Spatiotemporal Pattern of Land Cover Types on Loess Plateau

Ma Hui; Zhao Hongfei; Yue Chao; Zhao Jie; Li Yu; Wang Mengyu

doi:10.13961/j.cnki.stbctb.2023.06.041

您当前的位置：

首页 >

文章列表页 >

Spatiotemporal Pattern of Land Cover Types on Loess Plateau

- Spatiotemporal Pattern of Land Cover Types on Loess Plateau
- Bulletin of Soiland Water Conservation Vol. 43, Issue 6, Pages: 358-368(2023)
- 作者机构：
  
  1. 西北农林科技大学资源环境学院, 陕西杨凌,712100
  2. 中国科学院水利部水土保持研究所, 陕西杨凌,712100
  3. 临沂大学资源环境学院,山东,临沂,276000
- 作者简介：
- 基金信息：
- DOI：10.13961/j.cnki.stbctb.2023.06.041
  CLC： F301.2
- Published：2023
- 稿件说明：
移动端阅览
Ma Hui, Zhao Hongfei, Yue Chao, et al. Spatiotemporal Pattern of Land Cover Types on Loess Plateau[J]. Bulletin of Soiland Water Conservation, 2023, 43(6): 358-368.
DOI：

Ma Hui, Zhao Hongfei, Yue Chao, et al. Spatiotemporal Pattern of Land Cover Types on Loess Plateau[J]. Bulletin of Soiland Water Conservation, 2023, 43(6): 358-368. DOI： 10.13961/j.cnki.stbctb.2023.06.041.

摘要

[目的] 构建黄土高原地区长时序、高精度的土地覆盖数据集，对该区2001—2020年土地覆盖的时空格局进行分析，并为该地区生态环境保护和可持续发展提供科学依据。[方法] 利用多源、多时期土地覆盖产品和地面特征数据构建训练样本，并使用谷歌地球引擎（Google Earth Engine，GEE）平台和随机森林分类模型生成黄土高原地区土地覆盖（land cover of Loess Plateau，LCLP）数据集。在此基础上，通过空间分析和一元线性回归模型对黄土高原地区土地覆盖类型的时空格局进行分析。[结果] 基于随机森林验证集的结果显示，LCLP产品的总体精度和kappa系数均高于90%。基于独立验证集的精度验证结果显示，LCLP的总体精度较现有产品提高了0.58%～20.23%。同时，耕地、林地、草地、不透水面和裸地的分类精度均得到了提升。[结论] 本研究构建的LCLP数据集分类精度相较于其他产品有了显著提升，适用于反映黄土高原地区土地覆盖的变化。2001—2020年，黄土高原地区耕地和灌木呈现下降趋势，而林地、水体和不透水面呈现为极显著的上升趋势。从土地覆盖的变化情况来看，耕地和草地是其他土地覆盖类型新增的主要来源。

Abstract

[Objective] A long-term and high-precision land cover dataset was constructed for the Loess Plateau. The spatiotemporal pattern of land cover in 2001 and 2020 was analyzed in order to provide a scientific underpinning for initiatives concerning ecological environmental preservation and sustainable development within the region. [Methods] Training samples were constructed using multiple sources of land cover products and ground feature data from various time periods. The Google Earth Engine (GEE) platform and a random forest classification model were used to generate the land cover of Loess Plateau (LCLP) dataset. Spatial analysis and a univariate linear regression model were then used to analyze the spatiotemporal pattern of land cover types on the Loess Plateau. [Results] According to the validation set built using random forest

LCLP exhibited an overall accuracy and kappa coefficient greater than 90%. Moreover

based on the independent verification set

LCLP demonstrated an overall accuracy ranging from 0.58% to 20.23% higher than existing products. Additionally

the accuracy of the classification of various land cover types

including cultivated land

forest land

grassland

impervious surface

and bare land

was increased. [Conclusion] Compared with other datasets

LCLP significantly improved classification accuracy and is suitable for accurately reflecting land cover changes for the Loess Plateau region. During 2001-2020

there has been a decreasing trend in cultivated land and shrubs in the Loess Plateau region

while forest land

water bodies

and impervious surfaces have shown a significant increasing trend. From the perspective of land cover changes

cultivated land and grassland were the primary sources of newly added land cover types.

