王金亮教授团队学术论文在SCIE二区期刊Remote Sensing上发表

2022 年 12 月 10 日，以钟旭珍（云南师范大学地理学部地图学与地理信息系统专业 2022 级博士研究生）为第一作者，王金亮教授为通讯作者所撰写的题为“Linear and nonlinear characteristics of long-term NDVI using trend analysis: A case study of Lancang-Mekong River Basin”的学术论文在SCIE期刊 《Remote Sensing》 (中科院SCI期刊分区：2021年12月最新基础版二区，升级版二区，2022年IF 5.349)上线发表 (https://doi.org/10.3390/rs14246271)。

植被是陆地生态系统的主体，对区域生态系统环境变化有着重要指示。澜沧江-湄公河流域作为连接南亚、东南亚的重要纽带，监测其植被覆盖变化，可为陆地生态系统环境变化评估、流域水文水资源研究与管理等提供重要的数据支持和决策依据。研究以澜沧江-湄公河流域为研究区，基于2000-2021年MODIS NDVI数据，利用Sen斜率估计、Manna-Kendall检验、Hurst指数研究了其NDVI时空演变趋势和未来可持续性，并利用BFAST01方法对其突变类型和突变年份等非线性特征进行了检测和分析。结果表明：（1）近22年来，澜沧江-湄公河流域NDVI总体呈波动上升趋势，2021年 NDVI 值最大，为0.825，比 2000 年增加了4.29% 。但增加幅度各异：中国区域NDVI增长幅度最大达到7.25%，其次是泰国增长了7.21%，第三是缅甸和老挝，而柬埔寨和越南植被变化相对稳定。NDVI总体表现为南高北低，并以高植被覆盖度和较高植被覆盖度为主，其中植被覆盖度大于0.8的区域面积占比达到62%。（2）Sen-MK趋势表明，2000-2021年流域植被覆盖呈改善和退化趋势的面积分别占66.59%、18.88%。Hurst指数显示，未来NDVI将持续改善、退化及保持稳定的区域分别占60.14%、25.29%、14.53%，还有0.04%的区域未来发展趋势为不确定，应特别关注未来发展趋势退化的区域。（3）BFAST01检测到澜沧江-湄公河流域近22年NDVI有8种突变类型，突变主要发生在2002-2018年，2002-2004年和2014-2018年是发生断点最多的阶段。“中断：随着负中断增加”突变类型在此期间发生变化的比例最大为36.54%，占比最小的是“单调递减（带负中断）”仅0.65%。研究表明，运用常规趋势分析方法和BFAST突变检验相结合，可以更加准确地分析NDVI的时空变化与非线性突变性，从而为生态环境相关工作的开展提供科学的参考。

Figure 1. Lancang-Mekong River Basin. (a) Geographical location; (b) river network; (c) elevation.

Figure 2. Schematic diagram of BFAST01 trend mutation types.

Figure 3. Temporal change of the NDVI from 2000 to 2021. (a) Average annual; (b) seasonal;(c) spring; (d) summer; (e) autumn; (f) winter.

Figure 4. Spatial distribution of NDVI in the LMRB. (a) Average annual NDVI; (b) 2020 NDVI; (c) 2021 NDVI.

该论文得到了王金亮教授主持的国家重点研发计划多政府国际科技创新合作重点项目 “利用地理空间技术进行土地利用/土地覆盖变化对生态安全影响的环境监测与评估”（2018YFE0184300），国家自然科学基金项目 (41961060)，沱江流域高质量发展研究中心项目(TJGZL2022-15)，内江师范学院校级科研项目（2022YB17）共同资助。

这是钟旭珍同学读博士研究生以来发表的首篇 SCI/SCIE 学术论文，是她的博士期间发表的第一篇学术论文（详见录 1、2），也是王金亮教授导师团队 2022年的发表第15篇 SCI/SCIE 论文（详见录 3），让我们恭喜钟旭珍同学！希望她再接再厉！也热烈祝贺团队取好成绩！

附录 1 论文相关信息

标题：Linear and nonlinear characteristics of long-term NDVI using trend analysis: A case study of Lancang-Mekong River Basin

作者：Xuzhen Zhong^1,2,3,4,5, Jie Li^1,4,5,Jinliang Wang^1,3,4,5*, Jianpeng Zhang^1,4,5, Lanfang Liu^1,4,5,  and Jun Ma^1,6 

通讯作者：Jinliang Wang

作者单位： 

¹   Faculty of Geography, Yunnan Normal University, Kunming, Yunnan, 650500, China

²   School of Geography and Resource Science, Neijiang Normal University, Neijiang, Sichuan, 641100, China

³   Yunnan Normal University-Southwest United Graduate School, Kunming, 650500, China

⁴   Key Laboratory of Resources and Environmental Remote Sensing for Universities in Yunnan Kunming, Yunnan, 650500, China

⁵   Center for Geospatial Information Engineering and Technology of Yunnan Province, Kunming, Yunnan, 650500, China

⁶      Department of Geology, Tomsk State University, Tomsk 634050, Russia

*   Correspondence: E-mail: jlwang@ynnu.edu.cn; Tel.: 86 871 65941198

出版物： Remote Sensing

摘要：

Vegetation is the main body of the terrestrial ecosystem and is a significant indicator of the environmental changes in the regional ecosystem. As an essential link connecting South Asia and Southeast Asia, the Lancang-Mekong River Basin 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-Kendall 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 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 keep no-changed 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 mutations types in the Lancang-Mekong River Basin over the past 22 years. The mutations mainly occurred in 2002-2018, while 2002-2004 and 2014-2018 were the most frequent periods of breakpoints. The mutation type of "interruption: increase with negative break" was changed the most during this period, which accounts for 36.54%, and the smallest was "monotonic decrease (with negative break)”, 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 the ecological environment-related work.

