Biodiversity mapping via Natura 2000 conservation status and ebv assessment using airborne laser scanning in alkali grasslands

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Monitoring Spatial Variability and Temporal Dynamics of Phragmites Using Unmanned Aerial Vehicles

Littoral zones of freshwater lakes are exposed to environmental impacts from both terrestrial and aquatic sides, while substantial anthropogenic pressure also affects the high spatial and temporal variability of the ecotone. In this study, the possibility of monitoring seasonal and spatial changes in reed (Phragmites australis) stands using an unmanned aerial vehicle (UAV) based remote sensing technique was examined. Stands in eutrophic and mesotrophic parts of Lake Balaton including not deteriorating (stable) and deteriorating (die-back) patches, were tracked throughout the growing season using a UAV equipped with a normalized difference vegetation index (NDVI) camera. Photophysiological parameters of P. australis were also measured with amplitude modulated fluorescence. Parameters characterising the dynamics of seasonal changes in NDVI data were used for phenological comparison of eutrophic and mesotrophic, stable and die-back, terrestrial and aquatic, mowed and not-mowed patches of reed. It was shown that stable Phragmites plants from the eutrophic part of the lake reached specific phenological stages up to 3.5 days earlier than plants from the mesotrophic part of the lake. The phenological changes correlated with trophic (total and nitrate-nitrite nitrogen) and physical (organic C and clay content) properties of the sediment, while only minor relationships with air and water temperature were found. Phenological differences between the stable and die-back stands were even more pronounced, with ~34% higher rates of NDVI increase in stable than die-back patches, while the period of NDVI increase was 16 days longer. Aquatic and terrestrial parts of reed stands showed no phenological differences, although intermediate areas (shallow water parts of stands) were found to be less vigorous. Winter mowing of dried Phragmites sped up sprouting and growth of reed in the spring. This study showed that remote sensing-derived photophysiological and phenological variability within and between reed stands may provide valuable early indicators of environmental stress. The flexibility of the method makes it usable for mapping fine-scale temporal variability and spatial zonation within a stand, revealing ecophysiological hotspots that might require particular attention, and obtaining information vital for conservation and management of plants in the littoral zones

Area-Based Approach for Mapping and Monitoring Riverine Vegetation Using Mobile Laser Scanning

Peer reviewe

Observation of a local gravity potential isosurface by airborne lidar of Lake Balaton, Hungary

Airborne lidar is a remote sensing method commonly used for mapping surface topography in high resolution. A water surface in hydrostatic equilibrium theoretically represents a gravity potential isosurface. Here we compare lidar-based ellipsoidal water surface height measurements all around the shore of a major lake with a local high-resolution quasi-geoid model. The ellipsoidal heights of the 87 km2 we sampled all around the shore of the 597 km2 lake surface vary by 0.8m and strong spatial correlation with the quasi-geoid undulation was calculated (R2 = 0.91). After subtraction of the local geoid undulation from the measured ellipsoidal water surface heights, their variation was considerably reduced. Based on a network of water gauge measurements, dynamic water surface heights were also successfully corrected for. This demonstrates that the water surface heights of the lake were truly determined by the local gravity potential.We conclude that both the level of hydrostatic equilibrium of the lake and the accuracy of airborne lidar were sufficient for identifying the spatial variations of gravity potential

Global Island Monitoring Scheme (GIMS) : a proposal for the long-term coordinated survey and monitoring of native island forest biota

