{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T07:14:28Z","timestamp":1765610068695,"version":"build-2065373602"},"reference-count":88,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2020,10,2]],"date-time":"2020-10-02T00:00:00Z","timestamp":1601596800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"NASA Land Cover and Land Use Change Program","award":["NNX17AI14G"],"award-info":[{"award-number":["NNX17AI14G"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Monitoring forests is important for measuring overall success of the 2030 Agenda because forests play an essential role in meeting many Sustainable Development Goals (SDG), especially SDG 15. Our study evaluates the contribution of three satellite data sources (Landsat-8, Sentinel-2 and Sentinel-1) for mapping diverse forest types in Myanmar. This assessment is especially important because Myanmar is currently revising its classification system for forests and it is critical that these new forest types can be accurately mapped and monitored over time using satellite imagery. Our results show that using a combination of Sentinel-1 and Sentinel-2 yields the highest accuracy (89.6% \u00b1 0.16 percentage point(pp)), followed by Sentinel-2 alone (87.97% \u00b1 0.11 pp) and Landsat-8 (82.68% \u00b1 0.13 pp). The higher spatial resolution of Sentinel-2 Blue, Green, Red, Narrow Near Infrared and Short Wave Infrared bands enhances accuracy by 4.83% compared to Landsat-8. The addition of the Sentinel-2 Near Infrared and three Vegetation Red Edge bands further improve accuracy by 0.46% compared to using only Sentinel-2 Blue, Green, Red, Narrow Near Infrared and Short Wave Infrared bands. Adding the radar information from Sentinel-1 further increases the accuracy by 1.63%. We were able to map the two major forest types, Upper Moist and Upper Dry Mixed Deciduous Forest, which comprise 90% of our study area. Accuracies for these forest types ranged from 77 to 96% depending on the sensors used, demonstrating the feasibility of using satellite data to map forest categories from a newly revised classification system. Our results advance the ongoing development of the National Forest Monitoring System (NFMS) by the Myanmar Forest Department and United Nations-Food and Agriculture Organization (UN-FAO) and facilitates future monitoring of progress towards the SDGs.<\/jats:p>","DOI":"10.3390\/rs12193220","type":"journal-article","created":{"date-parts":[[2020,10,3]],"date-time":"2020-10-03T07:22:16Z","timestamp":1601709736000},"page":"3220","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["A Multi Sensor Approach to Forest Type Mapping for Advancing Monitoring of Sustainable Development Goals (SDG) in Myanmar"],"prefix":"10.3390","volume":"12","author":[{"given":"Sumalika","family":"Biswas","sequence":"first","affiliation":[{"name":"Conservation Ecology Center, Smithsonian Conservation Biology Institute, Front Royal, VA 22630, USA"}]},{"given":"Qiongyu","family":"Huang","sequence":"additional","affiliation":[{"name":"Conservation Ecology Center, Smithsonian Conservation Biology Institute, Front Royal, VA 22630, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9266-0716","authenticated-orcid":false,"given":"Anupam","family":"Anand","sequence":"additional","affiliation":[{"name":"Independent Evaluation Office, Global Environment Facility, Washington, DC 20006, USA"}]},{"given":"Myat Su","family":"Mon","sequence":"additional","affiliation":[{"name":"Remote Sensing and GIS Division, Forest Department, Ministry of Natural Resources and Environmental Conservation (MONREC), Nay Pyi Taw 15011, Myanmar"}]},{"given":"Franz-Eugen","family":"Arnold","sequence":"additional","affiliation":[{"name":"Food and Agriculture Organization of the United Nations, Nay Pyi Taw 15011, Myanmar"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3682-0153","authenticated-orcid":false,"given":"Peter","family":"Leimgruber","sequence":"additional","affiliation":[{"name":"Conservation Ecology Center, Smithsonian Conservation Biology Institute, Front Royal, VA 22630, USA"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,2]]},"reference":[{"key":"ref_1","first-page":"10","article-title":"Forests for sustainable development: A process approach to forest sector contributions to the UN 2030 Agenda for Sustainable Development","volume":"19","author":"Gregersen","year":"2017","journal-title":"Int. For. Rev."},{"key":"ref_2","unstructured":"Seymour, F., and Busch, J. (2016). Why forests? Why Now?: The Science, Economics, and Politics of Tropical Forests and Climate Change, Brookings Institution Press."},{"key":"ref_3","unstructured":"FAO (2017). Keeping an Eye on SDG 15, FAO."},{"key":"ref_4","unstructured":"Htun, K. (2009). Myanmar Forestry Outlook Study, FAO."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","unstructured":"Bhagwat, T., Hess, A., Horning, N., Khaing, T., Thein, Z.M., Aung, K.M., Aung, K.H., Phyo, P., Tun, Y.L., and Oo, A.H. (2017). Losing a jewel\u2014Rapid declines in Myanmar\u2019s intact forests from 2002-2014. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0176364"},{"key":"ref_7","unstructured":"FAO (2015). Global Forest Resources Assessment 2015: How are the World\u2019s Forests Changing?, Food and Agriculture Organization of the United Nations."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"4035","DOI":"10.1080\/0143116031000103853","article-title":"Remote sensing of tropical forest environments: Towards the monitoring of environmental resources for sustainable development","volume":"24","author":"Foody","year":"2003","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Franklin, S.E. (2001). Remote Sensing for Sustainable Forest Management, CRC Press.","DOI":"10.1201\/9781420032857"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"231","DOI":"10.4155\/cmt.11.18","article-title":"Advances in remote sensing technology and implications for measuring and monitoring forest carbon stocks and change","volume":"2","author":"Goetz","year":"2011","journal-title":"Carbon Manag."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1177\/030913339802200402","article-title":"Optical remote-sensing techniques for the assessment of forest inventory and biophysical parameters","volume":"22","author":"Wulder","year":"1998","journal-title":"Prog. Phys. Geogr."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1111\/j.1467-9493.2006.00237.x","article-title":"Tropical forest monitoring and remote sensing: A new era of transparency in forest governance?","volume":"27","author":"Fuller","year":"2006","journal-title":"Singap. J. Trop. Geogr."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.rse.2011.08.024","article-title":"A review of large area monitoring of land cover change using Landsat data","volume":"122","author":"Hansen","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.rse.2012.10.010","article-title":"Long-term land cover dynamics by multi-temporal classification across the Landsat-5 record","volume":"128","author":"Sexton","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e20000","DOI":"10.1002\/agg2.20000","article-title":"Retrospective tillage differentiation using the Landsat-5 TM archive with discriminant analysis","volume":"3","author":"Sharma","year":"2020","journal-title":"Agrosyst. Geosci. Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1641\/0006-3568(2004)054[0535:LRIEAO]2.0.CO;2","article-title":"Landsat\u2019s role in ecological applications of remote sensing","volume":"54","author":"Cohen","year":"2004","journal-title":"Bioscience"},{"key":"ref_17","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_18","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_19","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1080\/17538947.2012.713190","article-title":"Global characterization and monitoring of forest cover using Landsat data: Opportunities and challenges","volume":"5","author":"Townshend","year":"2012","journal-title":"Int. J. Digit. Earth"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.rse.2011.09.026","article-title":"Sentinels for science: Potential of Sentinel-1,-2, and-3 missions for scientific observations of ocean, cryosphere, and land","volume":"120","author":"Malenovsky","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Heckel, K., Urban, M., Schratz, P., Mahecha, M.D., and Schmullius, C. (2020). Predicting Forest Cover in Distinct Ecosystems: The Potential of Multi-Source Sentinel-1 and-2 Data Fusion. Remote Sens., 12.","DOI":"10.3390\/rs12020302"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Phiri, D., Simwanda, M., Salekin, S., R Nyirenda, V., Murayama, Y., and Ranagalage, M. (2020). Sentinel-2 Data for Land Cover\/Use Mapping: A Review. Remote Sens., 12.","DOI":"10.3390\/rs12142291"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Poortinga, A., Tenneson, K., Shapiro, A., Nquyen, Q., San Aung, K., Chishtie, F., and Saah, D. (2019). Mapping plantations in Myanmar by fusing landsat-8, sentinel-2 and sentinel-1 data along with systematic error quantification. Remote Sens., 11.","DOI":"10.3390\/rs11070831"},{"key":"ref_24","first-page":"595","article-title":"Combining Sentinel-1 and Sentinel-2 data for improved land use and land cover mapping of monsoon regions","volume":"73","author":"Steinhausen","year":"2018","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.rse.2019.01.019","article-title":"Comparison of Sentinel-2 and Landsat 8 imagery for forest variable prediction in boreal region","volume":"223","author":"Astola","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1080\/15481603.2017.1370169","article-title":"Landsat-8 vs. Sentinel-2: Examining the added value of sentinel-2\u2019s red-edge bands to land-use and land-cover mapping in Burkina Faso","volume":"55","author":"Forkuor","year":"2018","journal-title":"GISci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1016\/j.rse.2017.03.021","article-title":"Comparison of Sentinel-2 and Landsat 8 in the estimation of boreal forest canopy cover and leaf area index","volume":"195","author":"Korhonen","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_28","first-page":"1055","article-title":"Assessments of Sentinel 2 vegetation red-edge spectral bands for improving land cover classification","volume":"42","author":"Qiu","year":"2017","journal-title":"Proc. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.rse.2011.11.026","article-title":"Sentinel-2: ESA\u2019s optical high-resolution mission for GMES operational services","volume":"120","author":"Drusch","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_30","first-page":"417","article-title":"Potential improvement for forest cover and forest degradation mapping with the forthcoming Sentinel-2 program","volume":"40","author":"Belward","year":"2015","journal-title":"Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Hirschmugl, M., Sobe, C., Deutscher, J., and Schardt, M. (2018). Combined use of optical and synthetic aperture radar data for REDD+ applications in Malawi. Land, 7.","DOI":"10.3390\/land7040116"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chaves, E.D.M., Picoli, C.A.M., and Sanches, D.I. (2020). Recent Applications of Landsat 8\/OLI and Sentinel-2\/MSI for Land Use and Land Cover Mapping: A Systematic Review. Remote Sens., 12.","DOI":"10.3390\/rs12183062"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Sothe, C., Almeida, C.M., de Liesenberg, V., and Schimalski, M.B. (2017). Evaluating Sentinel-2 and Landsat-8 data to map sucessional forest stages in a subtropical forest in Southern Brazil. Remote Sens., 9.","DOI":"10.3390\/rs9080838"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Liu, Y., Gong, W., Hu, X., and Gong, J. (2018). Forest type identification with random forest using Sentinel-1A, Sentinel-2A, multi-temporal Landsat-8 and DEM data. Remote Sens., 10.","DOI":"10.3390\/rs10060946"},{"key":"ref_35","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":"ISPRS J. Photogramm. 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":"8703","DOI":"10.1080\/01431161.2018.1490976","article-title":"Land-cover mapping using Random Forest classification and incorporating NDVI time-series and texture: A case study of central Shandong","volume":"39","author":"Jin","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhang, Y., and Lin, H. (2012, January 22\u201327). Urban land cover mapping using random forest combined with optical and SAR data. Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany.","DOI":"10.1109\/IGARSS.2012.6352600"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1175","DOI":"10.1080\/01431161.2017.1395968","article-title":"A Random Forests classification method for urban land-use mapping integrating spatial metrics and texture analysis","volume":"39","author":"Shi","year":"2018","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"826","DOI":"10.1016\/j.envsoft.2010.01.004","article-title":"Predicting the potential habitat of oaks with data mining models and the R system","volume":"25","year":"2010","journal-title":"Environ. Model. Softw."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","unstructured":"Leimgruber, P., Kelly, D.S., Steininger, M.K., Brunner, J., M\u00fcller, T., and Songer, M. (2005). Forest cover change patterns in Myanmar (Burma) 1990\u20132000. Environ. Conserv., 356\u2013364.","DOI":"10.1017\/S0376892905002493"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Connette, G., Oswald, P., Songer, M., and Leimgruber, P. (2016). Mapping distinct forest types improves overall forest identification based on multi-spectral Landsat imagery for Myanmar\u2019s Tanintharyi Region. Remote Sens., 8.","DOI":"10.3390\/rs8110882"},{"key":"ref_44","unstructured":"Tint, K. (December, January 29). Community forestry. Proceedings of the National Workshop on\u201d Strengthening Re-Afforestation Programmes in Myanmar\u201d Resource Paper, Hamawbi, Myanmar."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"RG2004","DOI":"10.1029\/2005RG000183","article-title":"The shuttle radar topography mission","volume":"45","author":"Farr","year":"2007","journal-title":"Rev. Geophys."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.foreco.2011.11.036","article-title":"Factors affecting deforestation and forest degradation in selectively logged production forest: A case study in Myanmar","volume":"267","author":"Mon","year":"2012","journal-title":"For. Ecol. Manag."},{"key":"ref_47","unstructured":"USGS (2020, September 20). Landsat 8 (L8) Data Users Handbook, Available online: https:\/\/www.usgs.gov\/core-science-systems\/nli\/landsat\/landsat-8-data-users-handbook."