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Dissertation UNIVERSITA’ DEGLI STUDI DI ROMA TOR VERGATA Dipartimento di Ingegneria Civile e Ingegneria Informatica GeoInformation Doctorate Monitoring Forests: Parameters Estimation and Vegetation Classification with Multisource Remote Sensing Data A thesis submitted in partial fulfillment for the PhD degree (Dottore di Ricerca) Candidate: MSc. Gaia Vaglio Laurin Supervisors: Prof. Leila Guerriero, Ing. Fabio Del Frate January 2014 Abstract The work presented in this thesis covers two main areas of forest research with remote sensing data: the classification of forested landscapes, conducted in a tropical and an Alpine montane region, and the estimation of parameters of forestry interest, namely above ground biomass and the Shanon-Wiener arboreal diversity index. The thesis first introduces the need of monitoring forested landscapes, their changes and their resources, illustrating objectives, motivations and areas of innovation in the present research. The material and methods adopted in the research, with specifications on the study areas, and a short thesis outline, are also presented in the Introduction chapter. A short overview of techniques and sensors used in classification and estimation of the two forest parameters of interest is presented in Chapter 2, followed by the identification of some of the most recent challenges in remote sensing applied to forest studies, which have been object of the present thesis. In Chapter 3 the first case study is introduced, as published in Remote Sensing of Environment, addressing the integration of airborne lidar and vegetation types derived from aerial photography for mapping aboveground biomass. Chapter 4 presents the research paper as published by the International Journal of Remote Sensing, dealing with discrimination of vegetation types in alpine sites with ALOS PALSAR, RADARSAT-2 and lidar derived information. Chapter 5 illustrates the third case study, which is about optical and SAR sensor synergies for forest and land cover mapping in a tropical site in West Africa, according to the paper published in the International Journal of Applied Earth Observation and Geoinformation. In Chapter 6, the case study addresses the aboveground biomass estimation in an Africa tropical forest with lidar and hyperspectral data, a paper at its second review in the ISPRS Journal of Photogrammetry and Remote Sensing. The last case study is presented in Chapter 7, and deals with biodiversity mapping in a tropical West African forest with hyperspectral data, and is also a paper at its second review in PlosONE. Finally, the research summary and conclusions are presented in Chapter 8. 2 Acknowledgments These research years have been among the most exciting and interesting of my life, but this study effort was only possible thanks to the many persons who supported me. First, all my colleagues of the EO lab, with whom I shared ideas, hypothesis, laughs, hopes, troubles, and more and more for a long time: Lino, Giorgio, Chiara, Irene, Antonio, Matteo, Daniele, Reza, Ruggero, Andrei, Simone, Zina, Cristina. Three persons really inspired me more than others, and tried to transmit me the ‘sense’ of scientific research: Riccardo Valentini (La Tuscia University) who guided me with his vision and pushed and supported me toward invaluable scientific experiences; Qi Chen (University of Hawaii) who hosted and assisted me in all the ways before, during and after my months at the University of Hawaii; David Coomes (University of Cambridge) who always provided opportunity for research, exchange and collaboration. All of them gave me their trust, patience and time, and I really hope I will have a chance to keep on sharing and collaborating with them. My supervisors at the University of Rome Tor Vergata, Leila Guerriero and Fabio Del Frate, offered continuous assistance, encouragement and support during these years. Finally I have to thank my family: all my love to my mother who followed me on the other side of the globe and to my daughter with her sweetness and patience. 3 Table of Contents Abstract 2 Acknowledgments 3 Table of contents 4 Chapter 1 - Introduction 6 1.1 Thesis objectives, motivations and innovation 7 1.2 Materials and methods 15 1.2.1 The Sierra Nevada, U.S.A (study site 1) 16 1.2.2 The Alps, Bozen, Italy (study site 2) 16 1.2.3 Gola Rainforest National Park, Sierra Leone (study site 3) 17 1.3 Thesis outline 18 1.4 References 19 Chapter 2 – Remote sensing of forested landscapes 22 2.1 Land cover mapping 23 2.2 Estimation of forest parameters 25 2.2.1 Biomass estimation 26 2.2.2 Biodiversity estimation 29 2.3 Recent challenges in forest studies 30 2.3.1 Ancillary data usefulness in AGB LiDAR-based estimations 30 2.3.2 Ancillary data usefulness in discriminating vegetation types 32 2.3.3 Data fusion: evaluating the benefits of optical and RADAR 33 sensors integration for tropical land cover classification 2.3.4 Data fusion: evaluating the integration of LiDAR and 35 hyperspectral sensors for AGB estimation 2.3.5 Evaluating the impact of field data geolocation in 37 LiDAR-based AGB estimates 2.4 References 38 Chapter 3 – Integration of airborne LiDAR and vegetation types derived 47 from aerial photography for mapping aboveground live biomass – Research paper as published in Remote Sensing of Environment. 