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The increased availability of remote sensing products and new legislative agendas are driving a growing focus on farm‐level traceability and monitoring to tackle commodity‐driven deforestation. Here, we use data on land use change in Brazil (1985–2021) from Mapbiomas to demonstrate how analyses of the drivers of deforestation are sensitive to the scale of analysis: while pixel‐ or property‐level analyses identify proximate drivers of deforestation, analyses at larger scales (subnational regions or countries) capture more complex land use dynamics, including indirect land use change. We argue that initiatives which seek to monitor and address commodity‐driven deforestation—such as the European Union's deforestation due‐diligence regulation and the World Business Council on Sustainable Development's Greenhouse Gas Protocol—must be conscient of these wider land use dynamics. Only by measuring progress and defining success at multiple scales can initiatives for sustainable commodity sourcing create the right mix of incentives for addressing deforestation. [ABSTRACT FROM AUTHOR]

Biomass burning (BB) emissions negatively impact the biosphere and human lives. Orbital remote sensing and modelling are used to estimate BB emissions on regional to global scales, but these estimates are subject to errors related to the parameters, data, and methods available. For example, emission factors (mass emitted by species during BB per mass of dry matter burned) are based on land use and land cover (LULC) classifications that vary considerably across products. In this work, we evaluate how BB emissions vary in the PREP-CHEM-SRC emission estimator tool (version 1.8.3) when it is run with original LULC data from MDC12Q1 (collection 5.1) and newer LULC data from MapBiomas (collection 6.0). We compare the results using both datasets in the Brazilian Amazon and Cerrado biomes during the 2002–2020 time series. A major reallocation of emissions occurs within Brazil when using the MapBiomas product, with emissions decreasing by 788 Gg (−1.91% year−1) in the Amazon and emissions increasing by 371 Gg (2.44% year−1) in the Cerrado. The differences identified are mostly associated with the better capture of the deforestation process in the Amazon and forest formations in Northern Cerrado with the MapBiomas product, as emissions in forest-related LULCs decreased by 5260 Gg in the Amazon biome and increased by 1676 Gg in the Cerrado biome. This is an important improvement to PREP-CHEM-SRC, which could be considered the tool to build South America's official BB emission inventory and to provide a basis for setting emission reduction targets and assessing the effectiveness of mitigation strategies. [ABSTRACT FROM AUTHOR]

We analyzed the occurrences of fire foci between years 2001 and 2015 in Mato Grosso state, Brazil. For this, we used remote sensing data and we correlated fire with surface temperature, rainfall, wind speed, air temperature, relative humidity, and vegetation index. The data were analyzed within the state according to use and cover classes. We also spatialize the fire occurrence using Kernel density and analyze trends with the Mann–Kendall test. The year 2015 was the year with the highest average annual air temperature and lowest annual cumulative rainfall, and wet season with the lowest relative humidity and highest surface temperature. Therefore, it was the year with less difference in the number of fire foci between wet (40.53%) and dry (59.47%) seasons. The period 2002–2005 and the years 2007 and 2010 had the highest fire foci, representing about 66.5% of the total from 2001 to 2015. In these years, 80 to 83% of fire foci occurred in the dry season. The classes with the highest density of fire were Mining Area, Urban Area, Temporary Crops, and Pasture. The fire foci in Urbanized Area, Temporary Crops, Pasture and Forestry classes tended to decrease throughout the 15 years evaluated. Fire foci were negatively correlated with rainfall, relative humidity and soil water and positively correlated with wind and surface temperature. This demonstrates that public policies related to combating fires in Brazil must be intensified especially in years with prediction of extreme drought. [ABSTRACT FROM AUTHOR]

Remote sensing is valuable for estimating aboveground biomass (AGB) stocks. However, its application in agricultural and pasture areas is limited compared with forest areas. This study quantifies AGB in agriculture–pasture mosaics within Brazil's Campo Maior Complex (CMC). The methodology employs remote sensing cloud processing and utilizes an estimator to incorporate vegetation indices. The results reveal significant changes in biomass values among land use and land cover classes over the past ten years, with notable variations observed in forest plantation, pasture, sugar cane, and soybean areas. The estimated AGB values range from 0 to 20 Mg.ha−1 (minimum), 53 to 419 Mg.ha−1 (maximum), and 19 to 57 Mg.ha−1 (mean). In Forest formation areas, AGB values range from approximately 0 to 278 Mg.ha−1, with an average annual value of 56.44 Mg.ha−1. This study provides valuable insights for rural landowners and government officials in managing the semiarid territory and environment. It aids in decision making regarding agricultural management, irrigation and fertilization practices, agricultural productivity, land use and land cover changes, biodiversity loss, soil degradation, conservation strategies, the identification of priority areas for environmental restoration, and the optimization of resource utilization. [ABSTRACT FROM AUTHOR]

This article aims is to apply a methodology to identify Trip Generating Territories (TGT) in order to discuss the relationship between transport and land use. To achieve this goal, indirect methods were applied through digital processing of orbital remote sensing images along with spatial analysis, using the Kernel Density Estimation (KDE) method. The image processing results have revealed an overall accuracy of 71% for differentiation and characterization of intra-urban classes use through the adopted typologies. In this way, through data on land use and occupation, it was possible to map out the density of the trip generating territories associated to the main built surfaces and that can indicate a higher potential of trip attraction in the urban area. Additionally, the relationship between land use and public transport system was observed in the city of Petrolina-Brazil, the empirical area of study, and the highest concentration of TGT was observed to be located in the central portion of the city, on its surroundings and on the margins of some arterial roads. Thus, it is possible to extract, in the preliminary analysis, information to support the city's transport and mobility planning. [ABSTRACT FROM AUTHOR]

