{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T23:32:49Z","timestamp":1771716769473,"version":"3.50.1"},"reference-count":74,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,10]],"date-time":"2022-12-10T00:00:00Z","timestamp":1670630400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Multi-government International Science and Technology Innovation Cooperation Key Project of National Key Research and Development Program of China","award":["2018YFE0184300"],"award-info":[{"award-number":["2018YFE0184300"]}]},{"name":"the Multi-government International Science and Technology Innovation Cooperation Key Project of National Key Research and Development Program of China","award":["41961060"],"award-info":[{"award-number":["41961060"]}]},{"name":"the Multi-government International Science and Technology Innovation Cooperation Key Project of National Key Research and Development Program of China","award":["TJGZL2022-15"],"award-info":[{"award-number":["TJGZL2022-15"]}]},{"name":"the Multi-government International Science and Technology Innovation Cooperation Key Project of National Key Research and Development Program of China","award":["2022YB17"],"award-info":[{"award-number":["2022YB17"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2018YFE0184300"],"award-info":[{"award-number":["2018YFE0184300"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41961060"],"award-info":[{"award-number":["41961060"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["TJGZL2022-15"],"award-info":[{"award-number":["TJGZL2022-15"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022YB17"],"award-info":[{"award-number":["2022YB17"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Tuojiang River Basin High-quality Development Research Center Program of China","award":["2018YFE0184300"],"award-info":[{"award-number":["2018YFE0184300"]}]},{"name":"the Tuojiang River Basin High-quality Development Research Center Program of China","award":["41961060"],"award-info":[{"award-number":["41961060"]}]},{"name":"the Tuojiang River Basin High-quality Development Research Center Program of China","award":["TJGZL2022-15"],"award-info":[{"award-number":["TJGZL2022-15"]}]},{"name":"the Tuojiang River Basin High-quality Development Research Center Program of China","award":["2022YB17"],"award-info":[{"award-number":["2022YB17"]}]},{"name":"the Neijiang Normal University Program of China","award":["2018YFE0184300"],"award-info":[{"award-number":["2018YFE0184300"]}]},{"name":"the Neijiang Normal University Program of China","award":["41961060"],"award-info":[{"award-number":["41961060"]}]},{"name":"the Neijiang Normal University Program of China","award":["TJGZL2022-15"],"award-info":[{"award-number":["TJGZL2022-15"]}]},{"name":"the Neijiang Normal University Program of China","award":["2022YB17"],"award-info":[{"award-number":["2022YB17"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Vegetation is the main body of the terrestrial ecosystem and is a significant indicator of environmental changes in the regional ecosystem. As an essential link connecting South Asia and Southeast Asia, the Lancang-Mekong River Basin(LMRB) can provide essential data support and a decision-making basis for the assessment of terrestrial ecosystem environmental changes and the research and management of hydrology and water resources in the basin by monitoring changes in its vegetation cover. This study takes the Lancang-Mekong River Basin as the study area, and employs the Sen slope estimation, Mann\u2013Kendall test, and Hurst exponent based on the MODIS NDVI data from 2000 to 2021 to study the spatial and temporal evolution trend and future sustainability of its NDVI. Besides, the nonlinear characteristics such as mutation type and mutation year are detected and analyzed using the BFAST01 method. Results demonstrated that: (1) In the past 22 years, the NDVI of the Lancang-Mekong River Basin generally exhibited a fluctuating upward trend, and the NDVI value in 2021 was the largest, which was 0.825, showing an increase of 4.29% compared with 2000. However, the increase rate was different: China has the most considerable NDVI growth rate of 7.25%, followed by Thailand with an increase of 7.21%, Myanmar and Laos as the third, while Cambodia and Vietnam have relatively stable vegetation changes. The overall performance of NDVI is high in the south and low in the north, and is dominated by high and relatively high vegetation coverage, of which the area with vegetation coverage exceeding 0.8 accounts for 62%. (2) The Sen-MK trend showed that from 2000 to 2021, the area where the vegetation coverage in the basin showed a trend of increase and decrease accounted for 66.59% and 18.88%, respectively. The Hurst exponent indicated that the areas where NDVI will continue to increase, decrease, and remain unchanged in the future account for 60.14%, 25.29%, and 