Monitoring Forests: Parameters Estimation and Vegetation Classification with Multisource Remote Sensing Data

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. 5 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