| 基于GIS与分区Kriging的采煤沉陷区土壤有机碳含量空间预测 |
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| 引用本文： | 徐占军,张媛,张绍良,李乐乐,余明成.基于GIS与分区Kriging的采煤沉陷区土壤有机碳含量空间预测[J].农业工程学报,2018,34(10):253-259. |
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| 作者姓名： | 徐占军 张媛 张绍良 李乐乐 余明成 |
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| 作者单位： | 山西农业大学资源环境学院;中国矿业大学环境与测绘学院 |
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| 基金项目： | 国家青年基金（51304130）；山西省青年科技研究基金（2015021125）；山西农业大学校基金（20132-13）；国家自然科学基金 (51074154/E042203) |
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| 摘 要： | 中国煤炭产量占世界煤炭总产量的46.9%,年塌陷的耕地面积约为200 km~2,对农田土壤有机碳库扰动十分剧烈。由于农田的土壤有机碳库是减少陆地生态系统碳排放的最大潜在因素,中国以及世界上的其他煤炭开采大国必须更好地对煤炭开采区的土壤有机碳库进行科学管理,这也是煤炭低碳开采的重要途径。而预测精度好的煤炭开采沉陷区土壤有机碳含量空间预测方法是科学管理煤炭开采沉陷区土壤有机碳库的前提。该文以徐州九里煤炭开采沉陷区作为研究区,通过普通Kriging插值法和以结合沉陷积水情况为辅助变量的分区Kriging插值法这2种方法来对研究区的土壤有机碳含量进行了空间预测,并通过比较验证样点的预测值与实测值来对比2种方法的预测精度,确定每种方法的可行性。研究发现,结合区域内部积水情况来进行的分区Kriging插值法求得到的预测值与实测值的相关系数为0.7564,远高于直接进行Kriging插值得到的预测值与实测值的相关系数0.5086,并且两者的均方根误差分别为0.35和0.55,说明前者的预测精度更高。因此结合沉陷积水情况的分区Kriging插值模式是更适宜煤炭开采沉陷区土壤有机碳含量的空间预测模型。
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| 关 键 词： | 土壤 碳 GIS Kriging插值 煤炭开采沉陷区 |
| 收稿时间： | 2017/11/1 0:00:00 |
| 修稿时间： | 2018/3/23 0:00:00 |
Spatial prediction of soil organic carbon content in coal mining subsidence area based on GIS and partition Kriging |
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| Xu Zhanjun,Zhang Yuan,Zhang Shaoliang,Li Lele and Yu Mingcheng.Spatial prediction of soil organic carbon content in coal mining subsidence area based on GIS and partition Kriging[J].Transactions of the Chinese Society of Agricultural Engineering,2018,34(10):253-259. |
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| Authors: | Xu Zhanjun Zhang Yuan Zhang Shaoliang Li Lele and Yu Mingcheng |
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| Institution: | 1. College of Resources and Environment, Shanxi Agricultural University, Jinzhong, 030801, China;,1. College of Resources and Environment, Shanxi Agricultural University, Jinzhong, 030801, China;,2. School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou, 221008, China,1. College of Resources and Environment, Shanxi Agricultural University, Jinzhong, 030801, China; and 1. College of Resources and Environment, Shanxi Agricultural University, Jinzhong, 030801, China; |
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| Abstract: | Abstract: China''s coal production accounts for 46.9% of total world coal production, and the annual subsidence area is about 200 km2, which greatly disturbs the soil carbon pool of farmland. As the soil organic carbon (SOC) pool of farmland is the most potential factor reducing carbon emissions from terrestrial ecosystems, China and other major coal mining countries in the world must do better in managing the SOC pool in the coal mining area, which is also an important way for low-carbon coal mining. A spatial prediction method with good prediction accuracy of SOC content in subsidence area of coal mining is the prerequisite of scientific management of SOC pool in coal mining subsidence area. In order to determine a prediction method with high accuracy, taking Jiuli mining area in Xuzhou as a case, this paper predicted SOC content in the study area by the ordinary Kriging and the partition Kriging method. The ordinary Kriging directly spatially interpolated the SOC content based on the regionalized variable theory. In the partition Kriging method, the study area was categorized into non-waterlogged subsidence area, seasonally waterlogged subsidence area and wetland according to the subsidence and water-logging condition in the study area. With the method of partition Kriging, the spatial prediction value can be gained by summing up the mean value of SOC content in each separated area and the predictive residuals through spatial interpolation based on the residual data of the SOC content at each sampling point. Lastly this paper compared the prediction accuracy between the 2 methods with the measured values to determine the better method. It is found that the SOC content obtained by ordinary Kriging ranged from 9.34 to 16.252 g/kg, while the result of the partition Kriging was from 9.333 to 18.058 g/kg. The estimated ranges of the 2 methods were approximately same. Through the comparison, the spatial distribution of the SOC content obtained by ordinary Kriging showed no regularity because of neglecting the smooth transition between regions, while the spatial gradient features of SOC could be enhanced by the partition Kriging. The result showed that the correlation coefficient between the predicted and observed values obtained by the partition Kriging method was 0.7564, which was far higher than that by the ordinary Kriging method (0.5086). The root-mean-square error (RMSE), the mean absolute error (MAE) and the mean error (ME) of 2 methods were 0.35 and 0.55, 1.8511 and 1.2878, 0.0202 and 0.018, respectively. The partition Kriging performed much better than ordinary Kriging as it revealed much lower values of ME, MAE, and RMSE and a higher R2, indicating that the former has higher prediction accuracy. It is concluded that the partition Kriging interpolation model is a more appropriate spatial prediction model for SOC content in coal mining subsidence area, which provides the scientific basis for low-carbon land reclamation in mining area and even utilization of land resources in the region. |
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| Keywords: | soils carbon geographic information systems Kriging interpolation coal mining subsidence area |
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