Hyperspectral Compact Airborne Spectrographic Imager Research Articles

Mapping spatial distribution, percent cover and biomass of benthic vegetation in optically complex coastal waters using hyperspectral CASI and multispectral Sentinel-2 sensors

This work assessed the capability of Compact Airborne Spectrographic Imager (CASI) and satellite multispectral Sentinel-2 image data for mapping the distribution, percent cover (%cover) and biomass of submerged aquatic vegetation (SAV) in optically complex coastal waters of the Baltic Sea. As a first step, the distribution maps of SAV were created for brown macroalgae, green macroalgae and higher plants classes. Secondly, %cover maps were retrieved by building class level relationships between in situ estimated %cover and image reflectance. Thirdly, statistical models were built for estimating class specific SAV biomass as a function of SAV %cover. Finally, developed biomass models were applied to class specific %cover maps derived from the step 2 for landscape scale biomass estimation. CASI sensor had higher classification accuracy (78%) compared to Sentinel-2 sensor (69%). CASI also outperformed Sentinel-2 in the %cover assessment showing R2 values in the range of 0.55–0.73, while R2 values in the range of 0.36–0.49 were retrieved for Sentinel-2. However, both sensors provided similar distribution and %cover patterns of benthic vegetation. The %cover-biomass models showed a very good fit explaining 66–82% of variance of different SAV classes. Comparison of biomass estimates from both images revealed that the total dry biomass (t) was underestimated by Sentinel-2 by 10.6%. However, if biomasses were retrieved per unit area (t/km2), then both instruments resulted in nearly identical total SAV biomasses.

Fusion of Hyperspectral CASI and Airborne LiDAR Data for Ground Object Classification through Residual Network.

Modern satellite and aerial imagery outcomes exhibit increasingly complex types of ground objects with continuous developments and changes in land resources. Single remote-sensing modality is not sufficient for the accurate and satisfactory extraction and classification of ground objects. Hyperspectral imaging has been widely used in the classification of ground objects because of its high resolution, multiple bands, and abundant spatial and spectral information. Moreover, the airborne light detection and ranging (LiDAR) point-cloud data contains unique high-precision three-dimensional (3D) spatial information, which can enrich ground object classifiers with height features that hyperspectral images do not have. Therefore, the fusion of hyperspectral image data with airborne LiDAR point-cloud data is an effective approach for ground object classification. In this paper, the effectiveness of such a fusion scheme is investigated and confirmed on an observation area in the middle parts of the Heihe River in China. By combining the characteristics of hyperspectral compact airborne spectrographic imager (CASI) data and airborne LiDAR data, we extracted a variety of features for data fusion and ground object classification. Firstly, we used the minimum noise fraction transform to reduce the dimensionality of hyperspectral CASI images. Then, spatio-spectral and textural features of these images were extracted based on the normalized vegetation index and the gray-level co-occurrence matrices. Further, canopy height features were extracted from airborne LiDAR data. Finally, a hierarchical fusion scheme was applied to the hyperspectral CASI and airborne LiDAR features, and the fused features were used to train a residual network for high-accuracy ground object classification. The experimental results showed that the overall classification accuracy was based on the proposed hierarchical-fusion multiscale dilated residual network (M-DRN), which reached an accuracy of 97.89%. This result was found to be 10.13% and 5.68% higher than those of the convolutional neural network (CNN) and the dilated residual network (DRN), respectively. Spatio-spectral and textural features of hyperspectral CASI images can complement the canopy height features of airborne LiDAR data. These complementary features can provide richer and more accurate information than individual features for ground object classification and can thus outperform features based on a single remote-sensing modality.

How much benthic information can be retrieved with hyperspectral sensor from the optically complex coastal waters?

