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基于地形改进NPP指数的县域耕地产能测算
引用本文:张金亭,董艳超,叶宗达.基于地形改进NPP指数的县域耕地产能测算[J].农业工程学报,2020,36(10):227-234.
作者姓名:张金亭  董艳超  叶宗达
作者单位:武汉大学资源与环境科学学院,武汉 430000;广西壮族自治区自然资源生态修复中心,南宁 530000
基金项目:国家自然基金项目(41571385);中央高校自主科研项目(2042016kf0175)
摘    要:为了快速准确测算耕地产能,开展耕地质量和产能评价工作,落实"三位一体"的耕地占补政策,该研究尝试将耕地初级净生产力(NPP,Net Primary Productivity)运用于耕地产能测算。在具体提取NPP指数时,使用顾及地形要素对于太阳辐射影响改进的CASA模型,并在此基础上运用地理加权回归以及空间相关性的方法比较了NPP数据与耕地利用指数数据以验证NPP数据运用于耕地产能计算的可行性。研究结果表明:基于NPP获得的耕地产能数据总体呈现中部东南-西北轴线方向以及南部平原NPP指数较高东北、西南两侧较低的布局,而通过与耕地利用指数的地理加权回归发现两者有较强相关性。NPP指数可以直接用于耕地产能评价,能够提高工作效率和准确性。

关 键 词:遥感  地形  NPP  耕地产能  耕地质量  地理加权回归
收稿时间:2020/1/16 0:00:00
修稿时间:2020/3/30 0:00:00

Calculation of county-level cultivated land productivity based on NPP index corrected by topography
Zhang Jinting,Dong Yanchao,Ye Zongda.Calculation of county-level cultivated land productivity based on NPP index corrected by topography[J].Transactions of the Chinese Society of Agricultural Engineering,2020,36(10):227-234.
Authors:Zhang Jinting  Dong Yanchao  Ye Zongda
Institution:1. School of Resource and Environmental Sciences, Wuhan University, Wuhan 430000, China;; 2. Land Consolidation Center of Department of Land and Resources of Guangxi Zhuang Autonomous Region, Nanning 530000, China;
Abstract:This study aims to estimate the productivity of arable land in a quick, accurate and cost-saving way, for the quality/productivity evaluation of arable land, and the implementation of the "trinity" policy, i.e., requisition-compensation balance of cultivated land. An attempt was made to calculate the productivity of cultivated land by using the net primary productivity (NPP) index, in order to increase the accuracy of the evaluation system, while saving time and cost. Taking the county level as the research scale, and Binyang County as the research area, the obvious terrain difference can indicate the variation of solar radiation subjected to topographic factors, and fill the research lack of NPP database at the county level. A CASA (Carnegie-Ames-Stanford Approach) model was used to calculate the remote sensing and meteorological data when extracting NPP index. The influence of topographic factors (terrain) on solar radiation was also considered in a modified CASA model. A geographic weighted regression approach was selected to compare the obtained NPP data with the utilization index of cultivated land, in order to verify the application of NPP data for the production of cultivated land. A comparison analysis of local correlation coefficient was made to determine the region with a large difference between the terrain and solar radiation, further to find the main advantages of the modified CASA model. The results showed that in the productivity distribution of cultivated land, the high NPP index was generally in the direction of southeast-northwest axis in the central region, and on the southern plain, whereas the low NPP index was on both sides of northeast and southwest in the research area. There was little change in the topographically modified NPP index, but the concentration of distribution increased, while the productivity has been extended to dry land and paddy fields. The geographic weighted regression between the indexes of cultivated land use and NPP showed that there was a strong correlation in the same geographical location, and the correlation coefficient of dry land can reach 0.87, while paddy field was 0.80, indicating that NPP can well connect with the original cultivated land use index. It infers that the spatial autocorrelation of NPP index can be strong and sensitive to the factors affecting the productivity of cultivated land, such as soil conditions, terrain characteristics, crop differences, and transportation. The calculation of NPP index can be directly applied to the evaluation for the productivity of cultivated land, with the high efficiency and accuracy in the system. The proposed method can be more quickly and accurately applied to the dynamic estimation of cultivated land, the delimitation of basic farmland, the evaluation of land improvement benefits, and the transformation of medium and low yield farmland.
Keywords:remote sensing  topography  NPP  cultivated land productivity  quality of cultivated land  geographically weighted regression
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