The effects of vegetation on the ability to map soils using imaging spectrometer data

Remote sensing applied to tasks of mapping soil and rock surfaces must address the problem of vegetation cover in all but the most arid terrain. Masking out pixels with a high proportion of vegetation using a threshold on the near-infrared/red ratio is a popular strategy for live vegetation. The important effects of dead vegetation on the SWIR reflectance is usually ignored. Data gathered by the GER-II imaging spectrometer over a semi-arid area near Almaden, south central Spain were used to test the sensitivity of thematic soil mapping to variable cover of live and dead vegetation. After calibration to reflectance a least-squares unmixing analysis was performed using image end-members and proportions maps of vegetation and soil/rock components generated. Despite a low signal-to-noise ratio, three soil/rock and four vegetation endmembers were successfully mapped and validated from field estimates. A quantitative assessment was made of the effects of live and dead vegetation on the ability of the unmixing analysis to distinguish between granite and shale soils using synthetically mixed spectra gathered using field spectroradiometry and statistical analysis of the imaging spectrometer data. Dead vegetation was shown to have a greater impact on soil spectra than live vegetation. The ability to distinguish between the soils was lost at 50-60 per cent vegetation cover.     

Evaluating MODIS soil fractional cover for arid regions,using albedo from high-spatial resolution satellite imagery

Broad-scale high-temporal frequency satellite imagery is increasingly used for environmental monitoring. While the normalized difference vegetation index (NDVI) is the most commonly used index to track changes in vegetation cover, newer spectral mixture approaches aim to quantify sub-pixel fractions of photosynthesizing vegetation, non-photosynthesizing vegetation, and exposed soil. Validation of the unmixing products is essential to enable confident use of the products for management and decision-making. The most frequently used validation method is by field data collection, but this is very time consuming and costly, in particular in remote regions where access is difficult.

This study developed and demonstrates an alternative method for quantifying land-cover fractions using high-spatial resolution satellite imagery. The research aimed to evaluate the bare soil fraction in a sub-pixel product, MODIS Fract-G, for the natural arid landscapes of the far west of South Australia. Twenty-two sample regions, of 3400 sampling points each, were investigated across several arid land types in the study area. Albedo thresholds were carefully determined in Advanced Land Observing Satellite Panchromatic Remote-sensing Instrument Stereo Mapping (ALOS PRISM) images (2.5 m spatial resolution), which separated predominantly bare soil from predominantly vegetated or covered soil, and created classified images. Correlation analysis was carried out between MODIS Fract-G bare soil fractional cover and ALOS PRISM bare soil proportions for the same areas. Results showed much lower correlations than expected, though limited agreement was found in some specific areas. It is posited that the Moderate Resolution Imaging Spectroradiometer (MODIS) fractional cover product, which is based on unmixing using the NDVI and a cellulose absorption index (CAI) proxy, may be generally unable to separate soil from vegetation in situations where both indices are low. In addition, separation is hampered by the lack of ‘pure pixels’ in this heterogeneous landscape. This suggests that the MODIS fractional cover product, at least in its present form, is unsuited to monitor sparsely vegetated arid landscapes.     

Accuracy and applicability of linear spectral unmixing in delineating potential erosion areas in tropical watersheds

This study was undertaken to assess the accuracy of linear spectral unmixing (LSU) in estimating fractional abundance of land cover components and to examine its applicability in delineating potential erosion areas in tropical watershed. Five image end‐members (mixed vegetation, grass, Acacia auriculiformis, bare soil and water/shadow) were selected and used in different combinations in unmixing Landsat Enhanced Thematic Mapper (ETM) into fraction images. The accuracy assessment was conducted by comparing the land cover abundance estimates derived from unmixing with the land cover abundance measured from field‐validated classified QuickBird imagery. Good agreement was obtained using a four‐end‐member combination in which shadow was eliminated. The results suggest that LSU could be implemented for soil erosion detection. In general, soil erosion increases when vegetation cover decreases; hence, we used the fraction images to derive a bare soil/vegetation cover ratio and used that as a simple indicator to map high potential erosion areas. Comparison with field assessment of actual erosion levels in the study area showed that the technique is effective in identifying areas on which erosion control efforts should be concentrated.     