关键词

Keywords

references

邵明安,王云强,贾小旭.黄土高原生态建设与土壤干燥化[J].中国科学院院刊,2015,30(Z1):178-185.

孙鸿烈.中国生态问题与对策[M].北京:科学出版社,2011.

张志强,陈鹏飞,申维.长武县土地利用/覆被演变规律及其影响下生态服务价值变化研究[J].水土保持研究,2017,24(4):219-226,233.

毛盛林,上官周平.近20年黄土高原土地利用/植被覆盖变化特征及其成因[J].水土保持研究,2022,29(5):213-219.

李秀彬.全球环境变化研究的核心领域:土地利用/土地覆被变化的国际研究动向[J].地理学报,1996,51(6):553-558.

Foley J A, DeFries R, Asner G P, et al.Global consequences of land use [J].Science, 2005,309(5734):570-574.

Van D A, Peña-Arancibia J, Bruijnzeel L A.Land cover and water yield:inference problems when comparing catchments with mixed land cover [J].Hydrology and Earth System Sciences, 2012,16(9):3461-3473.

冯敏,孙艳玲,刘斌,等.华北地区植被NDVI与不同气候指标的相关分析[J].水土保持通报,2014,34(1):246-251.

张治国,胡友彪,郑永红,等.陆地土壤碳循环研究进展[J].水土保持通报,2016,36(4):339-345.

Yang Jie, Huang Xin.The 30 m annual land cover dataset and its dynamics in China from 1990 to 2019[J].Earth System Science Data, 2021, 13(8):3907-3925.

邹亚东,何亮,张晓萍,等.基于GEE数据平台的北洛河流域1970-2019年土地利用结构变化特征[J].水土保持通报,2022,41(6):209-219.

Loveland T R, Reed B C, Brown J F, et al.Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data [J].International Journal of Remote Sensing, 2000,21(6/7):1303-1330.

Gong Peng, Wang Jie, Yu Le, et al.Finer resolution observation and monitoring of global land cover:first mapping results with Landsat TM and ETM

data [J].International Journal of Remote Sensing, 2013,34(7):2607-2654.

Friedl M A, Sulla-Menashe D, Tan B, et al.MODIS Collection 5 global land cover:algorithm refinements and characterization of new datasets [J].Remote Sensing of Environment, 2010,114(1):168-182.

Bishop C M, Nasrabadi N M.Pattern Recognition and Machine Learning [M].US:Springer, 2006.

Abdi A M.Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data [J].Giscience & Remote Sensing, 2020,57(1):1-20.

Talukdar S, Singha P, Mahato S, et al.Land-use land-cover classification by machine learning classifiers for satellite observations:a review[J].Remote Sensing, 2020,12(7):1135.

Vali A, Comai S, Matteucci M.Deep learning for land use and land cover classification based on hyperspectral and multispectral earth observation data:a review [J].Remote Sensing, 2020,12(15):2495.

刘涵,宫鹏.21世纪逐日无缝数据立方体构建方法及逐年逐季节土地覆盖和土地利用动态制图:中国智慧遥感制图iMap(China) 1.0[J].遥感学报,2021,25(01):126-147.

Chen Jun, Chen Jin, Liao Anping, et al.Global land cover mapping at 30 m resolution:a POK-based operational approach [J].Isprs Journal of Photogrammetry Remote Sensing, 2015,103:7-27.

Zhang Xiao, Liu Liangyun, Chen Xidong, et al.GLC_FCS30:Global land-cover product with fine classification system at 30 m using time-series Landsat imagery [J].Earth System Science Data, 2021,13(6):2753-2776.

