Research Articles (Geography and Environmental Studies)

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    Phylogenetic structure of alien plant species pools from European donor habitats
    (John Wiley & Sons Ltd., 2021) Kalusova, Veronika; Cubino, Josep Padulles; Fristoe, Trevor S.; Chytry, Milan; Van Kleunen, Mark; Dawson, Wayne; Essl, Franz; Kreft, Holger; Mucina, Ladislav; Pergl, Jan; Pysek, Petr; Weigelt, Patrick; Winter, Marten; Lososova, Zdenka
    Aim: Many plant species native to Europe have naturalized worldwide. We tested whether the phylogenetic structure of the species pools of European habitats is related to the proportion of species from each habitat that has naturalized outside Europe (habitat’s donor role) and whether the donated species are more phylogenetically related to each other than expected by chance. Location: Europe (native range), the rest of the world (invaded range). Time period: Last c. 100 years. Major taxa studied: Angiospermae. Methods: We selected 33 habitats in Europe and analysed their species pools, including 9,636 plant species, of which 2,293 have naturalized outside Europe. We assessed the phylogenetic structure of each habitat as the difference between the observed and expected mean pairwise phylogenetic distance (MPD) for (a) the whole species pool and (b) subgroups of species that have naturalized outside Europe and those that have not. We used generalized linear models to test for the effects of the phylogenetic structure and the level of human influence on the habitat’s donor role.
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    Addressing the need for improved land cover map products for policy support
    (Elsevier, 2020-10) Szantoi, Zoltan; Geller, Gary N.; Tsendbazar, Nandin-Erdene; See, Linda; Griffiths, Patrick; Fritz, Steffen; Gong, Peng; Herold, Martin; Morah, Brice; Obregon, Andre
    The continued increase of anthropogenic pressure on the Earth’s ecosystems is degrading the natural environment and then decreasing the services it provides to humans. The type, quantity, and quality of many of those services are directly connected to land cover, yet competing demands for land continue to drive rapid land cover change, affecting ecosystem services. Accurate and updated land cover information is thus more important than ever, however, despite its importance, the needs of many users remain only partially attended. A key underlying reason for this is that user needs vary widely, since most current products – and there are many available – are produced for a specific type of end user, for example the climate modelling community. With this in mind we focus on the need for flexible, automated processing approaches that support on-demand, customized land cover products at various scales. Although land cover processing systems are gradually evolving in this direction there is much more to do and several important challenges must be addressed, including high quality reference data for training and validation and even better access to satellite data. Here, we 1) present a generic system architecture that we suggest land cover production systems evolve towards, 2) discuss the challenges involved, and 3) propose a step forward. Flexible systems that can generate on-demand products that match users’ specific needs would fundamentally change the relationship between users and land cover products – requiring more government support to make these systems a reality.
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    A synthesizing land-cover classification method based on Google Earth Engine : a case study in Nzhelele and Levhuvu catchments, South Africa
    (Springer, 2020-07-07) Zeng, Hongwei; Wu, Bingfang; Wang, Shuai; Musakwa, Walter; Tian, Fuyou; Mashimbye, Zama Eric; Poona, Nitesh; Syndey, Mavengahama
    This study designed an approach to derive land-cover in the South Africa with insufficient ground samples, and made a case demonstration in Nzhelele and Levhuvu catchments, South Africa. The method was developed based on an integration of Landsat 8, Sentinel-1, and Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), and the Google Earth Engine (GEE) platform. Random forest classifier with 300 trees is employed as land-cover classification model. In order to overcome the defect of insufficient ground data, the stratified sampling method was used to generate the training and validation samples from the existing land-cover product. Likewise, in order to recognize different land-cover categories, the percentile and monthly median composites were employed to expand input metrics of random forest classifier. Results showed that the overall accuracy of the land-cover of Nzhelele and Levhuvu catchments, South Africa in 2017–2018 reached to 76.43%. Three important results can be drawn from our research. 1) The participation of Sentinel-1 data can slightly improve overall accuracy of land-cover while its contribution on land-cover classification varied with land types. 2) Under-fitting problem was observed in the training of non-dominant land-cover categories using the random sampling, the stratified sampling method is recommended to make sure the classification accuracy of non-dominant classes. 3) When related reflectance bands participated in the training process, individual Normalized Difference Vegetation index (NDVI), Enhanced Vegetation Index (EVI), Soil Adjusted Vegetation Index (SAVI), Normalized Difference Built-up Index (NDBI) have little effect on final land-cover classification result.
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    National coastal assessment & coastal climate change vulnerability assessment: Implications for the future
    (2019-09) Luck-Vogel, Melanie
    In this presentation the legacy of the coastal flood and erosion risk assessments conducted from the National Coastal Assessment project and the Coastal Climate Change Vulnerability Assessment is explained and the way forward is lined out.
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    Vegetation mapping in the St Lucia estuary using very high-resolution multispectral imagery and LiDAR
    (Elsevier, 2016-05) Luck-Vogel, Melanie; Mbolambi, C.; Rautenbach, K.; Adams, J.; van Niekerk, L.
    This paper examines the value of very high-resolution multispectral satellite imagery and LiDAR-derived digital elevation information for classifying estuarine vegetation types. Satellite images used are fromtheWorldView-2, RapidEye, and SPOT-6 sensors in 2mand 5mresolution, respectively, acquired between 2010 and 2014. Ground truthing reference is a GIS-derived vegetation map based on field data from 2008. Supervised maximum likelihood classification produced satisfactory overall accuracies between 64.3% and 77.9% for the SPOT-6 and the WorldView-2 image, respectively,while the RapidEye-based classifications produced overall accuracies between 55.0% and 66.8%. The reasons for the misclassifications are mainly based on the highly dynamic environmental conditions causing discrepancies between the field data and satellite acquisition dates rather than technical issues. Dynamics in water levels and salinity caused rapid change in vegetation communities. Further, weather impacts such as floods and wind events caused water turbidity and led to bias in the reflective properties of the satellite images and thus misclassifications. These results show, however, that the spatial and spectral resolution of modern very high-resolution imagery is sufficient to satisfactory map estuarine vegetation and to monitor small-scale change. They emphasise, however, the importance of synchronisation of ground truthing data with actual image acquisition dates in these highly dynamic environments in order to achieve high classification accuracies. The results also highlight the importance of ancillary data for accurate interpretation of observed classification discrepancies and vegetation dynamics.