Airborne Imaging Spectrometer Data Research Articles

Spatial resolution, spectral metrics and biomass are key aspects in estimating plant species richness from spectral diversity in species‐rich grasslands

AbstractIncreasing evidence suggests that remotely sensed spectral diversity is linked to plant species richness. However, a conflicting spectral diversity–biodiversity relationship in grasslands has been found in previous studies. In particular, it remains unclear how well the spectral diversity–biodiversity relationship holds in naturally assembled species‐rich grasslands. To address the linkage between spectral diversity and plant species richness in a species‐rich alpine grassland ecosystem, we investigated (i) the trade‐off between spectral and spatial resolution in remote sensing data; (ii) the suitability of three different spectral metrics to describe spectral diversity (coefficient of variation, convex hull volume and spectral species richness) and (iii) the importance of confounding effects of live plant biomass, dead plant biomass and plant life forms on the spectral diversity–biodiversity relationship. We addressed these questions using remote sensing data collected with consumer‐grade cameras with four spectral bands and 10 cm spatial resolution on an unmanned aerial vehicle (UAV), airborne imaging spectrometer data (AVIRIS‐NG) with 372 bands and 2.5 m spatial resolution, and a fused data product of both datasets. Our findings suggest that a fused dataset can cope with the requirement of both high spatial‐ and spectral resolution to remotely measure biodiversity. However, in contrast to several previous studies, we found a negative correlation between plant species richness and spectral metrics based on the spectral information content (i.e. spectral complexity). The spectral diversity calculated based on the spectral complexity was sensitive to live and dead plant biomass. Overall, our results suggest that remote sensing of plant species diversity requires a high spatial resolution, the use of classification‐based spectral metrics, such as spectral species richness, and awareness of confounding factors (e.g. plant biomass), which may be ecosystem specific.

Detecting high-temperature anomalies from Sentinel-2 MSI images

High-temperature anomalies (HTAs) of the earth's surface, such as fires, volcanic activities, and industrial heat sources, have a profound impact on Earth's system. Sentinel-2 Multispectral Instrument (MSI) provides spatially-specific information for precisely measuring the location and extent of HTAs at a fine scale. However, detecting HTAs from MSI images remains challenging because the emitted radiance of an HTA in the short-wave infrared (SWIR) bands can be easily mixed with the reflected solar radiance background in the daytime; and an increasing number of atypical cases in MSI images need to be treated with the enhanced spatial resolution. A generic HTA detection approach that handles both anthropogenic and natural HTAs will broaden the scope of MSI applications. In this study, (i) we highlight two spectral characteristics of HTAs in the far-SWIR, near-SWIR, and NIR bands (i.e., (ρfar-SWIR - ρnear-SWIR)/ρNIR ≥ 0.45 and (ρfar-SWIR -ρnear-SWIR) ≥ ρnear-SWIR - ρNIR) that can effectively enhance HTAs from background geo-features, based on the reflectance spectra in airborne imaging spectrometer data. (ii) We propose a tri-spectral thermal anomaly index (TAI) that jointly uses the two high-temperature-sensitive SWIR bands and the high-temperature-insensitive NIR band to enhance HTAs, based on the above characteristics and a comprehensive sampling of different types of HTAs from 1,974 MSI images. (iii) We develop a TAI-based approach for MSI images to detect HTAs in general. The proposed approach was applied to detect different types of HTAs, including different biomass burnings, active volcanoes, and industrial HTAs, over a wide range of land-cover scenarios. Validations and comparisons demonstrate the proposed approach is reliable and performs better than the existing state-of-the-art HTA detection approaches. Evaluations on two types of small industrial HTAs, including operating kilns and enclosed landfill gas flares, show that the HTA detection probability of the TAI-based approach from time-series MSI images is ~ 84.91% and 88.23%, respectively. Further investigations show that the TAI-based approach also has good transferability in detecting HTAs from multispectral images acquired by Landsat-family satellites.