14.53%, respectively, and the future development trend of NDVI is uncertain, accounting for 0.04%. Thus, more attention should be paid to areas with a descending future development trend. (3) BFAST01 detected eight NDVI mutation types in the Lancang-Mekong River Basin over the past 22 years. The mutations mainly occurred in 2002\u20132018, while 2002\u20132004 and 2014\u20132018 were the most frequent periods of breakpoints. The mutation type of \u201cinterruption: increase with negative break\u201d was changed the most during this period, which accounts for 36.54%, and the smallest was \u201cmonotonic decrease (with negative break)\u201d, which only accounts for 0.65%. This research demonstrates that combining the conventional trend analysis method with the BFAST mutation test can more accurately analyze the spatiotemporal variation and nonlinear mutation of NDVI, thus providing a scientific reference to develop ecological environment-related work.<\/jats:p>","DOI":"10.3390\/rs14246271","type":"journal-article","created":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T04:34:20Z","timestamp":1670819660000},"page":"6271","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":39,"title":["Linear and Nonlinear Characteristics of Long-Term NDVI Using Trend Analysis: A Case Study of Lancang-Mekong River Basin"],"prefix":"10.3390","volume":"14","author":[{"given":"Xuzhen","family":"Zhong","sequence":"first","affiliation":[{"name":"Faculty of Geography, Yunnan Normal University, Kunming 650500, China"},{"name":"School of Geography and Resource Science, Neijiang Normal University, Neijiang 641100, China"},{"name":"Southwest United Graduate School, Yunnan Normal University, Kunming 650500, China"},{"name":"Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan, Kunming 650500, China"},{"name":"Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming 650500, China"}]},{"given":"Jie","family":"Li","sequence":"additional","affiliation":[{"name":"Faculty of Geography, Yunnan Normal University, Kunming 650500, China"},{"name":"Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan, Kunming 650500, China"},{"name":"Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming 650500, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7202-646X","authenticated-orcid":false,"given":"Jinliang","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Geography, Yunnan Normal University, Kunming 650500, China"},{"name":"Southwest United Graduate School, Yunnan Normal University, Kunming 650500, China"},{"name":"Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan, Kunming 650500, China"},{"name":"Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming 650500, China"}]},{"given":"Jianpeng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Geography, Yunnan Normal University, Kunming 650500, China"},{"name":"Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan, Kunming 650500, China"},{"name":"Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming 650500, China"}]},{"given":"Lanfang","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Geography, Yunnan Normal University, Kunming 650500, China"},{"name":"Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan, Kunming 650500, China"},{"name":"Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming 650500, China"}]},{"given":"Jun","family":"Ma","sequence":"additional","affiliation":[{"name":"Faculty of Geography, Yunnan Normal University, Kunming 650500, China"},{"name":"Department of Geology, Tomsk State University, Tomsk 634050, Russia"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101776","DOI":"10.1016\/j.ecoinf.2022.101776","article-title":"Vegetation coverage changes driven by a combination of climate change and human activities in Ethiopia, 2003\u20132018","volume":"71","author":"Yang","year":"2022","journal-title":"Ecol. Inform."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Zhang, X., Cao, Q., Chen, H., Quan, Q., Li, C., Dong, J., Chang, M., Yan, S., and Liu, J. (2022). Effect of Vegetation Carryover and Climate Variability on the Seasonal Growth of Vegetation in the Upper and Middle Reaches of the Yellow River Basin. Remote Sens., 14.","DOI":"10.3390\/rs14195011"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1016\/j.iswcr.2020.06.006","article-title":"Predicted trends of soil erosion and sediment yield from future land use and climate change scenarios in the Lancang\u2013Mekong River by using the modified RUSLE model","volume":"8","author":"Chuenchum","year":"2020","journal-title":"Int. Soil Water Conserv. Res."},{"key":"ref_4","unstructured":"IPCC (2022, August 08). Special Report on Climate Change and Land. Available online: https:\/\/www.ipcc.ch\/site\/assets\/uploads\/2019\/08\/4.-SPM_Approved_Microsite_FINAL.pdf."