In tropical regions, such as in the Amazon, the use of optical sensors is limited by high cloud coverage throughout the year. As an alternative, Synthetic Aperture Radar (SAR) products could be used, alone or in combination with optical images, to monitor tropical areas. In this sense, we aimed to select the best Land Use and Land Cover (LULC) classification approach for tropical regions using Sentinel family products. We choose the city of Belém, Brazil, as the study area. Images of close dates from Sentinel-1 (S-1) and Sentinel-2 (S-2) were selected, preprocessed, segmented, and integrated to develop a machine learning LULC classification through a Random Forest (RF) classifier. We also combined textural image analysis (S-1) and vegetation indexes (S-2). A total of six LULC classifications were made. Results showed that the best overall accuracy (OA) was found for the integration of S-1 and S-2 (91.07%) data, followed by S-2 only (89.53%), and S-2 with radiometric indexes (89.45%). The worse result was for S-1 data only (56.01). For our analysis the integration of optical products in the stacking increased de OA in all classifications. However, we suggest the development of more investigations with S-1 products due to its importance for tropical regions.

Urban ecosystem services (UES) is an essential approach to the development of sustainable cities and must be incorporated into urban planning to be able to improve humans’ life quality. This paper aimed to identify remote sensing (RS) data/techniques used in the literature in five years (2013–2017) for UES investigation and to analyze the similarity between them. For this purpose, we used the Scopus database of scientific journals, and a set of appropriate filters were applied. A total of 44 studies were selected, being 93.18% of them located in the Northern Hemisphere, mostly in Europe. The most common dataset used was the secondary data, followed by the Landsat family products. Land use and land cover (LULC) was the most common approach utilized, succeeded by radiometric indexes and band related. All four main classes (provision, regulation, supporting, and cultural) of ecosystem services (ES) were identified in the reviewed papers, wherein regulating services were the most popular modality mentioned. Seven different groups were established as having 100% of similarity between methods and ES results. Therefore, RS is identified in the literature as an important technique to reach this goal. However, we highlight the lack of studies in the southern hemisphere.

O uso do sensoriamento remoto voltado para a determinação de amostras de campo é de grande valia para estudos ambientais, uma vez que as imagens de satélite apresentam atributos capazes de avaliar a variabilidade espectral da superfície da água considerando uma área extensa. Desse modo, a abordagem deste trabalho objetiva definir um método de seleção estratificada de amostras baseada na variabilidade de imagens no espectro do visível e infravermelho oriundos do sensor Landsat-8/OLI. O método conta com a utilização de dados raster que representam o desvio padrão de uma série temporal de imagens Landsat-8/OLI e em seguida a definição automática de pontos de campo apoiada na técnica de amostragem estratificada aleatória. A escolha da imagem que deu origem a seleção dos pontos foi baseada na componente de maior variabilidade espectral por meio da técnica de Principal Componente. Como resultado foram obtidos vinte pontos representativos de um total de seis classes espectralmente semelhantes.

Polarimetric synthetic aperture radar (SAR) images are essential to understand forest structure and plan forest inventories with the purpose of natural resource management and environmental conservation efforts. We developed a method for estimating above-ground biomass (AGB) from power and phase-radar attributes in L-band images. The model was based on the variables “P_v” (from Freeman–Durden decomposition) and “σ°_HH”, complemented by the attributes of Touzi decomposition “α_S2”, “τ_m”, “ Φ_S3”, and “ Φ_S2”. The analyses demonstrated the contribution of volumetric, multiple, and direct scattering resulting from the interaction between the signal and the random structure of canopies and their forest biomass. The proposed model had good predictive capacity and a positive correlation (R² = 0.67 and = 0.81, respectively), with Syx = 56.9 Mg ha⁻¹ and a low average estimation error of 7.5% at R² = 0.81 in the validation. An additional exploratory analysis of the parallel polarimetric responses did not reveal a defined pattern for the different phytophysiognomies—although all indicated a predominance of multiple and/or volumetric scattering. This fact can be related to the floristic and structural variation in the primary forest units, the degree of human intervention in legal logging, and the differences among succession stages.