Remotely Sensing Variation in Ecological Strategies and Plant Traits of Willows in Perialpine Floodplains

AbstractNatural floodplains are characterized by a complex habitat mosaic. However, damming, water storage, and hydropower production affect many floodplains by altering their natural habitat diversity. Field sampling data and imaging spectroscopy are used in combination with statistical models to assess resource allocation strategies of willow stands in perialpine floodplains. Three contrasting floodplain reaches located along two rivers in Switzerland serve as test beds: The Sarine River is partitioned into an upstream and downstream segment under the influence of a dam and a hydropower plant, while the Sense River represents an undisturbed, natural floodplain. Airborne imaging spectrometer data allow mapping of spatially distributed Competitor/Stress tolerator/Ruderal (CSR) strategies using a partial least square modeling approach. Using cross validation, we demonstrate that a statistical modeling approach can reveal variations in CSR scores based on the StrateFy model. Such intraspecific variation of CSR scores cannot be captured by a strategy categorization based solely on the species. Results reveal that willows shifted toward more competition and less stress tolerance along hydrologically altered reaches compared to the willows strategy along the natural control. Moreover, the overall distribution of strategies indicates that stress factors (i.e., limiting growth factors), rather than disturbance (i.e., events leading to partial or total destruction), shape the plant traits of alluvial willow trees. Detailed assessments of resource allocation strategies contribute to a more complete understanding of the continuous and reciprocal shaping between flow regimes, landforms, and alluvial vegetation.

Minimizing soil moisture variations in multi-temporal airborne imaging spectrometer data for digital soil mapping

Bare soil surfaces vary spatially and temporally as a result of – among other - meteorological conditions and land management practices. Literature shows that most of the existing soil surface corrections are dependent on laboratory measurements. These measurements are time consuming and therefore unsuitable for large amounts of airborne or satellite spectroscopy data. Soil moisture and soil surface roughness present main challenges for in-field and remote sensing application of soil spectroscopy and soil moisture has substantial influence on surface reflectance. Here, we present a straightforward method for soil moisture reduction applicable to large amounts of airborne or satellite spectroscopy data.Multi-temporal airborne imaging spectrometer data was collected with the Airborne Prism EXperiment (APEX) in September 2013, May 2014 and May 2015 on the same pilot site. A new soil moisture index was developed to quantify the soil moisture of the airborne imaging spectroscopy data (R2 of 0.59). The index appeared to be closely related to the existing NINSOL index. A, laboratory based, independent soil moisture spectral dataset was used to quantify the correction factor per wavelength based on the wavelength dependent relative differences.After minimizing the effect of soil moisture variability, the airborne spectrometer data better approximate the reference dry-soil spectra (up to 21.3%). Then, the corrected data were tested for soil properties prediction (soil organic matter (SOM), sand silt and clay). The soil moisture corrected spectra performed marginally better for SOM (RMSE from 3.13 to 3.10) and silt (RMSE from 5.74 to 5.72), although not for clay (RMSE from 9.14 to 9.33) and sand (RMSE from 5.98 to 6.31). The methodology shows that field spectra can be corrected to better match laboratory conditions, focusing on soil moisture as main varying factor. Next step should include the various types of soil surface variation and their interactions. A better understanding of these interactions is needed and can be achieved by field-scale experiments and by modelling the different processes.

Validation of Global LAnd Surface Satellite (GLASS) fractional vegetation cover product from MODIS data in an agricultural region

ABSTRACTFractional vegetation cover (FVC) is an important parameter for describing the land surface vegetation conditions and widely used for land surface process simulations and global change studies. Global FVC products are mainly derived from satellite data and several global FVC products have been generated. Validation of the satellite FVC products is important before they can be applied. The objective of this study is to validate the newly generated Global LAnd Surface Satellite (GLASS) FVC product based on the time series of field FVC measurements in an agriculture region in the Heihe Basin of Northwest China. The high spatial resolution remotely sensed Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Compact Airborne Imaging Spectrometer (CASI) data were used to upscale the ground FVC measurements to validate the GLASS FVC product at 0.5 km spatial resolution. The results indicated that the GLASS FVC was highly accurate with the coefficient of determination (R2) of 0.86 and root-mean-square error (RMSE) of 0.087. Furthermore, the time series FVC profiles were consistent with the crop growing characteristics. It can be a reliable FVC product for agricultural applications.

