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The Amazon region comprises the largest tropical forest on the planet and is responsible for absorbing huge amounts of CO2 from the atmosphere. However, changes in land use and cover have contributed to an increase in greenhouse gas emissions, especially CO2, and in endangered indigenous lands and protected areas in the region. The objective of this study was to detect changes in CO2 emissions and removals associated with land use and land cover changes in the Brazilian Legal Amazon (BLA) through the analysis of multispectral satellite images from 2009 to 2019. The Gross Primary Production (GPP) and CO2Flux variables were estimated by the MODIS sensor onboard Terra and Aqua satellite, representing carbon absorption by vegetation during the photosynthesis process. Atmospheric CO2 concentration was estimated from the GOSAT satellite. The variables GPP and CO2Flux showed the effective flux of carbon in the BLA to atmosphere, which were weakly correlated with precipitation (r = 0.191 and 0.133). The forest absorbed 211.05 TgC annually but, due to its partial conversion to other land uses, the loss of 135,922.34 km2 of forest area resulted in 5.82 TgC less carbon being absorbed. Pasture and agriculture, which comprise the main land conversions, increased by 100,340.39 km2 and absorbed 1.32 and 3.19 TgC less, and emitted close to twice more, than forest in these areas. Atmospheric CO2 concentrations increased from 2.2 to 2.8 ppm annually in BLA, with hotspots observed in the southeast Amazonia, and CO2 capture by GPP showed an increase over the years, mainly after 2013, in the north and west of the BLA. This study brings to light the carbon dynamics, by GPP and CO2Flux models, as related to the land use and land cover in one of the biggest world carbon reservoirs, the Amazon, which is also important to fulfillment of international agreements signed by Brazil to reduce greenhouse gas emissions and for biodiversity conservation and other ecosystem services in the region. [ABSTRACT FROM AUTHOR]

Caatinga biome, located in the Brazilian semi-arid region, is the most populous semi-arid region in the world, causing intensification in land degradation and loss of biodiversity over time. The main objective of this paper is to determine and analyze the changes in land cover and use, over time, on the biophysical parameters in the Caatinga biome in the semi-arid region of Brazil using remote sensing. Landsat-8 images were used, along with the Surface Energy Balance Algorithm for Land (SEBAL) in the Google Earth Engine platform, from 2013 to 2019, through spatiotemporal modeling of vegetation indices, i.e., leaf area index (LAI) and vegetation cover (VC). Moreover, land surface temperature (LST) and actual evapotranspiration (ETa) in Petrolina, the semi-arid region of Brazil, was used. The principal component analysis was used to select descriptive variables and multiple regression analysis to predict ETa. The results indicated significant effects of land use and land cover changes on energy balances over time. In 2013, 70.2% of the study area was composed of Caatinga, while the lowest percentages were identified in 2015 (67.8%) and 2017 (68.7%). Rainfall records in 2013 ranged from 270 to 480 mm, with values higher than 410 mm in 46.5% of the study area, concentrated in the northern part of the municipality. On the other hand, in 2017 the lowest annual rainfall values (from 200 to 340 mm) occurred. Low vegetation cover rate was observed by LAI and VC values, with a range of 0 to 25% vegetation cover in 52.3% of the area, which exposes the effects of the dry season on vegetation. The highest LST was mainly found in urban areas and/or exposed soil. In 2013, 40.5% of the region's area had LST between 48.0 and 52.0 °C, raising ETa rates (~4.7 mm day−1). Our model has shown good outcomes in terms of accuracy and concordance (coefficient of determination = 0.98, root mean square error = 0.498, and Lin's concordance correlation coefficient = 0.907). The significant increase in agricultural areas has resulted in the progressive reduction of the Caatinga biome. Therefore, mitigation and sustainable planning is vital to decrease the impacts of anthropic actions. [ABSTRACT FROM AUTHOR]

