Predictive Modeling of Future Forest Cover Change Patterns in Southern Belize

remote sensing Article Predictive Modeling of Future Forest Cover Change Patterns in Southern Belize Carly Voight * , Karla Hernandez-Aguilar, Christina Garcia and Said Gutierrez Ya’axché Conservation Trust, Punta Gorda Town, Toledo District, Belize; [email protected] (K.H.-A.); [email protected] (C.G.); [email protected] (S.G.) * Correspondence: [email protected]; Tel.: +501-722-0108 Received: 1 February 2019; Accepted: 28 March 2019; Published: 5 April 2019 Abstract: Tropical forests and the biodiversity they contain are declining at an alarming rate throughout the world. Although southern Belize is generally recognized as a highly forested landscape, it is becoming increasingly threatened by unsustainable agricultural practices. Deforestation data allow forest managers to efficiently allocate resources and inform decisions for proper conservation and management. This study utilized satellite imagery to analyze recent forest cover and deforestation in southern Belize to model vulnerability and identify the areas that are the most susceptible to future forest loss. A forest cover change analysis was conducted in Google Earth Engine using a supervised classification of Landsat 8 imagery with ground-truthed land cover points as training data. A multi-layer perceptron neural network model was performed to predict the potential spatial patterns and magnitude of forest loss based on the regional drivers of deforestation. The assessment indicates that the agricultural frontier will continue to expand into recently untouched forests, predicting a decrease from 75.0% mature forest cover in 2016 to 71.9% in 2026. This study represents the most up-to-date assessment of forest cover and the first vulnerability and prediction assessment in southern Belize with immediate applications in conservation planning, monitoring, and management. Keywords: GIS; remote sensing; land cover change; land change model; deforestation; forest vulnerability; conservation planning; tropical forest; Belize 1. Introduction Throughout the world, deforestation, degradation, and fragmentation threaten the integrity of tropical forests and the biodiversity that they contain. About half of the world’s tropical forests have been cleared [1], and between 1980 and 2000, over 80% of new agricultural land originated from forests [2]. This deforestation has unknown long-term effects on biodiversity, rare species, ecosystem processes and functions, climate patterns, and the existence of important resources such as medicines and crop relatives [3,4]. Considering tropical forests’ critical function in sustaining biodiversity, ecosystem services, and local livelihoods, understanding the patterns of forest cover loss and implementing conservation actions in strategic locations to prevent deforestation is crucial. Belize is generally recognized as a highly forested country with a total mature forest cover of 57.6%, according to unpublished data from February 2017 [5]. The country does not necessarily contain “primary” or “virgin” broadleaf forest due to its history of land use practices performed by the ancient Maya and natural and anthropogenic disturbances, such as hurricanes, fires, and logging [6]. Therefore, areas that have not been cleared since 1980 are referred to in this study as “mature forests” instead of “primary forests”. The Maya Golden Landscape (MGL), located in the Toledo District in southern Belize, is still mostly forested and has retained a greater amount of mature forest cover than other areas in Belize. The 311,610-hectare MGL is a mosaic of private and national protected areas, private lands, and Maya and Hispanic communities (Figure1). The region includes the primary biological corridor Remote Sens. 2019, 11, 823; doi:10.3390/rs11070823 www.mdpi.com/journal/remotesensing Remote Sens. 2019, 11, 823 2 of 17 in southern Belize, which is the only remaining broadleaf forest link between the Maya Mountains and Remotethe Sens. lowland 2019, broadleaf11, x FOR PEER forests REVIEW that extend to the coast. This connection is critically important on both3 of a 17 national and regional scale as part of the Mesoamerican biological corridor. The MGL is also part of typicallyMesoamerica’s fine scale Selva and Maya, site‐specific which is[33]. the This second method largest could remaining be applied tropical more rainforest widely in to the conservation Americas, planningafter the in Amazon. other landscapes, The Selva Maya especially supports regions over 400with bird severe species, financial about one constraints, billion overwintering as a way to prioritizemigratory the bird protection individuals, of threatened and critical areas. populations of threatened species such as the jaguar, Yucatán black howler monkey, and Baird’s