Spectral response of a plant canopy with different soil backgrounds

The spectral behavior of a cotton canopy with four soil types alternately inserted underneath was examined at various levels of vegetation density. Measured composite spectra, representing various mixtures of vegetation with different soil backgrounds, were compared with existing measures of greenness, including the NIR-red band ratios, the perpendicular vegetation index (PVI), and the greenness vegetation index (GVI). Observed spectral patterns involving constant vegetation amounts with different soil backgrounds could not be explained nor predicted by either the ratio or the orthogonal greenness measures. All greenness measures were found to be strongly dependent on soil brightness. Furthermore, soil-induced greenness changes became greater with increasing amounts of vegetation up to 60% green cover. The results presented suggests that soil and plant spectra interactively mix in a nonadditive, partly correlated manner to produce composite canopy spectra.     

Assessing crop residue cover using shortwave infrared reflectance

Management of crop residues is an important consideration for reducing soil erosion and increasing soil organic carbon. Current methods of measuring residue cover are inadequate for characterizing the spatial variability of residue cover over large fields. The objectives of this research were to determine the spectral reflectance of crop residues and soils and to assess the limits of discrimination that can be expected in mixed scenes. Spectral reflectances of dry and wet crop residues plus three diverse soils were measured over the 400-2400 nm wavelength region. Reflectance values for scenes with varying proportions of crop residues and soils were simulated. Additional spectra of scenes with mixtures of crop residues, green vegetation, and soil were also acquired in corn, soybean, and wheat fields with different tillage treatments. The spectra of dry crop residues displayed a broad absorption feature near 2100 nm, associated with cellulose-lignin, that was absent in spectra of soils. Crop residue cover was linearly related (r²=0.89) to the Cellulose Absorption Index (CAI), which was defined as the relative depth of this absorption feature. Green vegetation cover in the scene attenuated CAI, but was linearly related to the Normalized Difference Vegetation Index (NDVI, r²=0.93). A novel method is proposed to assess soil tillage intensity classes using CAI and NDVI. Regional surveys of soil conservation practices that affect soil carbon dynamics may be feasible using advanced multispectral or hyperspectral imaging systems.     

Mitigating the effects of soil and residue water contents on remotely sensed estimates of crop residue cover

Crop residues on the soil surface decrease soil erosion and increase soil organic carbon and the management of crop residues is an integral part of many conservation tillage systems. Current methods of measuring residue cover are inadequate for characterizing the spatial variability of residue cover over large fields. The objectives of this research were to determine the effects of water content on the remotely sensed estimates of crop residue cover and to propose a method to mitigate the effects of water content on remotely sensed estimates of crop residue cover. Reflectance spectra of crop residues and soils were measured in the lab over the 400-2400 nm wavelength region. Reflectance of scenes with various residue cover fractions and water contents was simulated using a linear mixture model. Additional spectra of scenes with mixtures of crop residues and soil were also acquired in corn, soybean, and wheat fields with different tillage treatments and different water content conditions. Crop residue cover was linearly related to the cellulose absorption index (CAI), which was defined as the relative intensity of an absorption feature near 2100 nm. Water in the crop residue significantly attenuated CAI and changed the slope of the residue cover vs. CAI relationship. Without an appropriate correction, crop residue covers were underestimated as scene water content increased. Spectral vegetation water indices were poorly related to changes in the water contents of crop residues and soils. A new reflectance ratio water index that used the two bands located on the shoulders of the cellulose absorption feature to estimate scene water conditions was proposed and tested with data from corn, soybean, and wheat fields. The ratio water index was used to describe the changes in the slope of crop residue cover vs. CAI and improve the predictions of crop residue cover. These results indicate that spatial and temporal adjustments in the spectral estimates of crop residue cover are possible. Current mutispectral imaging systems will not provide reliable estimates of crop residue cover when scene water content varies. Hyperspectral data are not required, because the three narrow bands that are used for both CAI and the scene moisture correction could be incorporated in advanced multispectral sensors. Thus, regional surveys of soil conservation practices that affect soil carbon dynamics may be feasible using either advanced multispectral or hyperspectral imaging systems.     

Land‐cover classification in the Andes of southern Ecuador using Landsat ETM+ data as a basis for SVAT modelling

A land‐cover classification is needed to deduce surface boundary conditions for a soil–vegetation–atmosphere transfer (SVAT) scheme that is operated by a geoecological research unit working in the Andes of southern Ecuador. Landsat Enhanced Thematic Mapper Plus (ETM+) data are used to classify distinct vegetation types in the tropical mountain forest. Besides a hard classification, a soft classification technique is applied. Dempster–Shafer evidence theory is used to analyse the quality of the spectral training sites and a modified linear spectral unmixing technique is selected to produce abundancies of the spectral endmembers. The hard classification provides very good results, with a Kappa value of 0.86. The Dempster–Shafer ambiguity underlines the good quality of the training sites and the probability guided spectral unmixing is chosen for the determination of plant functional types for the land model. A similar model run with a spatial distribution of land cover from both the hard and the soft classification processes clearly points to more realistic model results by using the land surface based on the probability guided spectral unmixing technique.     

