Airborne Imaging Spectrometer For Applications Research Articles

Evaluating Variable Selection and Machine Learning Algorithms for Estimating Forest Heights by Combining Lidar and Hyperspectral Data

Machine learning has been employed for various mapping and modeling tasks using input variables from different sources of remote sensing data. For feature selection involving high- spatial and spectral dimensionality data, various methods have been developed and incorporated into the machine learning framework to ensure an efficient and optimal computational process. This research aims to assess the accuracy of various feature selection and machine learning methods for estimating forest height using AISA (airborne imaging spectrometer for applications) hyperspectral bands (479 bands) and airborne light detection and ranging (lidar) height metrics (36 metrics), alone and combined. Feature selection and dimensionality reduction using Boruta (BO), principal component analysis (PCA), simulated annealing (SA), and genetic algorithm (GA) in combination with machine learning algorithms such as multivariate adaptive regression spline (MARS), extra trees (ET), support vector regression (SVR) with radial basis function, and extreme gradient boosting (XGB) with trees (XGbtree and XGBdart) and linear (XGBlin) classifiers were evaluated. The results demonstrated that the combinations of BO-XGBdart and BO-SVR delivered the best model performance for estimating tropical forest height by combining lidar and hyperspectral data, with R2 = 0.53 and RMSE = 1.7 m (18.4% of nRMSE and 0.046 m of bias) for BO-XGBdart and R2 = 0.51 and RMSE = 1.8 m (15.8% of nRMSE and −0.244 m of bias) for BO-SVR. Our study also demonstrated the effectiveness of BO for variables selection; it could reduce 95% of the data to select the 29 most important variables from the initial 516 variables from lidar metrics and hyperspectral data.

Potential use of hyperspectral data to classify forest tree species

BackgroundRemote sensing techniques and data are becoming increasingly popular in forest management, e.g. for change detection and health condition analysis. Tree species recognition is a fundamental issue in taking forest inventories, especially in carbon budget modelling. Hyperspectral imagery provides an accurate classification results for large areas based on a relatively small amount of training data.ResultsA hyperspectral image of a forest stand in north-eastern Poland taken using an AISA (Airborne Imaging Spectrometer for Application) Eagle camera was transformed to extract the most valuable spectral differences and was classified into seven tree types (birch, European beech, oak, hornbeam, European larch, Scots pine, and Norway spruce) using nine classification algorithms. The highest overall accuracy and kappa coefficient were 90.3% and 0.9 respectively using three minimum noise fraction bands and maximum likelihood classifier.ConclusionsHyperspectral imaging of forests can be used to classify major forest tree species with a good degree of accuracy. It is time-efficient and user-friendly; however, the data and software required means that this approach is still expensive at present.

Surface soil clay content mapping at large scales using multispectral (VNIR–SWIR) ASTER data

ABSTRACT The potential of Visible Near-Infrared and Short-Wave Infrared (VNIR–SWIR, 400 nm–2500 nm) hyperspectral imagery for use in multivariate approaches and geostatistical techniques for mapping topsoil properties has been previously demonstrated. However, the use of VNIR–SWIR hyperspectral imagery remains costly, which limits the spatial scales over which it can be applied. This paper aims to evaluate the potential for substituting the more accessible Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) VNIR–SWIR multispectral data for hyperspectral imagery in mapping surface soil clay contents. This study used ASTER multispectral data (nine bands in the VNIR–SWIR spectral domain) acquired over the Cap-Bon region in Tunisia (2000 km2) and 262 surface soil samples collected within the ASTER scene that were subjected to laboratory analysis of the clay fraction (soil particles less than 2 µm). The approach followed two steps: i) estimation of surface soil clay contents for bare soil areas using a Multiple Linear Regression (MLR) model built from the 9 ASTER VNIR–SWIR bands and ii) spatial interpolation (co-kriging) of the soil sampling of measured points and the ASTER-estimates over the whole study area. The MLR model for estimating clay contents using ASTER multispectral data performed correctly ( = 0.60). In addition, this performance is only slightly lower than that obtained using hyperspectral imagery (specifically, an Airborne Imaging Spectrometer for Applications (AISA-DUAL) dual hyperspectral sensor) in a previous study. Moreover, the co-kriging process appeared to yield encouraging results for capturing the large range of variability of clay content values, although it was not able to represent the short scale variability ( = 0.43). Finally, the ASTER multispectral data, despite being underused in the mapping of soil properties, may open up new ways to perform more extensive mapping of surface soil properties in semi-arid contexts characterized by extensive bare and dry soil surfaces.

