A REVIEW OF - USE CHANGE MODELS Author Arnout van Soesbergen

Prepared for John D. and Catherine T. MacArthur Foundation

Acknowledgements Within UNEP-WCMC, support in preparing this report was provided by Sarah Darrah, Neil Burgess, Rebecca Mant and Fiona Danks. Rüdiger Schaldach (CESR, Kassel University), Jasper van Vliet (Institute for Environmental Studies, VU University 2 Amsterdam) and Aline Mosnier (International Institute for Applied Systems Analysis) served as external reviewers and provided comments on the draft report.

Published: April 2016

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ISBN: 978-92-807-3575-8 DEP/1999/CA

UNEP World Conservation Monitoring Centre (UNEP-WCMC) 219 Huntingdon Road, Cambridge CB3 0DL, UK UNEP promotes Tel: +44 1223 277314 environmentally sound www.unep-wcmc.org practices globally and in its own activities. Our distribution policy aims to reduce UNEP’s carbon footprint Contents

List of figures 5 List of tables 5 3 Glossary 6 Executive Summary 7 1 Introduction 21 1.1 Land cover, land-use and land functions 22 1.2 Land-use models 23

2 Drivers of land-use change 25 2.1 Proximate causes 26 2.2 Biophysical drivers 27 2.3 Feedbacks and interactions 28

3 Review of existing land-use models 29 3.1 Geographic land-use models 30 3.2 Economic land-use models 32 3.3 Integrated land-use models 33 3.4 Other model types 37

4 Land-use models and biodiversity 41 4.1 Global studies 43 4.2 Regional and national studies 44 4.3 Linking land-cover data products and habitat suitability 45

5 Land-use models and services 47 5.1 Global studies 48 5.2 Regional studies 48 6 Data for use in land-use models 51 6.1 Socioeconomic data 51 6.2 Baseline land-use and land-cover data 52 6.3 Environmental and topographical data 54 6.4 Model validation data 55

7 Scenarios for use in land-use models 57 7.1 Global scenarios 58 4 7.2 Regional scenarios 62 8 Conclusions and recommendations 63 8.1 General conclusions 63 8.2 Conclusions 64 8.3 Recommendations 65

References 66 LIST OF FIGURES AND TABLES

Figures No. Title Page 1 Relation between land-cover, land-use and land function and possible methods to collect 22 spatial data (source: Verburg et al. 2009) 2 Proximate causes of land-use change and underlying causes (reproduced from Geist and 26 Lambin 2002) 3 Global area of crop and across the RCPs. Grey area indicates the 90th percentile 35 of scenarios. is the part not covered by cropland or anthropogenically used 5 grassland (source: Van Vuuren et al. 2011)

Tables

No. Title Page 1 Overview of land-use models described in text 39 2 Examples of studies using land-use models and scenarios to assess changes in biodiversity 45 3 Classification schemes for land cover (based on Tomaselli et al. 2013) 45 4 FAO-LCCS main land-cover categories 46 5 Examples of studies using land-use models to assess changes in ecosystem services 49 6 Global and continental land-cover products 52 7 Global agricultural and datasets 54 8 Overview of the spatial, temporal and thematic properties of land-use and land-cover data 56 (source: Verburg et al. 2011) ABBREVIATIONS AVHRR Advanced Very High Resolution LC/LU Land Cover / Radiometer LCCS Land Cover Classification System, BIOSOS Biodiversity Multi-Source Monitoring developed by FAO System from Space to Species, EU-FP7 LCM Land Change Modeler project LCML Land Cover Meta Language CBD Convention on Biological Diversity LUCC Land-Use and Land-Cover Change CEPII Centre d’Etudes Prospectives et 6 d’Informations Internationales LUS Land-use systems CGE Computable General Equilibrium model MODIS Moderate resolution Imaging Spectroradiometer CORINE Coordination of Information on the Environment MSA Mean Species Abundance DEM Digital Elevation model NIES National Institute for Environmental Studies DSS Decision Support System NPP Net Primary Productivity FAO Food and Organisation of the United Nations NUTS Nomenclature of Territorial Units for Statistics FPU Food Production Unit PBL Netherlands Environmental Protection GCM Global Circulation Model Agency GEO Global Environment Outlook PE Partial Equilibrium model GHC General Habitat Categories PNNL Pacific Northwest National Laboratory GIS Geographic Information System PSS Policy Support System GTAP Global Trade Analysis Project RCP Representative Concentration Pathway IAM Integrated Assessment Model REDD Reducing Emissions from IFPRI International Food Policy Research and Degradation Institute REDD-PAC REDD+ Policy Assessment Centre IGBP International Geosphere-Biosphere SRES IPCC Special Report on Emission Programme Scenarios IIASA International Institute of Applied SRTM Shuttle Radar Topography Mission Systems Analysis SSP Shared Socioeconomic Pathway IPCC International Panel on ITE2M Integrated Tool for Economic and Ecological Modelling GLOSSARY Neural Network Model: A Neural Network Model Markov Chain Analysis: A statistical model that or 'artificial' neural network (ANN) is a model models the state of a system with a random variable designed to simulate the behaviour of biological that changes through time. The distribution of this neural networks, as in pattern recognition, language variable depends only on the current state and not processing, and problem solving, with the goal of on the sequence of events that preceded it. self-directed information processing. Transition Probability Matrix: A matrix that Cellular Automata Model: A grid based model describes the transitions of a Markov chain where where each cell changes state as a function of time each entry is a non-negative real number that according to a defined set of rules that includes the represents a probability. neighbouring cells. Executive Summary

This document aims to provide an overview of Many land-use models exist, operating at scales the current state of land-use modelling as well from local to global, and from coarse to fine 7 as the usability, applicability and availability of resolutions, and they can broadly be categorised modelling tools, particularly in relation to land- into: use change as a driver of change in biodiversity ● Geographic land-use models: models that and ecosystem services. A general overview of spatially allocate land-use types, based on existing models is presented along with their biophysical and infrastructural properties and data requirements and different scenarios the resulting suitability of land for a specific that can be used to drive these models. This use. document was prepared in the first instance to support UNEP-WCMC in the choices and ● Economic land-use models: models that assumptions underlying modelling frameworks use demand and supply functions as the main but is now being made available more widely as drivers of land-use change, giving total areas it can serve as a reference for other organisations of specific land-use types within defined to make informed choices on the capacity needs geographical regions. and options for the use of land-use models in ● Integrated land-use models: models that assessments of land-use change and resulting combine natural and human subsystems. impacts on biodiversity and ecosystem services. These often consist of a combination of Land-use models are important tools that can separate process models (e.g. economic and be used to explore potential future impacts on environmental) and are capable of spatially biodiversity and ecosystem services and evaluate explicit modelling, typically at large (global) potential trade-offs between different demands scales. for land use, and thus inform decision-making. ● Other model types: models that use a specific The complex relationships between land use approach such as machine learning or agent and biodiversity and ecosystem services make it based models. Most of these models are difficult to explore potential future changes with designed for a specific land-use system or a great certainty, but, particularly at larger scales, specific process, e.g. urban growth modelling or these modelling exercises can provide valuable forest change models. information that can help prioritise conservation action. Assessing the impacts of projected changes Other considerations include the choice of in land use on biodiversity and/or ecosystem baseline land-cover dataset and optimal spatial services can be done through modelling resolution to use in a study. Generally, for global approaches such as GLOBIO (Alkemade et studies, analyses are done at 0.5 degree (ca. al. 2009), PREDICTS (Newbold et al. 2014) 50 km) resolution (i.e., IMAGE, Hyde; Klein- or InVEST (Tallis et al. 2013), as well as more Goldewijk 2011) whereas continental studies may straightforward habitat-suitability and be done at 5 arc minutes (ca. 10 km) or even 1 km -function approaches. Some of these resolution. Ideally, baseline land-cover datasets approaches are described in more detail in have a temporal dimension so the land-use 8 similar reviews on mapping biodiversity (Hill et model can be validated. Currently, however, there al. 2016) and ecosystem services (Knight et al. are very few datasets based on 2016). that provide land-cover information for different time-periods which limits the options for their The choice of which land-use model and use in and validation of land-use models. biodiversity/ecosystem services assessment method to use in a particular study depends on the scope in terms of scale, geographic location and resources available. In general, land-use change modelling is complex, time consuming and requires many input datasets. For this reason, only a few models are made available outside the developing institutions. However, some of the freely available or limited cost tools such as CLUE (Verburg & Overmars 2009) and IDRISI Land Change Modeler (Clark labs) may be useful and viable for smaller projects. In addition, existing datasets of modelled land use at different spatial scales and resolution and under different scenarios can be sourced and used in projects. For example, global outputs from the IMAGE IAM (Alcamo 1994), or continental, national or regional results from the CLUE and LandSHIFT models. However, if data on spatially explicit land-use changes under specific scenarios or policy options are required, it will, in most cases, be necessary and more cost-effective to collaborate with partners from the land-use modelling community. Résumé analytique

Le présent document a pour objectif de fournir De nombreux modèles d’utilisation des terres un aperçu des modèles actuels d’utilisation existent à tous les niveaux et reposent sur des 9 des terres et de l’utilité, l’applicabilité et la résolutions plus ou moins affinées. Ils peuvent disponibilité des outils de modélisation, être sommairement regroupés comme suit : notamment en ce qui concerne les changements ● Modèles géographiques d’utilisation des d’affectation des terres en tant que moteurs terres : modèles d’utilisation spatiale des terres de l’évolution de la biodiversité et des services en fonction de leurs propriétés biophysiques écosystémiques. Plus concrètement, il présente et infrastructurelles et de leur pertinence pour les modèles existants et leurs besoins en matière une utilisation spécifique. de données ainsi que les différents scénarios envisageables. Initialement conçu pour soutenir ● Modèles économiques d’utilisation des les choix et les hypothèses relatifs aux cadres terres : modèles qui considèrent les fonctions de modélisation du PNUE-CMSC, cet aperçu d’offre et de demande comme les principaux global a été rendu plus largement accessible. moteurs des changements d’affectation des D’autres organisations peuvent ainsi prendre des terres et qui définissent la surface totale décisions éclairées sur les besoins en matière correspondant aux différents types d’utilisation de capacité et les possibilités d’exploitation des des terres au sein de régions géographiques modèles d’utilisation des terres afin d’évaluer spécifiques. les changements d’affectation des terres et leurs ● Modèles intégrés d’utilisation des terres : répercussions sur la biodiversité et les services modèles qui associent les sous-systèmes écosystémiques. naturels et humains. Il s’agit souvent Les modèles d’utilisation des terres sont des d’associations de modèles fondés sur des outils importants pour étudier les futures processus distincts (par exemple, économiques répercussions potentielles sur la biodiversité et environnementaux) relevant de modèles et les services écosystémiques, évaluer les spatialement explicites, généralement à grande possibles compromis entre les différentes échelle (mondiale). demandes relatives à l’utilisation des terres ● Autres types de modèles : modèles et éclairer ainsi le processus décisionnel. La reposant sur une approche spécifique comme complexité des liens entre l’utilisation des terres, l’apprentissage automatique ou les modèles d’une part, et les services écosystémiques et la fonctionnant avec un agent. Ils sont pour la biodiversité, d’autre part, ponctue d’incertitudes plupart conçus pour un système d’utilisation l’étude des changements futurs éventuels, mais des terres déterminé ou un processus ces exercices de modélisation, en particulier spécifique, par exemple la modélisation lorsqu’ils sont réalisés à grande échelle, peuvent de la croissance urbaine ou les modèles de cependant fournir des informations précieuses changement des forêts. susceptibles d’étayer la priorisation des actions de conservation. Il est possible d’évaluer les répercussions des D’autres aspects à prendre en considération dans changements d’affectation des terres prévus sur le cadre d’une étude concernent le choix de la la biodiversité et/ou les services écosystémiques résolution spatiale optimale et des ensembles à l’aide de méthodes de modélisation telles que de données de référence sur la couverture GLOBIO (Alkemade et al., 2009), PREDICTS terrestre. En général, les études mondiales (Newbold et al., 2014) ou InVEST (Tallis et al., s’appuient sur des analyses fondées sur une 2013), ou d’approches plus simples portant sur la résolution de 0,5 degré (environ 50 km) (par fonction du paysage et l’adéquation de l’habitat. exemple, IMAGE, Hyde ; Klein-Goldewijk, 2011) À cet égard, des analyses semblables sur la tandis que les études à l’échelle d’un continent 10 cartographie de la biodiversité (Hill et al., 2016) se fondent davantage sur une résolution de 5 et des services écosystémiques (Knight et al., minutes d’arc (environ 10 km), voire 1 km. Dans 2016) fournissent de plus amples informations. l’idéal, les ensembles de données de référence sur la couverture terrestre sont assortis d’une La sélection d’une méthode d’évaluation dimension temporelle qui permet de valider du modèle d’utilisation des terres et de la le modèle d’utilisation des terres. À l’heure biodiversité/des services écosystémiques dans actuelle, force est de constater que seuls de rares le cadre d’une étude particulière repose sur ensembles de données basés sur la télédétection différents facteurs : l’échelle, l’emplacement renseignent sur la couverture terrestre pour géographique et la disponibilité des ressources. différentes périodes, ce qui limite leur utilisation En général, la modélisation du changement pour la modélisation et la validation de d’affectation des terres est complexe et l’utilisation des terres. chronophage, et requiert de nombreux ensembles de données. C’est la raison pour laquelle peu de modèles sont disponibles, à l’exception de ceux des institutions conceptrices. Cependant, certains outils gratuits ou à bas coût, tels que CLUE (Verburg et Overmars, 2009) et IDRISI Land Change Modeler (Clark Labs), peuvent être utiles et pertinents pour des projets à échelle réduite. En outre, il est possible d’utiliser les ensembles de données existants sur la modélisation de l’utilisation des terres à des échelles spatiales et avec des résolutions différentes et suivant des scénarios divers, à l’instar des résultats mondiaux de l’initiative IMAGE IAM (Alcamo, 1994) ou des résultats régionaux, nationaux ou continentaux des modèles CLUE et LandSHIFT. Néanmoins, lorsque des données sur les changements d’affectation des terres spatialement explicites dans différents scénarios ou options politiques sont requises, la collaboration avec des partenaires spécialisés dans la modélisation de l’utilisation des terres s’impose, dans la plupart des cas, comme la méthode la plus rentable. Resumen