Spectral mixture analysis of agricultural crops: Endmember validation and biophysical estimation in potato plots

The spectral reflectance of agricultural crops is affected significantly by sub-pixel scale spectral contributions of background soils and shadows as viewed by a remote sensing instrument. This has meant the potential of remote sensing imagery has not been fully realized for extracting biophysical information and assessing ecological stress using methods such as vegetation indices (VIs). In this paper, we address this problem explicitly using spectral mixture analysis (SMA) to quantify the area abundance of plants, soils and shadows at sub-pixel scales with the aim of improving extraction of plant biophysical and structural information from remote sensing data. Different measurement strategies were tested in the field for acquiring reference endmember spectra of crop vegetation, soil and shadows using a field spectroradiometer for a set of potato plots in western Canada. Endmember measurements included sunlit and shadowed spectra of in situ crop targets, optically thick stacks and data from excised leaves, as well as cultivated, rough and compacted soils. All possible combinations of crop, soil and shadow endmember spectra were analysed using SMA to derive sets of sub-pixel scale component fractions from radiometer spectra acquired from a boom truck over replicate plot samples with a sensor field of view of 1.05 m. Digital video image frames captured simultaneously with the radiometer data were used to determine ground proportions of crop, soil and shadow for independent validation of the SMA fractions. Endmember fractions derived from excised leaves, cultivated soil and shadowed vegetation spectra showed the best agreement with ground truth data, with differences of only ±3.3%. These sub-pixel scale fractions were used in regression analyses to predict leaf area index, biomass and plant width with an average r2 value of 0.85 from SMA shadow fraction, which was a substantial improvement over the best VI results from NDVI, NGVI and SR (average r2 = 0.53). Perspectives on SMA at different stages in the growing season and for different crop types are provided with a recommendation that further SMA research is warranted for local to regional scale agricultural crop monitoring programmes.     

Spectral indices for estimating ecological indicators of karst rocky desertification

Karst rocky desertification is a process of land desertification associated with human disturbance of the fragile eco-geological setting of karst ecosystems. The fractional cover of photosynthetic vegetation (PV), non-photosynthetic vegetation (NPV), bare soil and exposed bedrock are key ecological indicators of the extent and degree of land degradation in karst regions. In this study, field spectral-reflectance measurements were used to develop a karst rocky desertification synthesis index (KRDSI) based on unique spectral features observed in non-vegetation land-cover types (NPV, bare soil and exposed bedrock) and were used to estimate the fractional cover of NPV, bare soil and exposed bedrock. Compared with linear spectral unmixing (LSU) using a tied-spectrum transform, the KRDSI is more consistent with the field measurement of non-vegetation land-cover fractions. This study indicates that ecological indicators of karst rocky desertification can be extracted relatively simply with the combination of vegetation indices and KRDSI values.     

Non-linear mixture modelling without end-members using an artificial neural network

Many methods of analysing remotely sensed data assume that pixels are pure, and so a failure to accommodate mixed pixels may result in significant errors in data interpretation and analysis. The analysis of data containing a large proportion of mixed pixels may therefore benefit from the decomposition of the pixels into their component parts. Methods for unmixing the composition of pixels have been used in a range of studies and have often increased the accuracy of the analyses. However, many of the methods assume linear mixing and require end-member spectra, but mixing is often non-linear and end-member spectra are difficult to obtain. In this paper, an alternative approach to unmixing the composition of image pixels, which makes no assumptions about the nature of the mixing and does not require end-member spectra, is presented. The method is based on an artificial neural network (ANN) and shown in a case study to provide accurate estimates of sub-pixel land cover composition. The results of this case study showed that accurate estimates of the proportional cover of a class and its areal extent may be made. It was also shown that there was a tendency for the accuracy of the unmixing to increase with the complexity of the network and the intensity of training. The results indicate the potential to derive accurate information from remotely sensed data sets dominated by mixed pixels.     