许晓聪,李冰洁,刘小平,等.全球2000-2015年30 m分辨率逐年土地覆盖制图[J].遥感学报,2021,25(9):1896-1916.

徐新良,刘纪远,张树文,等.中国多时期土地利用遥感监测数据集(CNLUCC).资源环境科学数据注册与出版系统(http://wwwresdccn/DOI), DOI:1012078/2018070201,2018.

傅伯杰.黄土高原土地利用变化的生态环境效应[J].科学通报,2022,67(32):3769-3779.

Sun Wengi, Ding Xiaotong, Su Jingbo, et al.Land use and cover changes on the Loess Plateau:a comparison of six global or national land use and cover datasets [J].Land Use Policy, 2022,119:106165.

朱源,刘宇,赵亮.2015年黄土高原地区土地利用/覆被栅格数据[J].中国科学数据(中英文网络版),2017,2(4):52-60.

赵鸣飞,王宇航,邢开雄,等.黄土高原山地森林群落植物区系特征与地理格局[J].地理学报,2014,69(7):916-925.

胡胜,邱海军,王宁练,等.地形对黄土高原滑坡的影响[J].地理学报,2021,76(11):2697-2709.

师玉锋,梁思琦,彭守璋.1901-2017年黄土高原地区气候干旱的时空变化[J].水土保持通报,2020,40(1):283-289.

Chen Yiping, Wang Kaibo, Lin Yishan, et al.Balancing green and grain trade [J].Nature Geoscience, 2015,8(10):739-741.

Farr T G, Rosen P A, Caro E, et al.The shuttle radar topography mission [J].Reviews of Geophysics, 2007,45(2):RG2004.

Potapov P, Li X, Hernandez-Serna A, et al.Mapping global forest canopy height through integration of GEDI and Landsat data [J].Remote Sensing of Environment, 2021,253:112165.

Liu Xiaoqiang, Su Yanjun, Hu Tianyu, et al.Neural network guided interpolation for mapping canopy height of China's forests by integrating GEDI and ICESat-2 data [J].Remote Sensing of Environment, 2022,269:112844.

Liu Xiaoping, Huang Yinghuai, Xu Xiaocong, et al.High-spatiotemporal-resolution mapping of global urban change from 1985 to 2015[J].Nature Sustainability, 2020,3(7):564-570.

Huang Xin, Song Yihong, Yang Jie, et al.Toward accurate mapping of 30 m time-series global impervious surface area(GISA) [J].International Journal of Applied Earth Observation Geoinformation, 2022,109:102787.

Pickens A H, Hansen M C, Hancher M, et al.Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series [J].Remote Sensing of Environment, 2020,243:111792.

Pekel J F, Cottam A, Gorelick N, et al.High-resolution mapping of global surface water and its long-term changes [J].Nature, 2016,540(7633):418-422.

Teluguntla P, Thenkabail P, Xiong J, et al.NASA Making Earth System Data Records for Use in Research Environments (MEaSUREs) Global Food Security-support Analysis Data (GFSAD) Cropland Extent 2015 Australia, New Zealand, China, Mongolia 30 m V001[R].US:NASA, 2017.

Potapov P, Turubanova S, Hansen M C, et al.Global maps of cropland extent and change show accelerated cropland expansion in the twenty-first century [J].Nature Food, 2022,3(1):19-28.

Fritz S, See L, Perger C, et al.A global dataset of crowdsourced land cover and land use reference data [J].Scientific Data, 2017,4(1):1-8.

Zhao Yuanyuan, Gong Peng, Yu Le, et al.Towards a common validation sample set for global land-cover mapping [J].International Journal of Remote Sensing, 2014,35(13):4795-4814.

Pal M.Random forest classifier for remote sensing classification [J].International Journal of Remote Sensing, 2005,26(1):217-222.

McFeeters S K.The use of the Normalized Difference Water Index(NDWI) in the delineation of open water features [J].International Journal of Remote Sensing, 1996,17(7):1425-1432.

