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  • 1
    In: Remote Sensing of Environment, Elsevier BV, Vol. 222 ( 2019-03), p. 195-203
    Type of Medium: Online Resource
    ISSN: 0034-4257
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2019
    detail.hit.zdb_id: 431483-9
    SSG: 11
    SSG: 14
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  • 2
    In: Remote Sensing, MDPI AG, Vol. 13, No. 17 ( 2021-08-24), p. 3342-
    Abstract: Increasing woody cover and overgrazing in semi-arid ecosystems are known to be the major factors driving land degradation. This study focuses on mapping the distribution of the slangbos shrub (Seriphium plumosum) in a test region in the Free State Province of South Africa. The goal of this study is to monitor the slangbos encroachment on cultivated land by synergistically combining Synthetic Aperture Radar (SAR) (Sentinel-1) and optical (Sentinel-2) Earth observation information. Both optical and radar satellite data are sensitive to different vegetation properties and surface scattering or reflection mechanisms caused by the specific sensor characteristics. We used a supervised random forest classification to predict slangbos encroachment for each individual crop year between 2015 and 2020. Training data were derived based on expert knowledge and in situ information from the Department of Agriculture, Land Reform and Rural Development (DALRRD). We found that the Sentinel-1 VH (cross-polarization) and Sentinel-2 SAVI (Soil Adjusted Vegetation Index) time series information have the highest importance for the random forest classifier among all input parameters. The modelling results confirm the in situ observations that pastures are most affected by slangbos encroachment. The estimation of the model accuracy was accomplished via spatial cross-validation (SpCV) and resulted in a classification precision of around 80% for the slangbos class within each time step.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2513863-7
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  • 3
    Online Resource
    Online Resource
    South African National Biodiversity Institute ; 2021
    In:  Bothalia, African Biodiversity & Conservation Vol. 51, No. 1 ( 2021-02-24)
    In: Bothalia, African Biodiversity & Conservation, South African National Biodiversity Institute, Vol. 51, No. 1 ( 2021-02-24)
    Abstract: Background: The understanding of the impact of land-use on the dynamics of phytoplankton assemblages during varying climate conditions on rivers is limited.Objective: To determine the impact of land-use types and flow on phytoplankton assemblages in the Sabie River.Methods: The relationships between land-use patterns, water quality and phytoplankton assemblages were analysed using canonical correspondence analyses (CCA).Results: Six main land-use types could be distinguished in the eight identified sub-catchments of the river. The CCA results showed that the land-use had a stronger correlation with phytoplankton classes during the higher flow conditions than during low flow conditions. The forestry land-use type had the strongest correlation with nitrate–nitrite concentrations in the Sabie River. Chlorophyll-a concentrations were higher during 2016, and a slightly lower number of genera were observed for the class Chlorophyceae. During 2017 the number of genera of the class Cyanophyceae decreased together with chlorophyll-a and total cell concentrations.Conclusion: Low flow conditions favoured filamentous genera that are common to mesotrophic conditions while higher flow conditions favoured single-celled small genera more common to oligotrophic waters with higher disturbance. This study showed that flow has the potential to enhance the impact of land-use on phytoplankton community dynamics in a river system and thereby may have further impacts on the health and activities of surrounding communities.
    Type of Medium: Online Resource
    ISSN: 2311-9284 , 0006-8241
    Language: Unknown
    Publisher: South African National Biodiversity Institute
    Publication Date: 2021
    detail.hit.zdb_id: 391485-9
    detail.hit.zdb_id: 2807660-6
    SSG: 12
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