Islands harbour evolutionary and ecologically unique biota, which are currently disproportionately threatened by a multitude of anthropogenic factors, including habitat loss, invasive species and climate change. Native forests on oceanic islands are important refugia for endemic species, many of which are rare and highly threatened. Long-term monitoring schemes for those biota and ecosystems are urgently needed: (i) to provide quantitative baselines for detecting changes within island ecosystems, (ii) to evaluate the effectiveness of conservation and management actions, and (iii) to identify general ecological patterns and processes using multiple island systems as repeated 'natural experiments'. In this contribution, we call for a Global Island Monitoring Scheme (GIMS) for monitoring the remaining native island forests, using bryophytes, vascular plants, selected groups of arthropods and vertebrates as model taxa. As a basis for the GIMS, we also present new, optimized monitoring protocols for bryophytes and arthropods that were developed based on former standardized inventory protocols. Effective inventorying and monitoring of native island forests will require: (i) permanent plots covering diverse ecological gradients (e.g. elevation, age of terrain, anthropogenic disturbance); (ii) a multiple-taxa approach that is based on standardized and replicable protocols; (iii) a common set of indicator taxa and community properties that are indicative of native island forests' welfare, building on, and harmonized with existing sampling and monitoring efforts; (iv) capacity building and training of local researchers, collaboration and continuous dialogue with local stakeholders; and (v) long-term commitment by funding agencies to maintain a global network of native island forest monitoring plots.Peer reviewe

Classification of Expansive Grassland Species in Different Growth Stages Based on Hyperspectral and LiDAR Data

Expansive species classification with remote sensing techniques offers great support for botanical field works aimed at detection of their distribution within areas of conservation value and assessment of the threat caused to natural habitats. Large number of spectral bands and high spatial resolution allows for identification of particular species. LiDAR (Light Detection and Ranging) data provide information about areas such as vegetation structure. Because the species differ in terms of features during the growing season, it is important to know when their spectral responses are unique in the background of the surrounding vegetation. The aim of the study was to identify two expansive grass species: Molinia caerulea and Calamagrostis epigejos in the Natura 2000 area in Poland depending on the period and dataset used. Field work was carried out during late spring, summer and early autumn, in parallel with remote sensing data acquisition. Airborne 1-m resolution HySpex images and LiDAR data were used. HySpex images were corrected geometrically and atmospherically before Minimum Noise Fraction (MNF) transformation and vegetation indices calculation. Based on a LiDAR point cloud generated Canopy Height Model, vegetation structure from discrete and full-waveform data and topographic indexes were generated. Classifications were performed using a Random Forest algorithm. The results show post-classification maps and their accuracies: Kappa value and F1 score being the harmonic mean of producer (PA) and user (UA) accuracy, calculated iteratively. Based on these accuracies and botanical knowledge, it was possible to assess the best identification date and dataset used for analysing both species. For M. caerulea the highest median Kappa was 0.85 (F1 = 0.89) in August and for C. epigejos 0.65 (F1 = 0.73) in September. For both species, adding discrete or full-waveform LiDAR data improved the results. We conclude that hyperspectral (HS) and LiDAR airborne data could be useful to id

NDVI as a Proxy for Estimating Sedimentation and Vegetation Spread in Artificial Lakes — Monitoring of Spatial and Temporal Changes by Using Satellite Images Overarching Three Decades

Observing wetland areas and monitoring changes are crucial to understand hydrological and ecological processes. Sedimentation-induced vegetation spread is a typical process in the succession of lakes endangering these habitats. We aimed to survey the tendencies of vegetation spread of a Hungarian lake using satellite images, and to develop a method to identify the areas of risk. Accordingly, we performed a 33-year long vegetation spread monitoring survey. We used the Normalized Difference Vegetation Index (NDVI) and the Modified Normalized Difference Water Index (MNDWI) to assess vegetation and open water characteristics of the basins. We used these spectral indices to evaluate sedimentation risk of water basins combined with the fact that the most abundant plant species of the basins was the water caltrop (Trapa natans) indicating shallow water. We proposed a 12-scale Level of Sedimentation Risk Index (LoSRI) composed from vegetation cover data derived from satellite images to determine sedimentation risk within any given water basin. We validated our results with average water basin water depth values, which showed an r = 0.6 (p < 0.05) correlation. We also pointed on the most endangered locations of these sedimentation-threatened areas, which can provide crucial information for management planning of water directorates and management organizations