A regional, remote sensing-based approach to mapping land degradation in the Little Karoo, South Africa

被引:5
|
作者
Kirsten, Tim [1 ,4 ]
Hoffman, Michael Timm [1 ]
Bell, Wesley Drummond [1 ]
Visser, Vernon [2 ,3 ]
机构
[1] Univ Cape Town, Dept Biol Sci, Plant Conservat Unit, ZA-7701 Rondebosch, South Africa
[2] Univ Cape Town, Ctr Stat Ecol Environm & Conservat, ZA-7701 Rondebosch, South Africa
[3] Stellenbosch Univ, Natl Inst Theoret & Computat Sci NITheCS, ZA-7602 Matieland, South Africa
[4] 1 Coligny Rd, ZA-7945 Kirstenhof, South Africa
关键词
Desertification; Drylands; Habitat condition; Land degradation assessment; Remote sensing; SUCCULENT KAROO; PLANT DIVERSITY; RANGELAND;
D O I
10.1016/j.jaridenv.2023.105066
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
There is growing global consensus that assessments of land degradation be conducted at regional or smaller scales. Working at this scale allows for locally relevant environmental and land use conditions to be incorporated into the assessment methodology. In this paper, a recently developed regional approach to assessing land degradation in the Hardeveld bioregion of the Succulent Karoo is applied to the Little Karoo region of this biome. The methodology uses fuzzy classification statistical techniques to combine field data with multiple Sentinel-2A and Landsat vegetation indices, as well as regionally modelled soil variables. The resultant habitat condition archetype map values show strong correlation with field observations of perennial plant and bare soil cover in 96 ground-truthed plots. The archetype map indicates that heavily degraded hotspots of high bare ground cover occur throughout the project region, although there is an overall lower average habitat condition in the western half of the Little Karoo. The mean habitat condition archetype value for the entire project area is 0.54 (standard deviation = 0.13), on a continuous scale where 0 and 1 represent the most degraded and pristine extremes, respectively. Random forest regression analysis of various environmental covariates of degradation indicates a strong relationship between habitat condition and topographic as well as rainfall variables, although the limited accuracy of modelled livestock data may obscure the negative impacts of overgrazing. The 30 m resolution habitat condition archetype map builds upon previous degradation research in the Little Karoo and has the potential to inform future conservation, restoration, and rangeland management decisions. The methodology was successfully transferred to a new region and provides an opportunity to improve reporting on the extent of land degradation across South Africa.
引用
收藏
页数:12
相关论文
共 50 条
  • [41] Long-term studies of land degradation in the Sneeuberg uplands, eastern Karoo, South Africa: A synthesis
    Boardman, J.
    Foster, I. D. L.
    Rowntree, K. M.
    Favis-Mortlock, D. T.
    Mol, L.
    Suich, H.
    Gaynor, D.
    GEOMORPHOLOGY, 2017, 285 : 106 - 120
  • [42] A Remote Sensing-based Soil Moisture Condition by an Evaporative Fraction Approach
    Chuang, Chi-Hung
    Yu, Hwa-Lung
    Taiwan Water Conservancy, 2021, 69 (01): : 56 - 66
  • [43] A triangulation approach for assessing and mapping land degradation in the Lepellane catchment of the greater Sekhukhune District, South Africa
    Nzuza, Phumlani
    Ramoelo, Abel
    Odindi, John
    Kahinda, Jean-Marc Mwenge
    Lindeque, Lehman
    SOUTH AFRICAN GEOGRAPHICAL JOURNAL, 2022, 104 (04) : 514 - 538
  • [44] REGIONAL MONITORING OF NATURAL EMERGENCIES BASED ON REMOTE SENSING OF LAND
    Kobernichenko, V. G.
    Ivanov, O. Yu.
    Zraenko, S. M.
    JOURNAL OF MINING INSTITUTE, 2005, 166 : 110 - 112
  • [45] Incorporating remote sensing-based ET estimates into the Community Land Model version 4.5
    Wang, Dagang
    Wang, Guiling
    Parr, Dana T.
    Liao, Weilin
    Xia, Youlong
    Fu, Congsheng
    HYDROLOGY AND EARTH SYSTEM SCIENCES, 2017, 21 (07) : 3557 - 3577
  • [46] An Ensemble Learning Approach for Urban Land Use Mapping Based on Remote Sensing Imagery and Social Sensing Data
    Huang, Zhou
    Qi, Houji
    Kang, Chaogui
    Su, Yuelong
    Liu, Yu
    REMOTE SENSING, 2020, 12 (19) : 1 - 18
  • [47] Research on satellite remote sensing-based intelligent monitoring technologies for new construction land
    Liu, Lirong
    Tang, Xinming
    Gan, Yuhang
    You, Shucheng
    Liu, Ke
    Luo, Zhengyu
    National Remote Sensing Bulletin, 2024, 28 (11) : 2828 - 2837
  • [48] Land Subsidence Induced by Rapid Urbanization in Arid Environments: A Remote Sensing-Based Investigation
    Aljammaz, Abdulaziz
    Sultan, Mohamed
    Izadi, Moein
    Abotalib, Abotalib Z.
    Elhebiry, Mohamed S.
    Emil, Mustafa Kemal
    Abdelmohsen, Karem
    Saleh, Mohamed
    Becker, Richard
    REMOTE SENSING, 2021, 13 (06)
  • [49] Remote sensing-based monitoring of land use and cover dynamics in surface lignite mining regions: a supervised classification approach
    Vlachogianni, Sofia
    Servou, Aikaterini
    Karalidis, Konstantinos
    Paraskevis, Nikolaos
    Menegaki, Maria
    Roumpos, Christos
    EARTH SCIENCE INFORMATICS, 2025, 18 (02)
  • [50] Remote sensing-based drought severity modeling and mapping using multiscale intelligence methods
    Ghasempour, Roghayeh
    Aalami, Mohammad Taghi
    Kirca, V. S. Ozgur
    Roushangar, Kiyoumars
    STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT, 2023, 37 (03) : 889 - 902