Fernanda Watanabe

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Early soybean yield estimation has become a fundamental tool for market policy and food security. Considering a heterogeneous crop, this study investigates the spatial and spectral variability in soybean canopy reflectance to achieve grain yield estimation. Besides allowing crop mapping, remote sensing data also provide spectral evidence that can be used as a priori knowledge to guide sample collection for prediction models. In this context, this study proposes a sampling design method that distributes sample plots based on the spatial and spectral variability in vegetation spectral indices observed in the field. Random forest (RF) and multiple linear regression (MLR) approaches were applied to a set of spectral bands and six vegetation indices to assess their contributions to the soybean yield estimates. Experiments were conducted with a hyperspectral sensor of 25 contiguous spectral bands, ranging from 500 to 900 nm, carried by an unmanned aerial vehicle (UAV) to collect images during the R5 soybean growth stage. The tests showed that spectral indices specially designed from some bands could be adopted instead of using multiple bands with MLR. However, the best result was obtained with RF using spectral bands and the height attribute extracted from the photogrammetric height model. In this case, Pearson’s correlation coefficient was 0.91. The difference between the grain yield productivity estimated with the RF model and the weight collected at harvest was 1.5%, indicating high accuracy for yield prediction.

The socio-environmental impacts caused by the collapse of a mining dam can be irreversible. In Brazil, the dam collapse at the Córrego do Feijão Mine was considered one of the worst disasters in the country. Remote sensing-based approaches have been used to detect and monitor areas affected by tailings from dam rupture. Therefore, it was proposed to identify the area affected by mining tailing mud, in Brumadinho, Minas Gerais State, through the analysis of three different spectral indices: Normalized Difference Vegetation Index (NDVI), Ferrous Minerals Ratio (FMR) and Clay Minerals Ratio (CMR). These indices were computed from the Operational Land Imager (OLI) images of Landsat-8. Different thresholds were tested to define the best range for delineating the affected area. For validation, the limits of the affected area, obtained from a higher resolution sensor, GeoEye-1, were used as reference. The methodology demonstrated great potential for detecting areas affected by dam failure. The indices NDVI and FMR delimited the area of interest with high performance, with precision varying between 95% and 92%; recall between 88% and 87%; F-score between 91% and 89%; and global accuracy between 84% and 80%, showing to be suitable mapping such disasters.