{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T16:54:22Z","timestamp":1770915262611,"version":"3.50.1"},"reference-count":103,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2019,6,26]],"date-time":"2019-06-26T00:00:00Z","timestamp":1561507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>As more data and technologies become available, it is important that a simple method is developed for the assessment of land use changes because of the global need to understand the potential climate mitigation that could result from a reduction in deforestation and forest degradation in the tropics. Here, we determined the threshold values of vegetation types to classify land use categories in Cambodia through the analysis of phenological behaviors and the development of a robust phenology-based threshold classification (PBTC) method for the mapping and long-term monitoring of land cover changes. We accessed 2199 Landsat collections using Google Earth Engine (GEE) and applied the Enhanced Vegetation Index (EVI) and harmonic regression methods to identify phenological behaviors of land cover categories during the leaf-shedding phenology (LSP) and leaf-flushing phenology (LFS) seasons. We then generated 722 mean phenology EVI profiles for 12 major land cover categories and determined the threshold values for selected land cover categories in the mid-LSP season. The PBTC pixel-based classified map was validated using very high-resolution (VHR) imagery. We obtained a cumulative overall accuracy of more than 88% and a cumulative overall accuracy of the referenced forest cover of almost 85%. These high accuracy values suggest that the very first PBTC map can be useful for estimating the activity data, which are critically needed to assess land use changes and related carbon emissions under the Reducing Emissions from Deforestation and forest Degradation (REDD+) scheme. We found that GEE cloud-computing is an appropriate tool to use to access remote sensing big data at scale and at no cost.<\/jats:p>","DOI":"10.3390\/rs11131514","type":"journal-article","created":{"date-parts":[[2019,6,27]],"date-time":"2019-06-27T02:50:15Z","timestamp":1561603815000},"page":"1514","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":64,"title":["Determination of Vegetation Thresholds for Assessing Land Use and Land Use Changes in Cambodia using the Google Earth Engine Cloud-Computing Platform"],"prefix":"10.3390","volume":"11","author":[{"given":"Manjunatha","family":"Venkatappa","sequence":"first","affiliation":[{"name":"School of Environment, Resources, and Development, Asian Institute of Technology, P.O. Box 4, Khlong Luang, Pathumthani 12120, Thailand"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8361-5283","authenticated-orcid":false,"given":"Nophea","family":"Sasaki","sequence":"additional","affiliation":[{"name":"School of Environment, Resources, and Development, Asian Institute of Technology, P.O. Box 4, Khlong Luang, Pathumthani 12120, Thailand"}]},{"given":"Rajendra Prasad","family":"Shrestha","sequence":"additional","affiliation":[{"name":"School of Environment, Resources, and Development, Asian Institute of Technology, P.O. Box 4, Khlong Luang, Pathumthani 12120, Thailand"}]},{"given":"Nitin Kumar","family":"Tripathi","sequence":"additional","affiliation":[{"name":"School of Engineering and Technology, Asian Institute of Technology, P.O. Box 4, Khlong Luang, Pathumthani 12120, Thailand"}]},{"given":"Hwan-Ok","family":"Ma","sequence":"additional","affiliation":[{"name":"International Tropical Timber Organization (ITTO), 1-1-1 Minato-Mirai, Nishi-Ku, Yokohama 220-0012, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2019,6,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Sandker, M., Suwarno, A., and Campbell, B.M. (2007). Will Forests Remain in the Face of Oil Palm Expansion? Simulating Change in Malinau, Indonesia. Ecol. Soc., 12.","DOI":"10.5751\/ES-02292-120237"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"50","DOI":"10.3389\/fenvs.2016.00050","article-title":"Sustainable Management of Tropical Forests Can Reduce Carbon Emissions and Stabilize Timber Production","volume":"4","author":"Sasaki","year":"2016","journal-title":"Front. Environ. Sci."