Thematic maps of land cover and use can assist in the environmental monitoring of semiarid regions, mainly due to the advent of climate change, such as drought, and pressures from anthropic activities, such as the advance of urban areas. The use of geotechnologies is key for its effectiveness and low operating cost. The objective was to evaluate and understand the spatiotemporal dynamics of the loss and gain of land cover and use in a region of the Brazilian semiarid region, and identify annual trends from changing conditions over 36 years (1985 to 2020), using cloud remote sensing techniques in Google Earth Engine (GEE). Thematic maps of land cover and land use from MapBiomas Brazil were used, evaluated by Mann–Kendall trend analysis. The Normalized Difference Vegetation Index (NDVI) was also determined from the digital processing of about 800 orbital images (1985 to 2020) from the Landsat series of satellites. The trend analysis for land cover and use detected, over time, the loss of forest areas and water bodies, followed by the advance of exposed soil areas and urban infrastructure. The modification of native vegetation directly influences water availability, and agricultural activities increase the pressure on water resources, mainly in periods of severe drought. The NDVI detected that the period from 2013 to 2020 was most affected by climatic variability conditions, with extremely low average values. Thematic maps of land cover and use and biophysical indices are essential indicators to mitigate environmental impacts in the Brazilian semiarid region. [ABSTRACT FROM AUTHOR]

The objective of this work was to carry out a multitemporal analysis of changes in land use and land cover in the municipality of Floresta, Pernambuco State in Brazil. Landsat images were used in the years 1985, 1989, 1993, 1997, 2001, 2005, 2009, 2014, and 2019, and the classes were broken down into areas: water, exposed soil, agriculture, and forestry, and using the Bhattacharya classifier, the classification was carried out for generating land use maps. The data was validated by the Kappa index and points collected in the field, and the projection of the dynamics of use for 2024 was constructed. The thematic maps of land use and coverage from 1985 to 2019 show more significant changes in the forest and exposed soil classes. The increase in the forest class and the consequent reduction in exposed soil are consequences of the interaction between climate and human activities and the quality of the spatial resolution of the satellite images used between the years analyzed. [ABSTRACT FROM AUTHOR]

Summary: Protected areas have numerous roles (such as biodiversity preservation, the development of scientific research and the sustainable use of natural resources), but they are under threat from political and economic forces. The 837 000-ha Serra do Divisor National Park (SDNP) in the south-western Brazilian Amazon combines the conservation of natural resources and the maintenance of the productive activities of c. 400 resident families. The Brazilian and Peruvian governments have proposed a road linking Acre (Brazil) to Ucayali (Peru) that would bisect the SDNP. Another threat to the SDNP is a bill proposing its downgrading to an 'environmental protection area'. This study aims to map the land cover of the SDNP and its surroundings from 1988 to 2018 and to analyse the dynamics of land-use change. Analysis of Landsat satellite images with supervised classification using the MaxVer algorithm show that, during the 30-year period, pasture showed the highest absolute land-cover gain, with 1986 ha in the interior and 7661 ha along the periphery of the SDNP. Only 1% of the park's primary forest was lost by 2018, but the proposed road and potential downgrading may result in accelerated deforestation and forest degradation in the near future. [ABSTRACT FROM AUTHOR]

Although global climate change is receiving considerable attention, the loss of biodiversity worldwide continues. In this study, dynamics of land use/land cover (LULC) change in the Paraguai/Jauquara Basin, Mato Grosso, Brazil, were investigated. Two analyses were performed using R software. The first was a comparative study of LULC among the LULC classes at the polygon scale, and the second was a spatio-temporal analysis of moving polygons restricted to the agricultural regions in terms of topology, size, distance, and direction of change. The data consisted of Landsat images captured in 1993, 1997, 2001, 2005, 2009, 2013, and 2016 and processed using ArcGIS software. The proposed analytical approach handled complex data structures and allowed for a deeper understanding of LULC change over time. The results showed that there was a statistically significant change from regions of natural vegetation to pastures, agricultural regions, and land for other uses, accompanied by a significant trend of expansion of agricultural regions, appearing to stabilize from 2005. Furthermore, different patterns of LULC change were found according to soil type and elevation. In particular, the purple latosol soil type presented the highest expansion indexes since 2001, and the elevated agricultural areas have been expanding and/or stabilizing since 1997. [ABSTRACT FROM AUTHOR]

Climate variability, land use and land cover changes (LULCC) have a considerable impact on runoff–erosion processes. This study analyzed the relationships between climate variability and spatiotemporal LULCC on runoff–erosion processes in different scenarios of land use and land cover (LULC) for the Almas River basin, located in the Cerrado biome in Brazil. Landsat images from 1991, 2006, and 2017 were used to analyze changes and the LULC scenarios. Two simulations based on the Soil and Water Assessment Tool (SWAT) were compared: (1) default application using the standard model database (SWATd), and (2) application using remote sensing multiple gridded datasets (albedo and leaf area index) downloaded using the Google Earth Engine (SWATrs). In addition, the SWAT model was applied to analyze the impacts of streamflow and erosion in two hypothetical scenarios of LULC. The first scenario was the optimistic scenario (OS), which represents the sustainable use and preservation of natural vegetation, emphasizing the recovery of permanent preservation areas close to watercourses, hilltops, and mountains, based on the Brazilian forest code. The second scenario was the pessimistic scenario (PS), which presents increased deforestation and expansion of farming activities. The results of the LULC changes show that between 1991 and 2017, the area occupied by agriculture and livestock increased by 75.38%. These results confirmed an increase in the sugarcane plantation and the number of cattle in the basin. The SWAT results showed that the difference between the simulated streamflow for the PS was 26.42%, compared with the OS. The sediment yield average estimation in the PS was 0.035 ton/ha/year, whereas in the OS, it was 0.025 ton/ha/year (i.e., a decrease of 21.88%). The results demonstrated that the basin has a greater predisposition for increased streamflow and sediment yield due to the LULC changes. In addition, measures to contain the increase in agriculture should be analyzed by regional managers to reduce soil erosion in this biome. [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]