Extracting urban vegetation characteristics using spectral mixture analysis and decision tree classifications

Urban vegetation cover is a critical component in urban systems modeling and recent advances in remote sensing technologies can provide detailed estimates of vegetation characteristics. In the present study we classify urban vegetation characteristics, including species and condition, using an approach based on spectral unmixing and statistically developed decision trees. This technique involves modeling the location and separability of vegetation characteristics within the spectral mixing space derived from high spatial resolution Quickbird imagery for the City of Vancouver, Canada. Abundance images, field based land cover observations and shadow estimates derived from a LiDAR (Light Detection and Ranging) surface model are applied to develop decision tree classifications to extract several urban vegetation characteristics. Our results indicate that along the vegetation-dark mixing line, tree and vegetated ground cover classes can be accurately separated (80% and 94% of variance explained respectively) and more detailed vegetation characteristics including manicured and mixed grasses and deciduous and evergreen trees can be extracted as second order hierarchical categories with variance explained ranging between 67% and 100%. Our results also suggest that the leaf-off condition of deciduous trees produce pixels with higher dark fractions resulting from branches and soils dominating the reflectance values. This research has important implications for understanding fine scale biophysical and social processes within urban environments.     

Mapping microphytobenthos biomass by non-linear inversion of visible-infrared hyperspectral images

This study presents an innovative approach to map microphytobenthos biomass and fractional cover in Bourgneuf Bay (French Atlantic coast) using Digital Airborne Imaging Spectrometer (DAIS) hyperspectral data. Microphytobenthos is a microalgae forming a biofilm on the mudflat. Its spatial distribution is heterogeneous so it varies on a finer scale than that of airborne instrument spatial resolution, leading to a “mixed pixel” problem. Moreover, some microphytobenthic species form, at low tide, a biofilm deposited at the surface of the sediment substrate. The resulting signal is a highly non-linear combination of spectral endmembers due to microscale intimate mixtures. This prevents the use of classical linear unmixing models to retrieve biomass from reflectance spectra. A Modified Gaussian Model (MGM) is therefore used to remove the effects of surface roughness, shadowing and any other unknown processes that contribute to the overall shape (continuum) of the reflectance spectra. Then, relationships between microphytobenthos biomass and spectral shapes are derived from a spectral database compiled from laboratory reflectance spectra of microalgal monospecific cultures with different biomasses. Finally, microphytobenthos biomass and fractional cover are retrieved from the DAIS image by comparing the reflectance spectra of each pixel to a library of synthetic spectra corresponding to combinations of various biomasses and substrate percent cover. This new approach, when compared to more classically used ones such as indices, linear unmixing or spectral distance analysis, is proven to enable a much more reliable determination of biomass despite the large variety of substrates found in Bourgneuf Bay.     

城市混合像元分解中土壤与不透水面纯像元选取方法的对比研究——以厦门为例

近年来混合像元分解在城市地表组分监测与分析中的应用逐渐成为城市遥感的一个热点。纯像元的选取是混合像元分解过程中的重点和关键所在。以沿海城市厦门为研究对象,根据不同的土壤和不透水面纯像元选取规则,使用2组12种不同的纯像元选取方法对2007年1月8日TM影像进行混合像元分解,对分解结果的模型适宜度进行了比较,并使用2006年12月25日SPOT5高分辨率影像对分解结果的精度进行了比较和评估。结果表明:混合像元分解在纯像元选取时,S端元选取兼顾低反射率裸土和高反射率裸露基岩的纯像元可以整体提高分解的模型适宜度和分解精度;适度提高I分量纯像元中高反射率纯像元的比例有助于改善整体尤其是S、W分量的分解效果。     

Scale dependence of biophysical structure in deforested areas bordering the Tapajós National Forest, Central Amazon

