Comparison of three modelling approaches of potential natural forest habitats in Bavaria, Germany

In the context of the EU Habitats Directive, which contains the obligation of environmental monitoring, nature conservation authorities face a growing demand for effective and competitive methods to survey protected habitats. Therefore the presented research study compared three modelling approaches (rule-based method with applied Bavarian woodland types, multivariate technique of cluster analysis, and a fuzzy logic approach) for the purpose of detecting potential habitat types. The results can be combined with earth observation data of different geometric resolution (ASTER, SPOT5, aerial photographs or very high resolution satellite data) in order to determine actual forest habitat types. This was carried out at two test sites, situated in the pre-alpine area in Bavaria (southern Germany). The results were subsequently compared to the terrestrial mapped habitat areas of the NATURA 2000 management plans. First results show that these techniques are a valuable support in mapping and monitoring NATURA 2000 forest habitats

GIS and Remote Sensing for Natura 2000 Monitoring in Mediterranean Biogeographic Region

NATURA 2000 areas monitoring is a key research topic on European countries since Habi-tat Directive specifies the obligation to implement monitoring systems for conservation status in Natura 2000 spaces. This can be achieved by combining GIS-based models of the Potential Natural Vegetation (PNV) with remote sensing classification or interpretation results. The presented study focuses on the implementation of a methodology to locate and detect changes in forest spaces of Natura 2000 Network. Location of different habitats types were carried out based on geo-factors and remote sensing interpretation, terrestrial mapping and analysis of natural habitat distribution for a test site. In order to derive the actual forest habitats, potential natural vegetation was derived from a defined rule-set, in which the habitat types with the highest possibility of occurrence could be ranked accordingly. The result of the modelling for pontential natural vegetation was verified using available satellite data (LANDSAT TM). This task was carried with a maxi-mum likelihood classification using the software PCI Geomatica. The results of the classi-fication and the GIS analysis are combined to obtain preliminary habitat types. These types were verified with existing Forest Management Plans, and compared with results of local terrestrial mapping and natural distribution of habitat types

Automated spatiotemporal landslide mapping over large areas using RapidEye time series data

In the past, different approaches for automated landslide identification based on multispectral satellite remote sensing were developed to focus on the analysis of the spatial distribution of landslide occurrences related to distinct triggering events. However, many regions, including southern Kyrgyzstan, experience ongoing process activity requiring continual multi-temporal analysis. For this purpose, an automated object-oriented landslide mapping approach has been developed based on RapidEye time series data complemented by relief information. The approach builds on analyzing temporal NDVI-trajectories for the separation between landslide-related surface changes and other land cover changes. To accommodate the variety of landslide phenomena occurring in the 7500 km2 study area, a combination of pixel-based multiple thresholds and object-oriented analysis has been implemented including the discrimination of uncertainty-related landslide likelihood classes. Applying the approach to the whole study area for the time period between 2009 and 2013 has resulted in the multi-temporal identification of 471 landslide objects. A quantitative accuracy assessment for two independent validation sites has revealed overall high mapping accuracy (Quality Percentage: 80%), proving the suitability of the developed approach for efficient spatiotemporal landslide mapping over large areas, representing an important prerequisite for objective landslide hazard and risk assessment at the regional scale

Challenges in UAS-Based TIR Imagery Processing: Image Alignment and Uncertainty Quantification

DFG, 357874777, FOR 2694: Large-Scale and High-Resolution Mapping of Soil Moisture on Field and Catchment Scales - Boosted by Cosmic-Ray NeutronsDFG, 414044773, Open Access Publizieren 2019 - 2020 / Technische Universität Berli

Mapping the fractional coverage of the invasive shrub Ulex europaeus with multi-temporal Sentinel-2 imagery utilizing UAV orthoimages and a new spatial optimization approach