• The Cerrado biome is highly complex in terms of LULC patterns rendering classification a big challenge. • Time series analysis and phenological metrics arise as valuable assets to perform LULC mapping. • Contextual information is a key factor to accurately classify vegetation and deforestation in the Cerrado. • We provide frameworks to integrate machine learning and image processing to perform LULC analysis The Brazilian Savannah, or Cerrado, has gained vital importance in the discussions about sustainable land development after the conversion of half of its natural vegetation. For the last two decades, most of the agricultural expansion in Brazil has occurred in this biome. This is related to technological improvements in agriculture as well as to environmental compliance policies that have effectively reduced soybean expansion in the Brazilian Amazon biome. Therefore, remotely sensed imagery, pattern recognition and image processing techniques have been employed to analyze and monitor the land dynamics over Cerrado. In this work, we present a brief review on Land Use and Land Cover mapping (LULC) in the Cerrado biome from an application perspective: natural vegetation, pastureland, agriculture, and deforestation. In this review we selected some studies whose results could contribute to the development of more detailed and accurate LULC maps for the Cerrado biome. [ABSTRACT FROM AUTHOR]

In the last 30 years, the growth of the agriculture and livestock industries in the Cerrado biome has caused severe changes in land use and land cover (LULC), and areas previously occupied by native vegetation are changing to agricultural monocultures (e.g., soybean or corn) and/or pastures. Thus, the objective of this study was to analyze the LULC changes for the years 1986, 1999, 2007, and 2016 based on Landsat time series and object-based image analysis (OBIA) for the Prata River Basin. Twelve LULC classes were mapped: riparian forest, cerrado, swampy grasslands, wetlands, semideciduous forest, pasture, agriculture, fallow agricultural land, barren land, eucalyptus, water bodies, and burnt area. The classifications presented results with an overall accuracy of more than 93% and a kappa coefficient of 0.92. In 2007, the pasture class had the highest increase in area (48.5%), with a total area of 118.32 km2 of Cerrado biome vegetation converted to pasture, and the classes banhado, riparian forest, swampy grasslands, and cerrado had the greatest reductions in area (41.58%, 29.67%, 25.44%, and 21.63%, respectively). More precisely, the wetlands class underwent the greatest decrease under the advancement of pasture in the studied period (− 36.2%). These changes are due to factors favorable to agropastoral practices, such as a flat relief and soil with good agricultural suitability. [ABSTRACT FROM AUTHOR]

Copyright of Revista Brasileira de Engenharia Agricola e Ambiental - Agriambi is the property of Revista Brasileira de Engenharia Agricola e Ambiental and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The objective of this study was to quantify and spatialize the agricultural composition and current land use in part of the Irrigation perimeter Nilo Coelho from the Landsat 8 satellite digital image classification. At first we performed the unsupervised classification image related to January 2014, identifying the separability within natural vegetation, water bodies, agricultural crops and anthropized areas. Then the supervised classification Maxver model was performed where it was observed that the fruit classes “Grape” (5.41%) “Acerola” (3.75%), “Coconut” (6.14%) “Guava” (6.17%), “Banana” (2.29%) and “Mango” (8.18%) showed dominance in the study area. These classes stood out in terms of yield in the area, pointing out that in that expansion which occurs in an area with agricultural crops in the irrigated Nilo Coelho, crops of mango, coco, guava, acerola, banana and grape also grow. In addition, we observed the emergence of new areas with passion fruit crops, melon, watermelon, corn, beans, onions, pumpkin, sweet potatoes, tomatoes, bell peppers, among other crops. Furthermore, we also found the occurrence of pastures containing buffel and elephant grasses, intercropping or not. The estimated overall accuracy was 80.52%, and the mapping defined as very good, with Kappa index equal to 0.7783. [ABSTRACT FROM AUTHOR]

The Brazilian Cerrado area is in rapid decline because of the expansion of modern agriculture. In this study, we used extensive field data and a 30-year chronosequence of Landsat images (1980-2010) to assess the effects of time since conversion of Cerrado into agriculture upon soil chemical attributes and soybean/corn yield in the Alto do Rio Verde watershed. We determined the rates of vegetation conversion into agriculture, the agricultural land use time since conversion, and the temporal changes in topsoil (0-20 cm soil depth) and subsurface (20-40 cm) chemical attributes of the soils. In addition, we investigated possible associations between fertilization/over-fertilization and land use history detected from the satellites. The results showed that 61.8% of the native vegetation in the Alto do Rio Verde watershed was already converted into agriculture with 31% of soils being used in agriculture for more than 30 years. While other fertilizers in cultivated soils (e.g., Ca, Mg, and P) have been compensated over time by soil management practices to keep crop yield high, large reductions in C (38%) and N (29%) were observed in old cultivated areas. Furthermore, soybean and cornfields having more than 10 years of farming presented higher values of P and Mg than the ideal levels necessary for plant development. Therefore, increased risks of over-fertilization of the soils and environmental contamination with these macronutrients were associated with soybean and cornfields having more than 10 years of farming, especially those with more than 30 years of agricultural land use. [ABSTRACT FROM AUTHOR]

