Remote Sensing Monitoring and Spatial Pattern Analysis of Non-Grain Production of Cultivated Land in Anhui Province, China

Author:

Zhi Junjun12ORCID,Cao Xinyue1,Liu Wangbing3,Sun Yang1,Xu Da4,Da Caiwei1,Jin Lei3,Wang Jin3,Zheng Zihao5,Lai Shuyuan1,Liu YongJiao1,Zhu Guohai1

Affiliation:

1. School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China

2. Engineering Technology Research Center of Resources Environment and GIS, Anhui Province, Wuhu 241002, China

3. Key Laboratory of Jianghuai Arable Land Resources Protection and Eco-Restoration, Hefei 230088, China

4. Urban and Rural Planning Management Service Center of Jin’an District, Lu’an 237100, China

5. College of Letters and Science, University of California, Santa Barbara, CA 93106, USA

Abstract

In recent years, non-grain production of cultivated land (NGPCL) has become increasingly prominent in China, seriously affecting food production and threatening the country’s food security. However, there is a lack of large-scale and high-precision methods for remote sensing identification of NGPCL. From the perspective of effective management of cultivated land resources, the characteristics of the spatial patterns of NGPCL, both on a large scale and at a patch scale, need to be further studied. For solving this problem, this paper uses the Google Earth engine (GEE) cloud computing platform and multi-source remote sensing data with a machine learning algorithm to determine the occurrence of NGPCL in Anhui province in 2019, and then uses nine selected landscape pattern indexes to analyze the spatial patterns of NGPCL from two aspects, specifically, economic development level and topography. The results show that: (1) terrain features, radar features, and texture features are beneficial to the extraction of NGPCL; (2) the degree of separation obtained by using an importance evaluation approach shows that spectral features have the highest importance, followed by index features with red edges, texture features, index features without red edges, radar features, and terrain features; and (3) the cultivated land in Anhui province in 2019 is mainly planted with food crops, and the phenomenon of NGPCL is more likely to occur in areas with high economic development levels and flat terrain. Aided by the GEE cloud platform, multi-source remote sensing data, and machine learning algorithm, the remote sensing monitoring approach utilized in this study could accurately, quickly, and efficiently determine NGPCL on a regional scale.

Funder

MOE (Ministry of Education in China) Youth Foundation Project of Humanities and Social Sciences

the Natural Science Foundation of China

Natural Science Foundation of Anhui province

Key Laboratory of Jianghuai Arable Land Resources Protection and Eco-restoration, the Ministry of Natural Resources

Anhui University Scientific Research Project

Undergraduate Innovation and Entrepreneurship Training Program of Anhui Normal University

Publisher

MDPI AG

Subject

Nature and Landscape Conservation,Ecology,Global and Planetary Change

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3