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Groundwater is the most ubiquitous source of liquid freshwater globally, yet its role in supporting diverse ecosystems is rarely acknowledged1,2. However, the location and extent of groundwater-dependent ecosystems (GDEs) are unknown in many geographies, and protection measures are lacking1,3. Here, we map GDEs at high-resolution (roughly 30 m) and find them present on more than one-third of global drylands analysed, including important global biodiversity hotspots4. GDEs are more extensive and contiguous in landscapes dominated by pastoralism with lower rates of groundwater depletion, suggesting that many GDEs are likely to have already been lost due to water and land use practices. Nevertheless, 53% of GDEs exist within regions showing declining groundwater trends, which highlights the urgent need to protect GDEs from the threat of groundwater depletion. However, we found that only 21% of GDEs exist on protected lands or in jurisdictions with sustainable groundwater management policies, invoking a call to action to protect these vital ecosystems. Furthermore, we examine the linkage of GDEs with cultural and socio-economic factors in the Greater Sahel region, where GDEs play an essential role in supporting biodiversity and rural livelihoods, to explore other means for protection of GDEs in politically unstable regions. Our GDE map provides critical information for prioritizing and developing policies and protection mechanisms across various local, regional or international scales to safeguard these important ecosystems and the societies dependent on them.

Globe-LFMC 2.0, an updated version of Globe-LFMC, is a comprehensive dataset of over 280,000 Live Fuel Moisture Content (LFMC) measurements. These measurements were gathered through field campaigns conducted in 15 countries spanning 47 years. In contrast to its prior version, Globe-LFMC 2.0 incorporates over 120,000 additional data entries, introduces more than 800 new sampling sites, and comprises LFMC values obtained from samples collected until the calendar year 2023. Each entry within the dataset provides essential information, including date, geographical coordinates, plant species, functional type, and, where available, topographical details. Moreover, the dataset encompasses insights into the sampling and weighing procedures, as well as information about land cover type and meteorological conditions at the time and location of each sampling event. Globe-LFMC 2.0 can facilitate advanced LFMC research, supporting studies on wildfire behaviour, physiological traits, ecological dynamics, and land surface modelling, whether remote sensing-based or otherwise. This dataset represents a valuable resource for researchers exploring the diverse LFMC aspects, contributing to the broader field of environmental and ecological research.

Carbon dioxide and methane emissions are the two primary anthropogenic climate-forcing agents and an important source of uncertainty in the global carbon budget. Uncertainties are further magnified when emissions occur at fine spatial scales (

Biogeographic history can set initial conditions for vegetation community assemblages that determine their climate responses at broad extents that land surface models attempt to forecast. Numerous studies have indicated that evolutionarily conserved biochemical, structural, and other functional attributes of plant species are captured in visible-to-short wavelength infrared, 400 to 2,500 nm, reflectance properties of vegetation. Here, we present a remotely sensed phylogenetic clustering and an evolutionary framework to accommodate spectra, distributions, and traits. Spectral properties evolutionarily conserved in plants provide the opportunity to spatially aggregate species into lineages (interpreted as lineage functional types or LFT) with improved classification accuracy. In this study, we use Airborne Visible/Infrared Imaging Spectrometer data from the 2013 Hyperspectral Infrared Imager campaign over the southern Sierra Nevada, California flight box, to investigate the potential for incorporating evolutionary thinking into landcover classification. We link the airborne hyperspectral data with vegetation plot data from 1372 surveys and a phylogeny representing 1,572 species. Despite temporal and spatial differences in our training data, we classified plant lineages with moderate reliability (Kappa = 0.76) and overall classification accuracy of 80.9%. We present an assessment of classification error and detail study limitations to facilitate future LFT development. This work demonstrates that lineage-based methods may be a promising way to leverage the new-generation high-resolution and high return-interval hyperspectral data planned for the forthcoming satellite missions with sparsely sampled existing ground-based ecological data.

