Remote sensing data and machine learning algorithms for canopy cover prediction in tropical agroforestry landscapes
This directory contains different scripts (in R, Python and ImageJ software) associated with manuscript on Estimation farm level and landscape range of canopy gap distribution in tropical agroforestry and forest landscapes. The analyses in the manuscript comprise Hemispherical Photographs processing, Canopy Gap Fraction estimation, Neural Network Regression of SAR Backscatter and canopy gap distribution. Please cite:
Numbisi, F. N.; Van Coillie, F. Does Sentinel-1A Backscatter Capture the Spatial Variability in Canopy Gaps of Tropical Agroforests? A Proof-of-Concept in Cocoa Landscapes in Cameroon. Remote Sens. 2020, 12, 4163. https://doi.org/10.3390/rs12244163.
Users are free to download and use the data and code in this repo for non-commerical purposes. If using and/or publishing analyses based on these scripts code, please be sure to cite the reference papers or this directory.