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At least 235 records · Page 13Linked to original sources
USGS2022

Quantifying aspects of rangeland health at watershed scales in Colorado using remotely sensed data products

During grazing permit renewals, the Bureau of Land Management assesses land health using indicators typically measured using field-based data collected from individual sites within grazing allotments. However, agency guidance suggests assessments be completed at larger spatial scales. We explored how the current generation of remotely sensed data products could be used to quantify aspects of land health at watershed scales in Colorado to provide broad spatial and temporal context for the land health assessment process. We found multiple indicators could be quantified using these data products and were relevant to land health standards. Within focal watersheds, bare ground cover decreased over the past 30 years, while annual herbaceous cover has increased over the last 10 years. Vegetation productivity was variable over time, but interannual fluctuations were consistent across watersheds. Remotely sensed data products can help resource managers understand how current conditions relate to broad spatial and temporal trends in the region and could provide another line of evidence for the land health assessment process. They may also identify target areas where management strategies, such as eradication of invasive annual grasses, should be focused, and could help resource managers communicate complex issues to the public.

Colorado↗
USGS2013

Comparative mineral mapping in the Colorado Mineral Belt using AVIRIS and ASTER remote sensing data

This report presents results of interpretation of spectral remote sensing data covering the eastern Colorado Mineral Belt in central Colorado, USA, acquired by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensors. This study was part of a multidisciplinary mapping and data integration project at the U.S. Geological Survey that focused on long-term resource planning by land-managing entities in Colorado. The map products were designed primarily for the regional mapping and characterization of exposed surface mineralogy, including that related to hydrothermal alteration and supergene weathering of pyritic rocks. Alteration type was modeled from identified minerals based on standard definitions of alteration mineral assemblages. Vegetation was identified using the ASTER data and subdivided based on per-pixel chlorophyll content (depth of 0.68 micrometer absorption band) and dryness (fit and depth of leaf biochemical absorptions in the shortwave infrared spectral region). The vegetation results can be used to estimate the abundance of fire fuels at the time of data acquisition (2002 and 2003). The AVIRIS- and ASTER-derived mineral mapping results can be readily compared using the toggleable layers in the GeoPDF file, and by using the provided GIS-ready raster datasets. The results relating to mineral occurrence and distribution were an important source of data for studies documenting the effects of mining and un-mined, altered rocks on aquatic ecosystems at the watershed level. These studies demonstrated a high correlation between metal concentrations in streams and the presence of hydrothermal alteration and (or) pyritic mine waste as determined by analysis of the map products presented herein. The mineral mapping results were also used to delineate permissive areas for various mineral deposit types.

Colorado↗
USGS2010

Monitoring the dynamics of an invasive emergent macrophyte community using operational remote sensing data

Potamogeton crispus L. (curly pondweed) is a cosmopolitan aquatic macrophyte considered invasive in North America and elsewhere. Its range is expanding and, on individual water bodies, its coverage can be dynamic both within and among years. In this study, we evaluate the use of free and low-cost satellite remote sensing data to monitor a problematic emergent macrophyte community dominated by P. crispus . Between 2000 and 2006, we acquired eight satellite images of 24,000-ha Lake Sharpe, South Dakota (USA). During one of the dates for which satellite imagery was acquired, we sampled the lake for P. crispus and other emergent macrophytes using GPS and photography for documentation. We used cluster analysis to assist in classification of the satellite imagery and independently validated results using the field data. Resulting estimates of emergent macrophyte coverage ranged from less than 20 ha in 2002 to 245 ha in 2004. Accuracy assessment indicated 82% of image pixels were correctly classified, with errors being primarily due to failure to identify emergent macrophytes. These results emphasize the dynamic nature of P. crispus -dominated macrophyte communities and show how they can be effectively monitored over large areas using low-cost remote sensing imagery. While results may vary in other systems depending on water quality and local flora, such an approach could be applied elsewhere and for a variety of macrophyte communities.