Dryland riparian woodlands are considered to be locally buffered from droughts by shallow and stable groundwater levels. However, climate change is causing more frequent and severe drought events, accompanied by warmer temperatures, collectively threatening the persistence of these groundwater dependent ecosystems through a combination of increasing evaporative demand and decreasing groundwater supply. We conducted a dendro-isotopic analysis of radial growth and seasonal (semi-annual) carbon isotope discrimination (Δ13 C) to investigate the response of riparian cottonwood stands to the unprecedented California-wide drought from 2012 to 2019, along the largest remaining free-flowing river in Southern California. Our goals were to identify principal drivers and indicators of drought stress for dryland riparian woodlands, determine their thresholds of tolerance to hydroclimatic stressors, and ultimately assess their vulnerability to climate change. Riparian trees were highly responsive to drought conditions along the river, exhibiting suppressed growth and strong stomatal closure (inferred from reduced Δ13 C) during peak drought years. However, patterns of radial growth and Δ13 C were quite variable among sites that differed in climatic conditions and rate of groundwater decline. We show that the rate of groundwater decline, as opposed to climate factors, was the primary driver of site differences in drought stress, and trees showed greater sensitivity to temperature at sites subjected to faster groundwater decline. Across sites, higher correlation between radial growth and Δ13 C for individual trees, and higher inter-correlation of Δ13 C among trees were indicative of greater drought stress. Trees showed a threshold of tolerance to groundwater decline at 0.5 m year-1 beyond which drought stress became increasingly evident and severe. For sites that exceeded this threshold, peak physiological stress occurred when total groundwater recession exceeded ~3 m. These findings indicate that drought-induced groundwater decline associated with more extreme droughts is a primary threat to dryland riparian woodlands and increases their susceptibility to projected warmer temperatures.

Each year, wildfires ravage the western U.S. and change the lives of millions of inhabitants. Situated in southern California, coastal Santa Barbara has witnessed devastating wildfires in the past decade, with nearly all ignitions started by humans. Therefore, estimating the risk imposed by unplanned ignitions in this fire-prone region will further increase resilience toward wildfires. Currently, a fire-risk map does not exist in this region. The main objective of this study is to provide a spatial analysis of regions at high risk of fast wildfire spread, particularly in the first two hours, considering varying scenarios of ignition locations and atmospheric conditions. To achieve this goal, multiple wildfire simulations were conducted using the FARSITE fire spread model with three ignition modeling methods and three wind scenarios. The first ignition method considers ignitions randomly distributed in 500 m buffers around previously observed ignition sites. Since these ignitions are mainly clustered around roads and trails, the second method considers a 50 m buffer around this built infrastructure, with ignition points randomly sampled from within this buffer. The third method assumes a Euclidean distance decay of ignition probability around roads and trails up to 1000 m, where the probability of selection linearly decreases further from the transportation paths. The ignition modeling methods were then employed in wildfire simulations with varying wind scenarios representing the climatological wind pattern and strong, downslope wind events. A large number of modeled ignitions were located near the major-exit highway running north–south (HWY 154), resulting in more simulated wildfires burning in that region. This could impact evacuation route planning and resource allocation under climatological wind conditions. The simulated fire areas were smaller, and the wildfires did not spread far from the ignition locations. In contrast, wildfires ignited during strong, northerly winds quickly spread into the wildland–urban interface (WUI) toward suburban and urban areas.

Riparian ecosystems fundamentally depend on groundwater, especially in dryland regions, yet their water requirements and sources are rarely considered in water resource management decisions. Until recently, technological limitations and data gaps have hindered assessment of groundwater influences on riparian ecosystem health at the spatial and temporal scales relevant to policy and management. Here, we analyze Sentinel-2-derived normalized difference vegetation index (NDVI; n = 5,335,472 observations), field-based groundwater elevation (n = 32,051 observations), and streamflow alteration data for riparian woodland communities (n = 22,153 polygons) over a 5-y period (2015 to 2020) across California. We find that riparian woodlands exhibit a stress response to deeper groundwater, as evidenced by concurrent declines in greenness represented by NDVI. Furthermore, we find greater seasonal coupling of canopy greenness to groundwater for vegetation along streams with natural flow regimes in comparison with anthropogenically altered streams, particularly in the most water-limited regions. These patterns suggest that many riparian woodlands in California are subsidized by water management practices. Riparian woodland communities rely on naturally variable groundwater and streamflow components to sustain key ecological processes, such as recruitment and succession. Altered flow regimes, which stabilize streamflow throughout the year and artificially enhance water supplies to riparian vegetation in the dry season, disrupt the seasonal cycles of abiotic drivers to which these Mediterranean forests are adapted. Consequently, our analysis suggests that many riparian ecosystems have become reliant on anthropogenically altered flow regimes, making them more vulnerable and less resilient to rapid hydrologic change, potentially leading to future riparian forest loss across increasingly stressed dryland regions.

