Integrating visible,near-infrared and short-wave infrared hyperspectral and multispectral thermal imagery for geological mapping at Cuprite,Nevada: a rule-based system

Previous research has shown that integrating hyperspectral visible and near-infrared (VNIR) / short-wave infrared (SWIR) with multispectral thermal infrared (TIR) data can lead to improved mineral and rock identification. However, inconsistent results were found regarding the relative accuracies of different classification methods for dealing with the integrated data set. In this study, a rule-based system was developed for integration of VNIR/SWIR hyperspectral data with TIR multispectral data and evaluated using a case study of Cuprite, Nevada. Previous geological mapping, supplemented by field work and sample spectral measurements, was used to develop a generalized knowledge base for analysis of both spectral reflectance and spectral emissivity. The characteristic absorption features, albedo and the location of the spectral emissivity minimum were used to construct the decision rules. A continuum removal algorithm was used to identify absorption features from VNIR/SWIR hyperspectral data only; spectral angle mapper (SAM) and spectral feature fitting (SFF) algorithms were used to estimate the most likely rock type. The rule-based system was found to achieve a notably higher performance than the SAM, SFF, minimum distance and maximum likelihood classification methods on their own.     

Regression-based synergy of optical, shortwave infrared and microwave remote sensing for monitoring the grain-size of intertidal sediments

A method is developed for monitoring the sediment grain-size of intertidal flats in the Westerschelde (southwest Netherlands), using information from both space-borne microwave (SAR) and optical/shortwave infrared remote sensing. Estimates of the backscattering coefficient were extracted from time-series of C-band ERS SAR imagery. Surface reflectance in the visible, near-infrared (VNIR) and shortwave infrared (SWIR) part of the electromagnetic spectrum, as well as spectral indices, were derived from matching multi-temporal Landsat TM imagery. In addition, surface reflectances were derived from a set of airborne multispectral (VNIR) CASI images, and hyperspectral (VNIR) measurements using a field spectroradiometer. The data were related to matching field measurements of surface characteristics, including sediment properties. Regression-based algorithms were developed to map the spatio-temporal distribution of mud content using (a) the C-band SAR backscattering coefficient, (b) surface reflectance in the green and SWIR, and (c) a combination of these, with corroborative field measurements. Mud content of the sediment has been successfully mapped by all three algorithms, but a combination of information from microwave and VNIR/SWIR provided best results. The algorithms were generally consistent in time, making them suitable for generating time-series and for monitoring. However, they should be validated and calibrated in order to be applicable to other intertidal areas.     

Deriving vegetation leaf water content from spectrophotometric data with orthogonal signal correction-partial least square regression

As an important element for the growth of plants, water is of great significance for real-time understanding of vegetation status, especially in the field of agricultural drought monitoring and forest fire prediction. Currently, vegetation leaf water content (LWC) estimation approaches employing remote sensing mainly include monitoring of spectral indices based on vegetation moisture feature bands and processing of continuous spectral data gained from hyperspectral remote sensing. In this article, a new approach, orthogonal signal correction-partial least square regression (OSC-PLSR), is introduced, to extract LWC from laboratory-measured reflectance spectra. Equivalent water thickness (EWT) and fuel moisture content (FMC) (based on both fresh and dry weight; FMC_w and FMC_d) were selected as indicators of LWC. Using the Leaf Optical Properties EXperiment (LOPEX) data set, first the relationships between LWC (EWT, FMC_w and FMC_d) and the spectral features of original reflectance were examined, via simple PLSR. Next, the OSC-PLSR was applied to evaluate its performance for estimating LWC (EWT, FMC_w and FMC_d) from reflectance spectra. Then, the vegetation moisture feature bands were derived from the analysis of OSC-PLSR latent variable loadings. According to the results, there are three major conclusions. (1) OSC-PLSR shows good performance for predicting all LWC indicators, and using only one latent variable, the OSC-PLSR model's complexity is greatly reduced compared with simple PLSR models. (2) Using both one and an optimal number of latent variables in OSC-PLSR models, FMC_w sees the best prediction, followed by EWT and lastly FMC_d; this order is opposite to the order of LWC data variation. (3) Through loading analysis of one-latent-variable OSC-PLSR models, the vegetation moisture feature bands can be retrieved. For EWT, the moisture-sensitive bands lie within one near-infrared (NIR) and two shortwave-infrared (SWIR) regions; for FMC_w and FMC_d, the moisture-sensitive bands lie within two SWIR regions. Also, the vegetation moisture-insensitive bands for EWT, FMC_w and FMC_d estimation are respectively acquired at around 1340, 1150 and 1330 nm.     