关键字：NDVI; spatial-temporal pattern; mutation detection; Hurst exponent; BFAST01; Lancang-Mekong River Basin

附录2 钟旭珍同学发表SCI论文清单

自2022年9月攻读博士至今，钟旭珍同学在王金亮教授指导下发表了1篇SCI学术论文，信息如下：

[1] Xuzhen Zhong, Jie Li, Jinliang Wang^*, Jianpeng Zhang, Lanfang Liu,  and Jun Ma. Linear and nonlinear characteristics of long-term NDVI using trend analysis: A case study of Lancang-Mekong River Basin [J]. Remote Sensing, 2022, 14(24), 6271. DOI: https://doi.org/10.3390/rs14246271  (中科院SCI期刊分区：2021年12月最新基础版二区，升级版二区，2022年IF 5.349)

附录 3 王金亮团队 2022 年01月01日至12月10日发表论文清单

自2022年01月01日至12月10日，王金亮教授团队发表学术论文19篇，其中15篇SCI/SCIE 论文、4篇CSCD论文，具体信息如下：

[19] Xuzhen Zhong, Jie Li, Jinliang Wang*, Jianpeng Zhang, Lanfang Liu,  and Jun Ma. Linear and nonlinear characteristics of long-term NDVI using trend analysis: A case study of Lancang-Mekong River Basin [J]. Remote Sensing, 2022, 14(24), 6271. DOI: https://doi.org/10.3390/rs14246271  (中科院SCI期刊分区：2021年12月最新基础版二区，升级版二区，2022年IF 5.349)

[18] Lanfang Liu, Jie Li, Jinliang Wang*, Fang Liu, Janine Cole , Jinming Sha , Yuanmei Jiao , Jingchun Zhou. The establishment of an eco-environmental evaluation model for southwest China and eastern South Africa based on the DPSIR framework [J]. Ecological Indicators, 2022,145, 109687. DOI: https://doi.org/10.1016/j.ecolind.2022.109687. (中科院SCI期刊分区：2021年12月最新基础版二区，升级版二区，2022年IF 6.263)

[17]Yuncheng Deng, Jiya Pan, Jinliang Wang*, Qianwei Liu, Jianpeng Zhang. Mapping of Forest Biomass in Shangri-La City Based on LiDAR Technology and Other Remote Sensing Data[J]. Remote Sensing, 2022,14,5816. DOI: https://doi.org/10.3390/rs14225816. (中科院SCI期刊分区：2021年12月最新基础版二区，升级版二区，2022年IF 5.349)

[16]邓云程,王金亮*,刘钱威,冯宝坤,张建鹏.提取林木胸径的F-LS算法[J].遥感信息,2022, 37(5): 77-84. DOI: https://doi.org/10.3969/j.issn.1000-3177.2022.05.012  (CSCD核心库)

[15] Yongcui Lan, Jinliang Wang*, Wenying Hu, Eldar Kurbanov, Janine Cole, Jinming Sha, Yuanmei Jiao, Jingchun Zhou. Spatial pattern prediction of forest wildfire susceptibility in Central Yunnan Province, China based on multivariate data[J]. Natural Hazards, 2022, Online. DOI: https://doi.org/10.1007/s11069-022-05689-x. (中科院SCI期刊分区：2021年12月最新基础版三区，升级版三区，2021年IF 3.158)

[14] Jiya Pan , Cheng Wang, Jinliang Wang* , Fan Gao, Qianwei Liu , Jianpeng Zhang, and Yuncheng Deng. Land Cover Classification Using ICESat-2 Photon Counting Data and Landsat 8 OLI Data: A Case Study in Yunnan Province, China[J]. IEEE Geoscience and Remote Sensing Letters, 2022.  DOI: https://doi.org/10.1109/LGRS.2022.3209725. （中科院SCI期刊分区：2021年12月基础版三区，升级版二区，2021年IF 5.343）

[13] Suling He, Jie Li, Jinliang Wang*, and Fang Liu. Evaluation and analysis of upscaling of different Land Use /Land Cover products (FORM-GLC30, GLC_FCS30, CCI_LC, MCD12Q1 and CNLUCC): a case study in China[J]. Geocarto International, 2022. DOI: 10.1080/10106049.2022.2127926.  (中科院SCI期刊分区：2021年12月基础版二区，升级 版，2021年IF 4.889)

