Object identification and characterization with hyperspectral imagery to identify structure and function of Natura 2000 habitats

Habitat monitoring of designated areas under the EU Habitats Directive requires every 6 years information on area, range, structure and function for the protected (Annex I) habitat types. First results from studies on heathland areas in Belgium and the Netherlands show that hyperspectral imagery can be an important source of information to assist the evaluation of the habitat conservation status. Hyperspectral imagery can provide continuous maps of habitat quality indicators (e.g., life forms or structure types, management activities, grass, shrub and tree encroachment) at the pixel level. At the same time, terrain managers, nature conservation agencies and national authorities responsible for the reporting to the EU are not directly interested in pixels, but rather in information at the level of vegetation patches, groups of patches or the protected site as a whole. Such local level information is needed for management purposes, e.g., exact location of patches of habitat types and the sizes and quality of these patches within a protected site. Site complexity determines not only the classification success of remote sensing imagery, but influences also the results of aggregation of information from the pixel to the site level. For all these reasons, it is important to identify and characterize the vegetation patches. This paper focuses on the use of segmentation techniques to identify relevant vegetation patches in combination with spectral mixture analysis of hyperspectral imagery from the Airborne Hyperspectral Scanner (AHS). Comparison with traditional vegetation maps shows that the habitat or vegetation patches can be identified by segmentation of hyperspectral imagery. This paper shows that spectral mixture analysis in combination with segmentation techniques on hyperspectral imagery can provide useful information on processes such as grass encroachment that determine the conservation status of Natura 2000 heathland areas to a large extent. A limitation is that both advanced remote sensing approaches and traditional field based vegetation surveys seem to cause over and underestimations of grass encroachment for specific categories, but the first provides a better basis for monitoring if specific species are not directly considered

Linking goniometer measurements to hyperspectral and multi-sensor imagery for retrieval of beach properties and coastal characterization

In June 2011, a multi-sensor airborne remote sensing campaign was flown at the Virginia Coast Reserve Long Term Ecological Research site with coordinated ground and water calibration and validation (cal/val) measurements. Remote sensing imagery acquired during the ten day exercise included hyperspectral imagery (CASI-1500), topographic LiDAR, and thermal infra-red imagery, all simultaneously from the same aircraft. Airborne synthetic aperture radar (SAR) data acquisition for a smaller subset of sites occurred in September 2011 (VCR\u2711). Focus areas for VCR\u2711 were properties of beaches and tidal flats and barrier island vegetation and, in the water column, shallow water bathymetry. On land, cal/val emphasized tidal flat and beach grain size distributions, density, moisture content, and other geotechnical properties such as shear and bearing strength (dynamic deflection modulus), which were related to hyperspectral BRDF measurements taken with the new NRL Goniometer for Outdoor Portable Hyperspectral Earth Reflectance (GOPHER). This builds on our earlier work at this site in 2007 related to beach properties and shallow water bathymetry. A priority for VCR\u2711 was to collect and model relationships between hyperspectral imagery, acquired from the aircraft at a variety of different phase angles, and geotechnical properties of beaches and tidal flats. One aspect of this effort was a demonstration that sand density differences are observable and consistent in reflectance spectra from GOPHER data, in CASI hyperspectral imagery, as well as in hyperspectral goniometer measurements conducted in our laboratory after VCR\u2711

Evaluating hyperspectral imagery for mapping the surface symptoms of dryland salinity