Water content estimation from hyperspectral images and MODIS indexes in Southeastern Arizona

Hyperspectral water retrievals from AVIRIS data, equivalent water thickness (EWT), were compared to in situ leaf water content and LAI measurements at a semiarid site in southeastern Arizona. Retrievals of EWT showed good correlation with field canopy water content measurements. Statistical analysis suggested that EWT was significantly different among seven community types, from savanna to agriculture. Four band-ratio indexes (NDVI, EVI, NDWI, and NDII) were derived from MODIS showing strong spatial agreement between maps of AVIRIS EWT and MODIS indexes, and good statistical agreement for the range of habitats at the site. Temporal patterns of these four indexes in all vegetation communities except creosote bush and agriculture showed distinct seasonal patterns that responded to the timing and amount of precipitation. Moreover, these time series captured different ecological responses among the different vegetation communities.     

Spectral mixture analysis for mapping abundance of urban surface components from the Terra/ASTER data

Using a linear unconstrained least squares (LSS) method and a non-linear artificial neural network (ANN) algorithm, we conducted a spectral mixture analysis to the Advanced Spaceborne Thermal Emission and Reflectance Radiometer (ASTER) image data in Yokohama city, Japan, for mapping the abundance of the urban surface components. ASTER is a newly developed research facility instrument. The regions of interest of four endmembers (Vegetation, Soil, High/Low albedo impervious surfaces) were determined in Maximum Noise Fraction (MNF) feature spaces. The spectral signatures of the four endmembers were then extracted from the ASTER VNIR (15-m resolution) and SWIR (30-m resolution) imagery by referring to high spatial resolution airborne imagery (The Airborne Imaging Spectrometer, AISA, with 2-m resolution) and land use/land cover map for training and testing the LSS and ANN algorithms. Experimental results indicate that ASTER VNIR and SWIR image data are capable of mapping the abundances of urban surface components with a reasonable accuracy and that the ANN outperforms the unconstrained LSS in this spectral mixture analysis.     

High spatial resolution burn severity mapping of the New Jersey Pine Barrens with WorldView-3 near-infrared and shortwave infrared imagery

The WorldView-3 (WV-3) sensor, launched in 2014, is the first high-spatial resolution scanner to acquire imagery in the shortwave infrared (SWIR). A spectral ratio of the SWIR combined with the near-infrared (NIR) can potentially provide an effective differentiation of wildfire burn severity. Previous high spatial resolution sensors were limited to data from the visible and NIR for mapping burn severity, for example using the normalized difference vegetation index (NDVI). Drawing on a study site in the Pine Barrens of New Jersey, USA, we investigate optimal processing methods for analysing WV-3 data, with a focus on the pre-fire minus post-fire differenced normalized burn ratio (dNBR). Although the imagery, originally acquired with a 3.7 m instantaneous field of view, was aggregated to 7.5 m pixels by DigitalGlobe due to current licensing constraints, a slight additional smoothing of the data was nevertheless found to help reduce noise in the multi-temporal dNBR imagery. The highest coefficient of determination (R²) of the regressions of dNBR with the field-based composite burn index was obtained with a dNBR ratio produced with the NIR1 and SWIR6 bands. Only a very small increase in R² was found when dNBR was calculated using the average of NIR1 and NIR2 for the NIR bands, and SWIR5 to SWIR8 for the SWIR bands. dNBR calculated using SWIR1 as the NIR band produced notably lower R² values than when either NIR1 or NIR2 were used. Differenced NDVI data was found to produce models with a much lower R² than dNBR, emphasizing the importance of the shortwave infrared region for monitoring fire severity. High spatial resolution dNBR data from WV-3 can potentially provide valuable information on finer details regarding burn severity patterns than can be obtained from Landsat 30 m data.     

