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Urban landslides are increasing globally, mainly caused by human-induced changes in hillsides. Most of these events have caused low-intensity damages to housing and infrastructure. A total of 2038 locations of landslides in the hydrological year of 2010 were used to model landslides' occurrence in the metropolitan region of São Paulo—Brazil—using a social–ecological–technological system's approach, which enables the analysis of urban landslides as the outcome of dynamic socioeconomic and infrastructural conditions alongside climatic and geophysical conditions. A multi-step model approach was used to select the best set of variables related to landslides' occurrence and assess their importance. The value of AUC of the model was 0.9087, denoting the high level of discrimination achieved. Antecedent rainfall played the most important role, followed by terrain slope. Informal settlements, associated with poor constructive practices and a lack of municipal inspection on civil works and buildings, as well as the number of households, which stands for built density and greater alteration in hillsides, yielded a slightly lower contribution. Other variables showed a marginal contribution. These results reinforce the role of local ordinances aimed at restricting occupation in steeper slopes and public policies to promote adequate housing and constructive practices. Future climate projections to MRSP point to the increase in intense rainfall days, making disasters caused by landslides a major source of risk. [ABSTRACT FROM AUTHOR]

In two small and isolated wetlands located at the interface of the Atlantic Forest and Brazilian savanna (Cerrado) in São Paulo State, Brazil, we employed a pixel-based supervised classification approach using a combination of panchromatic and multispectral bands obtained from Landsat 2, 5, 7, and CBERS-04A satellites (ranging from 80 to 2 m/pixel). In addition, we acquired DJI Phantom 4 Pro UAV-RGB images in twelve different periods with a resolution of +5 cm/pixel. Furthermore, we utilized 2D and 3D Electrical Resistivity Tomography (ERT) to obtain data on the surroundings and center of the wetlands. Finally, we conducted a climatological data analysis. The results from the multisource data allowed us to classify the ecosystems as geographically isolated wetlands (GIWs), for which we documented a seasonal month-to-month (12 months) spatial variation of inundated area, vegetation pattern, soil water interaction, and a point of surface and deep-subsurface water interaction. These results are essential for high-accuracy characterization of small wetlands' hydrodynamics and hydroperiods at the local scale. Our study contributes to optimizing GIWs understanding, monitoring, and reapplication of the methodology in other wetlands or small ecosystems. [ABSTRACT FROM AUTHOR]

Riparian zones consist of important environmental regions, specifically to maintain the quality of water resources. Accurately mapping forest vegetation in riparian zones is an important issue, since it may provide information about numerous surface processes that occur in these areas. Recently, machine learning algorithms have gained attention as an innovative approach to extract information from remote sensing imagery, including to support the mapping task of vegetation areas. Nonetheless, studies related to machine learning application for forest vegetation mapping in the riparian zones exclusively is still limited. Therefore, this paper presents a framework for forest vegetation mapping in riparian zones based on machine learning models using orbital multispectral images. A total of 14 Sentinel-2 images registered throughout the year, covering a large riparian zone of a portion of a wide river in the Pontal do Paranapanema region, São Paulo state, Brazil, was adopted as the dataset. This area is mainly composed of the Atlantic Biome vegetation, and it is near to the last primary fragment of its biome, being an important region from the environmental planning point of view. We compared the performance of multiple machine learning algorithms like decision tree (DT), random forest (RF), support vector machine (SVM), and normal Bayes (NB). We evaluated different dates and locations with all models. Our results demonstrated that the DT learner has, overall, the highest accuracy in this task. The DT algorithm also showed high accuracy when applied on different dates and in the riparian zone of another river. We conclude that the proposed approach is appropriated to accurately map forest vegetation in riparian zones, including temporal context. [ABSTRACT FROM AUTHOR]

In Brazil, irrigated agriculture is responsible for 46% of withdrawals of water bodies and 67% of use concerning the total water abstracted volume, representing the most significant consumptive use in the country. Understanding how different crops use water over time is essential for planning and managing water allocation, water rights, and farming production. In this work, we propose a methodology to estimate water used in agriculture irrigated by center pivots in the municipality of Itobi, São Paulo, in the Brazilian Savanna (known as Cerrado), which has strong potential for agricultural and livestock production. The methodology proposed for the water use estimate is based on mapping crops irrigated by center pivots for the 2015/2016 crop year and actual evapotranspiration (ETa). ETa is derived from the Operational Simplified Surface Energy Balance model (SSEBop) and parameterized for edaphoclimatic conditions in Brazil (SSEBop-Br). Three meteorological data sources (INMET, GLDAS, CFSv2) were tested for estimating ETa. The water use was estimated for each meteorological data source, relating the average irrigation balance and the total area for each crop identified in the map. We evaluated the models for each crop present in the center pivots through global accuracy and f1-score metrics, and f1-score was more significant than 0.9 for all crops. The potato was the crop that consumed the most water in irrigation, followed by soy crops, beans, carrots, and onions, considering the three meteorological data sources. The total water volume consumed by center pivots in the municipality of Itobi in the 2015/2016 agricultural year for each meteorological data source was 3.2 million m3 (INMET), 2.5 million m3; (GLDAS), and 1.8 million m3 (CFSv2). [ABSTRACT FROM AUTHOR]

With more than 80% of Brazilians living in cities, urbanization has had an important impact on climatic variations. São José dos Campos is located in a region experiencing rapid urbanization, which has produced a remarkable Urban Heat Island (UHI) effect. This effect influences the climate, environment and socio-economic development on a regional scale. In this study, the brightness temperatures and land cover types from Landsat TM images of São José dos Campos from 1986, 2001 and 2010 were analyzed for the spatial distribution of changes in temperature and land cover. A quantitative approach was used to explore the relationships among temperature, land cover areas and several indices, including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and Normalized Difference Built-up Index (NDBI). The results showed that urban and bare areas correlated positively with high temperatures. Conversely, areas covered in vegetation and water correlated positively with low temperatures. The indices showed that correlations between the NDVI and NDWI and temperature were low (<0.5); however, a moderate correlation was found between the NDBI and temperature. [ABSTRACT FROM AUTHOR]

The algorithms for determining sugarcane harvest dates are proposed; the algorithms allow the ability to monitor large areas and are based on the publicly available Synthetic Aperture Radar (SAR) and optical satellite data. Algorithm 1 uses the NDVI (Normalized Difference Vegetation Index) time series derived from Sentinel-2 data. Sharp and continuous decrease in the NDVI values is the main sign of sugarcane harvest. The NDVI time series allows the ability to determine most harvest dates. The best estimates of the sugarcane areas harvested per month have been obtained from March to August 2018 when cloudy pixel percentage is less than 45% of the image area. Algorithm 2 of the harvest monitoring uses the coherence time series derived from Sentinel-1 Single Look Complex (SLC) images and optical satellite data. Low coherence, demonstrating sharp growth upon the harvest completion, corresponds to the harvest period. The NDVI time series trends were used to refine the algorithm. It is supposed that the descending NDVI trend corresponds to harvest. The algorithms were used to identify the harvest dates and calculate the harvested areas of the reference sample of 574 sugarcane parcels with a total area of 3745 ha in the state of São Paulo, Brazil. The harvested areas identified by visual interpretation coincide with the optical-data algorithm (algorithm 1) by 97%; the coincidence with the algorithm based on SAR and optical data (algorithm 2) is 90%. The main practical applications of the algorithms are harvest monitoring and identification of the harvested fields to estimate the harvested area. [ABSTRACT FROM AUTHOR]