王金亮教授导师团队学术论文在Remote Sensing上线发表

2023年10月4日，以王思凯（云南师范大学地理学部地图学与地理信息系统专业2021级硕士研究生）为第一作者，王金亮教授为通讯作者所撰写的题为“Analysis of Land Use/Cover Changes and Driving Forces in a Typical Subtropical Region of South Africa”的学术论文在SCIE二区期刊Remote Sensing上线发表(https://doi.org/10.3390/rs15194823)。

土地利用/覆盖变化（Land Use/Cover Change, LUCC）是全球环境变化的重要组成，受到自然和社会经济因素的双重影响。以Landsat系列卫星影像为数据源，基于Google Earth Engine（GEE）平台和易康软件，选用基于像元的非监督分类、基于像元的监督分类和面向对象的监督分类三种不同机制的分类方法分别获取南非东部的夸祖鲁-纳塔尔省和姆普马兰加省1995-2020年每五年的LULC数据，分析其时空演变特征，结合基于最优参数的地理探测器（Optimal Parameters-based Geodetector, OPGD），探究该区域土地利用程度时空变化的驱动因素。研究结果表明：（1）三种分类方法中，面向对象的监督分类算法精度最高，其总体精度稳定在80-90%之间，基于像元的K-means分类算法精度最低，总体精度44.43-55.10%之间；（2）1995-2020年，研究区耕地面积呈波动性增长，林地和草地面积逐年缩减，建设用地面积逐年增长，水体和未利用地面积处于波动变化的状态；（3）社会经济因素的解释力普遍高于自然因素，其中人口增长和经济发展是该区域LUCC最主要的驱动因素。本研究对该区域制定合理的土地资源可持续发展战略，为相关部门在城乡规划、生态保护、环境治理等方面的管理与实施提供可靠的科学依据。

Figure 1. Location and topography of Kwazulu−Natal and Mpumalanga provinces.

Figure 2. Distribution of 7000 samples, taking 2020 as an example.

Figure 3. Spatiotemporal pattern of land use from 1995 to 2020 (derived from object-oriented classification).

Figure 4. Temporal changes in the area occupied by each land use type in the study area from 1995 to 2020.

Figure 5. Interactive detection results of driving force factors during 1995–2020.

该论文得到了王金亮教授主持的国家重点研发计划政府间国际科技创新合作重点专项：用地理空间技术监测和评估土地利用/土地覆被变化对区域生态安全的影响（2018YFE0184300），云南省高校高原山地资源环境遥感监测与评估科技创新团队（IRTSTYN）的资助。

这是王思凯同学硕士期间以第一作者发表的第1篇SCIE学术论文（详见录 1，2），也是王金亮教授导师团队 2023年发表的第6篇 SCI/SCIE 论文(详见录 3)，让我们恭喜王思凯同学！希望他再接再厉！也热烈祝贺团队取好成绩！

附录 1 论文相关信息

标题：Analysis of Land Use/Cover Changes and Driving Forces in a Typical Subtropical Region of South Africa

作者：Sikai Wang ^1,2,3, Suling He ^1,2,3, Jinliang Wang ^1,2,3,*, Jie Li ^1,2,3, Xuzhen Zhong ^1,2,3, Janine Cole ⁴, Eldar Kurbanov ⁵, and Jinming Sha ⁶

通讯作者：Jinliang Wang

作者单位：

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

wsk15368110405@outlook.com (S.W.); sulhe@user.ynnu.edu.cn (S.H.); jieli@ynnu.edu.cn (J.L.);

zxzxuzhen@njtc.edu.cn (X.Z.)

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

3  Center for Geospatial Information Engineering and Technology of Yunnan Province,

Kunming 650500, China

4  Council for Geoscience, Pretoria 0001, South Africa; jcole@geoscience.org.za

5  Center for Sustainable Forest Management and Remote Sensing, Volga State University of Technology, Yosh-kar-Ola 424000, Russia; kurbanovea@volgatech.net

6  School of Geographical Science, Fujian Normal University, Fuzhou 350007, China; jmsha@fjnu.edu.cn

出版物：Remote Sensing （2021年12月基础版二区，2022年12月最新升级版二区，2023年最新IF：5.349）

摘要：Land use/cover change (LULCC) is an integral part of global environmental change and is influenced by both natural and socioeconomic factors. This study aims to comprehensively analyze land use and land cover (LULC) in Kwazulu-Natal and Mpumalanga provinces in eastern South Africa from 1995 to 2020 and to identify the driving force behind LULCC. Utilizing Landsat series satellite imagery as a data source and based on the Google Earth Engine (GEE) platform and eCognition software   9.0, two different classification methods, pixel-based classification and object-oriented classification, were adopted to gather LULC data every five years. The spatiotemporal characteristics of the data were then analyzed. Using an optimal parameter-based geodetector (OPGD), this study explored the driving factors of LULCC in this region. The results show the fol-lowing: (1) Of the two classification methods examined, the object-oriented classification had higher accuracy, with an overall accuracy of 80–90%. The pixel-based classification had lower accuracy, with an overall accuracy of 62.33–72.14%. (2) From 1995 to 2020, the area of farmland in the study area showed a fluctuating increase, while the areas of forest and grassland declined annually. The area of constructed land increased annually, whereas the areas of water and unused land fluctuated over time. (3) Socioeconomic factors generally had greater explanatory power than natural factors, with population growth and economic development being the main drivers of LULCC in the region. This study provides a reliable scientific basis for the formulation of sustainable land resource development strategies in the area, as well as for the management and implementation of urban and rural planning, ecological protection, and environmental governance by relevant departments.

