欢迎访问林草资源研究
Technical Application

Grassland Vegetation Coverage Retrieval Based on Sentinel-2 Multi-Spectral Remote Sensing Data: A Case Study of Inner Mongolia Autonomous Region

  • Haijing TIAN ,
  • Lin WANG ,
  • Liliang HAN ,
  • Yunbao FAN ,
  • Jilin YANG
Expand
  • 1. Academy of Forestry Inventory and Planning,National Forestry and Grassland Administration,Beijing 100714,China
    2. Grassland Monitoring Center,National Forestry and Grassland Administration,Beijing 100714,China
    3. Key Laboratory of Land Surface Pattern and Simulation,Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China

Received date: 2022-05-11

  Revised date: 2022-07-17

  Online published: 2022-10-13

Abstract

High-precision grassland vegetation coverage remote sensing estimation model is important for quantitative assessment of grassland quality and fine management of grassland.Based on Sentinel-2 multi-spectral remote sensing data and measured sample plots in Inner Mongolia autonomous Region,grassland vegetation coverage was modeled and inverted.The results showed that:1)There was a significant correlation between the 23 vegetation indexes and the measured vegetation coverage (P<0.001),and the highest correlation coefficient was NDVI with a correlation coefficient of 0.834;2)The underestimation of sinusoidal function was more obvious in the high value part (coverage>75%),while the overestimation of linear function was more obvious in the low value part (coverage<25%),by using these two kinds of functions to simulate vegetation coverage,the results were better;3)The grassland types were divided into 6 groups to modeling vegetation coverage respectively.Finally,correlation coefficient between simulated and measured vegetation coverage for the 1 894 sample plots was R2=0.722,P<0.01,RMSE=12%;4)The vegetation coverage of different grassland types in Inner Mongolia from high to low was 78.91% for mountain meadow,73.7% for temperate meadow steppe,53.89% for lowland meadow,52.57% for temperate steppe,32.76% for temperate desert steppe,25.52% for temperate grassland desert and 19.29% for temperate desert.

Cite this article

Haijing TIAN , Lin WANG , Liliang HAN , Yunbao FAN , Jilin YANG . Grassland Vegetation Coverage Retrieval Based on Sentinel-2 Multi-Spectral Remote Sensing Data: A Case Study of Inner Mongolia Autonomous Region[J]. Forest and Grassland Resources Research, 2022 , 0(4) : 134 -140 . DOI: 10.13466/j.cnki.lyzygl.2022.04.017

References

[1] 中华人民共和国自然资源部. 第三次全国国土调查主要数据公报[R]. 2021.
[2] 章超斌, 李建龙, 张颖, 等. 基于RGB模式的一种草地盖度定量快速测定方法研究[J]. 草业学报, 2013, 22(4):220-226.
[3] 张学霞, 朱清科, 吴根梅, 等. 数码照相法估算植被盖度[J]. 北京林业大学学报, 2008, 30(1):164-169.
[4] 秦伟, 朱清科, 张学霞, 等. 植被覆盖度及其测算方法研究进展[J]. 西北农林科技大学学报:自然科学版, 2006, 34(9):163-170.
[5] 秦伟, 曹文洪, 左长清, 等. 考虑沟-坡分异的黄土高原大中流域侵蚀产沙模型[J]. 应用基础与工程科学学报, 2015, 23(1):12-29.
[6] 焦菊英, 王万忠. 人工草地在黄土高原水土保持中的减水减沙效益与有效盖度[J]. 草地学报, 2001(3):176-182.
[7] 张光辉, 梁一民. 植被盖度对水土保持功效影响的研究综述[J]. 水土保持研究, 1996(2):104-110.
[8] 包小庆, 何京丽, 邢恩德, 等. 草地水土保持科技发展战略[J]. 中国水利, 2008(21):66-68.
[9] 杜际增, 王根绪, 李元寿. 近45年长江黄河源区高寒草地退化特征及成因分析[J]. 草业学报, 2015, 24(6):5-15.
[10] 赵靖川, 刘树华. 植被变化对西北地区陆气耦合强度的影响[J]. 地球物理学报, 2015, 58(1):47-62.
[11] 纪磊. 若尔盖草地沙化程度的遥感监测及其植被特征与土壤养分的分析[D]. 雅安: 四川农业大学, 2012.
[12] 曹宁, 韩颖娟, 马宁. 荒漠化及植被盖度监测变化分析——以宁夏盐池县为例[J]. 农业网络信息, 2013(10):123-125.
[13] 内蒙古自治区自然资源厅. 内蒙古自治区第三次国土调查主要数据公报[R]. 2021.
[14] 田海静, 曹春香, 戴晟懋, 等. 准格尔旗植被覆盖度变化的时间序列遥感监测[J]. 地球信息科学学报, 2014, 16(1):126-133.
Outlines

/