The PROBA-V mission: the space segment

PROBA-V (Project for On-Board Autonomy – Vegetation) is an ESA (European Space Agency) mission developed within the framework of the Agency’s General Support Technology Programme (GSTP) devoted to the observation of the Earth’s vegetation, providing data continuity with the SPOT (Satellite pour l’Observation de la Terre) 4 and 5 VEGETATION payloads as a gap-filler to the ESA Sentinel-3 mission. The PROBA-V space segment is based on a three-axis stabilized PROBA small-satellite platform of about 140 kg equipped with a state-of-the-art compact 4-band multi-spectral imager with a large field of view. The instrument’s optomechanics is based on three very compact TMA (three mirror anastigmat) telescopes placed on an optical bench. At an altitude of 820 km, the instrument is able to provide daily coverage of the Earth in three VNIR (visible and near-infrared) bands and one SWIR (short-wave infrared) spectral band, with a spatial resolution of up to 100 m × 100 m at nadir for the VNIR. The instrument raw data will be downlinked with an X-band transmitter to the ground reception station in Kiruna, Sweden. The mission control centre is located in Redu, Belgium. The image processing centre, the so-called ‘user segment’, automatically accesses the raw data and is responsible for the processing and the dissemination of the data products towards the user community. The PROBA-V spacecraft was launched on board the new European launcher Vega on 7 May 2013. It is designed for a nominal mission lifetime of 2.5 years with a possible extension to 5 years.     

Sediment facies classification of a sandy shoreline by means of airborne imaging spectroscopy

Airborne imaging spectroscopy data (AISA Eagle and HyMap) were applied to classify the sediments of a sandy beach in seven sand type classes. On the AISA‐Eagle data, several classification strategies were tried out and compared with each other. The best classification results were obtained applying a linear discriminant classifier (LDC) in combination with feature selection based on sequential floating forward search (SFFS). The statistical LDC was used in a multiple binary approach. In the first step, the original bands were used in the classification, but transformation of the bands to wavelet coefficients enhanced the accuracy obtained. The combination of LDC with SFFS resulted in an overall accuracy of 82% (using three wavelet coefficients). Replacing the LDC with the non‐statistical SAM algorithm reduced the overall accuracy to 74% (using all bands or wavelet coefficients). When applying LDC, the optimal number of bands/wavelet coefficients to be used was defined: using more than two bands or three wavelet coefficients did not result in a higher classification accuracy. Finally, the HyMap data, featuring 126 bands in the VNIR‐SWIR range, were used to demonstrate that the VNIR range outperforms the SWIR range for this application.     

Employing crowdsourced geographic data and multi-temporal/multi-sensor satellite imagery to monitor land cover change: A case study in an urbanizing region of the Philippines

Land cover change (LCC) can have a significant impact on human and environmental well-being. LCC maps derived from historical remote sensing (RS) images are often used to evaluate the impacts of past LC changes and to construct models to predict future LC changes. Free moderate spatial resolution (~ 30 m) optical and synthetic aperture radar (SAR) RS imagery is now becoming increasingly available for this LCC monitoring. However, the classification algorithms used to extract LC information from these images typically require “training data” for classification (i.e. points or polygons with LC class labels), and acquiring this labelled training data can be difficult and time-consuming. Alternatively, crowdsourced geographic data (CGD) has become widely available from online sources like OpenStreetMap (OSM), and it may provide a useful source of training data for LCC monitoring. A major challenge with utilizing CGD for LCC mapping, however, is the presence of class labelling errors, and these errors can vary spatially (e.g. due to differing levels of CGD contributor expertise) and temporally (e.g. due to time lag between CGD creation and RS imagery acquisition). In this study, we investigated a new LCC mapping method which utilizes free Landsat (optical) and PALSAR mosaic (SAR) satellite imagery in combination with labelled LC data extracted from CGD sources (the OSM “landuse” and “natural” polygon datasets). A semi-unsupervised classification approach was employed for the LCC mapping to reduce the effects of class label noise in the CGD. The main motivation and benefit of the proposed method is that it does not require training data to be manually collected, allowing for a faster and more automated assessment of LCC. As a case study, we applied the method to map LCC in the Laguna de Bay area of the Philippines over the 2007–2015 period. The LCC map produced using our proposed approach achieved an overall classification accuracy of 90.2%, providing evidence that CGD and multi-temporal/multi-sensor satellite imagery, when combined, have a great potential for LCC monitoring.     