Mapping the occurrence patterns of invasive plant species and understanding their invasion dynamics is a crucial requirement for preventing further spread to so far unaffected regions. An established approach to map invasive species across large areas is based on the combination of satellite or aerial remote sensing data with ground truth data from fieldwork. Unmanned aerial vehicles (UAV, also referred to as unmanned aerial systems (UAS)) may represent an interesting and low-cost alternative to labor-intensive fieldwork. Despite the increasing use of UAVs in the field of remote sensing in the last years, operational methods to combine UAV and satellite data are still sparse. Here, we present a new methodological framework to estimate the fractional coverage (FC%) of the invasive shrub species Ulex europaeus (common gorse) on Chilo´e Island (south-central Chile), based on ultrahigh- resolution UAV images and a medium resolution intra-annual time-series of Sentinel-2. Our framework is based on three steps: 1) Land cover classification of the UAV orthoimages, 2) reduce the spatial shift between UAV-based land cover classification maps and Sentinel-2 imagery and 3) identify optimal satellite acquisition dates for estimating the actual distribution of Ulex europaeus. In Step 2 we translate the challenging co-registration task between two datasets with very different spatial resolutions into an (machine learning) optimization problem where the UAV-based land cover classification maps obtained in Step 1 are systematically shifted against the satellite images. Based on several Random Forest (RF) models, an optimal fit between varying land cover fractions and the spectral information of Sentinel-2 is identified to correct the spatial offset between both datasets. Considering the spatial shifts of the UAV orthoimages and using optimally timed Sentinel-2 acquisitions led to a significant improvement for the estimation of the current distribution of Ulex europaeus. Furthermore, we found that the Sentinel-2 acquisition from November (flowering time of Ulex europaeus) was particularly important in distinguishing Ulex europaeus from other plant species. Our mapping results could support local efforts in controlling Ulex europaeus. Furthermore, the proposed workflow should be transferable to other use cases where individual target species that are visually detectable in UAV imagery are considered. These findings confirm and underline the great potential of UAV-based groundtruth data for detecting invasive species

An object-based classification approach for mapping "migrant housing" in the mega-urban area of the Pearl River Delta (China)

Urban areas develop on formal and informal levels. Informal development is often highly dynamic, leading to a lag of spatial information about urban structure types. In this work, an object-based remote sensing approach will be presented to map the migrant housing urban structure type in the Pearl River Delta, China. SPOT5 data were utilized for the classification (auxiliary data, particularly up-to-date cadastral data, were not available). A hierarchically structured classification process was used to create (spectral) independence from single satellite scenes and to arrive at a transferrable classification process. Using the presented classification approach, an overall classification accuracy of migrant housing of 68.0% is attained

Using soil water isotopes to infer the influence of contrasting urban green space on ecohydrological partitioning

Acknowledgements We thank the German Research Foundation (DFG) for funding this project as part of the Research Training Group “Urban Water Interfaces (UWI)” (GRK 2032/2) and the Einstein Foundation for the support as part of the project “Modelling surface and groundwater with isotopes in urban catchments (MOSAIC)”. We are especially thankful to our colleagues of the TU Berlin Ecology Department for providing access to their property and assistance for site selection, in particular Birgit Seitz, and to the Department of Climatology, especially Dieter Scherer and Fred Meier, for providing the UCO climate data. Further, we thank our colleagues Esther Brakkee, Larissa Lachmann, Nina-Sophie Weiß, Christian Marx, Lukas Kleine, Wiebke Lehmann, Hauke Dämpfling, David Dubbert, Anna Wieland, Jonas Freymüller, Sylvia Jordan and Mikael Gillefalk for assistance in the sampling and installation of equipment and David Dubbert for help with the isotope analysis. Finally, we thank the Berlin Senate Department for the Environment, Transport and Climate Protection for providing groundwater data and well access. Financial support This research has been funded by the Deutsche Forschungsgemeinschaft (grant no. GRK 2032/2). The publication of this article was funded by the Open Access Fund of the Leibniz Association.Peer reviewedPublisher PD

