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Forest Dynamics with Sentinel 2 in Antanambe between 2005 and 2016 with the Snap Tool

Forest Dynamics with Sentinel 2 in Antanambe between 2005 and 2016 with the Snap Tool
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摘要 In order to protect and sustainably manage the forest in Madagascar, which is </span><span style="font-family:Verdana;">currently one of the countries still covered by forests, it is essential to use</span><span style="font-family:Verdana;"> technological advances, particularly with regard to remote sensing. It provides valuable data, and sometimes free with a wide range of spatial, spectral and temporal resolutions to meet the demands for information on forest resources that are increasingly numerous and requires ever increasing levels of accuracy. The present work presents a methodology for the analysis of forest dynamics in the Antanambe area for the period 2005-2016 for monitoring forest degradation in this forest area to be conserved. The Random Forest algorithm was used to classify a Sentinel 2 image collected on November 07, 2016 and compare with a classification result with LandSat 5 in 2005 to detect change. The per-pixel change detection of both results captured the change map to better interpret the situation. In order to protect and sustainably manage the forest in Madagascar, which is </span><span style="font-family:Verdana;">currently one of the countries still covered by forests, it is essential to use</span><span style="font-family:Verdana;"> technological advances, particularly with regard to remote sensing. It provides valuable data, and sometimes free with a wide range of spatial, spectral and temporal resolutions to meet the demands for information on forest resources that are increasingly numerous and requires ever increasing levels of accuracy. The present work presents a methodology for the analysis of forest dynamics in the Antanambe area for the period 2005-2016 for monitoring forest degradation in this forest area to be conserved. The Random Forest algorithm was used to classify a Sentinel 2 image collected on November 07, 2016 and compare with a classification result with LandSat 5 in 2005 to detect change. The per-pixel change detection of both results captured the change map to better interpret the situation.
作者 Tsiorinantenaina René Rakotoarison Aimé Richard Hajalalaina Elysa Nambinintsoa Safidinirina Tsiorinantenaina René Rakotoarison;Aimé Richard Hajalalaina;Elysa Nambinintsoa Safidinirina(School of Management and Technological Innovation, University of Fianarantsoa, Fianarantsoa, Madagascar;Laboratory of Computer Science and Mathematics Applied to Development, University of Fianarantsoa, Fianarantsoa, Madagas-car;Computer Science, Geomatics, Mathematics and Applications, Hosting Team Fianarantsoa, Fianarantsoa, Madagascar)
出处 《Advances in Remote Sensing》 2021年第3期92-101,共10页 遥感技术进展(英文)
关键词 Random Forest Detection Change Remote Sensing FOREST Madagascar Random Forest Detection Change Remote Sensing Forest Madagascar
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