Validation of Abundance Map Reference Data for Spectral Unmixing

The purpose of this study is to validate the accuracy of abundance map reference data (AMRD) for three airborne imaging spectrometer (IS) scenes. AMRD refers to reference data maps (“ground truth”) that are specifically designed to quantitatively assess the performance of spectral unmixing algorithms. While classification algorithms typically label whole pixels as belonging to certain ground cover classes, spectral unmixing allows pixels to be composed of fractions or abundances of each class. The AMRD validated in this paper were generated using our previously-proposed remotely-sensed reference data (RSRD) technique, which spatially aggregates the results of standard classification or unmixing algorithms from fine spatial-scale IS data to produce AMRD for co-located coarse-scale IS data. Validation of the three scenes was accomplished by estimating AMRD in 51 randomly-selected 10 m×10 m plots, using seven independent methods and observers. These independent estimates included field surveys by two observers, imagery analysis by two observers and RSRD by three algorithms. Results indicated statistically-significant differences between all versions of AMRD. Even AMRD from our two field surveys were significantly different for two of the four ground cover classes. These results suggest that all forms of reference data require validation prior to use in assessing the performance of classification and/or unmixing algorithms. Given the significant differences between the independent versions of AMRD, we propose that the mean of all (MOA) versions of reference data for each plot and class is most likely to represent true abundances. Our independent versions of AMRD were compared to MOA to characterize error and uncertainty. Best case results were achieved by a version of imagery analysis, which had mean coverage area differences of 2.0%, with a standard deviation of 5.6%. One of the RSRD algorithms was nearly as accurate, achieving mean differences of 3.0%, with a standard deviation of 6.3%. Further analysis of statistical equivalence yielded an overall zone of equivalence between [ − 7.0 % , 7.2 % ] for this version of RSRD. The relative accuracy of RSRD methods is promising, given their potential to efficiently generate scene-wide AMRD. These results provide the first known validated abundance level reference data for airborne IS data.

Fractional Vegetation Cover Estimation Method Through Dynamic Bayesian Network Combining Radiative Transfer Model and Crop Growth Model

Fractional vegetation cover (FVC) is an important parameter for describing the conditions of land surface vegetation and is widely used for Earth surface process simulations and global change studies. Regional FVC is primarily derived from remotely sensed data. However, current FVC estimation methods are mainly employed on remotely sensed data at a single time point, which can only reflect the instantaneous physical state of the land surface and ignore the important information from the vegetation growing characteristics. The vegetation growing characteristics have great potential to capture the temporal variations of FVC and, thus, can provide complementary information to improve the FVC estimation accuracy. In this paper, a dynamic Bayesian network method was proposed to estimate FVC from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance data through combining a radiative transfer model and a statistical crop growth model, which could synthetically use information from both remote sensing data and crop growing characteristics. The performance of the proposed method was investigated in a cropland area of the Heihe River Basin, covering the whole growing season of maize in 2012. The time series field FVC data were quantitatively measured using digital photography and then used to generate high-spatial-resolution FVC maps using the Advanced Spaceborne Thermal Emission and Reflection Radiometer and Compact Airborne Imaging Spectrometer data for evaluating the accuracy of FVC estimates from MODIS data. The validation results showed a satisfactory performance with a coefficient of determination $R^{2}$ of 0.956 and a root-mean-square error (RMSE) of 0.057, as compared with the performance ( $R^{2} = 0.817$ , $\text{RMSE} = 0.106$ ) of the FVC estimates using the lookup table method, which utilized the information from remote sensing data. These results indicated that the proposed method could effectively utilize the vegetation growth information and achieve reliable FVC estimates in the cropland area.

