Probabilistic description of vegetation ecotones using remote sensing

被引:10
|
作者
de Klerk, H. M. [1 ]
Burgess, N. D. [2 ,5 ]
Visser, V. [3 ,4 ]
机构
[1] Stellenbosch Univ, Dept Geog & Environm Studies, Chamber Mines Bldg,Ryneveld & Merriman St, ZA-7599 Stellenbosch, South Africa
[2] UN Environm World Conservat Monitoring Ctr UNEP W, 219 Huntington Rd, Cambridge, England
[3] Univ Cape Town, Dept Stat Sci, SEEC Ctr Stat Ecol Environm & Conservat, ZA-7701 Rondebosch, South Africa
[4] Univ Cape Town, African Climate & Dev Initiat, ZA-7701 Rondebosch, South Africa
[5] Univ Copenhagen, CMEC, Nat Hist Museum, Copenhagen, Denmark
基金
新加坡国家研究基金会;
关键词
Ecotone; Vegetation transition; Remote sensing; Probabilistic classifier; CAPE FLORISTIC REGION; AGULHAS PLAIN; GRADIENTS; PATTERN; TERRESTRIAL; PREDICTION; FOREST; MODEL;
D O I
10.1016/j.ecoinf.2018.06.001
中图分类号
Q14 [生态学(生物生态学)];
学科分类号
071012 ; 0713 ;
摘要
Ecotone transitions between vegetation types are of interest for understanding regional diversity, ecological processes and biogeographical patterns. Ecotones are seldom represented on vector, line-based vegetation maps, which imply an instantaneous change from one vegetation type to another. We use supervised, probabilistic classification of remotely sensed (RS) imagery to investigate the location, width and character of ecotones between acid Sandstone and alkaline Limestone fynbos on the Agulhas plain at the southern tip of Africa, known for rapid speciation of plants and exceptional plant biodiversity at the global scale. The resultant probability map, together with the probability graphs developed for a few transects across the transition, are able to map and describe (1) sharp, narrow ecotones (under five meters); (2) moderate ecotones that have a distinct band of transition (over a few hundred meters); and (3) complex ecotones that include slow transitions, interdigitated boundaries and outliers. The latter class of transitions include portions where vegetation types change sharply over a few meters, but due to the interdigitated boundaries they are mapped over hundreds of meters to a kilometre at a landscape scale. In this study area, our findings suggest that the character of the Agulhas limestone-acid ecotone is probably more complex than often noted. Moderate transitions and broad mosaics are difficult to indicate in a vector vegetation map, whereas RS probabilistic classifications can output images indicating core areas, important for key species and biodiversity pattern, and transitional zones, important for ecosystem processes and perhaps plant evolution, which distinction is important for conservation planning.
引用
收藏
页码:125 / 132
页数:8
相关论文
共 50 条
  • [41] REMOTE SENSING OF VEGETATION DYNAMICS IN AGRO-ECOSYSTEMS USING SMAP VEGETATION OPTICAL DEPTH AND OPTICAL VEGETATION INDICES
    Piles, M.
    Chaparro, D.
    Entekhabi, D.
    Konings, A. G.
    Jagdhuber, T.
    Camps-Valls, G.
    2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2017, : 4346 - 4349
  • [42] Monitoring vegetation condition using microwave remote sensing: the standardized vegetation optical depth index (SVODI)
    Moesinger, Leander
    Zotta, Ruxandra-Maria
    van Der Schalie, Robin
    Scanlon, Tracy
    de Jeu, Richard
    Dorigo, Wouter
    BIOGEOSCIENCES, 2022, 19 (21) : 5107 - 5123
  • [43] Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review
    Gao, Lin
    Wang, Xiaofei
    Johnson, Brian Alan
    Tian, Qingjiu
    Wang, Yu
    Verrelst, Jochem
    Mu, Xihan
    Gu, Xiangfa
    ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2020, 159 : 364 - 377
  • [44] Remote sensing of solar induced fluorescence of vegetation
    Smorenburg, K
    Courre'ges-Lacoste, GB
    Berger, M
    Buschmann, C
    Court, A
    Del Bello, U
    Langsdorf, G
    Lichtenthaler, HK
    Sioris, C
    Stoll, MP
    Visser, H
    REMOTE SENSING FOR AGRICULTURE, ECOSYSTEMS, AND HYDROLOGY III, 2002, 4542 : 178 - 190
  • [45] Hyperspectral remote sensing of vegetation and agricultural crops
    Thenkabail, Prasad S.
    Gumma, Murali Krishna
    Teluguntla, Pardhasaradhi
    Ahmed, Mohammed Irshad
    Photogrammetric Engineering and Remote Sensing, 2013, 79 (09):
  • [46] Vegetation Indices, Remote Sensing and Forest Monitoring
    Huete, Alfredo R.
    GEOGRAPHY COMPASS, 2012, 6 (09): : 513 - 532
  • [47] HYPERSPECTRAL REMOTE SENSING OF VEGETATION AND AGRICULTURAL CROPS
    Thenkabail, Prasad S.
    Gumma, Murali Krishna
    Teluguntla, Pardhasaradhi
    Mohammed, Irshad A.
    PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING, 2014, 80 (08): : 697 - 709
  • [48] Suppression of vegetation in multispectral remote sensing images
    Yu, Le
    Porwal, Alok
    Holden, Eun-Jung
    Dentith, Michael Charles
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2011, 32 (22) : 7343 - 7357
  • [49] REMOTE SENSING OF PHOTOSYNTHETIC ACTIVITY OF ARCTIC VEGETATION
    Kazantsev, Taras
    Raeim, Olaf
    2013 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS), 2013, : 3797 - 3800
  • [50] Remote sensing of soils and vegetation: regional aspects
    Kozoderov, V. V.
    Dmitriev, E. V.
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2008, 29 (09) : 2733 - 2748