黄土高原地区土地覆盖类型的时空格局

马慧; 赵洪飞; 岳超; 赵杰; 李昱; 王梦雨

doi:10.13961/j.cnki.stbctb.2023.06.041

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黄土高原地区土地覆盖类型的时空格局

试验研究

- 黄土高原地区土地覆盖类型的时空格局
- Spatiotemporal Pattern of Land Cover Types on Loess Plateau
- 水土保持通报 2023年43卷第6期页码：358-368
- 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：
  
  国家自然科学基金项目“黄土高原刺槐人工林对干旱胁迫的生理生态响应及其模拟”（41971132）
- DOI：10.13961/j.cnki.stbctb.2023.06.041
  中图分类号： F301.2
- 纸质出版：2023
- 稿件说明：
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马慧, 赵洪飞, 岳超, 等. 黄土高原地区土地覆盖类型的时空格局[J]. 水土保持通报, 2023,43(6):358-368.

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.
马慧, 赵洪飞, 岳超, 等. 黄土高原地区土地覆盖类型的时空格局[J]. 水土保持通报, 2023,43(6):358-368. DOI： 10.13961/j.cnki.stbctb.2023.06.041.

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

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