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1565","DOI":"10.1080\/0143116031000101675","article-title":"Review ArticleDigital change detection methods in ecosystem monitoring: A review","volume":"25","author":"Coppin","year":"2004","journal-title":"Int. J. Remote Sens."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"444","DOI":"10.1016\/j.rse.2003.10.022","article-title":"Impacts of imagery temporal frequency on land-cover change detection monitoring","volume":"89","author":"Lunetta","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_5","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_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1046\/j.1365-3040.1999.00395.x","article-title":"Selecting models to predict the timing of flowering of temperate trees: Implications for tree phenology modelling","volume":"22","author":"Chuine","year":"1999","journal-title":"Plant Cell Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"4485","DOI":"10.1080\/01431160500168686","article-title":"An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data","volume":"26","author":"Tucker","year":"2005","journal-title":"Int. J. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"823","DOI":"10.1080\/014311698215748","article-title":"Review article Multisensor image fusion in remote sensing: Concepts, methods and applications","volume":"19","author":"Pohl","year":"1998","journal-title":"Int. J. Remote Sens."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1080\/014311699213659","article-title":"Monitoring land-cover changes: A comparison of change detection techniques","volume":"20","author":"Mas","year":"1999","journal-title":"Int. J. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"4434","DOI":"10.1080\/01431161.2011.648285","article-title":"Object-based change detection","volume":"33","author":"Chen","year":"2012","journal-title":"Int. J. Remote Sens."},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.foreco.2015.06.003","article-title":"Assessing change in national forest monitoring capacities of 99 tropical countries","volume":"352","author":"Romijn","year":"2015","journal-title":"For. Ecol. Manag."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.rse.2015.01.018","article-title":"A global reference database from very high resolution commercial satellite data and methodology for application to Landsat derived 30m continuous field tree cover data","volume":"165","author":"Pengra","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Bey, A., D\u00edaz, A.S.P., Maniatis, D., Marchi, G., Mollicone, D., Ricci, S., Bastin, J.F., Moore, R., Federici, S., and Rezende, M. (2016). Collect earth: Land use and land cover assessment through augmented visual interpretation. Remote Sens., 8.","DOI":"10.3390\/rs8100807"},{"key":"ref_15","unstructured":"(2018, November 16). GEE Google Earth Engine. Available online: https:\/\/earthengine.google.com\/."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1016\/j.rse.2018.11.028","article-title":"Mapping tropical disturbed forests using multi-decadal 30 m optical satellite imagery","volume":"21","author":"Wang","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Nyland, K.E., Gunn, G.E., Shiklomanov, N.I., Engstrom, R.N., and Streletskiy, D.A. (2018). Land cover change in the lower Yenisei River using dense stacking of landsat imagery in Google Earth Engine. Remote Sens., 10.","DOI":"10.3390\/rs10081226"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kumar, L., and Mutanga, O. (2018). Google Earth Engine Applications Since Inception: Usage, Trends, and Potential. Remote Sens., 10.","DOI":"10.3390\/rs10101509"},{"key":"ref_19","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_20","unstructured":"(2018, October 15). GEE Earth Engine Data Catalog|Google Developers. Available online: https:\/\/developers.google.com\/earth-engine\/datasets\/."