Spatial variation of land-surface properties is a major challenge to ecological and biogeochemical studies in the Amazon basin. The scale dependence of biophysical variation (e.g., mixtures of vegetation cover types), as depicted in Landsat observations, was assessed for the common land-cover types bordering the Tapajós National Forest, Central Brazilian Amazon. We first collected hyperspectral signatures of vegetation and soils contributing to the optical reflectance of landscapes in a 600-km² region. We then employed a spectral mixture model AutoMCU that utilizes bundles of the field spectra with Monte Carlo analysis to estimate sub-pixel cover of green plants, senescent vegetation and soils in Landsat Thematic Mapper (TM) pixels. The method proved useful for quantifying biophysical variability within and between individual land parcels (e.g., across different pasture conditions). Image textural analysis was then performed to assess surface variability at the inter-pixel scale. We compared the results from the textural analysis (inter-pixel scale) to spectral mixture analysis (sub-pixel scale). We tested the hypothesis that very high resolution, sub-pixel estimates of surface constituents are needed to detect important differences in the biophysical structure of deforested lands. Across a range of deforestation categories common to the region, there was strong correlation between the fractional green and senescent vegetation cover values derived from spectral unmixing and texture analysis variance results (r²>0.85, p<0.05). These results support the argument that, in deforested areas, biophysical heterogeneity at the scale of individual field plots (sub-pixel) is similar to that of whole clearings when viewed from the Landsat vantage point.     

Synthesis of mixed pixel hyperspectral signatures

The general method of analysing mixed pixel spectral response is to decompose the actual spectra into several pure spectral components representing the signatures of the endmembers. This work suggests a reverse engineering of standardizing the mixed pixel spectrum for a certain spatial distribution of endmembers by synthesizing spectral signatures with varying proportions of standard spectral library data and matching them with the experimentally obtained mixed pixel signature. The idea is demonstrated with hyperspectral ultraviolet–visible–near-infrared (UV–vis–NIR) reflectance measurements on laboratory-generated model mixed pixels consisting of different endmember surfaces: concrete, soil, brick and vegetation and hyperspectral signatures derived from Hyperion satellite images consisting of concrete, soil and vegetation in different proportions. The experimental reflectance values were compared with the computationally generated spectral variations assuming linear mixing of pure spectral signatures. Good matching in the nature of spectral variation was obtained in most cases. It is hoped that using the present concept, hyperspectral signatures of mixed pixels can be synthesized from the available spectral libraries and matched with those obtained from satellite images, even with fewer bands. Thus enhancing the computational job in the laboratory can moderate the keen requirement of high accuracy of remote-sensor and band resolution, thereby reducing data volume and transmission bandwidth.     

A new tool for variable multiple endmember spectral mixture analysis (VMESMA)

Spectral mixture analysis is a widely used method to determine the sub‐pixel abundance of vegetation, soils and other spectrally distinct materials that fundamentally contribute to the spectral signal of mixed pixels. In this paper we present a computing and environmental analysis tool, named VMESMA, which extends the possibilities of conventional spectral unmixing. The basis is the categorization of the scene into different units or scene sub‐areas and software guidance for endmember selection, allowing for a better adaptation of the model to the conditions of the main cover types. For each pixel an individual combination of endmembers may be selected by automated matching to model quality criteria. This hierarchical assessment can incorporate a priori knowledge from different data sources, including information derived from the unmixing results. Based on an iterative feedback process, the unmixing performance may be improved at each stage until an optimum level is reached. VMESMA allows an immediate estimate of the proportions, which is very robust against external factors (e.g. illumination) and canopy shade. An application of VMESMA on hyperspectral data has been conducted to evaluate the possibilities to map residual sludge and sludge derivatives for two consecutive years with changing land surface conditions. The method offered greater flexibility and new possibilities to improve the understanding and modelling of the scene characteristics.     

Effects of soil composition and mineralogy on remote sensing of crop residue cover

The management of crop residues (non-photosynthetic vegetation) in agricultural fields influences soil erosion and soil carbon sequestration. Remote sensing methods can efficiently assess crop residue cover and related tillage intensity over many fields in a region. Although the reflectance spectra of soils and crop residues are often similar in the visible, near infrared, and the lower part of the shortwave infrared (400-1900 nm) wavelength region, specific diagnostic chemical absorption features are evident in the upper shortwave infrared (1900-2500 nm) region. Two reflectance band height indices used for estimating residue cover are the Cellulose Absorption Index (CAI) and the Lignin-Cellulose Absorption (LCA) index, both of which use reflectances in the upper shortwave infrared (SWIR). Soil mineralogy and composition will affect soil spectral properties and may limit the usefulness of these spectral indices in certain areas. Our objectives were to (1) identify minerals and soil components with absorption features in the 2000 nm to 2400 nm wavelength region that would affect CAI and LCA and (2) assess their potential impact on remote sensing estimates of crop residue cover. Most common soil minerals had CAI values ≤ 0.5, whereas crop residues were always > 0.5, allowing for good contrast between soils and residues. However, a number of common soil minerals had LCA values > 0.5, and, in some cases, the mineral LCA values were greater than those of the crop residues, which could limit the effectiveness of LCA for residue cover estimation. The LCA of some dry residues and live corn canopies were similar in value, unlike CAI. Thus, the Normalized Difference Vegetation Index (NDVI) or similar method should be used to separate out green vegetation pixels. Mineral groups, such as garnets and chlorites, often have wide ranges of CAI and LCA values, and thus, mineralogical analyses often do not identify individual mineral species required for precise CAI estimation. However, these methods are still useful for identifying mineral soils requiring additional scrutiny. Future advanced multi- and hyperspectral remote sensing platforms should include CAI bands to allow for crop residue cover estimation.     

