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Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco 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.)

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco 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.)

Copyright of Revista Foco (Interdisciplinary Studies Journal) is the property of Revista Foco 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.)

Vegetation indices (VIs) are designed to enhance the vegetation signal and minimize variations in solar irradiance and the effects of the plant canopy substrate. The time series of vegetation cover images present different frequency components, such as seasonal variations and long and short-term fluctuations, influenced mainly by climatic factors such as temperature and precipitation. Thus, the work aims to evaluate the influence of temperature and precipitation on the annual variation of NDVI (normalized difference vegetation index), RENDVI (red edge normalized difference vegetation index) and EVI (enhanced vegetation index), in an area of semideciduous seasonal forest, which covers part of the municipalities of Marmeleiro and Renascença, in the southwest of the State of Paraná. Seven images from the RapidEye satellite (level 3A) available for the year 2018 without cloud cover were used. Seasonal variation demonstrated that the highest values of vegetation IVs were found in summer, since temperature and precipitation contribute to raising them in the study area, with the response to temperature being faster than to precipitation. The EVI showed a better correlation with the minimum average temperature and the average temperature, because it is more influenced by the leaf area index (LAI), which is controlled by the seasons. On the other hand, NDVI and RENDVI are more influenced by precipitation and showed a greater correlation with accumulated precipitation between 30 and 60 days prior to imaging. [ABSTRACT FROM AUTHOR]

Copyright of Diversitas Journal is the property of Diversitas Journal 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.)

Green Chemistry aims to prevent pollution resulting from activities in the chemical sector. Generally, laboratory activities generate pollution because toxic substances are used to perform analyses, which produce waste once the analyses are completed. This study aimed to monitor the quality of the Uberaba River using more sustainable and non-destructive techniques through orbital remote sensing and to develop methods for estimating water quality at reduced costs using orbital sensors and machine learning. The values of water quality parameters, such as iron, nitrate, chloride, total phosphorus, and total nitrogen, were studied between 2018 and 2023. Orbital sensing data (spectral bands and vegetation indices) were taken from the exact geographic coordinates of the collection points. Data were analyzed using Pearson’s correlation coefficient and random forest regression analysis. The study concludes that it is possible to perform orbital remote sensing by estimating water quality through random forest regression, correlating the information obtained from Sentinel-2 images with the values of these parameters. This approach is a sustainable technique that does not generate waste and represents a 100% saving compared to conventional chemical analyses.

ABSTRACT In the current climate change scenario, the adoption of actions aimed at reducing wildland fires and, consequently, greenhouse gas emissions is urgent. The use of environmental satellites to trace wildland fires is an essential instrument in the development of fire prevention strategies. The objectives of this study were to (a) analyze the spatiotemporal variability in fire foci detection in the state of Pará, Brazil; (b) identify the main differences between data from AQUA and Suomi-NPP (S-NPP) satellites; and (c) determine the variables responsible for changes in fire detection at municipal level. Mean annual detection of fire foci was of 43,488 by AQUA based on data from 2003-2023, and of 156,038 by S-NPP from 2012-2023. During the overlap period, S-NPP detected 4.5 times more foci than AQUA. Despite the difference, both datasets were significantly correlated. Most fire foci were detected in August, September and November. São Félix do Xingu and Altamira were the municipalities with the highest number of detections, while Bajaru and Concórdia do Pará registered higher fire foci density. Of the 144 municipalities, 89 were classified as having extreme fire incidence. Deforested area was the variable that presented the highest correlation with municipal fire density, followed by pasture area, rainfall, urbanized area, forest area, agricultural area and demographic density. The results of this study could be used as basis for the development of public policies aiming at the reduction of wildland fire occurrence in Pará.

Copyright of Nativa is the property of Revista Nativa 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.)

Abstract The objective of this study is to evaluate changes in vegetation cover in the Ilhéus-Itabuna geographic intermediate region from 1984 to 2019 using the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Soil-Adjusted Vegetation Index (SAVI). Data was processed through the Google Earth Engine programming interface, using image collections from the TM/Landsat 5 and OLI/Landsat 8 sensors. After creating the indices, areas with different uses were selected, and the NDVI time series graphs were created. It was found that the vegetation density in the region decreased from 1984 to 1999 and increased from that year. The NDVI and EVI showed similar behavior, unlike the SAVI, which lower averages. Average NDVI values were higher in (June-August), due to the association of these conditions with higher soil moisture. In the pasture area, peaks of heat (January-March) drove vegetative activity due to the favorable climate and availability of water and sunlight.