Lithological discrimination of the Phenaimata felsic–mafic complex,Gujarat, India,using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)

This study deals with an evaluation of the efficacy of an Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image for lithological mapping. ASTER level-1B data in the visible near-infrared (VNIR), short wave infrared (SWIR) and thermal infrared (TIR) regions have been processed to generate a lithological map of the study area in and around the Phenaimata igneous complex, in mainland Gujarat, India. ASTER band combinations, band ratio images and spectral angle mapper (SAM) processing techniques were evaluated for mapping various lithologies. The reflectance and emissivity spectra of rock samples collected from the study area were obtained in the laboratory and were used as reference spectra for ASTER image analysis. The original data in the scaled digital number (DN) values were converted to radiance and then to relative reflectance by using a scene-derived correction technique prior to SAM classification. The SAM classification in the VNIR–SWIR region is found to be effective in differentiating felsic and mafic lithologies. The relative band depth (RBD) images were generated from the continuum-removed images of ASTER VNIR–SWIR bands. Four RBD combinations (3, 5, 6 and 8) were used to identify Al-OH (aluminium hydroxide), Fe-OH (iron hydroxide), Mg-OH (magnesium hydroxide) and CO₃ (carbonate) absorption from various lithological components. ASTER TIR spectral emittance data and the laboratory emissivity measurements show the presence of a number of discrete Si-O spectral features that can differentiate mafic and felsic rock types reflecting the lithological diversity around the regions of Phenaimata igneous complex. SAM classification using emittance data failed to distinguish the felsic and mafic lithology due to the wider spectral bandwidth. The felsic class comprises the granitoid composition of rocks. RBD12 and 13 images in the TIR region were used to derive the mafic index (MI) and the silica index (SI). The MI shows the highest value in regions of gabbro–basalt occurrence, while the SI indicates regions of high silica content. The MI is lowest in regions where granophyres occur. The complimentary attributes based on the spectral reflectance and emittance data resulted in the discrimination of silica-rich and silica-poor lithologies.     

Simultaneous retrieval of CO2 and aerosols in a plume from hyperspectral imagery: application to the characterization of forest fire smoke using AVIRIS data

Hyperspectral imagery is a widely used technique to study atmospheric composition. For several years, many methods have been developed to estimate the abundance of gases. However, existing methods do not simultaneously retrieve the properties of aerosols and often use standard aerosol models to describe the radiative impact of particles. This approach is not suited to the characterization of plumes, because plume particles may have a very different composition and size distribution from aerosols described by the standard models given by radiative transfer codes. This article presents a new method to simultaneously retrieve carbon dioxide (CO₂) and aerosols inside a plume, combining an aerosol retrieval algorithm using visible and near-infrared (VNIR) wavelengths and a CO₂ estimation algorithm using shortwave infrared (SWIR) wavelengths. The microphysical properties of the plume particles, obtained after aerosol retrieval, are used to calculate their optical properties in the SWIR. Then, a database of atmospheric terms is generated with the radiative transfer code, Moderate Resolution Atmospheric Transmission (MODTRAN). Finally, pixel radiances around the 2.0 μm absorption feature are used to retrieve the CO₂ abundances. After conducting a signal sensitivity analysis, the method was applied to two airborne visible/infrared imaging spectrometer (AVIRIS) images acquired over areas of biomass burning. For the first image, in situ measurements were available. The results show that including the aerosol retrieval step before the CO₂ estimation: (1) induces a better agreement between in situ measurements and retrieved CO₂ abundances (the CO₂ overestimation of about 15%, induced by neglecting aerosols has been corrected, especially for pixels where the plume is not very thick); (2) reduces the standard deviation of estimated CO₂ abundance by a factor of four; and (3) causes the spatial distribution of retrieved concentrations to be coherent.     

Atmospheric correction of optical imagery from MODIS and Reanalysis atmospheric products

