{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T09:58:59Z","timestamp":1780653539860,"version":"3.54.1"},"reference-count":47,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,6,12]],"date-time":"2018-06-12T00:00:00Z","timestamp":1528761600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DEB-1212183"],"award-info":[{"award-number":["DEB-1212183"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Fanjinshan National Nature Reserve (FNNR) is a biodiversity hotspot in China that is part of a larger, multi-use landscape where farming, grazing, tourism, and other human activities occur. The steep terrain and persistent cloud cover pose challenges to robust vegetation and land use mapping. Our objective is to develop satellite image classification techniques that can reliably map forest cover and land use while minimizing the cloud and terrain issues, and provide the basis for long-term monitoring. Multi-seasonal Landsat image composites and elevation ancillary layers effectively minimize the persistent cloud cover and terrain issues. Spectral vegetation index (SVI) products and shade\/illumination normalization approaches yield significantly higher mapping accuracies, compared to non-normalized spectral bands. Advanced machine learning image classification routines are implemented through the cloud-based Google Earth Engine platform. Optimal classifier parameters (e.g., number of trees and number of features for random forest classifiers) were achieved by using tuning techniques. Accuracy assessment results indicate consistent and effective overall classification (i.e., above 70% mapping accuracies) can be achieved using multi-temporal SVI composites with simple illumination normalization and elevation ancillary data, despite the fact limited training and reference data are available. This efficient and open-access image analysis workflow provides a reliable methodology to remotely monitor forest cover and land use in FNNR and other mountainous forested, cloud prevalent areas.<\/jats:p>","DOI":"10.3390\/rs10060927","type":"journal-article","created":{"date-parts":[[2018,6,12]],"date-time":"2018-06-12T10:58:32Z","timestamp":1528801112000},"page":"927","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":145,"title":["Mapping Vegetation and Land Use Types in Fanjingshan National Nature Reserve Using Google Earth Engine"],"prefix":"10.3390","volume":"10","author":[{"given":"Yu Hsin","family":"Tsai","sequence":"first","affiliation":[{"name":"Department of Geography, San Diego State University, San Diego, CA 92182-4493, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Douglas","family":"Stow","sequence":"additional","affiliation":[{"name":"Department of Geography, San Diego State University, San Diego, CA 92182-4493, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hsiang Ling","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Biology, San Diego State University, San Diego, CA 92182-4493, USA"},{"name":"Department of Forestry, National Chung Hsing University, Taichung City 402, Taiwan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rebecca","family":"Lewison","sequence":"additional","affiliation":[{"name":"Department of Biology, San Diego State University, San Diego, CA 92182-4493, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Li","family":"An","sequence":"additional","affiliation":[{"name":"Department of Geography, San Diego State University, San Diego, CA 92182-4493, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Shi","sequence":"additional","affiliation":[{"name":"Fanjingshan National Nature Reserve Administration, Jiangkou County, Guizhou 554400, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1126\/science.1058104","article-title":"Ecological degradation in protected areas: The case of Wolong Nature Reserve for giant pandas","volume":"292","author":"Liu","year":"2001","journal-title":"Science"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1038\/35002501","article-title":"Biodiversity hotspots for conservation priorities","volume":"403","author":"Myers","year":"2000","journal-title":"Nature"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.ecolmodel.2011.08.004","article-title":"Perception and decisions in modeling coupled human and natural systems: A case study from Fanjingshan National Nature Reserve, China","volume":"229","author":"Wandersee","year":"2012","journal-title":"Ecol. Model."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"247","DOI":"10.3368\/le.81.2.247","article-title":"Grain for green: Cost-effectiveness and sustainability of China\u2019s conservation set-aside program","volume":"81","author":"Uchida","year":"2005","journal-title":"Land Econ."