Integrated Topographic Corrections Improve Forest Mapping Using Landsat Imagery
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https://doi.org/10.1016/j.jag.2022.102716 <-- shared 2022 paper
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β€œHIGHLIGHTS:
β€’ [They] evaluated the impacts of topographic correction on forest mapping in the mountains.
β€’ The enhanced C-correction and the physical model reduced topographic effects.
β€’ The corrected Landsat imagery time series resulted in higher accuracy.
β€’ Terrain information improved classification but not as much as topographic correction.
β€’ [They] recommend using topographic correction for forest cover mapping..."
#GIS #spatial #AtmosphericCorrection #IlluminationCondition #LandCover #ModelComparison #TimeSeries #TopographicCorrection #remotesensing #comparasion #topographic #correction #NDVI #forest #vegetation #model #modeling #spatialanalyis #accuracy #forestcover #Russia #Georgia #CaucasusMountains #spatiotemporal #landsat #elevation #DEM
Integrated topographic corrections improve forest mapping using Landsat imagery

In mountainous environments, topography strongly affects the reflectance due to illumination effects and cast shadows, which introduce errors in land …

A detailed report on the methodologies in this paper on the #decisiontree classifiers for mapping #forestcover & change was also published by Trix Estomata in 2018: https://bit.ly/Estomata_2018
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Cheers to Estomata & Schmitt’s affiliation & supporting organizations for this #LoLManuscriptMonday feature: GIZ Philippines, the K&C Initiative of JAXA & the Forest Management Bureau of DENR Philippines #EOChat #GISChat πŸ›°οΈπŸŒ³πŸ₯³

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