基于决策树与NDVI时序变化检测的撂荒耕地的地形特征研究——以重庆市巫山县为例

夏玉松; 周启刚; 李辉; 张晓媛; 陈芳焱

doi:10.13961/j.cnki.stbctb.2024.04.039

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基于决策树与NDVI时序变化检测的撂荒耕地的地形特征研究——以重庆市巫山县为例

试验研究

- 基于决策树与NDVI时序变化检测的撂荒耕地的地形特征研究——以重庆市巫山县为例
- Topographic Characteristics of Abandoned Farmland Based on Decision Tree and NDVI Time Series Change Detection—A Case Study in Wushan County of Chongqing City
- 水土保持通报 2024年44卷第4期页码：383-393
- 作者机构：
  
  1. 重庆工商大学环境与资源学院,重庆,400067
  2. 重庆工商大学公共管理学院,重庆,400067
  3. 生态环境空间信息数据挖掘与大数据集成重庆市重点实验室,重庆,401320
  4. 重庆财经学院公共管理学院,重庆,401320
  5. 重庆财经学院讯飞人工智能学院,重庆,402160
- 作者简介：
- 基金信息：
  
  国家社会科学基金项目“西南粮食主产区生态安全屏障构建及实现路径研究”(22XJY006);重庆市自科基金项目(cstc2020jcyj-msxmX0582)
- DOI：10.13961/j.cnki.stbctb.2024.04.039
  中图分类号： X171.3
- 纸质出版：2024
- 稿件说明：
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夏玉松, 周启刚, 李辉, 等. 基于决策树与NDVI时序变化检测的撂荒耕地的地形特征研究——以重庆市巫山县为例[J]. 水土保持通报, 2024,44(4):383-393.

Xia Yusong, Zhou Qigang, Li Hui, et al. Topographic Characteristics of Abandoned Farmland Based on Decision Tree and NDVI Time Series Change Detection—A Case Study in Wushan County of Chongqing City[J]. Bulletin of Soiland Water Conservation, 2024, 44(4): 383-393.
夏玉松, 周启刚, 李辉, 等. 基于决策树与NDVI时序变化检测的撂荒耕地的地形特征研究——以重庆市巫山县为例[J]. 水土保持通报, 2024,44(4):383-393. DOI： 10.13961/j.cnki.stbctb.2024.04.039.

Xia Yusong, Zhou Qigang, Li Hui, et al. Topographic Characteristics of Abandoned Farmland Based on Decision Tree and NDVI Time Series Change Detection—A Case Study in Wushan County of Chongqing City[J]. Bulletin of Soiland Water Conservation, 2024, 44(4): 383-393. DOI： 10.13961/j.cnki.stbctb.2024.04.039.

摘要

[目的

]

揭示山区撂荒耕地的地形特征，为区域土地资源管理和农业可持续发展提供科学参考，[方法

]

以重庆市巫山县为研究区域，利用谷歌地球引擎(Google Earth Engine

GEE)云平台上的Landsat TM/ETM+/OLI和Sentinel-2数据，采用决策树与时间序列 NDVI变化检测法，对2017—2021年研究区的撂荒地信息进行提取和分析。[结果

]

①从时间序列特征上看，研究区内撂荒地面积整体呈上升趋势，2017—2021年的增加量为2 123.50 hm

，增长率为19.61%，区间内曲线形态上表现为“W”字形特征。空间上撂荒地呈全局分散，局部集中特征，主要沿着水系走向分布，显著集中于河流两侧，周边被坡耕地围绕。②研究区内撂荒地在不同的高程带和坡度带分布不同。撂荒地主要集中于高程1 000 m以下和坡度5°～20°范围内。高程1 500 m以下的区域，撂荒地面积和撂荒率表现“先增后减”的规律，2019年达到最高点；高程1 500 m以上的区域，撂荒地面积和撂荒率随着时间的变化呈现“先减后增”的规律，在2020年达到了最低值。③撂荒地在不同地形位等级下的分布指数表现为持续减少型，地形梯度1级、2级的分布指数大于1，为撂荒地的优势区。[结论

]

决策树与NDVI时序变化检测法结合能够精准识别撂荒地，识别精度为83.59%。

Abstract

[Objective] The topographic characteristics of abandoned farmland in mountainous areas were analysed in order to provide a scientific reference for regional land resource management and sustainable agricultural development. [Methods] Taking Wushan County of Chongqing City as the research area

this study used Landsat TM/ETM+/OLI and Sentinel-2 data on the Google Earth Engine (GEE) cloud platform and adopted decision tree and time series normalised difference vegetation index (NDVI) change detection methods. We extracted and analysed abandoned land information from the study area from 2017 to 2021. [Results] ① From the perspective of time series characteristics

the area of abandoned land in the study area showed an overall upward trend; the increase from 2017 to 2021 was 2 123.50 hm2

with a growth rate of 19.61%

and the curve shape within the interval showed a “W” shaped characteristic. Spatially

abandoned land was regionally dispersed and locally concentrated. It was mainly distributed along the direction of the water system

significantly concentrated on both sides of the river

and surrounded by sloping farmlands. ② The abandoned land in the study area was distributed differently across different elevation and slope zones. The abandoned land was concentrated at elevations below 1 000 m and slopes within a range of 5°~20°. In areas with an elevation below 1 500 m

the area of abandoned land and rate of abandonment showed a pattern of “first increasing and then decreasing

” reaching the highest point in 2019; in areas with an elevation above 1 500 m

the area of abandoned land and the rate of abandonment showed a pattern of “first decreasing and then increasing” over time. The law of “increase” reached its lowest value in 2020. ③ The distribution index of abandoned land under different terrain levels was continuously decreasing

and the distribution index of terrain gradients 1 and 2 was greater than 1

indicating the dominant area of abandoned land. [Conclusion] The combination of a decision tree and NDVI time series change detection methods can accurately identify abandoned land with a recognition accuracy of 83.59%.

关键词

Keywords

references

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