The fastest-growing and most destructive fires in the US (2001 to 2020).

The most destructive and deadly wildfires in US history were also fast. Using satellite data, we analyzed the daily growth rates of more than 60,000 fires from 2001 to 2020 across the contiguous US. Nearly half of the ecoregions experienced destructive fast fires that grew more than 1620 hectares in 1 day. These fires accounted for 78% of structures destroyed and 61% of suppression costs ($18.9 billion). From 2001 to 2020, the average peak daily growth rate for these fires more than doubled (+249% relative to 2001) in the Western US. Nearly 3 million structures were within 4 kilometers of a fast fire during this period across the US. Given recent devastating wildfires, understanding fast fires is crucial for improving firefighting strategies and community preparedness.

Satellite-enabled enviromics to enhance crop improvement.

Enviromics refers to the characterization of micro- and macroenvironments based on large-scale environmental datasets. By providing genotypic recommendations with predictive extrapolation at a site-specific level, enviromics could inform plant breeding decisions across varying conditions and anticipate productivity in a changing climate. Enviromics-based integration of statistics, envirotyping (i.e., determining environmental factors), and remote sensing could help unravel the complex interplay of genetics, environment, and management. To support this goal, exhaustive envirotyping to generate precise environmental profiles would significantly improve predictions of genotype performance and genetic gain in crops. Already, informatics management platforms aggregate diverse environmental datasets obtained using optical, thermal, radar, and light detection and ranging (LiDAR)sensors that capture detailed information about vegetation, surface structure, and terrain. This wealth of information, coupled with freely available climate data, fuels innovative enviromics research. While enviromics holds immense potential for breeding, a few obstacles remain, such as the need for (1) integrative methodologies to systematically collect field data to scale and expand observations across the landscape with satellite data; (2) state-of-the-art AI models for data integration, simulation, and prediction; (3) cyberinfrastructure for processing big data across scales and providing seamless interfaces to deliver forecasts to stakeholders; and (4) collaboration and data sharing among farmers, breeders, physiologists, geoinformatics experts, and programmers across research institutions. Overcoming these challenges is essential for leveraging the full potential of big data captured by satellites to transform 21st century agriculture and crop improvement through enviromics.

Drivers of mangrove vulnerability and resilience to tropical cyclones in the North Atlantic Basin.

The North Atlantic Basin (NAB) has seen an increase in the frequency and intensity of tropical cyclones since the 1980s, with record-breaking seasons in 2017 and 2020. However, little is known about how coastal ecosystems, particularly mangroves in the Gulf of Mexico and the Caribbean, respond to these new "climate normals" at regional and subregional scales. Wind speed, rainfall, pre-cyclone forest height, and hydro-geomorphology are known to influence mangrove damage and recovery following cyclones in the NAB. However, previous studies have focused on local-scale responses and individual cyclonic events. Here, we analyze 25 years (1996-2020) of mangrove vulnerability (damage after a cyclone) and 24 years (1996-2019) of short-term resilience (recovery after damage) for the NAB and subregions, using multi-annual, remote sensing-derived databases. We used machine learning to characterize the influence of 22 potential variables on mangrove responses, including human development and long-term climate trends. Our results document variability in the rates and drivers of mangrove vulnerability and resilience, highlighting hotspots of cyclone impacts, mangrove damage, and loss of resilience. Cyclone characteristics mainly drove vulnerability at the regional level. In contrast, resilience was driven by site-specific conditions, including long-term climate trends, pre-cyclone forest structure, soil organic carbon stock, and coastal development (i.e., proximity to human infrastructure). Coastal development is associated with both vulnerability and resilience at the subregional level. Further, we highlight that loss of resilience occurs mostly in areas experiencing long-term drought across the NAB. The impacts of increasing cyclone activity on mangroves and their coastal protection service must be framed in the context of compound climate change effects and continued coastal development. Our work offers descriptive and spatial information to support the restoration and adaptive management of NAB mangroves, which need adequate health, structure, and density to protect coasts and serve as Nature-based Solutions against climate change and extreme weather events.

Spectro-temporal analysis of the Paraopeba River water after the tailings dam burst of the Córrego do Feijão mine, in Brumadinho, Brazil.

Remote sensing is an important tool for environmental assessment, especially in the event of disasters such as the tailings dam burst at the Córrego do Feijão mine, located in the Paraopeba River basin, Brazil. Thus, this study aimed to carry out a spectro-temporal analysis of the Paraopeba River water given the dam burst, using multispectral images from the MSI sensor onboard Sentinel-2 satellites. For this analysis, sections along the river were defined by the creation of buffers, with 10-km intervals each, starting from the origin of the burst. For each section, the average visible to near-infrared (NIR) reflectance values per band and the Normalized Difference Water Index (NDWI) were obtained. We found that the red edge and NIR bands (B5, B6, B7, B8, and B8A) showed higher reflectance values when compared to the visible bands in the months immediately after the disaster, especially in the first 20 km. In these months, negative NDWI values were also found for almost all sections downstream, demonstrating the large volume of mining tailings in the Paraopeba River. The seasonal variation of the observed values indicates the resuspension of the material deposited at the river bottom with the beginning of the rainy season. Finally, we highlight the usefulness of the MSI/Sentinel-2 red edge and NIR bands for further studies on the monitoring from space of water bodies subjected to contamination by large amounts of mud with iron ore tailings and contaminants, as occurred in the state of Minas Gerais, southeastern Brazil.

