{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,16]],"date-time":"2026-01-16T00:01:22Z","timestamp":1768521682589,"version":"3.49.0"},"reference-count":56,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,25]],"date-time":"2021-11-25T00:00:00Z","timestamp":1637798400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Second Tibetan Plateau Scientific Expedition and Research Program (STEP)","award":["2019QZKK0404"],"award-info":[{"award-number":["2019QZKK0404"]}]},{"DOI":"10.13039\/501100001809","name":"the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42071349"],"award-info":[{"award-number":["42071349"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Sichuan Science and Technology Program","award":["2020JDJQ0003"],"award-info":[{"award-number":["2020JDJQ0003"]}]},{"name":"the CAS &quot;Light of West China&quot; Program","award":["Y9R2140149"],"award-info":[{"award-number":["Y9R2140149"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Afforestation is one of the most efficient ways to control land desertification in the middle section of the Yarlung Zangbo River (YZR) valley. However, the lack of a quantitative way to record the planting time of artificial forest (AF) constrains further management for these forests. The long-term archived Landsat images (including the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI)) provide a good opportunity to capture the temporal change information about AF plantations. Under the condition that there would be an abrupt increasing trend in the normalized difference vegetation index (NDVI) time-series curve after afforestation, and this characteristic can be thought of as the indicator of the AF planting time. To extract the indicator, an algorithm based on the Google Earth Engine (GEE) for detecting this trend change point (TCP) on the maximum NDVI time series within the growing season (May to September) was proposed. In this algorithm, the time-series NDVI was initially smoothed and segmented into two subspaces. Then, a trend change indicator Sdiff was calculated with the difference between the fitting slopes of the subspaces before and after each target point. A self-adaptive method was applied to the NDVI series to find the right year with the maximum TCP, which is recorded as the AF planting time. Based on the proposed method, the AF planting time of the middle section of the YZR valley from 1988 to 2020 was derived. The detected afforestation temporal information was validated by 222 samples collected from the field survey, with a Pearson correlation coefficient of 0.93 and a root mean squared error (RMSE) of 2.95 years. Meanwhile, the area distribution of the AF planted each year has good temporal consistency with the implementation of the eco-reconstruction project. Overall, the study provides a good way to map AF planting times that is not only helpful for sustainable management of AF areas but also provides a basis for further research on the impact of afforestation on desertification control.<\/jats:p>","DOI":"10.3390\/rs13234785","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"4785","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Temporal Information Extraction for Afforestation in the Middle Section of the Yarlung Zangbo River Using Time-Series Landsat Images Based on Google Earth Engine"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6787-7320","authenticated-orcid":false,"given":"Hao","family":"Fu","sequence":"first","affiliation":[{"name":"Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China"},{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4839-6791","authenticated-orcid":false,"given":"Wei","family":"Zhao","sequence":"additional","affiliation":[{"name":"Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China"},{"name":"Kathmandu Center for Research and Education, Chinese Academy of Sciences-Tribhuvan University, Beijing 100101, China"}]},{"given":"Qiqi","family":"Zhan","sequence":"additional","affiliation":[{"name":"Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Mengjiao","family":"Yang","sequence":"additional","affiliation":[{"name":"Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}]},{"given":"Donghong","family":"Xiong","sequence":"additional","affiliation":[{"name":"Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China"},{"name":"Kathmandu Center for Research and Education, Chinese Academy of Sciences-Tribhuvan University, Beijing 100101, China"}]},{"given":"Daijun","family":"Yu","sequence":"additional","affiliation":[{"name":"College of Earth Sciences, Chengdu University of Technology, Chengdu 610059, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1002\/ldr.1159","article-title":"Land Use And Climate Changes And Their Impacts on Runoff In the Yarlung Zangbo River Basin, China","volume":"25","author":"Liu","year":"2014","journal-title":"Land