Xu Hanqiu.Modification of normalised difference water index(NDWI) to enhance open water features in remotely sensed imagery [J].International Journal of Remote Sensing, 2006,27(14):3025-3033.

Feyisa G L, Meilby H, Fensholt R, et al.Automated water extraction index:a new technique for surface water mapping using Landsat imagery [J].Remote Sensing of Environment, 2014,140:23-35.

Zha Yong, Gao Jay, Ni Shaoxiang.Use of normalized difference built-up index in automatically mapping urban areas from TM imagery [J].International Journal of Remote Sensing, 2003,24(3):583-594.

Purevdorj T, Tateishi R, Ishiyama T, et al.Relationships between percent vegetation cover and vegetation indices [J].International Journal of Remote Sensing, 1998,19(18):3519-3535.

杨艳芬,王兵,王国梁,等.黄土高原生态分区及概况[J].生态学报,2019,39(20):7389-7397.

Liu Han, Li Jun, He Lin, et al.Superpixel-guided layer-wise embedding CNN for remote sensing image classification [J].Remote Sensing, 2019,11(2):174.

Woodcock C E, Loveland T R, Herold M, et al.Transitioning from change detection to monitoring with remote sensing:a paradigm shift [J].Remote Sensing of Environment, 2020,238:111558.

吉珍霞.黄土高原植被物候变化及其对气候因子的响应[D].甘肃兰州:甘肃农业大学.2022.

邵明安,贾小旭,王云强,等.黄土高原土壤干层研究进展与展望[J].地球科学进展,2016,31(1):14-22.

李华峰,张宝芝,王建明.子午岭天然山杨林枯死原因调查研究[J].甘肃科技,2008,24(23):166-177.

金思雨,彭祚登.刺槐和油松干旱胁迫响应研究进展[J].西北林学院学报,2022,37(4):79-91.

李妙宇,上官周平,邓蕾.黄土高原地区生态系统碳储量空间分布及其影响因素[J].生态学报,2021,41(17):6786-6799.

周书贵,邵全琴,曹巍.近20年黄土高原土地利用/覆被变化特征分析[J].地球信息科学学报,2016,18(2):190-199.

张家政,李崇贵,王涛.黄土高原植被覆盖时空变化及原因[J].水土保持研究,2022,29(1):224-230+241.

孙锐,陈少辉,苏红波.2000-2016年黄土高原不同土地覆盖类型植被NDVI时空变化[J].地理科学进展,2019,38(8):1248-1258.

周晖.融合多源遥感影像的森林资源变化监测研究[D].北京;北京林业大学,2020.

赵宏飞,何洪鸣,白春昱,等.黄土高原土地利用变化特征及其环境效应[J].中国土地科学,2018,32(7):49-57.

史晓亮,王馨爽.黄土高原草地覆盖度时空变化及其对气候变化的响应[J].水土保持研究,2018,25(4):189-194.

Views

444

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Remote sensing monitoring and soil and water conservation effects of abandoned farmland in core black soil protection area of Jilin Province from 2000 to 2023

Comparative analysis of landslide susceptibility based on different evaluation units——A case study of Ledu District in Haidong City， Qinghai Province

Precise measurement and caculation of carbon sink on check dams in Loess Plateau——A case study at Gaoxigou small watershed， Yulin City， Shaanxi Province

Spatiotemporal variations and driving forces of vegetation coverage in Three Gorges reservoir area during 2013—2023——A case study at Badong County， Hubei Province

Mechanism of sand-layer sliding induced by water content changes on sand covered loess slopes

Related Author

Feng Xinhui

Jiang Hailing

Yu Hailin

Sun Xihao

Wang Jingyi

Fu Jianing

Li Yuanyuan

Li Huamin

Related Institution

College of Geographic Science and Tourism， Jilin Normal University

School of Resources and Security， Henan Vocational College of Industry and Information Technology

School of Energy， Henan Polytechnic University

College of Soil and Water Conservation Science and Engineering， Northwest A&F University， Yangling

College of Forestry， Northwest A&F University， Yangling

⁰