Monitoramento da exploração florestal planejada com séries temporais landsat no município de Paragominas, Pará.

Abstract

The Amazon is an abundant region with strategic resources: hosting half of the planet's terrestrial species, with about 40 thousand species of plants, containing the largest hydrographic basin in the world. The activity of Sustainable Forest Management seeks to preserve natural resources, especially the forest resource that should be available in the future. Logging techniques reduce collateral damage to the remaining forest and wood waste, in addition to increasing productivity, and reducing extraction costs. Monitoring of forest exploitation can be enhanced with remote sensing and digital image processing techniques, in order to detect and quantify the impacts of logging and natural changes in the forest. Traditionally, multispectral satellite images have been used to estimate the intensity and impact area of selective logging. In this context, the present study used historical series of satellite images to assess the impacts of tree extraction, and remaining collateral damage in areas under forest management plan. The analysis considered changes in the forest explored before, and up to five years after forest exploitation, using remote sensing and geoprocessing tools. The study was carried out in an area of 61 thousand hectares of dense rain forest located in the municipality of Paragominas, Pará, in the forest management area of the Rio Capim farm. Eighteen Annual Production Units (UPAs) were analyzed, explored in the period from 2000 to 2016. The location data of UPAs and Work Units (UTs) were provided by the company Cikel, which were integrated into the Google Earth Engine platform for the processing of historical series of Landsat satellite images. Images from the Landsat 5 TM, 7 ETM + and 8 OLI / TIRS satellites were selected for the period studied. Then, still on the Earth Engine, the algorithms and codes, provided by the Institute of Man and Environment of the Amazon (Imazon), the spectral mixture model (MME), and the calculation of the NDFI index (Normalized Differencing Fraction Index) were executed. NDFI images were used to quantify the openings in the canopy of the areas explored with the Absolute Deviation from Median (DAM) algorithm applied to the time series. For filtering, organization of tabular data of areas by UTs and UPAs, and for the generation of statistics and graphs, the R language was used, through the Tidyverse library. The results of this work allowed us to observe the spatio-temporal changes caused by the forest management activity in the UPAs of Fazenda Rio Capim, associated with the activity of forest exploration. The impacted area of managed logging was on average 5% of UPAs, ranging from 1% to 13%. The sign of logging increased by 40% (5% in T1, increased to 7% in T2) on average one year after the extraction, decreasing 40% (returned to 5% in T3), in year T4 the reduction was plus 40% (5% in T3, going to 3% in T4), and from year T4 to year T5 the reduction was 33% (3% in T4 going to 2% in T5). The impact of the presence of clouds on Landsat images for monitoring logging was also assessed. The occurrence of clouds significantly limited the quantification of areas impacted by logging activities. For the possible monitoring areas, 8 UPAs (out of a total of 19) could be monitored with Landast images indicating low extraction impact (below the average of logging detection, which was 5%) and 10 UPAs, with average impact to moderate (who were on average or above average, which was 5%). As next studies, the results of MME and NDFI can be correlated with data from forest inventory and post-extraction for biophysical characterization of forest exploited with management. The results of this study indicate that time series of Landsat images can be effective to detect the impact of managed extraction and as an indicator of the quality of forest management.