[12]Juncheng Shi, Cheng Wang, Jinliang Wang*, Xiaohuan Xi*, Xuebo Yang, and Xue Ding*. Study on the LAI and FPAR inversion of maize from airborne LiDAR and hyperspectral data[J] INTERNATIONAL JOURNAL OF REMOTE SENSING 2022, VOL. 43, NO. 13, 4793-4809 HTTPS://DOI.ORG/10.1080/01431161.2022.2121187. （中科院SCI期刊分区: 2021年12月最新基础版三区,升级版三区；2021年IF 3.531）

[11] Chen Y, Wang J*, Kurbanov E, Thomas A, Sha J, Jiao Y, et al. Ecological security assessment at different spatial scales in central Yunnan Province, China[J]. PLoS ONE, 2022, 17(6): e0270267. DOI: https://doi.org/10.1371/journal.pone.0270267.  (中科院SCI期刊分区：2021年12月基础版三区，升级版三区；2022年最新IF 3.752)

[10] Jiya Pan, Jinliang Wang*, Fan Gao, and Guangjie Liu. Quantitative estimation and influencing factors of ecosystem soil conservation in Shangri-La, China[J]. Geocarto International, 2022.  DOI: https://doi.org/10.1080/10106049.2022.2091160.  (中科院SCI期刊分区：2021年12月基础版二区，升级版三区；2021年IF 4.889)

[9] Jianpeng Zhang, Jinliang Wang*, Feng Cheng, Weifeng Ma, Qianwei Liu, Guangjie Liu. Natural forest ALS-TLS point cloud data registration without control points[J]. Journal of Forestry Research, 2022, Online. DOI：https://doi.org/10.1007/s11676-022-01499-w. （中科院SCI期刊分区：2021 年12月基础版SCIE二区，升级版SCIE二区；2021年IF 2.149）

[8] PAN, J. Y. ,WANG, J. L.* , LIU, G. J. ,GAO, F. Estimation of ecological asset values in Shangri_la based on remotely sensed data [J]. Applied ecology and environmental research, 2022, 20(4):2879-2895. DOI: http://dx.doi.org/10.15666/aeer/2004_28792895 .  (中科院SCI 期刊分区：2021年12月基础版SCIE四区，升级版SCIE四区；2020-2021最新IF: 0.711)

[7]Jie Li, Suling He, Jinliang Wang*, Weifeng Ma, Hui Ye. Investigating the spatiotemporal changes and driving factors of nighttime light patterns in RCEP Countries based on remote sensed satellite images [J]. Journal of Cleaner Production, 2022, 131944. DOI: https://doi.org/10.1016 /j.jclepro.2022.131944. (中科院SCI期刊分区：2021年12月基础版SCIE一区，升级版 SCIE一区；Top：是；2020-2021最新IF: 9.297)

[6]何苏玲,王金亮*,角媛梅,周京春,农兰萍,朱泓.国土空间规划视角下资源环境承载力评价分析——以昆明市为例[J], 中国农业资源与区划, 2021,43(4): 119-127.  DOI: 10.7621 /CJARRP. 1005-9121. 20220413 （CSCD核心库; CSSCI）

[5]潘继亚, 王金亮*, 高帆. 滇西北高山峡谷典型区土地利用变化与生态安全评价 研究 [J]. 生态科学, 2022, 41(2): 29–40. (CSCD扩展库，北大核心)

[4] Jie Li, Jinliang Wang*, Jun Zhang, Chenli Liu, Suling He, Lanfang Liu. Growing-season vegetation coverage patterns and driving factors in the China-Myanmar Economic Corridor based on Google Earth Engine and geographic detector [J]. Ecological Indicators, 2022, 136, 108620. DOI: doi.org/10.1016/j.ecolind.2022.108620. （中科院 SCI 期刊分区：2021年12 月基础版 SCIE二区，升级版SCIE二区；2022最新IF: 6.263)

[3]农兰萍,王金亮*,玉院和.基于地理加权回归模型和不同植被特征参数的 TRMM 3B43 降尺度研究——以云南省为例[J].兰州大学学报(自然科学版), 2022, 58(01): 99-110+117. DOI:10.13885/j.issn.0455-2059.2022.01.011. ( CSCD 核心库)

[2] Jianpeng Zhang, Jinliang Wang*, Pinliang Dong, Weifeng Ma, Yicheng Liu, Qianwei Liu, Zhiyan Zhang. Tree stem extraction from TLS point-cloud data of natural forests based on geometric features and DBSCAN[J]. Geocarto International, Published online: 08 Feb 2022. DOI: 10.1080/10106049.2022.2034988 (中科院SCI期刊分区：2021年12月基础版SCIE区，升级版SCIE三区；2021年IF 4.889)

[1] Yuanhe Yu, Xingqi Sun, Jinliang Wang*, Jianpeng Zhang. Using InVEST to evaluate water yield services in Shangri-La, Northwestern Yunnan, China[J]. Peer J, 2022, online. DOI: doi.org/10.7717/peerj.12804 (中科院 SCI期刊分区：2021年12月基础版SCIE三区，升级版SCIE三区；2020-2021年IF: 2.984)

（云南高校资源与环境遥感重点实验室  供稿）