Airborne hyperspectral imagery has the potential to overcome the spectral and spatial resolution limitations of multispectral satellite imagery for monitoring salinity at both regional and farm scales. In particular, saline areas that have good cover of salt tolerant plants are difficult to map with multispectral satellite imagery. Hyperspectral imagery may provide a more reliable salinity mapping method because of its potential to discriminate halophytic plant cover from non - halophytes. HyMap and CASI airborne imagery ( at 3m ground resolution ) and Hyperion satellite imagery ( at 30 resolution ) were acquired over a 140 sq km dryland agricultural area in South Australia, which exhibits severe symptoms of salinity, including extensive patches of the perennial halophytic shrub samphire ( Halosarcia pergranulata ), sea barley grass ( Hordeum marinum ) and salt encrusted pans. The HyMap and Hyperion imagery were acquired in the dry season ( March and February respectively ) to maximise soil and perennial vegetation mapping. The optimum time of year to map sea barley grass, an annual species, was investigated through spectral discrimination analysis. Multiple reflectance spectra were collected of sea barley grass and other annual grasses with an ASD Fieldspec Pro spectrometer during the September spring flush and in November during late senescence. Comparing spectra of different species in November attempted to capture the spectral differences between the late senescing sea barley grass and other annual grasses. Broad NIR and SWIR regions were identified where sea barley grass differs significantly from other species in November during late senescence. The sea barley grass was therefore shown to have the potential to be discriminated and mapped with hyperspectral imagery at this time and as a result the CASI survey was commission for November. Other salinity symptoms were characterised by collecting single field and laboratory spectra for comparison to image derived spectra in order to provide certainty about the landscape components that were to be mapped. Endmembers spectra associated with saltpans and samphire patches were extracted from the imagery using automated endmember generation procedures or selected regions of interest and used in subsequent partial unmixing. Spectral subsets were evaluated for their ability to optimise salinity maps. The saltpan spectra contained absorption features consistent with montmorillonite and gypsum. A single gypsum endmember from one image strip successfully mapped saltpans across multiple images strips using the 1750 nm absorption feature as the input to matched filter unmixing. The individual spectra of green and red samphire are dominated by photosynthetic vegetation characteristics. The spectra of green samphire, often seen with red tips, exhibit peaks in both green and red wavebands whereas the red samphire spectra only contain a significant reflectance peak in the visible red wavelength region. For samphire, Mixture Tuned Matched Filtering using image spectra, containing all wavelength regions, from known samphire patches produced the most satisfactory mapping. Output salinity maps were validated at over 100 random sites. The HyMap salinity maps produced the most accurate results compared to CASI and Hyperion. HyMap successfully mapped highly saline areas with a good cover of samphire vegetation at Point Sturt without the use of multitemporal imagery or ancillary data such as topography or PIRSA soil attribute maps. CASI and Hyperion successfully mapped saltpan, however, their samphire maps showed a poor agreement with field data. These results suggest that perennial vegetation mapping requires all three visible, NIR and SWIR wavelength regions because the SWIR region contains important spectral properties related to halophytic adaptations. Furthermore, the unconvincing results of the CASI sea barley grass maps suggests that the optimal sensor for mapping both soil and vegetation salinity symptoms are airborne sensors with high spatial and spectral resolution, that incorporate the 450 to 1450 nm wavelength range, such as HyMap. This study has demonstrated that readily available software and image analysis techniques are capable of mapping indicators of varying levels of salinity. With the ability to map symptoms across multiple image strips, airborne hyperspectral imagery has the potential for mapping larger areas covering sizeable dryland agriculture catchments, closer in extent to single satellite images. This study has illustrated the advantage of the hyperspectral imagery over traditional soil mapping based on aerial photography interpretation such as the NLWRA Salinity 2000 and the PIRSA soil landscape unit maps. The HyMap salinity maps not only improved mapping of saline areas covered with samphire but also provided salinity maps that varied spatially within saline polygons.Thesis (Ph.D.)--School of Earth and Environmental Sciences, 2006

Fusion of pixel-based and object-based features for classification of urban hyperspectral remote sensing data

Hyperspectral imagery contains a wealth of spectral and spatial information that can improve target detection and recognition performance. Typically, spectral information is inferred pixel-based, while spatial information related to texture, context and geometry are deduced on a per-object basis. Existing feature extraction methods cannot fully utilize both the spectral and spatial information. Data fusion by simply stacking different feature sources together does not take into account the differences between feature sources. In this paper, we propose a feature fusion method to couple dimension reduction and data fusion of the pixel- and object-based features of hyperspectral imagery. The proposed method takes into account the properties of different feature sources, and makes full advantage of both the pixel- and object-based features through the fusion graph. Experimental results on classification of urban hyperspectral remote sensing image are very encouraging

Macroalgae and Eelgrass Mapping in Great Bay Estuary Using AISA Hyperspectral Imagery.

