Abstract: The Brazilian Pantanal biome, known for its rich biodiversity and wetlands, is experiencing frequent and destructive fires. Detecting and monitoring burnt areas is vital for comprehending their present ecological condition, a key indicator for climate change and protective measures. Optical remote sensing methods, traditionally used in fire mapping, have limitations due to atmospheric conditions. Microwave Synthetic Aperture Radar (SAR) is a promising alternative, excelling in challenging environments and demonstrating sensitivity to surface properties. This study aimed to assesses the potential of SAR images for detecting burnt areas in a conservation unit inserted in the Brazilian Pantanal after intense fires in 2020. For this, the Normalized Burn Ratio (NBR) index was calculated from Sentinel-2 images before and after fire, and then the difference between these images (dNBR). Differences in backscatter coefficients of pre- and post-fire SAR/Sentinel-1 images in the two polarizations (dVH and dVV) were also calculated. To detect burnt areas, the three differences were classified using the Random Forest algorithm. The results showed adequate coincidence of burned areas between dVH and dVV compared to dNBR and high accuracy values of the algorithm model, indicating consistency between SAR and optical data in identifying burnt areas. Abstract biome wetlands condition measures methods mapping conditions (SAR alternative properties 2020 this NBR (NBR Sentinel2 Sentinel 2 Sentinel- dNBR. . (dNBR) pre postfire post SAR/Sentinel1 SARSentinel1 SARSentinel SAR/Sentinel 1 SAR/Sentinel- model 202 (dNBR Sentinel1 20

Abstract The extraction of information from point cloud is usually done after the application of classification methods based on the geometric characteristics of the objects. However, the classification of photogrammetric point clouds can be carried out using radiometric information combined with geometric information to minimize possible classification issues. With this in mind, this work proposes an approach to the classification of photogrammetric point cloud, generated by correspondence of aerial images acquired by Remotely Piloted Aircraft System (RPAS). The proposed approach for classifying photogrammetric point clouds consists of a pixel-supervised classification method, based on a decision tree. To achieve this, three data sets were used, one to define which attributes allow discrimination between the classes and the definition of the thresholds. Initially, several attributes were extracted based on a training sample. The average and standard deviation values for the attributes of each class extracted were used to guide the decision tree definition. The defined decision tree was applied to the other two point clouds to validate the approach and for thematic accuracy assessment. The quantitative analyses of the classifications based on kappa coefficient of agreement, applied to both validation areas, reached values higher than 0.938.

Aunque la relación empírica entre imágenes satelitales y datos recolectados en campo del disco de Secchi ha sido demostrada por estudios previos en cuerpos de agua continentales, la creación de líneas base para el monitoreo espacio-temporal que permita identificar la estacionalidad del Índice de Estado Trófico calculado con el Disco de Secchi (IET-DS) ha sido poco explorado en el monitoreo de grandes lagos. Para realizar un ejemplo práctico de la dinámica espacio-estacional trófica en el embalse de Porto Primavera (Estado de São Paulo - Brasil) a partir de mayo de 2000 hasta abril de 2015, se utilizaron imágenes generadas por el sensor MODIS-Terra, e igualmente, dos trabajos de campo fueron realizados para obtener valores del Disco de Secchi (DS). Estos valores se usaron para ajustar y validar un modelo de correlación entre los valores de reflectancia de la banda centrada en los 645nm y el DS. Los resultados indicaron que el modelo tiene un alto coeficiente de determinación (R2 = 0,80) y un moderado error asociado a las estimaciones (RMSE = 0,47m). El análisis de la variación estacional del (IET-DS) muestra el siguiente patrón: la transparencia del lago es afectada por las estaciones seca y lluviosa. El valor promedio de las estaciones de muestreo localizadas en el lago lo clasifican como oligotrófico, las regiones próximas a la desembocadura del río Pardo, presentan una clasificación eutrófica durante gran parte del año; en las temporadas de verano y otoño esta condición se extiende a casi toda la región norte del lago.

Although the empirical relationship between satellite imagery and data collected in the field Secchi disk has been demonstrated by previous studies in inland water bodies, creating the basis line for spatiotemporal monitoring to identify seasonality index trophic state calculated with Secchi disk (TSISD) has been little explored in monitoring large lakes. To make a practical example of the spatioseasonal food web in the Porto Primavera reservoir (State of São Paulo Brazil) from May 2000 to April 2015, images generated by the MODISTerra sensor is used, and, also two field works were performed to obtain values Secchi Disk (SD). These values were used to adjust and validate a model of correlation between reflectance values centered on 645nm and DS band. The results indicated that the model has a high coefficient of determination (R2 = 0.80) and moderate error associated with the estimates (RMSE = 0,47m). The analysis of seasonal variation (IETDS) shows the following pattern: the transparency of the lake is affected by the dry and rainy seasons. The average value of the sampling stations located on the lake classify it as oligotrophic, next to the mouth of the Rio Pardo region, it has an eutrophic classification for a large part of the year; in the summer and fall seasons this condition extends to almost the entire northern region of the lake.

O cultivo da cana-de-açúcar no Brasil, embora assistido por técnicas modernas de plantio, é alvo constante de parasitas do sistema radicular. Por registrar seletivamente o fluxo espectral da radiação eletromagnética refletida pela vegetação, o sensoriamento remoto tornou-se uma poderosa ferramenta na detecção das plantas infectadas por patógenos do solo. Com o objetivo de caracterizar espectralmente a cana-de-açúcar sadia e infectada por nematoides e pela larva do besouro Migdolus fryanus, foram tomadas medidas radiométricas in situ e geradas curvas hiperespectrais de plantas sadias e infectadas. Técnicas específicas de análise espectral, como a determinação da posição da borda do vermelho limítrofe (Red Edge Position Determination - REPD) e diferentes índices espectrais foram avaliados para discriminar as três ocorrências. As curvas de reflectância mostraram diferenças em magnitude principalmente nos comprimentos de onda do vermelho e infravermelho próximo e, assim como a determinação do REP e os índices de clorofila b, NDVI, MCARI e TCARI, permitiram distinguir apenas entre plantas sadias e infectadas. As razões espectrais sensíveis aos pigmentos clorofila a e carotenoides, porém, discriminaram as três ocorrências, inclusive plantas infectadas por nematoides e Migdolus fryanus. A melhor discriminação foi obtida com o índice de carotenoides, um pigmento fortemente relacionado com estresse da planta

Despite all technological support used in sugarcane cultivation in Brazil, this planting is still subject to the action of root pathogens. Due to its ability to selectively record the spectral flux of electromagnetic radiation reflected by vegetation, remote sensing is a powerful tool in detecting plants infected by pests and parasites. The objective of this work was to characterize the spectral response of healthy sugarcane and its plants infected by nematodes and Migdolus fryanus beetle larva. In situ radiometric measurements were performed and specific spectral analysis techniques, such as Red Edge Position Determination (REPD) and different vegetation indices were evaluated to discriminate these occurrences. The reflectance curves generated showed differences in magnitude mainly at red and near-infrared wavelengths and, as well as REPD, chlorophyll b index, NDVI, MCARI and TCARI, were able to discriminate only healthy and infected plants. The spectral ratios sensitive to chlorophyll a and carotenoids pigments, however, discriminated the three occurrences, including nematode-infected plants and Migdolus fryanus. The best discrimination was obtained by the index of carotenoids, a pigment whose content is related to plant stress.