Degrad. Dev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"104794","DOI":"10.1016\/j.catena.2020.104794","article-title":"Grain size characteristics of aeolian sands and their implications for the aeolian dynamics of dunefields within a river valley on the southern Tibet Plateau: A case study from the Yarlung Zangbo river valley","volume":"196","author":"Zhou","year":"2021","journal-title":"Catena"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1648","DOI":"10.1016\/j.scitotenv.2017.10.137","article-title":"Monitoring of aeolian desertification on the Qinghai-Tibet Plateau from the 1970s to 2015 using Landsat images","volume":"619\u2013620","author":"Zhang","year":"2018","journal-title":"Sci. Total Environ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1987","DOI":"10.1007\/s11629-019-5509-5","article-title":"Spatial distribution and formation mechanism of aeolian sand in the middle reaches of the Yarlung Zangbo River","volume":"16","author":"Liu","year":"2019","journal-title":"J. Mt. Sci."},{"key":"ref_5","unstructured":"Wu, Y. (2016). The Study on the Relationship between Ecological Conservation and Rural Households\u2019 Income Improvement in Tibet. [Ph.D. Thesis, Beijing Forestry University]."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1038\/21867","article-title":"Rapid accumulation and turnover of soil carbon in a re-establishing forest","volume":"400","author":"Richter","year":"1999","journal-title":"Nature"},{"key":"ref_7","first-page":"53","article-title":"Biomass allocation and carbon density of Sophora moorcroftiana shrublands in the middle reaches of Yarlung Zangbo River, Xizang, China","volume":"41","author":"Cui","year":"2017","journal-title":"Chin. J. Plant Ecol."},{"key":"ref_8","first-page":"100127","article-title":"Carbon neutrality: Toward a sustainable future","volume":"2","author":"Chen","year":"2021","journal-title":"Innovation"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1007\/s10661-017-6183-0","article-title":"Changes in wind erosion over a 25-year restoration chronosequence on the south edge of the Tengger Desert, China: Implications for preventing desertification","volume":"189","author":"Ma","year":"2017","journal-title":"Environ. Monit. Assess."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.ecoleng.2005.10.015","article-title":"Ecological effects of desertification control and desertified land reclamation in an oasis\u2013desert ecotone in an arid region: A case study in Hexi Corridor, northwest China","volume":"29","author":"Su","year":"2007","journal-title":"Ecol. Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1007\/BF02837482","article-title":"Progress in sandy desertification research of China","volume":"14","author":"Tao","year":"2004","journal-title":"J. Geogr. Sci."},{"key":"ref_12","first-page":"100017","article-title":"Mechanisms of Plant Responses and Adaptation to Soil Salinity","volume":"1","author":"Zhao","year":"2020","journal-title":"Innovation"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"6501","DOI":"10.1109\/JSTARS.2021.3089851","article-title":"Spatiotemporal Patterns of Land Surface Temperature Change in the Tibetan Plateau Based on MODIS\/Terra Daily Product From 2000 to 2018","volume":"14","author":"Yang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1576","DOI":"10.1111\/gcb.14887","article-title":"Afforestation for climate change mitigation: Potentials, risks and trade-offs","volume":"26","author":"Doelman","year":"2020","journal-title":"Glob. Chang. Biol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1890\/0012-9615(1999)069[0535:PRIPGV]2.0.CO;2","article-title":"Plant Removals in Perennial Grassland: Vegetation Dynamics, Decomposers, Soil Biodiversity, and Ecosystem Properties","volume":"69","author":"Wardle","year":"1999","journal-title":"Ecol. Monogr."},{"key":"ref_16","unstructured":"Lemons, J., Victor, R., and Schaffer, D. (2003). Plant Diversity and Succession of Artificial Vegetation Types and Environment in an Arid Desert Region of China. Conserving Biodiversity in Arid Regions: Best Practices in Developing Nations, Springer."},{"key":"ref_17","unstructured":"Chen, S. (2009). Eco-Benefit of Ecoloigical Restoration and Reconstruction in Mining Area-Case Study of Phosphorite in Kunyang, Yunnan Province. [Master\u2019s Thesis, Kunming University of Science and Technology]."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.neunet.2013.01.012","article-title":"Change-point detection in time-series data by relative density-ratio estimation","volume":"43","author":"Liu","year":"2013","journal-title":"Neural Netw."