El objetivo de este documento es proporcionar Existen muchos modelos de uso de la tierra, que una panorámica del estado actual de los modelos operan de la escala local a la internacional y van 11 relativos al uso de la tierra, así como de la facilidad de las soluciones más burdas a las más brillantes. de uso, la aplicabilidad y la disponibilidad de las A grandes rasgos, pueden clasificarse de la forma herramientas para su elaboración, sobre todo siguiente: en relación con el cambio en el uso de la tierra ● Modelos geográficos del uso de la tierra: como factor impulsor de las variaciones en la modelos que utilizan un criterio espacial biodiversidad y los servicios de los ecosistemas. Se para asignar los tipos de uso de la tierra presenta una panorámica general de los modelos basándose en las propiedades biofísicas e existentes además de sus requisitos de datos y los infraestructurales y en la idoneidad resultante diferentes escenarios que pueden utilizarse para de la tierra para un uso específico. impulsarlos. En un principio, este documento se elaboró con el fin de apoyar al Centro Mundial ● Modelos económicos del uso de la tierra: de Vigilancia de la Conservación (CMVC) modelos que utilizan las funciones de la oferta del PNUMA en la toma de decisiones y en los y la demanda como principales impulsores del supuestos subyacentes a los marcos de elaboración cambio en el uso de la tierra y proporcionan de modelos, pero en la actualidad se ha ampliado la superficie total de los tipos específicos de su difusión, ya que puede servir de referencia uso de la tierra dentro de regiones geográficas para que otras organizaciones tomen decisiones determinadas. bien fundadas acerca de las opciones disponibles ● Modelos integrados del uso de la tierra: y las necesidades de capacidad de cara a la modelos que combinan subsistemas naturales y utilización de los modelos de uso de la tierra en las humanos. Suelen consistir en una combinación evaluaciones del cambio en el uso de la tierra y su de modelos de procedimientos distintos (por repercusión en la biodiversidad y los servicios de ejemplo, económicos y ambientales) y son los ecosistemas. capaces de elaborar modelos espacialmente Los modelos de uso de la tierra constituyen explícitos, normalmente a gran escala o a escala herramientas importantes que pueden utilizarse mundial. para explorar los posibles efectos futuros sobre la ● Otros tipos de modelo: modelos que utilizan biodiversidad y los servicios de los ecosistemas, un enfoque específico como los del aprendizaje evaluar las posibles soluciones intermedias entre automático o los basados en un agente. La las distintas demandas con respecto al uso de la mayoría de estos modelos están diseñados para tierra y tomar así decisiones bien fundamentadas. un sistema de uso de la tierra o un proceso Las complejas relaciones entre el uso de la determinados, por ejemplo, los relacionados tierra y la biodiversidad y los servicios de los con el crecimiento urbano o con el cambio ecosistemas dificultan el estudio de los posibles experimentado en los bosques. cambios futuros con un alto grado de seguridad, pero estos ejercicios de elaboración de modelos pueden proporcionar, sobre todo a gran escala, información valiosa que contribuya a priorizar las iniciativas de conservación. Evaluar los efectos que los cambios previstos en Otras consideraciones abarcan la elección del el uso de la tierra producen en la biodiversidad o conjunto de datos de referencia de la cubierta los servicios de los ecosistemas es posible gracias terrestre y la resolución espacial óptima que a enfoques de elaboración de modelos como deben utilizarse en un estudio. Por norma GLOBIO (Alkemade et al. 2009), PREDICTS general, para los estudios a escala mundial, los (Newbold et al. 2014) o InVEST (Tallis et al. análisis se llevan a cabo con una resolución de 2013), así como mediante enfoques más directos 0,5 grados, es decir, unos 50 km (por ejemplo, de idoneidad de hábitats y función paisajística. IMAGE, Hyde; Klein-Goldewijk 2011), mientras Algunos de estos enfoques se describen con más que los estudios continentales pueden realizarse 12 detalle en análisis similares sobre la cartografía con una resolución de 5 minutos de arco, es decir, de la biodiversidad (Hill et al. 2016)y los servicios unos 10 km, o incluso de 1 kilómetro. Lo ideal de los ecosistemas (Knight et al. 2016). es que el conjunto de datos de referencia sobre la cubierta terrestre cuente con una dimensión La elección del modelo del uso de la tierra y del temporal, de modo que el modelo de uso de método de evaluación de la biodiversidad y los la tierra pueda validarse. En la actualidad, sin servicios de los ecosistemas que se utilizarán en embargo, existen muy pocos conjuntos de datos un estudio en particular depende del objetivo basados en la teleobservación que proporcionen previsto en cuanto a la escala, la ubicación información sobre la cubierta terrestre en geográfica y los recursos de que se dispone. En diferentes periodos de tiempo, lo cual limita las general, la elaboración de modelos de cambio en opciones de su utilización en los modelos de uso el uso de la tierra es compleja y requiere tiempo y de la tierra y su validación. numerosos conjuntos de datos. Por este motivo, solo unos cuantos modelos están disponibles fuera de las instituciones que los desarrollan. No obstante, algunas de las herramientas gratuitas o con precio reducido, como CLUE (Verburg&Overmars 2009) e IDRISI Land Change Modeler (Clark Labs), pueden resultar útiles y viables para proyectos más pequeños. Además, los conjuntos de datos existentes sobre los modelos de uso de la tierra pueden obtenerse a diferentes resoluciones y escalas espaciales y con arreglo a distintos escenarios a fin de utilizarlos en los proyectos; por ejemplo, los productos mundiales aportados por el IMAGE IAM (Alcamo 1994) o los resultados continentales, nacionales o regionales de los modelos CLUE y LandSHIFT. Sin embargo, si se requieren datos espacialmente explícitos sobre los cambios en el uso de la tierra conforme a escenarios específicos o se precisan opciones de políticas, en la mayoría de los casos será necesario y más rentable colaborar con asociados de la comunidad de elaboración de modelos del uso de la tierra. Sumário executivo

Este documento tem como objetivo fornecer uma Existem muitos modelos de uso da terra, visão geral do estado atual da modelagem do uso operando em escalas do local ao global e, a partir 13 da terra, bem como a usabilidade, aplicabilidade de grosseiro a fino resoluções, e eles podem ser e disponibilidade de ferramentas de modelagem, amplamente classificados em: particularmente em relação à mudança do ● Geographic modelos de uso da terra: uso da terra como um motor da mudança no modelos que espacialmente alocar os tipos biodiversidade e serviços ecossistêmicos. Uma de uso da terra, com base nas propriedades visão geral dos modelos existentes é apresentado biofísicas e de infra-estrutura e a adequação junto com os seus requisitos de dados e diferentes resultante de terra para um uso específico. cenários que podem ser usados para conduzir esses modelos. Este documento foi elaborado em ● Modelos de uso da terra económicos: primeira instância para apoiar UNEP-WCMC nas modelos que usam de demanda e oferta funções escolhas e suposições subjacentes estruturas de como as principais causas da mudança do uso modelagem, mas está agora a ser disponibilizados da terra, dando áreas totais de tipos de uso da de forma mais ampla, pois ele pode servir como terra específicas dentro de regiões geográficas uma referência para outras organizações para definidas. fazer escolhas informadas sobre as necessidades ● Integrado modelos de uso da terra: de capacidade e opções para o uso de modelos de modelos que combinam subsistemas naturais uso da terra na avaliação de mudanças no uso da e humanos. Estes, muitas vezes consistem em terra e impactos sobre a biodiversidade e serviços uma combinação de modelos de processos ecossistêmicos. separados (por exemplo, económicos e Uso da terra modelos são ferramentas ambientais) e são capazes de modelar importantes que podem ser usados para espacialmente explícitos, normalmente em explorar os potenciais impactos futuros sobre grandes escalas (globais). biodiversidade e serviços ambientais e avaliar ● Outros tipos de modelo: modelos que potenciais soluções de compromisso entre usam uma abordagem específica, como a diferentes demandas de uso da terra, e, assim, aprendizagem de máquina ou agente modelos informar a tomada de decisão. As complexas baseados. A maioria destes modelos são relações entre uso do solo e biodiversidade e projetados para um sistema de uso da terra serviços ambientais tornam difícil para explorar específico ou um processo específico, por potenciais futuras mudanças com grande certeza, exemplo, modelagem crescimento urbano ou mas, particularmente em escalas maiores, alterar floresta modelos. estes exercícios de modelagem pode fornecer informações valiosas que podem ajudar a priorizar ações de conservação. Avaliar os impactos das mudanças projetadas Outras considerações incluem a escolha da linha no uso da terra sobre a biodiversidade e / ou de base do conjunto de dados de cobertura de serviços dos ecossistemas pode ser feito através terra e melhor resolução espacial para usar em de modelagem abordagens como GLOBIO um estudo. Geralmente, para estudos globais, (Alkemade et al. 2009), prevê (Newbold et as análises são feitas em 0,5 grau (cerca de al. 2014) ou investir (Tallis et al. 2013) , bem 50 km) resolução (ou seja, IMAGEM, Hyde; como habitat-adequação e de função paisagem Kleinman Goldewijk 2011) Considerando que abordagens mais simples. Algumas destas os estudos continental pode ser feito em 5 abordagens são descritos em mais detalhe em minutos de arco (cerca de 10 km) ou mesmo 1 14 avaliações semelhantes sobre a biodiversidade resolução km. Idealmente, basais conjuntos de mapeamento (Hill et al., 2016) e os serviços dos dados de cobertura da terra têm uma dimensão ecossistemas (Knight et ai. 2016). temporal para que o modelo de uso da terra pode ser validado. Atualmente, no entanto, há A escolha de qual modelo de uso da terra e muito poucos conjuntos de dados baseados da biodiversidade / ecossistemas método de em sensoriamento remoto que fornecem avaliação de serviços para usar em um estudo informações de cobertura da terra para diferentes particular depende do âmbito em termos de períodos de tempo, o que limita as opções para a escala, localização geográfica e os recursos sua utilização e validação de modelos de uso da disponíveis. Em geral, modelagem de mudanças terra. do uso da terra é complexo, demorado e requer muitos conjuntos de dados de entrada. Por esta razão, apenas alguns modelos são disponibilizados fora das instituições em desenvolvimento. No entanto, algumas das ferramentas de custos disponíveis gratuitamente ou limitados, tais como CLUE (Verburg & Overmars 2009) e IDRISI Land Change Modeler (Clark Labs) pode ser útil e viável para projetos menores. Além disso, bancos de dados existentes de uso da terra modelada em diferentes escalas e resolução espacial e sob diferentes cenários podem ser obtidos e usados em projetos. Por exemplo, saídas globais do IAM IMAGE (Alcamo 1994) ou resultados continentais, nacionais ou regionais dos modelos irão indicar e LandSHIFT. No entanto, se são necessários dados sobre as mudanças de uso da terra espacialmente explícitos sob cenários e opções de política específica, ele irá, na maioria dos casos, ser necessário e mais rentável para colaborar com parceiros da comunidade de modelagem de uso da terra. Резюме

Этот документ призван обеспечить обзор Существует множество моделей текущего состояния моделирования землепользования, работающих от локального 15 землепользования, а также удобство до глобального масштаба, и от грубого до использования, применимости и доступности тонкого разрешение, и они в целом можно инструментов моделирования, в частности, разделить на: в связи с изменением землепользования ● Географические модели в качестве движущей силы изменения землепользования: модели, биоразнообразия и экосистемных услуг. Общий которые пространственно выделяют обзор существующих моделей представлен типы землепользования, на основе наряду с их требованиями к данным и с их биофизических и инфраструктурных различными сценариями, которые могут быть свойств и в результате пригодности земли использованы для привода этих моделей. Этот для конкретного использования. документ был подготовлен в первую очередь для поддержки UNEP-WCMC в выборе и допущений, ● Экономические модели лежащих в основе систем моделирования, но землепользования: модели, которые в настоящее время доступны более широко, используют функций спроса и предложения как это может служить ориентиром для других в качестве основных факторов изменения организаций, чтобы сделать осознанный характера землепользования, что дает выбор на потребностях в области укрепления общей площади конкретных типов потенциала и опциях использовании моделей землепользования в пределах определенных землепользования при оценке изменений в географических регионов. землепользовании и в результате воздействия на ● Интегрированные модели биоразнообразие и экосистемные услуги. землепользования: модели, которые Модели землепользования являются важными сочетают в себе природные и человеческие инструментами, которые могут быть подсистемы. Они часто состоят из использованы для изучения потенциального комбинации отдельных моделей будущиего воздействия на биоразнообразие процесса (например, экономические и и экосистемные услуги, и чтобы оценить экологические) и результаты этих моделей потенциальные компромиссы между часто пространственно эксплицитные, различными требованиями землепользования, как правило, при больших (глобальных) и, таким образом, чтобы сделать процесс масштабах. принятия решений более осознанным. ● Другие типы моделей: модели, Сложные отношения между землепользованием которые используют особый подход, и биоразнообразием и экосистемными например машинное обучение или агент- услугами затрудняют изучить потенциальные ориентированные модели. Большинство будущие изменения с большой долей из этих моделей предназначены для уверенности, но, в частности, в более крупных конкретной системы землепользования масштабах, эти упражнения моделирования или конкретного процесса, например, могут дать полезную информацию, которая моделирование роста городов или модели может помочь расставить приоритеты действий изменения лесов. по охране природы. Оценка воздействия прогнозируемых Другие соображения включают выбор изменений в землепользовании на базового набора данных и оптимального биоразнообразие и / или экосистемные пространственного разрешения для услуги может быть сделано с помощью исследования. Как правило, для глобальных подходов моделирования, таких как GLOBIO исследований, анализы выполняются на (Alkemade и др. 2009), PREDIDTS (Newbold и 0,5 градуса (около 50 км) разрешением (т.е., др. 2014) или INVEST (Tallis и др. 2013) , а также IMAGE, Hyde; Klein- Goldewijk 2011); однако более простые среды обитания, пригодности континентальные исследования может быть и ландшафтно-функциональные подходы. сделано в течение 5 минут дуги (примерно 16 Некоторые из этих подходов описаны более 10 км) или даже 1 разрешение км. В идеале, подробно в аналогичных обзоров по вопросам базовые наборы данных землепользования биоразнообразия картирования (Hill и др. имеют временное измерение, поэтому модель 2016 г.) и экосистемных услуг (Knight и др. 2016 землепользования может быть проверена. г.). Однако в настоящее время существует очень мало наборов данных на основе Выбор землепользования модель и дистанционного зондирования, которые метод оценки услуг биоразнообразия / предоставляют информацию о земельном экосистемных для конкретного исследования покрове для различных временных периодов, зависит от сферы, в отношении зрения ограничивает возможности их использования масштаба, географического положения и в валидации моделей землепользования. имеющихся ресурсов. В целом, моделирование изменений в землепользовании является сложным и длительным, и требует много входных наборов данных. По этой причине, немного моделей доступны за пределами развивающихся институтов. Тем не менее, некоторые из свободных или недорогих инструментов, таких как CLUE (Вербург & Overmars 2009) и IDRISI Land Change Modeler (Clark Labs) может оказаться полезным и жизнеспособным для небольших проектов. Кроме того, существующие наборы данных моделируемой землепользования в различных пространственных масштабах и разрешений, и при различных сценариях могут быть получены и использованы в проектах. Например, глобальные выходы из IMAGE IAM (Alcamo 1994), или континентальные, национальные или региональные результаты моделей CLUE и LandSHIFT. Тем не менее, если требуются данные о пространственно-эксплицитных изменений в землепользовании при конкретных сценариях или вариантах политики, в большинстве случаев совместная работа с партнерами, которые являются специалистами моделирования землепользования, будет необходимы и более экономически эффективным. تقييم آثار التغريات املتوقعة يف استخدام األرايض عىل التنوع البيولوجي وتشمل االعتبارات األخرى خيار أسايس بيانات الغطاء األريض والتحليل و / أو خدمات النظام اإليكولوجي وميكن أن يتم من خالل النمذجة املكاين األمثل الستخدامها يف الدراسة. عموما، للدراسات العاملية، تتم النهج مثل PREDICTS, (Alkemade et al. 2009) GLOBIO التحليالت عند 0.5 درجة )حوايل 50 كم( القرار )أي ،IMAGE، Hyde (Newbold et al. 2014) أو Tallis et al. 2013) InVEST) وكذلك Klein-Goldewijk 2011(يف حني ميكن أن يتم الدراسات األوروبية يف موطن-مالءمة واملناظر الطبيعية وظيفة نهج أكرث وضوحا. ووصف 5 دقائق قوس )حوايل 10 كم( أو حتى 1 قرار كم. من الناحية املثالية، بعض من هذه الطرق مبزيد من التفصيل يف استعراض مامثلة عىل أساسية قواعد البيانات الغطاء األريض لها بعد زمني لذلك منوذج التنوع البيولوجي رسم الخرائط )Hill et al. 2016( وخدمات النظام استخدام األرايض ميكن التحقق من صحة. حاليا، ومع ذلك، هناك عدد 17 اإليكولوجي )Knight et al. 2016(. قليل جدا من مجموعات البيانات عىل أساس االستشعار عن بعد التي توفر معلومات الغطاء األريض ملختلف الوقت الفرتات مام يحد من االختيار منها استخدام األرايض منوذج والتنوع البيولوجي / النظم الخيارات املتاحة الستخدامها يف والتحقق من مناذج استخدام األرايض. اإليكولوجية طريقة تقييم الخدمات الستخدامها يف دراسة معينة يتوقف عىل نطاق من حيث الحجم، واملوقع الجغرايف واملوارد املتاحة. بشكل عام، وتغري استخدام األرايض ووضع مناذج معقدة، تستغرق وقتا طويال ويتطلب الكثري من مجموعات البيانات اإلدخال. لهذا السبب، يتم إجراء سوى عدد قليل من النامذج املتاحة خارج املؤسسات. ومع ذلك، بعض األدوات املتاحة تكلفة أو محدودة بحرية مثل CLUE (Verburg & Overmars 2009)، وIDRISI الند تغيري صانع التامثيل )Clark labs( قد يكون مفيدا ومجديا للمشاريع الصغرية. وباإلضافة إىل ذلك، قواعد البيانات املوجودة الستخدام األرايض عىل غرار يف نطاقات مكانية مختلفة، والقرار، وفقا لسيناريوهات مختلفة ميكن أن تكون مصادر واستخدامها يف املشاريع. عىل سبيل املثال، النواتج العاملية من Alcamo 1994) IAM IMAGE)، أو نتائج القارية أو الوطنية أو اإلقليمية من مناذج CLUE وLandSHIFT. ومع ذلك، إذا طلب بيانات عن التغريات يف استخدام األرايض واضحة مكانيا يف إطار سيناريوهات أو خيارات سياسة محددة، فإنه، يف معظم الحاالت، يكون من الرضوري وأكرث فعالية من حيث التكلفة للتعاون مع الرشكاء من املجتمع النمذجة استخدام األرايض. ملخص تنفيذي