Results Increases in nitrogen concentration and declining eelgrass beds in Great Bay Estuary have been observed in the last decades. These two parameters are clear indicators of the impending eutrophication for New Hampshire’s estuaries. The NH Department of Environmental Services (DES) in collaboration with the Piscataqua Region Estuaries Partnership adopted the assumption that eelgrass survival can be used as the target for establishing numeric water quality criteria for nutrients in NH’s estuaries. One of the hypotheses put forward regarding eelgrass decline is that an eutrophication response to nutrient increases in the Great Bay Estuary has been the proliferation of nuisance macroalgae, which has reduced eelgrass area in Great Bay Estuary. To determine the extent of this effect, mapping of eelgrass and nuisance macroalgae beds using hyperspectral imagery was suggested. A hyperspectral image was made by SpecTIR in August 2007 using an AISA Eagle sensor. The collected dataset was then used to map eelgrass and nuisance macroalgae throughout the Great Bay Estuary. Here we outline the procedure for mapping the macroalgae and eelgrass beds. Hyperspectral imagery was effective where known spectral signatures could be easily identified. Comprehensive eelgrass and macroalgae maps of the estuary could only be produced by combining hyperspectral imagery with ground-truth information and expert opinion. Macroalgae was predominantly located in areas where eelgrass formerly existed. Macroalgae mats have now replaced nearly 9% of the area formerly occupied by eelgrass in Great Bay

Macroalgae and eelgrass mapping in Great Bay Estuary using AISA hyperspectral imagery

Increase in nitrogen concentration and declining eelgrass beds in Great Bay Estuary have been observed in the last decades. These two parameters are clear indicators of the impending problems for NH’s estuaries. The NH Department of Environmental Services (DES) in collaboration with the New Hampshire Estuaries Project (NHEP) adopted the assumption that eelgrass survival can be used as the water quality target for nutrient criteria development for NH’s estuaries. One of the hypotheses put forward regarding eelgrass decline is that a possible eutrophication response to nutrient increases in the Great Bay Estuary has been the proliferation of nuisance macroalgae, which has reduced eelgrass area in Great Bay Estuary. To test this hypothesis, mapping of eelgrass and nuisance macroalgae beds using hyperspectral imagery was suggested. A hyperspectral imagery was conducted by SpecTIR in August 2007 using an AISA Eagle sensor. The collected dataset was used to map eelgrass and nuisance macroalgae throughout the Great Bay Estuary. This report outlines the configured procedure for mapping the macroalgae and eelgrass beds using hyperspectral imagery. No ground truth measurements of eelgrass or macroalgae were collected as part of this project, although eelgrass ground truth data was collected as part of a separate project. Guidance from eelgrass and macroalgae experts was used for identifying training sets and evaluating the classification results. The results produced a comprehensive eelgrass and macroalgae map of the estuary. Three recommendations are suggested following the experience gained in this study: conducting ground truth measurements at the time of the HS survey, acquiring the current DEM model of Great Bay Estuary, and examining additional HS datasets with expert eelgrass and macroalgae guidance. These three issues can improve the classification results and allow more advanced applications, such as identification of macroalgae types