In this paper we analyze the differences obtained in the atmospheric correction of optical imagery covering bands located in the Visible and Near Infra-Red (VNIR), Short-Wave Infra-Red (SWIR) and Themal-Infrared (TIR) spectral regions when atmospheric profiles extracted from different sources are used. In particular, three sensors were used, Compact High Resolution Imaging Spectrometer (CHRIS), Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) and Landsat5 Thematic Mapper (TM), whereas four atmospheric profiles sources were considered: i) local soundings launched near the sensor overpass time, ii) Moderate Resolution Radiometer (MODIS) atmospheric profiles product (MOD07), iii) Atmospheric Correction Parameter Calculator (ACPC) generated by the National Center for Environmental Prediction (NCEP) and iv) Modified Atmospheric Profiles from Reanalysis Information (MAPRI), which includes data from NCEP and National Center of Atmospheric Research (NCAR) Reanalysis project but interpolated to 34 atmospheric levels and resampled to 0.5° × 0.5°. MODIS aerosol product (MOD04) was also used to extract Aerosol Optical Thickness (AOT) values at 550 nm. Analysis was performed for three test dates (12th July 2003, 18th July 2004 and 13th July 2005) over an agricultural area in Spain. Results showed that air temperature vertical profiles were similar for the four sources, whereas dew point temperature profiles showed significant differences at some particular levels. Atmospheric profiles were used as input to MODTRAN4 radiative transfer code in order to compute atmospheric parameters involved in atmospheric correction, with the aim of retrieving surface reflectances in the case of VNIR and SWIR regions, and Land Surface Temperature (LST) in the case of the TIR region. For the VNIR and SWIR region, significant differences depending on the atmospheric profile used were not found, particularly in the Visible region in which the AOT content is the main parameter involved in the atmospheric correction. In the case of TIR, differences depending on the atmospheric profile used were appreciable, since in this case the main parameter involved in the atmospheric correction is the water vapor content, which depends on the vertical profile. In terms of LST retrieval from ASTER data (2004 test case), all profiles provided satisfactory results compared to the ones obtained when using a local sounding, with errors of 0.3 K for ACPC and MAPRI cases and 0.7 K for MOD07. When retrieving LST from TM data (2005 test case), errors for MOD07 and MAPRI were 0.6 and 0.9 K respectively, whereas ACPC provided an error of 2 K. The results presented in this paper show that the different atmospheric profile sources are useful for accurate atmospheric correction when local soundings are not available. In particular, MOD07 product provides atmospheric information at the highest spatial resolution, 5 km, although its use is limited from 2000 to present, whereas MAPRI provides historical information from 1970 to present, but at lower spatial resolution.     

ASTER Level 1A数据产品文件解析及应用

深入解析了ASTER数据的结构,研究如何正确读取数据中卫星的位置、速度、时间、姿态角、姿态变化率等与影像定位有关的数据,并研究了这些数据的变化规律。根据ASTER数据的特点,给出了该数据在辐射校正及影像定位方面的应用方法,使得影像能更好的在这些方面得到应用。     

Does single broadband or multispectral thermal data add information for classification of visible,near‐ and shortwave infrared imagery of urban areas?

The potential value of combining broadband and multispectral thermal infrared (TIR) data with multispectral and hyperspectral visible, near‐infrared (VNIR) and shortwave infrared (SWIR) data was investigated within the context of urban land‐cover classification. Using a case study of airborne Digital Airborne Imaging Spectrometer (DAIS) imagery of Strasbourg, France, the relative contribution of TIR wavelengths to classification accuracy was investigated for hyperspectral and simulated multispectral IKONOS, SPOT and Landsat Thematic Mapper (TM) bands. A support vector machines (SVM) classifier was used because this method was found to be very effective at handling the complex distributions of the heterogeneous land cover classes. The overall classification accuracy varied greatly with different band combinations. The inclusion of a single broad thermal band increased classification accuracy by as much as 20% for simulated IKONOS bands, but only 4% for hyperspectral VNIR and SWIR data. Adding multispectral TIR data raised the average accuracy approximately a further 10% for each band combination studied. Thermal wavelengths were found to be particularly useful for reducing the confusion between road and roof surfaces.     

基于ASTER遥感数据的矿物快速识别——以内蒙古萨麦地区为例

着力于充分利用遥感数据的时空特性及软件的智能开发,使在矿物识别中能获得事半功倍的效果。基于ASTER数据在可见光、近红外及热红外波段上的分布特性,利用USGS、JHU、ASU波谱库提供的矿物光谱,针对不同矿物类型的提取设计了特定的矿物识别模式。针对上述识别模式对ENVI进行二次开发利用,方便迅速地对内蒙古萨麦地区进行了矿物填图。研究结果表明:ASTER数据在识别矿物及矿物分类填图中展现出了优良的数据特性。ENVI二次开发功能强大,快捷有效。通过野外的勘探证实,分类效果良好。     