• Remotely sensed data in establishment of new boundary between biomes. • Land Use and Land Cover Change and the advance of the Cerrado biome on the edge of the Brazilian Amazonia. • Biophysical-atmospheric aspects altered with the anthropic advance and its influence on public environmental policies. The Amazonia is the largest tropical forest in the world, with approximately 5 million km2 occupying about 49.3% of Brazil in the North and Central-West regions and bordering the Cerrado sensu lato (Brazilian savannah). The border area between these two biomes is called the Cerrado-Amazonia transition that extends approximately 6240 km. Currently, these limits, as well as other areas of ecotonal stress, are not clearly defined mainly due to intense Land Use and Land Cover Change (LULCC). Remote sensing is a tool that tries to define and update these limits. In this context, the objective of this study was to use remote sensing tools to determine the transition between the two large Brazilian biomes. The study area comprised the transition between the Cerrado-Amazonia biomes in Brazil and an area composed of a buffer of 300 km was created around the official line of the abovementioned biomes. We used data from some remote sensors to calculate the composition of the previous year's pixel means for the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) (-1˜1), CO 2 Flux (μmol m−2 s-1), evaporative efficiency (mm yr-1), and Gross Primary Production (GPP) (g C m−2 d−1) variables. The statistical analyses were performed by softening the pixels of all the variables by means of the Kernel density (KD). Later, the analysis of the main components and the cluster between the biomes and the remote sensing variables were applied. Therefore, we sought to standardize the results and to find an overall mean between the standards to delimit a new area of the Amazonia and Cerrado biomes, called the Transition Area between Biomes (TAB). The variables analyzed by remote sensing were effective for identifying the Amazonia and Cerrado biomes characteristics that highlighted the evaporative efficiency variable in relation to other variables in both biomes. Thus, from the results achieved, a new boundary was determined that delimits the transition of the biomes that is different than the official government boundary. The analyses used in this study constitute important tools to foment the creation of public policies able to curb the anthropic actions on these biomes. [ABSTRACT FROM AUTHOR]

• The crisis in the sugar and alcohol sector contributed to the expansion on the north zone of Maceió. • Aceleração do Crescimento and Minha Casa Minha Vida programs boosted for urban expansion in Maceió. • The urban expansion of the northern zone contributed for increase on LST. • In the NDVI, the vegetation class reduced 49.81% on last 14 years. • In the NDBI, the built-up class increased 107.96% over the past 30 years. The development / urbanization process of Brazilian cities in the last decade occurred since the creation of the Programa de Aceleração de Crescimento (PAC) and Programa Minha Casa Minha Vida (PMCMV). In this study, we evaluated the impact of these programs on urban sprawl in the northern part of the Alagoan capital, Maceió, located in the Northeast of Brazil, by using orbital products from remote sensing (Normalized Difference Vegetation Index-NDVI, Normalized Difference Built-up Index-NDBI e Land Surface Temperature-LST) during the period from 1987 to 2017. Descriptive statistics (minimum, maximum, mean and standard deviation) and Pearson correlation analysis were applied to the NDVI, NDBI and LST in the selected years (1987, 1998, 2003 and 2017). The results obtained indicated significant changes in land use and land cover in the last thirty years verified by the NDVI with a gradual decrease in vegetation cover in areas north, east and northeast of the northern zone of Maceió over time, which it was also found by the NDBI. This gradual replacement of green areas by residential and commercial developments resulted in a significant increased LST, from 26.40 ± 1.63 °C in 1987 to 32.73 ± 3.20 °C in 2017, and in the latter, some regions indicated values higher than 35 °C and the difference between 2017-1987 oscillated between 4–10 °C. The PAC and PMCMV programs contributed significantly to a change in land use and land cover, which increased the extent of urbanized areas as well as changes in the local microclimate. [ABSTRACT FROM AUTHOR]

