Compact Airborne Spectral Imager Research Articles

Evaluating the portability of satellite derived chlorophyll-a algorithms for temperate inland lakes using airborne hyperspectral imagery and dense surface observations

This study evaluated the performances of twenty-nine algorithms that use satellite-based spectral imager data to derive estimates of chlorophyll-a concentrations that, in turn, can be used as an indicator of the general status of algal cell densities and the potential for a harmful algal bloom (HAB). The performance assessment was based on making relative comparisons between two temperate inland lakes: Harsha Lake (7.99 km2) in Southwest Ohio and Taylorsville Lake (11.88 km2) in central Kentucky. Of interest was identifying algorithm-imager combinations that had high correlation with coincident chlorophyll-a surface observations for both lakes, as this suggests portability for regional HAB monitoring. The spectral data utilized to estimate surface water chlorophyll-a concentrations were derived from the airborne Compact Airborne Spectral Imager (CASI) 1500 hyperspectral imager, that was then used to derive synthetic versions of currently operational satellite-based imagers using spatial resampling and spectral binning. The synthetic data mimics the configurations of spectral imagers on current satellites in earth’s orbit including, WorldView-2/3, Sentinel-2, Landsat-8, Moderate-resolution Imaging Spectroradiometer (MODIS), and Medium Resolution Imaging Spectrometer (MERIS). High correlations were found between the direct measurement and the imagery-estimated chlorophyll-a concentrations at both lakes. The results determined that eleven out of the twenty-nine algorithms were considered portable, with r2 values greater than 0.5 for both lakes. Even though the two lakes are different in terms of background water quality, size and shape, with Taylorsville being generally less impaired, larger, but much narrower throughout, the results support the portability of utilizing a suite of certain algorithms across multiple sensors to detect potential algal blooms through the use of chlorophyll-a as a proxy. Furthermore, the strong performance of the Sentinel-2 algorithms is exceptionally promising, due to the recent launch of the second satellite in the constellation, which will provide higher temporal resolution for temperate inland water bodies. Additionally, scripts were written for the open-source statistical software R that automate much of the spectral data processing steps. This allows for the simultaneous consideration of numerous algorithms across multiple imagers over an expedited time frame for the near real-time monitoring required for detecting algal blooms and mitigating their adverse impacts.

Chlorophyll content mapping of urban vegetation in the city of Valencia based on the hyperspectral NAOC index

Spatially distributed chlorophyll content of urban vegetation provides an important indicator of a plant's health status, which might depend on the habitat quality of the specific urban environment. Recent advances in optical remote sensing led to improved methodologies to monitor vegetation properties. The hyperspectral index NAOC (Normalized Area Over reflectance Curve) is one of these new tools that can be used for mapping chlorophyll content. In this paper we present the work done to quantify vegetation chlorophyll content over the city of Valencia (Spain) based on chlorophyll measurements of four representative tree species: the London plane tree (Platanus x. acerifolia), the Canarian date palm (Phoenix canariensis), the European nettle tree (Celtis australis) and the white mulberry (Morus alba). Measurements were acquired during the summer of 2011, in a field campaign in which for 320 leaf samples, chlorophyll content was measured both in the laboratory and by using a SPAD-502 chlorophyll meter. Both methods were correlated (R2>0.86), using best fit power type functions. During the field campaign an aircraft with a CASI (Compact Airborne Spectral Imager) hyperspectral sensor onboard overflew the city obtaining imagery with a spatial resolution of ∼1m suitable to identify individual urban trees. From the CASI data the NAOC index was calculated and linked with the laboratory chlorophyll content measurements. This led to a detailed chlorophyll content map with a RMSE of 15μgcm−2. Chlorophyll map analysis at the individual crown level suggests the applicability to identify trees with lowered chlorophyll content due to a suboptimal habitat quality.

Mapping functional vegetation abundance in a coastal dune environment using a combination of LSMA and MLC: a case study at Kenfig NNR, Wales