},{"key":"ref_5","first-page":"929","article-title":"Spatio-temporal changes of vegetation cover and their influencing factors in the Yellow River Basin from 1982 to 2015","volume":"30","author":"Zhang","year":"2021","journal-title":"Ecol. Environ. Sci."},{"key":"ref_6","first-page":"1","article-title":"Nonlinear trends and spatial pattern analysis of vegetation cover change in China from 1982 to 2018","volume":"42","author":"Luo","year":"2022","journal-title":"Acta Ecol. Sin."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Geng, S., Zhang, H., Xie, F., Li, L., and Yang, L. (2022). Vegetation Dynamics under Rapid Urbanization in the Guangdong\u2013Hong Kong\u2013Macao Greater Bay Area Urban Agglomeration during the Past Two Decades. Remote Sens., 14.","DOI":"10.3390\/rs14163993"},{"key":"ref_8","first-page":"e02215","article-title":"Interannual trends of vegetation and responses to climate change and human activities in the Great Mekong Subregion","volume":"38","author":"Han","year":"2022","journal-title":"Glob. Ecol. Conserv."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Higginbottom, T.P., and Symeonakis, E. (2020). Identifying Ecosystem Function Shifts in Africa Using Breakpoint Analysis of Long-Term NDVI and RUE Data. Remote Sens., 12.","DOI":"10.3390\/rs12111894"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1749","DOI":"10.1016\/j.rse.2007.08.018","article-title":"North American vegetation dynamics observed with multi-resolution satellite data","volume":"112","author":"Neigh","year":"2008","journal-title":"Remote Sens. Environ."},{"key":"ref_11","first-page":"1306","article-title":"Dynamic Variation of NDVI and Its Influencing Factors in the Pearl River Basin from 2000 to 2020","volume":"31","author":"Chen","year":"2022","journal-title":"Ecol. Environ. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"158416","DOI":"10.1016\/j.scitotenv.2022.158416","article-title":"Yeh. Responses of vegetation to changes in terrestrial water storage and temperature in global mountainous regions","volume":"851","author":"Zhang","year":"2022","journal-title":"Sci. Total. Environ."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"100027","DOI":"10.1016\/j.srs.2021.100027","article-title":"New insights of global vegetation structural properties through an analysis of canopy clumping index, fractional vegetation cover, and leaf area index","volume":"4","author":"Fang","year":"2021","journal-title":"Sci. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"137159","DOI":"10.1016\/j.scitotenv.2020.137159","article-title":"China\u2019s national nature reserve network shows great imbalances in conserving the country\u2019s mega-diverse vegetation","volume":"717","author":"Sun","year":"2020","journal-title":"Sci. Total Environ."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"101812","DOI":"10.1016\/j.ecoinf.2022.101812","article-title":"Quantifying the impacts of opencast mining on vegetation dynamics over eastern India using the long-term Landsat-series satellite dataset","volume":"71","author":"Ranjan","year":"2022","journal-title":"Ecol. Inform."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"106742","DOI":"10.1016\/j.ecoleng.2022.106742","article-title":"Using SPOT VEGETATION for analyzing dynamic changes and influencing factors on vegetation restoration in the Three-River Headwaters Region in the last 20 years (2000\u20132019), China","volume":"183","author":"Sun","year":"2022","journal-title":"Ecol. Eng."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"157227","DOI":"10.1016\/j.scitotenv.2022.157227","article-title":"Impacts of climate change on vegetation phenology over the Great Lakes Region of Central Asia from 1982 to 2014","volume":"845","author":"Gao","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"108818","DOI":"10.1016\/j.ecolind.2022.108818","article-title":"Detection of vegetation coverage changes in the Yellow River Basin from 2003 to 2020","volume":"138","author":"Liu","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1002\/joc.7273","article-title":"Spatial and temporal variability in extreme precipitation in the Pearl River Basin, China from 1960 to 2018","volume":"42","author":"Xu","year":"2022","journal-title":"Int. J. Climatol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"109429","DOI":"10.1016\/j.ecolind.2022.109429","article-title":"Temporal and spatial variation characteristics of vegetation coverage and quantitative analysis of its potential driving forces in the Qilian Mountains, China, 2000\u20132020","volume":"143","author":"Zuo","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"109101","DOI":"10.1016\/j.ecolind.2022.109101","article-title":"Vegetation variations and its driving factors in the transition zone between Tibetan Plateau and arid region","volume":"141","author":"Li","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_22","first-page":"101617","article-title":"Spatial relationships between NDVI and topographic factors at multiple scales in a watershed of the Minjiang River, China","volume":"69","author":"Xiong","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"157428","DOI":"10.1016\/j.scitotenv.2022.157428","article-title":"Evaluating the BFAST method to detect and characterise changing trends in water time series: A case study on the impact of droughts on the Mediterranean climate","volume":"846","author":"Maria","year":"2022","journal-title":"Sci. Total Environ."