وتهدف هذه الوثيقة إىل تقديم ملحة عامة عن الحالة الراهنة لنمذجة ● الجغرافية مناذج استخدام األرايض: مناذج التي تخصص مكانيا أنواع استخدام األرايض، فضال عن سهولة االستخدام، وتطبيق وتوافر أدوات استخدام األرايض، عىل أساس الخصائص الفيزيائية الحيوية والبنية 18 النمذجة، وخاصة فيام يتعلق بتغري استخدام األرايض كمحرك للتغيري التحتية وينجم عن ذلك من مالءمة األرايض الستخدام محدد. يف التنوع البيولوجي وخدمات النظم اإليكولوجية. ويقدم ملحة عامة ● مناذج استخدام األرايض االقتصادية: النامذج التي تستخدم وظائف عن النامذج الحالية مع متطلبات البيانات الخاصة بهم والسيناريوهات العرض والطلب كمحرك رئييس للتغيري استخدام األرايض، وإعطاء املختلفة التي ميكن استخدامها لدفع هذه النامذج. تم إعداد املناطق اإلجاملية ألنواع معينة استخدام األرايض داخل مناطق هذه الوثيقة يف املقام األول لدعم UNEP-WCMC يف الخيارات جغرافية محددة. واالفرتاضات التي تقوم عليها مناذج أطر ولكن يجري اآلن نرشها عىل نطاق أوسع ألنها ميكن أن تكون مبثابة مرجع للمنظامت األخرى ● املتكاملة مناذج استخدام األرايض: مناذج التي تجمع بني األنظمة إىل اتخاذ قرارات مستنرية بشأن احتياجات بناء القدرات والخيارات الفرعية الطبيعية والبرشية. غالبا ما تتألف هذه املجموعة من املتاحة ل استخدام مناذج استخدام األرايض يف تقييم التغري يف استخدام مجموعة من مناذج عملية منفصلة )مثل االقتصادية والبيئية( األرايض وينتج عنها من آثار عىل التنوع البيولوجي وخدمات النظم وقادرون عىل النمذجة واضح مكانيا، وعادة يف جداول )العاملية( اإليكولوجية. كبرية. مناذج استخدام األرايض هي أدوات هامة التي ميكن استخدامها ● أنواع منوذج أخرى: مناذج التي تستخدم نهج معني مثل تعلم آلة الستكشاف اآلثار املستقبلية املحتملة عىل التنوع البيولوجي وخدمات أو وكيل النامذج القامئة. تم تصميم معظم هذه النامذج لنظام النظم اإليكولوجية وتقييم املقايضات املحتملة بني مطالب مختلفة استخدام األرايض معني أو عملية معينة، عىل سبيل املثال، مناذج الستخدام األرايض، وبالتايل عملية صنع القرار. العالقات املعقدة بني النمو الحرضي أو تغيري الغابات النامذج. استخدام األرايض والتنوع البيولوجي وخدمات النظم اإليكولوجية تجعل من الصعب الستكشاف التغريات املستقبلية املحتملة بيقني كبري، ولكن، ال سيام يف نطاقات أوسع، ميكن لهذه التامرين النمذجة توفر املعلومات القيمة التي ميكن أن تساعد يف تحديد أولويات العمل الحفظ. وجود العديد من مناذج استخدام األرايض، والتي تعمل عىل مستويات من املحلية إىل العاملية، ومن الخشنة إىل غرامة القرارات، وأنها ميكن أن عىل نطاق واسع أن تصنيفها إىل: 执行摘要

本文件旨在提供建模工具土地使用模型的当前状 许多土地利用模式的存在，在从地方到全球规模 态的概况以及可用性，适用性和可用性，特别是 经营，由粗到细的分辨率，并且它们大致可分为: 19 有关土地使用的变化在生物多样性和生态系统服 ● 地理土地利用模式： 模式，空间分配土地利用 务变化的驱动力。现有型号的一般概述及其数据 类型的基础上，生物物理和基础设施的属性和 需求和可用于驱动这些模型不同的场景一起呈 土地用于特定用途的适用性结果。 现。该文件是在第一时间准备支持UNEP-WCMC 的选择和基本建模框架的假设, 但现在正在提供 ● 经济的土地利用模式： 使用需求和供给作为土 更广泛, 因为它可以作为其他组织的参考, 可对容 地利用变化的主要驱动力，所确定的地理区域 量的需求和选择明智的选择在土地利用变化的评 内给出具体的土地利用类型的总面积模型。 估和对生物多样性和生态系统服务产生的影响使 ● 综合用地模式： 结合了自然和人类的子系统模 用土地利用模式。 型。这些通常包括独立的过程模型（例如经济 土地利用模型是可以用来探讨未来对生物多样性 和环境）的组合，并且能够在空间上显式建模 和生态系统服务的潜在影响，并评估土地使用的 的，通常是在较大的（全局）尺度。 不同需求之间的潜在权衡, 从而告知决策的重要 ● 其他模型类型： 使用特定的方法，如机器学习 工具。土地利用与生物多样性和生态系统服务之 或基于代理的模型的模型。大多数这些模型被 间的复杂关系, 很难探讨未来以极大的肯定潜在 设计用于特定的土地利用系统或特定方法, 例 的变化，但是，尤其是在更大的尺度，这些建模 如城市增长模型或林改款车型。 练习可以提供有价值的信息，可以帮助优先保护 行动。 评估对生物多样性和/或生态系统服务在土地 其他考虑因素包括土地覆盖情况基线数据，并 利用预期变化的影响，可以通过建模来实现方 在研究中使用的最佳空间分辨率的选择。一般 法, 如GLOBIO (Alkemade et al. 2009), PREICTS 来说，对于全球研究，分析是在0.5度（约50 (Newbold et al. 2014) 或 INVEST (Tallis et al. 公里）分辨率进行（即 IMAGE, Hyde; Klein- 2013), 以及更直接的栖息地，适宜性和景观功能 Goldewijk 2011), 而大陆的研究可能会在5角分（ 的方法。一些这些方法的更详细的关于映射多样 约10公里), 甚至1完成公里的分辨率。理想情况 性类似的评论 (Hill et al. 2016) 和生态系统服务 下，土地覆盖情况基线数据集有一个时间维度使 (Knight et al. 2016) 描述的。 土地利用模型进行验证。然而，目前还有基于遥 感提供不同的时间段，限制的选项为他们在利用 其中，土地利用模式和生物多样性/生态系统服 20 和土地利用模式的验证土地覆盖信息很少的数据 务评估的方法在特定研究使用的选择取决于规 集。 模，地理位置和可用资源方面的范围。一般情况 下，土地利用变化造型复杂，耗时，而且需要很 多的输入数据集。由于这个原因，只有很少的模 型被显影机构外提供。然而，一些免费的或有限 的成本的工具，如CLUE（Verburg & Overmars 2009) 和 IDRISI 土地变化建模器 (Clark labs) 可能对较小的项目有用的和可行的。此外，建 模土地利用不同空间尺度和分辨率以及不同 情况下现有数据集可以采购和项目中使用。例 如，从IMAGE IAM (Alcamo 1994) 或从CLUE和 LandSHIFT车型欧陆，国家或地区的结果。但 是，如果需要在特定的场景或政策选项下的空间 明晰的土地利用变化数据，它会在大多数情况 下，是必要的和更具成本效益与土地利用建模领 域的合作伙伴进行合作。 1. Introduction

Over the coming decades, society will have to balance competing needs for land to feed the growing global population, to provide resources and energy to satisfy ever-accelerating 21 human consumption, to slow global warming and to reduce the rate of loss of ecosystem services and biodiversity. Decision makers need to balance these widely different demands and evaluate potential trade-offs.

Land-use models can help investigate the effects use change as a driver of change in biodiversity of a combination of drivers at different scale and ecosystem services. A general overview of levels, such as population increase, demand for currently existing models is presented along food, commodity prices and global warming, on with their data requirements and different land-use change and its environmental impacts. scenarios that can be used to drive these models. Essentially, the goals of land-use modelling It is assumed that the reader has a general are (a) to improve understanding of land-use understanding of the concepts of modelling and systems, (b) to explore the behaviour of land- the assessment of biodiversity and ecosystem use systems under changing environmental services. This document can serve as a reference conditions, or (c) to apply scenario analysis and for national and sub-national decision makers strategy development. to make informed choices on the capacity needs and options in utilising land-use models in This document aims to provide an overview of assessments of land-use change and resulting the current state of land-use modelling as well impacts on biodiversity and ecosystem services. as the usability, applicability and availability of modelling tools, particularly in relation to land- 1.1 LAND COVER, LAND USE AND LAND FUNCTIONS ‘Land cover’ refers to the physical surface schemes (Di Gregorio & Jansen 2000; Haines-Young characteristics of land (for example, the vegetation 2009). or the presence of built structures). Therefore, land Land-use systems exist when land uses are cover is directly observable, either in the field or systematically linked through temporal or spatial through remote sensing. Land use, on the other interactions such as crop rotations. Land-cover hand, describes the economic and social functions observations (e.g. in remote sensing data) alone of land, or the purposes for which humans exploit are usually not able to detect and analyse such land cover. FAO and UNEP (1999) define land use 22 land-use systems. Additional socioeconomic data as the “total of arrangement, activities and inputs (e.g. harvest statistics) are necessary to assess these that people undertake in a certain land-cover type". systems (Kruska et al. 2003). At a landscape level, Since major parts of the land surface are used for many different, interacting land-use systems may be such activities as agriculture, forestry, settlements present that supply a variety of goods and services to and infrastructure, land use can be considered as society (Verburg et al. 2009). The capacity of land to the most important factor affecting biodiversity provide goods and services is referred to as land-use globally (Sala et al. 2000). Land use can be inferred functions or ecosystem functions (de Groot 1992; from observable activities such as grazing or MA 2005; de Groot 2006; Verburg et al. 2009). Land structural elements in the landscape (i.e., roads). functions include provision of goods and services Clearly, land cover and land use are linked, but these related to land use but also include 'unintended linkages are complex. A single type of land cover, services' such as provision of aesthetic beauty, perhaps grassland, may support many uses, such as cultural heritage and preservation of biodiversity. livestock production, recreation and turf cutting, However, in many countries these unintended while a single use, say nature conservation, may services are now also actively planned or preserved take in a number of different cover types including and therefore no longer unintended. grassland, and wetland areas. However, while Figure 1 shows the relationship between land the distinction between cover and use is accepted, cover, land use and land function and possible they are often conflated in some classification methods of collecting spatial data.

Figure 1: Relation between land cover, land use and land function and possible methods to collect spatial data (source: Verburg et al. 2009)

Land system interactions Maps and data

Remote sensing, Land cover aerial photography, field mapping provision of goods Land cover and services

Census data, land cover maps Land function intent/purpose Land use supplemented by observations, land management inferred from landscape structure

provision of goods and services Quantification or valuation of goods Land use and services, analysis of the Land function socio-economic and environmental context 1.2 LAND-USE MODELS Land-use models can broadly be understood as Land-use models have been around for decades, tools that help us in understanding and analysing but advances in remote sensing, land inventory the sometimes complex linkages and feedbacks techniques and computing power have led to the between different drivers of land-use change. development of a wide variety of different land- However, there are a number of definitions of use and land-cover change models in the last five land-use models. For example, Heistermann et to ten years, mostly to address the many different al. (2006) define a land-use model as ‘a tool to processes, scales of analysis and research compute the change of area allocated to at least questions (Verburg et al. 2009). 23 one specific land-use type.’ Verburg et al. (2004) define a land-use model as a 'tool to support the analysis of the causes and consequences of land- use dynamics'.

Models that are capable of capturing (reproducing) aspects of the complex dynamics involved in land-use change can support understanding of these dynamics. Land-use models can be used to project demand for land for specific purposes and where resulting land-use changes will occur given different boundary conditions. They can, therefore, help in understanding the drivers of land-use change and which areas are likely to be under greatest pressure, and can thus provide support to land- use and policy decisions. Land-use models can also be used to explore alternative futures using scenarios; however, not all land-use models can be used for scenario analysis. Models that are simply based on an extrapolation of trends in land-use change are not suitable for scenario analysis, as they are only valid within the range of land-use changes on which they are based (Verburg et al. 2004). 24 2. Drivers of land-use change

Most land-use models consider land-use change as a function of a selection of socioeconomic and biophysical variables or 'driving forces' (Verburg et al. 2004). According to Heistermann 25 et al. (2006), a driver of land-use change is defined as causing a change in the total area allocated to a specific land-use type or a change in spatial distribution of land-use types. Drivers are scale-dependent, as changes in spatial arrangement of land use may not be detected if the analysis is carried out at coarse resolution or for a small extent. In addition, different drivers can have a dominant influence on the land-use system at different scales of analysis. For instance, at a local level this can be determined by local policy, whereas at a regional level this could be distance to market (Verburg et al. 2004).