The interactions between wind-blown sand transport, pioneer vegetation and succession vegetation in coastal dune fields play an important role in landform development and determine the balance between stabilization and re-activation of these aeolian landscapes. High-resolution mapping of vegetation communities across a dune field – in particular, the mixture of different functional plant types such as pioneer versus succession species – is critical for the calculation of landscape metrics that enable a rigorous and quantitative testing of numerical simulation models, as well as for informing targeted land management actions that maintain biodiversity and ecological functions. This article presents a method of using maximum likelihood classification (MLC) to inform linear spectral mixture analysis (LSMA) for quantifying sub-pixel abundance of sand, pioneer and succession vegetation in a coastal dune area in Wales, from archived imagery obtained from the Compact Airborne Spectral Imager (CASI) in 1997. LSMA is first applied to derive sub-pixel fractional abundances of soil, green vegetation (GV) and non-GV elements. An MLC is developed separately for automatically identifying pixels believed to contain a mixture of the two functional vegetation types, and this then serves as a basis for applying a transform that interprets the LSMA results in terms of sand and pioneer and succession vegetation communities. Very high resolution (0.1 m pixels) colour aerial photography, taken simultaneously with the CASI data, and field survey data from both 1997 and 2009 were used to aid the MLC and the transform algorithm and were also used for a limited validation exercise. The LSMA abundance maps achieved an overall accuracy of 82.7% (kappa coefficient κ = 0.78). The reduced MLC vegetation maps (four classes) achieved an overall accuracy of 98.2% (kappa coefficient κ = 0.96). Although it was not possible to validate the final pioneer and succession vegetation abundance maps quantitatively, a qualitative review of the results for selected locations within the dune field indicates the viability of applying MLC to help direct a transformation of LSMA abundance maps into functional vegetation abundance data.

Correction of reflectance anisotropy: a multi-sensor approach

Quantitative mapping by means of hyperspectral remote sensing (HRS) can be hampered by reflectance anisotropy emerging in large field of view (FOV) optics, and may contain spectral radiometric distortions. This paper presents an algorithm for the rectification of reflectance anisotropy for rough terrain. A new method is offered for correction of radiometric bias caused by topography and sensing geometry. The correction of HRS data of lawn grass is demonstrated, and the method is tested on a large park area. To record elevation we used airborne laser scanning data to obtain a digital surface model (DSM). The Compact Airborne Spectral Imager (CASI) recorded reflectance of the same area. Anisotropy of reflectance was recorded by a laboratory spectro-goniometer. An analysis of the effect of correction on the normalized difference vegetation index (NDVI) shows that even moderate slopes, medium sensor FOV and high illumination conditions will result in reflectance anisotropy. Further analysis shows a clear inverse relationship between sensitivity of interpretation and spatial or spectral resolutions. We conclude with an outlook on the utilization of this method among other pre-processing tasks.

Classification of boreal macrotidal littoral zone habitats in the Gulf of Maine: comparison of IKONOS and CASI multispectral imagery

Increasingly, remote sensing has become a useful tool for mapping and measuring terrestrial and aquatic environments. Advances in the spatial and spectral resolution of satellite-borne sensors have allowed affordable investigations of littoral macrotidal coastal systems that previously required more costly aircraft-based imagery. In this communication, we compare the results from analysis of a 4 m spatial resolution, multispectral IKONOS satellite image of the intertidal habitats of Islesboro, Maine, USA with that of an aerial compact airborne spectral imager survey of the same regions captured 4 years earlier. There was 72% agreement between the surveys in spite of the temporal gaps between the images. Accuracy varied by habitat class and the perceived error can be assigned to temporal and definitional issues rather than basic acquisition and analytic protocols. Most of the error can be explained by: (1) inadequacy of training sites, (2) temporal variations and (3) class definitions. We conclude that IKONOS imagery provides sufficient spatial and spectral resolution to map and monitor diverse intertidal habitats as found in the macrotidal Gulf of Maine.

Mapping river floodplain ecotopes by segmentation of spectral (CASI) and structural (LiDAR) remote sensing data

AbstractFloodplain land cover maps are important tools for river management and ecological assessment. These maps have to be revised regularly due to the dynamic nature of floodplains. Automation of the mapping process cuts the map processing time and can increase overall accuracy. Manual delineation and classification of vegetation is based on colour, contrasts (texture) and often‐stereoscopic view of aerial images. In this study, this is mimicked by the simultaneous use of both structural light detection and ranging (LiDAR) and compact airborne spectral imager (CASI) remote sensing data in an image segmentation routine fractal net evolution approach (FNEA). The segmentation results are tested against manually delineated ecotopes. Ecotope delineation improves when LiDAR and CASI are used simultaneously; the combination significantly lowers the number of segmented objects needed to accurately map ecotopes. Overall map accuracy of the LiDAR and CASI combination is 8–19% higher than single CASI and LiDAR, respectively. Copyright © 2009 John Wiley & Sons, Ltd.