Discriminating the early stages of Sirex noctilio infestation using classification tree ensembles and shortwave infrared bands

The woodwasp Sirex noctilio is causing extensive damage to Pinus patula trees in the summer rainfall areas of South Africa. The ability to remotely detect S. noctilio infestation remains crucial for monitoring purposes and for the effective deployment of suppression activities. In this study, we evaluated whether random forest and boosting trees can accurately discriminate between healthy trees and the early stages of S. noctilio infestation using reflectance measurements in the shortwave infrared (SWIR). Three variable selection methods, namely, a filter, the random forest out-of-bag samples and a wrapper algorithm, were used to select the smallest subset of SWIR bands. The results show that random forest produces better classification results than the competing boosting trees for all three variable selection methods, even when noise is introduced into the SWIR bands and class labels. The ability of the bands centred at 1990, 2009, 2028, 2047 and 2065 nm to discriminate between healthy trees and the early stages of infestation could be explained due to the rapid physiological changes that occur as a result of the toxic mucus and a fungus that S. noctilio injects into the tree. Overall, the results are encouraging and show that there is a link between the selected SWIR bands and existing physiological knowledge, thereby improving the chances of detecting the early stages of S. noctilio infestation at a canopy or landscape level.     

Feasibility of estimating leaf water content using spectral indices from WorldView-3’s near-infrared and shortwave infrared bands

A recently-launched high-resolution commercial satellite, DigitalGlobe’s WorldView-3, has 8 bands in the shortwave infrared (SWIR) wavelength region, which may be capable of estimating canopy water content at 3.7-m spatial resolution. WorldView-3 also has 8 multispectral bands at 1.24-m resolution with two bands in the near-infrared (NIR). The relative spectral response functions for WorldView-3 were provided by DigitalGlobe, Inc., and band reflectances were determined for reflectance spectra of PROSPECT model simulations and leaf data from maize, trees, grasses, and broadleaf herbaceous eudicots. For laboratory measurements, the range of leaf water contents was extended by including drying leaves and leaf stacks of corn, soybean, oaks, and maples. Correlations between leaf water content and spectral indices from model simulations suggested that indices using SWIR band 1 (center wavelength 1210 nm) had low variability with respect to leaf water content, but also low sensitivity. Other indices using SWIR band 5 (2165 nm) had the highest sensitivity, but also had high variability caused by different values of the leaf structure parameter in PROSPECT. Indices using SWIR bands 2, 3 and 4 (1570, 1660, and 1730 nm, respectively) had high correlations and intermediate variability from the leaf structure parameter. Spectral indices calculated from the leaf data had the same overall patterns as the simulations for variation and sensitivity; however, indices using SWIR band 1 had low correlations, and the best correlations were from indices that used SWIR bands 2, 3 and 4. Spectral indices for maize, grasses, and herbaceous crops and weeds had similar responses to leaf water content; tree leaves had higher index values and saturated at lower leaf water contents. The specified width of NIR band 2 (860–1040 nm) overlaps the water absorption feature at 970 nm wavelength; however, the normalized difference of NIR band 1 and 2 was insensitive to water content because NIR band 2’s spectral response was most heavily weighted to wavelengths less than 930 nm. The high spatial resolution of the WorldView-3 SWIR data will help analyze how variation among plant species and functional groups affects spectral responses to differences in canopy water content.     

Modeling seasonal changes in live fuel moisture and equivalent water thickness using a cumulative water balance index

Live fuel moisture, an important determinant of fire danger in Mediterranean ecosystems, exhibits seasonal changes in response to soil water availability. Both drought stress indices based on meteorological data and remote sensing indices based on vegetation water absorption can be used to monitor live fuel moisture. In this study, a cumulative water balance index (CWBI) for a time series spanning 1994-1997 and 1999-2001 was compared to field measured live fuel moisture and to equivalent water thickness (EWT) calculated from remote sensing data. A sigmoidal function was used to model the relationships between CWBI, live fuel moisture, and EWT. Both live fuel moisture and EWT reach minima at large CWBI deficits. Minimum and maximum live fuel moisture, minimum and maximum EWT, and the modeled inflection points of both live fuel moisture and EWT were found to vary with vegetation type. Modeled minimum and maximum EWT were also found to vary with vegetation biomass. Spatial variation in modeled EWT inflection points may be due to vegetation type and to local variation in soil moisture. Based on their temporal and spatial attributes, CWBI and EWT offer complimentary methods for monitoring live fuel moisture for fire danger assessment.     