Crown-level tree species classification from AISA hyperspectral imagery using an innovative pixel-weighting approach

Crown-level tree species classification is a challenging task due to the spectral similarity among different tree species. Shadow, underlying objects, and other materials within a crown may decrease the purity of extracted crown spectra and further reduce classification accuracy. To address this problem, an innovative pixel-weighting approach was developed for tree species classification at the crown level. The method utilized high density discrete LiDAR data for individual tree delineation and Airborne Imaging Spectrometer for Applications (AISA) hyperspectral imagery for pure crown-scale spectra extraction. Specifically, three steps were included: 1) individual tree identification using LiDAR data, 2) pixel-weighted representative crown spectra calculation using hyperspectral imagery, with which pixel-based illuminated-leaf fractions estimated using a linear spectral mixture analysis (LSMA) were employed as weighted factors, and 3) representative spectra based tree species classification was performed through applying a support vector machine (SVM) approach. Analysis of results suggests that the developed pixel-weighting approach (OA=82.12%, Kc=0.74) performed better than treetop-based (OA=70.86%, Kc=0.58) and pixel-majority methods (OA=72.26, Kc=0.62) in terms of classification accuracy. McNemar tests indicated the differences in accuracy between pixel-weighting and treetop-based approaches as well as that between pixel-weighting and pixel-majority approaches were statistically significant.

Short-term changes of remote sensing reflectancein a shallow-water environment: observations from repeated airborne hyperspectral measurements

ABSTRACTAn atmospheric correction algorithm has been developed for the Airborne Imaging Spectrometer for Applications (AISA) imagery over optically shallow waters in Sugarloaf Key of the Florida Keys. The AISA data were collected repeatedly during several days in May 2012, October 2012, and May 2013. Non-zero near-infrared (NIR) remote-sensing reflectance (Rrs) was accounted for through iterations, based on the relationship of field-measured Rrs between the NIR and red wavelengths. Validation showed mean ratios of 0.94–1.002 between AISA-retrieved and in situ Rrs in the blue to red wavelengths, with uncertainties generally <0.003 sr–1. Such an approach led to observations of short-term changes in AISA-retrieved Rrs from repeated measurements over waters with bottom types of seagrass meadow, sand, and patch reef. Some of these changes are larger than twofold the Rrs uncertainties from AISA retrievals, therefore representing statistically significant changes that can be well observed from airborne measurements. Through radiative transfer modelling, we demonstrated that short-term Rrs changes within 1 hour resulted primarily from sediment resuspension, while tides played a relatively minor role due to the small variation in tidal heights. A sensitivity analysis indicated that although Rrs generally increases with decreasing tide height but increasing suspended sediments, more changes were observed over sandy bottom than over seagrass. The case study suggests that repeated airborne measurements may be used to study short-term changes in shallow-water environments, and such a capacity may be enhanced with future geostationary satellite missions specifically designed to observe coastal ecosystems.

Spectral Unmixing of Forest Crown Components at Close Range, Airborne and Simulated Sentinel-2 and EnMAP Spectral Imaging Scale