Hydrobiologia↗
USGS2015

Operational thermal remote sensing and lava flow monitoring at the Hawaiian Volcano Observatory

Hawaiian volcanoes are highly accessible and well monitored by ground instruments. Nevertheless, observational gaps remain and thermal satellite imagery has proven useful in Hawai‘i for providing synoptic views of activity during intervals between field visits. Here we describe the beginning of a thermal remote sensing programme at the US Geological Survey Hawaiian Volcano Observatory (HVO). Whereas expensive receiving stations have been traditionally required to achieve rapid downloading of satellite data, we exploit free, low-latency data sources on the internet for timely access to GOES, MODIS, ASTER and EO-1 ALI imagery. Automated scripts at the observatory download these data and provide a basic display of the images. Satellite data have been extremely useful for monitoring the ongoing lava flow activity on Kīlauea's East Rift Zone at Pu‘u ‘Ō‘ō over the past few years. A recent lava flow, named Kahauale‘a 2, was upslope from residential subdivisions for over a year. Satellite data helped track the slow advance of the flow and contributed to hazard assessments. Ongoing improvement to thermal remote sensing at HVO incorporates automated hotspot detection, effusion rate estimation and lava flow forecasting, as has been done in Italy. These improvements should be useful for monitoring future activity on Mauna Loa.

Hawaii↗
USGS2013

Phenology-based, remote sensing of post-burn disturbance windows in rangelands

Wildland fire activity has increased in many parts of the world in recent decades. Ecological disturbance by fire can accelerate ecosystem degradation processes such as erosion due to combustion of vegetation that otherwise provides protective cover to the soil surface. This study employed a novel ecological indicator based on remote sensing of vegetation greenness dynamics (phenology) to estimate variability in the window of time between fire and the reemergence of green vegetation. The indicator was applied as a proxy for short-term, post-fire disturbance windows in rangelands; where a disturbance window is defined as the time required for an ecological or geomorphic process that is altered to return to pre-disturbance levels. We examined variability in the indicator determined for time series of MODIS and AVHRR NDVI remote sensing data for a database of ∼100 historical wildland fires, with associated post-fire reseeding treatments, that burned 1990–2003 in cold desert shrub steppe of the Great Basin and Columbia Plateau of the western USA. The indicator-based estimates of disturbance window length were examined relative to the day of the year that fires burned and seeding treatments to consider effects of contemporary variability in fire regime and management activities in this environment. A key finding was that contemporary changes of increased length of the annual fire season could have indirect effects on ecosystem degradation, as early season fires appeared to result in longer time that soils remained relatively bare of the protective cover of vegetation after fires. Also important was that reemergence of vegetation did not occur more quickly after fire in sites treated with post-fire seeding, which is a strategy commonly employed to accelerate post-fire vegetation recovery and stabilize soil. Future work with the indicator could examine other ecological factors that are dynamic in space and time following disturbance – such as nutrient cycling, carbon storage, microbial community composition, or soil hydrology – as a function of disturbance windows, possibly using simulation modeling and historical wildfire information.

California;Idaho;Nevada;Oregon;Utah;Washington↗
USGS2012

Relation of the spectroscopic reflectance of olivine to mineral chemistry and some remote sensing implications

Using high-resolution visible and near-infrared diffuse spectral reflectance, we have systematically investigated apparent wavelength shifts as a function of mineral chemistry in the Fe/Mg olivine series from Fo 11 to Fo 91 . The study also shows that trace amounts of nickel can be spectrally detected in the olivine structure. We show that significant compositional information can only be extracted at relatively high resolution, because the overall spectral characteristics of the olivines change only subtly as a function of the Fe/Mg ratio. Significant spectral variation as a function of grain size is also demonstrated, adding a further complication to the interpretation of remotely sensed data from olivine-rich surfaces. This laboratory study is expected to aid in the interpretation of remotely sensed data from both terrestrial and extraterrestrial bodies. Terrestrial applications may include the recognition of ultramafic, ultrabasic, and basaltic terrains which in themselves may have mineral potential. Among extraterrestrial applications, the asteroids are obvious candidates for further examination if instrumentation can provide the necessary wavelength coverage, resolution, and signal-to-noise ratio so that spectra can be compared to those laboratory data discussed here. Some permutations of Fe-Mg-Ni relations in olivines are discussed as they apply to the interpretation of asteroid surfaces and other extraterrestrial bodies.