Field-derived spectra of salinized soils and vegetation as indicators of irrigation-induced soil salinization

Salinization is a major cause of soil degradation in the Murray–Darling Basin of Australia. The objective of this research is to evaluate the utility of field-derived spectra of saline soils and related vegetation for characterizing and mapping the spatial distribution of irrigation-induced soil salinization. A FieldSpec FR hand-held spectrometer was used to measure the spectra of a range of salinized soils and associated vegetation. Strategies for mapping field-derived spectra using hyperspectral (HyMap) imagery were assessed, and a continuum-removed Spectral-Feature-Fitting (SFF) approach adopted. Field-derived spectra of the vegetation comprising of samphire, sea blite, and native grass species are also useful indicators of salinization; however, their absence is not necessarily an indicator of healthy soils. Distribution maps created using the SFF method and a restricted wavelength range of field-derived spectra provide an accurate record of the distribution of both vegetation and soil indicators of salinization at the time of image acquisition. Salinized soil and vegetation indicator class maps show a similar spatial distribution to soil salinization as mapped by ground-based geophysical surveys.     

A model for the estimation of fractional vegetation cover based on the relationship between vegetation and soil moisture

Based on surface temperature and the normalized difference vegetation index (NDVI), we calculated the temperature vegetation dryness index (TVDI). Using the relationship between TVDI and NDVI, we established a vegetation–soil moisture response model that captures the sensitivity of NDVI's response to changes in TVDI using a linear unmixing approach, and validated the model using Landsat Thematic Mapper (TM) images acquired in 1997, 2004 and 2006 and a Landsat Enhanced Thematic Mapper Plus (ETM+) image acquired in 2000. We determined the correlations between TVDI and field-measured soil moisture in 2006. TVDI was correlated significantly with soil moisture at depths of 0 to 10 cm and 10 to 20 cm, so TVDI can be used as an index that captures changes in soil moisture at these depths. By using fractional vegetation cover (FVC) data measured in the field to validate the estimated values, we estimated mean absolute errors of 0.043 and 0.137 for shrub and grassland vegetation coverage, respectively, demonstrating acceptable estimation accuracy. Based on these results, it is possible to estimate a region's FVC using the linear unmixing model. The results show bare land coverage values distributed similarly to TVDI values. In mountain areas, grassland coverage mostly ranged from 0.4 to 0.6. Shrub coverage mostly ranged from 0.4 to 0.6. Forest coverage was zero in most parts of the study area.     

Postfire soil burn severity mapping with hyperspectral image unmixing

Burn severity is mapped after wildfires to evaluate immediate and long-term fire effects on the landscape. Remotely sensed hyperspectral imagery has the potential to provide important information about fine-scale ground cover components that are indicative of burn severity after large wildland fires. Airborne hyperspectral imagery and ground data were collected after the 2002 Hayman Fire in Colorado to assess the application of high resolution imagery for burn severity mapping and to compare it to standard burn severity mapping methods. Mixture Tuned Matched Filtering (MTMF), a partial spectral unmixing algorithm, was used to identify the spectral abundance of ash, soil, and scorched and green vegetation in the burned area. The overall performance of the MTMF for predicting the ground cover components was satisfactory (r² = 0.21 to 0.48) based on a comparison to fractional ash, soil, and vegetation cover measured on ground validation plots. The relationship between Landsat-derived differenced Normalized Burn Ratio (dNBR) values and the ground data was also evaluated (r² = 0.20 to 0.58) and found to be comparable to the MTMF. However, the quantitative information provided by the fine-scale hyperspectral imagery makes it possible to more accurately assess the effects of the fire on the soil surface by identifying discrete ground cover characteristics. These surface effects, especially soil and ash cover and the lack of any remaining vegetative cover, directly relate to potential postfire watershed response processes.