Forest biochemical and biophysical variables and their spatial and temporal distribution are essential inputs to process-orientated ecosystem models. To provide this information, imaging spectroscopy appears to be a promising tool. In this context, the present study investigates the potential of spectral unmixing to derive sub-pixel crown component fractions in a temperate deciduous forest ecosystem. However, the high proportion of foliage in this complex vegetation structure leads to the problem of saturation effects, when applying broadband vegetation indices. This study illustrates that multiple endmember spectral mixture analysis (MESMA) can contribute to overcoming this challenge. Reference fractional abundances, as well as spectral measurements of the canopy components, could be precisely determined from a crane measurement platform situated in a deciduous forest in North-East Germany. In contrast to most other studies, which only use leaf and soil endmembers, this experimental setup allowed for the inclusion of a bark endmember for the unmixing of components within the canopy. This study demonstrates that the inclusion of additional endmembers markedly improves the accuracy. A mean absolute error of 7.9% could be achieved for the fractional occurrence of the leaf endmember and 5.9% for the bark endmember. In order to evaluate the results of this field-based study for airborne and satellite-based remote sensing applications, a transfer to Airborne Imaging Spectrometer for Applications (AISA) and simulated Environmental Mapping and Analysis Program (EnMAP) and Sentinel-2 imagery was carried out. All sensors were capable of unmixing crown components with a mean absolute error ranging between 3% and 21%.

Atmospheric Correction of AISA Measurements Over the Florida Keys Optically Shallow Waters: Challenges in Radiometric Calibration and Aerosol Selection

An Airborne Imaging Spectrometer for Applications (AISA) hyperspectral imager was deployed on a manned aircraft flown at 1305-m altitude to collect data over optically shallow waters in the Florida Keys with the ultimate goal of mapping water quality and benthic habitats. As a first step, we developed a practical atmospheric correction (AC) approach to derive surface remote-sensing reflectance ( ${{\bm{R}}_{{\bm{rs}}}}$ ) from AISA measurements using radiative transfer simulations and constraints obtained from field spectral ${{\bm{R}}_{{\bm{rs}}}}$ measurements. Unlike previously published method, the AC approach removes the surface Fresnel reflection and accounts for aircraft altitude and nonzero near-infrared (NIR) reflectance through iteration over the pre-established look-up tables (LUTs) based on MODTRAN calculations. Simulations and comparison with concurrent in situ ${{\bm{R}}_{{\bm{rs}}}}$ measurements show the feasibility of the approach in deriving surface ${{\bm{R}}_{{\bm{rs}}}}$ with acceptable uncertainties. The possibility of errors in the radiometric calibration of AISA is demonstrated, although a definitive assessment cannot be made due to lack of enough concurrent in situ measurements. The need for noise reduction and the difficulty in carrying out a vicarious calibration are also discussed to help advance the design of future AISA missions.

Automatic extraction of optimal endmembers from airborne hyperspectral imagery using iterative error analysis (IEA) and spectral discrimination measurements.

Pure surface materials denoted by endmembers play an important role in hyperspectral processing in various fields. Many endmember extraction algorithms (EEAs) have been proposed to find appropriate endmember sets. Most studies involving the automatic extraction of appropriate endmembers without a priori information have focused on N-FINDR. Although there are many different versions of N-FINDR algorithms, computational complexity issues still remain and these algorithms cannot consider the case where spectrally mixed materials are extracted as final endmembers. A sequential endmember extraction-based algorithm may be more effective when the number of endmembers to be extracted is unknown. In this study, we propose a simple but accurate method to automatically determine the optimal endmembers using such a method. The proposed method consists of three steps for determining the proper number of endmembers and for removing endmembers that are repeated or contain mixed signatures using the Root Mean Square Error (RMSE) images obtained from Iterative Error Analysis (IEA) and spectral discrimination measurements. A synthetic hyperpsectral image and two different airborne images such as Airborne Imaging Spectrometer for Application (AISA) and Compact Airborne Spectrographic Imager (CASI) data were tested using the proposed method, and our experimental results indicate that the final endmember set contained all of the distinct signatures without redundant endmembers and errors from mixed materials.

