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Special Issue:
农业生态环境-遥感合辑Agro-ecosystem & Environment—Romote sensing
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| From statistics to grids: A two-level model to simulate crop pattern dynamics |
| XIA Tian1, 2, WU Wen-bin2, ZHOU Qing-bo3, Peter H. VERBURG4, YANG Peng2, HU Qiong1, YE Li-ming5, ZHU Xiao-juan6 |
1 Key Laboratory for Geographical Process Analysis & Simulation, Hubei Province/College of Urban & Environmental Sci. Central China Normal University, Wuhan, Hubei 430079, P.R.China
2 Key Laboratory of Agricultural Remote Sensing, Ministry of Agriculture and Rural Affairs/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
3 Agricultural Information Institute of Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China
4 Institute for Environmental Studies, VU University Amsterdam, Amsterdam 1085, The Netherlands
5 Department of Geology, Ghent University, Ghent 9000, Belgium
6 Commercial and Economic Law School, China University of Political Science and Law, Beijing 100088, P.R.China |
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摘要
本研究提出一种统计数据空间化的方法构建多时像农作物种植格局空间数据集来解决数据缺失的问题。该方法采用两层嵌套结构实现土地利用层和农作物层模拟，其中第一层模拟的耕地数据用于控制第二层农作物种植格局空间模拟范围。第二层农作物层采用空间迭代的方法按分配规则进行农作物面积统计数据空间化，最终实现农作物空间格局动态模拟。该模型在中国黑龙江省地区进行2000-2019年农作物空间格局模拟，结果表明模型模拟精度较高，能够实现长时间序列的农作物种植面积统计数据空间化应用，未来该模型能广泛应用于农业土地系统各方面研究及生产应用。
Abstract Crop planting patterns are an important component of agricultural land systems. These patterns have been significantly changed due to the combined impacts of climatic changes and socioeconomic developments. However, the extent of these changes and their possible impacts on the environment, terrestrial landscapes and rural livelihoods are largely unknown due to the lack of spatially explicit datasets including crop planting patterns. To fill this gap, this study proposes a new method for spatializing statistical data to generate multitemporal crop planting pattern datasets. This method features a two-level model that combines a land-use simulation and a crop pattern simulation. The output of the first level is the spatial distribution of the cropland, which is then used as the input for the second level, which allocates crop censuses to individual gridded cells according to certain rules. The method was tested using data from 2000 to 2019 from Heilongjiang Province, China, and was validated using remote sensing images. The results show that this method has high accuracy for crop area spatialization. Spatial crop pattern datasets over a given time period can be important supplementary information for remote sensing and thus support a wide range of application in agricultural land systems.
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Received: 05 February 2021
Accepted: 16 April 2021
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| Fund: The research described in this paper is supported and financed by the National key Research and Development Program of China (2019YFA0607400), and the Fundamental Research Funds for the Central Universities, China (CCNU19TS045). |
| About author: XIA Tian, E-mail: xiatian@ccnu.edu.cn; Correspondence WU Wen-bin, Tel: +86-10-82105070, E-mail: wuwenbin@caas.cn; ZHU Xiao-juan, Mobile: +86-13581895899, E-mail: xiaojuanzh@cupl.edu.cn |
Cite this article:
XIA Tian, WU Wen-bin, ZHOU Qing-bo, Peter H. VERBURG, YANG Peng, HU Qiong, YE Li-ming, ZHU Xiao-juan.
2022.
From statistics to grids: A two-level model to simulate crop pattern dynamics. Journal of Integrative Agriculture, 21(6): 1786-1789.
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