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"893","DOI":"10.1016\/j.rse.2009.01.007","article-title":"Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors","volume":"113","author":"Chander","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.rse.2016.04.008","article-title":"Preliminary analysis of the performance of the Landsat 8\/OLI land surface reflectance product","volume":"185","author":"Vermote","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1080\/01431161.2010.519002","article-title":"Continuous fields of land cover for the conterminous United States using Landsat data: First results from the Web-Enabled Landsat Data (WELD) project","volume":"2","author":"Hansen","year":"2011","journal-title":"Remote Sens. Lett."},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Liu, Q., Guo, Y., Liu, G., and Zhao, J. (2014, January 19\u201321). Classification of Landsat 8 OLI image using support vector machine with Tasseled Cap Transformation. Proceedings of the 2014 10th International Conference on Natural Computation (ICNC), Xiamen, China.","DOI":"10.1109\/ICNC.2014.6975915"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"111592","DOI":"10.1016\/j.rse.2019.111592","article-title":"A reporting framework for Sustainable Development Goal 15: Multi-scale monitoring of forest degradation using MODIS, Landsat and Sentinel data","volume":"237","author":"Mondal","year":"2020","journal-title":"Remote Sens. Environ."},{"key":"ref_53","unstructured":"Flores-Anderson, A.I., Herndon, K.E., Thapa, R.B., and Cherrington, E. (2019). The SAR Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation."},{"key":"ref_54","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_55","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/0734-189X(84)90197-X","article-title":"Segmentation of a high-resolution urban scene using texture operators","volume":"25","author":"Conners","year":"1984","journal-title":"Comput. Vis. Graph. Image Process."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"390","DOI":"10.1016\/j.rse.2006.02.022","article-title":"Retrieving forest structure variables based on image texture analysis and IKONOS-2 imagery","volume":"102","author":"Kayitakire","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1080\/014311600210993","article-title":"Incorporating texture into classification of forest species composition from airborne multispectral images","volume":"21","author":"Franklin","year":"2000","journal-title":"Int. J. Remote Sens."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.rse.2011.12.003","article-title":"Random Forest classification of Mediterranean land cover using multi-seasonal imagery and multi-seasonal texture","volume":"121","author":"Atkinson","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"2310","DOI":"10.1109\/36.868888","article-title":"The use of decision tree and multiscale texture for classification of JERS-1 SAR data over tropical forest","volume":"38","author":"Simard","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"8650","DOI":"10.1073\/pnas.0912668107","article-title":"Quantification of global gross forest cover loss","volume":"107","author":"Hansen","year":"2010","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_61","unstructured":"McGrew, J.C., and Monroe, C.B. (2009). An Introduction to Statistical Problem Solving in Geography, Waveland Press."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"356","DOI":"10.1016\/j.rama.2018.01.001","article-title":"Nondestructive estimation of standing crop and fuel moisture content in tallgrass prairie","volume":"71","author":"Sharma","year":"2018","journal-title":"Rangel. Ecol. Manag."},{"key":"ref_63","unstructured":"R Core Team, R. (2016). A Language and Environment for Statistical Computing [Computer Software Manual], R Core Team."},{"key":"ref_64","first-page":"18","article-title":"Classification and regression by randomForest","volume":"2","author":"Liaw","year":"2002","journal-title":"R News"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.rse.2014.02.015","article-title":"Good practices for estimating area and assessing accuracy of land change","volume":"148","author":"Olofsson","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Bousbih, S., Zribi, M., Lili-Chabaane, Z., Baghdadi, N., El Hajj, M., Gao, Q., and Mougenot, B. (2017). Potential of Sentinel-1 radar data for the assessment of soil and cereal cover parameters. Sensors, 17.","DOI":"10.3390\/s17112617"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.rse.2011.08.026","article-title":"The next Landsat satellite: The Landsat data continuity mission","volume":"122","author":"Irons","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1080\/22797254.2017.1419441","article-title":"The potentials of Sentinel-2 and LandSat-8 data in green infrastructure extraction, using object based image analysis (OBIA) method","volume":"51","author":"Labib","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Lisein, J., Michez, A., Claessens, H., and Lejeune, P. (2015). Discrimination of deciduous tree species from time series of unmanned aerial system imagery. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0141006"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"083670","DOI":"10.1117\/1.JRS.8.083670","article-title":"Estimating the age of deciduous forests in northeast China with Enhanced Thematic Mapper Plus data acquired in different phenological seasons","volume":"8","author":"Li","year":"2014","journal-title":"J. Appl. Remote Sens."