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Open AccessArticle

High-Resolution Vegetation Mapping Using eXtreme Gradient Boosting Based on Extensive Features

1
College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
2
Laboratoire Évolution et Diversité Biologique, UMR 5174 (CNRS/IRD/UPS), 31062 Toulouse Cedex 9, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(12), 1505; https://doi.org/10.3390/rs11121505
Received: 27 May 2019 / Revised: 13 June 2019 / Accepted: 18 June 2019 / Published: 25 June 2019
Accurate mapping of vegetation is a premise for conserving, managing, and sustainably using vegetation resources, especially in conditions of intensive human activities and accelerating global changes. However, it is still challenging to produce high-resolution multiclass vegetation map in high accuracy, due to the incapacity of traditional mapping techniques in distinguishing mosaic vegetation classes with subtle differences and the paucity of fieldwork data. This study created a workflow by adopting a promising classifier, extreme gradient boosting (XGBoost), to produce accurate vegetation maps of two strikingly different cases (the Dzungarian Basin in China and New Zealand) based on extensive features and abundant vegetation data. For the Dzungarian Basin, a vegetation map with seven vegetation types, 17 subtypes, and 43 associations was produced with an overall accuracy of 0.907, 0.801, and 0.748, respectively. For New Zealand, a map of 10 habitats and a map of 41 vegetation classes were produced with 0.946, and 0.703 overall accuracy, respectively. The workflow incorporating simplified field survey procedures outperformed conventional field survey and remote sensing based methods in terms of accuracy and efficiency. In addition, it opens a possibility of building large-scale, high-resolution, and timely vegetation monitoring platforms for most terrestrial ecosystems worldwide with the aid of Google Earth Engine and citizen science programs. View Full-Text
Keywords: vegetation mapping; XGBoost; simplified field survey; Dzungarian Basin; New Zealand vegetation mapping; XGBoost; simplified field survey; Dzungarian Basin; New Zealand
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MDPI and ACS Style

Zhang, H.; Eziz, A.; Xiao, J.; Tao, S.; Wang, S.; Tang, Z.; Zhu, J.; Fang, J. High-Resolution Vegetation Mapping Using eXtreme Gradient Boosting Based on Extensive Features. Remote Sens. 2019, 11, 1505. https://doi.org/10.3390/rs11121505

AMA Style

Zhang H, Eziz A, Xiao J, Tao S, Wang S, Tang Z, Zhu J, Fang J. High-Resolution Vegetation Mapping Using eXtreme Gradient Boosting Based on Extensive Features. Remote Sensing. 2019; 11(12):1505. https://doi.org/10.3390/rs11121505

Chicago/Turabian Style

Zhang, Heng; Eziz, Anwar; Xiao, Jian; Tao, Shengli; Wang, Shaopeng; Tang, Zhiyao; Zhu, Jiangling; Fang, Jingyun. 2019. "High-Resolution Vegetation Mapping Using eXtreme Gradient Boosting Based on Extensive Features" Remote Sens. 11, no. 12: 1505. https://doi.org/10.3390/rs11121505

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