Geostatistical modeling using LiDAR-derived prior knowledge with SPOT-6 data to estimate temperate forest canopy cover and above-ground biomass via stratified random sampling |
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| Institution: | 1. AMAP Lab, IRD, CIRAD, CNRS, INRA, Montpellier University, Montpellier, France;2. Institut des sciences et industries du vivant et de l''environnement, Montpellier, France;3. French Institute of Pondicherry, Puducherry, India;4. Landscape Ecology and Plant Production Systems Unit, Université libre de Bruxelles, Brussels, Belgium;5. Department of Botany and Plant Physiology, University of Buea, Buea, Cameroon;6. Institut de Recherche pour le Développement, UMR-DIADE, Montpellier, France;7. Evolutionary Biology and Ecology, Faculté des Sciences, Université libre de Bruxelles, Brussels, Belgium;8. Herbarium et Bibliothèque de Botanique africaine, Université libre de Bruxelles, Brussels, Belgium;9. Université Paul Sabatier, CESBIO, Toulouse, France;10. Laboratoire de Botanique systématique et d''Ecologie, Département des Sciences Biologiques, Ecole Normale Supérieure, Université de Yaoundé I, Yaoundé, Cameroon;11. Center for Tropical Forest Science — Forest Global Earth observatory, Smithsonian Tropical Research Institute, Washington, USA;12. Centre de coopération Internationale en Recherche Agronomique pour le Développement, UMR-ECOFOG, Kourou, France;13. Department of Biological Sciences, Washington State University, Vancouver, USA;14. Technische Universität Dresden, Faculty of Environmental Sciences, Institute of Forest Growth and Forest Computer Sciences, Tharandt, Germany;1. Department of Agricultural, Food and Forestry Systems, Università degli Studi di Firenze, Italy;2. Northern Research Station, U.S. Forest Service, Saint Paul, MN, USA;3. Department of Economics and Statistics, Università di Siena, Italy;4. Department of Biosciences and Territory, University of Molise, Pesche (IS), Italy;1. Department of Geography, University of Hawai''i at Mānoa, 422 Saunders Hall, 2424 Maile Way, Honolulu, HI 96822, USA;2. CMCC — Centro Mediterraneo sui i Cambiamenti Climatici, via Augusto Imperatore (Euro-Mediterranean Center for Climate Change), IAFENT Division, via Pacinotti 5, Viterbo, 01100, Italy;3. Department for Innovation in Biological, Agro-food and Forest Systems, Tuscia University, Viterbo, 01100, Italy;1. National Satellite Meteorological Center, China Meteorological Administration, Beijing, China;2. Center for Forest Operations and Environment, Northeast Forestry University, Harbin, China;3. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing, China;4. Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China;5. International Institute for Earth System Science, Nanjing University, Nanjing, China |
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| Abstract: | Forest canopy cover (CC) and above-ground biomass (AGB) are important ecological indicators for forest monitoring and geoscience applications. This study aimed to estimate temperate forest CC and AGB by integrating airborne LiDAR data with wall-to-wall space-borne SPOT-6 data through geostatistical modeling. Our study involved the following approach: (1) reference maps of CC and AGB were derived from wall-to-wall LiDAR data and calibrated by field measurements; (2) twelve discrete LiDAR flights were simulated by assuming that LiDAR data were only available beneath these flights; (3) training/testing samples of CC and AGB were extracted from the reference maps inside and outside the simulated flights using stratified random sampling; (4) The simple linear regression, ordinary kriging and regression kriging model were used to extend the sparsely sampled CC/AGB data to the entire study area by incorporating a selection of SPOT-6 variables, including vegetation indices and texture variables. The regression kriging model was superior at estimating and mapping the spatial distribution of CC and AGB, as it featured the lowest mean absolute error (MAE; 11.295% and 18.929 t/ha for CC and AGB, respectively) and root mean squared error (RMSE; 17.361% and 21.351 t/ha for CC and AGB, respectively). The predicted and reference values of both CC and AGB were highly correlated for the entire study area based on the estimation histograms and error maps. Finally, we concluded that the regression kriging model was superior and more effective at estimating LiDAR-derived CC and AGB values using the spatially-reduced samples and the SPOT-6 variables. The presented modeling workflow will greatly facilitate future forest growth monitoring and carbon stock assessments for large areas of temperate forest in northeast China. It also provides guidance on how to take full advantage of future sparsely collected LiDAR data in cases where wall-to-wall LiDAR coverage is not available from the perspective of geostatistics. |
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| Keywords: | Geostatistical modeling LiDAR SPOT-6 Canopy cover Above-ground biomass |
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