ANALYSIS OF THE PREDICTORS INFORMATIVITY OF FOREST SOIL CARBON STOCKS MODELING (THE REPUBLIC OF KARELIA AND THE KARELIAN ISTHMUS)
Forest ecosystems are multifunctional, they perform various functions and provide ecosystem services to people. The subject of the paper is to model carbon stocks in the forest soils at the regional level. The area of interest is the Republic of Karelia and the Karelian Isthmus, located in the northern and middle taiga zones in northwestern Russia. One of the research phases is a preparation of predictors, a correlation analysis, and an analysis of the predictors informativity. Correlation analysis is necessary to identify the dependence of variables and preprocess data. We used the computing platform - Google Earth Engine (GEE) for geospatial data analysis on a regional scale.The authors analyzed 29 predictors: spectral bands of composite satellite seasonal images (spring, summer, autumn); climatic (average annual temperatures, average annual precipitation, etc.); soil characteristics (cation exchange capacity, sand, silt content, coarse fragments, etc.); elevation data (digital elevation model); forest data (forest canopy height). The Landsat-5 satellite data had additional processing: the collection USGS Landsat-5 Level-2, Collection-2, Tier-1 images collection was filtered by the study area boundary, the time period (from 2005 to 2010), and the cloudiness. Prepared predictors in GEE were uploaded as a CSV table based on a 10 x 10 km grid containing predictor values in the same sequence. We automatically created the grid points using the QGIS software package.