ABSTRACT The management of natural and planted forests can be conducted sustainably by implementing techniques that consider the spatial and temporal variability of the plant and soil. In this context, precision silviculture through remote sensing can play a vital role, mainly when using Unmanned Aerial Vehicles (UAVs) equipped with specific sensors. In the present study, an automated computational routine (based on computer vision techniques) was developed and validated to perform forest inventory in commercial Eucalyptus grandis forests, using an orthophoto mosaic obtained with an RGB sensor built-in to a UAV. The developed routine employs computer vision techniques, including template matching to locate plants, Delaunay triangulation to create a mesh and indicate the predominant orientations of the planting rows, and an adaptation of the Hough transform to estimate the analytical parameters of each row. These parameters are refined using linear regression to generate the lines best fitting the input data. Finally, the failure segments on each row are identified by detecting the plants in each row. A simulation of regular point distribution on the segment is then used to identify the planting failure. This process allows the geolocation of each failure point for replanting to be quantified. The routine has significant potential in the forest inventory, allowing the geolocation of failures with an overall accuracy of 0.97 and 0.99, respectively, and a maximum positional error of 0.15 m and 0.20 m, respectively.

The objective of this study was to evaluate the correlations between vegetation indices (VI), obtained by RGB camera on flights of Remotely piloted aircraft System (RPA), with yield maps of agricultural crops. Monitoring was carried out during 4 harvest seasons: soybean 2018/19, maize (2019), soybean 2019/20 and wheat (2020), in two areas of a rural property located in Toledo, Paraná. During the harvests, periodic flights were performed using DJI-branded RPA Phantom 3 Advanced. For the generation of orthomosaic, Agisoft PhotoScan software (Free trial) was used. After the RGB bands normalization, the vegetation indices MPRI, VARI, GLI and ExG were calculated for 3 flight dates in each harvest in the study areas. At the end of the crop cycle, samples were collected to create the yield maps. With the yield data, descriptive statistics analyzes were performed and, later, the correlation between the VIs and the yields of each harvest was performed using Spearman's correlation coefficient (rs). According to the research, it would be suggested that the farmer carry out surveys with RPA with RGB camera in soybean crop, mainly in R7 stage, in maize at VT (bolting) stage and in wheat at tillering stage, since these phenological stages showed higher correlations and between the VIs and the yield of each crop. The pairs of VIs MPRI and VARI, GLI and ExG were similar as vegetative indicators, so only two of them would already have the capacity to represent the variations existing in the areas of the study between the dates.

Abstract Evapotranspiration (ET) is one of the most important processes in the hydrologic cycle, constituting the main responsible for water losses at the surface. Several evapotranspiration models use information from surface temperature and vegetation indices captured by remote sensors such as MODIS and LANDSAT to estimate the ETc value. The objective of this study is to apply SSEBop model to estimate ETc of soybean in a field experiment under four water regimes, using high-resolution multispectral and thermal images collected from remotely piloted aircraft (RPA). Surface temperature and NDVI maps were generated as sources for evapotranspiration estimation. From a Python script, spatial variability maps of ETc were generated at different phenological stages of the crop. The quality of the model for ETc estimates was performed by comparing the modeling results with leaf transpiration data measured in the field using an infrared gas analyzer, whose results showed a good correlation (R2 = 0.76). These results demonstrated the possibility of transferring a model originally developed for processing low to medium-resolution satellite images to high-resolution spatial-temporal images acquired by RPA with small adaptations in the original algorithm, generating great potential for new studies on an experimental and field scale.

