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.

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.

An approach to monitor vegetation without the influence of cloud cover and employing Synthetic Aperture Radar (SAR) data was developed by the DPSVI (Dual Polarization SAR Vegetation Index), based on Sentinel-1 mission data. However, DPSVI performance in areas of dense vegetation and variation in vegetation classes is unknown. Thus, this paper aimed to investigate the performance of DPSVI in the monitoring of vegetation in the Brazilian Atlantic Forest biome, as well as to propose modifications to improve its capacity to monitor the vegetation and investigate seasonal and spatial influences on the performance of the proposed index (DPSVIm). Three approaches were adopted: 1) use Sentinel-1 and Landsat 8 scenes obtained from four hydrological years (2015–2016 to 2018–2019), to compute monthly DPSVI, DPSVIm, NDVI (Normalized Difference Vegetation Index), and EVI (Enhanced Vegetation Index) indices, to investigate seasonal influences; 2) perform topographic correction in Landsat 8 and Sentinel-1 data to verify the influence of relief on DPSVIm performance; and 3) confronting DPSVIm with an attribute of Atlantic Forest fragments: the above ground biomass (AGB). The scenes were processed on the Google Earth Engine platform. The DPSVIm was able to better distinguish the vegetation, compared to the DPSVI, mainly in forest planting areas and Atlantic Forest, which evidences the proposed improvements. The rainfall regime made the DPSVIm performance seasonal in comparison with the NDVI and EVI (Normalized Difference Vegetation Index and Enhanced Vegetation Index), presenting better agreements in the dry season and worse in the rainy season. There was a greater agreement between DPSVIm and EVI in the results to monitor dense vegetation since EVI does not saturate as NDVI does. Finally, the DPSVIm has a good fit to AGB, being able to distinguish different levels of AGB from 50 to 200 Mg ha, approximately. The study allowed improving the DPSVI making the DPSVIm adequate to quantify vegetation in the study area, even considering the effects of topography and rainfall regime. The latter environmental factor should be addressed in future studies, searching to mitigate this source of temporal uncertainty in DPSVIm.

Fast-growing Eucalyptus spp. forest plantations and their resultant wood products are economically important and may provide a low-cost means to sequester carbon for greenhouse gas reduction. The development of advanced and optimized frameworks for estimating forest plantation attributes from lidar remote sensing data combined with statistical modeling approaches is a step towards forest inventory operationalization and might improve industry efficiency in monitoring and managing forest resources. In this study, we first developed and tested a framework for modeling individual tree attributes in fast-growing Eucalyptus forest plantation using airborne lidar data and linear mixed-effect models (LME) and assessed the gain in accuracy compared to a conventional linear fixed-effects model (LFE). Second, we evaluated the potential of using the tree-level estimates for determining tree attribute uniformity across different stand ages. In the field, tree measurements, such as tree geolocation, species, genotype, age, height (Ht), and diameter at breast height (dbh) were collected through conventional forest inventory practices, and tree-level aboveground carbon (AGC) was estimated using allometric equations. Individual trees were detected and delineated from lidar-derived canopy height models (CHM), and crown-level metrics (e.g., crown volume and crown projected area) were computed from the lidar 3-D point cloud. Field and lidar-derived crown metrics were combined for ht, dbh, and AGC modeling using an LME. We fitted a varying intercept and slope model, setting species, genotype, and stand (alone and nested) as random effects. For comparison, we also modeled the same attributes using a conventional LFE model. The tree attribute estimates derived from the best LME model were used for assessing forest uniformity at the tree level using the Lorenz curves and Gini coefficient (GC). We successfully detected 96.6% of the trees from the lidar-derived CHM. The best LME model for estimating the tree attributes was composed of the stand as a random effect variable, and canopy height, crown volume, and crown projected area as fixed effects. The %RMSE values for tree-level height, dbh, and AGC were 8.9%, 12.1%, and 23.7% for the LFE model and improved to 7.3%, 7.1%, and 13.6%, respectively, for the LME model. Tree attributes uniformity was assessed with the Lorenz curves and tree-level estimations, especially for the older stands. All stands showed a high level of tree uniformity with GC values approximately 0.2. This study demonstrates that accurate detection of individual trees and their associated crown metrics can be used to estimate Ht, dbh, and AGC stocks as well as forest uniformity in fast-growing Eucalyptus plantations forests using lidar data as inputs to LME models. This further underscores the high potential of our proposed approach to monitor standing stock and growth in Eucalyptus—and similar forest plantations for carbon dynamics and forest product planning.