Chaparral shrubs in southern California may be vulnerable to frequent fire and severe drought. Drought may diminish postfire recovery or worsen impact of short-interval fires. Field-based studies have not shown the extent and magnitude of drought effects on recovery, which may vary among chaparral types and climatic zones. We tracked regional patterns of shrub cover based on June-solstice Landsat Normalized Difference Vegetation Index series, compared between the periods 1984-1989 and 2014-2018. High spatial resolution ortho-imagery was used to map shrub cover in distributed sample plots, to empirically constrain the Landsat-based estimates of mature-stage lateral canopy recovery. We evaluated precipitation, climatic water deficit (CWD), and Palmer Drought Severity Index in summer and wet seasons preceding and following fire, as regional predictors of recovery in 982 locations between the Pacific Coast and inland deserts. Wet-season CWD was the strongest drought-metric predictor of recovery, contributing 34-43 % of explanatory power in multivariate regressions (R2 =0.16-0.42). Limited recovery linked to drought was most prevalent in transmontane chamise chaparral; impacts were minor in montane areas, and in mixed and montane chaparral types. Elevation was correlated negatively to recovery of transmontane chamise; this may imply acute drought sensitivity in resprouts which predominate seedlings at higher elevations. Landsat Visible Atmospherically Resistant Index (sensitive to live-fuel moisture) was evaluated as a landscape-scale predictor of recovery and explained the greatest amount of variance in a multivariate regression (R2 = 0.53). We find that drought severity was more closely related to recovery differences among twice-burned sites than was fire-return interval. Summarily, drought has a major role in long-term shrub cover reduction within xeric chaparral ecotones bounding the Mojave Desert and Colorado Desert, likely in tandem with other global change stressors.

The 2017–2027 National Academies' Decadal Survey, Thriving on Our Changing Planet, recommended Surface Biology and Geology (SBG) as a “Designated Targeted Observable” (DO). The SBG DO is based on the need for capabilities to acquire global, high spatial resolution, visible to shortwave infrared (VSWIR; 380–2500 nm; ~30 m pixel resolution) hyperspectral (imaging spectroscopy) and multispectral midwave and thermal infrared (MWIR: 3–5 μm; TIR: 8–12 μm; ~60 m pixel resolution) measurements with sub-monthly temporal revisits over terrestrial, freshwater, and coastal marine habitats. To address the various mission design needs, an SBG Algorithms Working Group of multidisciplinary researchers has been formed to review and evaluate the algorithms applicable to the SBG DO across a wide range of Earth science disciplines, including terrestrial and aquatic ecology, atmospheric science, geology, and hydrology. Here, we summarize current state-of-the-practice VSWIR and TIR algorithms that use airborne or orbital spectral imaging observations to address the SBG DO priorities identified by the Decadal Survey: (i) terrestrial vegetation physiology, functional traits, and health; (ii) inland and coastal aquatic ecosystems physiology, functional traits, and health; (iii) snow and ice accumulation, melting, and albedo; (iv) active surface composition (eruptions, landslides, evolving landscapes, hazard risks); (v) effects of changing land use on surface energy, water, momentum, and carbon fluxes; and (vi) managing agriculture, natural habitats, water use/quality, and urban development. We review existing algorithms in the following categories: snow/ice, aquatic environments, geology, and terrestrial vegetation, and summarize the community-state-of-practice in each category. This effort synthesizes the findings of more than 130 scientists.