Journal of Geophysical Research Solid Earth↗
USGS2024

From pixels to riverscapes: How remote sensing and geospatial tools can prioritize riverscape restoration at multiple scales

Prioritizing restoration opportunities effectively across entire riverscape networks (i.e., riverine landscape including floodplain and stream channel networks) can be difficult when relying on in-channel, reach-scale monitoring data, or watershed-level summaries that fail to capture riverscape heterogeneity and the information necessary to implement restoration actions. Leveraging remote sensing and geospatial tools to develop spatially continuous information across nested hierarchical scales may support increased understanding of local riverscape reaches in their broader network context. Using riparian (vegetation) and geomorphic (elevation) indicators to assess status of riverscape health, along with a measure of restoration capacity (valley bottom area), could be adapted to fit specific management goals related to riverscape restoration. Frameworks using remotely sensed vegetation and elevation data to prioritize restoration continuously across riverscapes at restoration-relevant, reach-scales may uphold the ecosystem services provided by riverscapes. By incorporating local knowledge and identifying caveats for using these datasets, continuous inferences can be applied at network scales (watershed to regional extent and reach-scale resolution) to prioritize restoration over a wide variety of ecoregions.

California, Nevada↗
USGS2024

A framework for disaggregating remote-sensing cropland into rainfed and irrigated classes at continental scale

Agriculture consumes the largest share of freshwater globally; therefore, distinguishing between rainfed and irrigated croplands is essential for agricultural water management and food security. In this study, a framework incorporating the Budyko model was used to differentiate between rainfed and irrigated cropland areas in Africa for eight remote sensing landcover products and a high-confidence cropland map (HCCM). The HCCM was generated for calibration and validation of the crop partitioning framework as an alternative to individual cropland masks which exhibit high disagreement. The accuracy of the framework in partitioning the HCCM was evaluated using an independent validation dataset, yielding an overall accuracy rate of 73 %. The findings of this study indicate that out of the total area covered by the HCCM (2.36 million km 2 ), about 461,000 km 2 (19 %) is irrigated cropland. The partitioning framework was applied on eight landcover products, and the extent of irrigated areas varied between 19 % and 30 % of the total cropland area. The framework demonstrated high precision and specificity scores, indicating its effectiveness in correctly identifying irrigated areas while minimizing the misclassification of rainfed areas as irrigated. This study provides an enhanced understanding of rainfed and irrigation patterns across Africa, supporting efforts towards achieving sustainable and resilient agricultural systems. Consequently, the approach outlined expands on the suite of remote sensing landcover products that can be used for agricultural water studies in Africa by enabling the extraction of irrigated and rainfed cropland data from landcover products that do not have disaggregated cropland classes.

International Journal of Applied Earth Observation↗
USGS2003

Use of radar remote sensing (RADARSAT) to map winter wetland habitat for shorebirds in an agricultural landscape

Many of todays agricultural landscapes once held vast amounts of wetland habitat for waterbirds and other wildlife. Successful restoration of these landscapes relies on access to accurate maps of the wetlands that remain. We used C-band (5.6-cm-wavelength), HH-polarized radar remote sensing (RADARSAT) at a 38° incidence angle (8-m resolution) to map the distribution of winter shorebird ( Charadriiformes ) habitat on agricultural lands in the Willamette Valley of western Oregon. We acquired imagery on three dates (10 December 1999, 27 January 2000, and 15 March 2000) and simultaneously collected ground reference data to classify radar signatures and evaluate map accuracy of four habitat classes: (1) wet with 50% vegetation (considered optimal shorebird habitat), (2) wet with > 50% vegetation, (3) dry with 50% vegetation, and (4) dry with > 50% vegetation. Overall accuracy varied from 45 to 60% among the three images, but the accuracy of focal class 1 was greater, ranging from 72 to 80%. Class 4 coverage was stable and dominated maps (40% of mapped study area) for all three dates, while coverage of class 3 decreased slightly throughout the study period. Among wet classes, class 1 was most abundant (about 30% coverage) in December and January, decreasing in March to approximately 15%. Conversely, class 2 increased dramatically from January to March, likely due to transition from class 1 as vegetation grew. This approach was successful in detecting optimal habitat for shorebirds on agricultural lands. For modest classification schemes, radar remote sensing is a valuable option for wetland mapping in areas where cloud cover is persistent.