Copyright of Revista Ceres is the property of Revista Ceres and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Greenhouse gas (GHG) sources and sinks are an important global concern. Monitoring the spatiotemporal variations of GHG concentrations, particularly carbon dioxide (CO 2), is crucial for identifying potential sources and sinks and moving toward a sustainable future. Therefore, via a time-series of remote data and multispectral images, this study evaluates the CO 2 spatiotemporal dynamics and related factors during 2015–2018 in one of the world's main agricultural frontier areas, the state of Mato Grosso (SMT), Brazil, which is both experiencing continued deforestation and attempting to achieve sustainable food production. In this study, data was obtained from the measurement of column-averaged carbon dioxide (CO 2) dry air mole fraction in the atmosphere, set as X CO2 from Orbiting Carbon Observatory-2 satellite from January 2015 to December 2018. The enhanced vegetation index data were obtained from the Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor, and rainfall data were obtained from the Climate Hazards Group InfraRed Precipitation with Station dataset. From a series of Landsat-8 satellite images, it was possible to distinguish land use and land cover classes and estimate the CO 2 flux in the SMT. The results showed that the temporal variability of CO 2 flux is correlated positively with rainfall, while X CO2 is negatively correlated with rainfall. Regarding spatial variability, we observed that forest areas that were converted to other land uses resulted in higher values that characterize with sources, and that the highest and lowest average concentrations of CO 2 occurred in the dry and rainy months, respectively, for X CO2 , which might be the result of differences in the vertical resolution of the CO 2 column and scale. In contrast, areas with large continuous forest areas tended to have lower values and contribute positively to the carbon balance as sinks, thereby mitigating climate change impacts. Therefore, not only X CO2 but also CO 2 flux are directly related to changes in land use and land cover (LULC) in complex systems that are affected by climatic variables and processes, such as photosynthesis and soil respiration. • X CO2 is inversely related to rainfall, with highest concentration in drier periods. • Human actions in land use and land cover change increase atmospherical CO 2. • Remote sensing to locate and understand the sources and sinks of carbon dioxide. [ABSTRACT FROM AUTHOR]

The objective of this study was to quantify and analyze the level of anthropic changes and its relations with land use, land cover, and soil types in the Diamantino river sub-basin, Mato Grosso State. For this purpose, satellite images of Landsat-5 and Landsat-8 were used, which were processed and classified with the Spring software. The thematic classes were quantified using the ARCgis software. The degree of human disturbance was verified by the Anthropic Transformation Index. The soil types in the basin were quantified, considering a cut-out of the Soils Map from Mato Grosso State, also using ARCgis. Among the classes mapped, pasture presented a significant expansion in the last 20 years, to the detriment of suppression from the natural vegetation in all sub-basins. The soil types Red Latosol and Red-Yellow are prominent in the Diamantino river sub-basin. The calculation of the Anthropic Transformation Index indicated that the level of changes at the Diamantino river sub-basin increased. In all sub-basins, this level was degraded between 1993 and 2013. In this study, the main factor that favored the increase of the Anthropic Transformation Index was the expansion of pasture. [ABSTRACT FROM AUTHOR]

Expansion of agricultural lands have shaped Brazilian Cerrado landscapes in recent decades; however, the environmental consequences of these transformations are still poorly assessed. This paper presents a multidisciplinary approach used to assess historical land-use and land-cover (LULC) changes and their impacts on the environment in southeast Mato Grosso State, a region where the Cerrado has been intensively converted into agricultural lands. The methodology encompassed three main stages: (1) quantifying LULC changes using remote sensing data, (2) assessing LULC change impacts on vulnerable lands (e.g. erosion prone areas and wetlands), and (3) summarizing preceding information into key environmental indicators, assessed within the Pressure-State-Response framework (PSR) of the Organization for Economic Co-operation and Development (OECD). The results indicated a drastic landscape transformation in the selected area, which evolved from predominantly vegetated to a consolidated agricultural region. Crops expanded at high rates from 1985 to 1995, occupying the majority of the lands suited for agriculture. In the following decade, crops continued to expand and encroached into fragile environments (e.g. wetlands and more erodible soils). As a result, from 1985 to 2005, the area lost approximately 42% of its natural vegetation and erosion risks increased significantly. Our integration of land-use change information with intrinsic environmental vulnerabilities allowed a deeper understanding of LULC changes consequences and provided environmental indicators. This offered a synoptic view of how LULC changes occurred and how they affected the environment at a landscape scale. Furthermore, the assessment of the indicators using the PSR framework, helped to clarify cause–effect relationships thus furnishing key information of value to decision-makers and future comparisons with other areas. [ABSTRACT FROM AUTHOR]

We assess the water balance of the Brazilian Cerrado based on remotely sensed estimates of precipitation (TRMM), evapotranspiration (MOD16), and terrestrial water storage (GRACE) for the period from 2003 to 2010. Uncertainties for each remotely sensed data set were computed, the budget closure was evaluated using measured discharge data for the three largest river basins in the Cerrado, and the Mann-Kendall test was used to evaluate temporal trends in the water balance components and measured river discharge. The results indicate an overestimation of discharge data, due mainly to the overestimation of rainfall by TRMM version 6. However, better results were obtained when the new release of TRMM 3B42 v7 was used instead. Our results suggest that there have been (a) significant increases in average annual evapotranspiration over the entire Cerrado of 51 ± 15 mm yr−1, (b) terrestrial water storage increases of 11 ± 6 mm yr−1 in the northeast region of the Brazilian Cerrado, and (c) runoff decreases of 72 ± 11 mm yr−1 in isolated spots and in the western part of the State of Mato Grosso. Although complete water budget closure from remote sensing remains a significant challenge due to uncertainties in the data, it provides a useful way to evaluate trends in major water balance components over large regions, identify dry periods, and assess changes in water balance due to land cover and land use change. [ABSTRACT FROM AUTHOR]

