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.

In recent years, it is possible to realize a change in the profile of deforestation in the Amazon, reflecting increasing rates of “little deforestations”, result from the diversification of productive activities related to family farming. In the Amazon, the state of Pará stands out for its strategic location when considering the Arc of Deforestation in advance, and also for its contribution in the distribution of lands by the Agrarian Reform, whose settlements add 1.055 units and 221.04 families installed. In this context, this paper aims to investigate the influence of the settlement projects of agrarian reform have on deforestation in the city of Novo Repartimento (PA) from 2000 to 2013. Keywords: Settlement; Conservation; Deforestation; Dynamics

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.

The urbanization process represented by an increased supply of housing and transport infrastructure has taken place at an accelerated rate in several regions of Brazil, especially in the metropolitan areas of the Brazilian Amazon. Despite the existence of environmental policies that guide the proper disposal of civil construction waste (CCW) in Brazil, the impacts of these policies are still negligible, pointing to the need to establish other metrics such as the measurement of greenhouse gas (GHG) emissions in CO₂eq associated with civil construction waste. This work aims to evaluate, in the second-largest city in the Brazilian Amazon, the environmental impact generated by the transportation of CCW to disposal sites, having as indicators the volume of this waste and the CO₂ emissions produced during a whole year. A literature review on life cycle carbon emissions assessment in building construction and CO₂ emissions in transportation are provided to establish the background of the research methodology. Data collection was carried out by searching large generators of construction waste, the companies responsible for transporting construction waste, and the types of vehicles used. Calculation of GHG emissions from CCW transportation was based on the method described in the 2006 IPCC Guidelines. The study identified a volume of waste of around 1244 m³/month, with a generation of 40,440 kgCO₂/year, only from small and large generators. Besides the damage identified in this study, there is also the dumping of CCW into urban streams in the city which is causing negative impacts on sanitation and drainage systems. The results point to the need to strengthen local policies to mitigate the impacts of the existing CCW to contribute to a more sustainable city.

Flood susceptibility mapping is essential for urban planning and disaster risk management, especially in rapidly urbanizing areas exposed to extreme rainfall events. This study applies an integrated approach combining Geographic Information Systems (GIS), map algebra, and the Analytic Hierarchy Process (AHP) to assess flood-prone zones in Ananindeua, Pará, Brazil. Five geoenvironmental criteria—rainfall, land use and land cover (LULC), slope, soil type, and drainage density—were selected and weighted using AHP to generate a composite flood susceptibility index. The results identified rainfall and slope as the most influential criteria, with both contributing to over 184 km² of high-susceptibility area. Spatial patterns showed that flood-prone zones are concentrated in flat urban areas with high drainage density and extensive impermeable surfaces. CHIRPS rainfall data were validated using Pearson’s correlation (r = 0.83) and the Nash–Sutcliffe efficiency (NS = 0.97), confirming the reliability of the precipitation input. The final susceptibility map, categorized into low, medium, and high classes, was validated using flood events derived from Sentinel-1 SAR data (2019–2025), of which 97.2% occurred in medium- or high-susceptibility zones. These findings demonstrate the model’s strong predictive performance and highlight the role of unplanned urban expansion, land cover changes, and inadequate drainage in increasing flood risk. Although specific to Ananindeua, the proposed methodology can be adapted to other urban areas in Brazil, provided local conditions and data availability are considered.