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|Title:||Deriving a forest cover map in Kyrgyzstan using a hybrid fusion strategy||Authors:||Jia, T
Forest cover map
|Issue Date:||2019||Publisher:||Molecular Diversity Preservation International (MDPI)||Source:||Remote sensing, 2019, v. 11, no. 19, 2325 How to cite?||Journal:||International journal of environmental research and public health||Abstract:||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 Considering its outdated and incomplete forest statistics, the Kyrgyzstan Republic urgently needs a forest cover map for assessing its current forest resources and assisting national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 n 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.||URI:||http://hdl.handle.net/10397/81597||ISSN:||2072-4292||EISSN:||2072-4292||DOI:||10.3390/rs11192325||Rights:||© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
The following publication Jia T, Li Y, Shi W, Zhu L. Deriving a Forest Cover Map in Kyrgyzstan Using a Hybrid Fusion Strategy. Remote Sensing. 2019; 11(19):2325, is available at https://doi.org/10.3390/rs11192325
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Citations as of May 6, 2020
Citations as of May 6, 2020
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