This article demonstrates the importance of examining level-dependent deforestation trajectories that may be undetected within regional analyses. We examine Amazon-wide, sub-regional, settlement, and farm-level deforestation trajectories in the Brazilian Amazon and suggest factors that underlie level-dependent differences in spatial patterns and temporal magnitudes of deforestation from 1970 to 2001. At a sub-regional level, we find significant variation in frequency, magnitude, and intensity of deforestation associated with context-specific processes, including areas that have stopped deforesting, are rapidly reforesting, while at the same time increasing economic value of forest-based production. We use remote-sensing data from 1970 to 2001. For Amazon-wide and state-level analyses, we use data available from Brazil's National Institute for Space Research. For sub-regional, settlement, and farm-lot levels, time-series Landsat images and aerial photographs and detailed field-based research are used to reconstruct the history of deforestation. Our analysis empirically demonstrates that understanding deforestation trajectories requires differentiating underlying causes at different levels of analysis and to be wary of overarching explanations and solutions that ignore differences in scale. [ABSTRACT FROM AUTHOR]

Abstract: An increased frequency of droughts is predicted for the Amazon rainforest in the 21st century, which, combined with deforestation, could exacerbate fire occurrence in the region. There is ample evidence of the association between fire use and deforestation in the land use and land cover change (LULCC) processes occurring in the Amazon region, but there are no studies on the actual spatial structuring and spatial association between these events. The present study evaluates the existence of such relationships through the use of remotely sensed data and spatial analysis techniques for an active deforestation frontier covering portions of the states of Rondônia and Mato Grosso in the Brazilian Amazon. A map of burn scars for the year 2005 was produced using a Linear Spectral Mixture Model (LSMM) transformation of Landsat Thematic Mapper (TM) images, with subsequent unsupervised classification and manual editing. Annual and aggregated maps of deforested areas up to 2005, produced by the Brazilian Amazon Deforestation Estimation Project (PRODES), were also used. The amount of burn scar occurrences inside both recent (2002–2005) and old (prior to 2002) deforested areas was then determined, and the spatial structure of both variables was assessed using Mantel tests for multiple aggregation scales. A partial Mantel test was also used to test the spatial correlation between burn scars and deforested areas, accounting for the existence of spatial structure. The results show that there is a significant spatial association between recent deforestation and the occurrence of fires. In addition, we identified a large amount of burned areas (∼55%) within older deforested areas. These results highlight the following: 1) the direct role of fire in the land use and land cover change processes in the Brazilian Amazon, and 2) that fire also widely affects previously degraded vegetation, with significant implications for current estimates of forest fire-associated atmospheric carbon emission in the Amazon region. [Copyright &y& Elsevier]

Abstract: Understanding actor-specific responsibility for deforestation in the Brazilian Amazon is key in adjusting policy and resource allocation in the face of current forest destruction. However, previous research shows that there is great variability in such assessments. To contribute to the ongoing discussions on forest conservation and rural development policies in the Amazon, this paper studies actor-specific deforestation and its environmental effects in four municipalities situated along the Transamazon Highway. We used spatially explicit methods that integrate a database of 8281 georeferenced properties with a time series of remote-sensing data covering four periods between 1986 and 2007. We also included landscape ecology metrics as improved indicators of the complex environmental effects of forest fragmentation. The analysis demonstrates that smallholders (defined as colonists who own less than 100ha of land) were responsible for 23% of total deforestation in the study region while accounting for 55% of the total properties. We also explored the relationship between property size and deforestation at the property level, finding that it closely follows a power distribution. Property deforestation increased with property size, while the percentage of property deforestation decreased. In spite of this, compliance with current legal requirements to maintain 50% of property forest cover was not statistically different between smallholders and largeholders. In comparison to municipalities dominated by medium- and large-scale ranchers, the smallholder-dominated municipality of Medicilândia showed better performance in all applied landscape metrics with well-established relationships with the provision of important environmental goods and services. Although all studied municipalities showed severe accumulated deforestation, Medicilândia experienced an abrupt decrease in municipal deforestation after 1999 to just 0.03%year−1, while municipalities dominated by larger holders maintained or increased their previous deforestation rates to between 0.90% and 1.34%year−1 in the same period. This indicates that the smallholders’ productions schemes in our study area might present potential for agricultural frontier stabilization based on improved land-use efficiency. The policy implications of our findings are discussed, especially with regard to the role of smallholders in productive forest conservation. [Copyright &y& Elsevier]

The Soy Moratorium is a pledge agreed to by major soybean companies not to trade soybean produced in deforested areas after 24th July 2006 in the Brazilian Amazon biome. The present study aims to identify soybean planting in these areas using the MOD13Q1 product and TM/Landsat-5 images followed by aerial survey and field inspection. In the 2009/2010 crop year, 6.3 thousand ha of soybean (0.25% of the total deforestation) were identified in areas deforested during the moratorium period. The use of remote sensing satellite images reduced by almost 80% the need for aerial survey to identify soybean planting and allowed monitoring of all deforested areas greater than 25 ha. It is still premature to attribute the recent low deforestation rates in the Amazon biome to the Soy Moratorium, but the initiative has certainly exerted an inhibitory effect on the soybean frontier expansion in this biome. [ABSTRACT FROM AUTHOR]