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1016\/j.ecolind.2011.08.011","article-title":"Trend analysis of vegetation dynamics in Qinghai\u2013Tibet Plateau using Hurst Exponent","volume":"14","author":"Peng","year":"2012","journal-title":"Ecol. Indic."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"He, C., Yan, F., Wang, Y., and Lu, Q. (2022). Spatiotemporal Variation in Vegetation Growth Status and Its Response to Climate in the Three-River Headwaters Regio. China Remote Sens., 14.","DOI":"10.3390\/rs14195041"},{"key":"ref_26","unstructured":"Wang, J.S., Bi, R.T., He, P., Xu, L.S., Liu, A.C., and Cao, C.B. (2022). Dynamic characteristics of NDVI during main growth seasons in the Chinese Loess Plateau effect by climate change. Chin. J. Ecol., 1\u201313. Available online: http:\/\/h-s.kns.cnki.net.njtc.vpn358.com\/kcms\/detail\/21.1148.q.20220621.0853.002.html."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez, B., S\u00e1nchez-Ruiz, S., Campos-Taberner, M., Garc\u00eda-Haro, F., and Gilabert, M. (2022). Exploring Ecosystem Functioning in Spain with Gross and Net Primary Production Time Series. Remote Sens., 14.","DOI":"10.3390\/rs14061310"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Qu, G., Dai, X., Cheng, J., Li, W., Wang, M., Liu, W., Yang, Z., Shan, Y., Ren, J., and Lu, H. (2022). Characterization of Long-Time Series Variation of Glacial Lakes in Southwestern Tibet: A Case Study in the Nyalam County. Remote Sens., 14.","DOI":"10.3390\/rs14194688"},{"key":"ref_29","first-page":"102378","article-title":"Dynamic changes of vegetation coverage in China-Myanmar economic corridor over the past 20 years","volume":"102","author":"Li","year":"2021","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_30","first-page":"9210","article-title":"A time-series approach to detect urbanized areas using biophysical indicators and landsat satellite imagery","volume":"14","author":"Zhang","year":"2021","journal-title":"IEEE J.-Stars"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"112438","DOI":"10.1016\/j.rse.2021.112438","article-title":"Performance stability of the MODIS and VIIRS LAI algorithms inferred from analysis of long time series of products","volume":"260","author":"Yan","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Tehrani, N.A., Mollalo, A., Farhanj, F., Pahlevanzadeh, N., and Janalipour, M. (2021). Time-series analysis of COVID-19 in Iran: A remote sensing perspective. COVID-19 Pandemic, Geospatial Information, and Community Resilience, CRC Press.","DOI":"10.1201\/9781003181590-25"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"101001","DOI":"10.1088\/2515-7620\/ac9459","article-title":"Analyzing the critical locations in response of constructed and planned dams on the Mekong River Basin for environmental integrity","volume":"4","author":"Gao","year":"2022","journal-title":"Environ. Res. Commun."},{"key":"ref_34","first-page":"78","article-title":"Evaluation on the Suitability of Vegetation Ecological Factors in Lancanjiang-Mekong River Basin","volume":"41","author":"Zhang","year":"2017","journal-title":"Heilongjiang Environ. J."},{"key":"ref_35","first-page":"102352","article-title":"Deriving drought indices from MODIS vegetation indices (NDVI\/EVI) and Land Surface Temperature (LST): Is data reconstruction necessary?","volume":"101","author":"Xie","year":"2021","journal-title":"Int. J. Appl. Earth Obs."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1016\/S0034-4257(01)00337-6","article-title":"Normalisation of directional effects in 10-day global syntheses derived from VEGETATION\/SPOT","volume":"81","author":"Duchemin","year":"2002","journal-title":"remote sens. Environ."},{"key":"ref_37","first-page":"969","article-title":"Review on VI Compositing Algorithm","volume":"28","author":"Long","year":"2013","journal-title":"Remote Sens. Technol. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"109342","DOI":"10.1016\/j.ecolind.2022.109342","article-title":"The contributions of natural and anthropogenic factors to NDVI variations on the Loess Plateau in China during 2000\u20132020","volume":"143","author":"Zhang","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"He, S., Shao, H., Xian, W., Zhang, S., Zhong, J., and Qi, J. (2021). Extraction of Abandoned Land in Hilly Areas Based on the Spatio-Temporal Fusion of Multi-Source Remote Sensing Images. Remote Sens., 13.","DOI":"10.3390\/rs13193956"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"109164","DOI":"10.1016\/j.ecolind.2022.109164","article-title":"Impacts of climate change and human activities on vegetation NDVI in China\u2019s Mu Us Sandy Land during 2000\u20132019","volume":"142","author":"Lin","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1417","DOI":"10.1080\/01431168608948945","article-title":"Characteristics of maximum-value composite images from temporal AVHRR data","volume":"7","author":"Holben","year":"1986","journal-title":"Int. J. Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.rse.2006.06.018","article-title":"Land-cover change detection using multi-temporal MODIS NDVI data","volume":"105","author":"Lunetta","year":"2006","journal-title":"Remote Sens. Environ."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1007\/978-94-011-2546-8_20","article-title":"A Rank-Invariant Method of Linear and Polynomial Regression Analysis","volume":"Volume 23","author":"Raj","year":"1992","journal-title":"Henri Theil\u2019s Contributions to Economics and Econometrics"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.ecolind.2014.07.031","article-title":"Spatio-temporal analysis of vegetation variation in the Yellow River Basin","volume":"51","author":"Jiang","year":"2015","journal-title":"Ecol. Indic."},{"key":"ref_45","first-page":"7798","article-title":"The spatio-temporal variations of vegetation cover in the Yellow River Basin from 2000 to 2010","volume":"33","author":"Yuan","year":"2013","journal-title":"Acta Ecol. Sin."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"269","DOI":"10.1137\/S0036144501394387","article-title":"Stochastic models that separate fractal dimension and the Hurst effect","volume":"46","author":"Gneiting","year":"2004","journal-title":"SIAM Rev."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ecolind.2020.106642","article-title":"Analysis and prediction of vegetation dynamic changes in China: Past, present and future","volume":"117","author":"Zhou","year":"2020","journal-title":"Ecol. Indic."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"799","DOI":"10.3390\/applmech3030047","article-title":"Spectral Properties of Water Hammer Wave","volume":"3","author":"Sarker","year":"2022","journal-title":"Appl. Mech. Rev."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"770","DOI":"10.1061\/TACEAT.0006518","article-title":"Long-term storage capacity of reservoirs. Trans","volume":"116","author":"Hurst","year":"1951","journal-title":"Am. Soc. Civ. Eng."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Sawut, R., Li, Y., Kasimu, A., and Ablat, X. (2022). Examining the spatially varying effects of climatic and environmental pollution factors on the NDVI based on their spatially heterogeneous relationships in Bohai Rim, China. J. Hydrol., 128815.","DOI":"10.1016\/j.jhydrol.2022.128815"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.rse.2017.11.017","article-title":"Large-scale detection of vegetation dynamics and their potential drivers using MODIS images and BFAST: A case study in Quebec, Canada","volume":"206","author":"Fang","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.rse.2009.08.014","article-title":"Detecting trend and seasonal changes in satellite image time series","volume":"114","author":"Verbesselt","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_53","first-page":"100622","article-title":"Analysis of trends and changes in the successional trajectories of tropical forest using the Landsat NDVI time series","volume":"24","author":"Berveglieri","year":"2021","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"112619","DOI":"10.1016\/j.rse.2021.112619","article-title":"Investigating aerosol vertical distribution using CALIPSO time series over the Middle East and North Africa (MENA), Europe, and India: A BFAST-based gradual and abrupt change detection","volume":"264","author":"Brakhasi","year":"2021","journal-title":"Remote Sens. Environ."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"113267","DOI":"10.1016\/j.rse.2022.113267","article-title":"Characterizing ecosystem change in wetlands using dense earth observation time series","volume":"281","author":"Horion","year":"2022","journal-title":"Remote Sens. Environ."},{"key":"ref_56","unstructured":"R Core Team (2018). R: A Language and Environment for Statistical Computing, R Core Team. Available online: https:\/\/www.R-project.org."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.3390\/rs5031117","article-title":"Shifts in global vegetation activity trends","volume":"5","author":"Verbesselt","year":"2013","journal-title":"Remote Sens."},{"key":"ref_58","unstructured":"Mekong River Commission (2019). Annual Mekong Flood Report: Aspects of Hydrology and Extreme Weather Phenomena in Flood and Drought Forecasting in the Lower Mekong Basin (LMB), MRC Secretariat. Available online: https:\/\/www.mrcmekong.org\/assets\/Publications\/Annual-Mekong-Flood-Report-2017.pdf."