关键字：land use/cover change; object-oriented classification; driving factor analysis; optimal

parameter-based geodetector

附录2 王思凯同学发表论文清单

[1] Wang, S.; He, S.; Wang, J.*; Li, J.; Zhong, X.; Janine, C.; Eldar K.; Sha, J. Analysis of Land Use/Cover Changes and Driving Forces in a Typical Subtropical Region of South Africa. Remote Sensing, 2023, 15, 4823. https://doi.org/10.3390/rs15194823

附录 3 王金亮教授团队 2023 年发表论文清单

自2023年01月01日至10月05日，王金亮教授导师团队发表学术论文10篇，其中SCIE论文6篇，CSCD3篇，普刊1篇。具体信息如下：

[10]Sikai Wang, Suling He, Jinliang Wang *, Jie Li, Xuzhen Zhong, Janine Cole, Eldar Kurbanov, Jinming Sha. Analysis of Land Use/Cover Changes and Driving Forces in a Typical Subtropical Region of South Africa[J], Remote Sensing. 2023，15(19): 4823.   https://doi.org/10.3390/rs15194823 （2021年12月基础版二区，2022年12月最新升级版二区，2023年最新IF：5.349）

[9] Hong Zhu, Feng Cheng, Jinliang Wang*, Yuanmei Jiao, Jingchun Zhou, Jinming Sha, Fang Liu, and Lanping Nong. Variation in the Ecological Carrying Capacity and Its Driving Factors of the Five Lake Basins in Central Yunnan Plateau, China[J]. Sustainability 2023, 15, 14442.  https://doi.org/10.3390/su151914442（中科院SCI期刊分区：2021年12月基础版三区，2022年12月最新升级版三区，2023年最新IF3.889）

[8] 邵大江,叶辉,王金亮*,周京春,角媛,沙晋明. 基于机器学习均值化的地质灾害易发性评价[J]. 云南大学学报（自然科学版）, 2023, 45(3): 653-665. DOI:10.7540/j.ynu.20220109.  (CSCD核心库)

[7] Jie Li, Hui Wang, Jinliang Wang*, Jianpeng Zhang, Yongcui Lan, Yuncheng Deng. Combining Multi-Source Data and Feature Optimization for Plastic-Covered Greenhouse Extraction and Mapping Using the Google Earth Engine: A Case in Central Yunnan Province, China [J]. Remote Sensing, 2023, 15, 3287. DOI: https:// doi.org/10.3390/rs15133287. (中科院SCI期刊分区：2021年12月基础版二区，2022年12月最新升级版二区Top，2023年最新IF5.349)

[6]张建鹏;王金亮*;刘广杰;麻卫峰;刘钱威;邓云程. 基于地基雷达点云主方向的林下植被自动滤除[J], 遥感技术与应用, 2023，38（2）：405-412. DOI：10.11873/j.issn.1004-0323.2023.2.0405  (CSCD核心库)

[5]何苏玲，贺增红，潘继亚，王金亮*．基于多模型的县域土地利用/土地覆盖模拟[J/OL]．自然资源遥感. 2023-04-03网络首发. https://kns.cnki.net/kcms/detail/10.1759.P. 20230331.1810.004.html  (CSCD核心库)

[4]成钊，王金亮*，何苏玲，祁兰兰. 基于多源数据的滇中地区生态韧性度研究[J]. 云南地理环境研究, 2023, 35(02)：7-16

[3] Jianpeng Zhang, Jinliang Wang*, Weifeng Ma, Yuncheng Deng, Jiya Pan, Jie Li. Vegetation Extraction from Airborne Laser Scanning Data of Urban Plots Based on Point Cloud Neighborhood Features [J]. Forests, 2023, 14(4), 691. DOI: https://doi.org/10.3390/f14040691. (中科院SCI期刊分区：2021年12月基础版三区， 2022年12月最新升级版二区，2022年IF 3.282)

[2] Jun Ma, Jianpeng Zhang, Jinliang Wang *, Vadim Khromykh, Jie Li, Xuzheng Zhong. Global Leaf Area Index Research over the Past 75 Years: A Comprehensive Review and Bibliometric Analysis [J]. Sustainability, 2023. DOI: https://doi.org/10.3390/su15043072. (中科院SCI期刊分区：2021年12月基础版三区，2022年12月最新升级版三区，2022年IF 3.889)

[1] Lei Liang, Jinliang Wang*, Fei Deng, Deyang Kong. Mapping Pu'er tea plantations from GF-1 images using Object-Oriented Image Analysis (OOIA) and Support Vector Machine (SVM) [J]. PLOS ONE, 2023. DOI: https://doi.org/10.1371/journal.pone. 0263969. (中科院SCI期刊分区： 2021年12月基础版三区，2022年12月最新升级版三区，2022年IF 3.752)

（云南省高校资源与环境遥感重点实验室  供稿）