Oregon↗
USGS2003

Use of radar remote sensing (RADARSAT) to map winter wetland habitat for shorebirds in an agricultural landscape

Many of today's agricultural landscapes once held vast amounts of wetland habitat for waterbirds and other wildlife. Successful restoration of these landscapes relies on access to accurate maps of the wetlands that remain. We used C-band (5.6-cm-wavelength), HH-polarized radar remote sensing (RADARSAT) at a 38?? incidence angle (8-m resolution) to map the distribution of winter shorebird (Charadriiformes) habitat on agricultural lands in the Willamette Valley of western Oregon. We acquired imagery on three dates (10 December 1999, 27 January 2000, and 15 March 2000) and simultaneously collected ground reference data to classify radar signatures and evaluate map accuracy of four habitat classes: (1) wet with ??? 50% vegetation (considered optimal shorebird habitat), (2) wet with > 50% vegetation, (3) dry with ??? 50% vegetation, and (4) dry with > 50% vegetation. Overall accuracy varied from 45 to 60% among the three images, but the accuracy of focal class 1 was greater, ranging from 72 to 80%. Class 4 coverage was stable and dominated maps (40% of mapped study area) for all three dates, while class 3 coverage decreased slightly throughout the study period. Among wet classes, class 1 was most abundant (30% coverage) in December and January, decreasing in March by 15%. Conversely, class 2 increased dramatically from January to March, likely due to transition from class 1 as vegetation grew. This approach was successful in detecting optimal habitat for shorebirds on agricultural lands. For modest classification schemes, radar remote sensing is a valuable option for wetland mapping in areas where cloud cover is persistent. ?? 2003 Springer-Verlag New York Inc.

Environmental Management↗
USGS2023

Integrating remote sensing with ground-based observations to quantify the effects of an extreme freeze event on black mangroves (Avicennia germinans) at the landscape scale

Climate change is altering the frequency and intensity of extreme weather events. Quantifying ecosystem responses to extreme events at the landscape scale is critical for understanding and responding to climate-driven change but is constrained by limited data availability. Here, we integrated remote sensing with ground-based observations to quantify landscape-scale vegetation damage from an extreme climatic event. We used ground- and satellite-based black mangrove ( Avicennia germinans ) leaf damage data from the northern Gulf of Mexico (USA and Mexico) to examine the effects of an extreme freeze in a region where black mangroves are expanding their range. The February 2021 event produced coastal temperatures as low as − 10 °C in some areas, exceeding thresholds for A. germinans damage and mortality. We used Sentinel-2 surface reflectance data to assess vegetation greenness before and after the freeze, along with ground-based observations of A. germinans leaf damage. Our results show a negative, nonlinear threshold relationship between A. germinans leaf damage and minimum temperature, with a temperature threshold for leaf damage near − 6 °C. Satellite-based analyses indicate that, at the landscape scale, damage was particularly severe along the central Texas coast, where the freeze event affected > 2000 ha of A. germinans -dominated coastal wetlands. Our analyses highlight the value of pairing remotely sensed data with regional, ground-based observations for quantifying and extrapolating the effects of extreme freeze events on mangroves and other tropical, cold-sensitive plants. The results also demonstrate how extreme freeze events govern the expansion and contraction of mangroves near northern range limits in North America.

Gulf of Mexico↗
USGS2019

Remote sensing of tracer dye concentrations to support dispersion studies in river channels

In river channels the flow field influences the dispersion of biota, contaminants, and other suspended or dissolved materials. Insight on patterns and rates of dispersion can be gained by injecting a pulse of visible dye and observing spatial and temporal variations in dye concentration as the pulse moves downstream. We evaluated the potential of passive optical remote sensing to enhance such tracer experiments by providing spatially distributed concentration information. During tests performed in both an experimental flume facility and a large natural channel, we made field measurements of Rhodamine WT dye concentration and above-water spectral reflectance. At Korea's River Experiment Center, a small unmanned aircraft system (sUAS) was used to acquire hyperspectral images of a sinuous outdoor flume. On the Kootenai River in northern Idaho, USA, field spectra were collected from a boat and hyperspectral image data and high resolution aerial photographs were obtained from manned aircraft. We modified an Optimal Band Ratio Analysis (OBRA) algorithm to identify wavelength combinations that yielded strong correlations between a spectrally based quantity X and dye concentration C. For both the flume and field tests, we obtained very strong (R^2 from 0.94 to 0.99) relationships between X and C across a broad range of visible wavelengths. On the Kootenai, we found that X vs. C relations derived from field spectra could be applied to airborne hyperspectral images and that dye concentrations could be estimated nearly as reliably from relatively simple three-band images as from hyperspectral data. These results imply that remote sensing could become a powerful tool for mapping dye patterns. Such a capability would advance our understanding of dispersion processes by enabling more rigorous testing of numerical flow models.

Journal of Ecohydraulics↗
USGS2012

Modelling rating curves using remotely sensed LiDAR data

Accurate stream discharge measurements are important for many hydrological studies. In remote locations, however, it is often difficult to obtain stream flow information because of the difficulty in making the discharge measurements necessary to define stage-discharge relationships (rating curves). This study investigates the feasibility of defining rating curves by using a fluid mechanics-based model constrained with topographic data from an airborne LiDAR scanning. The study was carried out for an 8m-wide channel in the boreal landscape of northern Sweden. LiDAR data were used to define channel geometry above a low flow water surface along the 90-m surveyed reach. The channel topography below the water surface was estimated using the simple assumption of a flat streambed. The roughness for the modelled reach was back calculated from a single measurment of discharge. The topographic and roughness information was then used to model a rating curve. To isolate the potential influence of the flat bed assumption, a 'hybrid model' rating curve was developed on the basis of data combined from the LiDAR scan and a detailed ground survey. Whereas this hybrid model rating curve was in agreement with the direct measurements of discharge, the LiDAR model rating curve was equally in agreement with the medium and high flow measurements based on confidence intervals calculated from the direct measurements. The discrepancy between the LiDAR model rating curve and the low flow measurements was likely due to reduced roughness associated with unresolved submerged bed topography. Scanning during periods of low flow can help minimize this deficiency. These results suggest that combined ground surveys and LiDAR scans or multifrequency LiDAR scans that see 'below' the water surface (bathymetric LiDAR) could be useful in generating data needed to run such a fluid mechanics-based model. This opens a realm of possibility to remotely sense and monitor stream flows in channels in remote locations.

Hydrological Processes↗
USGS2012

Spectroscopic remote sensing for material identification, vegetation characterization, and mapping

Identifying materials by measuring and analyzing their reflectance spectra has been an important procedure in analytical chemistry for decades. Airborne and space-based imaging spectrometers allow materials to be mapped across the landscape. With many existing airborne sensors and new satellite-borne sensors planned for the future, robust methods are needed to fully exploit the information content of hyperspectral remote sensing data. A method of identifying and mapping materials using spectral feature analyses of reflectance data in an expert-system framework called MICA (Material Identification and Characterization Algorithm) is described. MICA is a module of the PRISM (Processing Routines in IDL for Spectroscopic Measurements) software, available to the public from the U.S. Geological Survey (USGS) at http://pubs.usgs.gov/of/2011/1155/. The core concepts of MICA include continuum removal and linear regression to compare key diagnostic absorption features in reference laboratory/field spectra and the spectra being analyzed. The reference spectra, diagnostic features, and threshold constraints are defined within a user-developed MICA command file (MCF). Building on several decades of experience in mineral mapping, a broadly-applicable MCF was developed to detect a set of minerals frequently occurring on the Earth's surface and applied to map minerals in the country-wide coverage of the 2007 Afghanistan HyMap data set. MICA has also been applied to detect sub-pixel oil contamination in marshes impacted by the Deepwater Horizon incident by discriminating the C-H absorption features in oil residues from background vegetation. These two recent examples demonstrate the utility of a spectroscopic approach to remote sensing for identifying and mapping the distributions of materials in imaging spectrometer data.

Conference Paper↗
USGS2023

Editorial: Remote sensing of volcanic gas emissions from the ground, air, and space

When magma rises in volcanic systems, volatile species exsolve from the melt and are outgassed to the atmosphere. The melt composition and temperature, depth at which degassing occurs, extent of gas-water-rock interactions, and volume of ascending magma are all factors that determine the composition and rate of gas emissions at the surface. Interpreted in a petrological framework, gas measurements thus provide information on these fundamental parameters of volcanic systems. Volcanic gases have traditionally been sampled in the field and later analyzed with standard laboratory methods, but remote sensing measurements are playing an increasingly central role in characterizing emissions and the volcanoes from which they originate. The 17 contributions in this Research Topic summarize the state-of-the-art in volcanic gas remote sensing and identify key areas in which the field could further improve our understanding of global volcanism and its impact on Earth’s environment in the next decade.