The emission of soil carbon dioxide (CO 2) in agricultural areas is a process that results from the interaction of several factors such as climate, soil, and land management practices. Agricultural practices directly affect the carbon dynamics between the soil and atmosphere. Herein, we evaluated the temporal variability (2020/2021 crop season) of soil CO 2 emissions and its relationship with related variables, such as the CO 2 flux model, enhanced vegetation index (EVI), gross primary productivity (GPP), and leaf area index (LAI) from orbital data and soil temperature, soil moisture, and soil CO 2 emissions from in situ collections from native forests, productive pastures, degraded pastures, and areas of high-yield potential soybean and low-yield potential soybean production. A significant influence (p < 0.01) was observed for all variables and between the different land uses and occupation types. September and October had lower emissions of soil CO 2 and low means of soil moisture and soil temperature, and no differences were observed among the treatments. On the other hand, there was a significant effect of the CO 2 flux model in productive pastures, high-yield potential soybean areas, and low-yield potential soybean areas. The months with the highest CO 2 flux values in the model, regardless of land use and land cover, were October and November, which is the beginning of the rainy season. There were positive correlations between soil CO 2 emissions and GPP (0.208), LAI (0.354), EVI (0.363), and soil moisture (0.280) and negative correlations between soil CO 2 emissions and soil temperature (−0.240) and CO 2 flux model (−0.314) values. Land use and land cover showed negative correlations with these variables, except for the CO 2 flux model variable. Soil CO 2 emission values were lower for high-yield potential soybean areas (averages from 0.834 to 6.835 μmol m−2 s−1) and low-yield potential soybean areas (from 0.943 to 5.686 μmol m−2 s−1) and higher for native forests (from 2.279 to 8.131 μmol m−2 s−1), whereas the opposite was true for the CO 2 flux model. • The LULCs showed positive correlation with CO 2 Flux variable. • The highest mean FCO2 was found in the native forest area. • Positive CO 2 flux values indicate carbon loss in soybean areas. [ABSTRACT FROM AUTHOR]

Land cover mapping over large areas is essential to address a wide spectrum of socio-environmental challenges. For this reason, many global or regional land cover products are regularly released to the scientific community. Yet, the remote sensing community has not fully addressed the challenge to extract useful information from vast volumes of satellite data. Especially, major limitations concern the use of inadequate classification schemes and "black box" methods that may not match with end-users conceptualization of geographic features. In this paper, we introduce a knowledge-driven methodological approach to automatically process Sentinel-2 time series in order to produce pre-classifications that can be adapted by end-users to match their requirements. The approach relies on a conceptual framework inspired from ontologies of scientific observation and geographic information to describe the representation of geographic entities in remote sensing images. The implementation consists in a three-stage classification system including an initial stage, a dichotomous stage and a modular stage. At each stage, the system firstly relies on natural language semantic descriptions of time series of spectral signatures before assigning labels of land cover classes. The implementation was tested on 75 time series of Sentinel-2 images (i.e. 2069 images) in the Southern Brazilian Amazon to map natural vegetation and water bodies as required by a local end-user, i.e. a non-governmental organization. The results confirmed the potential of the method to accurately detect water bodies (F-score = 0.874 for bodies larger than 10 m) and map natural vegetation (max F-score = 0.875), yet emphasizing the spatial heterogeneity of accuracy results. In addition, it proved to be efficient to provide rapid estimates of degraded riparian forests at watershed level (R 2 = 0.871). Finally, we discuss potential improvements both in the system's implementation, e.g. considering additional characteristics, and in the conceptual framework, e.g. moving from pixel- to object-based image analysis and evolving towards a hybrid system combining data- and knowledge-driven approaches. • End-user requirements are rarely taken into account in large scale land cover maps. • We developed a knowlegde-driven approach to automatically map large areas. • We introduced an ontology-based conceptual framework. • We implemented a system to classify Sentinel 2 times series in the Brazilian Amazon. • The system was efficient to map vegetation gradients and small water bodies. [ABSTRACT FROM AUTHOR]