Regrowth after fire is critical to the persistence of chaparral shrub communities in southern California, which has been subject to frequent fire events in recent decades. Fires that recur at short intervals of 10 years or less have been considered an inhibitor of recovery and the major cause of 'community type-conversion' in chaparral, primarily based on studies of small extents and limited time periods. However, recent sub-regional investigations based on remote sensing suggest that short-interval fire (SIF) does not have ubiquitous impact on postfire chaparral recovery. A region-wide analysis including a greater spatial extent and time period is needed to better understand SIF impact on chaparral. This study evaluates patterns of postfire recovery across southern California, based on temporal trajectories of Normalized Difference Vegetation Index (NDVI) derived from June-solstice Landsat image series covering the period 1984-2018. High spatial resolution aerial images were used to calibrate Landsat NDVI trajectory-based estimates of change in fractional shrub cover (dFSC) for 294 stands. The objectives of this study were (1) to assess effects of time between fires and number of burns on recovery, using stand-aggregate samples (n = 294) and paired single- and multiple-burn sample plots (n = 528), and (2) to explain recovery variations among predominant single-burn locations based on shrub community type, climate, soils, and terrain. Stand-aggregate samples showed a significant but weak effect of SIF on recovery (p

Regional maps of vegetation structure are necessary for delineating species habitats and for supporting conservation and ecological analyses. A systematic approach that can discriminate a wide range of meaningful and detailed vegetation classes is still lacking for neotropical savannas. Detailed vegetation mapping of savannas is challenged by seasonal vegetation dynamics and substantial heterogeneity in vegetation structure and composition, but fine spatial resolution imagery (

Temporal trajectories of apparent vegetation abundance based on the multi-decadal Landsat image series provide valuable information on the postfire recovery of chaparral shrublands, which tend to mature within one decade. Signals of change in fractional shrub cover (FSC) extracted from time-sequential Normalized Difference Vegetation Index (NDVI) data can be systematically biased due to spatial variation in shrub type, soil substrate, or illumination differences associated with topography. We evaluate the effects of these variables in Landsat-derived metrics of FSC and postfire recovery, based upon three chaparral sites in southern California which contain shrub community ecotones, complex terrain, and soil variations. Detailed validations of prefire and postfire FSC are based on high spatial resolution ortho-imagery; cross-stratified random sampling is used for variable control. We find that differences in the composition and structure of shrubs (inferred from ortho-imagery) can substantially influence FSC-NDVI relations and impact recovery metrics. Differences in soil type have a moderate effect on the FSC-NDVI relation in one of the study sites, while no substantial effects were observed due to variation of terrain illumination among the study sites. Arithmetic difference recovery metrics - based on NDVI values that were not normalized with unburned control plots - correlate in a moderate but significant manner with a change in FSC (R2 values range 0.47-0.59 at two sites). Similar regression coefficients resulted from using Landsat visible reflectance data alone. The lowest correlations to FSC resulted from Soil-Adjusted Vegetation Index (SAVI) and are attributed to the effects of the soil-adjustment factor in sparsely vegetated areas. The Normalized Burn Ratio and Normalized Burn Ratio 2 showed a moderate correlation to FSC. This study confirms the utility of Landsat NDVI data for postfire recovery evaluation and implies a need for stratified analysis of postfire recovery in some chaparral landscapes.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM's sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. This study advances our understanding of the nuances associated with mapping xeric and mesic regions, and highlights the importance of incorporating complementary algorithms and sensors to accurately characterize the heterogeneity of dryland ecosystems.

Globe-LFMC is an extensive global database of live fuel moisture content (LFMC) measured from 1,383 sampling sites in 11 countries: Argentina, Australia, China, France, Italy, Senegal, Spain, South Africa, Tunisia, United Kingdom and the United States of America. The database contains 161,717 individual records based on in situ destructive samples used to measure LFMC, representing the amount of water in plant leaves per unit of dry matter. The primary goal of the database is to calibrate and validate remote sensing algorithms used to predict LFMC. However, this database is also relevant for the calibration and validation of dynamic global vegetation models, eco-physiological models of plant water stress as well as understanding the physiological drivers of spatiotemporal variation in LFMC at local, regional and global scales. Globe-LFMC should be useful for studying LFMC trends in response to environmental change and LFMC influence on wildfire occurrence, wildfire behavior, and overall vegetation health.

