USGS Science⌕ Search
Home/Archive/USGS

USGS · 70271969

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

E. Natasha Stavros·Janice Coen·Birgit Peterson·Harshvardhan Singh·Kama Kennedy·Carlos Ramirez·David Schimel
View original source ↗Export metadata ↓

Abstract

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).

Explore related subjects

Remote Sensing Applications: Society and EnvironmentArticle

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

E. Natasha Stavros, Janice Coen, Birgit Peterson, Harshvardhan Singh, Kama Kennedy, Carlos Ramirez, David Schimel. 2018. Use of imaging spectroscopy and LIDAR to characterize fuels for fire behavior prediction. https://doi.org/10.1016/j.rsase.2018.04.010

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related USGS reports

USGS2026

Regreening, restoring, and reconnecting a southwestern wetland ecosystem – the Zeedyk wetland

Alluvial wetland ecosystems are vital as biodiversity hotspots but are increasingly threatened by anthropogenic stressors and drought. These pressures are especially acute in arid and semi-arid regions, where eco-hydrologic connectivity is fragile and recovery is slow. This study quantifies the efficacy of nature-based solutions, particularly the ‘Zeedyk approach,’ which employs low-tech Natural Infrastructure in Dryland Streams (NIDS)—including rock detention structures—to slow surface water, raise groundwater tables, and restore wetland function at a spring-fed wetland in Cebolla Canyon, New Mexico, U.S.A. Our results depict a Restoration Feedback Loop that captures stages of change from a healthy wetland in 1935, altered by 20th-century agriculture and grazing, to the re-establishment of the historical flow regime by 2024 documented through an 89-year archive of aerial imagery (1935–2024). By the end of our study period, the Spring-Fed Wetland had expanded by roughly 229% of the original 1935 area, to 4.13 ha. Using 40 years of satellite data, we assess changes in vegetation and hydrology with remote sensing indices. Spatial and temporal analyses reveal significant increases in vegetation greenness and wetness, particularly in an Expanded Wetland subregion, which exhibited ∼3.5x higher wetness and ∼1.5x higher greenness trends compared to adjacent areas. Monthly metrics highlight seasonal variability, with increases in greenness linked to monsoonal rainfall and lateral water redistribution, indicating that restoration impacts extend beyond the primary wetland. This study demonstrates the utility of cloud-based platforms like Google Earth Engine and USGS EarthExplorer for long-term monitoring of wetland restoration, while quantifying the efficacy of the ‘Zeedyk approach’ and demonstrating its potential as a scalable method to restore and conserve wetland meadows in other arid and semi-arid landscapes.

New Mexico↗
USGS2026

Comparative crop yield forecasting using satellite-derived biophysical and agro-climatic predictors in Sub-Saharan Africa

Timely and accurate crop yield forecasting is central to food security early warning systems, particularly in climate-vulnerable regions. While operational forecasting frameworks commonly rely on precipitation and vegetation indices such as NDVI, their ability to provide actionable lead time remains limited. Here, we evaluate the added value of satellite-derived biophysical Essential Climate Variables (ECVs): Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FAPAR), for forecasting millet yield in Burkina Faso (BF) and maize yield in South Africa (ZA) and Malawi (MW). Using Random Forest models, we quantify forecast skill across the growing season at both national and sub-national scales. Results show that LAI and FAPAR provide effective forecast lead times of approximately 4 months in BF, 2 months in ZA, and up to 6 months in MW relative to harvest. At peak performance, Mean Absolute Percentage Error (MAPE) reaches 19.8% (LAI) and 23.8% (FAPAR) in BF, 12.0% and 9.8% in ZA, and 21.8% and 20.8% in MW, respectively. Across countries, biophysical parameters often outperform NDVI and precipitation, particularly in arid and semi-arid regions. At the sub-national level, LAI and FAPAR enable classification of administrative units into high and moderate-skill forecast units, revealing strong spatial heterogeneity linked to crop dominance. However, forecast skill declines where the target crop is not the dominant type, highlighting an important limitation for operational deployment. Overall, the findings suggest that satellite-derived biophysical parameters can provide earlier and more spatially resolved yield signals than commonly used predictors, with potential to improve the timeliness and effectiveness of food security early warning systems.

Remote Sensing Applications: Society and Environme↗
USGS2025

Monitoring cyanobacteria temporal trends in a hypereutrophic lake using remote sensing: From multispectral to hyperspectral

Cyanobacterial harmful algal blooms (cyanoHABs) and associated cyanotoxins are a concern for inland waters. Due to the extensive spatial coverage and frequent availability of satellite images, multispectral remote sensing tools demonstrate utility for monitoring these blooms. The next frontier for remote sensing of cyanoHABs in inland waters is hyperspectral data. Recent and upcoming hyperspectral satellite missions using narrow wavelength imaging spectrometers could have a major impact on advancing our ability to detect, quantify, and characterize cyanobacterial blooms. This study compares multispectral and hyperspectral remote sensing capabilities and processing tools for monitoring cyanoHAB dynamics. We evaluated the temporal trends of cyanoHABs in Clear Lake, California, a hypereutrophic lake with diverse cyanobacteria genera based on 38 sampling events over a five-year monitoring period (2019–2023). We validated the Sentinel-3 Ocean and Land Color Instrument (multispectral) Cyanobacteria Index algorithm for Clear Lake using in situ cyanobacteria measurements, which complemented our field-based evaluation of cyanobacteria trends in Clear Lake. We then demonstrate the advantages of hyperspectral data from both in situ spectroradiometer measurements and full-lake hyperspectral satellite images. We apply the Spectral Mixture Analysis for Surveillance of HABs (SMASH) workflow, a Multiple Endmember Spectral Mixture Analysis (MESMA) algorithm, to the hyperspectral images to assess the potential of satellite imaging spectrometer data to identify cyanobacteria genera – the first study to test this tool outside its original study sites. We developed a Clear Lake-specific cyanobacteria spectral library using our field spectroradiometer measurements to improve SMASH performance in Clear Lake, which supports the continued development of this tool.

California↗