In this study maximum-likelihood, supervised classification along with post-classification change detection was applied to satellite images for 1986, 1989, 1996, 2001, and 2009, in order to map land-cover changes within the Apodi Valley region of northeastern Brazil. The supervised classification was carried out on the six reflective bands and ground truthed. The classification results were then further refined using ancillary data, visual interpretation and expert knowledge of the area along with GIS. Post-classification change detection then generated a change image in the form of cross-tabulations. Fifteen land cover classes existed within the area, and reclamation processes during the 1990's changed them substantially, with conflicting changes being caused primarily by lack of both stability and consistency in the government's land use policies. The result was extensive vegetation degradation and water logging in parts of the study area. [ABSTRACT FROM AUTHOR]

This study's overarching aim is to establish the areal extent and characteristics of the rapid sugarcane expansion and land use change in São Paulo state (Brazil) as a result of an increase in the demand for ethanol, using Landsat type remotely sensed data. In 2003 flex fuel automobiles started to enter the Brazilian consumer market causing a dramatic expansion of sugarcane areas from 2.57 million ha in 2003 to 4.45 million ha in 2008. Almost all the land use change, for the sugarcane expansion of crop year 2008/09, occurred on pasture and annual crop land, being equally distributed on each. It was also observed that during the 2008 harvest season, the burned sugarcane area was reduced to 50% of the total harvested area in response to a protocol that aims to cease sugarcane straw burning practice by 2014 for mechanized areas. This study indicates that remote sensing images have efficiently evaluated important characteristics of the sugarcane cultivation dynamic providing quantitative results that are relevant to the debate of sustainable ethanol production from sugarcane in Brazil. [ABSTRACT FROM AUTHOR]

Abstract: The objective of this study was to better understand the linkages between spatial patterns of forest use, land use, and rates of deforestation. We focused on a region of the Brazilian Amazon characterized by a variety of spatial patterns of deforestation and that experienced rapid deforestation in the last decade: the colonization project “Pedro Peixoto” in the extreme East of Acre State. By combining time series of remote sensing data and household survey data, we tested the hypothesis that each spatial pattern of deforestation is associated with specific socio-economic characteristics of land managers. The statistical results support our hypothesis. However, spatial patterns of deforestation are not a consequence of the main economic activities. Rather, spatial patterns of forest use are linked to the pre-conceived design of the settlement project. The main economic activities are influenced by factors such as the property size, their location and disposition, and the availability of infrastructure. [Copyright &y& Elsevier]

Abstract: The intensification of irrigated agriculture in the semi-arid region of Brazilian North-east results in a change of natural vegetation by irrigated fruit crops. New applications of remote sensing technologies are presented in this paper to estimate the impact of this land use change on regional water consumption – and ultimately the water balance – in Low-Middle São Francisco River basin. Ten Landsat images for a period from 2001 to 2007 were used, together with the locally calibrated Surface Energy Balance Algorithm for Land (SEBAL) and agro-meteorological data to derive information on regional actual evapotranspiration (ET), biomass production (BIO), and crop water productivity (CWP). The Landsat-based results revealed that regional mean ET for irrigated crops was 3.6mmd−1 being higher than for natural vegetation (1.4mmd−1). Similar incremental ET values between natural and irrigated ecosystems were found from micro-meteorological field experiments. The consequence of this land use change on São Francisco River''s downstream stream flow was assessed by estimating volumetric incremental evapotranspiration at the regional scale. The bio-physical crop water productivity per unit of actual evapotranspiration (CWPET) varied between 0.4 and 1.7l of wine per m3 of water for wine grapes; 1.7 and 4.0kg of fruits per m3 of water for table grapes; and 2.2 and 5.0kg of fruits per m3 of water for mangos. The accompanying paper (Part A) describes the calibration and validation of SEBAL steps witnessed under the actual field conditions in this study area. [Copyright &y& Elsevier]

The ability to model factors influencing land use can be significantly improved by incorporating variables derived from geographic information systems with more detailed survey data. While remote sensing data have the advantage of providing land cover measurements for large areas, survey data collected from households provide a more detailed account of land use. We estimate land use decisions in the Brazilian Amazon with land cover data derived from satellite images merged with observations from a household panel. We focus on the rapidly expanding cattle industry or "pecuarizaçao" (cattleization) as well as a potentially competing land use strategy: the planting and harvesting of annual and perennial crops. We identify spatial error in our initial estimations and as a result use corrected models of land use with greater explanatory power and efficiency. Estimation results indicate that both pasture creation and agriculture are determined by similar household and spatial characteristics; however, the impacts of these determinants are in opposing directions for both land uses estimated. We conclude from our survey and satellite comparisons that while remote sensing data may overestimate the extent of pasture, the overestimation is minor. Finally, based on the current extent of pasture, we also conclude that policy that focuses on reducing deforestation within these long-established settlements is most likely inefficient. Rather, policy that addresses the land use practices employed by recent settlers may be more effective in reducing further pasture creation. [ABSTRACT FROM AUTHOR]