Automated, reliable cloud masks over snow-covered terrain would improve the retrieval of snow properties from multispectral satellite sensors. The U.S. Geological Survey and NASA chose the currently operational cloud masks based on global performance across diverse land cover types. This study assesses errors in these cloud masks over snow-covered, midlatitude mountains. We use 26 Landsat 8 scenes with manually delineated cloud, snow, and land cover to assess the performance of two cloud masks: CFMask for the Landsat 8 OLI and the cloud mask that ships with Moderate-Resolution Imaging Spectroradiometer (MODIS) surface reflectance products MOD09GA and MYD09GA. The overall precision and recall of CFMask over snow-covered terrain are 0.70 and 0.86; the MOD09GA cloud mask precision and recall are 0.17 and 0.72. A plausible reason for poorer performance of cloud masks over snow lies in the potential similarity between multispectral signatures of snow and cloud pixels in three situations: (1) Snow at high elevation is bright enough in the "cirrus" bands (Landsat band 9 or MODIS band 26) to be classified as cirrus. (2) Reflectances of "dark" clouds in shortwave infrared (SWIR) bands are bracketed by snow spectra in those wavelengths. (3) Snow as part of a fractional mixture in a pixel with soils sometimes produces "bright SWIR" pixels that look like clouds. Improvement in snow-cloud discrimination in these cases will require more information than just the spectrum of the sensor's bands or will require deployment of a spaceborne imaging spectrometer, which could discriminate between snow and cloud under conditions where a multispectral sensor might not.

Understanding atmospheric water vapor patterns can inform regional understanding of water use, climate patterns and hydrologic processes. This research uses Airborne Visible Infrared Imaging Spectrometer (AVIRIS) reflectance and water vapor imagery to investigate spatial patterns of water vapor in California's Central Valley on a June date in 2013, and 2015, and relates these patterns to surface characteristics and atmospheric properties. We analyze water vapor imagery at two scales: regional and agricultural field, to examine how the slope, intercept, and trajectory of water vapor interact with the landscape in a highly diverse and complex agricultural setting. At the field scale, we found significant quadratic relationships between water vapor slope and wind magnitude in both years (p

Although gross primary productivity (GPP) is estimated with remote sensing over large regions of Earth, urban areas are usually excluded due to the lack of light use efficiency (LUE) parameters for urban vegetation and the spatial heterogeneity of urban land cover. Here, we estimated midsummer GPP, both within and among vegetation and land-use types, across the Minneapolis-Saint Paul, Minnesota metropolitan region. We derived LUE parameters for urban vegetation types using estimates of GPP from tree sap flow and eddy covariance CO2 flux observations, and from fraction of absorbed photosynthetically active radiation based on 2 m resolution WorldView-2 satellite imagery. Mean GPP per unit land area (including vegetation, impervious surfaces, and soil) was 2.64 g C m−2 d−1, and was 4.45 g C m−2 d−1 per unit vegetated area. Mapped GPP estimates were within 11.4% of estimates from independent tall tower eddy covariance measurements. Turf grass GPP had a larger coefficient of variation (0.18) than other vegetation classes (~0.10). Vegetation composition was largely consistent across the study area. Excluding golf courses, mean land-use GPP for the total study area varied more by percent vegetation cover (R2 = 0.98, p < 0.001) than by variability within vegetation classes (R2 = 0.21, p = 0.19). Urban GPP in general was less than half that of natural forests and grasslands in the same climate zone.

