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

Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept

The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver: 1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators; 2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species; 3. A proposal for a cost-effective biodiversity monitoring system. There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme. The issues that we faced were many: 1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset. 2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything. 3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration. 4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output. EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data. EBONE in its initial development, focused on three main indicators covering: (i) the extent and change of habitats of European interest in the context of a general habitat assessment; (ii) abundance and distribution of selected species (birds, butterflies and plants); and (iii) fragmentation of natural and semi-natural areas. For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles: using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples. For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved. Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’. With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations

Ash Tree Identification Based on the Integration of Hyperspectral Imagery and High-density Lidar Data

Monitoring and management of ash trees has become particularly important in recent years due to the heightened risk of attack from the invasive pest, the emerald ash borer (EAB). However, distinguishing ash from other deciduous trees can be challenging. Both hyperspectral imagery and Light detection and ranging (LiDAR) data are two valuable data sources that are often used for tree species classification. Hyperspectral imagery measures detailed spectral reflectance related to the biochemical properties of vegetation, while LiDAR data measures the three-dimensional structure of tree crowns related to morphological characteristics. Thus, the accuracy of vegetation classification may be improved by combining both techniques. Therefore, the objective of this research is to integrate hyperspectral imagery and LiDAR data for improving ash tree identification. Specifically, the research aims include: 1) using LiDAR data for individual tree crowns segmentation; 2) using hyperspectral imagery for extraction of relative pure crown spectra; 3) fusing hyperspectral and LiDAR data for ash tree identification. It is expected that the classification accuracy of ash trees will be significantly improved with the integration of hyperspectral and LiDAR techniques. Analysis results suggest that, first, 3D crown structures of individual trees can be reconstructed using a set of generalized geometric models which optimally matched LiDAR-derived raster image, and crown widths can be further estimated using tree height and shape-related parameters as independent variables and ground measurement of crown widths as dependent variables. Second, with constrained linear spectral mixture analysis method, the fractions of all materials within a pixel can be extracted, and relative pure crown-scale spectra can be further calculated using illuminated-leaf fraction as weighting factors for tree species classification. Third, both crown shape index (SI) and coefficient of variation (CV) can be extracted from LiDAR data as invariant variables in tree’s life cycle, and improve ash tree identification by integrating with pixel-weighted crown spectra. Therefore, three major contributions of this research have been made in the field of tree species classification:1) the automatic estimation of individual tree crown width from LiDAR data by combining a generalized geometric model and a regression model, 2) the computation of relative pure crown-scale spectral reflectance using a pixel-weighting algorithm for tree species classification, 3) the fusion of shape-related structural features and pixel-weighted crown-scale spectral features for improving of ash tree identification

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

Automated identification of river hydromorphological features using UAV high resolution aerial imagery

European legislation is driving the development of methods for river ecosystem protection in light of concerns over water quality and ecology. Key to their success is the accurate and rapid characterisation of physical features (i.e., hydromorphology) along the river. Image pattern recognition techniques have been successfully used for this purpose. The reliability of the methodology depends on both the quality of the aerial imagery and the pattern recognition technique used. Recent studies have proved the potential of Unmanned Aerial Vehicles (UAVs) to increase the quality of the imagery by capturing high resolution photography. Similarly, Artificial Neural Networks (ANN) have been shown to be a high precision tool for automated recognition of environmental patterns. This paper presents a UAV based framework for the identification of hydromorphological features from high resolution RGB aerial imagery using a novel classification technique based on ANNs. The framework is developed for a 1.4 km river reach along the river Dee in Wales, United Kingdom. For this purpose, a Falcon 8 octocopter was used to gather 2.5 cm resolution imagery. The results show that the accuracy of the framework is above 81%, performing particularly well at recognising vegetation. These results leverage the use of UAVs for environmental policy implementation and demonstrate the potential of ANNs and RGB imagery for high precision river monitoring and river management