tapir [6]. Figure 1. Location of study area and protected areas of the Maya Golden Landscape. Figure 1. Location of study area and protected areas of the Maya Golden Landscape. The MGL is becoming increasingly threatened as unsustainable land use practices reduce the 2. Materialsland’s capacity and Methods to provide life-supporting ecosystem services. The region is farmed predominantly through slash-and-burn agriculture. Traditionally, farmers will cultivate a plot until it decreases in 2.1.productivity, Forest Cover at Classification which point and it will Change be left Analysis to re-grow natural vegetation for a period of ten to fifteen years. During this fallow period, the soil is able to regain fertility for subsequent cultivation. However, Forest cover was classified utilizing a supervised classification of Landsat 8 Operational Land in the last few decades, the fallow period of most plots has been reduced to two or three years due to Imager (OLI) data in Google Earth Engine (GEE) for 2014, 2016, and 2017. For the remote sensing a shortage of land brought about by a steady increase in population [7,8]. Consequently, the soil is analysis, surface reflectance Landsat 8 imagery was used for the study area (path 19, row 49). not able to completely regain its fertility, resulting in more numerous and shorter agricultural cycles Orthorectificationand increased deforestation and coregistration [9]. While were farmers completed continue on toall clear the imagery secondary-growth used in this forests study that by were USGS priorleft to fallow, integration they have in also the begunGEE to(Google, cultivate Inc., mature MountainView, forests that have CA) not platform. been cleared The inimagery the recent was atmosphericallypast. There has corrected also been using an increase LaSRC in othersoftware. unsustainable Clouds and agricultural cloud shadows practices were in the masked MGL such with as the CFmasklarge-scale function. citrus Image and banana composites plantations were [10 created]. by selecting imagery with the lowest possible cloud coverProtected (<10%) area using and sustainable the composite livelihood algorithm managers available in Belize in GEE. and throughout False color the composites tropics have and normalizedlimited resources. difference Therefore, vegetation detailed indexes information (NDVI on = the (NIR location − RED)/(NIR of sites vulnerable + RED)) to were forest computed conversion to enhanceis necessary the interpretability to prioritize areas of the for imagery. law enforcement and compliance, sustainable management, and communityA stratified outreach. random However, sampling thedesign location was used and sizeto select of sites training most and vulnerable validation to future data to tropical perform the supervised classification of Landsat imagery [34]. Of the 1000 sampling points, 700 were randomly selected as training data and the remaining 300 were used as validation points. The training and validation data were primarily created from field surveys that were conducted within the MGL to collect ground‐truthed GPS points of land cover types. Training points located in remote areas that could not be visited in the field due to safety or cost constraints were visually interpreted using freely available high‐resolution satellite imagery and aerial imagery from Google EarthTM. Ecosystem layers [35] and fire point data [36] were used as reference materials to help interpretation, especially of non‐ Remote Sens. 2019, 11, 823 3 of 17 deforestation remain unknown. Thus, a need exists to identify the potential future spatial distribution and magnitude of forest loss to strategically implement conservation efforts where they will be the most effective. Data-driven models of deforestation patterns can assist forest protection and management organizations in conservation planning to efficiently allocate resources and produce the greatest conservation impact. A plethora of research has focused on the locations and rates of forest cover change based on remote sensing technology. Recently, the results of these studies have been used to identify the predictors of change and to assess specific areas where forest loss is likely to occur in the future [11–20]. Deforestation vulnerability and future forest cover change can be predicted using simulation models and empirical models [21]. These models assess how and why changes occurred in the past to understand the main drivers of deforestation in the present and therefore predict where and how much change will arise in the future. The most common methods used in land cover prediction modeling are weights of evidence [18], logistic regression [14,22], and multi-layer perceptron (MLP) neural networks [23]. Recent research has shown that MLP

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