A geographically weighted random forest approach for evaluate forest change drivers in the Northern Ecuadorian Amazon.

ABSTRACT: The Tropical Andes region includes biodiversity hotspots of high conservation priority whose management strategies depend on the analysis of forest dynamics drivers (FDDs). These depend on complex social and ecological interactions that manifest on different space-time scales and are commonly evaluated through regression analysis of multivariate datasets. However, processing such datasets is challenging, especially when time series are used and inconsistencies in data collection complicate their integration. Moreover, regression analysis in FDD characterization has been criticized for failing to capture spatial variability; therefore, alternatives such as geographically weighted regression (GWR) have been proposed, but their sensitivity to multicollinearity has not yet been solved. In this

SUBMITTER: Santos F 

PROVIDER: S-EPMC6927660 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature