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USGS2007

Geographic information systems, remote sensing, and spatial analysis activities in Texas, 2002-07

Geographic information system (GIS) technology has become an important tool for scientific investigation, resource management, and environmental planning. A GIS is a computer-aided system capable of collecting, storing, analyzing, and displaying spatially referenced digital data. GIS technology is particularly useful when analyzing a wide variety of spatial data such as with remote sensing and spatial analysis. Remote sensing involves collecting remotely sensed data, such as satellite imagery, aerial photography, or radar images, and analyzing the data to gather information or investigate trends about the environment or the Earth's surface. Spatial analysis combines remotely sensed, thematic, statistical, quantitative, and geographical data through overlay, modeling, and other analytical techniques to investigate specific research questions. It is the combination of data formats and analysis techniques that has made GIS an essential tool in scientific investigations. This document presents information about the technical capabilities and project activities of the U.S. Geological Survey (USGS) Texas Water Science Center (TWSC) GIS Workgroup from 2002 through 2007.

Fact Sheet↗

USGS2009

Geographic information systems, remote sensing, and spatial analysis activities in Texas, 2008-09

Geographic information system (GIS) technology has become an important tool for scientific investigation, resource management, and environmental planning. A GIS is a computer-aided system capable of collecting, storing, analyzing, and displaying spatially referenced digital data. GIS technology is useful for analyzing a wide variety of spatial data. Remote sensing involves collecting remotely sensed data, such as satellite imagery, aerial photography, or radar images, and analyzing the data to gather information or investigate trends about the environment or the Earth's surface. Spatial analysis combines remotely sensed, thematic, statistical, quantitative, and geographical data through overlay, modeling, and other analytical techniques to investigate specific research questions. It is the combination of data formats and analysis techniques that has made GIS an essential tool in scientific investigations. This fact sheet presents information about the technical capabilities and project activities of the U.S. Geological Survey (USGS) Texas Water Science Center (TWSC) GIS Workgroup during 2008 and 2009. After a summary of GIS Workgroup capabilities, brief descriptions of activities by project at the local and national levels are presented. Projects are grouped by the fiscal year (October-September 2008 or 2009) the project ends and include overviews, project images, and Internet links to additional project information and related publications or articles.

Fact Sheet↗

USGS2016

Removing sun glint from optical remote sensing images of shallow rivers

Sun glint is the specular reflection of light from the water surface, which often causes unusually bright pixel values that can dominate fluvial remote sensing imagery and obscure the water-leaving radiance signal of interest for mapping bathymetry, bottom type, or water column optical characteristics. Although sun glint is ubiquitous in fluvial remote sensing imagery, river-specific methods for removing sun glint are not yet available. We show that existing sun glint-removal methods developed for multispectral images of marine shallow water environments over-correct shallow portions of fluvial remote sensing imagery resulting in regions of unreliable data along channel margins. We build on existing marine glint-removal methods to develop a river-specific technique that removes sun glint from shallow areas of the channel without overcorrection by accounting for non-negligible water-leaving near-infrared radiance. This new sun glint-removal method can improve the accuracy of spectrally-based depth retrieval in cases where sun glint dominates the at-sensor radiance. For an example image of the gravel-bed Snake River, Wyoming, USA, observed-vs.-predicted R 2 values for depth retrieval improved from 0.66 to 0.76 following sun glint removal. The methodology presented here is straightforward to implement and could be incorporated into image processing workflows for multispectral images that include a near-infrared band.

Earth Surface Processes and Landforms↗

USGS2003

The U.S. Geological Survey Land Remote Sensing Program

In 2002, the U. S. Geological Survey (USGS) launched a program to enhance the acquisition, preservation, and use of remotely sensed data for USGS science programs, as well as for those of cooperators and customers. Remotely sensed data are fundamental tools for studying the Earth's land surface, including coastal and near-shore environments. For many decades, the USGS has been a leader in providing remotely sensed data to the national and international communities. Acting on its historical topographic mapping mission, the USGS has archived and distributed aerial photographs of the United States for more than half a century. Since 1972, the USGS has acquired, processed, archived, and distributed Landsat and other satellite and airborne remotely sensed data products to users worldwide. Today, the USGS operates and manages the Landsats 5 and 7 missions and cooperates with the National Aeronautics and Space Administration (NASA) to define and implement future satellite missions that will continue and expand the collection of moderate-resolution remotely sensed data. In addition to being a provider of remotely sensed data, the USGS is a user of these data and related remote sensing technology. These data are used in natural resource evaluations for energy and minerals, coastal environmental surveys, assessments of natural hazards (earthquakes, volcanoes, and landslides), biological surveys and investigations, water resources status and trends analyses and studies, and geographic and cartographic applications, such as wildfire detection and tracking and as a source of information for The National Map. The program furthers these distinct but related roles by leading the USGS activities in providing remotely sensed data while advancing applications of such data for USGS programs and a wider user community.

Fact Sheet↗

USGS2015

Remote sensing systems – Platforms and sensors: Aerial, satellites, UAVs, optical, radar, and LiDAR

The American Society of Photogrammetry and Remote Sensing defined remote sensing as the measurement or acquisition of information of some property of an object or phenomenon, by a recording device that is not in physical or intimate contact with the object or phenomenon under study (Colwell et al., 1983). Environmental Systems Research Institute (ESRI) in its geographic information system (GIS) dictionary defines remote sensing as “collecting and interpreting information about the environment and the surface of the earth from a distance, primarily by sensing radiation that is naturally emitted or reflected by the earth’s surface or from the atmosphere, or by sending signals transmitted from a device and reflected back to it (ESRI, 2014).” The usual source of passive remote sensing data is the measurement of reflected or transmitted electromagnetic radiation (EMR) from the sun across the electromagnetic spectrum (EMS); this can also include acoustic or sound energy, gravity, or the magnetic field from or of the objects under consideration. In this context, the simple act of reading this text is considered remote sensing. In this case, the eye acts as a sensor and senses the light reflected from the object to obtain information about the object. It is the same technology used by a handheld camera to take a photograph of a person or a distant scenic view. Active remote sensing, however, involves sending a pulse of energy and then measuring the returned energy through a sensor (e.g., Radio Detection and Ranging [RADAR], Light Detection and Ranging [LiDAR]). Thermal sensors measure emitted energy by different objects. Thus, in general, passive remote sensing involves the measurement of solar energy reflected from the Earth’s surface, while active remote sensing involves synthetic (man-made) energy pulsed at the environment and the return signals are measured and recorded.

Book chapter↗

USGS2010

Identification of plant species by using high spatial and spectral resolution thermal infrared (8.0–13.5 μm) imagery

High spatial and spectral resolution thermal infrared imagery (8.0–13.5 μm) from the SEBASS airborne sensor was used to analyze and map tree canopy spectral features at the State Arboretum of Virginia, near Boyce, Virginia. Fifty tree species were analyzed and about half were directly identified with varying degrees of success on the basis of spectral matched filtering that utilized laboratory-measured leaf spectra as the target signatures. Spectral averages of pixels extracted from SEBASS emissivity data compared favorably with laboratory spectra of leaves collected from individual tree species. Best results were obtained from species having relatively strong spectral contrast, wide and flat leaves, closed planophile canopies, and/or large canopy areas. Tree species having small leaves or unfavorable leaf orientations showed spectral attenuation likely resulting from cavity blackbody effects. Increased spatial resolution and better image calibration and atmospheric correction might lead to further improvements in thermal infrared plant species identification.

Remote Sensing of the Environment↗

USGS2013

Controls on variations in MODIS fire radiative power in Alaskan boreal forests: implications for fire severity conditions

Fire activity in the Alaskan boreal forest, though episodic at annual and intra-annual time scales, has experienced an increase over the last several decades. Increases in burned area and fire severity are not only releasing more carbon to the atmosphere, but likely shifting vegetation composition in the region towards greater deciduous dominance and a reduction in coniferous stands. While some recent studies have addressed qualitative differences between large and small fire years in the Alaskan boreal forest, the ecological effects of a greater proportion of burning occurring during large fire years and during late season fires have not yet been examined. Some characteristics of wildfires that can be detected remotely are related to fire severity and can provide new information on spatial and temporal patterns of burning. This analysis focused on boreal wildfire intensity (fire radiative power, or FRP) contained in the Moderate Resolution Imaging Spectroradiometer (MODIS) daily active fire product from 2003 to 2010. We found that differences in FRP resulted from seasonality and intra-annual variability in fire activity levels, vegetation composition, latitudinal variation, and fire spread behavior. Our studies determined two general categories of active fire detections: new detections associated with the spread of the fire front and residual pixels in areas that had already experienced front burning. Residual pixels had a lower average FRP than front pixels, but represented a high percentage of all pixels during periods of high fire activity (large fire years, late season burning, and seasonal periods of high fire activity). As a result, the FRP from periods of high fire activity was less intense than those from periods of low fire activity. Differences related to latitude were greater than expected, with higher latitudes burning later in the season and at a higher intensity than lower latitudes. Differences in vegetation type indicate that coniferous vegetation is the most fire prone, but deciduous vegetation is not particularly fire resistant, as the proportion of active fire detections in deciduous stands is roughly the same as the fraction of deciduous vegetation in the region. Qualitative differences between periods of high and low fire activity are likely to reflect important differences in fire severity. Large fire years are likely to be more severe, characterized by more late season fires and a greater proportion of residual burning. Given the potential for severe fires to effect changes in vegetation cover, the shift toward a greater proportion of area burning during large fire years may influence vegetation patterns in the region over the medium to long term.

Alaska↗

USGS2021

Satellites for long-term monitoring of inland U.S. lakes: The MERIS time series and application for chlorophyll-a

Lakes and other surface fresh waterbodies provide drinking water, recreational and economic opportunities, food, and other critical support for humans, aquatic life, and ecosystem health. Lakes are also productive ecosystems that provide habitats and influence global cycles. Chlorophyll concentration provides a common metric of water quality, and is frequently used as a proxy for lake trophic state. Here, we document the generation and distribution of the complete MEdium Resolution Imaging Spectrometer (MERIS; Appendix A provides a complete list of abbreviations) radiometric time series for over 2300 satellite resolvable inland bodies of water across the contiguous United States (CONUS) and more than 5,000 in Alaska. This contribution greatly increases the ease of use of satellite remote sensing data for inland water quality monitoring, as well as highlights new horizons in inland water remote sensing algorithm development. We evaluate the performance of satellite remote sensing Cyanobacteria Index (CI)-based chlorophyll algorithms, the retrievals for which provide surrogate estimates of phytoplankton concentrations in cyanobacteria dominated lakes. Our analysis quantifies the algorithms' abilities to assess lake trophic state across the CONUS. As a case study, we apply a bootstrapping approach to derive a new CI-to-chlorophyll relationship, ChlBS, which performs relatively well with a multiplicative bias of 1.11 (11%) and mean absolute error of 1.60 (60%). While the primary contribution of this work is the distribution of the MERIS radiometric timeseries, we provide this case study as a roadmap for future stakeholders' algorithm development activities, as well as a tool to assess the strengths and weaknesses of applying a single algorithm across CONUS.

Alaska, Minnesota↗

USGS2022

Satellite solar-induced chlorophyll fluorescence and near-infrared reflectance capture complementary aspects of dryland vegetation productivity dynamics

Mounting evidence indicates dryland ecosystems play an important role in driving the interannual variability and trend of the terrestrial carbon sink. Nevertheless, our understanding of the seasonal dynamics of dryland ecosystem carbon uptake through photosynthesis [gross primary productivity (GPP)] remains relatively limited due in part to the limited availability of long-term data and unique challenges associated with satellite remote sensing across dryland ecosystems. Here, we comprehensively evaluated longstanding and emerging satellite vegetation proxies in their ability to capture seasonal dryland GPP dynamics. Specifically, we evaluated: 1) reflectance-based proxies normalized difference vegetation index (NDVI), soil adjusted vegetation index (SAVI), near infrared reflectance index (NIR v ), and kernel NDVI (kNDVI) from the MODerate resolution Imaging Spectroradiometer (MODIS); and 2) newly available physiologically-based proxy solar-induced chlorophyll fluorescence (SIF) from the TROPOspheric Monitoring Instrument (TROPOMI). As a performance benchmark, we used GPP estimates from a robust network of 21 western United States eddy covariance tower sites that span representative gradients in dryland ecosystem climate and functional composition. We found that NIR v and SIF were the best performing GPP proxies and captured complementary aspects of seasonal GPP dynamics across dryland ecosystem types. NIR v offered better performance than the other proxies across relatively low-productivity, sparsely non-evergreen vegetated sites (R 2 = 0.59 ± 0.13); whereas SIF best captured seasonal dynamics across relatively high-productivity sites, including evergreen-dominated sites (R 2 = 0.74 ± 0.07). Notably, across grass-dominated sites, all reflectance-based proxies (NDVI, SAVI, NIR v and kNDVI) showed significant seasonal bias (hysteresis) that strengthened with the total fraction of woody vegetation cover, likely due to seasonal patterns in woody vegetation reflectance that are unrelated to or decoupled from GPP. Future efforts to fully integrate the complementary strengths of NIR v and SIF could significantly improve our understanding and representation of dryland GPP dynamics in satellite-based models.

Arizona, California, Colorado, Idaho, New Mexico, ↗

USGS2026

Aquatic reflectance derived from Sentinel-2 Multispectral Imager data for inland waters in the conterminous United States

Satellite-based earth observation is a robust tool for tracking change in ecosystems. While terrestrially focused applications of remote sensing have empowered wide adoption for research and management, remote sensing of inland aquatic ecosystems remains comparably nascent. This divergence, in part, stems from the lack of standardized, accessible, and near real-time remotely sensed surface reflectance, atmospherically corrected for aquatic environments. To date, surface reflectance products at national scales and with minimal latency are typically designed exclusively for terrestrial environments. Rectifying this situation can be accomplished by applying aquatic-focused atmospheric correction algorithms independent of those used for terrestrial ecosystems. As a first step to filling this data gap, we present the first national scale, dynamically updated, analysis-ready, aquatic reflectance dataset for inland water derived from Sentinel-2 for the conterminous United States.

conterminous United States↗

USGS2014

Spectral properties of Ca-sulfates: Gypsum, bassanite, and anhydrite

This study of the spectral properties of Ca-sulfates was initiated to support remote detection of these minerals on Mars. Gypsum, bassanite, and anhydrite are the currently known forms of Ca-sulfates. They are typically found in sedimentary evaporites on Earth, but can also form via reaction of acidic fluids associated with volcanic activity. Reflectance, emission, transmittance, and Raman spectra are discussed here for various sample forms. Gypsum and bassanite spectra exhibit characteristic and distinct triplet bands near 1.4–1.5 μm, a strong band near 1.93–1.94 μm, and multiple features near 2.1–2.3 μm attributed to H 2 O. Anhydrite, bassanite, and gypsum all have SO 4 combination and overtone features from 4.2–5 μm that are present in reflectance spectra. The mid-IR region spectra exhibit strong SO 4 ν 3 and ν 4 vibrational bands near 1150–1200 and 600–680 cm −1 (~8.5 and 16 μm), respectively. Additional weaker features are observed near 1005–1015 cm −1 (~10 μm) for ν 1 and near 470–510 cm −1 (~20 μm) for ν 2 . The mid-IR H 2 O bending vibration occurs near 1623–1630 cm −1 (~6.2 μm). The visible/near-infrared region spectra are brighter for the finer-grained samples. In reflectance and emission spectra of the mid-IR region the ν 4 bands begin to invert for the finer-grained samples, and the ν 1 vibration occurs as a band instead of a peak and has the strongest intensity for the finer-grained samples. The ν 2 vibration is a sharp band for anhydrite and a broad peak for gypsum. The band center of the ν 1 vibration follows a trend of decreasing frequency (increasing wavelength) with increasing hydration of the sample in the transmittance, Raman, and reflectance spectra. Anhydrite forms at elevated temperatures compared to gypsum, and at lower temperature, salt concentration, and pH than bassanite. The relative humidity controls whether bassanite or gypsum is stable. Thus, distinguishing among gypsum, bassanite, and anhydrite via remote sensing can provide constraints on the geochemical environment.

American Mineralogist↗

USGS1980

EROS: A space program for Earth resources

Within the technology of the space age lies a key to increased knowledge about the resources and environment of the Earth. This key is remote sensing detecting the nature of an object without actually touching it. Although the photographic camera is the most familiar remote-sensing device, other instrument systems, such as scanning radiometers and radar, also can produce photographs and images. On the basis of the potential of this technology, and in response to the critical need for greater knowledge of the Earth and its resources, the Department of the Interior established the Earth Resources Observation Systems (EROS) Program to gather and use remotely sensed data collected by satellite and aircraft of natural and manmade features on the Earth's surface.

South Dakota↗

USGS2011

Earth observing data and methods for advancing water harvesting technologies in the semi-arid rain-fed environments of India

The paper develops approaches and methods of modeling and mapping land and water productivity of rain-fed crops in semi-arid environments of India using hyperspectral, hyperspatial, and advanced multispectral remote sensing data and linking the same to field-plot data and climate station data. The overarching goal is to provide information to advance water harvesting technologies in the agricultural croplands of the semi-arid environments of India by conducting research in a representative pilot site in Jodhpur, Rajasthan, India. ?? 2011 IEEE.

Conference Paper↗

USGS2002

Mapping Chinese tallow with color-infrared photography

Airborne color-infrared photography (CIR) (1:12,000 scale) was used to map localized occurrences of the widespread and aggressive Chinese tallow (Sapium sebiferum), an invasive species. Photography was collected during senescence when Chinese tallow's bright red leaves presented a high spectral contrast within the native bottomland hardwood and upland forests and marsh land-cover types. Mapped occurrences were conservative because not all senescing tallow leaves are bright red simultaneously. To simulate low spectral but high spatial resolution satellite/airborne image and digital video data, the CIR photography was transformed into raster images at spatial resolutions approximating 0.5 in and 1.0 m. The image data were then spectrally classified for the occurrence of bright red leaves associated with senescing Chinese tallow. Classification accuracies were greater than 95 percent at both spatial resolutions. There was no significant difference in either forest in the detection of tallow or inclusion of non-tallow trees associated with the two spatial resolutions. In marshes, slightly more tallow occurrences were mapped with the lower spatial resolution, but there were also more misclassifications of native land covers as tallow. Combining all land covers, there was no difference at detecting tallow occurrences (equal omission errors) between the two resolutions, but the higher spatial resolution was associated with less inclusion of non-tallow land covers as tallow (lower commission error). Overall, these results confirm that high spatial (???1 m) but low spectral resolution remote sensing data can be used for mapping Chinese tallow trees in dominant environments found in coastal and adjacent upland landscapes.

Photogrammetric Engineering and Remote Sensing↗

USGS2003

Foraminifera as bioindicators in coral reef assessment and monitoring: The foram index

Coral reef communities are threatened worldwide. Resource managers urgently need indicators of the biological condition of reef environments that can relate data acquired through remote-sensing, water-quality and benthic-community monitoring to stress responses in reef organisms. The "FORAM" (Foraminifera in Reef Assessment and Monitoring) Index (FI) is based on 30 years of research on reef sediments and reef-dwelling larger foraminifers. These shelled protists are ideal indicator organisms because: ??? Foraminifers are widely used as environmental and paleoenvironmental indicators in many contexts; ??? Reef-building, zooxanthellate corals and foraminifers with algal symbionts have similar water-quality requirements; ??? The relatively short life spans of foraminifers as compared with long-lived colonial corals facilitate differentiation between long-term water-quality decline and episodic stress events; ??? Foraminifers are relatively small and abundant, permitting statistically significant sample sizes to be collected quickly and relatively inexpensively, ideally as a component of comprehensive monitoring programs; and ??? Collection of foraminifers has minimal impact on reef resources. USEPA guidelines for ecological indicators are used to evaluate the FI. Data required are foraminiferal assemblages from surface sediments of reef-associated environments. The FI provides resource managers with a simple procedure for determining the suitability of benthic environments for communities dominated by algal symbiotic organisms. The FI can be applied independently, or incorporated into existing or planned monitoring efforts. The simple calculations require limited computer capabilities and therefore can be applied readily to reef-associated environments worldwide. In addition, the foraminiferal shells collected can be subjected to morphometric and geochemical analyses in areas of suspected heavy-metal pollution, and the data sets for the index can be used with other monitoring data in detailed multidimensional assessments.

Environmental Monitoring and Assessment↗

USGS2016

Object-based forest classification to facilitate landscape-scale conservation in the Mississippi Alluvial Valley

The Mississippi Alluvial Valley is a floodplain along the southern extent of the Mississippi River extending from southern Missouri to the Gulf of Mexico. This area once encompassed nearly 10 million ha of floodplain forests, most of which has been converted to agriculture over the past two centuries. Conservation programs in this region revolve around protection of existing forest and reforestation of converted lands. Therefore, an accurate and up to date classification of forest cover is essential for conservation planning, including efforts that prioritize areas for conservation activities. We used object-based image analysis with Random Forest classification to quickly and accurately classify forest cover. We used Landsat band, band ratio, and band index statistics to identify and define similar objects as our training sets instead of selecting individual training points. This provided a single rule-set that was used to classify each of the 11 Landsat 5 Thematic Mapper scenes that encompassed the Mississippi Alluvial Valley. We classified 3,307,910±85,344 ha (32% of this region) as forest. Our overall classification accuracy was 96.9% with Kappa statistic of 0.96. Because this method of forest classification is rapid and accurate, assessment of forest cover can be regularly updated and progress toward forest habitat goals identified in conservation plans can be periodically evaluated.

Arkansas, Louisiana, Mississippi, Missouri↗

USGS2017

Multi-scale 46-year remote sensing change detection of diamond mining and land cover in a conflict and post-conflict setting

The town of Tortiya was created in the rural northern region of Côte d′Ivoire in the late 1940s to house workers for a new diamond mine. Nearly three decades later, the closure of the industrial-scale diamond mine in 1975 did not diminish the importance of diamond profits to the region's economy, and resulted in the growth of artisanal and small-scale diamond mining (ASM) within the abandoned industrial-scale mining concession. In the early 2000s, the violent conflict that arose in Côte d′Ivoire highlighted the importance of ASM land use to the local economy, but also brought about international concerns that diamond profits were being used to fund the rebellion. In recent years, cashew plantations have expanded exponentially in the region, diversifying economic activity, but also creating the potential for conflict between diamond mining and agricultural land uses. As the government looks to address the future of Tortiya and this potential conflict, a detailed spatio-temporal understanding of the changes in these two land uses over time may assist in informing policymaking. Remotely sensed imagery presents an objective and detailed spatial record of land use/land cover (LULC), and change detection methods can provide quantitative insight regarding regional land cover trends. However, the vastly different scales of ASM and cashew orchards present a unique challenge to comprehensive understanding of land use change in the region. In this study, moderate-scale categories of LULC, including cashew orchards, uncultivated forest, urban space, mining/ bare, and mixed vegetation, were produced through supervised classification of Landsat multispectral imagery from 1984, 1991, 2000, 2007, and 2014. The fine-scale ASM land use was identified through manual interpretation of annually acquired high resolution satellite imagery. Corona imagery was also integrated into the study to extend the temporal duration of the remote sensing record back to the period of industrial-scale mining. These different-scale analyses were then integrated to create a record of 46 years of mining activity and land cover change in Tortiya. While similar in spatial extent, the mining/ bare class in the integrated analysis exhibits a substantially different spatial distribution than in the original classifications. This additional information regarding the locations of ASM activity in the Tortiya area is important from a policy and planning perspective. The results of this study also suggest that LULC classifications of Landsat imagery do not consistently capture areas of ASM in the Côte d′Ivoire landscape.

Remote Sensing Applications: Society and Environme↗

USGS2018

Use of imaging spectroscopy and LIDAR to characterize fuels for fire behavior prediction

To protect ecosystem services and the increasing wildland urban interface in a world with fire, comprehensive maps of wildland fuels are needed to predict fire behavior and effects. Traditionally, fuels have been categorized into a classification scheme whereby a single metric represents vegetation composition and structure, which can then be parameterized based on variable vegetation amount and condition. Remote sensing has been used to extrapolate between known field plots across the landscape, however until recently, those technologies have had limited ability to characterize fuels (e.g., composition, horizontal and vertical connectivity). Using new technologies (imaging spectroscopy and LIDAR), the objectives of this study are to assess: 1) how fuel characteristics observed from remote sensing affect categorical fuel classifications, and 2) how fuel characteristics affect landscape-scale fire behavior (spread rate, areal extent and perimeter). The analysis was conducted over the 2014 California King Fire that burned ~40,000 ha over lands with varying use and history and has unique remote sensing observations from before and after the fire. This analysis compares fuel classifications from a synergistic field, model, and Landsat approach (LANDFIRE) and products derived from the Airborne Visible/Infrared Imaging Spectrometer and LIDAR (MapFUELS). Each classification focuses on different fuel characteristics, which were then used to compare differences in a fire simulation model (CAWFE) and actual fire behavior. The results show that fuel characteristic inputs such as horizontal connectivity or fuel type and vertical structure affect fire spread rate and final fire extent (respectively). These results present the opportunity for future integration of fuel characteristics observed at coarser resolutions (900 m 2 ) into predictions of fire behavior a similar spatial resolutions (as opposed to the current standard based on empirical relationships between fuel and fire behavior at ~12 m 2 resolution).

Remote Sensing Applications: Society and Environme↗