Remote estimation of cyanobacteria-dominance in inland waters

Remote sensing of the concentration ratio of phycocyanin (PC) to chlorophyll a (Chl-a) is important for water management, as it provides critical knowledge regarding the phytoplankton community. Using the observed in situ datasets, a simple empirical model was developed to estimate PC:Chl-a based on the band ratio index of Rrs(550)/Rrs(620) (Rrs: remote sensing reflectance) (R2 = 0.84; RMSE = 1.01). This simple model exhibited relatively high validation accuracy using the independent validation dataset. In addition, the model can be successfully applied to AISA (Airborne Imaging Spectrometer for Application) image data, indicating the practicality of the developed model for determining the dominance of cyanobacteria among the total phytoplankton from airborne image data in inland waters. However, the present model cannot be used directly to estimate PC:Chl-a in extremely turbid waters with total suspended matter (TSM) concentrations higher than 25 mg/l. For these waters, the model parameters may require local optimization according to the conditions of the water under analysis. The findings of this study indicate that our proposed model is able to detect the dominance of cyanobacteria among the phytoplankton in inland waters, where the turbidity is not too much high. This study improves our understanding of the species composition of phytoplankton biomass in optically complex inland bodies of water.

Impact of discrete wavelet transform on discriminating airborne hyperspectral tropical rainforest tree species

National University of Uzbekistan, Institute of Mathematics, 100095 Tashkent, UzbekistanAbstract. Discriminating tropical rainforest tree species is still a challenging task due to a vari-ety of species with high spectral similarityand duetoverylimited studies conducted in this area.Weareinvestigatingtheeffectofdiscretewavelettransform(DWT)onenhancingdiscriminationof tropical rainforest tree species. For this purpose, airborne imaging spectrometer for applica-tions (AISA) airborne hyperspectral data obtained from Malaysian’s rainforest area are used; sixtree species were selected from the study area. For comparison purposes, the performance ofDWT is compared with the original reflectance, first, and second derivative spectra by usingfive different spectral measure techniques. An overall discrimination accuracy of ∼74% isobtained with DWT using Euclidean distance, which outperforms the original reflectanceand first and second derivatives by ∼16.6, 11.9, and 22.1%, respectively. The results suggesta significant impact of the DWTapproach on improving tropical rainforest tree species discrimi-nation.

An inversion model for deriving inherent optical properties of inland waters: Establishment, validation and application

The inherent optical properties (IOPs) of natural waters are the most significant factors affecting light propagation within water columns, and thus play indispensable roles on estimation of aquatic biomass, primary production, and carbon pools. Despite its importance, no IOPs retrieval model was specifically developed for inland water bodies, although significant efforts were made on oceanic inversion models. In addition, for inland waters, an IOPs-based model is often preferred for estimating chlorophyll-a (Chl-a) concentration, an application of IOPs, over empirical and some semi-empirical algorithms. Then developing a model for estimating both IOPs and Chl-a is of significance for understanding the bio-optical properties and occurrence of algal blooms in eutrophic reservoirs, lakes and estuaries. In this paper, an IOPs Inversion Model of Inland Waters (IIMIW) for deriving natural water IOPs and estimating Chl-a is proposed and validated. The results indicate that this model can be used to accurately retrieve absorption coefficients at 443nm and 665nm with R2=0.8347 and R2=0.7550 respectively for Indiana study sites, and to estimate Chl-a from the derived absorption coefficients at high accuracies (R2=0.9292 and a mean relative error 21.65%) with samples collected from eight different study sites in the world and in different seasons. The model was also applied on Airborne Imaging Spectrometer for Application (AISA) images to map IOPs and Chl-a. Through validation by in situ measured Chl-a, results directly show that IIMIW can predict Chl-a with good accuracy even using the AISA bands, to as well indirectly prove that non-water absorption coefficients are retrieved accurately, at least within red and near-infrared region. Further biogeochemical information can be derived from these maps as well. These promising mapping results reveal possible remote routine surveillance of bio-optical states of inland waters.

Characterization of Forest Crops with a Range of Nutrient and Water Treatments Using AISA Hyperspectral Imagery

This research examined the utility of Airborne Imaging Spectrometer for Applications (AISA) hyperspectral imagery for estimating the biomass of three forest crops—sycamore, sweetgum, and loblolly pine—planted in experimental plots with a range of fertilization and irrigation treatments on the Savannah River Site near Aiken, South Carolina. Both vegetation index (VI) and red-edge positioning (REP) approaches were investigated to estimate the biomass associated with 12 treatment conditions. The optimum band pairs using the VI approach for biomass estimation were located mainly in the visible, NIR, and/or water absorption region around 970 nm, depending on the treatment conditions. Both the selected hyperspectral variables (i.e., VI and REP) resulted in good performance for biomass estimation for a range of treatment conditions except for those associated with loblolly pine. The hyperspectral variables were also examined to determine if they were able to identify the optimum fertilization treatment level. For the fertilization treatment conditions with good biomass estimation (R 20.9), their optimum treatment levels were successfully identified.

