Long-term Landsat-based monthly burned area dataset for the Brazilian biomes using Deep Learning

Fire is a significant agent of landscape transformation on Earth, and a dynamic and ephemeral process that is challenging to map. Difficulties include the seasonality of native vegetation in areas affected by fire, the high levels of spectral heterogeneity due to the spatial and temporal variability of the burned areas, distinct persistence of the fire signal, increase in cloud and smoke cover surrounding burned areas, and difficulty in detecting understory fire signals. To produce a large-scale time-series of burned area, a robust number of observations and a more efficient sampling strategy is needed. In order to overcome these challenges, we used a novel strategy based on a machine-learning algorithm to map monthly burned areas from 1985 to 2020 using Landsat-based annual quality mosaics retrieved from minimum NBR values. The annual mosaics integrated year-round observations of burned and unburned spectral data (i.e., RED, NIR, SWIR-1, and SWIR-2), and used them to train a Deep Neural Network model, which resulted in annual maps of areas burned by land use type for all six Brazilian biomes. The annual dataset was used to retrieve the frequency of the burned area, while the date on which the minimum NBR was captured in a year, was used to reconstruct 36 years of monthly burned area. Results of this effort indicated that 19.6% (1.6 million km2) of the Brazilian territory was burned from 1985 to 2020, with 61% of this area burned at least once. Most of the burning (83%) occurred between July and October. The Amazon and Cerrado, together, accounted for 85% of the area burned at least once in Brazil. Native vegetation was the land cover most affected by fire, representing 65% of the burned area, while the remaining 35% burned in areas dominated by anthropogenic land uses, mainly pasture. This novel dataset is crucial for understanding the spatial and long-term temporal dynamics of fire regimes that are fundamental for designing appropriate public policies for reducing and controlling fires in Brazil

Woody aboveground biomass mapping of the brazilian savanna with a multi-sensor and machine learning approach

The tropical savanna in Brazil known as the Cerrado covers circa 23% of the Brazilian territory, but only 3% of this area is protected. High rates of deforestation and degradation in the woodland and forest areas have made the Cerrado the second-largest source of carbon emissions in Brazil. However, data on these emissions are highly uncertain because of the spatial and temporal variability of the aboveground biomass (AGB) in this biome. Remote-sensing data combined with local vegetation inventories provide the means to quantify the AGB at large scales. Here, we quantify the spatial distribution of woody AGB in the Rio Vermelho watershed, located in the centre of the Cerrado, at a high spatial resolution of 30 metres, with a random forest (RF) machine-learning approach. We produced the first high-resolution map of the AGB for a region in the Brazilian Cerrado using a combination of vegetation inventory plots, airborne light detection and ranging (LiDAR) data, and multispectral and radar satellite images (Landsat 8 and ALOS-2/PALSAR-2). A combination of random forest (RF) models and jackknife analyses enabled us to select the best remote-sensing variables to quantify the AGB on a large scale. Overall, the relationship between the ground data from vegetation inventories and remote-sensing variables was strong (R2 = 0.89), with a root-mean-square error (RMSE) of 7.58 Mg ha−1 and a bias of 0.43 Mg ha−1

Zoneamento geoambiental como subsídio aos projetos de reforma agrária. Estudo de caso: assentamento rural Pirituba II (SP)