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"9477","DOI":"10.1073\/pnas.0706436105","article-title":"Ecological and socioeconomic effects of China\u2019s policies for ecosystem services","volume":"105","author":"Liu","year":"2008","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"16297","DOI":"10.1073\/pnas.1316036110","article-title":"Integrated assessments of payments for ecosystem services programs","volume":"110","author":"Liu","year":"2013","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"699","DOI":"10.1016\/j.ecolecon.2007.09.017","article-title":"China\u2019s sloping land conversion program: Institutional innovation or business as usual?","volume":"65","author":"Bennett","year":"2008","journal-title":"Ecol. Econ."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1179","DOI":"10.1038\/4351179a","article-title":"China\u2019s environment in a globalizing world","volume":"435","author":"Liu","year":"2005","journal-title":"Nature"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1093\/jpe\/rtm005","article-title":"Remote sensing imagery in vegetation mapping: A review","volume":"1","author":"Xie","year":"2008","journal-title":"J. Plant Ecol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/0034-4257(91)90062-B","article-title":"Radiometric rectification: Toward a common radiometric response among multidate, multisensor images","volume":"35","author":"Hall","year":"1991","journal-title":"Remote Sens. Environ."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/0034-4257(92)90076-V","article-title":"Evaluation of simplified procedures for retrieval of land surface reflectance factors from satellite sensor output","volume":"41","author":"Moran","year":"1992","journal-title":"Remote Sens. Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1109\/LGRS.2005.857030","article-title":"A Landsat surface reflectance dataset for North America, 1990\u20132000","volume":"3","author":"Masek","year":"2006","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/0034-4257(94)90134-1","article-title":"A modified soil adjusted vegetation index","volume":"48","author":"Qi","year":"1994","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2016.10.027","article-title":"Detecting historical changes to vegetation in a Cambodian protected area using the Landsat TM and ETM+ sensors","volume":"187","author":"Davies","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1175\/EI133.1","article-title":"Assessment of tropical forest degradation with canopy fractional cover from Landsat ETM+ and IKONOS imagery","volume":"9","author":"Wang","year":"2005","journal-title":"Earth Interact."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2636","DOI":"10.3390\/s7112636","article-title":"Sensitivity of the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) to topographic effects: A case study in high-density cypress forest","volume":"7","author":"Matsushita","year":"2007","journal-title":"Sensors"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/01431160600746456","article-title":"A survey of image classification methods and techniques for improving classification performance","volume":"28","author":"Lu","year":"2007","journal-title":"Int. J. Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/S0034-4257(01)00209-7","article-title":"Estimation and mapping of forest stand density, volume, and cover type using the k-nearest neighbors method","volume":"77","author":"Ek","year":"2001","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","first-page":"321","article-title":"Evaluating Seasonal Variability as an Aid to Cover-Type Mapping from Landsat Thematic Mapper Data in the Northwest","volume":"61","author":"Schriever","year":"1995","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_21","first-page":"1129","article-title":"Improved Forest Classification in the Northern Lake States Using Multi-Temporal Landsat Imagery","volume":"61","author":"Wolter","year":"1995","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6143","DOI":"10.1080\/01431160903401379","article-title":"Comparing techniques for vegetation classification using multi-and hyperspectral images and ancillary environmental data","volume":"31","author":"Sluiter","year":"2010","journal-title":"Int. J. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1329","DOI":"10.1080\/01431160500444806","article-title":"Integration of environmental variables with satellite images in regional scale vegetation classification","volume":"27","year":"2006","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/S0378-1127(03)00113-0","article-title":"Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification","volume":"183","author":"Dorren","year":"2003","journal-title":"For. Ecol. Manag."