{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T14:32:04Z","timestamp":1777905124990,"version":"3.51.4"},"reference-count":41,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T00:00:00Z","timestamp":1607558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012245","name":"Science and Technology Planning Project of Guangdong Province","doi-asserted-by":"publisher","award":["2016A020228009"],"award-info":[{"award-number":["2016A020228009"]}],"id":[{"id":"10.13039\/501100012245","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) can detect an abrupt change that was undetected by Residual Trend analysis (RESTREND), but it is usually combined with the Global Inventory for Mapping and Modeling Studies (GIMMS) Normalized Difference Vegetation Index (NDVI), which cannot detect detailed vegetation changes in small areas. Hence, we used Time Series Segmentation and Residual Trend analysis (TSS-RESTREND) and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI (MOD-TR) to analyze the vegetation dynamic of the Pearl River Delta region (PRD) in this study. To choose the most suitable MODIS NDVI from MOD13Q1 (250 m), MOD13A1 (500 m), and MOD13A2 (1 km), whole and local comparison of results of the break year and MOD-TR were used. Meanwhile, a comparison of vegetation change at the city-scale was also implemented. Moreover, to reduce insignificant trend pixels in TSS-RESTREND, a combination method of TSS-RESTREND and RESTREND (CTSS-RESTREND) was proposed. We found that: (1) MOD13Q1 and MOD13A1 two NDVI were suitable for combination with TSS-RESTREND to detect vegetation change in PRD, but MOD13Q1 was a better choice when considering the accuracy of local detailed vegetation change; (2) CTSS-RESTREND could detect more pixels with a significant change (i.e., significant increase and significant decrease) than those of TSS-RESTREND and RESTREND. Also, its effectiveness could be verified by Landsat data; (3) at the city-scale, the CTSS-RESTREND detected that only vegetation decreases in Shenzhen, Foshan, Dongguan, and Zhongshan were higher than vegetation increases, but, significant vegetation changes (i.e., decreases and increases) were mainly concentrated in Huizhou, Jiangmen, Zhaoqing, and Guangzhou.<\/jats:p>","DOI":"10.3390\/rs12244049","type":"journal-article","created":{"date-parts":[[2020,12,10]],"date-time":"2020-12-10T20:18:22Z","timestamp":1607631502000},"page":"4049","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":23,"title":["Detecting Vegetation Change in the Pearl River Delta Region Based on Time Series Segmentation and Residual Trend Analysis (TSS-RESTREND) and MODIS NDVI"],"prefix":"10.3390","volume":"12","author":[{"given":"Zhu","family":"Ruan","sequence":"first","affiliation":[{"name":"Key Laboratory of Ocean and Marginal Sea Geology, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"School of Environment, Jinan University, Guangzhou 511443, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yaoqiu","family":"Kuang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ocean and Marginal Sea Geology, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China"},{"name":"School of Environment, Jinan University, Guangzhou 511443, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yeyu","family":"He","sequence":"additional","affiliation":[{"name":"Key Laboratory of Ocean and Marginal Sea Geology, Guangzhou Institute of Geochemistry, Chinese Academy of Sciences, Guangzhou 510640, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"},{"name":"School of Environment, Jinan University, Guangzhou 511443, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Zhen","sequence":"additional","affiliation":[{"name":"School of Economics, Zhejiang University of Finance and Economics, Hangzhou 310018, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Song","family":"Ding","sequence":"additional","affiliation":[{"name":"School of Environment, Jinan University, Guangzhou 511443, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,12,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1016\/j.envpol.2018.10.114","article-title":"Urban vegetation loss and ecosystem services: The influence on climate regulation and noise and air pollution","volume":"245","author":"Szlafsztein","year":"2019","journal-title":"Environ. Pollut."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1126\/science.1184984","article-title":"Terrestrial gross carbon dioxide uptake: Global distribution and covariation with climate","volume":"329","author":"Beer","year":"2010","journal-title":"Science"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2633","DOI":"10.1175\/JCLI-D-17-0236.1","article-title":"Impact of earth greening on the terrestrial water cycle","volume":"31","author":"Zeng","year":"2018","journal-title":"J. Clim."