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Regional Scale Rain-Forest Height Mapping Using Regression-Kriging of Spaceborne and Airborne LiDAR Data: Application on French Guiana.

, , , , , , , , and . Remote. Sens., 8 (3): 240 (2016)

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Template Phenology for Vegetation Models., , , , , , , , and . IGARSS (4), page 1042-1045. IEEE, (2009)Stability in time and consistency between atmospheric corrections: Assessing the reliability of Sentinel-2 products for biodiversity monitoring in tropical forests., , , , and . Int. J. Appl. Earth Obs. Geoinformation, (2022)Regional Scale Rain-Forest Height Mapping Using Regression-Kriging of Spaceborne and Airborne LiDAR Data: Application on French Guiana., , , , , , , , and . Remote. Sens., 8 (3): 240 (2016)Inverting Aboveground Biomass-Canopy Texture Relationships in a Landscape of Forest Mosaic in the Western Ghats of India Using Very High Resolution Cartosat Imagery., , , , , , , , , and . Remote. Sens., 9 (3): 228 (2017)Regional scale rain-forest height mapping using regression-kriging of spaceborne and airborne LiDAR data: Application on French Guiana., , , , , , , , and . IGARSS, page 4109-4112. IEEE, (2015)Monitoring canopy grain of tropical forest using Fourier-based textural ordination (FOTO) of very high resolution images., , , , and . IGARSS, page 4324-4326. IEEE, (2007)Airborne Lidar Sampling Pivotal for Accurate Regional AGB Predictions from Multispectral Images in Forest-Savanna Landscapes., , , , , and . Remote. Sens., 12 (10): 1637 (2020)Exploring the Biophysical Drivers of Amazon Phenology: Preparing Data Sets to Improve Dynamic Global Vegetation Models., , , , , , , and . IGARSS (2), page 137-140. IEEE, (2008)Equatorial Forests Display Distinct Trends in Phenological Variation: A Time-Series Analysis of Vegetation Index Data from Three Continents., , , , , , and . IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 9 (8): 3505-3511 (2016)Sensing Tropical Forest Phenology and Productivity from the Field to the Satellite., , , , , and . IGARSS, page 716-719. IEEE, (2021)