The Baltic Sea represents an optically complex case 2 water type, where high concentrations of water column constituents limit acquisition of benthic information. Different preprocessing steps were applied to the hyperspectral compact airborne spectrographic imager (CASI) image to extract as much useful benthic information as possible. Atmospheric correction, minimum noise fraction transform, and sun-glint correction were performed to acquire water surface reflectance data. Additionally, a water column removal procedure was applied to acquire bottom reflectance data. Although retrieved CASI water surface reflectance spectra generally matched the magnitudes and shapes of in situ measured spectra, then the applied water column correction algorithm did not yield accurate bottom reflectance spectra. Therefore, both benthic habitat and bathymetry maps were retrieved from the CASI sea surface image data set. An image-based supervised classification method produced a good quality benthic habitat map from the shallow Pakri study area (depth R 2 value of 0.88 and a root-mean-square error of 0.32 m. The assessment of the benthic substrate detectability limits in the Baltic Sea revealed that, at the wavelength of deepest light penetration (near 570 nm), the depth restriction for CASI benthic substrate detection was 7.6 m for sand, 5.0 m for green macroalgae, 3.0 m for higher order vegetation, and 3.1 m for brown macroalgae. The depth limit to which bathymetric mapping is practical in our study site was estimated to be around 3.5 to 4.0 m.

Fine Classification of Planting Structure in the Middle Reaches of Heihe River Basin based on Hyperspectral Compact Airborne Spectrographic Imager(CASI) Data

Fine Classification of Planting Structure in the Middle Reaches of Heihe River Basin based on Hyperspectral Compact Airborne Spectrographic Imager(CASI) Data

Análisis espectral del arrecife coralino de Cayos Arcas, Campeche, México

La utilización de sensores remotos aerotransportados con alta resolución espectral o hiperespectrales, ha venido incrementándose, paulatinamente, en estudios de elementos sobre la superficie terrestre. En particular, el estudio de cuerpos de agua mediante estos instrumentos ha recibido un gran impulso en los últimos años, en diversos campos de investigación. El objetivo de este trabajo es mostrar una caracterización espacial del arrecife coralino de Cayos Arcas utilizando el sensor hiperespectral aerotransportado CASI (Compact Airborne Spectrographic Imager). Los resultados obtenidos mediante el procesamiento de imágenes muestran que es posible la detección de sedimento (distribución y patrones de transporte), la batimetría y las características fisiográficas de la región entre los aspectos más importantes.

Mapping an inland wetland complex using hyperspectral imagery

The goal is to determine the extent to which heterogeneous inland wetland vegetation communities and their dominant species, as well as adjacent upland vegetation types, can be mapped using 4‐m hyperspectral Compact Airborne Spectrographic Imager (CASI) data. Two classification algorithms, the maximum‐likelihood classifier (MLC) and the spectral angle mapper (SAM), are applied to CASI data acquired over an inland wetland complex located in southern Ontario, Canada. Application of the MLC algorithm to all bands of the CASI data produced better classification results than use of the SAM. Using the MLC, 10 classes were identified with an overall accuracy of 92%. This approach permitted differentiation between areas of shrub‐dominated vegetation communities, floating aquatic communities, emergent aquatics and shallow open water. In the SAM classification, 11 image‐derived spectral endmembers were generated. Wetland classes identified were shrub‐dominated wetlands, floating aquatic vegetation communities, shallow open water and moderately turbid shallow open water. Upland vegetation types were accurately mapped with both algorithms. Reasons why the SAM did not perform as well as the MLC in this complex environment are suggested. It is concluded that high‐resolution hyperspectral data can provide information needed by wetland managers about inland wetland plant communities and their dominant species.

A scale-wise model inversion method to retrieve canopy biophysical parameters from hyperspectral remote sensing data

Biophysical parameters, such as leaf area index (LAI) and leaf chlorophyll content, play an important role in precision agriculture management, forest ecology monitoring, and global change research. Although many efforts have been made, robust and accurate estimation of these parameters from remote sensing data is still a challenge. In this study, a scale-wise scheme was developed for inverting a physical surface model. With this scheme, the model parameters were gradually approximated with dynamic scales during iterations. This scale-wise inversion method was validated based on the coupled PROSPECT and SAIL model using simulated and hyperspectral Compact Airborne Spectrographic Imager (CASI) data over agricultural fields. It was also compared to the widely used Marquet–Levenberg (ML) optimization method. With the simulated data, the results showed that the scale-wise inversion method generated very accurate LAI and leaf chlorophyll content, with maximum errors of less than 0.03 and 0.10 µg/cm2, respectively. Using the same initial values, the errors for the ML method were large, and for some cases the iterations were not converged. The results also showed that the scale-wise method was not sensitive to noise in the data. With the hyperspectral CASI data, the retrieved LAI values were shown to have a strong correlation with the ground measurements, with a root mean square error (RMSE) between them of as low as 0.36 and a R2 of 0.91. The RMSE and R2 values for the ML method are 0.79 and 0.76, respectively.