Minimizing Reflectance Anisotropy Effects in Airborne Spectroscopy Data Using Ross–Li Model Inversion With Continuous Field Land Cover Stratification

The spectral and radiometric quality of airborne imaging spectrometer data is affected by the anisotropic reflectance behavior of the imaged surface. Illumination and observation angle-dependent patterns of surface reflected radiation propagate into products, hinder quantitative assessment of biophysical/biochemical parameters, and decrease the comparability of data from multiple flight lines. The Ross–Li model, originally developed for multiangular observations, can be inverted to estimate and correct for surface anisotropy effects. This requires land cover be stratified into distinct types of scattering behavior. When the observations subsumed in these classes cover a range of view angles, a pseudo multiangular view on the surface can be employed to invert the Ross–Li model. A discrete land cover classification, however, bears the risk of inappropriate scattering correction resulting in spatial artifacts in the corrected data, predominantly in transition regions of two land cover types (e.g., soil and sparse vegetation with varying fractions). We invert the Ross–Li model on continuous land cover fraction layers. We decompose land cover in dominating structural types using linear spectral unmixing. Ross–Li kernel weights and formulations are estimated for each type independently; the correction is then applied pixel-wise according to the fractional distribution. The corrected Airborne Prism EXperiment imaging spectrometer data show significant reduction of anisotropic reflectance effects of up to 90% (average 60% to 75%, $p=0.05$ ), measured in the overlapping regions of adjacent flight lines. No spatial artifacts or spectral irregularities are observed after correction.

Correction of Reflectance Anisotropy Effects of Vegetation on Airborne Spectroscopy Data and Derived Products

Directional effects in airborne imaging spectrometer (IS) data are mainly caused by anisotropic reflectance behavior of surfaces, commonly described by bi-directional reflectance distribution functions (BRDF). The radiometric and spectral accuracy of IS data is known to be highly influenced by such effects, which prevents consistent comparison of products. Several models were developed to approximate surface reflectance anisotropy for multi-angular observations. Few studies were carried out using such models for airborne flight lines where only a single observation is available for each ground location. In the present work, we quantified and corrected reflectance anisotropy on a single airborne HyMap flight line using a Ross-Li model. We stratified the surface in two vegetation structural types (different in vertical structuring) using spectral angle mapping, to generate a structure dependent set of angular observations. We then derived a suite of products [indices (structure insensitive pigment index, normalized difference vegetation index, simple ratio index, and anthocyanin reflectance index) and inversion-based (SAIL/PROSPECT-leaf area index, Cw, Cdm, Cab)] from corrected and uncorrected images. Non-parametric analysis of variance (Kruskal-Wallis test) showed throughout significant improvements in products from corrected images. Data correction resulting in airborne nadir BRDF adjusted reflectance (aNBAR) showed uncertainty reductions from 60 to 100% (p-value = 0.05) as compared to uncorrected and nadir observations. Using sparse IS data acquisitions, the use of fully parametrized BRDF models is limited. Our normalization scheme is straightforward and can be applied with illumination and observation geometry being the only a priori information. We recommend aNBAR generation to precede any higher level airborne IS product generation based on reflectance data.

High spatial resolution mapping of elevated atmospheric carbon dioxide using airborne imaging spectroscopy: Radiative transfer modeling and power plant plume detection