Spectral characteristics of plant communities from salt marshes: A case study from Chongming Dongtan, Yangtze estuary, China

The spectral reflectance of recently formed salt marshes at the mouth of the Yangtze River, which are undergoing invasion by Spartina alterniflora, were assessed to determine the potential utility of remotely sensed data in assessing future invasion and changes in species composition. Following a review of published research on remote sensing of salt marshes, 53 locations along three transects were sampled for paired data on plant species composition and spectral reflectance using a FieldSpec™ Pro JR Field Portable Spectroradiometer. Spectral data were processed concerning reflectance, and the averaged reflectance values for each sample were reanalysed to correspond to a 12-waveband bandset of the Compact Airborne Spectral Imager. The spectral data were summarised using principal components analysis (PCA) and the relationships between the vegetation composition, and the PCA axes of spectral data were examined. The first PCA axis of the reflectance data showed a strong correlation with variability in near infrared reflectance and ‘brightness’, while the second axis was correlated with visible reflectance and ‘greenness’. Total vegetation cover, vegetation height, and mudflat cover were all significantly related to the first axis. The implications of this in terms of the ability of remote sensing to distinguish the various salt marsh species and in particular the invasive species S. alterniflora were discussed. Major differences in species with various physiognomies could be recognised but problems occurred in separating early colonising S. alterniflora from other species at that stage. Further work using multi-seasonal hyperspectral data might assist in solving these problems.

Mapping mixed vegetation communities in salt marshes using airborne spectral data

The aim of this study is to evaluate a new neural network classifier using spectrally sampled image data to map mixed halophytic vegetation in tidal environments. The work is based on the concept of vegetation communities, mixtures of several species, characteristic of salt marshes. The study site is the Venice lagoon, and the material available is a spectrally sampled Compact Airborne Spectral Imager (CASI) image, in conjunction with ground truth for precise characterization of vegetation communities. Detailed observations of vegetation species and of their fractional abundance were collected for 36 Regions Of Interest (ROI): such field polygons are used for classification training and accuracy assessment. To select the most significant spectral channels, the Spectral Reconstruction method was applied to the image data: a set of 6 bands was selected as optimal for classification, out of the 15 available. The spatial heterogeneity of salt-marsh vegetation is significant and even at the spatial resolution of the airborne CASI image data, mixed pixels are observed. The Vegetation Community based Neural Network Classifier (VCNNC) is introduced to cope with a situation where no pure pixels exist, and was applied to the set of 6 selected bands. Both quantitative and qualitative comparisons of classification results of VCNNC with those of conventional Neural Network Classifier (NNC), trained and assessed on exactly the same data sets, shows that VCNNC's accuracy is substantially higher (≈ 91%) than that of NNC (≈ 84%), while the Kappa coefficient is 0.87 for VCNNC and 0.75 for the NNC method.

Multivariate texture‐based segmentation of remotely sensed imagery for extraction of objects and their uncertainty

In this study, a segmentation procedure is proposed, based on grey‐level and multivariate texture to extract spatial objects from an image scene. Object uncertainty was quantified to identify transitions zones of objects with indeterminate boundaries. The Local Binary Pattern (LBP) operator, modelling texture, was integrated into a hierarchical splitting segmentation to identify homogeneous texture regions in an image. We proposed a multivariate extension of the standard univariate LBP operator to describe colour texture. The paper is illustrated with two case studies. The first considers an image with a composite of texture regions. The two LBP operators provided good segmentation results on both grey‐scale and colour textures, depicted by accuracy values of 96% and 98%, respectively. The second case study involved segmentation of coastal land cover objects from a multi‐spectral Compact Airborne Spectral Imager (CASI) image, of a coastal area in the UK. Segmentation based on the univariate LBP measure provided unsatisfactory segmentation results from a single CASI band (70% accuracy). A multivariate LBP‐based segmentation of three CASI bands improved segmentation results considerably (77% accuracy). Uncertainty values for object building blocks provided valuable information for identification of object transition zones. We conclude that the (multivariate) LBP texture model in combination with a hierarchical splitting segmentation framework is suitable for identifying objects and for quantifying their uncertainty.

Artificial neural networks to predict corn yield from Compact Airborne Spectrographic Imager data

In the light of recent advances in spectral imaging technology, highly flexible modeling methods must be developed to estimate various soil and crop parameters for precision farming from airborne hyperspectral imagery. The potential of artificial neural networks (ANNs) for the development of in-season yield mapping and forecasting systems was examined. Hyperspectral images of corn ( Zea mays L.) plots in eastern Canada, subjected to different fertilization rates and various weed management protocols, were acquired by a compact airborne spectral imager. Statistical and ANN approaches along with various vegetation indices were used to develop yield prediction models. Principal component analysis was used to reduce the number of input variables. Greater prediction accuracy (about 20% validation RMSE) was obtained with an ANN model than with either of the three conventional empirical models based on normalized difference vegetation index, simple ratio, or photochemical reflectance index. No clear difference was observed between ANNs and stepwise multiple linear regression models. Although the high potential usefulness of ANNs was confirmed, particularly in the creation of yield maps, further investigations are needed before their application at the field scale can be generalized.