Quality criteria benchmark for hyperspectral imagery

Hyperspectral data appear to be of a growing interest over the past few years. However, applications for hyperspectral data are still in their infancy as handling the significant size of the data presents a challenge for the user community. Efficient compression techniques are required, and lossy compression, specifically, will have a role to play, provided its impact on remote sensing applications remains insignificant. To assess the data quality, suitable distortion measures relevant to end-user applications are required. Quality criteria are also of a major interest for the conception and development of new sensors to define their requirements and specifications. This paper proposes a method to evaluate quality criteria in the context of hyperspectral images. The purpose is to provide quality criteria relevant to the impact of degradations on several classification applications. Different quality criteria are considered. Some are traditionnally used in image and video coding and are adapted here to hyperspectral images. Others are specific to hyperspectral data.We also propose the adaptation of two advanced criteria in the presence of different simulated degradations on AVIRIS hyperspectral images. Finally, five criteria are selected to give an accurate representation of the nature and the level of the degradation affecting hyperspectral data

Restoration of Oyster (Crassostrea virginica) Habitat for Multiple Estuarine Species Benefits

Increase in nitrogen concentration and declining eelgrass beds in Great Bay Estuary have been observed in the last decades. These two parameters are clear indicators of the impending problems for NH’s estuaries. The NH Department of Environmental Services (DES) in collaboration with the New Hampshire Estuaries Project (NHEP) adopted the assumption that eelgrass survival can be used as the water quality target for nutrient criteria development for NH’s estuaries. One of the hypotheses put forward regarding eelgrass decline is that a possible eutrophication response to nutrient increases in the Great Bay Estuary has been the proliferation of nuisance macroalgae, which has reduced eelgrass area in Great Bay Estuary. To test this hypothesis, mapping of eelgrass and nuisance macroalgae beds using hyperspectral imagery was suggested. A hyperspectral imagery was conducted by SpecTIR in August 2007 using an AISA Eagle sensor. The collected dataset was used to map eelgrass and nuisance macroalgae throughout the Great Bay Estuary. This report outlines the configured procedure for mapping the macroalgae and eelgrass beds using hyperspectral imagery. No ground truth measurements of eelgrass or macroalgae were collected as part of this project, although eelgrass ground truth data was collected as part of a separate project. Guidance from eelgrass and macroalgae experts was used for identifying training sets and evaluating the classification results. The results produced a comprehensive eelgrass and macroalgae map of the estuary. Three recommendations are suggested following the experience gained in this study: conducting ground truth measurements at the time of the HS survey, acquiring the current DEM model of Great Bay Estuary, and examining additional HS datasets with expert eelgrass and macroalgae guidance. These three issues can improve the classification results and allow more advanced applications, such as identification of macroalgae types

Geodatabase Development to Support Hyperspectral Imagery Exploitation

Geodatabase development for coastal studies conducted by the Naval Research Laboratory (NRL) is essential to support the exploitation of hyperspectral imagery (HSI). NRL has found that the remote sensing and mapping science community benefits from coastal classifications that group coastal types based on similar features. Selected features in project geodatabases relate to significant biological and physical forces that shape the coast. The project geodatabases help researchers understand factors that are necessary for imagery post processing, especially those features having a high degree of temporal and spatial variability. NRL project geodatabases include a hierarchy of environmental factors that extend from shallow water bottom types and beach composition to inland soil and vegetation characteristics. These geodatabases developed by NRL allow researchers to compare features among coast types. The project geodatabases may also be used to enhance littoral data archives that are sparse. This paper highlights geodatabase development for recent remote sensing experiments in barrier island, coral, and mangrove coast types

Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery

This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the linear mixing model. The estimation of the unknown endmember spectra is conducted in a unified manner by generating the posterior distribution of abundances and endmember parameters under a hierarchical Bayesian model. This model assumes conjugate prior distributions for these parameters, accounts for non-negativity and full-additivity constraints, and exploits the fact that the endmember proportions lie on a lower dimensional simplex. A Gibbs sampler is proposed to overcome the complexity of evaluating the resulting posterior distribution. This sampler generates samples distributed according to the posterior distribution and estimates the unknown parameters using these generated samples. The accuracy of the joint Bayesian estimator is illustrated by simulations conducted on synthetic and real AVIRIS images