Please use this identifier to cite or link to this item: https://ktisis.cut.ac.cy/handle/10488/19254
Title: Mapping understory invasive plant species with field and remotely sensed data in Chitwan, Nepal
Authors: Dai, Jie 
Roberts, Dar A. 
Stow, Douglas Alan 
An, Li 
Hall, Sharon J. 
Yabiku, Scott T. 
Kyriakidis, Phaedon 
Major Field of Science: Engineering and Technology
Field Category: Civil Engineering
Keywords: Invasive species;Understory vegetation;Spectral mixture analysis;Maxent;Landsat;Mikania micrantha;Chitwan National Park
Issue Date: 1-Dec-2020
Source: Remote Sensing of Environment, 2020, vol. 250, articl. no 112037
Volume: 250
Journal: Remote Sensing of Environment 
Abstract: Monitoring invasive species distribution and prevalence is important, but direct field-based assessment is often impractical. In this paper, we introduce and validate a cost-effective method for mapping understory invasive plant species. We utilized Landsat imagery, spectral mixture analysis (SMA) and a maximum entropy (Maxent) modeling framework to map the spatial extent of Mikania micrantha in Chitwan National Park, Nepal and community forests within its buffer zone. We developed a spectral library from reference and image sources and applied multiple endmember SMA (MESMA) to selected Landsat imagery. Incorporating the resultant green vegetation and shade fractions into Maxent, we mapped the distribution of understory M. micrantha in the study area, with training and testing Area under Curve (AUC) values around 0.80, and kappa around 0.55. In vegetated places, especially mature forests, an increase in green vegetation fraction and decrease in shade fraction was associated with higher likelihood of M. micrantha presence. In addition, the inclusion of elevation as a model input further improved map accuracy (AUC around 0.95; kappa around 0.80). Elevation, a surrogate for distance to water in this case, proved to be the determining factor of M. micrantha's distribution in the study area. The combination of MESMA and Maxent can provide significant opportunities for understanding understory vegetation distribution, and contribute to ecological restoration, biodiversity conservation, and provision of sustainable ecosystem services in protected areas.
URI: https://ktisis.cut.ac.cy/handle/10488/19254
ISSN: 0034-4257
DOI: 10.1016/j.rse.2020.112037
Rights: © The Authors
Type: Article
Affiliation : San Diego State University 
University of California 
Arizona State University 
Pennsylvania State University 
Cyprus University of Technology 
Appears in Collections:Άρθρα/Articles

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