Cloud shading limits surface radiation, thus reducing vegetation water stress and, presumably, flammability. Since the early 1970s, cloud observations from airfields in coastal Southern California (CSCA) indicate reductions of ~25–50% in warm-season frequency of daytime stratus clouds at many sites, including fire-prone wildland-urban interface zones. We use 10 years of meteorological, surface radiation, and cloud observations to statistically model the effects of clouds on warm-season surface energy fluxes in CSCA. Forcing our model with cloud observations, we estimate that reduced warm-season cloud shading since the 1970s significantly enhanced daytime solar radiation and evaporative demand throughout much of CSCA, particularly in greater Los Angeles and northern San Diego. Correlation with burned area and live fuel moisture implicates stratus cloud shading as an important driver of warm-season wildfire activity in CSCA. Large reductions in cloud shading have likely enhanced warm-season wildfire potential in many CSCA areas when and where fuels are not limiting.

The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite-based sensors can repeatedly record the visible and near-infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100-m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short-wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14-bit digitization, absolute radiometric calibration

The biodiversity and high productivity of coastal terrestrial and aquatic habitats are the foundation for important benefits to human societies around the world. These globally distributed habitats need frequent and broad systematic assessments, but field surveys only cover a small fraction of these areas. Satellite-based sensors can repeatedly record the visible and near-infrared reflectance spectra that contain the absorption, scattering, and fluorescence signatures of functional phytoplankton groups, colored dissolved matter, and particulate matter near the surface ocean, and of biologically structured habitats (floating and emergent vegetation, benthic habitats like coral, seagrass, and algae). These measures can be incorporated into Essential Biodiversity Variables (EBVs), including the distribution, abundance, and traits of groups of species populations, and used to evaluate habitat fragmentation. However, current and planned satellites are not designed to observe the EBVs that change rapidly with extreme tides, salinity, temperatures, storms, pollution, or physical habitat destruction over scales relevant to human activity. Making these observations requires a new generation of satellite sensors able to sample with these combined characteristics: (1) spatial resolution on the order of 30 to 100-m pixels or smaller; (2) spectral resolution on the order of 5 nm in the visible and 10 nm in the short-wave infrared spectrum (or at least two or more bands at 1,030, 1,240, 1,630, 2,125, and/or 2,260 nm) for atmospheric correction and aquatic and vegetation assessments; (3) radiometric quality with signal to noise ratios (SNR) above 800 (relative to signal levels typical of the open ocean), 14-bit digitization, absolute radiometric calibration

Fire impacts many vegetated ecosystems across the world. The severity of a fire is major component in determining post-fire effects, including soil erosion, trace gas emissions, and the trajectory of recovery. In this study, we used imaging spectroscopy data combined with Multiple Endmember Spectral Mixture Analysis (MESMA), a form of spectral mixture analysis that accounts for endmember variability, to map fire severity of the 2013 Rim Fire. We evaluated four endmember selection approaches: Iterative Endmember Selection (IES), count-based within endmember class (In-CoB), Endmember Average Root Mean Squared Error (EAR), and Minimum Average Spectral Angle (MASA). To reduce the dimensionality of the imaging spectroscopy data we used uncorrelated Stable Zone Unmixing (uSZU). Fractional cover maps derived from MESMA were validated using two approaches: (1) manual interpretation of fine spatial resolutionWorldView-2 imagery; and (2) ground plots measuring the Geo Composite Burn Index (GeoCBI) and the percentage of co-dominant and dominant trees with green, brown, and black needles. Comparison to reference data demonstrated fairly high correlation for green vegetation and char fractions (r2 values as high as 0.741 for the MESMA ash fractions compared to classifiedWorldView-2 imagery and as high as 0.841 for green vegetation fractions). The combination of uSZU band selection and In-CoB endmember selection had the best trade-offbetween accuracy and computational efficiency. This study demonstrated that detailed fire severity retrievals based on imaging spectroscopy can be optimized using techniques that would be viable also in a satellite-based imaging spectrometer.