Airborne multispectral data for quantifying leaf area index, nitrogen concentration, and photosynthetic efficiency in agriculture

Airborne multispectral data were acquired by the Compact Airborne Spectral Imager (CASI) for an agricultural area in Denmark with the purpose of quantifying vegetation amount and variations in the physiological status of the vegetation. Spectral reflectances, vegetation indices, and red edge positions were calculated on the basis of the CASI data and compared to field measurements of green leaf area index (LAI; L) and canopy nitrogen concentrations ( N c) sampled at 16 sites. Because of the variety of the samples with respect to vegetation type, leaf age, and phenological developmental stage, the data of L and N c were uncorrelated. The scattering effect of leaves effectuated a robust linear relationship between L and near-infrared (NIR) reflectance ( r=.93), whereas the N c (vegetative period) was significantly correlated with the spectral reflectance in the green ( r=−.88) and far-red wavebands ( r=−.94). The correlations between vegetation indices and L were also important, in particular, for the enhanced vegetation index (EVI; r=.88), whereas the red edge position correlated less significantly with N c ( r=.78). Assuming L and N c to be responsible for most of the spatial variability in the CO 2 assimilation rates, remote sensing-based maps of these variables were produced for use in a coupled sun/shade photosynthesis/transpiration model. The predicted rates of net photosynthesis and transpiration compared reasonably with eddy covariance measurements of CO 2 and water vapour fluxes recorded at four different crop fields. The results allowed evaluation of the spatial variations in the photosynthetic light, nitrogen, and water use efficiencies. While photosynthesis was linearly related to the transpiration, the light use efficiency (LUE) was found to be dependent on nitrogen concentrations.

Suspended Particulate Matter detection in the North Sea by hyper spectral airborne remote sensing

A spectral model is developed describing the relation between optical spectral reflectance and sampled suspended particulate matter concentration. This reflectance model is based on inherent optical properties of North Sea colouring water constituents. Hyper spectral airborne observations by a CASI scanner are carried out in the Dutch coastal zone and interpreted making use of the reflectance model. A non linear relation between reflectance and suspended particulate matter concentration is found. Variability of sampled suspended matter concentration is high, in contrast to this the variability of interpreted spectral measurements is much lower. Abbreviations: IOP – inherent optical properties; CASI – compact airborne spectral imager; SPM – suspended particulate matter; CHL – chlorophyll; FWHM – full width half maximum; r.m.s – root mean square.

Remote sensing estimates of inherent optical properties in a coastal environment

High resolution aircraft remote sensing imagery and in situ optical data were coupled to characterize the spatial and temporal variability of the inherent optical properties in the near-surf zone off Fort Walton Beach, Florida in August 1994. Upwelling radiance measurements at SeaWiFS wavelengths were collected over a uniform, highly reflective white sand bottom at a ground resolution of 2..5 m using the CASI sensor (Compact Airborne Spectral Imager). Following atmospheric correction, the total remote sensing reflectance signal was partitioned into bottom and water volume reflectance components, using measurements of bottom albedo, water depth, and the diffuse attenuation coefficient at the time of the overflight. The water components were entered in the SeaWiFS biooptical model to derive spectral absorption and scattering coefficients. After applying minor algorithm and coefficient adjustments, model results compared favorably with in situ measurements. The biooptical model was subsequently applied to the aircraft imagery to describe the spatial distribution of absorption and scattering. Elevated absorption and particle scattering were observed over the sand bar and shoreward (a555=0.19 m −1, b555=0.7 m −1). The temporal variability of the inherent optical properties over a I-week period was similar to the spatial variability along a 500-in offshore transect.

Monitoring and preliminary interpretation of in-river turbidity and remote sensed imagery for suspended sediment transport studies in the Humber catchment

Rivers are the main source of sediment to the marine environment in global terms. This sediment is predominantly suspended sediments. Calculation of suspended sediment flux requires the combination of suspended sediment concentration and water discharge records. A continuous record of suspended sediment concentration is derived for Land Ocean Interaction Study (LOIS) river monitoring sites using an innovative dual turbidity sensor system. Calibration of turbidity data, to provide a record of suspended sediment concentration, is achieved by comparisons with bulk water/sediment samples collected from Humber rivers, covering the full turbidity range usually encountered in British rivers. The equipment, methodlologies and preliminary results are described, along with the potential errors and limitations. One of the functions of remote sensed data is to help understand the applicability of such point measurements. LOIS is using such data, acquired by imaging spectrometers mounted in an aircraft, to map the spatial distribution of suspended sediment concentration. A semi-empirical relationship has been developed between suspended sediment concentrations and the irradiance reflectance recorded in a near infra red band by the compact airborne spectral imager (CASI). The resulting data on the spatial distribution of suspended sediment concentration are illustrated by an example from the lower part of the River Ouse, Yorkshire. The next stage of the programme will integrate the field science and remote sensing studies with other LOIS rivers research on particle properties, river bank erosion and sediment source studies.