Vegetation fires remain as one of the most important processes governing land use and land cover change in tropical areas. The large area extent of fire prone areas associated with human activities makes satellite remote sensing of active fires a valuable tool to help monitor biomass burning in those regions. However, identification of active fire fronts under optically thick clouds is not possible through passive remote sensing, often resulting in omission errors. Previous analyses of fire activity either ignored the cloud obscuration problem or applied corrections based on the assumption that fire occurrence is not impacted by the presence of clouds. In this study we addressed the cloud obscuration problem in the Brazilian Amazon region using a pixel based probabilistic approach, using information on previous fire occurrence, precipitation and land use. We implemented the methodology using data from the geostationary GOES imager, covering the entire diurnal cycle of fire activity and cloud occurrence. Our assessment of the method indicated that the cloud adjustment reproduced the number of potential fires missed within 1.5% and 5% of the true fire counts on annual and monthly bases respectively. Spatially explicit comparison with high resolution burn scar maps in Acre state showed a reduction of omission error (from 58.3% to 43.7%) and only slight increase of commission error (from 6.4% to 8.8%) compared to uncorrected fire counts. A basin-wide analysis of corrected GOES fire counts during 2005 showed a mean cloud adjustment factor of approximately 11%, ranging from negligible adjustment in the central and western part of the Brazilian Amazon to as high as 50% in parts of Roraima, Para and Mato Grosso. [Copyright &y& Elsevier]

Remotely sensed data are the best and perhaps the only possible way for monitoring large-scale, human-induced land occupation and biosphere-atmosphere processes in regions such as the Brazilian tropical savanna (Cerrado). Landsat imagery has been intensively employed for these studies because of their long-term data coverage (>30 years), suitable spatial and temporal resolutions, and ability to discriminate different land-use and land-cover classes. However, cloud cover is the most obvious constraint for obtaining optical remote sensing data in tropical regions, and cloud cover analysis of remotely sensed data is a requisite step needed for any optical remote sensing studies. This study addresses the extent to which cloudiness can restrict the monitoring of the Brazilian Cerrado from Landsat-like sensors. Percent cloud cover from more than 35 500 Landsat quick-looks were estimated by the K-means unsupervised classification technique. The data were examined by month, season, and El Niño Southern Oscillation event. Monthly observations of any part of the biome are highly unlikely during the wet season (October-March), but very possible during the dry season, especially in July and August. Research involving seasonality is feasible in some parts of the Cerrado at the temporal satellite sampling frequency of Landsat sensors. There are several limitations at the northern limit of the Cerrado, especially in the transitional area with the Amazon. During the 1997 El Niño event, the cloudiness over the Cerrado decreased to a measurable but small degree (5% less, on average). These results set the framework and limitations of future studies of land use/land cover and ecological dynamics using Landsat-like satellite sensors. [ABSTRACT FROM AUTHOR]

Although pasture degradation has been a regional concern in Amazonian ecosystems, our ability to characterize and monitor pasture degradation under different environmental and human-related conditions is still limited. Regional analysis of pasture dynamic patterns was conducted using high-frequency temporal satellite data and ancillary data to better understand pasture degradation under varied soil, environmental, and pasture management conditions in the state of Rondônia, Brazil. The 10-day normalized difference vegetation index (NDVI) composite derived from Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m resolution was used to characterize different grass phenological patterns for 32 counties in Rondônia between 2001 and 2003. Six pasture greenness classes showed that high greenness pasture classes dominated in young pastures, while low greenness pasture classes were least common. As pastures aged, the proportion of high greenness pasture classes decreased and the proportion of low greenness pastures increased, indicating a decrease in forage productivity over time in Rondônia. The magnitude of productivity decline depended on environmental constraints and land use systems. To refine this analysis, trajectories of pasture change were determined using spectral mixture analysis applied to Landsat time series data from 1988 to 2001 with the focus on two counties that show contrasting patterns of potential of grass production: Pimenteira, representing the “degraded” pasture category, and Governador Jorge Teixeira, as the “productive” pasture category. The results revealed a clear pasture degradation pattern in Pimenteira, related to low soil fertility and dry climate conditions, while Governador Jorge Teixeira, with better soil fertility and intermediate precipitation, did not show signs of pasture degradation through time. [ABSTRACT FROM AUTHOR]

Mapping and monitoring land degradation in areas under human-induced stresses have become urgent tasks in remote sensing whose importance has not yet been fully appreciated. In this study, a surface cover index (SCI) is developed to evaluate and map potential land degradation risks associated with deforestation and accompanying soil erosion in a Western Brazilian Amazon rural settlement study area. The relationships between land-use and land-cover (LULC) types and land degradation risks as well as the impacts of LULC change on land degradation are examined. This research indicates that remotely sensed data can be effectively used for identification and mapping of land degradation risks and monitoring of land degradation changes in the study area. Sites covered by mature forest and advanced successional forests have low land degradation risk potential, while some types of initial successional forests, agroforestry/perennial agriculture and pasture have higher risk potential. Deforestation and associated soil erosion are major causes leading to land degradation, while vegetation regrowth reduces such problems. [ABSTRACT FROM AUTHOR]

The Brazilian Amazon forest and cerrado savanna encompasses a region of enormous ecological, climatic, and land-use variation. Satellite remote sensing is the only tractable means to measure the biophysical attributes of vegetation throughout this region, but coarse-resolution sensors cannot resolve the details of forest structure and land-cover change deemed critical to many land-use, ecological, and conservation-oriented studies. The Carnegie Landsat Analysis System (CLAS) was developed for studies of forest and savanna structural attributes using widely available Landsat Enhanced Thematic Mapper Plus (ETM+) satellite data and advanced methods in automated spectral mixture analysis. The methodology of the CLAS approach is presented along with a study of its sensitivity to atmospheric correction errors. CLAS is then applied to a mosaic of Landsat images spanning the years 1999–2001 as a proof of concept and capability for large-scale, very high resolution mapping of the Amazon and bordering cerrado savanna. A total of 197 images were analyzed for fractional photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and bare substrate covers using a probabilistic spectral mixture model. Results from areas without significant land use, clouds, cloud shadows, and water bodies were compiled by the Brazilian state and vegetation class to understand the baseline structural typology of forests and savannas using this new system. Conversion of the satellite-derived PV data to woody canopy gap fraction was made to highlight major differences by vegetation and ecosystem classes. The results indicate important differences in fractional photosynthetic cover and canopy gap fraction that can now be accounted for in future studies of land-cover change, ecological variability, and biogeochemical processes across the Amazon and bordering cerrado regions of Brazil. [ABSTRACT FROM AUTHOR]