Invasive species change detection using artificial neural networks and CASI hyperspectral imagery

For monitoring and controlling the extent and intensity of an invasive species, a direct multi-date image classification method was applied in invasive species (salt cedar) change detection in the study area of Lovelock, Nevada. With multidate Compact Airborne Spectrographic Imager (CASI) hyperspectral data sets, two types of hyperspectral CASI input data and two classifiers have been examined and compared for mapping and monitoring the salt cedar change. The two types of input data are all two-date original CASI bands and 12 principal component images (PCs) derived from the two-date CASI images. The two classifiers are an artificial neural network (ANN) and linear discriminant analysis (LDA). The experimental results indicate that (1) the direct multitemporal image classification method applied in land cover change detection is feasible either with original CASI bands or PCs, but a better accuracy was obtained from the CASI PCA transformed data; (2) with the same inputs of 12 PCs, the ANN outperforms the LDA due to the ANN's non-linear property and ability of handling data without a prerequisite of a certain distribution of the analysis data.

The delineation of tree crowns in Australian mixed species forests using hyperspectral Compact Airborne Spectrographic Imager (CASI) data

In mixed-species forests of complex structure, the delineation of tree crowns is problematic because of their varying dimensions and reflectance characteristics, the existence of several layers of canopy (including understorey), and shadowing within and between crowns. To overcome this problem, an algorithm for delineating tree crowns has been developed using eCognition Expert and hyperspectral Compact Airborne Spectrographic Imager (CASI-2) data acquired over a forested landscape near Injune, central east Queensland, Australia. The algorithm has six components: 1) the differentiation of forest, non-forest and understorey; 2) initial segmentation of the forest area and allocation of segments (objects) to larger objects associated with forest spectral types (FSTs); 3) initial identification of object maxima as seeds within these larger objects and their expansion to the edges of crowns or clusters of crowns; 4) subsequent classification-based separation of the resulting objects into crown or cluster classes; 5) further iterative splitting of the cluster classes to delineate more crowns; and 6) identification and subsequent merging of oversplit objects into crowns or clusters. In forests with a high density of individuals (e.g., regrowth), objects associated with tree clusters rather than crowns are delineated and local maxima counted to approximate density. With reference to field data, the delineation process provided accuracies > ∼70% (range 48–88%) for individuals or clusters of trees of the same species with diameter at breast height (DBH) exceeding 10 cm (senescent and dead trees excluded), with lower accuracies associated with dense stands containing several canopy layers, as many trees were obscured from the view of the CASI sensor. Although developed using 1-m spatial resolution CASI data acquired over Australian forests, the algorithm has application elsewhere and is currently being considered for integration into the Definiens product portfolio for use by the wider community.

Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data

Radiative transfer theory and modeling assumptions were applied at laboratory and field scales in order to study the link between leaf reflectance and transmittance and canopy hyper-spectral data for chlorophyll content estimation. This study was focused on 12 sites of Acer saccharum M. (sugar maple) in the Algoma Region, Canada, where field measurements, laboratory-simulation experiments, and hyper-spectral compact airborne spectrographic imager (CASI) imagery of 72 channels in the visible and near-infrared region and up to 1-m spatial resolution data were acquired in the 1997, 1998, and 1999 campaigns. A different set of 14 sites of the same species were used in 2000 for validation of methodologies. Infinite reflectance and canopy reflectance models were used to link leaf to canopy levels through radiative transfer simulation. The closed and dense (LAI>4) forest canopies of Acer saccharum M. used for this study, and the high spatial resolution reflectance data targeting crowns, allowed the use of optically thick simulation formulae and turbid-medium SAILH and MCRM canopy reflectance models for chlorophyll content estimation by scaling-up and by numerical model inversion approaches through coupling to the PROSPECT leaf radiative transfer model. Study of the merit function in the numerical inversion showed that red edge optical indices used in the minimizing function such as R/sub 750//R/sub 710/ perform better than when all single spectral reflectance channels from hyper-spectral airborne CASI data are used, and in addition, the effect of shadows and LAI variation are minimized.

