Remote Sensing Mapping and Analysis of Spatiotemporal Patterns of Land Use and Cover Change in the Helong Region of the Loess Plateau Region (1986-2020)

被引:1
|
作者
Li, Jingyu [1 ]
Chen, Yangbo [1 ]
Gu, Yu [1 ]
Wang, Meiying [1 ]
Zhao, Yanjun [1 ]
机构
[1] Sun Yat Sen Univ, Sch Geog & Planning, Guangzhou 510275, Peoples R China
关键词
land cover mapping; spatiotemporal analysis of land use and cover data; random forest; Google Earth Engine; YELLOW-RIVER; RANDOM FOREST; CLASSIFICATION; SOIL; TOPOGRAPHY; TERRACES; SEDIMENT; MACHINE; PRODUCT; RUNOFF;
D O I
10.3390/rs16193738
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Land use and cover change (LUCC) is directly linked to the sustainability of ecosystems and the long-term well-being of human society. The Helong Region in the Loess Plateau has become one of the areas most severely affected by soil and water erosion in China due to its unique geographical location and ecological environment. The long-term construction of terraces and orchards is one of the important measures for this region to combat soil erosion. Despite the important role that terraces and orchards play in this region, current studies on their extraction and understanding remain limited. For this reason, this study designed a land use classification system, including terraces and orchards, to reveal the patterns of LUCC and the effectiveness of ecological restoration projects in the area. Based on this system, this study utilized the Random Forest classification algorithm to create an annual land use and cover (LUC) dataset for the Helong Region that covers eight periods from 1986 to 2020, with a spatial resolution of 30 m. The validation results showed that the maps achieved an average overall accuracy of 87.54% and an average Kappa coefficient of 76.94%. This demonstrates the feasibility of the proposed design and land coverage mapping method in the study area. This study found that, from 1986 to 2020, there was a continuous increase in forest and grassland areas, a significant reduction in cropland and bare land areas, and a notable rise in impervious surface areas. We emphasized that the continuous growth of terraces and orchards was an important LUCC trend in the region. This growth was primarily attributed to the conversion of grasslands, croplands, and forests. This transformation not only reduced soil erosion but also enhanced economic efficiency. The products and insights provided in this study help us better understand the complexities of ecological recovery and land management.
引用
收藏
页数:21
相关论文
共 29 条
  • [21] Analyzing Land Cover and Land Use Changes Using Remote Sensing Techniques: A Temporal Analysis of Climate Change Detection with Google Earth Engine
    Afzal, Mozina
    Ali, Kamran
    Kasi, Mumraiz Khan
    Rehman, Masood Ur
    Khoshkholgh, Mohammad Ali
    Haq, Bushra
    Shah, Syed Ahmed
    2023 IEEE 22ND INTERNATIONAL CONFERENCE ON TRUST, SECURITY AND PRIVACY IN COMPUTING AND COMMUNICATIONS, TRUSTCOM, BIGDATASE, CSE, EUC, ISCI 2023, 2024, : 2018 - 2023
  • [22] Land use and land cover (LULC) of the Republic of the Maldives: first national map and LULC change analysis using remote-sensing data
    Fallati, Luca
    Savini, Alessandra
    Sterlacchini, Simone
    Galli, Paolo
    ENVIRONMENTAL MONITORING AND ASSESSMENT, 2017, 189 (08)
  • [23] Spatio-temporal land use/land cover change analysis and assessment of urban heat island in Ghana: A focus on the Greater Accra Region
    Borsah, Abraham Aidoo
    Boah, Evans Annan
    Brantson, Eric Thompson
    JOURNAL OF AFRICAN EARTH SCIENCES, 2025, 221
  • [24] Mapping of land-based aquaculture regions in Southeast Asia and its Spatiotemporal change from 1990 to 2020 using time-series remote sensing data
    Zhang, Junyao
    Yang, Xiaomei
    Wang, Zhihua
    Liu, Yueming
    Liu, Xiaoliang
    Ding, Yaxin
    INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 2023, 124
  • [25] Temporal Land-Use/Land-Cover Change Analysis in Kotla Sub-Watershed of Rupnagar District (Punjab) Using Remote Sensing and GIS
    Digra, Amritpal
    Kaushal, Arun
    Loshali, D. C.
    Kaur, Samanpreet
    Bhavsar, Dhruval
    JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING, 2022, 50 (07) : 1371 - 1391
  • [26] Toward understanding land use land cover changes and their effects on land surface temperature in yam production area, Cote d'Ivoire, Gontougo Region, using remote sensing and machine learning tools (Google Earth Engine)
    Aka, Kadio S. R.
    Akpavi, Semihinva
    Dibi, N'Da Hyppolite
    Kabo-Bah, Amos T.
    Gyilbag, Amatus
    Boamah, Edward
    FRONTIERS IN REMOTE SENSING, 2023, 4
  • [27] Time series analysis of remote sensing images of vegetation cover change in the Faro-Benoue-Bouba Ndjidda ecological landscape, north region of Cameroon
    Delanot, Tanougong Nkondjoua Armand
    Martin, Tchamba Ngankam
    REMOTE SENSING APPLICATIONS-SOCIETY AND ENVIRONMENT, 2022, 27
  • [28] Spatio-temporal Patterns of Land Use/Land Cover Change in the Bhutan-Bengal Foothill Region Between 1987 and 2019: Study Towards Geospatial Applications and Policy Making
    Chamling, Meelan
    Bera, Biswajit
    EARTH SYSTEMS AND ENVIRONMENT, 2020, 4 (01) : 117 - 130
  • [29] Urban Sprawl and Changes in Land-Use Efficiency in the Beijing-Tianjin-Hebei Region, China from 2000 to 2020: A Spatiotemporal Analysis Using Earth Observation Data
    Zhou, Meiling
    Lu, Linlin
    Guo, Huadong
    Weng, Qihao
    Cao, Shisong
    Zhang, Shuangcheng
    Li, Qingting
    REMOTE SENSING, 2021, 13 (15)