Detection of stressed oil palms from an airborne sensor using optimized spectral indices

Existing vegetation indices and red-edge techniques have been widely used for the assessment of vegetation status and vegetation health from remote-sensing instruments. This study proposed and applied optimized Airborne Imaging Spectrometer for Applications (AISA) airborne hyperspectral indices in assessing and mapping stressed oil palm trees. Six vegetation indices, four red-edge techniques, a standard supervised classifier and three optimized AISA spectral indices were compared in mapping diseased oil palms using AISA airborne hyperspectral imagery. The optimized AISA spectral indices algorithms used newly defined reflectance values at wavelength locations of 734 nm (near-infrared (NIR)) and 616 nm (red). The selection of these two bands was based on laboratory statistical analysis using field spectroradiometer reflectance data. These two bands were then applied to the AISA airborne hyperspectral imagery using the three optimized algorithms for AISA data. The newly formulated AISA hyperspectral indices were D2 = R 616/R 734, normalized difference vegetation index a (NDVIa) = (R 734 – R 616)/(R 734 + R 616) and transformed vegetation index a (TVIa) = ((NDVIa + 0.5)/(abs (NDVIa + 0.5)) × [abs (NDVIa + 0.5)]1/2. The classification results from the optimized AISA hyperspectral indices were compared with the other techniques and the optimized AISA spectral indices obtained the highest overall accuracy. D2 and NDVIa obtained 86% of overall accuracy followed by TVIa with 84% of overall accuracy.

Estimation of chlorophyll- a concentration in turbid productive waters using airborne hyperspectral data

Algorithms based on red and near infra-red (NIR) reflectances measured using field spectrometers have been previously shown to yield accurate estimates of chlorophyll- a concentration in turbid productive waters, irrespective of variations in the bio-optical characteristics of water. The objective of this study was to investigate the performance of NIR-red models when applied to multi-temporal airborne reflectance data acquired by the hyperspectral sensor, Airborne Imaging Spectrometer for Applications (AISA), with non-uniform atmospheric effects across the dates of data acquisition. The results demonstrated the capability of the NIR-red models to capture the spatial distribution of chlorophyll- a in surface waters without the need for atmospheric correction. However, the variable atmospheric effects did affect the accuracy of chlorophyll- a retrieval. Two atmospheric correction procedures, namely, Fast Line-of-sight Atmospheric Adjustment of Spectral Hypercubes (FLAASH) and QUick Atmospheric Correction (QUAC), were applied to AISA data and their results were compared. QUAC produced a robust atmospheric correction, which led to NIR-red algorithms that were able to accurately estimate chlorophyll- a concentration, with a root mean square error of 5.54 mg m −3 for chlorophyll- a concentrations in the range 2.27–81.17 mg m −3.

Large-scale mapping of the riverbanks, mud flats and salt marshes of the Scheldt basin, using airborne imaging spectroscopy and LiDAR

