Estratificação em cerrado sensu stricto a partir de imagens de sensoriamento remoto e técnicas geoestatísticas

Abstract

Remote sensing images are currently used as a source of auxiliary data for the inventory of native forests. These images when combined with geo-statistical techniques can provide gains in accuracy in inventory estimates. Accordingly, the aim of this study was to: (a) evaluate the spatial dependence structure of canopy reflectance values in a Cerrado Sensu Stricto fragment; (b) determine the correlations between the spectral and the wood volume data; (c) evaluate the pre-stratification efficiency based on the reflectance values from images of Landsat 5 TM satellite in a Cerrado fragment combined to kriging estimator and compare the random stratified sampling (RSS) estimates to systematic sampling (SS) estimates through the variable of interest in the forest inventory. The study area corresponds to a Cerrado Sensu Stricto fragment in Cônego Marinho city, MG. The forest inventory data were obtained from 41 plots distributed systematically. The wood volume was obtained by volumetric equations. The spectral data were collected from image in the satellite Landsat 5 TM. The spectral data were composed by TM1, TM2, TM3, TM4 and TM5 bands and the NDVI and SR vegetation indices. The spectral data have undergone a variographic analysis and were correlated with the total wood volume. Then, the stratification was carried out in the area from the spectral data. All the spectral variables showed spatially structured. The wood volume presented the highest correlations with the reflectance variables in TM4 band (r = -0,638) and reflectance in TM5 band (r = -0,501). The inventory error for SS was 19.11%, and ranged from 13.42% to 18.39% for different stratifications. Best stratifications were generated by spectral variables that presented the highest correlation values to the wood volume and those also presented the highest degree of spatial dependence (DE).