Version 1
: Received: 14 September 2024 / Approved: 15 September 2024 / Online: 16 September 2024 (10:03:51 CEST)
How to cite:
Kombate, A.; Fotso Kamga, G. A.; Goïta, K. Modelling Canopy Height of Forest-Savannah Mosaics in Togo Using ICESat-2 and GEDI Spaceborne LiDAR and Multisource Satellite Data. Preprints2024, 2024091176. https://doi.org/10.20944/preprints202409.1176.v1
Kombate, A.; Fotso Kamga, G. A.; Goïta, K. Modelling Canopy Height of Forest-Savannah Mosaics in Togo Using ICESat-2 and GEDI Spaceborne LiDAR and Multisource Satellite Data. Preprints 2024, 2024091176. https://doi.org/10.20944/preprints202409.1176.v1
Kombate, A.; Fotso Kamga, G. A.; Goïta, K. Modelling Canopy Height of Forest-Savannah Mosaics in Togo Using ICESat-2 and GEDI Spaceborne LiDAR and Multisource Satellite Data. Preprints2024, 2024091176. https://doi.org/10.20944/preprints202409.1176.v1
APA Style
Kombate, A., Fotso Kamga, G. A., & Goïta, K. (2024). Modelling Canopy Height of Forest-Savannah Mosaics in Togo Using ICESat-2 and GEDI Spaceborne LiDAR and Multisource Satellite Data. Preprints. https://doi.org/10.20944/preprints202409.1176.v1
Chicago/Turabian Style
Kombate, A., Guy Armel Fotso Kamga and Kalifa Goïta. 2024 "Modelling Canopy Height of Forest-Savannah Mosaics in Togo Using ICESat-2 and GEDI Spaceborne LiDAR and Multisource Satellite Data" Preprints. https://doi.org/10.20944/preprints202409.1176.v1
Abstract
Quantifying forest carbon storage to better manage climate change and its effects requires accurate estimation of forest structural parameters such as canopy height. Variables from remote sensing data and machine-learning models are tools that are being increasingly used for this purpose. This study modelled canopy height of forest-savannah mosaics in the Sudano-Guinean zone of Togo. Relative heights were extracted from GEDI and ICESat-2 products, which were combined with optical, radar and topographic variables for canopy height modelling. We tested four methods: Random Forest (RF); Support Vector Machine (SVM); Extreme Gradient Boosting (XGBoost); and Deep Neural Network (DNN). The RF algorithm obtained the best predictions using 98% relative height (RH98). The best performing result was obtained from variables extracted from GEDI data (r = 0.84; RMSE = 4.15 m; MAE = 2.36 m), compared to ICESat-2 (r = 0.65; RMSE = 5.10 m; MAE = 3.80 m). Models that were developed during the study, from the combination of multisource and multisensor data, can be applied over large areas in forest-savannah mosaics, thereby contributing to better monitoring of forest dynamics according to the objectives and requirements of REDD+.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
