LANDSAT-BASED WOODY VEGETATION COVER MONITORING IN SOUTHERN AFRICAN SAVANNAHS

被引:11
|
作者
Symeonakis, E. [1 ]
Petroulaki, K. [1 ]
Higginbottom, T. [1 ]
机构
[1] Manchester Metropolitan Univ, Sch Sci & Environm, Chester St, Manchester M15 GD, Lancs, England
来源
XXIII ISPRS CONGRESS, COMMISSION VII | 2016年 / 41卷 / B7期
关键词
Land degradation; woody vegetation cover; bush encroachment monitoring; South Africa; Landsat; random forests;
D O I
10.5194/isprsarchives-XLI-B7-563-2016
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Mapping woody cover over large areas can only be effectively achieved using remote sensing data and techniques. The longest continuously operating Earth-observation program, the Landsat series, is now freely-available as an atmospherically corrected, cloud masked surface reflectance product. The availability and length of the Landsat archive is thus an unparalleled Earth-observation resource, particularly for long-term change detection and monitoring. Here, we map and monitor woody vegetation cover in the Northwest Province of South Africa, an area of more than 100,000km(2) covered by 11 Landsat scenes. We employ a multi-temporal approach with dry-season data from 7 epochs between 1990 to 2015. We use 0.5m-pixel colour aerial photography to collect >15,000 point samples for training and validating Random Forest classifications of (i) woody vegetation cover, (ii) other vegetation types (including grasses and agricultural land), and (iii) non-vegetated areas (i.e. urban areas and bare land). Overall accuracies for all years are around 80% and overall kappa between 0.45 and 0.66. Woody vegetation covers a quarter of the Province and is the most accurately mapped class (balanced accuracies between 0.74-0.84 for the 7 epochs). There is a steady increase in woody vegetation cover over the 25-year-long period of study in the expense of the other vegetation types. We identify potential woody vegetation encroachment 'hot spots' where mitigation measures might be required and thus provide a management tool for the prioritisation of such measures in degraded and food-insecure areas.
引用
收藏
页码:563 / 567
页数:5
相关论文
共 50 条
  • [21] TOWARDS A DEEP LEARNING FRACTIONAL WOODY VEGETATION COVER MONITORING FRAMEWORK
    Symeonakis, Elias
    Korkofigkas, Antonis
    Higginbottom, Thomas
    Boyd, James
    Arnau-Rosalen, Eva
    Stamou, Giorgos
    Karantzalos, Konstantinos
    2022 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS 2022), 2022, : 5905 - 5908
  • [22] Woody Cover Fractions in African Savannas From Landsat and High-Resolution Imagery
    Nagelkirk, Ryan L.
    Dahlin, Kyla M.
    REMOTE SENSING, 2020, 12 (05)
  • [23] Landsat-based land cover mapping in the lower Yuna River watershed in the Dominican Republic
    Laba, M
    Smith, SD
    Degloria, SD
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 1997, 18 (14) : 3011 - 3025
  • [24] Departures of Rangeland Fractional Component Cover and Land Cover from Landsat-Based Ecological Potential in Wyoming, USA
    Rigge, Matthew
    Homer, Collin
    Shi, Hua
    Wylie, Bruce
    RANGELAND ECOLOGY & MANAGEMENT, 2020, 73 (06) : 856 - 870
  • [25] EVALUATION OF RULE-BASED CLASSIFIER FOR LANDSAT-BASED AUTOMATED LAND COVER MAPPING IN SOUTH AFRICA
    Salmon, B. P.
    Wessels, K. J.
    van den Bergh, E.
    Steenkamp, K.
    Kleynhans, W.
    Swanepoel, D.
    Roy, D.
    Kovalskyy, V.
    2013 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2013, : 4301 - 4304
  • [26] Guidance on and comparison of machine learning classifiers for Landsat-based land cover and land use mapping
    Shih, Hsiao-chien
    Stow, Douglas A.
    Tsai, Yu Hsin
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2019, 40 (04) : 1248 - 1274
  • [27] A Landsat-based vegetation trend product of the Tibetan Plateau for the time-period 1990–2018
    Fabian Ewald Fassnacht
    Christopher Schiller
    Teja Kattenborn
    Xinquan Zhao
    Jiapeng Qu
    Scientific Data, 6
  • [28] Monitoring change in the spatial heterogeneity of vegetation cover in an African savanna
    Murwira, Amon
    Skidmore, Andrew K.
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2006, 27 (11) : 2255 - 2269
  • [29] Biomass Increases Go under Cover: Woody Vegetation Dynamics in South African Rangelands
    Mograbi, Penelope J.
    Erasmus, Barend F. N.
    Witkowski, E. T. F.
    Asner, Gregory P.
    Wessels, Konrad J.
    Mathieu, Renaud
    Knapp, David E.
    Martin, Roberta E.
    Main, Russell
    PLOS ONE, 2015, 10 (05):
  • [30] Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error
    Sexton, Joseph O.
    Song, Xiao-Peng
    Feng, Min
    Noojipady, Praveen
    Anand, Anupam
    Huang, Chengquan
    Kim, Do-Hyung
    Collins, Kathrine M.
    Channan, Saurabh
    DiMiceli, Charlene
    Townshend, John R.
    INTERNATIONAL JOURNAL OF DIGITAL EARTH, 2013, 6 (05) : 427 - 448