Os projetos de reforma agrária apresentam dificuldades de planejamento para uso e ocupação da terra. Esses problemas afetam a qualidade de vida das famílias, a produtividade e a sustentabilidade ambiental. Isso se deve à carência de estudos interdisciplinares detalhados de diagnósticos e zoneamentos ambientais para implantação, desenvolvimento e gestão desses assentamentos. Assim, o objetivo principal desse estudo é estabelecer o zoneamento geoambiental no assentamento rural Pirituba II (Itapeva/Itaberá/SP) e analisar o uso desse instrumento para melhorar os projetos de reforma agrária que visem a sustentabilidade socioambiental. Para isso, foram realizadas fotointerpretação de fotos aéreas (escala 1:25.000) e verificações em campo para detalhar as informações ambientais básicas de drenagem, geologia estrutural, de unidades fisiográficas, e pedológicas. Essas informações permitiram a compreensão da evolução e dinâmica da paisagem. A partir da caracterização das unidades fisiográficas colúvio-aluvionares da área foram estabelecidos os fatores e processos endógenos e exógenos que resultaram na formação das paisagens. Isto permitiu estabelecer as zonas geoambientais (unidades aloestratigráficas). Essas foram divididas em subzonas geoambientais pela análise estrutural e fisiográfica, para posteriormente determinar as potencialidades e limitações de tais unidades. Dessa forma, mapas temáticos foram elaborados quanto à: suscetibilidade à erosão, indicação de áreas para proteção ambiental e adequação a culturas anuais. A aplicação do zoneamento geoambiental no assentamento Pirituba II forneceu um estudo detalhado e integrado do meio físico para planejamento local visando a sustentabilidade socioambiental. Portanto, esse zoneamento pode ser uma ferramenta útil para a gestão territorial e melhoria dos projetos de reforma agrária.The environmental diagnostic studies that aim planning for land reform settlements are few and still present some gaps. These affect the life quality of families, productivity and environmental sustentability. Geoenvironmental zoning is based on the integration of physical aspects, and for this reason it may contribute with information that will be used for the environmental analysis of these settlements. The aim of the present study is to evaluate the geoenvironmental zoning applied to the Pirituba II Settlement (Itapeva/Itaberá/SP) as a reliable tool and instrument for the definition of lines that can help in the sustainable implementation of land reform projects, as much by the social view as by the environmental focus. For this the drainage, structural geology, physiographic unities and pedological basic environmental information were detailed through field and laboratory works (aerial photointerpretation). This information have enabled better undestanding of the landscape dynamic and evolution. Physiographic characterization for colluvial and alluvial units of the studied area permitted to establish the factors and processes, both endogenetic and geomorphic, that resulted in the landscape formation. The geoenvironmental zoning was defined by this purpose, which generate subdividing operations into structural and physiographic analysis, for as much as the potentiality and limitation determination of them as entities. The following thematic maps were obtained, therefore: erosion vulnerability, environmental protection indication and agricultural annual rotation. The results of the geoenvironmental zoning work in the Pirituba II Settlement allowed the definition of environmental planning detailed strategies in agreement with sustainable reality.Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP

Detection of large-scale forest canopy change in pan-tropical humid forests 2000-2009 with the SeaWinds Ku-band scatterometer

We analyzed the 10-year record (1999–2009) of SeaWinds Ku-band microwave backscatter from humid tropical forest regions in South America, Africa, and Indonesia/Malaysia. While backscatter was relatively stable across much of the region, it declined by 1–2 dB in areas of known large-scale deforestation, and increased by up to 1–2 dB in areas of secondary forest or plantation forest growth and in major metropolitan areas. The reduction in backscatter over 142 18.5 km × 18.5 km blocks of tropical forest was correlated with gross forest cover loss (as determined from Landsat data analysis) (R = −0.78); this correlation improved when restricted to humid tropical forest blocks in South America with high initial forest cover (R = −0.93, n = 22). This study shows that scatterometer-based analyses can provide an important geophysical data record leading to robust identification of the spatial patterns and timing of large-scale change in tropical forests. The coarse spatial resolution of SeaWinds (∼10 km) makes it unsuitable for mapping deforestation at the scale of land-use activity. However, due to a combination of instrument stability, sensitivity to canopy change and insensitivity to atmospheric effects, and straight-forward data processing, Ku-band scatterometery can provide a fully independent assessment of large-scale tropical forest canopy dynamics which may complement the interpretation of higher resolution optical remote sensing