For maintaining the tidal waterways in the Scheldt basin, including the rivers Rupel and Durme and a large part of the Nete catchment, and for ecological monitoring of the mud flats, salt marshes and riverbank vegetation, the Flemish government needs detailed maps of these rivers and their bank structures. These maps indicate not only vegetation types, plant associations and sediment types but also hard structures, such as quays, locks, sluices and roads. Different remote sensing techniques were used to collect the data necessary to produce the required detailed maps. During the months of July and August 2007 an airborne flight campaign took place to collect hyperspectral and LiDAR data of the Scheldt basin and the Nete catchments. These rivers have a total length of about 240 km. The Airborne Imaging Spectrometer for Applications (AISA) Eagle sensor acquired hyperspectral data in 32 spectral bands covering the visible/near-infrared (VIS/NIR) part of the electromagnetic spectrum with a ground resolution of 1 m. A multiple binary classification algorithm based on Fisher's linear discriminant analysis (LDA) was used to map the salt marshes and riverbank vegetation. Ground truth information, that is vegetation and sediment types, together with their geographical locations collected around the time of the flight campaign, was used to train the classifier in the later classification step. Laser scanning was performed using the Riegl LMS-Q560. The LiDAR dataset obtained had a resolution of at least 1 point per m2 and was used to produce a digital elevation model (DEM) that contains all elements of the terrain. From this DEM a digital terrain model (DTM) was derived by applying appropriate filtering techniques. The elevation models were used primarily to derive information on the height, slope and aspect of the banks and dikes, but they also served as expert knowledge in the classification of the mud flats and bank vegetation. Overall, this work illustrates how airborne hyperspectral and LiDAR data can be used to derive highly detailed maps of the sediments, vegetation and hard structures along tidal rivers in large river basins. It also shows how large datasets can be handled in an expert system, in combination with different classification techniques, to produce the required result and accuracy.

Operational Atmospheric Correction for Imaging Spectrometers Accounting for the Smile Effect

Hyperspectral pushbroom imagers are affected by a number of artifacts, such as pixel nonuniformity, spectral smile, and keystone. These have to be taken into account during system correction, orthorectification, or atmospheric correction, as performed in processing and archiving facilities (PAFs). This contribution is presenting an efficient and accurate smile correction method integrated in the atmospheric correction. The proposed technique will be used in the PAF of the German hyperspectral Environmental Mapping and Analysis Program mission. The spectral smile shift across the detector array is parametrized with a fourth-order polynomial function for each channel based on the instrument optical design model or measured laboratory data. Alternatively, spectral smile shifts can be calculated from image data using channels in atmospheric absorption regions. The concept for the time-optimized processor is outlined, and the results are presented for simulated EnMAP data and existing pushbroom imagery [HYPERION, AISA (Airborne Imaging Spectrometer for Applications), and HYSPEX (Hyperspectral Camera)].

Imaging spectroscopy in soil-water based site suitability assessment for artificial regeneration to Scots pine

In a humid northern boreal climate, the success rate of artificial regeneration to Scots pine ( Pinus sylvestris L.) can be improved by including a soil water content (SWC) based assessment of site suitability in the reforestation planning process. This paper introduces an application of airborne visible-near-infrared imaging spectroscopic data to identify suitable subregions of forest compartments for the low SWC-tolerant Scots pine. The spatial patterns of understorey plant species communities, recorded by the AISA (Airborne Imaging Spectrometer for Applications) sensor, were demonstrated to be dependant on the underlying SWC. According to the nonmetric multidimensional scaling and correlation results twelve understorey species were found to be most abundant on sites with high soil SWCs. The abundance of bare soil, rocks and abundance of more than ten species indicated low soil SWCs. The spatial patterns of understorey are attributed to time-stability of the underlying SWC patterns. A supervised artificial neural network (radial basis functional link network, probabilistic neural network) approach was taken to classify AISA imaging spectrometer data with dielectric (as a measure volumetric SWC) ground referencing into regimes suitable and unsuitable for Scots pine. The accuracy assessment with receiver operating characteristics curves demonstrated a maximum of 74.1% area under the curve values which indicated moderate success of the NN modelling. The results signified the importance of the training set’s quality, adequate quantity (>2.43 points/ha) and NN algorithm selection over the NN algorithm training parameter optimization to perfection. This methodology for the analysis of site suitability of Scots pine can be recommended, especially when artificial regeneration of former mixed wood Norway spruce ( Picea abies L. Karst) - downy birch ( Betula pubenscens Ehrh.) stands is being considered, so that artificially regenerated areas to Scots pine can be optimized for forestry purposes.