Frontiers in Earth Science↗
USGS2024

Detecting avalanche path ground cover and vegetation change across multiple scales through time using remote sensing tools

Large-magnitude avalanches often alter vegetation composition, avalanche path dimensions, and subsequent avalanche return periods. Understanding temporal changes in individual avalanche path trimlines, runout zones, and geomorphic characteristics helps forecasters, planners, and engineers estimate potential avalanche destructive size and impact on infrastructure or settlements in the runout zone. Understanding these changes on a large scale also provides information on post-cycle avalanche distribution. Here, we use remote sensing platforms and change detection techniques to examine vegetation change in avalanche paths in Montana and Colorado. In northwest Montana, we implemented a novel approach using lidar, aerial imagery, and a random forest model to classify imagery-observed vegetation within avalanche paths. We calculated spatially explicit avalanche return periods using a physically based spatial interpolation method and characterized the vegetation within those return period zones. In Colorado, we investigated changes in avalanche path vegetation characteristics prior to and after a widespread large-magnitude avalanche cycle. The highest frequency of avalanche return periods was broadly characterized by grassland and shrubland, but topography greatly influences vegetation classes and return periods. Furthermore, statistically significant differences in lidar-derived vegetation canopy height exist between categorical return periods. We used optical sensors from satellite imagery to analyze changes in Normalized Difference Vegetation Index (NDVI) to calculate ground cover change over time. NDVI, a measure of near-infrared and red bands within the imagery, allowed us to distinguish between green vegetation (e.g., trees and shrubs) and non-vegetated ground cover (e.g., dead and downed trees, rocks, and dirt) within avalanche paths. For this study, we calculated changes in NDVI values by comparing imagery from 2018 to imagery from 2019 after a widespread large magnitude avalanche cycle occurred in March 2019 in Colorado, United States. We applied a filtering process to reduce error, classified NDVI change based on the value distribution, and then calculated area change of all areas within each avalanche path. We completed this process for 1633 avalanche paths throughout Colorado. We found that using NDVI difference values pre- and post-avalanche cycle allowed us to identify ground cover change in avalanche paths throughout Colorado. These changes span from a slight expansion of existing avalanche paths to substantial landscape disturbance. For example, a size D5 avalanche caused severe ground cover change in 18% of one single path near Aspen, Colorado. This suggests that large magnitude avalanches can redefine avalanche path dimensions and could impact subsequent avalanche size and frequency. Using NDVI from satellite imagery is a simple way to detect ground cover changes in avalanche paths on a large scale or in remote areas. In general, remote sensing products to detect and examine vegetation and ground cover change in avalanche paths can help inform avalanche distribution and benefit planning efforts.

Montana↗
USGS1995

Recent data and information system initiatives for remotely sensed measurements of the land surface

As part of the International Satellite Land Satellite Climatology Program (ISLSCP) Workshop on Remote Sensing of the Land Surface for Studies of Global Change, five invited presentations were given on recent data initiatives relevant to the ISLSCP community. The presentations are summarized in this paper along with some observations by the authors on data systems for the land sciences community. The invited presentations are by no means all inclusive but were selected as examples of current data activities, representing a range of topics associated with data for land sciences including: the generation of global and local scale data sets, the reworking of historical data sets, new data initiatives and some programmatic aspects of land data base development. This paper serves to provide information on these data initiatives and to air some of the issues concerning land science data systems that were raised at the meeting.

Remote Sensing of Environment↗
USGS2013

Estimating wildfire risk on a Mojave Desert landscape using remote sensing and field sampling

Predicting wildfires that affect broad landscapes is important for allocating suppression resources and guiding land management. Wildfire prediction in the south-western United States is of specific concern because of the increasing prevalence and severe effects of fire on desert shrublands and the current lack of accurate fire prediction tools. We developed a fire risk model to predict fire occurrence in a north-eastern Mojave Desert landscape. First we developed a spatial model using remote sensing data to predict fuel loads based on field estimates of fuels. We then modelled fire risk (interactions of fuel characteristics and environmental conditions conducive to wildfire) using satellite imagery, our model of fuel loads, and spatial data on ignition potential (lightning strikes and distance to roads), topography (elevation and aspect) and climate (maximum and minimum temperatures). The risk model was developed during a fire year at our study landscape and validated at a nearby landscape; model performance was accurate and similar at both sites. This study demonstrates that remote sensing techniques used in combination with field surveys can accurately predict wildfire risk in the Mojave Desert and may be applicable to other arid and semiarid lands where wildfires are prevalent.

Mojave Desert↗