In general, land-use change in developed and land-use and land-cover changes in those regions such as Europe is mainly a result of regions are often proceeding rapidly (Dolman et changes in production systems (crops, fertilizer, al. 2003). Effectively, the changes in the tropics livestock numbers). Whilst these changes can today are the same as the changes that happened lead to higher productivity and subsequent in temperate regions over the past millennia. abandonment of (van Vliet et Globally, changes in forest cover, mainly through al. 2015), they do not generally cause major shifts deforestation, and changes in agricultural areas in land cover such as from forest to agriculture. and management, are the most significant types These processes are generally slow and policy- of land-use change. Land-use change through driven. These shifts in production systems follow urban expansion is of less importance as the thousands of years of land-use change, with the spatial extent associated with this process is dramatic change from natural to more human- minimal, although this depends on the scale dominated systems occurring hundreds or of analysis. However, rural-urban linkages also thousands of years ago. In the tropics, however, influence land-use change, and this impact can demand for new agricultural land from a rapidly be greater than urban expansion itself (Seto et al. expanding human population continues to be 2012; Delgado et al. 2003). the main driving force for land-cover changes, 2.1 PROXIMATE CAUSES Proximate causes or land management actions (Liu 2013). Figure 2 summarises some of the are local or direct human modifications that main proximate causes of land-use change and cause changes in the landscape. Proximate underlying causes. causes are human activities or immediate actions Land management is also a proximate cause at the local level, such as agricultural expansion, that is determined by the societal setting that directly impact forest cover (Geist & Lambin (Heistermann et al. 2006) and impacts land 2002). use at different spatial scales. Examples of land 26 Underlying causes of proximate causes are, for management include farming practices such instance, social processes such as agricultural as fertilizer use that can increase crop yields or policies or population dynamics. In addition, practices such as slash and burn and selective characteristics of societies, such as cultural logging to manage tree density in forests. background (Rockwell 1994), wealth and lifestyle, Political decisions, such as policy interventions in can be considered underlying causes as they developed countries and development projects in have an impact on the demand for land-intensive developing countries, can also underpin land-use commodities (Delgado et al. 2003), which leads change at local to regional scales (Batistella 2001). to agricultural expansion. International trade also Governance, law enforcement, land tenure and has an impact on demand for such commodities, access to markets are also very important factors such socioeconomic and environmental driving land-use change (Geist & Lambin 2002). interactions are also known as telecoupling

Figure 2: Proximate causes of land-use change and underlying causes (reproduced from Geist & Lambin 2002)

Infrastructure Agricultural Wood extension expansion extraction Other factors Transport Permanent Cultivation Commerical Pre-Disposing (roads, railroads, etc.) (large-scale vs. smallholder (state-run, private, Environmental Factors subsistence vs. commercial) growth coalition, etc.) Markets (land characteristics, e.g. soil (public & private, Shifting Cultivation Fuelwood quality, topography, forest e.g. sawmills) (slash & burn vs. (mainly domestic usage) fragmentation, etc.) traditional swidden) Settlements Polewood Biophysical Drivers ( lines, electrical Cattle Ranching (mainly domestic usage) (triggers, e.g. fires, droughts, grids, sanitation, etc.) (large-scale vs. smallholder) flood, pests) Charcoal Production Proximate causes Proximate Private Company Colonisation (domestic & industrial uses) Social Trigger Event (hydropower, mining, (incl. transmigration (e.g. war, revolution, social disorder, oil exploration) & resettlement projects) abrupt displacements, economic shocks, abrupt policy shifts)

Demographic Economic Technological Policy & Cultural factors factors factors institutional factors factors Natural Increment Market Growth & Agro-technical Formal Policies Public Attitudes, (fertility, mortality) Commercialisation Change (e.g. on economic Values & Beliefs developement credits) Migration (e.g. in/extensification) (e.g. unconcern about Economic forest, frontier mentality) (in/out migration) Applications in Policy Climate Structures (e.g. corruption, Population the Wood Sector Individual & Urbanisation mismanagement) Density (e.g. mainly wastage) Household & Industrialisation Property Rights Agricultural Behavior Population (e.g. land races, titling) (e.g. unconcern about Special Variables Production Factors Distribution (e.g. price increases, forest, rent-seeking, (e.g. mainly wastage) imitation) Life Cycle Features comparative cost advantages)

Underlying causes 2.2 BIOPHYSICAL DRIVERS Biophysical drivers in most cases do not ‘drive’ Climate change drives land-use change, as land-use change directly (Verburg et al. 2004), changes in temperature and precipitation result but rather cause land-cover changes, for example, in changes in land and water regimes, that through climate change and influencing land-use can drive a shift in vegetation and agricultural allocation decisions. Key biophysical drivers for cultivation. Many land-use models (e.g. land-use change are climate (Ogallo et al. 2000), LandSHIFT) use crop-growth or vegetation- freshwater availability (Rosegrant et al. 2002) growth models or data to assess what different and soil conditions as all of these affect land areas are suitable for expansion now, as well as 27 suitability. where there will be changes in the future. 2.3 FEEDBACKS AND INTERACTIONS Models of land use change are often used to Very few studies have attempted to include assess the impact of land cover on biophysical interactions between land use change and processes such as climate variability, land landscape processes. One example is a study degradation and ecosystem stability and by Claessens et al. (2009) who coupled a land diversity. However, biophysical responses to land use model (CLUE) with a landscape process cover changes in turn can drive land use change. model capable of simulating water, tillage One example is the influence land cover can have erosion and sedimentation. This study concluded 28 on climate, particularly in relation to carbon that including feedback mechanisms did not sequestration and emission (Veldkamp & Lambin lead to large changes in total quantities of land 2001). Global climate models take into account use change but resulted in important differences impacts of land-use change on the biophysical in the spatial patterns of land use and soil atmospheric processes only in a unidirectional redistribution. manner, i.e. very limited feedback of the climate system on land-cover is incorporated. 3 Review of existing land-use models

There are a number of reviews that characterise and classify land-use models. Most notable are the reviews by Lambin et al. (2000) focusing on agricultural intensification models, Irwin and Geoghegan (2001) using the degree of spatial explicitness and economic rationale 29 for classification, Briassoulis (2000) who applied the criterion of modelling tradition to distinguish statistical/econometric, spatial interaction, optimisation and integrated models, and Veldkamp and Lambin (2001) focusing on models' ability to reproduce and predict intensification processes. Verburg et al. (2004), rather than classifying models, focuses on features of land-use systems that need to be taken into account by land-use modelers and, as such, provides useful information for this review. More recent reviews include those of Heistermann et al. (2006; described below), Rosa et al. (2014) who compared land-use models applied in tropical regions and identified shortfalls and research needs and the review of Brown et al. (2013) who identify five key types of approaches based on emphasis on pattern versus process and projection versus explanation. The five approaches identified are: machine learning models, cellular models, sector based economic models, spatially disaggregated economic models and agent based models.

Heistermann et al. (2006) reviews land-use of both the human and the environment sub- models at continental to global scales and system and operate in a spatially explicit manner categorises them into a) geographic land-use on regional to global scales. The models reviewed models, including empirical-statistical and rule- in the next section follow the classification or process-based models b) economic land-use by Heistermann et al. (2006) but also include models and c) integrated models. Geographic models reviewed by Schaldach and Priess (2008) models are those that allocate area or commodity and others. All described models are summarised demand on suitable locations with suitability in Table 1. based on local characteristics. Economic models Many land-use models are developed for specific use supply and demand of land-intensive geographical regions or case studies. While some commodities as a base for allocation of land of these models provide useful results for their (albeit at large geographical scales), while specific application, these are not included in integrated models combine these two approaches this review as they are generally only applicable with an economic analysis of world markets in the regions they are designed for and are thus and policies to quantify demand and supply not flexible enough to be considered for other and allocation of land use based on geographic areas. Regional models that are flexible enough analysis. Schaldach and Priest (2008) provide to be applied in other regions are included. an overview of integrated models of the land system. These are models that include processes 3.1 GEOGRAPHIC LAND-USE MODELS Geographic land-use models are characterised relationships between observed land use and by the spatial allocation of land-use types, based spatial variables. The model has been applied on on biophysical and socioeconomic properties the European scale in the EURURALIS project and the resulting suitability of land for a specific (Verburg et al. 2006), as well as in other projects. use. As a result of increasing availability of data DINAMICO EGO (Environment for through remote sensing, and the development Geoprocessing Objects; Soares-Filho et al. 2009) and availability of Geographic Information is a cellular automata model developed by the Systems (GIS), many geographic land-use models 30 remote sensing lab at the Federal University of have been developed, mostly at local or regional Minas Gerais in Brazil that has been used for a scales. number of studies into LUCC, mostly applied Geographic land-use models are capable of within Brazil, as well as a number of studies in capturing supply-side constraints based on land support of REDD projects. The model uses a resources and spatial determination of land use. 'weights of evidence' method that generates a However, they cannot endogenously treat the map of change potential based on a number of interplay between supply, demand and trade explanatory variables and past trends that involve (Heistermann et al. 2006). expert knowledge.

Empirical-statistical models Rule based/process based models The CLUE model framework (Veldkamp & Fresco In contrast to empirical-statistical models, that 1996) is probably the most well known and most are based on statistical relationships between frequently used land-use model globally. Over drivers and historical land-use changes, rule- the years, the model has evolved and different or process-based models imitate processes versions have been developed (CLUE, CLUE-s, addressing the interaction of various components Dyna-CLUE and CLUE-Scanner). The Dyna- forming a system (Lambin et al. 2000). For CLUE version of the model (Verburg & Overmars example the SALU model (Stéphenne & Lambin 2009) is available as freeware (although it can 2001) simulates spatially explicit changes of land only be applied with a limited extent). The key use at a coarse resolution for the Sahel Zone assumption underlying the CLUE models is that using a sequence of agricultural land-use changes observed spatial relations between land-use that are typical for many regions, i.e., agricultural types and potential explanatory factors represent expansion at the most extensive technological currently active processes and remain valid in level is followed by agricultural intensification the future. Logistic regression is used to derive once a land threshold is reached. Land systems models using (externally calculated) demand for land intensive commodities and supply defined by More recently, land change modellers have the local biomass productivity in a specific cell. started to use the land systems approach whereby Productivity is influenced by climate change and the modelling units are land use systems that technological changes. represent human-environmental interactions in mosaic (van Asselen & Verburg The CLUMondo model (van Asselen & Verburg 2013). Land systems models are also classed as 2013) uses the land system change approach integrated models as they combine economic with a land systems classification that represent and environmental processes. An example of the typical combinations of land cover, livestock and 31 land system approach is the model developed land use intensity rather than using factors such by Letourneau et al. (2012) who uses a land as population density and market accessibility use systems (LUS) approach to model global as a proxy for land-use intensity. This has the land change. This model forms part of the land advantage that population and accessibility use systems module of the IMAGE integrated are independent driving factors of land system assessment model described in section 3.3.1. change so that different land systems can The LUS are combinations of land cover, land occur with the same population density due use (such as livestock, cropland and pasture), to differences in location factors and external population and accessibility and were modelled demand (van Asselen & Verburg 2013). at 5 arc minute resolution (~10 x 10 km). Areas Unlike most land use models where demand for and management intensities are determined by different land cover types is allocated one-to-one global economic models based on globaltrends to changes in the spatial distribution of these and the choice between land cover change or land cover types, CLUMondo simulates changes intensification of agriculture to fulfil demand in land systems capable of providing various is not endogenous. This approach is advocated goods at the same time. This means that the same to better inform the land sharing/land sparing production or area can be fulfilled by multiple debate (Phalan et al. 2011; Tscharntke et al. combinations of land systems. The model uses 2012; van Asselen & Verburg 2013) as it can show an initial land systems map at 5 arc minute regionally variable outcomes of expansion of resolution based on land cover (Hansen et al. based on local factors that either 2003), cropland area (Ramankutty et al. 2008), constrain or promote land system conversion. built-up area (Schneider et al. 2009), livestock Another example of a land use system model is density (FAO, 2007) and intensity of agricultural the LandSHIFT model framework (Schaldach et production (Neumann et al. 2010). It then al. 2006). LandSHIFT is a tool for medium-term determines suitability for land systems using scenario analysis (20-50 years) and assessment of empirical relationships between the land systems environmental impacts of land use change and and socioeconomic and biophysical factors based the model simulates spatial-temporal dynamics on Van Asselen and Verburg (2012). These land of settlement, crop cultivation and livestock systems compete with each other in an iterative grazing. LandSHIFT is based on the concept of procedure until all demand is fulfilled. Demand land use systems (Mather 2006) as it couples is determined by macro-scale economic models model components representing anthropogenic for 24 world regions based on the IMAGE model and environmental systems. Crop yields and regions (van Asselen & Verburg 2013). net primary productivity (NPP) of grassland are simulated in a productivity module and land use change is then simulated in a LUC module 3.2 ECONOMIC LAND-USE MODELS In economic models, demand and supply Economic land-use models have the advantage functions for tradable commodities are the that they can consistently address demand, main drivers of land-use change. This means supply and trade via price mechanisms. However, that most of these models will model total they are often limited in accounting for supply- area of specific land-use types within defined side constraints, such as behaviour not related geographical regions, but, in most cases, these to price mechanisms, as well as incorporating are not made spatially explicit. Most global the impact of demand on actual land-use change 32 economic models are equilibrium models that processes. Technically, most of these models explain land allocation by demand and supply are not land use models as they focus on market structures of the land-intensive sectors, where structure for land-intensive commodities and not the main mechanism is to equate demand and on allocation of land specifically (Heistermann supply under exogenously defined constraints 2006). Below are listed some of the most well- (Heistermann et al. 2006). Computable General known (global) economic models. Equilibrium models (CGEs) attempt to model IMPACT (Rosegrant et al. 2001) developed at all markets explicitly and assume these to be in the International Food Policy Research Institute equilibrium in every time-step, whereas Partial (IFPRI) is a global partial equilibrium model Equilibrium models (PEs) only model a subset (PE) focusing on the agricultural sector only and of the market and ignore or parameterise the is developed to study the impacts of alternative remaining markets. This approach therefore lacks future development pathways on food security feedbacks with other sectors. A key assumption by modelling agricultural commodity supply, of economic-based models is that people will demand, trade and prices. The model currently seek to maximise utility, either in financial or includes 40 agricultural commodities and models commodity gains (Evans et al. 2001). However, these at the scale of food production units (FPU). Walker (2004) found household demography These are a combination of 115 geo-political to be a main factor affecting land allocation regions with 126 water basins resulting in 281 decisions. Therefore, household economy FPUs. The latest version includes a water module, taking into account social and economic factors allowing for the separate treatment of rain-fed might be a more suitable approach than profit and irrigated crops and allowing for yield and maximisation approaches (Walker 2004). area reductions as a result of lack of water.

The Global Trade Analysis Project (GTAP; Hertel & Tsigas 1997) is a global computable general equilibrium model with consideration for the impacts of non-agricultural sectors on agriculture. Outputs from this model are very similar to those from the IMPACT model and include sectoral production growth rates, land use and yield factors describing changes in land productivity due to technological changes. GTAP has been linked to other models such as IMAGE in order to improve calculations of yields and feed livestock (van Meijl et al. 2006). The Common Agricultural Policy Regionalised The MIRAGE (Modelling International Impact model (CAPRI) is an agricultural Relationships in Applied General Equilibrium) economic model developed to assess agricultural model (Decreux & Valin 2007) is a multi-region, policies within the EU (Britz 2005). The model multi-sector computable general equilibrium links approximately 280 administrative regions at model (CGE) that can be used to analyse trade the NUTS-2 level with a global agricultural trade policies. Originally developed by CEPII in France model. For each of the NUTS-2 regions, CAPRI but contributed to and extensively used by simulates changes in crop areas and yields for 35 IFPRI, the model has been used to model land- crops, herd sizes of 13 animal processes as well use implications of biofuel policies in the EU. as feeding and fertilizing practise. The crops, MIRAGE uses the GTAP7 database, a publicly 33 which include fallow land, exhaust the available available global database with bilateral trade agricultural area. An extension of the model, information for 113 regions and 57 commodities, CAPRI-Spat disaggregates the NUTS-2 regions to and thus models across these regions and a grid of 1 x 1 km resolution for the EU-27 (Leip et commodities. al. 2008).

