INVENTORY AND MAPPING OF TEMPERATE FOREST IN HIDALGO, MEXICO THROUGH SPOT AND FIELD DATA

Remote sensors combined with geospatial analytical methods provide important tools for measuring forest biophysical variables at a lower cost and at broader spatial and temporal scales than traditional forest inventories. The objective of this study was to analyze the relationship between data of the National Inventory of Forest and Soils (INFyS) of Mexico and spectral data from images of the SPOT platform for spatial estimation of basal area, timber volume and overlapping tree cover in the temperate and mesophyll forests of Hidalgo, Mexico. Four approaches to the analysis were used to generate models that describe the inventory and the distribution of the variables of interest: 1) multiple linear regression, 2) K-nearest neighbor (K-nn), 3) ratio and regression estimators, and 4) traditional forest inventory. The estimations derived from the first three methods are within the confidence interval of 95 % of the traditional forest inventory, and the values derived from ratio and regression estimators produced narrower confidence intervals. The analysis of the results indicate significant correlation between the INFyS data and the spectral bands of the SPOT satellite, particularly with the green, near infrared and mid infrared bands, as well as with the indexes and simple ratios based on these bands.