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

A method for automatic segmentation and splitting of hyperspectral images of raspberry plants collected in field conditions

Abstract Hyperspectral imaging is a technology that can be used to monitor plant responses to stress. Hyperspectral images have a full spectrum for each pixel in the image, 400–2500 nm in this case, giving detailed information about the spectral reflectance of the plant. Although this technology has been used in laboratory-based controlled lighting conditions for early detection of plant disease, the transfer of such technology to imaging plants in field conditions presents a number of challenges. These include problems caused by varying light levels and difficulties of separating the target plant from its background. Here we present an automated method that has been developed to segment raspberry plants from the background using a selected spectral ratio combined with edge detection. Graph theory was used to minimise a cost function to detect the continuous boundary between uninteresting plants and the area of interest. The method includes automatic detection of a known reflectance tile which was kept constantly within the field of view for all image scans. A method to split images containing rows of multiple raspberry plants into individual plants was also developed. Validation was carried out by comparison of plant height and density measurements with manually scored values. A reasonable correlation was found between these manual scores and measurements taken from the images (r2 = 0.75 for plant height). These preliminary steps are an essential requirement before detailed spectral analysis of the plants can be achieved

Development of robust hyperspectral indices for the detection of deviations of normal plant state

This research was conducted to assess the potential of hyperspectral indices to detect iron defi-ciency in capital-intensive multi-annual crop systems. A well-defined hyperspectral multi-layer dataset was constructed for a peach orchard in Zaragoza, Spain, consisting of hyperspectral measurements at various monitoring levels (leaf, crown, airborne). Trees were subjected to four different treatments of iron application (0 g / tree, 60 g / tree, 90 g / tree, and 120 g / tree). Ground-based measurements were used to characterise the on-site peach (Prunus persica L.) orchard in terms of chlorophyll, dry matter, water content, and leaf area index (LAI). Indices were extracted from the spectral profiles by means of band reduction techniques based on logistic regression and narrow-waveband ratioing involving all possible two-band combinations. Physiological interpreta-tions extracted from leaf-level experiments were extrapolated to crown- and airborne level. It was concluded from leaf level measurements that a decrease in leaf chlorophyll concentration resulted due to iron deficiency. The results suggested that spectral bands and narrow waveband ratio vege-tation indices, selected via multivariate logistic regression classification, were able to distinguish iron untreated and iron treated trees (C-values>0.8). Moreover, the most appropriate indices ob-tained in this manner fulfilled the expectations by being highly correlated (R2>0.6) to the measured chlorophyll concentrations. The visible part of the spectrum, mostly dominated by the amount of pigments (e.g. chlorophyll, carotenoids), provided the most discriminative spectral region (505 - 740 nm) in this study. The discriminatory performance of a combined chlorophyll and soil-adjusted vegetation index was compared to the results of the selected vegetation indices to estimate the effects of soil background and LAI on those indices.Belgian Science PolicyPeer reviewe

Heathland conservation status mapping through integration of hyperspectral mixture analysis and decision tree classifiers

Monitoring the conservation status of natural habitats is an essential aspect of effective conservation management. Not only data on habitat occurrence are needed, but also detailed information on the structural and functional characteristics of the habitat patches is crucial for an adequate conservation status assessment. Classification of hyperspectral remote sensing images performs well in discriminating dominant land cover and vegetation classes, but the accuracy drops significantly for the classification of more subtle differences in conservation status that are related to structural characteristics. This study proposes a method to facilitate ecological conservation status assessment based on decision tree modeling of subpixel fraction estimates steered by ecological expert knowledge. In particular, it contributes to the spatially explicit assessment of an important structural aspect of dry heathland vegetation, namely the heather age structure, using Airborne Hyperspectral line-Scanner radiometer (AHS-160) data of the Kalmthoutse Heide in northern Belgium. We implemented a subpixel unmixing approach to identify the percentage of heather, sand and shadow in each heather pixel, and subsequently applied a decision tree classification to allocate each pixel to a certain age class. As such, our method provides a tool that contributes to the information required for an appropriate management and successful conservation of natural heathlands

COMPACT HYPERSPECTRAL IMAGING SYSTEM (COSI) FOR SMALL REMOTELY PILOTED AIRCRAFT SYSTEMS (RPAS) – SYSTEM OVERVIEW AND FIRST PERFORMANCE EVALUATION RESULTS

This paper gives an overview of the new COmpact hyperSpectral Imaging (COSI) system recently developed at the Flemish Institute for Technological Research (VITO, Belgium) and suitable for remotely piloted aircraft systems. A hyperspectral dataset captured from a multirotor platform over a strawberry field is presented and explored in order to assess spectral bands co-registration quality. Thanks to application of line based interference filters deposited directly on the detector wafer the COSI camera is compact and lightweight (total mass of 500g), and captures 72 narrow (FWHM: 5nm to 10 nm) bands in the spectral range of 600-900 nm. Covering the region of red edge (680 nm to 730 nm) allows for deriving plant chlorophyll content, biomass and hydric status indicators, making the camera suitable for agriculture purposes. Additionally to the orthorectified hypercube digital terrain model can be derived enabling various analyses requiring object height, e.g. plant height in vegetation growth monitoring. Geometric data quality assessment proves that the COSI camera and the dedicated data processing chain are capable to deliver very high resolution data (centimetre level) where spectral information can be correctly derived. Obtained results are comparable or better than results reported in similar studies for an alternative system based on the Fabry–Pérot interferometer