},{"key":"ref_25","first-page":"249","article-title":"Supervised machine learning: A review of classification techniques","volume":"31","author":"Kotsiantis","year":"2007","journal-title":"Informatica"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.isprsjprs.2011.11.002","article-title":"An assessment of the effectiveness of a random forest classifier for land-cover classification","volume":"67","author":"Ghimire","year":"2012","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Boser, B.E., Guyon, I., and Vapnik, V. (1992, January 27\u201329). A training algorithm for optimal margin classifiers. Proceedings of the Fifth Annual Workshop on Computational Learning Theory, Pittsburgh, PA, USA.","DOI":"10.1145\/130385.130401"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.isprsjprs.2010.11.001","article-title":"Support vector machines in remote sensing: A review","volume":"66","author":"Mountrakis","year":"2011","journal-title":"Photogramm. Eng. Remote Sens."},{"key":"ref_29","unstructured":"Breiman, L., Friedman, J., Stone, C.J., and Olshen, R.A. (1984). Classification and regression trees, Routledge."},{"key":"ref_30","unstructured":"Bishop, C.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"17","DOI":"10.3389\/feart.2017.00017","article-title":"Exploring Google Earth Engine platform for big data processing: Classification of multi-temporal satellite imagery for crop mapping","volume":"5","author":"Shelestov","year":"2017","journal-title":"Front. Earth Sci."},{"key":"ref_33","first-page":"36","article-title":"Mapping woody vegetation clearing in Queensland, Australia from Landsat imagery using the Google Earth Engine","volume":"1","author":"Johansen","year":"2015","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Parente, L., and Ferreira, L. (2018). Assessing the Spatial and Occupation Dynamics of the Brazilian Pasturelands Based on the Automated Classification of MODIS Images from 2000 to 2016. Remote Sens., 10.","DOI":"10.3390\/rs10040606"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1016\/j.isprsjprs.2016.01.011","article-title":"Random forest in remote sensing: A review of applications and future directions","volume":"114","author":"Belgiu","year":"2016","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1016\/j.rse.2017.06.031","article-title":"Google Earth Engine: Planetary-scale geospatial analysis for everyone","volume":"202","author":"Gorelick","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_38","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_39","unstructured":"Zhou, Z. (1990). Department of Forestry of Guizhou Province; Fanjingshan National Nature Reserve Administration Office. Research on the Fanjing Mountain, Guizhou People\u2019s Publishing House."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rse.2017.03.026","article-title":"Cloud detection algorithm comparison and validation for operational Landsat data products","volume":"194","author":"Foga","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_41","unstructured":"(2018, June 11). California Native Plant Society (CNPS) Vegetation Committee Rapid Assessment (RA) Protocol. Available online: http:\/\/www.cnps.org\/cnps\/vegetation\/pdf\/protocol-combined-2016.pdf."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/j.rse.2004.08.003","article-title":"Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery","volume":"93","author":"Wu","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2061","DOI":"10.1016\/S1872-2032(06)60031-0","article-title":"Multi-scale analysis on wintering habitat selection of Reeves\u2019s pheasant (Syrmaticus reevesii) in Dongzhai National Nature Reserve, Henan Province, China","volume":"26","author":"Xu","year":"2006","journal-title":"Acta Ecol. Sin."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/S0034-4257(97)00104-1","article-title":"On the relation between NDVI, fractional vegetation cover, and leaf area index","volume":"62","author":"Carlson","year":"1997","journal-title":"Remote Sens. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"396","DOI":"10.1016\/j.rse.2016.07.016","article-title":"Classification and assessment of land cover and land use change in southern Ghana using dense stacks of Landsat 7 ETM+ imagery","volume":"184","author":"Coulter","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1080\/2150704X.2016.1177243","article-title":"Quantifying canopy fractional cover and change in Fanjingshan National Nature Reserve, China using multi-temporal Landsat imagery","volume":"7","author":"Tsai","year":"2016","journal-title":"Remote Sens. Lett."},{"key":"ref_47","unstructured":"Planet Team (2018, June 11). Planet Application Program Interface: In Space for Life on Earth. San Francisco, CA. Available online: https:\/\/www.planet.com."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/6\/927\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T15:08:23Z","timestamp":1760195303000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/6\/927"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,6,12]]},"references-count":47,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2018,6]]}},"alternative-id":["rs10060927"],"URL":"https:\/\/doi.org\/10.3390\/rs10060927","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,6,12]]}}}