Chlorophyll Fluorescence Effects on Vegetation Apparent Reflectance: II. Laboratory and Airborne Canopy-Level Measurements with Hyperspectral Data

Relationships found between Compact Airborne Spectrographic Imager (CASI) hyperspectral canopy reflectance measurements at laboratory and field levels with PAM-2000 chlorophyll fluorescence data are presented. This is a continuation of the paper where relationships at the leaf level between leaf reflectance and chlorophyll fluorescence were found and demonstrated to be consistent with theory using the Fluorescence-Reflectance-Transmittance (FRT) model. Experiments using the hyperspectral CASI sensor in the laboratory to observe a canopy of maple seedlings are performed as an intermediate step to demonstrate the link between the results at leaf-level and the CASI field canopy levels. Scene observations of the seedlings utilizing a long-pass blocking filter showed that apparent canopy reflectance in the laboratory is affected by changes in fluorescence emissions. A laboratory experiment on seedlings subjected to diurnally induced change shows the strong link between CASI canopy reflectance optical indices in the 680–690-nm region and Fv/Fm dark-adapted chlorophyll fluorescence. Stressed and healthy maple seedlings are used to demonstrate the use of optical indices calculated from the 680–690-nm spectral region to track changes in steady-state fluorescence: the curvature index R683 2/(R675·R691) and the R685/R655 ratio calculated from the canopy reflectance are related to leaf-measured Ft, Fm′ and ΔF/Fm′ steady-state features, and are in agreement with theoretical simulations using the leaf Fluorescence-Reflectance-Transmittance model. To test these findings in a field setting, airborne field hyperspectral CASI data of 2-m spatial resolution, 7.5-nm spectral resolution, and 72 channels was used, collected in deployments over 12 sites of Acer saccharum M. in the Algoma Region, Ontario (Canada) in 1997 and 1998. A field sampling campaign was carried out for biochemical contents of leaf chlorophyll and carotenoids, chlorophyll fluorescence, and leaf reflectance and transmittance. Leaf-level relationships obtained between optical indices and physiological indicators were scaled up to canopy level through canopy reflectance models using input model parameters related to the canopy structure and viewing geometry at the time of data acquisition. Results show that scaled-up optical indices in the 680–690-nm region are related to Fv/Fm chlorophyll fluorescence measured in the 20×20-m study sites. Consistency between leaf, laboratory, and field canopy hyperspectral data is shown in this and the previous paper, demonstrating the effect of fluorescence on observations of apparent vegetation reflectance.

Synoptic Measurements of Chlorophyll- a and Suspended Particulate Matter in a Transitional Zone from Polluted to Clean Seawater Utilizing Airborne Remote Sensing and Ground Measurements, Haifa Bay (SE Mediterranean)

The hyperspectral Compact Airborne Spectrographic Imager (CASI) sensor was implemented to monitor water quality in a transitional zone from polluted to clean seawater, in Haifa Bay and adjacent river estuaries, at the northern part of the Mediterranean coast of Israel. Synoptic measurements of optical data acquired from the airborne scanner were used to map chlorophyll- a (chl- a) and suspended particulate matter (SPM) concentrations in surface waters in the study area. This airborne hyperspectral scanner was found as an expedient monitoring tool for the relatively small geographic area of the current study, as it enabled to reveal the patchy distribution, and sharp concentration changes of the mapped water characteristics. The distribution of SPM concentrations in Haifa Bay was mainly dictated by the polluted riverine inputs, with concentrations between 1 and 3 mg l −1 at its seaward border and higher by more than 1 order of magnitude at the river estuaries. The chl- a concentrations mapped and measured in this survey were unusually low (<2 μg l −1) due to a long-period intermission of anthropogenic phosphate and nitrate input to the bay. SPM and chl- a spatial distributions along the lower rivers system exhibit variations which could be plausibly explained by the hydrological structure and geochemical impacts on the riverine water sources. The correlation between SPM and some particulate heavy metal concentrations was found as a useful tool for monitoring such environmental hazardous substances.