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The occurrence of forest fires has increased significantly in recent years across the planet. Events of this nature have resulted in the leveraging of new automated methodologies to identify and map burned areas. In this paper, we introduce a unified data-driven framework capable of mapping areas damaged by fire by integrating time series of remotely sensed multispectral images, statistical modeling, and unsupervised classification. We collect and analyze multiple remote-sensing images acquired by the Landsat-8, Sentinel-2, and Terra satellites between August–October 2020, validating our proposal with three case studies in Brazil and Bolivia whose affected regions have suffered from recurrent forest fires. Besides providing less noisy mappings, our methodology outperforms other evaluated methods in terms of average scores of 90%, 0.71, and 0.65 for overall accuracy, F1-score, and kappa coefficient, respectively. The proposed method provides spatial-adherence mappings of the burned areas whose segments match the estimates reported by the MODIS Burn Area product. [ABSTRACT FROM AUTHOR]

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]

Detection of urban area extents by means of remotely sensed data is a difficult task, especially because of the multiple, diverse definitions of what an "urban area" is. The models of urban areas listed in technical literature are based on the combination of spectral information with spatial patterns, possibly at different spatial resolutions. Starting from the same data set, "urban area" extraction may thus lead to multiple outputs. If this is done in a well-structured framework, however, this may be considered as an advantage rather than an issue. This paper proposes a novel framework for urban area extent extraction from multispectral Earth Observation (EO) data. The key is to compute and combine spectral and multi-scale spatial features. By selecting the most adequate features, and combining them with proper logical rules, the approach allows matching multiple urban area models. Experimental results for different locations in Brazil and Kenya using High-Resolution (HR) data prove the usefulness and flexibility of the framework. [ABSTRACT FROM AUTHOR]

Due to the El Niño phenomenon, the 1997-1998 dry season in Roraima (Brazil, Amazonia) was particularly pronounced. Consequently, vegetation fires spread widely and were monitored by many satellites in real time. Satellite images are currently being used to monitor vegetation fires either globally for climate studies or more regionally for impact assessment. After reviewing different satellite data used for impact assessment, this paper focuses on the contribution of SPOT-4's imagery provided by high resolution HRVIR and coarse resolution VEGETATION sensors. These sensors are described with emphasis on those characteristics of potential benefit for forest mapping and fire detection. Early images of Roraima from SPOT-4 are analysed and interpreted to delineate the areas already damaged by fire. VEGETATION images provide a first estimate of damaged areas on a regional scale and an indication of the main ecosystems affected. SPOT HRVIR is used to establish a much more precise classification of various ecosystems. Each vegetation class is associated with a biomass density. From the known burned areas, an estimate of burned biomass during the 1998 dry season is computed, as well as total carbon release. On an intensive study site of 20 400 km[sup 2], 3060 km[sup 2] of savannahs and crops and 6980 km[sup 2] of forest have been burned; the corresponding carbon release is estimated as 210 000 t for croplands and savannahs and 23 M t for the evergreen seasonal forest. The estimated burnt surface areas derived from VEGETATION are then cross-validated with HRVIR and thus an attempt is made to extrapolate the burned biomass with the help of VEGETATION on a regional scale. [ABSTRACT FROM AUTHOR]

A catastrophic tailings dam failure disaster occurred in Brumadinho, Brazil on 25 January 2019, which resulted in over 270 casualties, 24,000 residents evacuated, and a huge economic loss. Environmental concerns were raised for the potential pollution of water due to tailings waste entering the Paraopeba River. In this paper, a detailed analysis has been carried out to investigate the disaster conditions of the Brumadinho dam failure using satellite images with different spatial resolutions. Our in-depth analysis reveals that the hazard chain caused by this failure contained three stages, namely dam failure, mudflow, and the hyperconcentrated flow in the Paraopeba River. The variation characteristics of turbidity of the Rio Paraopeba River after the disaster have also been investigated using high-resolution remote sensing images, followed by a qualitative analysis of the impacts on the downstream reservoir of the Retiro Baixo Plant that was over 300 km away from the dam failure origin. It is believed that, on the one hand, the lack of dam stability management at the maintenance stage was the main cause of this disaster. On the other hand, the abundant antecedent precipitation caused by extreme weather events should be a critical triggering factor. Furthermore, the spatiotemporal pattern mining of global tailings dam failures revealed that the Brumadinho dam disaster belonged to a Consecutive Hot Spot area, suggesting that the regular drainage inspection, risk assessment, monitoring, and early warning of tailings dam in Consecutive Hot Spot areas still need to be strengthened for disaster mitigation. [ABSTRACT FROM AUTHOR]