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Shelestov, A., Lavreniuk, M., Kussul, N., Novikov, A., and Skakun, S. (2017, January 23\u201328). Large scale crop classification using Google earth engine platform. Proceedings of the 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Fort Worth, TX, USA.","DOI":"10.1109\/IGARSS.2017.8127801"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Langner, A., Miettinen, J., Kukkonen, M., Vancutsem, C., Simonetti, D., Vieilledent, G., Verhegghen, A., Gallego, J., and Stibig, H.J. (2018). Towards operational monitoring of forest canopy disturbance in evergreen rain forests: A test case in continental Southeast Asia. Remote Sens., 10.","DOI":"10.3390\/rs10040544"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Tsai, Y.H., Stow, D., Chen, H.L., Lewison, R., An, L., and Shi, L. (2018). Mapping vegetation and land use types in Fanjingshan National Nature Reserve using google earth engine. Remote Sens., 10.","DOI":"10.3390\/rs10060927"},{"key":"ref_24","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_25","doi-asserted-by":"crossref","first-page":"166","DOI":"10.1016\/j.rse.2017.02.021","article-title":"Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine","volume":"202","author":"Huang","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1080\/22797254.2018.1451782","article-title":"Using Google Earth Engine to detect land cover change: Singapore as a use case","volume":"51","author":"Sidhu","year":"2018","journal-title":"Eur. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1016\/j.apgeog.2017.12.006","article-title":"Characterising the land surface phenology of Africa using 500 m MODIS EVI","volume":"90","author":"Adole","year":"2018","journal-title":"Appl. Geogr."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e01436","DOI":"10.1002\/ecs2.1436","article-title":"Emerging opportunities and challenges in phenology: A review","volume":"7","author":"Tang","year":"2016","journal-title":"Ecosphere"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2747\/1548-1603.43.1.1","article-title":"Regional Scale Land Cover Characterization Using MODIS-NDVI 250 m Multi-Temporal Imagery: A Phenology-Based Approach","volume":"43","author":"Knight","year":"2006","journal-title":"GIScience Remote Sens."},{"key":"ref_30","unstructured":"(2018, November 17). USGS Remote Sensing Phenology, Available online: https:\/\/phenology.cr.usgs.gov\/."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"489","DOI":"10.1080\/01431161.2014.999167","article-title":"Phenology-based classification of vegetation cover types in Northeast China using MODIS NDVI and EVI time series","volume":"36","author":"Yan","year":"2015","journal-title":"Int. J. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/S0168-1923(01)00233-7","article-title":"Response of tree phenology to climate change across Europe","volume":"108","author":"Chmielewski","year":"2001","journal-title":"Agric. For. Meteorol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"55","DOI":"10.2307\/2390090","article-title":"Relationships Between First Flowering Date and Temperature in the Flora of a Locality in Central England","volume":"9","author":"Fitter","year":"2006","journal-title":"Funct. Ecol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"20069","DOI":"10.1029\/2000JD000115","article-title":"Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999","volume":"106","author":"Zhou","year":"2001","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"496","DOI":"10.1016\/j.rse.2007.05.011","article-title":"Interannual vegetation phenology estimates from global AVHRR measurements","volume":"112","author":"Maignan","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"7320","DOI":"10.3390\/rs6087320","article-title":"Global-Scale Associations of Vegetation Phenology with Rainfall and Temperature at a High Spatio-Temporal Resolution","volume":"6","author":"Clinton","year":"2014","journal-title":"Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"310","DOI":"10.3390\/rs6010310","article-title":"Cross-Comparison of Vegetation Indices Derived from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Landsat-8 Operational Land Imager (OLI) Sensors","volume":"6","author":"Li","year":"2013","journal-title":"Remote Sens."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/0034-4257(94)00098-8","article-title":"Classification of multispectral images based on fractions of endmembers: Application to land-cover change in the Brazilian Amazon","volume":"52","author":"Adams","year":"1995","journal-title":"Remote Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1080\/01431169308904331","article-title":"Monitoring vegetation changes in Al Madinah, Saudi Arabia, using Thematic Mapper data","volume":"14","author":"Alwashe","year":"1993","journal-title":"Int. J. Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1115","DOI":"10.1080\/01431169408954145","article-title":"Fourier analysis of multi-temporal AVHRR data applied to a land cover classification","volume":"15","author":"Andres","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"045501","DOI":"10.1088\/1748-9326\/6\/4\/045501","article-title":"Satellite observations of high northern latitude vegetation productivity changes between 1982 and 2008: Ecological variability and regional differences","volume":"6","author":"Beck","year":"2011","journal-title":"Environ. Res. Lett."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"035501","DOI":"10.1088\/1748-9326\/6\/3\/035501","article-title":"Does NDVI reflect variation in the structural attributes associated with increasing shrub dominance in arctic tundra?","volume":"6","author":"Boelman","year":"2011","journal-title":"Environ. Res. Lett."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"180028","DOI":"10.1038\/sdata.2018.28","article-title":"Tracking vegetation phenology across diverse North American biomes using PhenoCam imagery","volume":"5","author":"Richardson","year":"2018","journal-title":"Sci. Data"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"012005","DOI":"10.1088\/1755-1315\/98\/1\/012005","article-title":"Land Cover Analysis by Using Pixel-Based and Object-Based Image Classification Method in Bogor","volume":"98","author":"Amalisana","year":"2017","journal-title":"IOP Conf. Ser. Earth Environ. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2335","DOI":"10.1111\/j.1365-2486.2009.01910.x","article-title":"Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982-2006","volume":"15","author":"White","year":"2009","journal-title":"Glob. Chang. Biol."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1654","DOI":"10.2134\/agronj2007.0170","article-title":"Corn and Soybean Mapping in the United States Using MODIS Time-Series Data Sets","volume":"99","author":"Chang","year":"2007","journal-title":"Agron. J."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"3340","DOI":"10.1109\/TGRS.2012.2183137","article-title":"Fitting the multitemporal curve: A fourier series approach to the missing data problem in remote sensing analysis","volume":"50","author":"Brooks","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1919","DOI":"10.1109\/TGRS.2009.2035615","article-title":"A Nonlinear Harmonic Model for Fitting Satellite Image Time Series: Analysis and Prediction of Land Cover Dynamics","volume":"48","author":"Carrao","year":"2010","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/TGRS.2016.2580576","article-title":"Spatially and Temporally Weighted Regression: A Novel Method to Produce Continuous Cloud-Free Landsat Imagery","volume":"55","author":"Chen","year":"2017","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.isprsjprs.2018.01.006","article-title":"Harmonic regression of Landsat time series for modeling attributes from national forest inventory data","volume":"137","author":"Wilson","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_51","unstructured":"Roy, D.P., and Yan, L. (2018). Robust Landsat-based crop time series modelling. Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"363","DOI":"10.1016\/j.rse.2018.02.046","article-title":"Extracting the full value of the Landsat archive: Inter-sensor harmonization for the mapping of Minnesota forest canopy cover (1973\u20132015)","volume":"209","author":"Vogeler","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"1496","DOI":"10.1109\/LGRS.2015.2409982","article-title":"First Results from the Phenology-Based Synthesis Classifier Using Landsat 8 Imagery","volume":"12","author":"Simonetti","year":"2015","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_54","unstructured":"Simonetti, E., Simonetti, D., and Preatoni, D. (2014). Phenology-Based Land cover Classification Using Landsat 8 Time Series, European Commission Joint Research Center. Report EUR 26841 EN."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.asr.2018.09.018","article-title":"Object-based rice mapping using time-series and phenological data","volume":"63","author":"Zhang","year":"2018","journal-title":"Adv. Space Res."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1186\/s13021-018-0097-1","article-title":"Landsat phenological metrics and their relation to aboveground carbon in the Brazilian Savanna","volume":"13","author":"Schwieder","year":"2018","journal-title":"Carbon Balance Manag."