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"111259","DOI":"10.1016\/j.rse.2019.111259","article-title":"Detecting hotspots of interactions between vegetation greenness and terrestrial water storage using satellite observations","volume":"231","author":"Xie","year":"2019","journal-title":"Remote. Sens. Environ."},{"key":"ref_5","first-page":"1","article-title":"Assessing the Influence of Vegetation on the Water Budget of Tropical Areas","volume":"51","author":"Casagrande","year":"2018","journal-title":"Ifac-Pap."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.rse.2012.01.017","article-title":"Greenness in semi-arid areas across the globe 1981\u20132007\u2014An earth observing satellite based analysis of trends and drivers","volume":"121","author":"Fensholt","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_7","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_8","doi-asserted-by":"crossref","first-page":"245","DOI":"10.2307\/1907187","article-title":"Nonparametric Tests against Trend","volume":"13","author":"Mann","year":"1945","journal-title":"Econometrica"},{"key":"ref_9","unstructured":"Kendall, M.G. (1948). Rank Correlation Methods, Griffin."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1080\/01621459.1968.10480934","article-title":"Estimates of the regression coefficient based on Kendall\u2019s tau","volume":"63","author":"Sen","year":"1968","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/S0140-1963(03)00121-6","article-title":"Discrimination between climate and human-induced dryland degradation","volume":"57","author":"Evans","year":"2004","journal-title":"J. Arid Environ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"969","DOI":"10.1007\/s10980-012-9751-2","article-title":"Distinguishing between human-induced and climate-driven vegetation changes: A critical application of RESTREND in inner Mongolia","volume":"27","author":"Li","year":"2012","journal-title":"Landsc. Ecol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"5471","DOI":"10.3390\/rs70505471","article-title":"Land degradation assessment using residual trend analysis of GIMMS NDVI3g, soil moisture and rainfall in Sub-Saharan West Africa from 1982 to 2012","volume":"7","author":"Ibrahim","year":"2015","journal-title":"Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"89","DOI":"10.18172\/cig.2945","article-title":"Land degradation trend assessment over Iberia during 1982\u20132012","volume":"42","author":"Gouveia","year":"2016","journal-title":"Cuad. Investig. Geogr\u00e1fica"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.scitotenv.2019.05.158","article-title":"Mapping precipitation-corrected NDVI trends across Namibia","volume":"684","author":"Wingate","year":"2019","journal-title":"Sci. Total Environ."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.rse.2012.06.022","article-title":"Limits to detectability of land degradation by trend analysis of vegetation index data","volume":"125","author":"Wessels","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"642","DOI":"10.1111\/j.1365-2486.2011.02578.x","article-title":"Trend changes in global greening and browning: Contribution of short-term trends to longer-term change","volume":"18","author":"Verbesselt","year":"2012","journal-title":"Glob. Chang. Biol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.rse.2013.10.019","article-title":"Automated mapping of vegetation trends with polynomials using NDVI imagery over the Sahel","volume":"141","author":"Jamali","year":"2014","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.rse.2017.05.018","article-title":"Detecting dryland degradation using Time Series Segmentation and Residual Trend analysis (TSS-RESTREND)","volume":"197","author":"Burrell","year":"2017","journal-title":"Remote Sens. Environ."},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"2970","DOI":"10.1016\/j.rse.2010.08.003","article-title":"Phenological change detection while accounting for abrupt and gradual trends in satellite image time series","volume":"114","author":"Verbesselt","year":"2010","journal-title":"Remote Sens. Environ."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"6929","DOI":"10.3390\/rs6086929","article-title":"A non-stationary 1981\u20132012 AVHRR NDVI3g time series","volume":"6","author":"Pinzon","year":"2014","journal-title":"Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2342","DOI":"10.1109\/JSTARS.2019.2906466","article-title":"The addition of temperature to the TSS-RESTREND methodology significantly improves the detection of dryland degradation","volume":"12","author":"Burrell","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3853","DOI":"10.1038\/s41467-020-17710-7","article-title":"Anthropogenic climate change has driven over 5 million km2 of drylands towards desertification","volume":"11","author":"Burrell","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Liu, C., Melack, J., Tian, Y., Huang, H., Jiang, J., Fu, X., and Zhang, Z. (2019). Detecting land degradation in eastern China grasslands with time series segmentation and residual trend analysis (TSS-RESTREND) and GIMMS NDVI3g data. Remote Sens., 11.","DOI":"10.3390\/rs11091014"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"7735","DOI":"10.1080\/01431161.2020.1763509","article-title":"Land change syndromes identification in temperate forests of Hindukush Himalaya Karakorum (HHK) mountain ranges","volume":"41","author":"Munawar","year":"2020","journal-title":"Int. J. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Cui, C., Zhang, W., Hong, Z., and Meng, L. (2020). Forecasting NDVI in multiple complex areas using neural network techniques combined feature engineering. Int. J. Digit. Earth, 1733\u20131749.","DOI":"10.1080\/17538947.2020.1808718"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Nyamekye, C., Sch\u00f6nbrodt-Stitt, S., Amekudzi, L.K., Zoungrana, B.J.B., and Thiel, M. (2020). Usage of MODIS NDVI to evaluate the effect of soil and water conservation measures on vegetation in Burkina Faso. Land Degrad. Dev., 1\u201313.","DOI":"10.1002\/ldr.3654"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Hamunyela, E., Rosca, S., Mirt, A., Engle, E., Herold, M., Gieseke, F., and Verbesselt, J. (2020). Implementation of BFASTmonitor Algorithm on Google Earth Engine to Support Large-Area and Sub-Annual Change Monitoring Using Earth Observation Data. Remote Sens., 12.","DOI":"10.3390\/rs12182953"},{"key":"ref_30","unstructured":"Burrell, A. (2020, October 05). TSS.RESTREND: Time Series Segmentation of Residual Trends. Available online: https:\/\/rdrr.io\/cran\/TSS.RESTREND\/man\/TSSRESTREND.html."},{"key":"ref_31","unstructured":"Detsch, F. (2020, October 05). gimms: Download and Process GIMMS NDVI3g Data. Available online: https:\/\/github.com\/environmentalinformatics-marburg\/gimms."},{"key":"ref_32","unstructured":"Hijmans, R.J. (2020, October 05). raster: Geographic Data Analysis and Modeling. Available online: https:\/\/rspatial.org\/raster."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.rse.2004.03.014","article-title":"A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky\u2013Golay filter","volume":"91","author":"Chen","year":"2004","journal-title":"Remote Sens. Environ."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"408","DOI":"10.1016\/j.agrformet.2017.10.026","article-title":"Vegetation phenology on the Qinghai-Tibetan Plateau and its response to climate change (1982\u20132013)","volume":"248","author":"Zhang","year":"2018","journal-title":"Agric. and Forest Meteorology"},{"key":"ref_35","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_36","doi-asserted-by":"crossref","first-page":"361","DOI":"10.2307\/1913018","article-title":"Tests of Equality Between Sets of Coefficients in Two Linear Regressions: An Expository Note","volume":"38","author":"Fisher","year":"1970","journal-title":"Econometrica"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1080\/13658810802443457","article-title":"Geographical detectors-based health risk assessment and its application in the neural tube defects study of the Heshun region, China","volume":"24","author":"Wang","year":"2010","journal-title":"Int. J. Geogr. Inf. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"40092","DOI":"10.1038\/srep40092","article-title":"Quantifying influences of physiographic factors on temperate dryland vegetation, Northwest China","volume":"7","author":"Du","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"593","DOI":"10.1080\/15481603.2020.1760434","article-title":"An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: Cases with different types of spatial data","volume":"57","author":"Song","year":"2020","journal-title":"GIScience Remote Sens."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1007\/s11442-010-0483-4","article-title":"Spatial patterns and driving forces of land use change in China during the early 21st century","volume":"20","author":"Liu","year":"2010","journal-title":"J. Geogr. Sci."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"359","DOI":"10.1002\/ldr.3221","article-title":"A significant increase in the normalized difference vegetation index during the rapid economic development in the Pearl River Delta of China","volume":"30","author":"Hu","year":"2019","journal-title":"Land Degrad. Dev."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/24\/4049\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:43:32Z","timestamp":1760179412000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/24\/4049"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,12,10]]},"references-count":41,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2020,12]]}},"alternative-id":["rs12244049"],"URL":"https:\/\/doi.org\/10.3390\/rs12244049","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,10]]}}}