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

The micro-level foundations and dynamics of political corporate social responsibility: hegemony and passive revolution through civil society

Exploration of the political roles firms play in society is a flourishing stream within corporate social responsibility (CSR) research. However, few empirical studies have examined multiple levels of political CSR at the same time from a critical perspective. We explore both how the motivations of managers and internal organizational practices affect a company’s choice between competing CSR approaches, and how the different CSR programs of corporate and civil society actors compete with each other. We present a qualitative interpretative case study of how a French children’s clothing retailer develops CSR practices in response to accusations of poor working conditions and child labor in its supply chain. The company’s CSR approach consists of superficial practices, such as supplier audits by a cooperative business-organized nongovernmental organization (NGO) and philanthropic activities, which enable managers to silence more radical alternative models defended by other NGOs, activists, and trade unions. By this approach, the core business model based on exploitative low-cost country sourcing remains intact through self-regulated CSR. Through the case study, we develop a framework of dynamism in competing CSR programs. We discuss the implications of our study for CSR researchers, company managers, and policy makers

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