Impact of drought associated with high temperatures on Coffea canephora plantations: a case study in Espírito Santo State, Brazil.

Droughts are major natural disasters that affect many parts of the world all years and recently affected one of the major conilon coffee-producing regions of the world in state of Espírito Santo, which caused a huge crisis in the sector. Therefore, the objective of this study was to conduct an analysis with technical-scientific basis of the real impact of drought associated with high temperatures and irradiances on the conilon coffee (Coffea canephora Pierre ex Froehner) plantations located in the north, northwest, and northeast regions of the state of Espírito Santo, Brazil. Data from 2010 to 2016 of rainfall, air temperature, production, yield, planted area and surface remote sensing were obtained from different sources, statistically analyzed, and correlated. The 2015/2016 season was the most affected by the drought and high temperatures (mean annual above 26 °C) because, in addition to the adverse weather conditions, coffee plants were already damaged by the climatic conditions of the previous season. The increase in air temperature has higher impact (negative) on production than the decrease in annual precipitation. The average annual air temperatures in the two harvest seasons that stood out for the lowest yields (i.e. 2012/2013 and 2015/2016) were approximately 1 °C higher than in the previous seasons. In addition, in the 2015/2016 season, the average annual air temperature was the highest in the entire series. The spatial and temporal distribution of Enhanced Vegetation Index values enabled the detection and perception of droughts in the conilon coffee-producing regions of Espírito Santo. The rainfall volume accumulated in the periods from September to December and from April to August are the ones that most affect coffee yield. The conilon coffee plantations in these regions are susceptible to new climate extremes, as they continue to be managed under irrigation and full sun. The adoption of agroforestry systems and construction of small reservoirs can be useful to alleviate these climate effects, reducing the risk of coffee production losses and contributing to the sustainability of crops in Espírito Santo.

Leaf development stages and ontogenetic changes in passionfruit (Passiflora edulis Sims.) are detected by narrowband spectral signal.

During shoot development, leaves undergo various ontogenetic changes, including variation in size, shape, and geometry. Passiflora edulis (passionfruit) is a heteroblastic species, which means that it experiences conspicuous changes throughout development, enabling a morphological distinction between the juvenile and adult vegetative phases. Quantification of heteroblasty requires a practical, inexpensive, reliable, and non-destructive method, such as remote sensing. Moreover, relationships among ontogenetic changes and spectral signal at leaf level can be scaled up to support precision agriculture in passion fruit crops. In the present study, we used laboratory spectroscopic measurements (400-2500 nm) and narrowband vegetation indexes (or hyperspectral vegetation indexes - HVIs) to evaluate ontogenetic changes related to development and aging in P. edulis leaves. We also assessed leaf pigment concentration to further support the application of biochemical-related narrowband indexes. We report that 30-d-old leaves can be discriminated into developmental stages through their spectral signals. MSI (Moisture Stress Index) and NDVI750 (Normalized Difference Vegetation Index ρ750) contribute most to the variation of age (15 to 30-d-old leaves) and developmental stage (phytomer positions along the plant axis) in passionfruit leaves. PRI (Photochemical Reflectance Index) played an important role in detecting age and development alterations, including heteroblasty. A biochemical and spectral comparison of pigments revealed that spectroscopy offered potential for diagnosing phenology in P. edulis, as some narrowband indexes correlated strongly with chlorophylls and carotenoids content. Narrowband vegetation indexes are found to be a suitable tool for monitoring passionfruit crops.

Wildfires as a major challenge for natural regeneration in Atlantic Forest.

The natural regeneration management is a good strategy of ecological restoration of the Atlantic Forest, one of the most devastated biomes on the planet. However, the frequent occurrence of wildfires is one of the challenges to the success of this method. The objective of this study was to evaluate the effects of wildfires on forest dynamics in Atlantic Forest. The studied area was explored during the coffee cycle when plantations replaced primary forests. We used remote sensing data to analyze the forest dynamics over a period of 50 years (1966-2016). We used the INPE burn database to find the occurrence of hot spots from 1998 to 2016. During this period, we selected the years most affected by the fires for the identification of fire scars using the Normalized Burn Ratio spectral index. From this set of information, we used the methodology of weights of evidence to relate forest dynamics and wildfire events with biophysical and anthropic variables. The results showed that in 1966 the forest area accounted for 8.01% of the land cover, and in 2016 this number rose to 18.55% due to the spontaneous natural regeneration process. The regenerating areas were mainly related to the proximity of the remaining fragments and the portions of the landscape receiving the least amount of global solar radiation. The proximity to urban areas, roads and highways, damaged regeneration and favored both deforestation and wildfire events. Fire scars preferentially occur where there is greater sun exposure. It is possible to observe a negative correlation between the natural regeneration process and the fire scars. We concluded that fire severity is one of the factors that shape the landscape of the region while slowing the regeneration process in preferential areas.