},{"key":"ref_19","first-page":"100110","article-title":"Bridging the knowledge gap on the evolution of the Asian monsoon during 26\u201316 Ma","volume":"2","author":"Xie","year":"2021","journal-title":"Innovation"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"106444","DOI":"10.1016\/j.quascirev.2020.106444","article-title":"Climate change, vegetation history, and landscape responses on the Tibetan Plateau during the Holocene: A comprehensive review","volume":"243","author":"Chen","year":"2020","journal-title":"Quat. Sci. Rev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.rse.2015.11.032","article-title":"The global Landsat archive: Status, consolidation, and direction","volume":"185","author":"Wulder","year":"2016","journal-title":"Remote. Sens. Environ."},{"key":"ref_22","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_23","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.rse.2019.03.034","article-title":"Long-term continuity in land surface phenology measurements: A comparative assessment of the MODIS land cover dynamics and VIIRS land surface phenology products","volume":"226","author":"Moon","year":"2019","journal-title":"Remote Sens. Environ."},{"key":"ref_24","first-page":"27","article-title":"Optimal dates for assessing long-term changes in tree-cover in the semi-arid biomes of South Africa using MODIS NDVI time series (2001\u20132018)","volume":"81","author":"Cho","year":"2019","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1975","DOI":"10.1029\/2018JD030007","article-title":"Spatiotemporal Variability in Land Surface Temperature Over the Mountainous Region Affected by the 2008 Wenchuan Earthquake from 2000 to 2017","volume":"124","author":"Zhao","year":"2019","journal-title":"J. Geophys. Res.-Atmos."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1016\/S0378-1127(03)00113-0","article-title":"Improved Landsat-based forest mapping in steep mountainous terrain using object-based classification","volume":"183","author":"Dorren","year":"2003","journal-title":"For. Ecol. Manag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1080\/02757259609532305","article-title":"Digital change detection in forest ecosystems with remote sensing imagery","volume":"13","author":"Coppin","year":"1996","journal-title":"Remote. Sens. Rev."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Pause, M., Schweitzer, C., Rosenthal, M., Keuck, V., Bumberger, J., Dietrich, P., Heurich, M., Jung, A., and Lausch, A. (2016). In Situ\/Remote Sensing Integration to Assess Forest Health\u2014A Review. Remote. Sens., 8.","DOI":"10.3390\/rs8060471"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.rse.2007.03.010","article-title":"Trajectory-based change detection for automated characterization of forest disturbance dynamics","volume":"110","author":"Kennedy","year":"2007","journal-title":"Remote. Sens. Environ."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1886","DOI":"10.1016\/j.rse.2009.04.004","article-title":"Evaluation of earth observation based long term vegetation trends\u2014Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data","volume":"113","author":"Fensholt","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.rse.2009.08.017","article-title":"An automated approach for reconstructing recent forest disturbance history using dense Landsat time series stacks","volume":"114","author":"Huang","year":"2010","journal-title":"Remote. Sens. Environ."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"112026","DOI":"10.1016\/j.rse.2020.112026","article-title":"Threshold- and trend-based vegetation change monitoring algorithm based on the inter-annual multi-temporal normalized difference moisture index series: A case study of the Tatra Mountains","volume":"249","author":"Ochtyra","year":"2020","journal-title":"Remote. Sens. Environ."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Yang, Y., Luo, J., Huang, Q., Wu, W., and Sun, Y. (2019). Weighted Double-Logistic Function Fitting Method for Reconstructing the High-Quality Sentinel-2 NDVI Time Series Data Set. Remote. Sens., 11.","DOI":"10.3390\/rs11202342"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.geosus.2021.01.002","article-title":"A long-term record (1995\u20132019) of the dynamics of land desertification in the middle reaches of Yarlung Zangbo River basin derived from Landsat data","volume":"2","author":"Zhan","year":"2021","journal-title":"Geogr. Sustain."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Pan, L., Xia, H., Zhao, X., Guo, Y., and Qin, Y. (2021). Mapping Winter Crops Using a Phenology Algorithm, Time-Series Sentinel-2 and Landsat-7\/8 Images, and Google Earth Engine. Remote. Sens., 13.","DOI":"10.3390\/rs13132510"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"126532","DOI":"10.1016\/j.jhydrol.2021.126532","article-title":"Time-lag effects of climatic change and drought on vegetation dynamics in an alpine river basin of the Tibet Plateau, China","volume":"600","author":"Zuo","year":"2021","journal-title":"J. Hydrol."