{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T09:41:59Z","timestamp":1762508519273,"version":"build-2065373602"},"reference-count":76,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,6,23]],"date-time":"2021-06-23T00:00:00Z","timestamp":1624406400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100011350","name":"State Key Laboratory of Remote Sensing Science","doi-asserted-by":"publisher","award":["OFSLRSS202001"],"award-info":[{"award-number":["OFSLRSS202001"]}],"id":[{"id":"10.13039\/501100011350","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The warming climate has rapidly altered vegetation growth in drylands, and consequently, has put great pressure on sustainable livelihoods. Various datasets have been applied from local to global scale to study vegetation dynamics and there is a lack of solid comparison among multiple datasets. Note that vegetation growth might shift over time and the greening and browning components over a long-time span might be masked by a linear trend. Here, we aim to monitor the long-term and nonlinear dynamics in vegetation greenness for Inner Mongolia (an important part of dryland Asia). As a useful tool that indicates vegetation greenness, NDVI (Normalized Difference Vegetation Index) and LAI (Leaf Area Index) integrals derived from the GIMMS (Global Inventory Modelling and Mapping Studies) NDVI3g and the GIMMS LAI3g products are applied. During the period of 1982-2016, NDVI\/LAI integrals have an overall acceptable consistency in characterizing the trends of vegetation greenness, with NDVI large\/small integrals and LAI large\/small integrals increase at a rate of 0.96, 1.72, 2.23, and 3.13 per decade, respectively. Inner Mongolia experienced a noticeable greening process (71% and 82% greening area in NDVI large\/small integrals, 67% and 73% greening area in LAI large\/small integrals), despite the fragmentally distributed browning trends in eastern and partial northern Inner Mongolia. As inferred from nonlinear trend analysis, we found the greening process is still prevalent. The browning of eastern Inner Mongolia under the linear analysis was actually transferring from browning to greening, while the greening trend in northern Inner Mongolia was changing to browning. Increased occurrences in the frequency of breakpoints after 1999 suggest that previously stable vegetation ecology is more sensitive to external disturbances such as altered climatic impact and anthropogenic intervention.<\/jats:p>","DOI":"10.3390\/rs13132446","type":"journal-article","created":{"date-parts":[[2021,6,23]],"date-time":"2021-06-23T03:22:00Z","timestamp":1624418520000},"page":"2446","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Continuously Vegetation Greening over Inner Mongolia for the Past Three Decades"],"prefix":"10.3390","volume":"13","author":[{"given":"Hui","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0394-7972","authenticated-orcid":false,"given":"Xin","family":"Zhang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}]},{"given":"Yi","family":"Shang","sequence":"additional","affiliation":[{"name":"School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8348-6477","authenticated-orcid":false,"given":"Giri","family":"Kattel","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Water and Agriculture Program (WEAP), Department of Infrastructure Engineering, The University of Melbourne, Melbourne 3010, Australia"},{"name":"Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China"}]},{"given":"Lijuan","family":"Miao","sequence":"additional","affiliation":[{"name":"School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"Leibniz Institute of Agricultural Development in Transition Economies (IAMO), 06120 Halle, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1794","DOI":"10.1111\/1365-2664.13420","article-title":"Long-term impacts of changed grazing regimes on the vegetation of heterogeneous upland grasslands","volume":"56","author":"Pakeman","year":"2019","journal-title":"J. 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