4 Chapter 4 – Discrimination of vegetation types in alpine sites with 58 ALOS PALSAR, RADARSAT-2, and LiDAR-derived information – Research paper as published in International Journal of Remote Sensing. Chapter 5 – Optical and SAR sensor synergies for forest and land 77 cover mapping in a tropical site in West Africa – Research paper as published in International Journal of Applied Earth Observation and Geoinformation. Chapter 6 – Above ground biomass estimation in an African tropical 88 forest with LiDAR and hyperspectral data - Research paper as submitted to Journal of Photogrammetry and Remote Sensing. Chapter 7 – Biodiversity mapping in a tropical West African forest 131 with airborne hyperspectral data - Research paper as submitted to Plos One. Chapter 8 – Research summary 158 8.1 Challenges addressed 158 8.2 Conclusion 165 8.3 References 167 Appendix 1 – Curriculum Vitae and publications list 169 5 Chapter 1 Introduction Forest is defined as land spanning more than 0.5 hectares with trees higher than 5 meters and a canopy cover of more than 10 percent, or trees able to reach these thresholds in situ. It does not include land that is predominantly under agricultural or urban land use (FAO 2010). Forest ecosystems are characterized by the dominant vegetation type, stand structure, climate, soil type, and topography; local climate determine the biome level division in tropical, boreal and temperate forests. In the last three decades the international community has debated on climate change and global warming, and since the 1994 the United Nation Framework Convention on Climate Change entered into force, with the ultimate aim of preventing dangerous human interference with the climate system. Carbon dioxide has constantly increased in the last decades (Fig. 1) and the CO2 emissions are the first responsible for greenhouse effects, modifying the radiative balance of the earth, which results in increased heat absorbed and trapped into the atmosphere and thus in global warming (NOAA 2007) . Figure 1 – The Keeling curve: atmospheric carbon dioxide concentration in parts per million in the last 50 years (NOAA 2007). 6 1. Introduction Oceans are the major sinks of carbon on earth, but soil and vegetation are the first responsible – through photosynthesis – of CO2 removal from atmosphere (IPCC/GRID- Arendal 2001), with about half of forest biomass made by carbon. Deforestation is considered the responsible of about 10-20% of global annual greenhouse gases emissions. Considering the constraints of reducing emissions from industrialized countries and the increasing emission from emerging economies (i.e. Brazil, India), to avoid deforestation and forest degradation is possibly the best option to quickly and efficiently reduce carbon emissions and mitigate on-going climate change. This is one of the main reasons behind the increase in forest studies in recent years. Monitoring of forest resources is therefore essential and it can be realized by means of retrieval and classification of remote sensing data, which allow generalizing to large areas the local in situ observations. Estimation of forest biophysical and ecological parameters, among which are found woody biomass and forest biodiversity, is important for forest inventory, management and for scientific purposes (Parresol, 1999). Classification of forests, discrimination of different forest types, and mapping their extent is also essential to management and conservation activities and to assess degradation. Both retrieval and classification activities, based on remote sensing data, are needed for the full understanding of ecosystem functioning in a changing climate scenario, and its management and conservation. 1.1 Thesis objectives, motivations, innovation The main goal of this research is to innovatively use remote sensing data to produce information on important forest characteristics, such as forest parameters and classification into distinguishable vegetation classes. The main motivation behind this research is the desire to contribute to forest conservation by means of improving methods and tools for its monitoring, bringing ecology and engineering issues closer. Forests are complex ecosystems, having different and often site-specific characteristics. The
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