Analyzing temporal trends of urban evaporation using generalized additive models

This study aimed to gain new insights into urban hydrological balance (in particular, the evaporation from paved surfaces). Hourly evaporation data were obtained simultaneously from two high-resolution weighable lysimeters. These lysimeters are covered in two pavement sealing types commonly used for sidewalks in Berlin, namely cobble-stones and concrete slabs. A paired experiment in field conditions is designed to determine the mechanism by which these two types of soil sealing affect the evaporation rate under the same climatic conditions. A generalized additive model (GAM) is applied to explain how the climatic conditions interact with soil sealing and to evaluate the variation of evaporation rate according to pavement type. Moreover, taking the advantage of the fact that the experimental design is paired, the study fits a new GAM where the response variable is the difference between the evaporation rate from the two lysimeters and its explanatory variables are the climatic conditions. As a result, under the same climatic conditions, cobble-stones are more prone to increasing the evaporation rate than concrete slabs when the precipitation accumulated over 10 h, solar radiation, and wind speed increases. On the other hand, concrete slabs are more inclined to increase the evaporation rate than cobblestones when the relative humidity increases. GAM represents a robust modeling approach for comparing different sealing types in order to understand how they alter the hydrological balanceFunding: The German Research Foundation DFG (GRK 2032) and the Open Access Publication Fund of TU Berlin.Peer ReviewedPostprint (published version

Towards detecting swath events in TerraSAR-X time series to establish NATURA 2000 grassland habitat swath management as monitoring parameter

Spatial monitoring tools are necessary to respond to the threat of global biodiversity loss. At the European scale, remote sensing tools for NATURA 2000 habitat monitoring have been requested by the European Commission to fulfill the obligations of the EU Habitats Directive. This paper introduces a method by which swath events in semi-natural grasslands can be detected from multi-temporal TerraSAR-X data. The investigated study sites represent rare and endangered habitats (NATURA 2000 codes 6410, 6510), located in the Döberitzer Heide nature conservation area west of Berlin. We analyzed a time series of 11 stripmap images (HH-polarization) covering the vegetation period affected by swath (June to September 2010) at a constant 11-day acquisition rate. A swath detection rule was established to extract the swath events for the NATURA 2000 habitats as well as for six contrasting pasture sites not affected by swath. All swath events observed in the field were correctly allocated. The results indicate the potential to allocate semi-natural grassland swath events to 11-day-periods using TerraSAR-X time series. Since the conservation of semi-natural grassland habitats requires compliance with specific swath management rules, the detection of swath events may thus provide new parameters for the monitoring of NATURA 2000 grassland habitats.DLR/50EE092

Evaluation of Remote-Sensing-Based Landslide Inventories for Hazard Assessment in Southern Kyrgyzstan

Large areas in southern Kyrgyzstan are subjected to high and ongoing landslide activity; however, an objective and systematic assessment of landslide susceptibility at a regional level has not yet been conducted. In this paper, we investigate the contribution that remote sensing can provide to facilitate a quantitative landslide hazard assessment at a regional scale under the condition of data scarcity. We performed a landslide susceptibility and hazard assessment based on a multi-temporal landslide inventory that was derived from a 30-year time series of satellite remote sensing data using an automated identification approach. To evaluate the effect of the resulting inventory on the landslide susceptibility assessment, we calculated an alternative susceptibility model using a historical inventory that was derived by an expert through combining visual interpretation of remote sensing data with already existing knowledge on landslide activity in this region. For both susceptibility models, the same predisposing factors were used: geology, stream power index, absolute height, aspect and slope. A comparison of the two models revealed that using the multi-temporal landslide inventory covering the 30-year period results in model coefficients and susceptibility values that more strongly reflect the properties of the most recent landslide activity. Overall, both susceptibility maps present the highest susceptibility values for similar regions and are characterized by acceptable to high predictive performances. We conclude that the results of the automated landslide detection provide a suitable landslide inventory for a reliable large-area landslide susceptibility assessment. We also used the temporal information of the automatically detected multi-temporal landslide inventory to assess the temporal component of landslide hazard in the form of exceedance probability. The results show the great potential of satellite remote sensing for deriving detailed and systematic spatio-temporal information on landslide occurrences, which can significantly improve landslide susceptibility and hazard assessment at a regional scale, particularly in data-scarce regions such as Kyrgyzstan.BMBF, 03G0809, Verbundprojekt WTZ Zentralasien: TIPTIMON - Tien Shan - Pamir Monitoring Programm - Spätkänozoische Geodynamik, Klimainteraktionen und resultierende Risiken in Zentralasie