Estimation of subpixel land surface temperature using an endmember index based technique: A case examination on ASTER and MODIS temperature products over a heterogeneous area

Land surface temperature (LST) is a key parameter in numerous environmental studies. Surface heterogeneity induces uncertainty in estimating subpixel temperature. To take an advantage of simultaneous, multi-resolution observations at coincident nadirs by the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and the MODerate-resolution Imaging Spectroradiometer (MODIS), LST products from the two sensors were examined for a portion of suburb area in Beijing, China. We selected Soil-Adjusted Vegetation Index (SAVI), Normalized Multi-band Drought Index (NMDI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI) as representative remote sensing indices for four land cover types (vegetation, bare soil, impervious and water area), respectively. By using support vector machines, the overall classification accuracy of the four land cover types with inputs of the four remote sensing indices, extracted from ASTER visible near infrared (VNIR) bands and shortwave infrared (SWIR) bands, reached 97.66%, and Kappa coefficient was 0.9632. In order to lower the subpixel temperature estimation error caused by re-sampling of remote sensing data, a disaggregation method for subpixel temperature using the remote sensing endmember index based technique (DisEMI) was established in this study. Firstly, the area ratios and statistical information of endmember remote sensing indices were calculated from ASTER VNIR/SWIR data at 990 m and 90 m resolutions, respectively. Secondly, the relationship between the 990 m resolution MODIS LST and the corresponding input parameters (area ratios and endmember indices at the 990 m resolution) was trained by a genetic algorithm and self-organizing feature map artificial neural network (GA-SOFM-ANN). Finally, the trained models were employed to estimate the 90 m resolution subpixel temperature with inputs of area ratios and endmember indices at the 90 m resolution. ASTER LST product was used for verifying the estimated subpixel temperature, and the verified results indicate that the estimated temperature distribution was basically consistent with that of ASTER LST product. A better agreement was found between temperatures derived by our proposed method (DisEMI) and the ASTER 90 m data (R² = 0.709 and RMSE = 2.702 K).     

Spectral mixture analyses of hyperspectral data acquired using a tethered balloon

Tethered balloon remote sensing platforms can be used to study radiometric issues in terrestrial ecosystems by effectively bridging the spatial gap between measurements made on the ground and those acquired via airplane or satellite. In this study, the Short Wave Aerostat-Mounted Imager (SWAMI) tethered balloon-mounted platform was utilized to evaluate linear and nonlinear spectral mixture analysis (SMA) for a grassland-conifer forest ecotone during the summer of 2003. Hyperspectral measurement of a 74-m diameter ground instantaneous field of view (GIFOV) attained by the SWAMI was studied. Hyperspectral spectra of four common endmembers, bare soil, grass, tree, and shadow, were collected in situ, and images captured via video camera were interpreted into accurate areal ground cover fractions for evaluating the mixture models. The comparison between the SWAMI spectrum and the spectrum derived by combining in situ spectral data with video-derived areal fractions indicated that nonlinear effects occurred in the near infrared (NIR) region, while nonlinear influences were minimal in the visible region. The evaluation of hyperspectral and multispectral mixture models indicated that nonlinear mixture model-derived areal fractions were sensitive to the model input data, while the linear mixture model performed more stably. Areal fractions of bare soil were overestimated in all models due to the increased radiance of bare soil resulting from side scattering of NIR radiation by adjacent grass and trees. Unmixing errors occurred mainly due to multiple scattering as well as close endmember spectral correlation. In addition, though an apparent endmember assemblage could be derived using linear approaches to yield low residual error, the tree and shade endmember fractions calculated using this technique were erroneous and therefore separate treatment of endmembers subject to high amounts of multiple scattering (i.e. shadows and trees) must be done with caution. Including the short wave infrared (SWIR) region in the hyperspectral and multispectral endmember data significantly reduced the Pearson correlation coefficient values among endmember spectra. Therefore, combination of visible, NIR, and SWIR information is likely to further improve the utility of SMA in understanding ecosystem structure and function and may help narrow uncertainties when utilizing remotely sensed data to extrapolate trace glas flux measurements from the canopy scale to the landscape scale.