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"10232","DOI":"10.3390\/rs61010232","article-title":"The spectral response of the Landsat-8 operational land imager","volume":"6","author":"Barsi","year":"2014","journal-title":"Remote Sens."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"3957","DOI":"10.1109\/JSTARS.2016.2574360","article-title":"Discriminating rangeland management practices using simulated hyspIRI, landsat 8 OLI, sentinel 2 MSI, and VEN\u03bcs spectral data","volume":"9","author":"Sibanda","year":"2016","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"274","DOI":"10.1016\/j.rse.2018.11.012","article-title":"Empirical cross sensor comparison of Sentinel-2A and 2B MSI, Landsat-8 OLI, and Landsat-7 ETM+ top of atmosphere spectral characteristics over the conterminous United States","volume":"221","author":"Chastain","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.rse.2018.07.006","article-title":"Mapping and assessment of vegetation types in the tropical rainforests of the Western Ghats using multispectral Sentinel-2 and SAR Sentinel-1 satellite imagery","volume":"216","author":"Erinjery","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"R\u00fcetschi, M., Schaepman, M.E., and Small, D. (2018). Using multitemporal sentinel-1 c-band backscatter to monitor phenology and classify deciduous and coniferous forests in northern switzerland. Remote Sens., 10.","DOI":"10.3390\/rs10010055"},{"key":"ref_76","first-page":"3","article-title":"Modelling above Ground Biomass of Mangrove Forest Using Sentinel-1 Imagery","volume":"4","author":"Argamosa","year":"2018","journal-title":"ISPRS Ann. Photogramm. Remote Sens. Spat. Inf. Sci."},{"key":"ref_77","unstructured":"Jena, F.-S.-U. (2012). SAR Theory and Applications to Forest Cover and Disturbance Mapping and Forest Biomass Assessment, Friedrich-Schiller-University."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Niemi, M.T., and Vauhkonen, J. (2016). Extracting canopy surface texture from airborne laser scanning data for the supervised and unsupervised prediction of area-based forest characteristics. Remote Sens., 8.","DOI":"10.3390\/rs8070582"},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.rse.2018.09.002","article-title":"The Harmonized Landsat and Sentinel-2 surface reflectance data set","volume":"219","author":"Claverie","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_80","unstructured":"Emerton, L., and Aung, Y.M. (2013). The Economic Value of Forest Ecosystem Services in Myanmar and Options for Sustainable Financing, International Management Group."},{"key":"ref_81","unstructured":"Assessment, I.H.L.C. (2011). Integrated Household Living Conditions Survey 2009-10 Myanmar: Poverty Profile, United Nations Development Programme."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"360","DOI":"10.1046\/j.1523-1739.2002.00219.x","article-title":"Status review of the protected-area system in Myanmar, with recommendations for conservation planning","volume":"16","author":"Rao","year":"2002","journal-title":"Conserv. Biol."},{"key":"ref_83","unstructured":"(2020, August 14). Southeastern Asia: Central Myanmar (formerly Burma)|Ecoregions|WWF. Available online: https:\/\/www.worldwildlife.org\/ecoregions\/im0205."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.rse.2011.09.022","article-title":"Landsat: Building a strong future","volume":"122","author":"Loveland","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1011","DOI":"10.1126\/science.320.5879.1011a","article-title":"Free access to Landsat imagery","volume":"320","author":"Woodcock","year":"2008","journal-title":"Science"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.rse.2012.01.010","article-title":"Opening the archive: How free data has enabled the science and monitoring promise of Landsat","volume":"122","author":"Wulder","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1002\/2014EO260003","article-title":"Sentinel satellites initiate new era in earth observation","volume":"95","author":"Showstack","year":"2014","journal-title":"Eos Trans. Am. Geophys. Union"},{"key":"ref_88","unstructured":"(2020, August 13). International Cooperation|Copernicus. Available online: https:\/\/www.copernicus.eu\/en\/about-copernicus\/international-cooperation."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/19\/3220\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:16:10Z","timestamp":1760177770000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/19\/3220"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,2]]},"references-count":88,"journal-issue":{"issue":"19","published-online":{"date-parts":[[2020,10]]}},"alternative-id":["rs12193220"],"URL":"https:\/\/doi.org\/10.3390\/rs12193220","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2020,10,2]]}}}