Assessing the utility of airborne hyperspectral and LiDAR data for species distribution mapping in the coastal Pacific Northwest, Canada

To effectively manage forested ecosystems an accurate characterization of species distribution is required. In this study we assess the utility of hyperspectral Airborne Imaging Spectrometer for Applications (AISA) imagery and small footprint discrete return Light Detection and Ranging (LiDAR) data for mapping 11 tree species in and around the Gulf Islands National Park Reserve, in coastal South-western Canada. Using hyperspectral imagery yielded producer's and user's accuracies for most species ranging from > 52–95.4 and > 63–87.8%, respectively. For species dominated by definable growth stages, pixel-level fusion of hyperspectral imagery with LiDAR-derived height and volumetric canopy profile data increased both producer's (+ 5.1–11.6%) and user's (+ 8.4–18.8%) accuracies. McNemar's tests confirmed that improvements in overall accuracies associated with the inclusion of LiDAR-derived structural information were statistically significant ( p < 0.05). This methodology establishes a specific framework for mapping key species with greater detail and accuracy then is possible using conventional approaches (i.e., aerial photograph interpretation), or either technology on its own. Furthermore, in the study area, acquisition and processing costs were lower than a conventional aerial photograph interpretation campaign, making hyperspectral/LiDAR fusion a viable replacement technology.

Using hyperspectral remote sensing to estimate chlorophyll‐a and phycocyanin in a mesotrophic reservoir

This research estimates phytoplankton pigment concentrations (chlorophyll‐a (chl‐a) and phycocyanin (PC)) from hyperspectral Airborne Imaging Spectrometer for Applications (AISA) imagery. AISA images were acquired for a meso‐eutrophic reservoir in Central Indiana, USA. Concurrent with the airborne image acquisition, in situ water samples and reflectances were collected. The water samples were subsequently analysed for pigment concentrations, and in situ measured reflectance spectra were used for calibrating the AISA images. Spectral indices, derived from the AISA reflectance spectra, were regressed against the measured pigment concentrations to derive algorithms for estimating chl‐a and PC. The relationship between the pigment concentrations and the spectral indices were analysed and evaluated. The results indicate that the highest correlation occurred between chl‐a and a near‐infrared to red ratio (coefficient of determination R 2 = 0.78) and between PC and the reflectance trough at 628 nm (R 2 = 0.80). The relationship between PC and the reflectance at 628 nm provides an approach to the estimation of cyanobacteria concentration from hyperspectral imagery, which facilitates water‐quality authorities or management agencies in making well‐informed management decisions.

Hyperspectral remote sensing of cyanobacterial pigments as indicators for cell populations and toxins in eutrophic lakes

The growth of mass populations of toxin-producing cyanobacteria is a serious concern for the ecological status of inland waterbodies and for human and animal health. In this study we examined the performance of four semi-analytical algorithms for the retrieval of chlorophyll a (Chl a) and phycocyanin (C-PC) from data acquired by the Compact Airborne Spectrographic Imager-2 (CASI-2) and the Airborne Imaging Spectrometer for Applications (AISA) Eagle sensor. The retrieval accuracies of the semi-analytical models were compared to those returned by optimally calibrated empirical band-ratio algorithms. The best-performing algorithm for the retrieval of Chl a was an empirical band-ratio model based on a quadratic function of the ratio of reflectance at 710 and 670 nm ( R 2 = 0.832; RMSE = 29.8%). However, this model only provided a marginally better retrieval than the best semi-analytical algorithm. The best-performing model for the retrieval of C-PC was a semi-analytical nested band-ratio model ( R 2 = 0.984; RMSE = 3.98 mg m − 3). The concentrations of C-PC retrieved using the semi-analytical model were correlated with cyanobacterial cell numbers ( R 2 = 0.380) and the particulate and total (particulate plus dissolved) pools of microcystins ( R 2 = 0.858 and 0.896 respectively). Importantly, both the empirical and semi-analytical algorithms were able to retrieve the concentration of C-PC at cyanobacterial cell concentrations below current warning thresholds for cyanobacteria in waterbodies. This demonstrates the potential of remote sensing to contribute to early-warning detection and monitoring of cyanobacterial blooms for human health protection at regional and global scales.