At the end of 2019, the outbreak of COVID-19 was reported in Wuhan, China. The outbreak spread quickly to several countries, becoming a public health emergency of international interest. Without a vaccine or antiviral drugs, control measures are necessary to understand the evolution of cases. Here, we report through spatial analysis the spatial pattern of the COVID-19 outbreak. The study site was the State of São Paulo, Brazil, where the first case of the disease was confirmed. We applied the Kernel Density to generate surfaces that indicate where there is higher density of cases and, consequently, greater risk of confirming new cases. The spatial pattern of COVID-19 pandemic could be observed in São Paulo State, in which its metropolitan region standed out with the greatest cases, being classified as a hotspot. In addition, the main highways and airports that connect the capital to the cities with the highest population density were classified as medium density areas by the Kernel Density method.It indicates a gradual expansion from the capital to the interior. Therefore, spatial analyses are fundamental to understand the spread of the virus and its association with other spatial data can be essential to guide control measures.

Bark beetles cause significant tree mortality in western North America. The United States Forest Service coordinates annual insect and disease surveys (IDS) by observers in airplanes to map and quantify the tree mortality caused by beetles. The subjective nature of these surveys means that accuracy evaluation is important for characterizing uncertainty. Furthermore, the metric reported for quantifying tree mortality recently changed (2012–2018 depending in region) from killed trees per acre to percent tree mortality within damage polygons, posing challenges for linking older and newer records. Here we evaluated IDS severity estimates in a beetle-affected forest in northern Idaho, USA using fine-resolution satellite imagery, which permitted greater areal coverage than field data. We first used well-established methods to map beetle-caused tree mortality in two WorldView-2 (WV2) images with a high accuracy relative to field observations. Trees-per-acre measurements within collocated IDS polygons were then converted to percent mortality using three methods and evaluated with the WV2 maps. The overall accuracies for the three methods ranged from 35–38% (for methods that used five percent-mortality classes) and 49–56% (three classes). When IDS and WV2 estimates of mortality severity that were within ±15% of each other were considered accurate, overall accuracies were 71–78%. Within the aerial survey damage polygons, the total mortality area tended to be overestimated relative to WV2. WV2 imagery identified ~50% more mortality across the study region compared with the IDS methods, with most of the difference occurring where damage was low severity or in wilderness areas. Severity of Douglas-fir beetle-caused tree mortality was estimated the most accurately, whereas severity of mountain pine beetle-caused tree mortality was estimated the least accurately. Future studies that control for temporal ambiguity between IDS and satellite imagery, as well as IDS spatial error, might provide better assessments of IDS severity accuracy. Our study increases the usefulness of the rich aerial survey database by providing estimates of uncertainty in the IDS database of tree mortality severity.

Land surface temperature (LST) acquired from remote sensing observations is essential to monitor surface energy and water exchange processes at the land-atmosphere interface. Most LST retrieval methodologies are developed focusing on Northern hemisphere. Consequently, Southern hemisphere has a great need for investigating the performance of LST retrieval algorithms already consolidated in the literature. In this paper, we compared a Split-window (SW) and a Single-channel (SC) method to retrieve LST from Landsat 8 OLI/TIRS images in a dune field, Southern Brazil. To validate the results, the Atmospheric Correction Parameter Calculator (ACPC) tool and Radiative Transfer Equation (RTE) were used. Results demonstrated that both methodologies are in accordance with the RTE, despite they overestimated the LST. Analysis of variance (ANOVA) indicated that the means are not statistically significant (0.05 level). The correlations between LST retrieved and RTE were strong, producing R² of 0.984 and 0.973 for the SW and SC, respectively, and RMSE values of 1.18 and 1.6. SW also exhibited the best values of MSD (±0.983) and Bias (0.773), thus reinforcing its superior performance. SW can be applied with an accuracy of 1.18 K in Southern Brazil, without needing complex modeling or specific radiosonde.

The objective of this study was to test the performance of canopy data obtained from Airborne Laser Scanner (ALS) in generating estimates of above-ground biomass (AGB) of Araucaria angustifolia (Bertol.) Kuntze individuals. A cloud of ALS points located in a fragment of native urban forest in Curitiba, Paraná was used. The procedures consisted of: classifying points; obtaining and smoothing the Canopy Height Model (CHM); detecting peaks and segmenting canopy using eCognition software. Mathematical models were adjusted to estimate the AGB from the crown areas. Two equations were required to estimate the individual AGB, while R² (%) values of 96.19 and 98.89 were found. The total AGB stock found was 264.333 kg. The LiDAR technology and the methods for obtaining the information used in this work constitute non-destructive and precise tools for quantifying biomass in native forests.

Developing the capacity to monitor species diversity worldwide is of great importance in halting biodiversity loss. To this end, remote sensing plays a unique role. In this study, we evaluate the potential of Global Ecosystem Dynamics Investigation (GEDI) data, combined with conventional satellite optical imagery and climate reanalysis data, to predict in situ alpha diversity (Species richness, Simpson index, and Shannon index) among tree species. Data from Sentinel-2 optical imagery, ERA-5 climate data, SRTM-DEM imagery, and simulated GEDI data were selected for the characterization of diversity in four study areas. The integration of ancillary data can improve biodiversity metrics predictions. Random Forest (RF) regression models were suitable for estimating tree species diversity indices from remote sensing variables. From these models, we generated diversity index maps for the entire Cerrado using all GEDI data available in orbit. For all models, the structural metric Foliage Height Diversity (FHD) was selected; the Renormalized Difference Vegetation Index (RDVI) was also selected in all species diversity models. For the Shannon model, two GEDI variables were selected. Overall, the models indicated performances for species diversity ranging from (R² = 0.24 to 0.56). In terms of RMSE%, the Shannon model had the lowest value among the diversity indices (31.98%). Our results suggested that the developed models are valuable tools for assessing species diversity in tropical savanna ecosystems, although each model can be chosen based on the objectives of a given study, the target amount of performance/error, and the availability of data.