Abstract. At local scales, emissions of methane and carbon dioxide are highly uncertain. Localized sources of both trace gases can create strong local gradients in its columnar abundance, which can be discerned using absorption spectroscopy at high spatial resolution. In a previous study, more than 250 methane plumes were observed in the San Juan Basin near Four Corners during April 2015 using the next-generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) and a linearized matched filter. For the first time, we apply the iterative maximum a posteriori differential optical absorption spectroscopy (IMAP-DOAS) method to AVIRIS-NG data and generate gas concentration maps for methane, carbon dioxide, and water vapor plumes. This demonstrates a comprehensive greenhouse gas monitoring capability that targets methane and carbon dioxide, the two dominant anthropogenic climate-forcing agents. Water vapor results indicate the ability of these retrievals to distinguish between methane and water vapor despite spectral interference in the shortwave infrared. We focus on selected cases from anthropogenic and natural sources, including emissions from mine ventilation shafts, a gas processing plant, tank, pipeline leak, and natural seep. In addition, carbon dioxide emissions were mapped from the flue-gas stacks of two coal-fired power plants and a water vapor plume was observed from the combined sources of cooling towers and cooling ponds. Observed plumes were consistent with known and suspected emission sources verified by the true color AVIRIS-NG scenes and higher-resolution Google Earth imagery. Real-time detection and geolocation of methane plumes by AVIRIS-NG provided unambiguous identification of individual emission source locations and communication to a ground team for rapid follow-up. This permitted verification of a number of methane emission sources using a thermal camera, including a tank and buried natural gas pipeline.

The 2010 BP Deepwater Horizon (DWH) oil spill damaged thousands of km2 of intertidal marsh along shorelines that had been experiencing elevated rates of erosion for decades. Yet, the contribution of marsh oiling to landscape-scale degradation and subsequent land loss has been difficult to quantify. Here, we applied advanced remote sensing techniques to map changes in marsh land cover and open water before and after oiling. We segmented the marsh shorelines into non-oiled and oiled reaches and calculated the land loss rates for each 10% increase in oil cover (e.g. 0% to >70%), to determine if land loss rates for each reach oiling category were significantly different before and after oiling. Finally, we calculated background land-loss rates to separate natural and oil-related erosion and land loss. Oiling caused significant increases in land losses, particularly along reaches of heavy oiling (>20% oil cover). For reaches with ≥20% oiling, land loss rates increased abruptly during the 2010-2013 period, and the loss rates during this period are significantly different from both the pre-oiling (p < 0.0001) and 2013-2016 post-oiling periods (p < 0.0001). The pre-oiling and 2013-2016 post-oiling periods exhibit no significant differences in land loss rates across oiled and non-oiled reaches (p = 0.557). We conclude that oiling increased land loss by more than 50%, but that land loss rates returned to background levels within 3-6 years after oiling, suggesting that oiling results in a large but temporary increase in land loss rates along the shoreline.

Amazonia has experienced large-scale regional droughts that affect forest productivity and biomass stocks. Space-borne remote sensing provides basin-wide data on impacts of meteorological anomalies, an important complement to relatively limited ground observations across the Amazon's vast and remote humid tropical forests. Morning overpass QuikScat Ku-band microwave backscatter from the forest canopy was anomalously low during the 2005 drought, relative to the full instrument record of 1999-2009, and low morning backscatter persisted for 2006-2009, after which the instrument failed. The persistent low backscatter has been suggested to be indicative of increased forest vulnerability to future drought. To better ascribe the cause of the low post-drought backscatter, we analyzed multiyear, gridded remote sensing data sets of precipitation, land surface temperature, forest cover and forest cover loss, and microwave backscatter over the 2005 drought region in the southwestern Amazon Basin (4°-12°S, 66°-76°W) and in adjacent 8°x10° regions to the north and east. We found moderate to weak correlations with the spatial distribution of persistent low backscatter for variables related to three groups of forest impacts: the 2005 drought itself, loss of forest cover, and warmer and drier dry seasons in the post-drought vs. the pre-drought years. However, these variables explained only about one quarter of the variability in depressed backscatter across the southwestern drought region. Our findings indicate that drought impact is a complex phenomenon and that better understanding can only come from more extensive ground data and/or analysis of frequent, spatially-comprehensive, high-resolution data or imagery before and after droughts.