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Parks, S.A., Holsinger, L.M., Voss, M.A., Loehman, R.A., and Robinson, N.P. (2018). Mean composite fire severity metrics computed with google earth engine offer improved accuracy and expanded mapping potential. Remote Sens., 10.","DOI":"10.3390\/rs10060879"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Sazib, N., Mladenova, I., and Bolten, J. (2018). Leveraging the google earth engine for drought assessment using global soil moisture data. Remote Sens., 10.","DOI":"10.3390\/rs10081265"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Campos-Taberner, M., Moreno-Mart\u00ednez, \u00c1., Garc\u00eda-Haro, F.J., Camps-Valls, G., Robinson, N.P., Kattge, J., and Running, S.W. (2018). Global estimation of biophysical variables from Google Earth Engine platform. Remote Sens., 10.","DOI":"10.3390\/rs10081167"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Markert, K.N., Schmidt, C.M., Griffin, R.E., Flores, A.I., Poortinga, A., Saah, D.S., Muench, R.E., Clinton, N.E., Chishtie, F., and Kityuttachai, K. (2018). Historical and operational monitoring of surface sediments in the Lower Mekong Basin using Landsat and Google Earth Engine cloud computing. Remote Sens., 10.","DOI":"10.3390\/rs10060909"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Mateo-Garc\u00eda, G., G\u00f3mez-Chova, L., Amor\u00f3s-L\u00f3pez, J., Mu\u00f1oz-Mar\u00ed, J., and Camps-Valls, G. (2018). Multitemporal cloud masking in the Google Earth Engine. Remote Sens., 10.","DOI":"10.3390\/rs10071079"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Ito, E., Araki, M., Tani, A., Kanzaki, M., Saret, K., Seila, D., Phearak, P., Sopheap, L., and Sopheavuth, P. (2007, January 23\u201328). Leaf-shedding phenology in tropical seasonal forests of Cambodia estimated from NOAA satellite images. Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain.","DOI":"10.1109\/IGARSS.2007.4423810"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"135","DOI":"10.3390\/rs6010135","article-title":"A phenology-based classification of time-series MODIS data for rice crop monitoring in Mekong Delta, Vietnam","volume":"6","author":"Son","year":"2013","journal-title":"Remote Sens."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"6041","DOI":"10.3390\/rs70506041","article-title":"Phenology-Based Vegetation Index Differencing for Mapping of Rubber Plantations Using Landsat OLI Data","volume":"7","author":"Fan","year":"2015","journal-title":"Remote Sens."},{"key":"ref_65","first-page":"82","article-title":"Forest reference emission level and carbon sequestration in Cambodia","volume":"7","author":"Sasaki","year":"2016","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_66","first-page":"248","article-title":"de Pilot de guerra","volume":"36","year":"1958","journal-title":"Nov. Col\u00b7Lecci\u00f3 Lletres"},{"key":"ref_67","first-page":"34","article-title":"Assessment of carbon stocks of semi-evergreen forests in Cambodia","volume":"5","author":"Chheng","year":"2016","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_68","unstructured":"(2018, November 19). MoE Cambodia Evnvironment Outlook. Available online: https:\/\/wedocs.unep.org\/bitstream\/handle\/20.500.11822\/8689\/Cambodia_environment_outlook.pdf?sequence=3&isAllowed=y."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"1633","DOI":"10.5194\/hess-11-1633-2007","article-title":"Updated world map of the K\u00f6ppen-Geiger climate classification","volume":"11","author":"Peel","year":"2007","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_70","unstructured":"(2018, November 19). MoA Climate Change Priorities Action Plan for Agriculture, Forestry and Fisheries Sector 2016\u20132020. Available online: http:\/\/www.twgaw.org\/wp-content\/uploads\/2016\/08\/MAFF-CCPAP-2016-2020_final_CLEAN.pdf."},{"key":"ref_71","unstructured":"(2018, November 20). FAO GIEWS Global Information and Early Warning System on Food and Agriculture GIEWS Country Brief Cambodia. Available online: http:\/\/www.fao.org\/giews\/countrybrief\/country.jsp?code=KHM."