3.3 INTEGRATED LAND-USE MODELS Integrated land-use models are models that The GLOBIOM model (Havlik et al. 2011), combine economic and environmental processes developed by the International Institute of and thus overcome the limitations associated Applied Systems Analysis (IIASA) is a global with purely geographical and economic land-use integrated land-use model designed to analyse models. In most cases, integrated models consist the competition for land use between the main of a combination of separate economic and land-based production sectors: agriculture, environmental process models capable of spatially forestry and bio-energy. The model is global explicit modelling of crop functional types, such in extent and includes 18 of the world's most as the EPIC vegetation model (Izaurralde et al. important crops, as well as livestock production, 2006) used by the GLOBIOM model, and the forestry commodities and bio-energy. The Lund-Potsdam-Jena global vegetation model model simulates demand and supply quantities for managed land (LPJmL; Bondeau et al. 2007) in 30 world regions at time-steps of 10 years up used by the Nexus and LandSHIFT models, as to 2050, and allocates land use in a spatially described in the sections below. explicit way at a resolution of 50 x 50 km. There are regional versions of the model available that provide more detailed spatial representations of land-use changes and/or include more crops (i.e. GLOBIOM-EU includes 27 crops). GLOBIOM uses spatially explicit crop productivity and input use for different management systems estimated by the EPIC vegetation model (Izaurralde et al. 2006). GEONAMICA, developed by the Research and tillage. Water balance and water quality in Institute for Knowledge Systems is a commercial sub-catchments is calculated using the SWAT application framework that can be used to model (Soil Water Assessment Tool; Arnold construct integrated models of the land system. & Fohrer 2005). Other models included in the The framework can be applied at various scales framework are the EPIC model for crop growth, ranging from regional to high-resolution local ANIMO to model biodiversity (Frede et al. 2002) levels (e.g., 100 x 100 m). A number of regional and AGRISIM for agricultural trade. Linkages integrated models have been developed based between all these models do not account for on this framework for use as Decision Support feedback loops (Schaldach & Priess 2008). 34 Systems (DSS; Engelen et al. 2000) and Policy The Nexus land-use model (Souty et al. 2012) Support Systems (PSS; van Delden et al. 2007). is a global model that includes interactions The MedAction PSS (van Delden et al. 2007) between food demand, biomass energy and was developed to support the planning and forest preservation that drive food prices and policymaking in Mediterranean watersheds land-use changes. Agricultural intensification analysing land degradation, , in the model is based on nonlinear relations water management and sustainable farming. The with chemical inputs. The model focuses on model integrates three human sub-system model agricultural with forest area as a rest components – water management, land-use and category. Outputs are modelled at a resolution crop choice, and profit – with three environment of ~0.5 x 0.5 km for 12 regions of the globe for sub-system models – climate and weather model, six land-use types. Potential and actual crop hydrology and soil model and a vegetation yields for 11 functional crop types are calculated model. There is a high level of integration by the LPJmL model. Functional crop types are between all the model components. For instance, defined as “generalised and climatically adapted soil moisture calculated by the hydrology model plant prototypes designed to capture the most feeds into the plant growth model to simulate widespread types of agricultural plant traits” biomass growth (Schaldach & Priess 2008). (Bondeau et al. 2007). The Integrated Tool for Economic and Ecological The Model of Agricultural Production and its Modelling (ITE2M; Reiher et al. 2003) was Impact on the Environment (MagPIE; Lotze- developed to assess landscape services of Campen et al. 2008), is a global, spatially explicit different land-use options. The framework model of land-use change. The model focuses consists of a network of interdisciplinary models on agricultural production and land and water that address agro-economy, agricultural policy for the most important agricultural crop and and environmental services. Spatial scales range livestock production types in 10 economic from 25 x 25 m to sub-catchment levels. The core regions worldwide. The model derives land-use of the tool is the ProLand model which is a static patterns for grid cells at a resolution of three by bio-economic model that can be used to predict three degrees (ca. 300 x 300 km at the equator) optimal land-use distributions from an economic incorporating regional economic conditions and point of view. Outputs comprise information spatially explicit data on potential crop yields and on land rent, land use and land management land and water constraints. such as crop rotation, fertilizer application Integrated assessment models the scenario assumptions under which they have been run. The IMAGE model was used to develop Integrated assessment models (IAMs), are the RCP2.6 scenario, which is characterised typically large-scale models that combine natural by optimistic assumptions regarding land use, and human subsystems and can thus be classified energy efficiency and bio-energy. The IAM has as a type of integrated models although they are been used to generate the RCP6.0 scenario, which not specifically focused on land use modelling. sees emissions rise until 2080 and then decline. They comprise several modules representing The MESSAGE model has been used to develop human energy use, industrial development, the RCP8.5 scenario. This scenario assumes the agriculture and land-use or land-cover changes greatest increase in human population with 35 and scenarios of future development. The key emissions continuing to rise throughout the 21st purpose of IAMs is the modelling of climate century, and, finally, the GCAM model has been change and options for climate mitigation, used to generate the RCP4.5 scenario, which can and, as such, their key predictions are of be considered a medium-impact scenario with anthropogenic emissions rising until 2040 and then declining. (Harfoot et al. 2014). However, they also produce projections of land cover and use including Spatially explicit outputs, including land use deforestation rates, agricultural crop cultivation and land-use transitions for each of these RCPs, areas, types and inputs. These land-cover/land- are available from the RCP database (IIASA use (LC/LU) projections can therefore be used in 2009). The RCP land-use scenarios have been assessing changes to biodiversity and ecosystem harmonised with historical land-use data services under given scenarios. from the Hyde 3.1 database (Klein Goldewijk et al. 2011). This harmonised dataset includes There have been many IAMs developed over the fractional land-use patterns and underlying land- years (Moss et al. 2008); however, this review, in use transitions such as those between cropland, line with the review of Harfoot et al. (2014), will pasture, primary land and secondary recovering only focus on the four models used to develop the land as well as to and from urban land. These representative concentration pathways (RCPs) data are available for the period 1500-2005 based for the IPCC’s fifth assessment report (Moss et on Hyde 3.1 data and from 2005-2100 based on al. 2010). These models are well described and RCP projections at 0.5 x 0.5 degree resolution all provide land-cover/land-use projections for globally. Global differences in trajectories of areas the period 2000-2100 with varying time-steps. under specific land-use types for each of these The projections of each of these IAMs depend on models and RCP scenarios are shown in Figure 3.

Figure 3: Global area of crop and grassland across the RCPs. Grey area indicates the 90th percentile of scenarios. Vegetation is the part not covered by cropland or anthropogenically used grassland (source: Van Vuuren et al. 2011) The Model of Energy Supply Strategy Alternatives and their General Environment (MESSAGE) consists of a collection of models and modules developed by IIASA. The framework is able to resolve 11 world regions at time-steps of 10 years. MESSAGE uses the DIMA (dynamic integrated model of forestry and alternative land use; Rokityanskiy et al. 2007) for a spatially explicit representation of interactions and 36 feedbacks between and human land use. Crops and natural vegetation are represented by the agricultural-ecological zones (AEZ) module (Fischer et al. 2002).

The Global Climate Assessment Model (GCAM) is available as a community tool developed by the Pacific Northwest National Laboratory (PNNL) group. The framework resolves 14 world regions at time-steps of 15 years, with recent developments to the model increasing the number of time-steps to every five or one The Integrated Model to Assess the Global year. Agriculture and land use in the model is Environment (IMAGE; Alcamo 1994) was represented by the agLU model (Brenkert et al. originally developed by RIVM (National Institute 2003; Sands & Leimbach 2003). for Public Health and the Environment) in the Netherlands and is now being further developed A major disadvantage of most integrated by PBL (Netherlands Environmental Protection assessment models is that they only assess land- Agency) which is part of RIVM. The current use changes at very coarse resolutions, with the version of IMAGE is 3.0 and defines 24 world exception of the IMAGE model which can also regions. The model consists of an energy model operate at high resolution (5 arc minutes for (TIMER), an agricultural model (GTAP; Hertel & IMAGE 3.0). Recently, however, there have been Tsigas 1997), a vegetation model (TVM; van Meijl efforts to downscale IAM land-use change output et al. 2006) and a land-cover/land-use model (from 0.5 degree globally to 30 metres for the US) developed by Alcamo et al. (1998). The land-use which would make these datasets more suitable model provides land use and cover on a 0.5 x for impact analyses (West et al. 2014). Another 0.5 degree grid globally. Other outputs such as issue with IAMs, highlighted in comparative demography, energy supply and demand are all studies across similar scenarios, is that land-use represented at regional level. changes generated by IAMS can be very different than those in regional land-use models, in many The Asia-Pacific Integrated Model (AIM) cases with opposing directions of change (Busch was developed at the National Institute for 2006). Environmental Studies (NIES) and Kyoto University and comprises a dynamic optimisation model, AIM/IMPACT (policy), and AIM/CGE (Global), a general equilibrium model that resolves 24 world regions. However, the model represents the Asia-Pacific region in greater detail, and the details of the agriculture and food sub-system module are not very well described (Harfoot et al. 2014). 3.4 OTHER MODEL TYPES

Urban growth models Agent-based models Most land-use models are able to simulate Agent-based land-use models consist of a multiple land-use types. However, some models number of 'agents' that interact with each other are specifically developed to simulate urban as well as with their environment and can make growth, for example, the Sleuth UGM (Dietzel decisions and change their actions based on & Clarke 2007) model. This model started as a these interactions. The agents can have their cellular automata model to simulate the spread own model of their environment based on the 37 of wildfire and behaviour, but has since been interactions with it, and changes in the whole developed into a urban-growth and land-use system of agents depend on the aggregated model with model development encouraged by behaviour of each individual agent (Matthews publicly available source code. The model is a et al. 2007). Advantages of this type of model combination of an urban-growth model (UGM) are their ability to model individual decision- with the Deltatron Land Cover Model (LCD) and making entities and that they can incorporate has been applied throughout the world in urban social processes and non-monetary influences on settings. decision-making.

Typical agents in agent-based models are, for instance, households. For example, the Integrated Model for Simulating Household and Ecosystem Dynamics (IMSHED) was developed to study the impact of an increase in rural population on forests and giant panda habitats in China (An et al. 2005). In this study, demand for fuel wood was modelled as a function of household size, composition and types of crops grown. Results showed that policies that encouraged family planning or increased use of electricity could help to preserve forest and panda habitat. Machine learning models by reviewing historical changes between land cover maps of two time-periods combined with IDRISI land change modeller (LCM) which is maps of driver variables (e.g. distance to roads available in IDRISI GIS (Clark labs) or as an or accessibility to forest) to create a layer of extension to ArcGIS consists of a suite of tools expression of transition potential which is the that can combine LUCC analysis and modelling likelihood that a land-use will transition in the with biodiversity and greenhouse gas emission future. Transitions can be modelled with logistic assessments. The model has been applied to regression, neural network or with a similarity assess priority areas for implementation of weighted instance-based learning algorithm. REDD policies (e.g. in Tanzania, Lin et al. 2013). 38 Future change in land-use can be modelled by The model utilises a neural network approach Markov chain analysis or through a transition and contains a MAXENT (Maximum Entropy probability matrix (i.e. based on an econometric Modelling, Phillips et al. 2004) component to model). Since LCM uses neural networks (i.e. simulate species distribution that can be linked lacking process knowledge), it is more limited to changes in habitat. The model can also be for developing alternative scenarios as the linked to the MARXAN conservation planning relationship between explanatory variables and software. The land-use change module works change potential cannot be easily modified. Model/ Spatial Framework Reference resolution Scale Classification Availability CLUE Veldkamp & Depends on Globally Geographical Dyna-Clue (various Fresco (1996) input applicable more freely available versions) (CLUE) suitable for (limited extent) smaller scale Verburg & studies Overmars (2009) (Dyna-CLUE) IDRISI LCM Clark labs Depends on National, sub- Machine Available input national learning (license required) 39 DINAMICO Soares-Filho et al. Depends on National, sub- Geographical Freely available EGO (2009) input national SALU Stephenne & 2.5 x 3.75 Sudano-Sahel Rule-based Not available Lambin (2001) degree (Regional) IMPACT Rosegrant et al. 281 FPUs Global Economic Not available (2001) GTAP Hertel (1997) 113 regions Global Economic Not available CAPRI Britz (2005) NUTS-2 EU (Regional) Economic Not available regions/1 x 1 km MIRAGE Decreux & Valin 113 GTAP Global Economic Not available (2007) regions GLOBIOM Havlik et al. (2011) 0.5 x 0.5 degree Global Integrated Not available GEONAMICA RIKS up to 100 x Regional Integrated Not available 100 m ITE2M Reiher et al. up to 25 x 25 m Regional Integrated Not available (2003) Nexus Souty et al. (2012) 0.5 x 0.5 degree Global Integrated Not available MagPIE Lotze-Campen 300 x 300 km Global Integrated Not available et al. (2008) IMAGE RIVM 0.5 x 0.5 degree Global IAM Not available IAM NIES 0.5 x 0.5 degree Global IAM Not available MESSAGE IIASA 0.5 x 0.5 degree Global IAM Not available GCAM PNNL 0.5 x 0.5 degree Global IAM Not available CLUMondo Van Asselen & 5 arc min Global Land System Freely available Verburg (2013) LandSHIFT Schaldach et al. 30 arc sec - 5 Globally Land System Not available (2006) arc min applicable SLEUTH Dietzel & Clark Depends on Globally Urban growth Available (2007) input applicable IMSHED An et al. (2005) Depends on Globally Agent based Not available input applicable

Table 1: Overview of land-use models/frameworks described in text 40 4. Land-use models and biodiversity

A number of studies have focused on modelling land-use change to assess impacts on biodiversity, although most of these studies have focused on changes in forest cover only and 41 resulting impacts on biodiversity. Land-use models can also be used to project changes in habitat that can be used as a proxy for biodiversity.