Carbon dioxide is emitted from the combustion of fossil fuels and is an important contributor to anthropogenic climate change. Multiple current and planned satellite missions are designed to quantify atmospheric carbon dioxide concentrations on a global scale, but most of these sensors do not have the spatial resolution necessary to resolve point sources such as fossil fuel power plants. Airborne imaging spectrometer data, such as those from the Airborne Visible InfraRed Imaging Spectrometer (AVIRIS), can have multiple, contiguous bands covering shortwave infrared (SWIR) absorption features produced by carbon dioxide. Therefore, high spatial resolution data from AVIRIS-like sensors may offer a means for detecting plumes and retrieving carbon dioxide concentrations for point source emissions. The objectives of this study include modeling minimum carbon dioxide anomalies detectable in AVIRIS data under different conditions and applying a Cluster-Tuned Matched Filter for detection of carbon dioxide plumes in simulated data and in AVIRIS images acquired over power plants. Radiative transfer simulations were used to model the residual radiance produced by increased absorption by carbon dioxide as concentration was elevated above background levels within a 0–500m layer. Carbon dioxide anomalies, surface reflectance, water vapor concentration, solar zenith angle, sensor height, and aerosol scattering were varied in simulation sets and the resulting residual radiance spectra were compared against noise equivalent delta radiance (NEdL) for the “classic” and “next generation” AVIRIS instruments. Sensitivity to carbon dioxide anomalies improved with increased surface reflectance and declined with increased water vapor concentration, solar zenith angle, sensor height, and aerosol scattering. Zero to 500m concentration anomalies as low as 100 parts per milion by volume (ppm) for AVIRIS C and 25ppm for AVIRIS NG produced residual radiance values that exceeded SWIR NEdL. Carbon dioxide concentrations modeled for a generic power plant emissions scenario using a plume dispersion model were combined with randomly-generated reflectance spectra to create simulated images with varying surface reflectance and NEdL. For these simulated images, true positive and false positive detection rates improved as background reflectance increased and as NEdL decreased. Apparent plumes were detected in all four AVIRIS C images acquired over power plants, although the characteristics of the plumes varied according to solar-plume-sensor geometry. Improvements in modeling may allow retrieval of plume concentration, providing a valuable means for quantifying point source emissions and a basis for comparison with column concentrations retrieved from in situ measurements and coarse resolution satellite data.

Retrieval of spruce leaf chlorophyll content from airborne image data using continuum removal and radiative transfer

We investigate combined continuum removal and radiative transfer (RT) modeling to retrieve leaf chlorophyll a & b content (Cab) from the AISA Eagle airborne imaging spectrometer data of sub-meter (0.4 m) spatial resolution. Based on coupled PROSPECT-DART RT simulations of a Norway spruce (Picea abies (L.) Karst.) stand, we propose a new Cab sensitive index located between 650 and 720nm and termed ANCB650–720. The performance of ANCB650–720 was validated against ground-measured Cab of ten spruce crowns and compared with Cab estimated by a conventional artificial neural network (ANN) trained with continuum removed RT simulations and also by three previously published chlorophyll optical indices: normalized difference between reflectance at 925 and 710nm (ND925&710), simple reflectance ratio between 750 and 710nm (SR750/710) and the ratio of TCARI/OSAVI indices. Although all retrieval methods produced visually comparable Cab spatial patterns, the ground validation revealed that the ANCB650–720 and ANN retrievals are more accurate than the other three chlorophyll indices (R2=0.72 for both methods). ANCB650–720 estimated Cab with an RMSE=2.27μgcm−2 (relative RRMSE=4.35%) and ANN with an RMSE=2.18μgcm−2 (RRMSE=4.18%), while SR750/710 with an RMSE=4.16μgcm−2 (RRMSE=7.97%), ND925&710 with an RMSE=9.07μgcm−2 (RRMSE=17.38%) and TCARI/OSAVI with an RMSE=12.30μgcm−2 (RRMSE=23.56%). Also the systematic RMSES was lower than the unsystematic one only for the ANCB650–720 and ANN retrievals. Our results indicate that the newly proposed index can provide the same accuracy as ANN except for Cab values below 30μgcm−2, which are slightly overestimated (RMSE=2.42μgcm−2). The computationally efficient ANCB650–720 retrieval provides accurate high spatial resolution airborne Cab maps, considerable as a suitable reference data for validating satellite-based Cab products.