},{"key":"ref_72","unstructured":"Sim, K., Sou, S., Sam, C., Chou, P., and Neang, M. (2018, November 28). Impacts of Climate Change on Rice Production in Cambodia. Available online: https:\/\/www.researchgate.net\/publication\/264540118_The_Impact_of_Climate_Change_on_Rice_Production_in_Cambodia."},{"key":"ref_73","unstructured":"FREL, C. (2018, November 17). Initial Forest Reference Level for Cambodia under the UNFCCC Framework. Available online: https:\/\/redd.unfccc.int\/files\/cambodia_frl_rcvd17112016.pdf."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"2911","DOI":"10.1016\/j.rse.2010.07.010","article-title":"Detecting trends in forest disturbance and recovery using yearly Landsat time series: 2. TimeSync\u2014Tools for calibration and validation","volume":"114","author":"Cohen","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"045023","DOI":"10.1088\/1748-9326\/2\/4\/045023","article-title":"Monitoring and estimating tropical forest carbon stocks: Making REDD a reality","volume":"2","author":"Gibbs","year":"2007","journal-title":"Environ. Res. Lett."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.3390\/rs70101048","article-title":"Mapping deciduous rubber plantation areas and stand ages with PALSAR and landsat images","volume":"7","author":"Kou","year":"2015","journal-title":"Remote Sens."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Kou, W., Liang, C., Wei, L., Hernandez, A., and Yang, X. (2017). Phenology-Based Method for Mapping Tropical Evergreen Forests by Integrating of MODIS and Landsat Imagery. Forests, 8.","DOI":"10.3390\/f8020034"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2016.02.016","article-title":"Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine","volume":"185","author":"Dong","year":"2016","journal-title":"Remote Sens. Environ."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.ecolind.2015.03.039","article-title":"Mapping paddy rice areas based on vegetation phenology and surface moisture conditions","volume":"56","author":"Qiu","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_80","first-page":"1","article-title":"Mapping paddy rice planting area in rice-wetland coexistent areas through analysis of Landsat 8 OLI and MODIS images","volume":"46","author":"Zhou","year":"2016","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/S0034-4257(02)00096-2","article-title":"Overview of the radiometric and biophysical performance of the MODIS vegetation indices","volume":"83","author":"Huete","year":"2002","journal-title":"Remote Sens. Environ."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.rse.2018.09.008","article-title":"Tracking annual cropland changes from 1984 to 2016 using time-series Landsat images with a change-detection and post-classification approach: Experiments from three sites in Africa","volume":"218","author":"Xu","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1080\/07038992.2014.987376","article-title":"Forest Monitoring Using Landsat Time Series Data: A Review","volume":"40","author":"Banskota","year":"2014","journal-title":"Can. J. Remote Sens."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Xiong, J., Thenkabail, P.S., Tilton, J.C., Gumma, M.K., Teluguntla, P., Oliphant, A., Congalton, R.G., Yadav, K., and Gorelick, N. (2017). Nominal 30-m cropland extent map of continental Africa by integrating pixel-based and object-based algorithms using Sentinel-2 and Landsat-8 data on google earth engine. Remote Sens., 9.","DOI":"10.3390\/rs9101065"},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"1154","DOI":"10.1175\/1520-0442(1997)010<1154:GSAOVP>2.0.CO;2","article-title":"Global-Scale Assessment of Vegetation Phenology Using NOAA\/AVHRR Satellite Measurements","volume":"10","author":"Moulin","year":"1997","journal-title":"J. Clim."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.rse.2015.08.004","article-title":"Mapping rice paddy extent and intensification in the Vietnamese Mekong River Delta with dense time stacks of Landsat data","volume":"169","author":"Kontgis","year":"2015","journal-title":"Remote Sens. Environ."