Spectral indices for detecting surface water bodies play a crucial role in environmental studies, on the other hand, these indices behave differently depending on the study site or the bands used. Therefore, this study proposed an evaluation of the performance of spectral indices in detecting surface water, using a scene from the Landsat-9 satellite and implementing a new index called NIR-Green Water Index (NGWI). After a visual inspection, positive performance was found in all indices in the detection of surface water, especially those formulated using the green band, such as the Normalized Difference Water Index (NDWI), (NGWI) and Water Ratio Index (WRI). During the Pearson correlation and Separability Index analysis between the "Water" and "Non-water" classes, the Automated Water Extraction Index (AWEI) demonstrated the highest average separability, reaching 1.24, and the lowest correlation, with 0.15, which places it as the index with the best detection estimates. The NGWI stood out especially when compared to other indices, showing a moderate behavior with an average separability of 1.15, surpassing the WRI (1.12) and the MNDWI (1.13), in addition to an average Pearson correlation of 0.17, just behind AWEI (0.16) and WRI (0.15). Although all the indices mentioned have demonstrated usefulness in water detection, it was observed that more complex indices, with a more elaborate formulation and less sensitivity to shadows, such as AWEI, produced results comparable to simpler indices, such as NDWI and MNDWI.

Soil moisture content can be used to predict drought impact on agricultural yield better than precipitation. Remote sensing is viable source of soil moisture data in instrument-scarce areas. However, space-based soil moisture estimates lack suitability for daily and high-resolution agricultural, hydrological, and environmental applications. This study aimed to assess the potential of the random forest machine learning technique to enhance the spatial resolution of remote soil moisture products from the SMAP satellite. Models were built using random forest for spatial downscaling of SMAP-L3-E, then visually and statistically evaluated for disaggregation quality. The impact of topography, soil properties, and precipitation on the downscaled soil moisture was examined. The relationship between downscaled soil moisture and in-situ soil moisture was analyzed. The results indicate that the proposed method demonstrated spatial and hydrological coherence, along with a satisfactory downscaling quality. Statistical validation indicated suitable generalization error for scientific and practical use (RMSE < 0.05 cm3 cm-3). Random forest effectively achieved spatial downscaling of SMAP-L3-E in the study area. Principal component and spatial analysis revealed dependence of downscaled soil moisture on elevation, soil organic carbon content, clay content, and saturated hydraulic conductivity, mainly under near-saturation conditions. Regarding validation against in-situ data, downscaled soil moisture explained in-situ soil moisture well under low soil water content ( = 0.624). Downscaling performance deteriorates for water contents between 0.40 to 0.50 cm3 cm-3, suggesting inadequacy under near saturation conditions at a daily temporal frequency. However, coarser temporal aggregations (7 to 10 days) yielded an average 0.98 correlation coefficient, regardless of saturation conditions. These results could potentially be applied in irrigation planning, soil physics studies and hydrology monitoring, to forecasting the occurrence of droughts, leaching of contaminants, surface runoff modeling, carbon cycle studies, soil's capacity to store and provide nutrients.

Disorderly urbanization and the consequent changes in urban land use have resulted in the recurrence of urban flood events, demonstrating the need for improvement in urban planning and expansion policies. In this context, the mapping of land use and occupation through remote sensing is an important tool to support decision-making by the public administration. With advances in technology, the possibility of obtaining images by means of VANTS (Unmanned Aerial Vehicles) became popular. Therefore, this research aimed to evaluate the effectiveness of using aerial images of very high spatial resolution obtained by UAV, as a tool for mapping and monitoring intra-lot permeability rates. The study was carried out in the urban area of the city of Irati - PR, using an orthomosaic with a spatial resolution of 3 cm from the study area. The results demonstrated the potential of the proposed methodology to obtain data from land use urban differences. Although manual classification has as a disadvantage the large time required for its execution, especially for large areas, the very high spatial resolution, in addition to the quality of the images obtained by UAV, bring a new perspective within this theme, especially in relation to the best possibility of monitoring and oversight. [ABSTRACT FROM AUTHOR]

Copyright of Informe Gepec is the property of Informe Gepec 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.)

Copyright of Entorno Geográfico is the property of Universidad del Valle 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.)

ABSTRACT Considering the spectral differences between broadleaf weeds and narrow-leaf crops and the influence of terrain and soil variables on weed infestations, integrating such information into a machine-learning algorithm can lead to accurate weed maps. Therefore, we aim to evaluate the effectiveness of these variables in classifying the occurrence of broadleaf weeds in narrow-leaf crops. Weed data was collected at georeferenced points across two areas covering 200 ha (pasture) and 106 ha (sorghum), creating classes 0 (absence) and 1 (presence). For each sample point, we obtained 11 variables: soil clay content, cation exchange capacity, soil organic matter, terrain elevation, slope, NDVI, EVI, CIgreen, BGND (derived from PlanetScope images), and spatial information (X and Y coordinates). These variables were used as predictors of broadleaf weed presence and absence in the Random Forest classification algorithm. The presence and absence of broadleaf weeds were correctly classified in 84% and 74% of all predictions in the test sample sets for pasture and sorghum areas, respectively. This strategy represents an efficient way to map and manage the occurrence of broadleaf weeds in narrow-leaf crops.