Near-surface soil moisture estimation by combining airborne L-band brightness temperature observations and imaging hyperspectral data at the field scale

The observation of spatially distributed soil moisture fields is an essential component for a large range of hydrological, climate, and agricultural applications. While direct measurements are expensive and limited to small spatial domains, the inversion of airborne and satellite L-band radiometer data has shown the potential to provide spatial estimates of near surface soil moisture from the local up to the global scale. When using L-band radiometer observations for soil moisture retrieval, a major limitation is the attenuation of the microwave signal by the vegetation, hampering the signal inversion and thereby making spatially distributed plant information necessary. Usually vegetation types are considered with a vegetation type specific global parameterization, e.g., for leaf area index (LAI). Within this study we evaluate and address the effect of spatially varying LAI on high spatial resolution (pixel size 50 m) airborne L-band brightness temperature of crop canopies that are usually regarded homogeneous. To account for within field variations of LAI we used airborne imaging spectrometer data (pixel size 1.5 m) to empirically create maps of LAI using spectral greenness vegetation indices. We found clear (R2<0.90) functional relationships between spatially varying L-band brightness temperature and LAI variations within crop canopies that in literature are usually assumed homogeneous. Very good (R2 = 0.93) near surface soil moisture estimates were achieved using multi-variate regression and adding plant specific spectral information to the independent variable set for final soil moisture retrieval. The study shows that a multi-sensor campaign using airborne L-band radiometer and imaging spectrometers provide a powerful data set for monitoring patterns of near surface soil moisture and vegetation canopy at the field scale with high accuracy.

Mapping alteration minerals at Malmbjerg molybdenum deposit, central East Greenland, by Kohonen self-organizing maps and matched filter analysis of HyMap data

The Malmbjerg molybdenum deposit in central East Greenland is a world-class porphyry molybdenum deposit. The porphyry molybdenum deposit occurs within a granite stock intruded into sedimentary rocks. The Malmbjerg molybdenum deposit is associated with a pronounced zone of hydrothermal alteration. The reflectance spectra of rock samples from the Malmbjerg alteration assemblage show absorption features of a number of minerals including topaz, jarosite, goethite, muscovite, phengite, epidote, chlorite and smectite. This study investigated the Malmbjerg alteration assemblage using airborne imaging spectrometer data recorded by the HyMap imaging system. The HyMap data were analysed using an unsupervised classification based on Kohonen self-organizing maps and partial spectral unmixing based on the matched filter algorithm. The mapping results show the spatial distribution of jarosite, goethite, phengite, epidote/chlorite, smectite, topaz and non-altered sediments. The remote-sensing mapping results bring new information for the alteration assemblage at Malmbjerg, especially for the occurrence and the spatial distribution of a phengite zone in the altered sediments overlying the molybdenum deposit and for the propylitic alteration zone. The study is an example of detailed characterization by imaging spectrometry of alteration assemblages associated with porphyry molybdenum deposits. This research also shows the potential of imaging spectrometry as a tool for geological mapping and exploration in the Arctic regions of East Greenland.

Mapping of semi-arid iron bearing red sands on emerged areas around lake marshes (Tablas de Daimiel, Spain) using hyperspectral DAIS 7915 spectrometer data

Wetlands are particularly sensitive environments receiving attention from the natural sciences community due to their wealth of both flora and fauna, and often considered as natural parks. In the Tablas de Daimiel (La Mancha, Central Spain), Digital Airborne Imaging Spectrometer data (DAIS 7915) have been analyzed to map geological processes on areas around the receding wetland which have never been flooded by water in the past. Sediments permanently exposed to the atmosphere dehydrate and oxide, developing different mineralogical associations arranged on planation surfaces. Such planation surfaces are key in the geological knowledge of recent climate change and landscape evolution. Progressive iron oxide/hydroxide rate and decarbonation can be spectrally followed on the Holocene sands framing the current marshy area. Such mineralogical changes are geologically registered on flat surfaces at different heights over the receding shore of the paleolake. Interacting erosion and sedimentation processes are responsible for the development of the flat morphological surfaces with increasing dryness. Maps are built for four different morphological units consisting of planation surfaces following chronologically the receding marsh during the last 2000 years before the present. Interactive spectral responses of mineralogical associations are described on the imagery, field and laboratory spectra.

