Discriminating Forest Successional Stages, Forest Degradation, and Land Use in Central Amazon Using ALOS/PALSAR-2 Full-Polarimetric Data

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.

Removal of Ionospheric Effects from Sigma Naught Images of the ALOS/PALSAR-2 Satellite

“…The climate of the region is classified by Köppen as AmW, with an average annual temperature of 25 • C and average relative humidity of 85%. The average annual rainfall is 1909 mm with a dry season between July and November [12].…”

“…This site is situated in a relatively flat area with an elevation between 80 and 180 m [12,13]. The vegetation in the TNF is mainly formed by dense and open ombrophylous rainforests.…”

“…These forests are composed of a continuous canopy of perennial trees with heights between 25 and 30 m, and occasional emergent trees that reach up to 50-60 m in height. The vegetation is also formed by open rainforests, secondary forests, and anthropic areas (crops and pastures), mainly in the surroundings of the national forest [12].…”

“…Optical sensor systems, commonly used to map and monitor land use and land cover, are ineffective in tropical regions, and especially in the Amazon, due to frequent cloud cover and adverse atmospheric conditions, which often make it difficult to obtain information from these images during rainy season [8]. In recent years, the use of active sensors (synthetic aperture radar (SAR)) has grown because of its advantages of being able to generate its own energy source, not being influenced by clouds or aerosols, and sensitivity to variations in the forest structure [9][10][11][12][13].…”

“…The greater penetration of microwaves allows us to understand the different forest strata that make up the forest cover at different stages of ecological succession. This makes it possible to improve the level of thematic characterization of the landscape, enabling the discrimination of different land use types [12][13][14][15][16][17][18].…”