Remote detection of invasive plants: a review of spectral, textural and phenological approaches

被引:201
|
作者
Bradley, Bethany A. [1 ]
机构
[1] Univ Massachusetts, Dept Environm Conservat, Amherst, MA 01003 USA
关键词
Aerial photograph; Hyperspectral; Invasive plant; Object-based classification; Phenology; Satellite remote sensing; KNAPWEED CENTAUREA-MACULOSA; HYPERSPECTRAL IMAGERY; SATELLITE IMAGERY; LIGUSTRUM-LUCIDUM; CLIMATE-CHANGE; LAKE VICTORIA; COVER; CHEATGRASS; FOREST; WEED;
D O I
10.1007/s10530-013-0578-9
中图分类号
X176 [生物多样性保护];
学科分类号
090705 ;
摘要
Remote sensing image analysis is increasingly being used as a tool for mapping invasive plant species. Resulting distribution maps can be used to target management of early infestations and to model future invasion risk. Remote identification of invasive plants based on differences in spectral signatures is the most common approach, typically using hyperspectral data. But several studies have found that textural and phenological differences are also effective approaches for identifying invasive plants. I review examples of remote detection of invasive plants based on spectral, textural and phenological analysis and highlight circumstances where the different approaches are likely to be most effective. I also review sources and availability of remotely sensed data that could be used for mapping and suggest field data collection approaches that would support the analysis of remotely sensed data. Remote mapping of biological invasions remains a relatively specialized research topic, but the distinct cover, morphology and/or seasonality of many invaded versus native ecosystems suggests that more species could be detected remotely. Remote sensing can sometimes support early detection and rapid response directly, however, accurately detecting small, nascent populations is a challenge. However, even maps of heavily infested areas can provide a valuable tool for risk assessment by increasing knowledge about temporal and spatial patterns and predictors of invasion.
引用
收藏
页码:1411 / 1425
页数:15
相关论文
共 50 条
  • [1] Remote detection of invasive plants: a review of spectral, textural and phenological approaches
    Bethany A. Bradley
    Biological Invasions, 2014, 16 : 1411 - 1425
  • [2] Future directions in remote sensing for detection of invasive plants.
    Root, R
    Kokaly, R
    Brown, K
    Anderson, GL
    ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY, 2001, 221 : U51 - U51
  • [3] The identification and remote detection of alien invasive plants in commercial forests: An Overview
    Peerbhay, Kabir
    Mutanga, Onisimo
    Ismail, Riyad
    SOUTH AFRICAN JOURNAL OF GEOMATICS, 2016, 5 (01): : 49 - 67
  • [4] Systematic review and best practices for drone remote sensing of invasive plants
    Singh, Kunwar K.
    Surasinghe, Thilina D.
    Frazier, Amy E.
    METHODS IN ECOLOGY AND EVOLUTION, 2024, 15 (06): : 998 - 1015
  • [5] Invasive plants:: approaches and predictions
    Rejmánek, M
    AUSTRAL ECOLOGY, 2000, 25 (05) : 497 - 506
  • [6] Assessing the environmental impacts of invasive alien plants: a review of assessment approaches
    Bartz, Robert
    Kowarik, Ingo
    NEOBIOTA, 2019, (43) : 69 - 99
  • [7] Textural approaches for vineyard detection and characterization using very high spatial resolution remote sensing data
    Delenne, C.
    Durrieu, S.
    Rabatel, G.
    Deshayes, M.
    Bailly, J. S.
    Lelong, C.
    Couteron, P.
    INTERNATIONAL JOURNAL OF REMOTE SENSING, 2008, 29 (04) : 1153 - 1167
  • [8] New approaches for early detection and rapid response to invasive plants in the United States
    Westbrooks, RG
    WEED TECHNOLOGY, 2004, 18 : 1468 - 1471
  • [9] Advances in Remote Sensing and Machine Learning Methods for Invasive Plants Study: A Comprehensive Review
    Zaka, Muhammad Murtaza
    Samat, Alim
    REMOTE SENSING, 2024, 16 (20)
  • [10] Change identification of remote sensing images based on textural and spectral features
    Lin, YZ
    Hsieh, PF
    IGARSS 2005: IEEE International Geoscience and Remote Sensing Symposium, Vols 1-8, Proceedings, 2005, : 2141 - 2144