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Open AccessArticle

Deriving a Forest Cover Map in Kyrgyzstan Using a Hybrid Fusion Strategy

by Tao Jia 1,2,*, Yuqian Li 1, Wenzhong Shi 2 and Ling Zhu 3
1
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China
2
Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong, China
3
School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(19), 2325; https://doi.org/10.3390/rs11192325
Received: 22 July 2019 / Revised: 29 September 2019 / Accepted: 3 October 2019 / Published: 6 October 2019
(This article belongs to the Section Forest Remote Sensing)
Forests have potential economic value and play a significant role in maintaining ecological balance. Considering its outdated and incomplete forest statistics, the Kyrgyzstan Republic urgently needs a forest cover map for assessing its current forest resources and assisting national policies on improving rural livelihood and sustainability. This study adopted a hybrid fusion strategy to develop a forest cover map for the Kyrgyzstan Republic with improved accuracy. The fusion strategy uses the merits of the GlobeLand30 in 2010 and the USGS TreeCover2010, the benefits of auxiliary geographic information, and the advantages of the stacking learning method in classification. Additionally, we explored the influence of different forest definitions, based on the tree cover percentage value in the USGS TreeCover2010, on the accuracy of forest cover. Results suggested that the accuracy of our model can be improved significantly by including auxiliary geographic features and feeding the optimal size of training samples. Thereafter, using our model, forest cover maps were derived at different tree cover threshold values in the USGS TreeCover2010. Importantly, the forest cover map at the tree cover threshold value of 40% was determined as the most accurate one with the kappa value of 0.89, whose spatial extent constitutes about 2.4% of the entire territory. This estimated forest cover percentage suggests a low estimation of forest resources based on rigorous definition, which can be valuable for reviewing and amending the current national forest policies. View Full-Text
Keywords: Forest cover map; GlobeLand30; USGS TreeCover2010; data fusion; stacking learning Forest cover map; GlobeLand30; USGS TreeCover2010; data fusion; stacking learning
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Jia, T.; Li, Y.; Shi, W.; Zhu, L. Deriving a Forest Cover Map in Kyrgyzstan Using a Hybrid Fusion Strategy. Remote Sens. 2019, 11, 2325.

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