Margin-based feature selection for hyperspectral data

A margin-based feature selection approach is explored for hyperspectral data. This approach is based on measuring the confidence of a classifier when making predictions on a test data. Greedy feature flip and iterative search algorithms, which attempts to maximise the margin-based evaluation functions, were used in the present study. Evaluation functions use linear, zero–one and sigmoid utility functions where a utility function controls the contribution of each margin term to the overall score. The results obtained by margin-based feature selection technique were compared to a support vector machine-based recurring feature elimination approach. Two different hyperspectral data sets, one consisting of 65 bands (DAIS data) and other with 185 bands (AVIRIS data) were used. With digital airborne imaging spectrometer (DAIS) data, the classification accuracy by greedy feature flip algorithm and sigmoid utility function was 93.02% using a total of 24 selected features in comparison to an accuracy of 91.76% with full set of 65 features. The results suggest a significant increase in classification accuracy with 24 selected features. The classification accuracy (93.4%) achieved by the iterative search margin-based algorithm with 20 selected features using sigmoid utility function is also significantly more accurate than that achieved with 65 features. To judge the usefulness of margin-based feature selection approaches, another hyperspectral data set consisting of 185 features was used. A total of 65 selected features were used to evaluate the performance of margin-based feature selection approach. The results suggest a significantly improved performance by greedy feature flip-based feature selection technique with this data set also. This study also suggest that margin-based feature selection algorithms provide a comparable performance to support vector machine-based recurring feature elimination approach.

Using spectral information from the NIR water absorption features for the retrieval of canopy water content

Canopy water content (CWC) is important for mapping and monitoring the condition of the terrestrial ecosystem. Spectral information related to the water absorption features at 970nm and 1200nm offers possibilities for deriving information on CWC. In this study, we compare the use of derivative spectra, spectral indices and continuum removal techniques for these regions. Hyperspectral reflectance data representing a range of canopies were simulated using the combined PROSPECT+SAILH model. Best results in estimating CWC were obtained by using spectral derivatives at the slopes of the 970nm and 1200nm water absorption features. Real data from two different test sites were analysed. Spectral information at both test sites was obtained with an ASD FieldSpec spectrometer, whereas at the second site HyMap airborne imaging spectrometer data were also acquired. Best results were obtained for the derivative spectra. In order to avoid the potential influence of atmospheric water vapour absorption bands the derivative of the reflectance on the right slope of the canopy water absorption feature at 970nm can best be used for estimating CWC.

Indicator kriging applied to absorption band analysis in hyperspectral imagery: A case study from the Rodalquilar epithermal gold mining area, SE Spain

A new approach to the analysis of hyperspectral data for the purpose of surface compositional mapping is presented in this paper. We use an interpolated value of the absorption band position and the absorption band depth for the diagnostic of mineral absorption features. Using thresholds for this depth and position, the data is transformed to indicator [0,1] values. By kriging these values, we obtain the probability of exceeding certain absorption depth and the probability of a pixel exhibiting absorption features within a specified wavelength region. Using Bayesian statistics, the indicator kriging derived probabilities are used to produce a hard classification result. By adapting the prior probabilities to the dominant mineralogy in the various alteration facies mapped, data stratification is achieved. The classification results are compared to results derived using the spectral angle mapper and maximum likelihood classification. In addition, the results are statistically compared to field spectral data classified into dominant mineralogy. The indicator approach and the spectral angle mapper produce favourable results relative to field data and in comparison to the maximum likelihood classifier. A data set from the Rodalquilar high-sulfidation epithermal gold system in SE Spain, consisting of HyMAP airborne imaging spectrometer data and ASD field spectra focusing on the key minerals alunite, kaolinite, illite and chlorite, is used to illustrate the methodology.