• Recently, the advancement of agriculture in Brazil has caused very serious problems, such as deforestation. • Future impacts of the advancement of agriculture in the Cerrado/Atlantic forest ecotone in 2033, 2050, 2080 and 2100. • The future LULC scenarios were created with a cellular automata-Markov forecast model. • The results indicated the advancement of crop agriculture and decreases in important areas. • The results can help the effectiveness of public policies to reduce deforestation in the area. Recently, the advancement of agriculture in Brazil has caused very serious problems, such as deforestation and an increase in the amount of pesticides and suspended sediment that have negatively impacted the environment, especially in the Prata River basin, which is located in the Serra da Bodoquena region, Brazil. Thus, the objective of this study was to analyze the future changes in land use and land cover of the advancement of agriculture in the native vegetation areas of the Cerrado/Atlantic forest ecotone in the Prata River basin in 2033, 2050, 2080 and 2100. To map the future land use and land cover (LULC) in the study area, an object-based classification approach was used on Landsat satellite imagery from 1986, 1999, 2007 and 2016. The future LULC scenarios were created with a cellular automata-Markov forecast model. The simulation results of the LULC changes were considered satisfactory, as evidenced by the values obtained from the kappa for agreement (κ standard) = 0.73, kappa for no information (κ no) = 0.79 and kappa for grid-cell level location (κ location) = 0.85 indices. The modeled future scenarios of LULC indicated the advancement of crop agriculture and decreases in wetlands (banhado), savannahs, riparian forests, seasonal semideciduous forests and wet grasslands, which can be considered a warning about the loss of biodiversity of fauna and flora in the Serra da Bodoquena National Park, mainly in the Prata River basin. [ABSTRACT FROM AUTHOR]

Landsat 5 has produced imagery for decades that can now be viewed and manipulated in Google Earth Engine, but a general, automated way of producing a coherent time series from these images—particularly over cloudy areas in the distant past—is elusive. Here, we create a land use and land cover (LULC) time series for part of tropical Mato Grosso, Brazil, using the Bayesian Updating of Land Cover: Unsupervised (BULC-U) technique. The algorithm built backward in time from the GlobCover 2009 data set, a multi-category global LULC data set at 300 m resolution for the year 2009, combining it with Landsat time series imagery to create a land cover time series for the period 1986–2000. Despite the substantial LULC differences between the 1990s and 2009 in this area, much of the landscape remained the same: we asked whether we could harness those similarities and differences to recreate an accurate version of the earlier LULC. The GlobCover basis and the Landsat-5 images shared neither a common spatial resolution nor time frame, But BULC-U successfully combined the labels from the coarser classification with the spatial detail of Landsat. The result was an accurate fine-scale time series that quantified the expansion of deforestation in the study area, which more than doubled in size during this time. Earth Engine directly enabled the fusion of these different data sets held in its catalog: its flexible treatment of spatial resolution, rapid prototyping, and overall processing speed permitted the development and testing of this study. Many would-be users of remote sensing data are currently limited by the need to have highly specialized knowledge to create classifications of older data. The approach shown here presents fewer obstacles to participation and allows a wide audience to create their own time series of past decades. By leveraging both the varied data catalog and the processing speed of Earth Engine, this research can contribute to the rapid advances underway in multi-temporal image classification techniques. Given Earth Engine's power and deep catalog, this research further opens up remote sensing to a rapidly growing community of researchers and managers who need to understand the long-term dynamics of terrestrial systems. [ABSTRACT FROM AUTHOR]

Background: Brazilian Amazon forests contain a large stock of carbon that could be released into the atmosphere as a result of land use and cover change. To quantify the carbon stocks, Brazil has forest inventory plots from different sources, but they are unstandardized and not always available to the scientific community. Considering the Brazilian Amazon extension, the use of remote sensing, combined with forest inventory plots, is one of the best options to estimate forest aboveground biomass (AGB). Nevertheless, the combination of limited forest inventory data and different remote sensing products has resulted in significant differences in the spatial distribution of AGB estimates. This study evaluates the spatial coverage of AGB data (forest inventory plots, AGB maps and remote sensing products) in undisturbed forests in the Brazilian Amazon. Additionally, we analyze the interconnection between these data and AGB stakeholders producing the information. Specifically, we provide the first benchmark of the existing field plots in terms of their size, frequency, and spatial distribution. Results: We synthesized the coverage of forest inventory plots, AGB maps and airborne light detection and ranging (LiDAR) transects of the Brazilian Amazon. Although several extensive forest inventories have been implemented, these AGB data cover a small fraction of this region (e.g., central Amazon remains largely uncovered). Although the use of new technology such as airborne LiDAR cover a significant extension of AGB surveys, these data and forest plots represent only 1% of the entire forest area of the Brazilian Amazon. Conclusions: Considering that several institutions involved in forest inventories of the Brazilian Amazon have different goals, protocols, and time frames for forest surveys, forest inventory data of the Brazilian Amazon remain unstandardized. Research funding agencies have a very important role in establishing a clear sharing policy to make data free and open as well as in harmonizing the collection procedure. Nevertheless, the use of old and new forest inventory plots combined with airborne LiDAR data and satellite images will likely reduce the uncertainty of the AGB distribution of the Brazilian Amazon. [ABSTRACT FROM AUTHOR]