},{"key":"ref_37","first-page":"102376","article-title":"Mapping cropping intensity in Huaihe basin using phenology algorithm, all Sentinel-2 and Landsat images in Google Earth Engine","volume":"102","author":"Pan","year":"2021","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1016\/j.rse.2017.03.026","article-title":"Cloud detection algorithm comparison and validation for operational Landsat data products","volume":"194","author":"Foga","year":"2017","journal-title":"Remote. Sens. Environ."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.rse.2015.12.024","article-title":"Characterization of Landsat-7 to Landsat-8 reflective wavelength and normalized difference vegetation index continuity","volume":"185","author":"Roy","year":"2016","journal-title":"Remote. Sens. Environ."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/01431160412331269698","article-title":"Random forest classifier for remote sensing classification","volume":"26","author":"Pal","year":"2005","journal-title":"Int. J. Remote. Sens."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Phan, T.N., Kuch, V., and Lehnert, L.W. (2020). Land Cover Classification using Google Earth Engine and Random Forest Classifier\u2014The Role of Image Composition. Remote. Sens., 12.","DOI":"10.3390\/rs12152411"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_43","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_44","doi-asserted-by":"crossref","first-page":"596","DOI":"10.1016\/j.jaridenv.2005.03.007","article-title":"Analysis of Sahelian vegetation dynamics using NOAA-AVHRR NDVI data from 1981\u20132003","volume":"63","author":"Anyamba","year":"2005","journal-title":"J. Arid. Environ."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"370","DOI":"10.1016\/j.isprsjprs.2017.06.013","article-title":"Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications","volume":"130","author":"Zhu","year":"2017","journal-title":"ISPRS J. Photogramm. Remote. Sens."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"201","DOI":"10.1007\/s00704-019-02817-9","article-title":"A new statistical method for detecting trend turning","volume":"138","author":"Zuo","year":"2019","journal-title":"Theor. Appl. Climatol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"9949","DOI":"10.1007\/s10661-013-3304-2","article-title":"Mapping afforestation and deforestation from 1974 to 2012 using Landsat time-series stacks in Yulin District, a key region of the Three-North Shelter region, China","volume":"185","author":"Liu","year":"2013","journal-title":"Environ. Monit. Assess."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/s10479-016-2185-5","article-title":"Multi-sensor slope change detection","volume":"263","author":"Cao","year":"2018","journal-title":"Ann. Oper. Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"989","DOI":"10.1080\/01431168908903939","article-title":"Review Article Digital change detection techniques using remotely-sensed data","volume":"10","author":"Singh","year":"1989","journal-title":"Int. J. Remote. Sens."},{"key":"ref_50","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_51","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_52","first-page":"199","article-title":"Multitemporal settlement and population mapping from Landsat using Google Earth Engine","volume":"35","author":"Patel","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Kennedy, R.E., Yang, Z., Gorelick, N., Braaten, J., Cavalcante, L., Cohen, W.B., and Healey, S. (2018). Implementation of the LandTrendr Algorithm on Google Earth Engine. Remote. Sens., 10.","DOI":"10.3390\/rs10050691"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Wang, C., Jia, M., Chen, N., and Wang, W. (2018). Long-Term Surface Water Dynamics Analysis Based on Landsat Imagery and the Google Earth Engine Platform: A Case Study in the Middle Yangtze River Basin. Remote. Sens., 10.","DOI":"10.3390\/rs10101635"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1080\/01431161.2014.995276","article-title":"Integrating multiple texture methods and NDVI to the Random Forest classification algorithm to detect tea and hazelnut plantation areas in northeast Turkey","volume":"36","author":"Akar","year":"2015","journal-title":"Int. J. Remote. Sens."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"7677","DOI":"10.1080\/01431161.2010.527392","article-title":"Extracting structural attributes from IKONOS imagery for Eucalyptus plantation forests in KwaZulu-Natal, South Africa, using image texture analysis and artificial neural networks","volume":"32","author":"Gebreslasie","year":"2011","journal-title":"Int. J. Remote. Sens."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4785\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:35:59Z","timestamp":1760168159000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4785"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,25]]},"references-count":56,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13234785"],"URL":"https:\/\/doi.org\/10.3390\/rs13234785","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,25]]}}}