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This study investigated the pattern distribution of fires in the Brazilian Amazon and their relationship with deforested areas from 2003 to 2022. The primary objective was to analyze fire spots’ spatial and temporal variation. First, we examine whether open vegetation (non-forest areas) in the Amazon exhibit a higher density of fire spots than forested areas. Second, we analyze how the density of fire spots in deforested areas varies with the time elapsed since deforestation. Using recent fire monitoring data and satellite imagery, the study employed statistical techniques to reveal significant patterns and trends. Our findings indicate that fire spot density is associated with deforestation in forested lands; however, this relationship does not hold for non-forest ecosystems. Fire spots are more frequent in years with higher deforestation rates, exhibiting similar trends in both forested and non-forested areas. Nevertheless, fire spot density is generally higher in originally forested lands subjected to anthropogenic activities compared to non-forest areas experiencing similar disturbances. In contrast, fires are more common in non-forest ecosystems with minimal or no disturbance compared to forested areas. Additionally, we observe variations in fire spot density over time in deforested areas, with higher densities occurring in the year the forest was cleared. In non-forest vegetation, the frequency of fires is similar in natural and anthropized areas, but both are lower compared to areas where vegetation has been recently suppressed. This study enhances our understanding of the environmental impacts of deforestation and provides valuable insights for formulating management and conservation policies in the Amazon.

The increasing pressure from agriculture and pasture activities over non-forest vegetation areas revealed the need of INPE to extend forest regular monitoring to non-forested areas. Non-forest vegetation consists of savannahs, shrub lands, grasslands or seasonal floodplains that occupy 279 thousand km2 (6,63 %) of Brazilian Amazon biome extension. Addressed ecosystem services of non-forest vegetation vary from climate, soil, carbon storage, biodiversity, water and fire regulation to cultural benefits and living of the population. The challenge in monitoring non-forest removal lies in climatic seasonality, high variability of phytophysiognomies and cloud coverage. In this work we developed a method to map non-forest vegetation from 2000 to 2021 in a pilot area enclosing five municipalities in Roraima and Amapá states. Considering the total area of 47 thousand km2, 58 % was originally forest and 37 % was non-forest vegetation. In 2020, soybean planted areas occupied 60 thousands hectares in these municipalities. We adapted PRODES protocol to non-forest features using visual interpretation of Landsat and SENTINEL-2 satellite images. Regional expertise supported the interpretation keys to distinguish non-forest removal from seasonal changes or other land use changes. A baseline map for 2000 is provided together with biannual and annual increments for 2002–2018 and 2019–2021, respectively. Results show removal of non-forested areas even overpassing deforestation in some municipalities. Accumulated non-forest removal was 3.133,06 km2 or 17,44 % of the non-forest area enclosed. This work consolidated the method to be applied in the Brazilian Amazon biome allowing a uniform historical mapping series of deforestation and non-forest removal.

Applying remote sensing technology to map and monitor agriculture and its impacts can greatly contribute for the proper development of this activity, promoting efficient food, fiber and energy production. For that, not only remote sensing images are needed, but also ground truth information, which is a key factor for the development and improvement of methodologies using remote sensing data. While a variety of images are current available, inclusive cost-free images, field reference data is scarcer. For agricultural applications, especially in tropical regions such as Brazil, where the agriculture is very dynamic and diverse (recent agricultural frontiers, crop rotations, multiple cropping systems, several management practices, etc.), and cultivated over a vast territory, this task is not trivial. One way of boosting the researches in agricultural remote sensing is to stimulate people to share their data, and to foster different groups to use the same dataset, so distinct methods can be properly compared. In this context, our group created the LEM Benchmark Database (a project funded by the ISPRS Scientific Initiative project - 2017) from the Luiz Eduardo Magalhães (LEM) municipality, Bahia State, Brazil. The database contains a set of pre-processed multitemporal satellite images (Landsat-8/OLI, Sentinel-2/MSI and SAR band-C Sentinel-1) and shapefiles of agricultural fields with their correspondent monthly land use classes, covering the period of one Brazilian crop year (2017–2018). In this paper we present the first results obtained with this database.

The monitoring of agricultural activities at a regular basis is crucial to assure that the food production meets the world population demands, which is increasing yearly. Such information can be derived from remote sensing data. In spite of topic’s relevance, not enough efforts have been invested to exploit modern pattern recognition and machine learning methods for agricultural land-cover mapping from multi-temporal, multi-sensor earth observation data. Furthermore, only a small proportion of the works published on this topic relates to tropical/subtropical regions, where crop dynamics is more complicated and difficult to model than in temperate regions. A major hindrance has been the lack of accurate public databases for the comparison of different classification methods. In this context, the aim of the present paper is to share a multi-temporal and multi-sensor benchmark database that can be used by the remote sensing community for agricultural land-cover mapping. Information about crops in situ was collected in Luís Eduardo Magalhães (LEM) municipality, which is an important Brazilian agricultural area, to create field reference data including information about first and second crop harvests. Moreover, a series of remote sensing images was acquired and pre-processed, from both active and passive orbital sensors (Sentinel-1, Sentinel-2/MSI, Landsat-8/OLI), correspondent to the LEM area, along the development of the main annual crops. In this paper, we describe the LEM database (crop field boundaries, land use reference data and pre-processed images) and present the results of an experiment conducted using the Sentinel-1 and Sentinel-2 data.