Abstract
We discriminated different successional forest stages, forest degradation, and land use classes in the Tapajós National Forest (TNF), located in the Central Brazilian Amazon. We used full polarimetric images from ALOS/PALSAR-2 that have not yet been tested for land use and land cover (LULC) classification, neither for forest degradation classification in the TNF. Our specific objectives were: (1) to test the potential of ALOS/PALSAR-2 full polarimetric images to discriminate LULC classes and forest degradation; (2) to determine the optimum subset of attributes to be used in LULC classification and forest degradation studies; and (3) to evaluate the performance of Random Forest (RF) and Support Vector Machine (SVM) supervised classifications to discriminate LULC classes and forest degradation. PALSAR-2 images from 2015 and 2016 were processed to generate Radar Vegetation Index, Canopy Structure Index, Volume Scattering Index, Biomass Index, and Cloude–Pottier, van Zyl, Freeman–Durden, and Yamaguchi polarimetric decompositions. To determine the optimum subset, we used principal component analysis in order to select the best attributes to discriminate the LULC classes and forest degradation, which were classified by RF. Based on the variable importance score, we selected the four first attributes for 2015, alpha, anisotropy, volumetric scattering, and double-bounce, and for 2016, entropy, anisotropy, surface scattering, and biomass index, subsequently classified by SVM. Individual backscattering indexes and polarimetric decompositions were also considered in both RF and SVM classifiers. Yamaguchi decomposition performed by RF presented the best results, with an overall accuracy (OA) of 76.9% and 83.3%, and Kappa index of 0.70 and 0.80 for 2015 and 2016, respectively. The optimum subset classified by RF showed an OA of 75.4% and 79.9%, and Kappa index of 0.68 and 0.76 for 2015 and 2016, respectively. RF exhibited superior performance in relation to SVM in both years. Polarimetric attributes exhibited an adequate capability to discriminate forest degradation and classes of different ecological succession from the ones with less vegetation cover.
Highlights
The Legal Amazonia, known as the Brazilian Amazon, covers a continuous area of more than five million square kilometers [1], with approximately three million and two hundred thousand square kilometers of tropical forest [2]
In 2019, a total of 89,186 heat points were identified in the Legal Amazonia, corresponding to an increase of 30.5% when compared to the same period in 2018 (68,345 heat points) [13]
In the southeastern portion of Pará state, we find the Tapajós National Forest (TNF), a federal conservation unit that comprises many of the unique attributes of the Brazilian Amazon, such as well-preserved forested areas with primary forest/old growth forest and forest in different successional stages
Summary
The Legal Amazonia, known as the Brazilian Amazon, covers a continuous area of more than five million square kilometers [1], with approximately three million and two hundred thousand square kilometers of tropical forest [2]. Between 2010 and 2019, the Legal Amazonia, which includes the states of Acre, Amapá, Amazonas, Mato Grosso, Pará, Rondônia, Roraima, Tocantins, and part of the Maranhão, lost about 65,348 km of its forest [7]. This loss corresponds to the gross carbon emission of 4366 million metric tons of carbon dioxide (Mt CO2) in this area [8]. The latest deforestation rate (from August 2018 to July 2019), released by the Project of Deforestation Monitoring of the Brazilian Amazon Forest by satellite (PRODES), showed a 29.5% deforestation increase in relation to the deforestation rate from 2018 [7]
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