Relationships between land use and biodiversity land-use change on biodiversity will be more are complex and often bi-directional, making it significant than other global change processes difficult to identify cause and effect relationships. such as climate change, nitrogen deposition, Land-use and land-management practices can species introductions and changing atmospheric play an important role in sustaining biodiversity, concentrations of carbon dioxide globally but biodiversity resources can also determine (Chapin III et al. 2000; Sala et al. 2000; Haines- land use in some cases (Haynes-Young 2009). Young 2009). It is projected that, by 2100, the impact of 42

Huston (2005) identifies three stages of land-use This concept is extended by Imhoff et al. (2004) change that can predict changes in distribution and Haberl et al. (2007) who argue that an of human population density and land-use important indicator of the impact of human intensity, impacting different components of activity on biodiversity is the HANPP or human biodiversity over time. The first phase or agrarian appropriation of net primary production. It is stage is driven by primary productivity. The estimated that for Europe, the HANPP may second phase is a transition from an agrarian be more than 70% (Imhoff et al. 2004). It is to an industrial economy in which foods and suggested that HANPP could potentially be a 'top raw materials can be transported away from level indicator' of pressure of agricultural activity agricultural centres, allowing human settlements on biodiversity (Firbank et al. 2008). to be located away from production. The third Land-use modelling to assess impacts on and final stage constitutes a further increase in biodiversity is a relatively new field of research. independence where human settlements can A Scopus (www.scopus.com) search conducted occupy any portion of the landscape without on the 25th of November 2014 using the key any necessary links to production centres - this word search terms 'land-use change model' is called the information phase. These stages and 'biodiversity' yielded 301 results, of which represent a preferred distribution of land uses just over 50% of articles were produced in in relation to productivity with a tendency to the last 4 years (since 2011) and only 21 papers minimise negative impacts on diversity of plants were published prior to 2004. Most studies and small animals but with strong negative do not specifically focus on the assessment of effects on large vertebrates that compete more biodiversity, but tend to focus on agricultural directly with human land use (Huston 2005). expansion and deforestation with assumed Understanding these principles is important impacts on biodiversity and ecosystem services. to inform land-use models to explain variation The section below describes key examples of in growth rates and ecological consequences of studies that have utilised a land-use model or different economic drivers. results from (global) land-use or integrated- assessment models to analyse the impact on biodiversity under scenarios of change at global and regional scales. 4.1 GLOBAL STUDIES Only a handful of studies exist that combine Alkemade et al. (2009), again using land-use land-use models with assessments of biodiversity change results from the IMAGE model, assessed and biodiversity change and these are dominated global terrestrial biodiversity loss under a set of by studies utilising the IMAGE global land-use policy options with the GLOBIO3 model. This dataset for different scenarios. model uses the mean species abundance (MSA) approach as an indicator of biodiversity. In this Jetz et al. (2007) analysed the impact of approach, the mean abundance of species in their combined climate and land-use change on the original, undisturbed ecosystem is compared to distribution of global land bird species under 43 their abundance in a current or future ecosystem. the Millennium Ecosystem Assessment scenarios The study concluded that MSA loss will likely to 2100. Land-use changes were modelled using continue during the coming decades, and the the IMAGE model. The study concluded that, (effective) protection of 20% of all ecosystems particularly in the near future, land-use change would lead to a small reduction in the rate of loss. in tropical countries may lead to greater species loss than climate change. Visconti et al. (2011) used the GLOBIO/HYDE model to assess future declines of biodiversity Dobrovolski et al. (2011) also used results from globally for four scenarios from the Millennium the IMAGE model at 0.5 degree resolution for the Ecosystem Assessment until 2050. Land-use 21st century under the IPCC Special Report on changes were derived from the IMAGE integrated Emission Scenarios (SRES) and intersected these assessment model and spatially allocated onto with a total of nine maps of global biodiversity grid cells at relatively high resolution (6 arc conservation priorities such as the Last of the minutes) with the GLOBIO/HYDE model. Wild and Biodiversity Hotspots datasets, in Biodiversity loss was assessed using a weighted order to analyse the impact of global agricultural species richness metric at country level. Mexico expansion on conservation priorities. ranked highest in terms of decline in richness for mammals, although most countries in the top ten of national and global losses were found in Africa.

Newbold et al. (2015) applied the PREDICTS (Projecting Responses of Ecological Diversity in Changing Terrestrial Systems) model to assess changes in biodiversity due to changes in anthropogenic pressures at global scale up to 2100, projecting rapid losses of biodiversity under a business-as-usual land use scenario and losses concentrated mostly in biodiverse but economically poor countries. The analysis used the harmonized land-use scenarios associated with the IPCCs Representative Concentration Pathways scenarios (Hurtt et al. 2011). PREDICTS has also been applied to the Rio+20 scenarios (Netherlands Environmental Assessment Agency 2010), which describe efforts to mitigate land-use change itself, through yield and technological improvements or through human dietary shifts. 4.2 REGIONAL AND NATIONAL STUDIES While global studies are few and tend to be Akber and Shrestha (2013) use the Dyna- based on the same models and biodiversity CLUE model and the GLOBIO3 model to impact assessment methods, there are numerous assess the impacts on biodiversity of three regional and national studies utilising a range of land-management scenarios in the Chiang Rai land-use models and biodiversity assessments. province of Thailand for 2030, concluding that Freely available or low-priced land-use models, if current rates of deforestation continue there such as CLUE (various versions), DINAMICA will be a significant decrease in biodiversity, as 44 EGO and IDRISI Land Change Modeler, are measured by the MSA scores. more dominant in the literature, however, as Lin et al. (2013) used the IDRISI Land Change reflected by the following examples of studies at Modeler to project deforestation in Tanzania the regional or national scale that utilise land-use for the year 2020 as part of a multi-criteria models and scenarios to assess potential impacts assessment to identify potential areas for REDD+ on biodiversity. project development, using biodiversity as a Verburg et al. (2011) used the CLUE-Scanner co-benefit evaluation criterion, with biodiversity model to assess the impacts of land-use changes change assessed as a function of distance to on biodiversity under different policy scenarios protected areas. Results were presented as a for the European Union. Biodiversity was suitability map displaying areas of high, medium assessed using indicators for habitat quality and and low suitability for future REDD+ projects landscape connectivity, and results showed that development. land-use changes have spatially different impacts Spangenberg et al. (2012) assessed risks to on biodiversity and that effectiveness of policies biodiversity under different scenarios from the is very much region and context dependent. ALARM project (Settele et al. 2005) to 2080 using Pérez-Vega et al. (2012) applied the IDRISI Land- a spatially explicit land-use model for Europe Change Modeler and the DINAMICA EGO model (MOLUSC) which uses interpretation of future to a deciduous tropical forest in western Mexico trends in current European policy that have an to assess biodiversity loss. Biodiversity was impact on land use. The study concluded that assessed by looking at changes in areas of high current EU policies to protect biodiversity appear species richness and rarity. The study concluded to be insufficient to reverse ongoing losses. that the approximate assessment of biodiversity Table 2 summarises key global and regional by both models was more accurate than a random studies using land-use models and scenarios to model. assess changes in biodiversity. Soares-Filho et al.(2006) simulated forest change in Amazonia using the cellular-automata land- use model DINAMICA EGO for eight different scenarios up to 2050 and linked the decline in forest area (ca.40%) to the changes in extent of suitable habitat for 382 mammalian species. In contrast, Bird et al. (2012) used these same projections of forest change for Amazonia to assess the extinction risk of 814 Amazonian bird species, and found that the number of threatened species is projected to increase from 3% to 8–11%. Biodiversity Reference Location LU model model/method Scenario Visconti et al. (2011) Global IMAGE/GLOBIO/ Weighted MEA HYDE species richness Alkemade et al. (2009) Global IMAGE GLOBIO3/MSA MEA Jetz et al. (2007) Global IMAGE Map overlay MEA Dobrovolski et al. (2011) Global IMAGE Map overlay SRES Akear & Shreshta (2013) Thailand Dyna-CLUE GLOBIO3 Land management Newbold et al. (2015) Global MAGE/MINICAM/ PREDICTS RCP AIM/MESSAGE 45 Lin et al. (2013) Tanzania IDRISI Land Distance to PAs BAU/past trends Change Modeler Perez-Vega et al. (2012) Mexico IDRISI Land Species Past trends Change Modeler/ richness/rarity DINAMICO EGO Soares-Filho et al. (2006)/ Brazil DINAMICA EGO Suitable habitat Regional, up to 2050 Bird et al. (2012) Verburg et al. (2011) Europe CLUE-Scanner Habitat change Targeted conservation policies Spangenberg et al. (2012) Europe MOLUSC DPSIR ALARM (Settele et al. 2005)

Table 2: Examples of studies using land-use models and scenarios to assess changes in biodiversity

4.3 LINKING LAND-COVER DATA PRODUCTS AND HABITAT SUITABILITY Land-cover maps are the basis of habitat maps monitoring as different datasets and classification and biodiversity indicator extraction. In order to systems can make comparisons difficult. Tomaselli link land-use model outputs to biodiversity, it is et al. (2013) provide a translation of some of the important to have different land-cover/land-use most common LU/LC classification systems (LU/LC) classes that represent different habitats. (CORINE, FAO-LCCS and IGBP) to existing However, having a high number of LU/LC classes in habitat taxonomies with a focus on Mediterranean land-change models increases the computational NATURA 2000 sites. This study concluded that, time of these models significantly and decreases the of the three classification schemes studied, the allocation accuracy. Choosing an appropriate LU/LC FAO-LCCS provides the finest discrimination of classification system for habitat mapping is crucial, natural and semi-natural types. Table 3 provides an particularly in terms of long-term overview of these classification schemes.

LC Classification scheme Brief description Reference CORINE Land Cover (CLC) Classification system largely used in EU. Non Bossard et al. consistent (some classes are a mix between land-cover (2000) and land-use categories).Only virtually expandable. International Geosphere- Classification system developed to cover the Belward (1996) Biosphere Programme entire Earth's surface. Not comprehensive since (IGBP) DISCover Land Cover marine environments are not considered. Neither Classification Scheme hierarchical, nor expandable. FAO Land Cover Classification system based on a set of independent Di Gregorio and Classification Scheme diagnostic criteria and a tool for harmonizing Jansen (1998), (LCCS) different LU/LC legends. Di Gregorio (2005)

Table 3: Classification schemes for land cover (based on Tomaselliet al. 2013) FAO-LCCS is the result of an initiative towards Kosmidou et al. (2014) provide a harmonisation an internationally-agreed reference base for of LCCS with the General Habitat Categories land cover (Di Gregorio & Jansen 2000). Any (GHC) classification system. GHC was developed given land-cover class in LCCS is defined by a to provide a primary structure for recording combination of a set of independent attributes ecosystems or habitats in an easy but consistent or classifiers (e.g., height, cover, life form). This manner (Kosmidou et al. 2014) and is the approach reduces the total number of classes result of a number of EU projects, including since it removes impractical combinations but the BIOHAB, a framework for coordination of also allows for the use of the most appropriate biodiversity and habitats, and the European 46 indicators. More detail in the description of a Biodiversity Observation Network (EBONE) land-cover feature is linked to the increase in the on surveillance and monitoring of biodiversity number of classifiers used. Class boundaries are through habitats. The GHCs are characterised by then defined by the number of classifiers, placing life form, leaf type, phenology, height and cover the emphasis no longer on the class name but and are further detailed by environment, site, on the set of classifiers used to define the class management and species composition. Non-life (Di Gregorio & Jansen 2000). There are eight form habitats have been added to complete the major land-cover types based on three classifiers: system for bare land areas. GHC consists of six presence of vegetation, edaphic condition and 'super categories': urban (URB), cultivated (CUL), artificiality of cover. These land-cover 'super sparsely vegetated (SPV), trees and shrubs (TRS), categories' are detailed in Table 4. herbaceous wetland (HER) and other herbaceous (HER). Class Description A11 Cultivated and managed areas A number of conversions exist in the literature A12 Natural and semi-natural terrestrial between habitat suitability (i.e., IUCN habitats) vegetation and land-cover products. For instance, Rondinini A23 Cultivated aquatic or regularly et al. (2011) developed habitat suitability scores flooded areas for terrestrial mammals based on GLOBCOVER A24 Natural and semi-natural aquatic or v2.1 classification. Subsequently, Visconti (2011) regularly flooded vegetation adapted these habitat suitability scores to link B15 Artificial surface and associated with the GLC2000 classification by providing areas a conversion between Globcover and GLC2000 B16 Bare areas classes. Another crosswalk between GLC2000 B27 Artificial water bodies, snow and and IUCN habitats has been provided by Foden B28 Natural water bodies, snow and ice et al. ( 2013). In addition, a full conversion between FAO-LCCS and CORINE, IGBDISCover Table 4: FAO-LCCS main land-cover categories and UMD legends is provided by Herold et al. (2009).

More recently, the EU-FP7 project Biodiversity Multi-Source Monitoring System from Space to Species (BIOSOS, www.biosos.eu) has developed a standardised procedure for classifying LC/LU classes according to FAO-LCCS from very high resolution satellite and airborne (i.e. LIDAR), and translating these into general habitat categories (Adamo et al. 2014). 5. Land-use models and ecosystem services

Land-use and land-management decisions have major impacts on ecosystems and the goods and services they provide to people (Daily 1997). Changes in land use or land management 47 can cause changes in the provision and value of ecosystem services, often increasing the provision and value of some services but at the same time decreasing others. A number of studies in recent years have been devoted to the quantification of ecosystem services. However, assessing projected changes in ecosystem services is more difficult as (ideally spatially explicit) information is needed on future landscape characteristics. Land-use models can provide this information, although since the assessment of ecosystem services in landscapes is still a relatively new field of research, not many studies have been carried out. A Scopus (www.scopus.com) search using the search terms 'land-use change model' combined with 'ecosystem services' yielded only 142 articles, all of which were produced after 2000, with more than 50% of those produced in the last 3 years. In part, this result relates to the relatively new interest in the concept of ecosystem services, which did not become part of mainstream literature until 1997 when the influential book 'Nature's services: societal dependence on natural ecosystems' was published (Daily 1997). In the sections below are examples of studies that these studies are fully integrated assessments but have utilised land-use models and scenarios to rather use (raster) outputs from land use models assess potential changes in ecosystem services to subsequently assess changes in ecosystem provision at different scales. Some of these services. Therefore, while these studies have used studies also include assessments of changes in specific land use models, similar assessments of biodiversity or suitable habitat for biodiversity, ecosystem services would likely be possible with as reviewed in the previous section. None of outputs from other land use models. 48 5.1 GLOBAL STUDIES Nelson et al. (2010) assessed changes in Strassburg et al. (2012) used the Global Forest provision for two global Model (G4M) to simulate deforestation under scenarios of land-use change. This study used different prices for carbon within REDD-eligible two very simple global scenarios of land-use countries to quantify the potential impacts change, where estimates of global areal change in on species extinctions increased by forest urban land and crop land between 2000 and 2015 loss and decreased by carbon conservation. It were spatially allocated to the grid cell level at 5 concluded that adequately funded and broadly km resolution. Changes in crop provision, water implemented carbon-based forest conservation availability, carbon storage and species habitat can play an important role in biodiversity were then assessed using the InVEST ecosystem conservation. services modelling system.

