Next Article in Journal
Capturing Coastal Dune Natural Vegetation Types Using a Phenology-Based Mapping Approach: The Potential of Sentinel-2
Previous Article in Journal
Climate and Land-Use Change Effects on Soil Carbon Stocks over 150 Years in Wisconsin, USA
Article Menu
Issue 12 (June-2) cover image

Export Article

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
  |  
PDF [11254 KB, uploaded 27 June 2019]
  |  

Abstract

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
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Supplementary material

SciFeed

Share & Cite This Article

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.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top