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Wang, J., Xiao, X., Qin, Y., Dong, J., Zhang, G., Kou, W., Jin, C., Zhou, Y., and Zhang, Y. (2015). Mapping paddy rice planting area in wheat-rice double-cropped areas through integration of Landsat-8 OLI, MODIS, and PALSAR images. Sci. Rep., 5.","DOI":"10.1038\/srep10088"},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Goldblatt, R. (2017). High Spatial Resolution Visual Band Imagery Outperforms Medium Resolution Spectral Imagery for Ecosystem Assessment in the Semi-Arid Brazilian Sert\u00e3o. Remote Sens., 9.","DOI":"10.3390\/rs9121336"},{"key":"ref_89","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_90","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_91","doi-asserted-by":"crossref","first-page":"187","DOI":"10.6090\/jarq.46.187","article-title":"Tree Biomass Carbon Stock Estimation using Permanent Sampling Plot Data in Different Types of Seasonal Forests in Cambodia","volume":"46","author":"Samreth","year":"2012","journal-title":"Jpn. Agric. Res. Q."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Shumway, R.H., and Stoffer, D.S. (2017). Time Series Analysis and its Applications, Springer International Publishing.","DOI":"10.1007\/978-3-319-52452-8"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1016\/j.rse.2014.03.017","article-title":"Remote sensing of spring phenology in northeastern forests: A comparison of methods, field metrics and sources of uncertainty","volume":"148","author":"White","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"084001","DOI":"10.1088\/1748-9326\/11\/8\/084001","article-title":"Changes in growing season duration and productivity of northern vegetation inferred from long-term remote sensing data","volume":"11","author":"Myneni","year":"2016","journal-title":"Environ. Res. Lett."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"2979","DOI":"10.1111\/gcb.13200","article-title":"Satellite chlorophyll fluorescence measurements reveal large-scale decoupling of photosynthesis and greenness dynamics in boreal evergreen forests","volume":"22","author":"Walther","year":"2016","journal-title":"Glob. Chang. Biol."},{"key":"ref_96","first-page":"25","article-title":"The match and mismatch between photosynthesis and land surface phenology of deciduous forests","volume":"214\u2013215","author":"Gonsamo","year":"2015","journal-title":"Agric. For. Meteorol."},{"key":"ref_97","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_98","doi-asserted-by":"crossref","unstructured":"Ito, E., Khorn, S., Lim, S., Pol, S., Tith, B., Pith, P., Tani, A., Kanzaki, M., Kaneko, T., and Okuda, Y. (2007). Comparison of the leaf area index (LAI) of two types of dipterocarp forest on the west bank of the Mekong river, Cambodia. Forest Environments in the Mekong River Basin, Springer.","DOI":"10.1007\/978-4-431-46503-4_19"},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Ren, S., Yi, S., Peichl, M., and Wang, X. (2018). Diverse responses of vegetation phenology to climate change in different Grasslands in Inner Mongolia during 2000\u20132016. Remote Sens., 10.","DOI":"10.3390\/rs10010017"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"6985","DOI":"10.5194\/bg-12-6985-2015","article-title":"Trends and climatic sensitivities of vegetation phenology in semiarid and arid ecosystems in the US Great Basin during 1982\u20132011","volume":"12","author":"Tang","year":"2015","journal-title":"Biogeosciences"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1016\/j.scitotenv.2017.07.237","article-title":"Land surface phenology: What do we really \u2018see\u2019 from space?","volume":"618","author":"Helman","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"150066","DOI":"10.1038\/sdata.2015.66","article-title":"The climate hazards infrared precipitation with stations\u2014A new environmental record for monitoring extremes","volume":"2","author":"Funk","year":"2015","journal-title":"Sci. Data"},{"key":"ref_103","unstructured":"(2019, January 24). NASA MODIS Products Table | LP DAAC:: NASA Land Data Products and Services, Available online: https:\/\/lpdaac.usgs.gov\/products\/modis_products_table."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/13\/1514\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:01:30Z","timestamp":1760187690000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/11\/13\/1514"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,6,26]]},"references-count":103,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2019,7]]}},"alternative-id":["rs11131514"],"URL":"https:\/\/doi.org\/10.3390\/rs11131514","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,6,26]]}}}