5.2 REGIONAL STUDIES Shoyama and Yamagata (2014) used the Dyna- Geneletti (2013) used the IDRISI Land Change CLUE model and the InVEST ecosystem service Modeler (LCM) and the InVest ecosystem service modelling system to assess changes in ecosystem modelling system to explore the impact of future service provision in the Kushiro watershed land-use zoning policies on ecosystem services in northern Japan under different land-use in the Araucania administrative region in Chile, management scenarios for 2050. The study concluding that the spatial configuration of land showed that, without conservation measures and uses is equally important to the size of land uses. with no significant increase in the timber and Polasky et al. (2010) applied the InVEST model to agricultural industries until 2060, there would be assess trade-offs between provision of ecosystem a decline in supporting and provisioning services. services under different land-use scenarios for Lawler et al. (2014) assessed the changes the state of Minnesota, USA, between 1992 and in provision of ecosystem services for the 2001. The alternative land-use scenarios were contiguous United States under two scenarios based on simple statistical allocation of land- of land-use change between 2001 and 2051. The use types on a baseline land-cover map. The scenarios represented continuation of 1990s results showed that a scenario of agricultural trends and high crop demand reflective of the expansion generated the highest private returns recent past. It was found that large differences in to landowners but lowest net social benefit of land-use trajectories generate increases in carbon all scenarios. Due to clear financial rewards storage, timber production, food production for landowners for commodity production, as from increased yields (even with declines in opposed to non-market ecosystem services, cropland area), and greater than 10% decreases agricultural expansion is more likely to emerge in habitat availability for a quarter of modelled than more conservation-oriented landscapes, species. even though the latter would generate higher net social values. Swetnam et al. (2011) applied a rule-based Benin, West Africa for 2050 under the UNEP- approach in a GIS framework to create two GEO 4 Markets First scenario, with climate different land-cover maps for the Eastern Arc projections based on the IPCC AR4 A2A scenario, Mountains of Tanzania for 2025 to assess changes concluding that large areas are projected to lose in carbon stocks. The study concluded that under 50% of their current economic value by 2050. a sustainable development scenario, 4% of carbon Many studies are specific to Europe, for instance, stocks would be lost, whereas under a business-as- the ESTIMAP modelling framework (Zulian et usual scenario 41% of carbon stocks would be lost. al. 2014) includes a high-resolution land-use Van Soesbergen and Arnell (2015) used modelled model (EU-CLUEScanner100 (Lavalle et al. 2011)) 49 land use data from the LandSHIFT model to and can be used to assess changes in supply assess a number of potential future ecosystem of ecosystem services through linkages with services or functions in East Africa, the Mekong separate modules. Currently, the framework is and parts of the Andes. This study used assumed linked to four such modules: outdoor recreation, links between land cover types and ecosystem crop pollination, coastal protection and air function provision quantified by area based on a quality regulation. method developed by Kienast et al. (2009). Table 5 summarises the above-listed global and Heubes (2012) used the LandSHIFT model and regional studies applying land-use modelling niche-based modelling to assess changes in and scenarios to assess potential changes in distribution of non-timber forest products in ecosystem services.

Ecosystem Ecosystem services Reference Location Land use model Services model included Nelson et al. Global Spatial allocation of InVEST Water, carbon, habitat (2010) estimated change in cropland and urban land Shoyama & Japan Dyna-CLUE InVEST Habitat quality, Yamagata carbon, timber, water (2014) Lawler et al. United States Econometric LU model carbon, food (2014) storage model/ production, habitat timber yield model/ habit affiliations Geneletti Chile IDRISI Land Change InVEST Water purification, soil (2013) Modeler conservation, habitat, carbon sequestration, timber production Polasky Minnesota, USA Land-cover maps of InVEST Carbon, water quality et al. (2011) recent change Swetnam Eastern Arc Rule-based land-cover Rule-based Carbon et al. (2011) Mountains, Tanzania maps Heubes Northern Benin LandSHIFT Species NTFP et al. (2012) distribution model (Biomod) Van East Africa, LandSHIFT Ecosystem Bundled provisioning Soesbergen & Mekong, Andes Functions (Kienast and regulating Arnell (2015) et al. 2009) services Zulian et al. EU EU-CLUESCanner 100 ESTIMAP Crop pollination, (2014) framework coastal protection, outdoor recreation, air quality regulation

Table 5: Examples of studies using land-use models to assess changes in ecosystem services 50 6. Data for use in land-use models

6.1 SOCIOECONOMIC DATA 51 Economic data concerns factors related to The NASA Socioeconomic Data and Applications monetary gains and losses, for instance capital Center (SEDAC) hosted by CIESIN at Columbia available or land prices (Rosa et al. 2014). University (http://sedac.ciesin.columbia. Examples of social data are family size and edu/) provides freely-available high-resolution demography but also data on people’s values and global gridded datasets on (among other behaviours. For instance, differing assumptions things) population, GDP, poverty estimates about farmers and farming practices on land and malnutrition. Data on market access at clearing and implementation of agro-forestry high resolution is available from the Institute practices can lead to significant differences for Environmental studies at VU University in model predictions as shown by Dale et al. Amsterdam (Verburg et al. 2011). However, (1994). Socioeconomic data are rarely available other socioeconomic data useful for land-use at high resolutions and are usually only modelling such as data on land-tenure systems, available at country or, at best, sub-national development policies and governance remains level (Heistermann et al. 2006). In recent years, difficult to obtain. Even at low spatial resolutions, however, advances have been made in developing such data greatly benefits land-use modelling in high-resolution (e.g., 1 x 1 km) socioeconomic order to characterise regional differences in land- datasets. use dynamics. However, due to more opaque and un-standardised collection methods, these data are generally more susceptible to uncertainty and low comparability and are costly to collect (Heistermann et al. 2006). 6.2 BASELINE LAND-USE AND LAND-COVER DATA In order to initialise, calibrate and validate land- Land-cover products based on remote sensing use models, data on current and/or historical have been developed using data from the land use is needed. Baseline land-use data in AVHRR (Loveland et al. 2000), SPOT-Vegetation most cases are statistical data such as FAO (e.g., GLC2000, Bartholomé & Belward 2005) data, which is available at national, regional MODIS (Friedl et al. 2002) and MERIS (e.g., or local scales. Agricultural land-use types are GLOBCOVER, Arino et al. 2007) sensors. While often reported as part of an agricultural census land-cover products based on remote sensing 52 providing data on cropping areas, but in many are very useful, particularly at broad strategic cases these data also provide information on scales, there has been limited integration of land management such as , fertilizer these datasets with collected data on the ground application and crop yields (Verburg et al. 2011). to allow for detection of more subtle aspects of Forestry statistics often also include information change (Haines-Young 2009). Table 6 lists the about management practices. Other types of most common sources of land-use and land- LC/LU information include field survey and cover data at global and continental scales. participatory maps and cadastral information.

Land-cover Temporal Classification product Reference Extent Spatial Resolution Resolution system Availability GLC2000 Bartholomé Global 30 arc seconds ca. 2000 LCCS Freely & Belward available (2005) Globcover Defourny Global 300 metres ca. 2005 LCCS Freely et al. (2006) available IGBPDiscover Loveland Global 30 arc seconds 1992-1993 IGBP Freely & Belward available (1997) HYDE 3.1 Klein Global 5 arc minutes 10000 BC Cropland/pasture Freely Goldewijk - 2000 AD available (2011) MODIS VCF Di Miceli Global 30 arc seconds 2000/2010 3 functional Freely et al. (2011) types (tree, bare, available herbaceous) MODIS NASA LP Global 500 metres 2001-2012 IGBP Freely (MCD12Q1) DAAC available (2013) CORINE EEA (2013) Europe 100 x 100 and 250 1990, 2000 CORINE Freely x 250 metres and 2006 available

Table 6: Global and continental land-cover products 53

Most models will use simple allocation al. 2011). Table 7 lists some of the key global algorithms to combine non-spatial census land- land-management datasets that can be used in use data with spatially explicit land-cover data land-use modelling. All these datasets are based to obtain spatial patterns of land use. However, on remote sensing data with the exception of since there are major inconsistencies among the Hyde 3.1 dataset, which was modelled. Most these data sources, these land-use patterns current land-use models focus on changes in land are of limited quality (Heistermann et al. use but do not account for differences in land 2006). Integration of census data with remote management (Verburg et al. 2011), and when land sensing is most commonly applied to determine management is taken into account this usually agricultural land uses. By assuming that census only applies to crop productivity (Schaldach data represents the true area of agricultural et al. 2011). Land-use intensification is equally land, satellite data can then be used to spatially important as land cover towards global change disaggregate these data in each administrative since it impacts on environment and human unit. However, if definitions of agricultural well-being. However, since this data cannot be land-use classes differ between countries, multi- captured with remote sensing approaches, as country or global maps based on this approach with land-cover products, there is only a limited run the risk of being inconsistent (Verburg et amount of such data available globally. Product Reference Extent Spatial Resolution Temporal Resolution Availability Agricultural Ramankutty Global 30 arc seconds Circa 2000 Freely available lands in the et al. (2008) year 2000 Global crop Monfreda Global 5 arc min Circa 2000 Freely available distribution et al. (2008) Global irrigation Siebert Global 5 arc min Circa 2000 Freely available areas et al. (2005) GAEZ IIASA Global 30 arc seconds Baseline (1961-1999), Freely available 54 2030s, 2050s, 2100 IIASA-IFPRI Fritz et al. Global 30 arc seconds 2005 Freely available global cropland (2015) and fieldsize

Table 7: Global agricultural and land management datasets

6.3 ENVIRONMENTAL AND TOPOGRAPHICAL DATA Environmental data availability is generally Roads are important drivers of land-use change better than socioeconomic data as many of these as they provide access to resources. Deadman et datasets can be obtained from either remote al. (2004) and Geist and Lambin (2002) showed sensing (e.g., topography) or interpolation of that there is a causal relationship between point data (e.g., climate data) on regular grids road construction and deforestation driven by that are often freely available. Key environmental economic and cultural factors. However, in many datasets for land-use modelling are soil datasets land-use models roads are treated as static inputs that, in combination with climate data, provide (Rosa et al. 2013). Models that do incorporate spatially explicit information on suitability for changes in the road network use the DINAMICA different land-use types. Data on other biological or IDRISI road constructor modules (Messina & factors such as plant growth rates, agricultural Walsh 2001; Soares-Filho et al. 2010; Lapola et al. yield and crop nutrient demands are also 2010; Carlson et al. 2012). important. Forest re-growth has also been used as an input into land-use models in the Amazon as a predictor for deforestation, assuming a negative correlation between deforestation and distance to re-growth (Soares-Filho et al. 2002). Topographic data usually derives from remote sensing sources such as the Shuttle Radar Topography Mission (SRTM) Digital Elevation Models, available at 90m or 1 km spatial resolution. As well as elevation, these data can be used to derive maps of slope, aspect and terrain ruggedness, which determine suitability for different land-use types. Other data that are important for land-use models are datasets of road networks. The global roads dataset gRoads (CIESIN 2013) is currently the best available and most comprehensive spatial dataset of roads compiled from multiple sources and representing road networks globally between 1980 and 2010. 6.4 MODEL VALIDATION DATA Often, land-use models are validated by of this data and it can, therefore, not be used for comparing model results for a historic period inter-comparison. There are other land-cover with actual changes in land use as they have datasets with a temporal component such as occurred. This makes it necessary to have land- the Hyde 3.1 dataset (Klein Goldewijk 2011) that use data for other years than those used for the provide historical land-use change, however, parameterisation of the model. There should also this dataset has a relatively coarse temporal and be a sufficient time-period between the years of spatial resolution. available data to properly compare observed and Uncertainty in data sources also forms an 55 simulated dynamics (Verburg et al. 2004). In the obstacle to the validation of land-use models. For case of modelling future land-use conditions, instance, remote sensing data may overestimate ideally the time-period between parameterisation the total cultivated area, which can lead to an and validation data should be as long as the time- overestimation of potential food production period for the model projections for the future. capacities or an underestimation of land-use Some land-cover datasets based on remote intensity. These structural differences between sensing have been produced for more than one data sources can be larger than the land-cover time-period but these cannot always be directly change over a period of one or more decades compared due to differences in classification (Pontius et al. 2008). schemes or resolution. The MODIS land-cover type product (MCD12Q1) does provide global Table 8 provides an overview of the various types land-cover data at high resolution (500 metres) of data and their properties that can be used in for a number of years (2001-2010). However, as validation of land-use models. shown by Scharlemann et al. (2010), there is inconsistency in the analysis and classification Spatial Spatial Temporal Thematic Data source resolution extent resolution Temporal extent properties Remote Dependent Dependent Frequent. Depending on Land-cover sensing/Aerial on sensor on sensor. Depending on launching and classes. photography (remote Coverage sensor/satellite. lifetime of sensor. Classification is sensing is limited Few remote based on sensor mostly in case of sensing data characteristics and between 0.6 clouds (not are available user preferences. m and 1 km). for radar). before 1970s. Except for aerial 56 photographs. Census/ Administrative Often Infrequent. Country specific Focus on survey data units. national level. Depending on depending on economic sectors census, often statistical system. (mostly agriculture less than every and forestry). 10 years. Land-use Dependent Varying. Often made for - Varying and fixed maps based on scale of one year only. within a specific on field mapping map. survey (often between 1:25,000 and 1:1 million). Participatory Dependent Often For one Participatory back Depending maps on scale of restricted to moment only. casting possible. on purpose of mapping. territory of mapping. one or more communities. Cadastral Precise Dependent Continuously Often available for Limited to tenure information information at on cadastral updated. long time period. conditions with property level. system. limited information about land use especially in urban environments.

Table 8: Overview of the spatial, temporal and thematic properties of land-use and land-cover data (source: Verburg et al. 2011) 7. Scenarios for use in land-use models

In order to use land-use models to project future extent and distribution of land-cover/ land-use types, it is necessary to drive these models with a set of assumptions on, for 57 instance, population growth or environmental policies. Such assumptions can be derived from scenarios and these can be used as fixed, constant assumptions or used dynamically where they change during model simulation. Scenarios are widely used in land-use planning (Verburg et al. 2006), conservation planning (Osvaldo et al. 2000) and ecosystem service assessment (MA 2005; Walz et al. 2007). In addition to the use of scenarios, land-use models can be used to explore the impacts of different policy options such as policies on biofuels or REDD implementation.

Scenarios can be defined as 'internally consistent quantification whereby key indicators (e.g., and realistic narratives describing potential economic growth, population change) are ranked future states' (Peterson et al. 2003). The term on a +/- scale. However, further quantification scenario is generally used to describe qualitative, using models is often necessary to derive the often socioeconomic storylines (e.g., the IPCC needed input drivers for land-use models. This SRES emission scenarios), as well as quantitative quantification is mostly done using global projections of the future. These storylines are economic models such as the IMPACT and typically constructed using existing conditions GTAP models that provide projections of yields and processes but incorporate likely future and production for a range of commodities at changes in drivers. Most scenarios used in national scales. environmental modelling are ultimately based Depending on the extent of analysis, global, on these socioeconomic storylines. However, regional or even local scenarios may be used. The in order for such scenarios to be used in sections below give examples of the most widely land-use models, they have to be quantified. used global scenarios in land-use modelling Quantification of qualitative storylines can as well as some examples of regional-scale be done in a number of ways. Often, scenario scenarios. development includes some form of preliminary 7.1 GLOBAL SCENARIOS

Millennium Ecosystem Assessment ● Techno Garden: This scenario portrays a globally connected world relying strongly on The Millennium Ecosystem Assessment (MA technology, and on highly-managed and often 2005) provides a set of four scenarios that engineered ecosystems to deliver needed goods were used to explore alternative development and services. Overall, eco-efficiency improves, pathways and impacts of these on ecosystems, but it is overshadowed by the risks inherent in ecosystem services and human wellbeing over large-scale human-made solutions. 58 the next 50 years. As described in chapter four, the MA scenarios have been widely used in global UNEP GEO Scenarios assessments of land-use change and impacts on biodiversity as they were quantified with the The Global Environment Outlook (GEO), led by IMAGE model. the United Nations Environment Programme (UNEP), is a consultative, participatory process The MA consists of the following four scenarios, that builds capacity for conducting integrated which were developed using a mix of qualitative environmental assessments for reporting on the and quantitative methods: state, trends and outlooks of the environment ● Global Orchestration: Depicting a (UNEP 2012). worldwide-connected society in which global Thus far, five GEO reports have been produced, markets are well developed. In this scenario, analysing environmental status and trends as supra-national institutions are well placed to well as describing outlooks. The most recent deal with global environmental problems, such published report is the GEO-5 and, unlike the as climate change and fisheries. However, their previous GEO reports, the outlook in this report reactive approach to ecosystem management focuses on choices and strategies leading to a makes them vulnerable to surprises arising sustainable future, for which two very different from delayed action or unexpected regional storylines were developed: changes. ● “Conventional world” scenarios - a view of ● Order from Strength: This scenario represents the world in 2050 assuming business-as-usual a regionalised and fragmented world concerned paths and behaviours. with security and protection, emphasising primarily regional markets, paying little ● “Sustainable world” scenarios - an alternative attention to the common goods, and with that leads to results consistent with our current an individualistic attitude toward ecosystem understanding of and agreed-upon management. goals and targets on the road to 2050.