Remote sensing of forest biophysical variables using HyMap imaging spectrometer data

This study systematically evaluated linear predictive models between vegetation indices (VI) derived from radiometrically corrected airborne imaging spectrometer (HyMap) data and field measurements of biophysical forest stand variables ( n=40). Ratio-based and soil-line-related broadband VI were calculated after HyMap reflectance had been spectrally resampled to Landsat TM channels. Hyperspectral VI involved all possible types of two-band combinations of ratio VI (RVI) and perpendicular VI (PVI) and the red edge inflection point (REIP) computed from two techniques, inverted Gaussian Model and Lagrange Interpolation. Cross-validation procedure was used to assess the prediction power of the regression models. Analyses were performed on the entire data set or on subsets stratified according to stand age. A PVI based on wavebands at 1088 nm and 1148 nm was linearly related to leaf area index (LAI) ( R 2=0.67, RMSE=0.69 m 2 m −2 (21% of the mean); after removal of one forest stand subjected to clearing measures: R 2=0.77, RMSE=0.54 m 2 m −2 (17% of the mean). A PVI based on wavebands at 885 nm and 948 nm was linearly related to the crown volume (VOL) ( R 2=0.79, RMSE=0.52). VOL was derived from measured biophysical variables through factor analysis (varimax rotation). The study demonstrates that for hyperspectral image data, linear regression models can be applied to quantify LAI and VOL with good accuracy. For broadband multispectral data, the accuracy was generally lower. It can be stated that the hyperspectral data set contains more information relevant to the estimation of the forest stand variables LAI and VOL than multispectral data. When the pooled data set was analysed, soil-line-related VI performed better than ratio-based VI. When age classes were analysed separately, hyperspectral VI performed considerably better than broadband VI. Best hyperspectral VI in relation with LAI were typically based on wavebands related to prominent water absorption features. Such VI are related to the total amount of canopy water; as the leaf water content is considered to be relatively constant in the study area, variations of LAI are retrieved.

Simulating Landsat ETM+ imagery using DAIS 7915 hyperspectral scanner data

Any application in remote sensing requires specific datasets that are defined in terms of the spectral and spatial characteristics of the sensor. The question as to which spatial resolution is preferable for a particular problem is a difficult one, and represents a critical decision in the planning of a project. Remote sensing data with different spatial resolutions are often required. In such cases, an alternative to the purchase of new images is simulation. Simulation is often used to generate images that will be acquired by a future sensor such as using aircraft imagery to simulate a new, as yet unavailable, orbital sensor. Simulation has usually been carried out as a spatial degradation process. However, to make a realistic simulation it is necessary to take a number of factors into consideration. Two such factors are the point spread function (PSF) and the spectral response of the sensor. In this study, Digital Airborne Imaging Spectrometer (DAIS 7915) data for a test site from the La Mancha Alta region of Spain are used to simulate a Landsat Enhanced Thematic Mapper Plus (ETM+) image. The results are promising in that comparable images are produced by considering the PSF and spectral response of the Landsat ETM+.

Radiative transfer modeling within a heterogeneous canopy for estimation of forest fire fuel properties

Imaging spectrometer data were acquired over conifer stands to retrieve spatially distributed information on canopy structure and foliage water content, which may be used to assess fire risk and to manage the impact of forest fires. The study relied on a comprehensive field campaign using stratified systematic unaligned sampling ranging from full spectroradiometric characterization of the canopy to conventional measurements of biochemical and biophysical variables. Airborne imaging spectrometer data (DAIS7915 and ROSIS) were acquired parallel to the ground measurements, describing the canopy reflectance of the observed forest. Coniferous canopies are highly heterogeneous and thus the transfer of incident radiation within the canopy is dominated by its structure. We demonstrated the viability of radiative transfer representation and compared the performance of two hybrid canopy reflectance models, GeoSAIL and FLIGHT, within this heterogeneous medium. Despite the different nature and canopy representation of these models, they yielded similar results. Subsequently, the inversion of a hyperspectral GeoSAIL version demonstrated the feasibility of estimating structure and foliage water content of a coniferous canopy based on radiative transfer modeling. Estimates of the canopy variables showed reasonably accurate results and were validated through ground measurements.