Research on remote sensing identification of abandoned farmland in agricultural and animal husbandry interzone：Taking Ledu District, Haidong City, Qinghai Province as an example

doi:10.14088/j.cnki.issn0439-8114.2024.01.036

Abstract

Abstract: In order to achieve timely and accurate identification of farmland, remote sensing technology was used to identify and extract abandoned farmland in the agricultural pastoral transitional zone, and to understand the spatial distribution characteristics of abandoned farmland.Based on the Google Earth Engine （GEE） platform, the study area’s Sentienel-1 and Sentienel-2 remote sensing images were called and preprocessed. The random forest algorithm was used to conduct land use classification research in the study area,and obtain the monthly maximum NDVI composite data of the study area from 2017 to 2022 through the GEE platform. Combined with the NDVI summer and spring differences and NDVI summer and autumn differences of abandoned and non abandoned farmland samples, segmentation thresholds to extract abandoned farmland in the study area were set. The results showed that the overall classification accuracy OA of the study area from 2017 to 2022 was ≥0.85, and the Kappa coefficient was ≥0.80. The overall classification effect was good, and it could be used for subsequent farmland extraction;from a horizontal scale, the abandoned farmland in the study area was mainly distributed in the north-south mountainous areas, followed by along the banks of the Huangshui River;from a vertical scale perspective, as the altitude increased, the abandonment rate followed a normal distribution, with abandoned farmland concentrated between 2 000 and 2 500 meters. The abandonment rate increased with the increase of slope, which was closely related to the decline in farmland quality and the difficulty in utilizing agricultural machinery caused by the increase of slope.Compared to traditional land use remote sensing classification research, abandoned farmland identification research conducted using the GEE platform could quickly obtain the distribution of abandoned farmland at the regional scale, providing reference for extracting abandoned farmland and land use protection in the region.

Key words: farmland, abandoned farmland, spatial distribution characteristics, GEE, NDVI, abandonment rate, Ledu District,Haidong City,Qinghai Province

CLC Number:

S127

YE Peng-shuai, YANG Hai-zhen, MA Tao, HU Bi-xia, BAO Xi-wen, ZHAO Zhi-zhong. Research on remote sensing identification of abandoned farmland in agricultural and animal husbandry interzone：Taking Ledu District, Haidong City, Qinghai Province as an example[J]. HUBEI AGRICULTURAL SCIENCES, 2024, 63(1): 199-205.

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