● Adapting Mosaic: This scenario depicts a GEO-4 (UNEP 2007) consisted of four storylines fragmented world resulting from discredited that focus on the implications of actions, global institutions. It sees the rise of local approaches and societal choices at regional and ecosystem management strategies and the global levels for environment and human well- strengthening of local institutions. Investments being. The GEO-4 storylines can be summarised in human and social capital are geared towards as follows: improving knowledge about ecosystem ● The “Markets First” scenario envisages a functioning and management, resulting in world in which market-driven developments a better understanding of the importance converge on the currently prevailing values and of resilience, fragility, and local flexibility of expectations in industrialised countries. ecosystems. ● In a “Policy First” world, strong actions are ● The A1 storyline and scenario family represents undertaken by governments in an attempt to a world of rapid economic growth and low achieve specific social and environmental goals. global population growth, peaking in 2050 and then declining. It also anticipates a ● The “Security First” scenario assumes a world rapid introduction of new and more efficient full of large disparities, where inequality and technologies. There are three main variants to conflict, brought about by socioeconomic and this scenario that make different assumptions environmental stresses, prevail. about sources of energy. A1F is a fossil intensive ● The “Sustainability First” scenario pictures a world, A1T assumes no fossil fuels and A1B has a world in which a new development paradigm balanced mix across all sources. 59 emerges in response to the challenge of ● The A2 storyline represents a heterogeneous sustainability supported by new, more world where the population continuously equitable values and institutions. increases throughout the century but where The GEO-4 as well as the GEO-5 scenarios have economic growth is more regionally oriented, been quantified with the IMPACT agro-economic more fragmented and slower than the other model, and land-use projections for these storylines. scenarios have been made using the IMAGE ● The B1 storyline represents a convergent world model. Since the GEO-4 scenarios have been with similar population growth as the A1 around for longer, more studies have used these storyline, but with rapid changes in economic scenarios in assessment of land-use changes, e.g., structures focused on a service and information Schaldach et al. (2006) used the GEO-4 scenarios economy. Furthermore, a reduction in material to model land-use change for Africa using the intensity and introduction of clean and LandSHIFT model. resource efficient technologies is assumed.

IPCC-SRES ● The B2 storyline represents a world that has a continuously (but lower than A2) growing The IPCC Special Report on Emissions population but is focused on local solutions Scenarios (SRES; Nakicenovic & Swart 2000) to economic, social and environmental comprises 40 scenarios grouped into four sustainability and has intermediate economic different storylines with each storyline detailing development. different developmental pathways that lead to different emission scenarios for atmospheric The SRES scenarios have been used extensively concentrations of greenhouse gases and aerosols. to model future (global) land-use changes, The scenarios described in the report have been mostly using Integrated Assessment Models used to create projections of future climate such as IMAGE. However, they have also been change in the IPCC Third Assessment Report used in other regions using different models. For (TAR) published in 2001 and the IPCC Fourth example, Rounsevell et al. (2006) used the SRES Assessment Report (AR4) published in 2007. The scenarios to develop land-use projections for the four storylines can be summarised as follows: EU. IPCC-RCP and SSP ● SSP1: Sustainable development proceeds at a reasonably high pace, inequalities are lessened, For its Fifth Assessment Report, the IPCC used technological change is rapid and directed a new approach to scenario development, where toward environmentally friendly processes, scenarios were defined in terms of radiative including lower-carbon energy sources and forcing as Representative Concentration high productivity of land. This SSP has low Pathways (RCPs). These RCPs define endpoints mitigation and adaptation challenges and of radiative forcing that can be used in climate is somewhat similar to the SRES B1 and A1T modelling but also to analyse the world scenarios. 60 development needed to achieve a certain level of anthropogenic influence on the climate. These ● SSP2: This represents a pathway with RCPs are associated with Shared Socioeconomic moderate challenges in terms of mitigation Pathways (SSPs) that provide pathways of and adaptation and can be considered an climate policies necessary to prevent or adapt to intermediate case between SSP1 and SSP3. a certain level of climate change (i.e., mitigation ● SSP3: Unmitigated emissions are high due to and adaptation pathways). Five SSPs have been moderate economic growth, a rapidly growing defined with the following narrative starting population and slow technological change points (adapted from O'Neill et al. 2014): in the energy sector. There is high inequality and large numbers of people are vulnerable to climate change with low adaptive capacity. Similar to SRES A2 scenario.

● SSP4: A mixed world, with rapid technological development in key emitting regions leading to high mitigative capacity in places where it matters most. In other regions, development is slow, however, with high inequality leaving these regions vulnerable to climate change and with limited adaptive capacity.

● SSP5: High energy demand, mostly met with carbon-based fuels. Low investment in alternative energy and few options for mitigation. Rapid economic development and improved human capital with more equitable distribution of resources and slower population growth leading to a less vulnerable world with higher adaptive capacity to climate change. Similar to SRES A1F1 scenario. Sustainable Development Scenarios for ● Business as usual ("Growth first"): a future Rio+20 world that would result from a continuation of current policies and practices which are The Global Sustainable Development Scenarios primarily geared toward achieving sufficiently are a set of scenarios that were developed high levels of economic growth. specifically for the Rio+20 UN Conference on Sustainable Development held in Rio in ● Dynamics as usual ("Keep it up"): a future world 2012. These scenarios describe ultimate goals that results from a continuation of incremental and targets as well as pathway characteristics, progress, in line with historical patterns and policies, actions and investment needs to achieve trends. 61 sustainability goals by 2050 (Roehrl 2012). The ● Catch-up scenario ("Growth first with catch- scenarios were developed by different scenario up"): a future world which continues to focus teams, using a variety of models and different on economic growth as the primary objective, foci. For example, IIASA (Institute for Applied but makes special efforts to achieve catch-up Systems Analysis) developed scenarios on energy growth in the Least Developed Countries. pathways for sustainable development whereas PBL (Netherlands Environmental Assessment ● Green economy scenario ("Green growth"): Agency) focused more on food, land and a world that focuses on growth and partial biodiversity. The latter used the GLOBIO model environmental objectives. Economic to assess options to prevent biodiversity loss instruments are the preferred means to achieve under three sustainable development scenarios policy objectives, which are increasingly framed with the same endpoints for 2050. Other key in terms of eco-efficiency. model teams and institutes that developed ● Climate change scenario ("IPCC world"): a scenarios for Rio+20 are the Stockholm future world that considers climate change as Environment Institute (SEI), OECD, FEEM and the most important threat to humanity and GSG. In addition to the quantified scenarios, takes decisive action in terms of mitigation and a total of eight qualitative storylines were adaptation. developed to provide further context (adapted from Roehrl 2012): ● Planetary boundaries scenario ("One planet world"): a future world that emphasises action to ensure that humanity develops within a range of planetary boundaries to avoid global environmental collapse.

● Development scenario ("MDG+ world"): a future world that emphasises poverty reduction initiatives that primarily address social, education and health goals, but also take into account selected economic and environmental issues.

● Sustainable development scenario ("SD21 world"): policy follows an integrated approach to economic, social and environmental goals and major institutional change. 7.2 REGIONAL SCENARIOS While global scenarios are extremely useful National and local scenarios for global and larger-scale land-use modelling studies, they are not always the best option Eastern Arc Mountains, Tanzania to explore alternative futures and impacts at The Valuing the Arc project in Tanzania regional or local scales addressing regionally- developed two socioeconomic scenarios for specific issues. For some studies, very specific the Eastern Arc Mountains in Tanzania using scenarios have been developed. However, key informant interviews and stakeholder 62 there are also more generic – but still regional workshops. The scenarios were developed for – scenarios available, which can be useful the year 2025 and were quantified by developing depending on the scope and scale of the intended rules through a participatory process. These rules study. Below are a few examples of regional and were subsequently transformed into spatially local scenarios that can be and have been used in explicit maps representing possible end-points, land-use modelling. using Boolean rules in a GIS model followed by a grading process to identify preferences for change CCAFS regional scenarios (Swetnam et al. 2011).

The CGIAR research program on Climate Change Agricultural systems in Kenyan highlands Agriculture and Food Security (CCAFS) has Herrero et al. (2014) developed three recently developed regional socioeconomic socioeconomic scenarios of how Kenya might scenarios for East Africa, West Africa, South evolve in the coming 20 years and used these to Asia, South East Asia and Latin America up assess the potential impacts on the agricultural to 2050. These scenarios have been developed sector. The scenarios were developed through with stakeholders from policy, private sector, workshops with key stakeholders (planners, NGOs and CSOs from several countries in each policy makers, researchers) with an interest region and focussed on exploring key regional in Kenya's future agricultural development. socioeconomic and governance uncertainties. All The scenario storylines were quantified and socioeconomic storylines have been quantified translated into spatial data layers such as farming with the IMPACT and GLOBIOM models to systems and population density. examine alternative futures for global food supply, demand, trade, prices and food security. These model quantifications included different climate change alternatives based on the IPCC RCP scenarios. The GLOBIOM model results also include a spatial allocation of land-use changes under these scenarios whereas the IMPACT results can be used to drive a land-use model. The latter has been done for a number of countries in East Africa, South East Asia and Latin America using the LandSHIFT model as part of UNEP- WCMC's Commodities and Biodiversity project. 8. Conclusions and recommendations

8.1 GENERAL CONCLUSIONS 63 Land-use models are important tools that can instance, most models incorporate agricultural be used to explore potential future impacts on management to some degree but none of the biodiversity and ecosystem services and evaluate reviewed models explicitly incorporates forest potential trade-offs between different land and water management. demands, and thus inform decision-making. The link between land cover and the function(s) The complex relationships between land use provided by that land are not straightforward and and biodiversity and ecosystem services make depend on local and contextual factors. Many it difficult to explore potential future changes land-use models are not able or only partially with great certainty, but, particularly at larger able to address these issues. For example, models scales, these modelling exercises can provide focussing on deforestation are only capable of valuable information that can help prioritise addressing complete deforestation and not forest conservation action. Modelling the impacts of degradation. Models that simulate agricultural potential future land-use changes on biodiversity expansion can only partially account for changes and ecosystem services is generally a multi-step in management practices such as changes in approach involving several datasets, models inputs, potentially overestimating the extent of and assessment methods, introducing and agricultural expansion. Land-system models such propagating uncertainty at each step. as LandSHIFT and CLU-Mondo are capable of This review has documented land-use modelling representing some of these processes, but can tools that are widely used and applicable at still be improved in many aspects. regional to global scales and are particularly Many of these issues are related to limitations in useful for assessing impacts of land-use data. Most land-use models use remote sensing change on biodiversity and ecosystem services. land-cover data as a starting point. However, Significant progress has been made, specifically there are large inconsistencies between cropland in the last decade, on the representation of areas from these data and agricultural statistics different driving factors of land-use change (e.g., from FAO) in many areas around the world within these models. However, there are still a (Ramankutty et al. 2008). Another limitation number of limitations to land-use modelling. is the lack of consistent land-cover data for For example, land-use models are still limited different time-periods necessary for model in addressing feedbacks between society and spin-up and validation. New datasets tend to environment, i.e., models that incorporate focus on new classification schemes and higher changes in land use or land-use planning as a resolutions, and are usually only developed for consequence of (modelled) land-use change, one time-period. Application of new algorithms which can improve understanding of importance and classification schemes to older (e.g., Landsat) of actors and driving forces in land-use change data for different time-periods would be a step processes. Aspects of land management are also forward in addressing this limitation. not fully accounted for in land-use models. For 8.2 CONCLUSIONS It is clear that many different modelling tools Biodiversity assessment and approaches to modelling land-use change The approaches to assessing biodiversity based on and potential impacts on biodiversity and modelled land-use changes have been described ecosystem services exist. Land use models are in Section 4. Globally, most assessments have used important tools that can be used to explore land-use data from Integrated Assessment Models potential future impacts on biodiversity and in combination with species richness, abundance ecosystem services and evaluate potential or habitat suitability models. The most widely used 64 trade-offs between different land demands biodiversity model thus far has been the GLOBIO and thus inform decision-making. The model. However, the more data driven PREDICTS complex relationships between land-use and model is gaining credibility. The advantage of the biodiversity and ecosystem services make it PREDICTS approach is that it looks at the change difficult to explore potential future changes in 'naturalness' of an area, thereby providing with great certainty but particularly at larger a more unique assessment of biodiversity loss scales these modelling exercises can provide that is not solely based on number of species but valuable information that can help prioritise rather on decline of what was originally there. conservation action. Modelling the impacts of For local or regional studies, generally a variety of potential future land-use changes on biodiversity approaches is used such as relatively simple overlays and ecosystem services is generally a multi-step or more advanced habitat suitability models. The approach involving several datasets, models approaches taken tend to depend on the type of and assessment methods, introducing and land-use modelling, (biodiversity) data availability propagating uncertainty at each step. and focus of the study.

Scale and extent Ecosystem services assessment The required scale and extent of analysis for a Current ecosystem services are often assessed in project will determine the appropriate type of land- a qualitative manner, which is not possible for use model and datasets that can be used. Based on future situations. Some examples exist however a review of land-use models for the tropics, Rosa et of studies linking modelled land cover types al. (2014) concluded that there is no optimal scale with ecosystem function provision which can in terms of resolution or extent to model land-use be projected into the future (Van Soesbergen & change since each scale of analysis gives different Arnell 2015). However, the method of linking insights into the processes and outcomes (Rosa et land-cover types with ecosystem functions could al. 2013; Rindfuss et al. 2004; Rindfuss et al. 2007). be refined using (expert-based) weightings. The maximum possible resolution of land-use In addition, it would be possible to run freely model outputs depends on the complexity of the available ecosystem services models such as models and computing power available. Generally, InVEST or Co$ting Nature/WaterWorld2 with for global studies, analyses are done at 0.5 degree modelled land-use data to quantify potential (ca. 50 km) resolution (i.e., IMAGE/ Hyde) future ecosystem services. whereas continental studies may be done at 5 arc minute (ca. 10 km) or even 1 km resolution. For example, the LandSHIFT model has been applied to model land-use changes in Africa at 5 arc minute resolution based on GEO-4 scenarios (Schaldach et al. 2006; Alcamo et al. 2011).

2 www.policysupport.org 8.3 RECOMMENDATIONS Land-use modelling is very complex and time- consuming, in most cases requiring significant expertise and capacity in terms of understanding of drivers, spatial data and analysis and statistical relationships. The choice of land-use model to be used in a project therefore depends on the purpose, scale and scope of the study as well as the resources and capacity available. For larger 65 projects it may be more cost-effective to partner with land-use model developers, not least since many of these models have not been made available. However, some of the freely available or limited cost tools such as CLUE and CLUMondo may be useful for smaller projects. Furthermore, there are many existing datasets of modelled land use under a range of future scenarios that can be sourced and used for projects. For example, the IMAGE and Hyde datasets but also results from the CLUE and LandSHIFT models which have been used in global, continental and regional studies at relatively high resolution. References

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