ABSTRACTThe Río de la Plata estuary, located in southeast South America, is of great social and environmental relevance for the region. In the summer of 2018–2019, an exceptional cyanobacterial bloom was detected along the northern coast of the estuary, which greatly affected tourism and recreation, as reported in previous studies and the public press. This is the first work that uses satellite images to quantitatively monitor the evolution of this bloom in an intermediate zone of the estuary, including its initial and ending stages. Chlorophyll-a threshold maps of 10 and 24 µg/L were detected from Sentinel-2 imagery between November 2018-May 2019, while the presence of cyanobacteria was confirmed from phytoplankton field samples and a continuous (point) record of phycocyanin fluorescence. The combination of both satellite and in-situ information was crucial to analyze the spatiotemporal variability of the bloom, and to study its qualitative relationship to environmental drivers: local winds, discharge of the Río de la Plata main tributaries, discharge of a local tributary, water temperature and salinity. Our study shows the complexity of short-term bloom dynamics in a coastal region of the Río de la Plata, particularly highlighting the role of local winds on surface transport and mixing conditions.

Research Purpose: Proposes a methodology for space-time analysis of fires/burn on a microregional scale based on free data from the burning program of INPE –National Institute for Space Research and use of free software. Methodology: The methodological referral covered the reference on the subject, the acquisition of accessible vector and matrix data and records from the INPE fires/burn database. The product validation was referenced in the publications of INPE fires and reports. Findings: The results revealed that of the 209 burn detected in the South Coast Microregion of the State of Espírito Santo, Brazil between 2009 and 2019, 60.3% or 126 occurred in land uses of "Pasture" and in "Temporary Crops". It also showed 4 registers inside the Conservation Units -UCs and 26 the damping zone of 3 km of the UCs. The monthly analysis indicated the highest records in the months marked by low rainfall and dry indices, with the highlight for July to September, concentrating 45.9% or 93 of the 209 occurrences. Originality/Value: The use of the methodology offers an important contribution to control and monitoring actions, which can be applied in municipalities or regions that need studies of this nature, because it considered the occurrences in the territory from reference data of accessible fires, charting with free software places most susceptible to disasters of this nature.

This article begins by observing the spatio-temporal heterogeneity of deforestation in Northern Benin over the past four decades. In order to understand the underlying reasons for these spatial disparities, we conducted a thorough inquiry among residents of areas where woody cover has diverged significantly from the regional trend. Utilizing textual statistical methods, our analysis of these interviews has yielded deeper insights into specific local nuances in the population's perceptions of the ongoing environmental changes within their territories. Furthermore, our investigations have underscored a worsening of social and land inequalities that impact the entire region. The persistence of deforestation in Northern Benin, alongside the expression of economic and social vulnerability among the surveyed populations, mirrors situations already observed in several other rural regions of West Africa.

Abstract One of the major challenges for effective agricultural activity monitoring systems is defining robust indicators of spatial and temporal variability for the main risk factors associated with crop production. In this context, this study aimed to analyze the potential of the Temperature-Vegetation Dryness Index (TVDI), obtained by terrestrial and orbital sensors from soybean production areas in southern Brazil, in generating spatial and temporal patterns of the main risk factor, surface moisture, to be incorporated in operational agricultural monitoring systems. For this purpose, MODIS Terra and Landsat-8 OLI/TIRS sensor images were used, as well as data from surface positioned sensors to serve as a reference. The study area encompassed one soybean crop area, soybean mapped crop areas near the experimental area, and the municipality of Carazinho-RS. The experimental area was analyzed during the soybean growing season. As the TVDI data estimated by OLI/TIRS and MODIS sensors were coherent and robust, both sensors can be used in conjunction for agricultural risk monitoring. Its main features are continuous monitoring of large production regions by TVDIMODIS and spatial distribution detailing by TVDIOLI/TIRS in critical periods to water deficit.