{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T03:48:34Z","timestamp":1760240914482,"version":"build-2065373602"},"reference-count":38,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2019,10,6]],"date-time":"2019-10-06T00:00:00Z","timestamp":1570320000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41401453"],"award-info":[{"award-number":["41401453"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>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 Considering its outdated and incomplete forest statistics, the Kyrgyzstan Republic urgently needs a forest cover map for assessing its current forest resources and assisting national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.<\/jats:p>","DOI":"10.3390\/rs11192325","type":"journal-article","created":{"date-parts":[[2019,10,7]],"date-time":"2019-10-07T03:34:01Z","timestamp":1570419241000},"page":"2325","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Deriving a Forest Cover Map in Kyrgyzstan Using a Hybrid Fusion Strategy"],"prefix":"10.3390","volume":"11","author":[{"given":"Tao","family":"Jia","sequence":"first","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China"},{"name":"Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong, China"}]},{"given":"Yuqian","family":"Li","sequence":"additional","affiliation":[{"name":"School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430072, China"}]},{"given":"Wenzhong","family":"Shi","sequence":"additional","affiliation":[{"name":"Department of Land Surveying and Geo-informatics, The Hong Kong Polytechnic University, Kowloon 999077, Hong Kong, China"}]},{"given":"Ling","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Geomatics and Urban Spatial Information, Beijing University of Civil Engineering and Architecture, Beijing 100044, China"}]}],"member":"1968","published-online":{"date-parts":[[2019,10,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1146\/annurev-ecolsys-110512-135914","article-title":"The structure, distribution, and biomass of the world\u2019s forests","volume":"44","author":"Pan","year":"2013","journal-title":"Annu. Rev. Ecol. Evol. Syst."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.apgeog.2018.05.011","article-title":"Aboveground biomass estimation using multi-sensor data synergy and machine learning algorithms in a dense tropical forest","volume":"96","author":"Ghosh","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_3","unstructured":"MacDicken, K., Jonsson, \u00d6., Pi\u00f1a, L., Marklund, L., Maulo, S., Contessa, V., Adikari, Y., Garzuglia, M., Lindquist, E., and Reams, G. (2016). Global Forest Resources Assessment 2015: How Are the World\u2019s Forests Changing?, Food and Agriculture Organistation of the United Nations (FAO)."},{"key":"ref_4","first-page":"1","article-title":"Forest cover mapping in post-Soviet Central Asia using multi-resolution remote sensing imagery","volume":"7","author":"Yin","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.rse.2013.09.015","article-title":"Using the regression estimator with Landsat data to estimate proportion forest cover and net proportion deforestation in Gabon","volume":"151","author":"Sannier","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.rse.2014.11.027","article-title":"Eastern Europe\u2019s forest cover dynamics from 1985 to 2012 quantified from the full Landsat archive","volume":"159","author":"Potapov","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1191\/0309133305pp432ra","article-title":"Satellite remote sensing of forest resources: three decades of research development","volume":"29","author":"Boyd","year":"2005","journal-title":"Prog. Phys. Geogr. Earth Environ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.foreco.2014.07.025","article-title":"Using a remote sensing-based, percent tree cover map to enhance forest inventory estimation","volume":"331","author":"McRoberts","year":"2014","journal-title":"For. Ecol. Manag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"619","DOI":"10.1080\/07038992.2016.1207484","article-title":"Remote Sensing Technologies for Enhancing Forest Inventories: A Review","volume":"42","author":"White","year":"2016","journal-title":"Can. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"717","DOI":"10.1016\/j.rse.2017.09.029","article-title":"Mapping forest change using stacked generalization: An ensemble approach","volume":"204","author":"Healey","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"3969","DOI":"10.1016\/j.patcog.2015.06.001","article-title":"On the usefulness of one-class classifier ensembles for decomposition of multi-class problems","volume":"48","author":"Krawczyk","year":"2015","journal-title":"Pattern Recognit."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.knosys.2013.01.018","article-title":"Analysing the classification of imbalanced data-sets with multiple classes: Binarization techniques and ad-hoc approaches","volume":"42","author":"Lopez","year":"2013","journal-title":"Knowledge-Based Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3091","DOI":"10.1080\/01431160310001648019","article-title":"Supervised image classification by MLP and RBF neural networks with and without an exhaustively defined set of classes","volume":"25","author":"Foody","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"3294","DOI":"10.1080\/01431161.2017.1292073","article-title":"Improving specific class mapping from remotely sensed data by cost-sensitive learning","volume":"38","author":"Silva","year":"2017","journal-title":"Int. J. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.isprsjprs.2015.02.010","article-title":"Geographic stacking: Decision fusion to increase global land cover map accuracy","volume":"103","author":"Clinton","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"8207","DOI":"10.1080\/01431161.2010.532831","article-title":"Land-cover classification in a moist tropical region of Brazil with Landsat TM imagery","volume":"32","author":"Li","year":"2011","journal-title":"Int. J. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1080\/17538947.2013.856959","article-title":"Integrating global land cover products for improved forest cover characterization: An application in North America","volume":"7","author":"Song","year":"2014","journal-title":"Int. J. Digit. Earth"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"2914","DOI":"10.1016\/j.rse.2008.02.010","article-title":"North American forest disturbance mapped from a decadal Landsat record","volume":"112","author":"Masek","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"548","DOI":"10.1016\/j.rse.2010.10.001","article-title":"Regional-scale boreal forest cover and change mapping using Landsat data composites for European Russia","volume":"115","author":"Potapov","year":"2011","journal-title":"Remote Sens. Environ."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1959","DOI":"10.1080\/01431160412331291297","article-title":"GLC2000: A new approach to global land cover mapping from Earth observation data","volume":"26","author":"Bartholome","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.rse.2009.08.016","article-title":"MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets","volume":"114","author":"Friedl","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"4795","DOI":"10.1080\/01431161.2014.930202","article-title":"Towards a common validation sample set for global land-cover mapping","volume":"35","author":"Zhao","year":"2014","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"850","DOI":"10.1126\/science.1244693","article-title":"High-Resolution Global Maps of 21st-Century Forest Cover Change","volume":"342","author":"Hansen","year":"2013","journal-title":"Science"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1016\/j.isprsjprs.2014.09.002","article-title":"Global land cover mapping at 30m resolution: A POK-based operational approach","volume":"103","author":"Chen","year":"2015","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Yang, Z., Dong, J., Liu, J., Zhai, J., Kuang, W., Zhao, G., Shen, W., Zhou, Y., Qin, Y., and Xiao, X. (2017). Accuracy Assessment and Inter-Comparison of Eight Medium Resolution Forest Products on the Loess Plateau, China. ISPRS Int. J. Geo-Information, 6.","DOI":"10.3390\/ijgi6050152"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.rse.2016.06.012","article-title":"Earth science data records of global forest cover and change: Assessment of accuracy in 1990, 2000, and 2005 epochs","volume":"184","author":"Feng","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Chen, J., Cao, X., Peng, S., and Ren, H. (2017). Analysis and Applications of GlobeLand30: A Review. ISPRS Int. J. Geo-Information, 6.","DOI":"10.3390\/ijgi6080230"},{"key":"ref_28","unstructured":"Ridder, R.M. (2007). Global Forest Resources Assessment 2010: Options and Recommendations for a Global Remote Sensing Survey of Forests, FAO. FAO Resour Assess Programme Work Paper."},{"key":"ref_29","unstructured":"Wan, Z.M. (2019, October 04). MOD11A2: MODIS\/Terra Land Surface Temperature and Emissivity 8-Day L3 Global 1 km Grid SIN V006, Available online: https:\/\/lpdaac.usgs.gov\/products\/mod11a2v006\/."},{"key":"ref_30","unstructured":"Jarvis, A., Reuter, H.I., Nelson, A., and Guevara, E. (2019, October 04). Hole-filled seamless SRTM data (Version 4). Available online: http:\/\/srtm.csi.cgiar.org."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"610","DOI":"10.1109\/TSMC.1973.4309314","article-title":"Textural Features for Image Classification","volume":"3","author":"Haralick","year":"1973","journal-title":"IEEE Trans. Syst. Man, Cybern."},{"key":"ref_32","unstructured":"Akosa, J. (2017, January 2\u20135). Predictive Accuracy: A Misleading Performance Measure for Highly Imbalanced Data. Proceedings of the SAS Global Forum 2017, Orlando, FL, USA."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Whalen, S., Pandey, G., and Pandey, G. (2013, January 7\u201310). A Comparative Analysis of Ensemble Classifiers: Case Studies in Genomics. Proceedings of the 2013 IEEE 13th International Conference on Data Mining, Dallas, TX, USA.","DOI":"10.1109\/ICDM.2013.21"},{"key":"ref_34","first-page":"2825","article-title":"Scikit-learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_35","first-page":"83","article-title":"Forest Rehabilitation in Kyrgyzstan","volume":"Volume 20-IV","author":"Lee","year":"2009","journal-title":"Keep Asia Green Volume IV \u201cWest and Central Asia\u201d"},{"key":"ref_36","unstructured":"Atamuradov, A., and Karryeva, S. (2005). Global Forest Resources Assessment: Turkmenistan Country Report, Food and Agriculture Organistation of the United Nations (FAO)."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2188","DOI":"10.1016\/j.patcog.2005.02.010","article-title":"A study on the performances of dynamic classifier selection based on local accuracy estimation","volume":"38","author":"Didaci","year":"2005","journal-title":"Pattern Recognit."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1890\/1540-9295(2007)5[80:SHIUER]2.0.CO;2","article-title":"Spatial heterogeneity in urban ecosystems: Reconceptualizing land cover and a framework for classification","volume":"5","author":"Cadenasso","year":"2007","journal-title":"Front. Ecol. Environ."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/19\/2325\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:27:52Z","timestamp":1760189272000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/19\/2325"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,10,6]]},"references-count":38,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["rs11192325"],"URL":"https:\/\/doi.org\/10.3390\/rs11192325","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2019,10,6]]}}}