},{"key":"ref_59","first-page":"533","article-title":"Global Major Weather and Climate Events in 2018 and the Possible Causes","volume":"45","author":"Sun","year":"2019","journal-title":"Meteorol. Mon."},{"key":"ref_60","first-page":"538","article-title":"Global Major Weather and Climate Events in 2019 and the Possible Causes","volume":"4","author":"Yin","year":"2020","journal-title":"Meteorol. Mon."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Yasmi, Y., Durst, P., Haq, R.U., and Broadhead, J. (2017). Forest Change in the Greater Mekong Subregion (GMS): An Overview of Negative and Positive Drivers, FAO Regional Office for Asia and the Pacific.","DOI":"10.18356\/cb44f175-en"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1016\/j.rse.2018.05.018","article-title":"Increasing global vegetation browning hidden in overall vegetation greening: Insights from time-varying trends","volume":"214","author":"Pan","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.foreco.2012.07.050","article-title":"Effects of cascade hydropower dams on the structure and distribution of riparian and upland vegetation along the middle-lower Lancang-Mekong River","volume":"284","author":"Li","year":"2012","journal-title":"For. Ecol. Manag."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"106530","DOI":"10.1016\/j.catena.2022.106530","article-title":"Vegetation cover changes in China induced by ecological restoration-protection projects and land-use changes from 2000 to 2020","volume":"217","author":"Cai","year":"2022","journal-title":"Catena"},{"key":"ref_65","first-page":"1222","article-title":"Spatial-Temporal Dynamic Changes of Vegetation Cover in Lancang River Basin during 2001\u20132010","volume":"34","author":"Fan","year":"2012","journal-title":"Resour. Sci."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"707","DOI":"10.1016\/j.rse.2018.12.020","article-title":"Assessing the accuracy of detected breaks in Landsat time series as predictors of small scale deforestation in tropical dry forests of Mexico and Costa Rica","volume":"221","author":"Smith","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.rse.2012.02.022","article-title":"Near real-time disturbance detection using satellite image time series","volume":"123","author":"Verbesselt","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1157","DOI":"10.1002\/joc.5868","article-title":"Regional differences in shifts of temperature trends across China between 1980 and 2017","volume":"39","author":"Li","year":"2019","journal-title":"Int. J. Climatol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"5327","DOI":"10.1029\/2018WR022905","article-title":"Multidecadal trajectory of riverine nitrogen and phosphorus dynamics in rural catchments","volume":"54","author":"Dupas","year":"2018","journal-title":"Water Resour. Res."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1002\/ldr.3282","article-title":"Mapping European ecosystem change types in response to land-use change, extreme climate events, and land degradation","volume":"30","author":"Horion","year":"2019","journal-title":"Land Degrad. Dev."},{"key":"ref_71","first-page":"187","article-title":"Spatio-temporal change characteristics of vegetation coverage and its relationship with meteorological factors in the Greater Mekong Subregion","volume":"46","author":"Qiu","year":"2022","journal-title":"J. Nanjing For. Univ. (Nat. Sci. Ed.)"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Lasaponara, R., Abate, N., Fattore, C., Aromando, A., Cardettini, G., and Di Fonzo, M. (2022). On the Use of Sentinel-2 NDVI Time Series and Google Earth Engine to Detect Land-Use\/Land-Cover Changes in Fire-Affected Areas. Remote Sens., 14.","DOI":"10.3390\/rs14194723"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"108620","DOI":"10.1016\/j.ecolind.2022.108620","article-title":"Growing-season vegetation coverage patterns and driving factors in the China-Myanmar Economic Corridor based on Google Earth Engine and geographic detector","volume":"136","author":"Li","year":"2022","journal-title":"Ecol. Indic."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Liu, J., Liu, S., Tang, X., Ding, Z., Ma, M., and Yu, P. (2022). The Response of Land Surface Temperature Changes to the Vegetation Dynamics in the Yangtze River Basin. Remote Sens., 14.","DOI":"10.3390\/rs14205093"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6271\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:37:58Z","timestamp":1760146678000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/24\/6271"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,10]]},"references-count":74,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2022,12]]}},"alternative-id":["rs14246271"],"URL":"https:\/\/doi.org\/10.3390\/rs14246271","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,10]]}}}