干旱区生态环境知识资源中心(Arid): 基于多光谱数据的荒漠矿区土壤有机质估算模型

Arid

	基于多光谱数据的荒漠矿区土壤有机质估算模型
其他题名	Estimation model of soil organic matter in desert mining area based on multispectral image data
	夏楠; 塔西甫拉提?特依拜; 丁建丽; 依力亚斯江?努尔麦麦提; 张东; 刘芳
来源期刊	农业工程学报
ISSN	1002-6819
出版年	2016
卷号	32 期号:6 页码:263-267
中文摘要	目前运用高光谱数据估算土壤有机质的模型精度已经可以达到精准农业的要求,但其数据的整理和运算过程较为复杂且观测尺度较小。为节省资源,提高效率并为多光谱遥感估算土壤有机质积累经验,该文将Landsat8_OLI多光谱遥感影像各波段的反射率数据与地面土壤有机质SOM(soil organic matter)实测数据相结合,利用SPSS软件及多元线性回归分析方法建立基于反射率R、反射率倒数1/R、反射率倒数对数LN(1/R)、反射率一阶导数FDR(first derivative reflectance)的土壤有机质定量估算模型,精度检验后择取最优模型通过多光谱遥感波段运算的方式推广至整个研究区。结果表明:FDR模型的精度更高,RMSE为0.215,F检验结果为4.072,预测值与实际值之间的决定系数R~2为0.963。基于该模型估算研究区空间范围的土壤有机质含量,得出土壤有机质含量在0~5 g/kg之间的面积占总研究区的84.065%,>10 g/kg的面积仅仅为0.001 5%。在4种土地类型中工矿用地SOM平均含量为最高的7.35 g/kg,受开采的煤炭中有机质影响较大。裸地面积2 674.44 km~2,占研究区面积的63%,SOM平均含量6.12 g/kg;盐渍地和荒漠林地SOM含量偏低。总之,运用多光谱遥感数据估算干旱区土壤有机质的方法可行,也为遥感估算其他地表参数提供参考。
英文摘要	Soil is related closely to human living and vegetation grow. The quality of soil organic matter (SOM) influences plant development. Scientists take a variety of researcheson soil. Many findings focus on the estimation of SOM using remote sensing data, which are usually hyperspectral and multispectral. The former has a detailed result of band information, while the latter provides a macroscopical and convenient way to get in whole area. In addition, processing hyperspectral data needs a strong mathematical background and software technology, while processing multispectraldata needs less. To apply the multispectral method to make decisions on buildings and planning is of great significance. In order to save resources, increase efficiency and accuracy, in May 2014, we collected soil samples in the various layers of 0~10, 10~20 and 20~30 cm, and there were totally 45 points marked by GPS(global positioning system) on Google Earth. The weighed aluminum box was used to hold some soil in each layer. The collections were taken back and dried for 24 h. Then the dried soil was weighed and the soil moisture was calculated. Meanwhile, the image needed pretreatment. The atmospheric correction should be taken to remove bands’ noises to get clear data. Then the pixels of the image for each sample point were used to establish models. And at the same time, other soil was crushed and sieved in 2 mm, and the SOM was measured by the potassium dichromate volumetric method. The final work was to combine the reflectance data of multispectral image and the measured SOM data. We used the reflectance(R), the reflectance reciprocal(1/R), the reflectance reciprocal’s logarithm (ln(1/R)), the reflectance’s first derivative(FDR) and the measured SOM to build multiple linear regression models, and then, it was found that the FDR model had a better precision with the R~2 of 0.963 between the predicted and the measured. This meant that the more effective approach could be applied to express the regional SOM if needed. By the FDR model, we predicted the SOM content in study area. It showed that the area with SOM content of 0~5 g/kg was 84.065% of the whole area and that with SOM content of greater than 10 g/kg was 0.001 5%. The greatest SOM value was 13.065 g/kg,and the inferior was closed to 0.355 g/kg, which was very low. The SOM content in the Wu caiwan area was lower than that in Qitai County, for the former’s SOM was less than 1% in the most area. It also indicated that the highest average SOM content in the mining area was 7.35 g/kg, which was influenced by the organic matter in coal. The bare land’s area was 2 674.44 km~2, accounting for 63% of all area, and the mean SOM content was 6.12 g/kg. The saline land and desert woodland had lower SOM content because of the development of water-soil loss, salinization and desertification. The low SOM content and less precipitation made the area a desert increasingly. Further more, we found that in the arid area, the soil moisture content was extremely low, so it was not only influenced weakly by moisture to using remote sensing means to estimate SOM, but also formed an advantageous method which provided a higher simulation precision. All in all, it is imperative to restore the ecologic environment in the study area. Measures should be taken immediately. Choosing appropriate vegetation to plant in desert will be the key to the restoring works, while enhancing supervision of the Kalamaili Nature Reserve and controlling grazing will contribute to slow down those negative phenomena above.
中文关键词	土壤 ; 遥感 ; 光谱分析 ; 荒漠 ; 建模 ; 多光谱 ; 估算
英文关键词	SOM soils remote sensing spectrum analysis desert SOM modeling multispectral estimation
语种	中文
国家	中国
收录类别	CSCD
WOS类目	REMOTE SENSING ; AGRICULTURE MULTIDISCIPLINARY
WOS研究方向	Remote Sensing ; Agriculture
CSCD记录号	CSCD:5667675
来源机构	新疆大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/234603
作者单位	新疆大学资源与环境科学学院, 绿洲生态教育部重点实验室, 乌鲁木齐, 新疆 830046, 中国
推荐引用方式 GB/T 7714	夏楠,塔西甫拉提?特依拜,丁建丽,等. 基于多光谱数据的荒漠矿区土壤有机质估算模型[J]. 新疆大学,2016,32(6):263-267.
APA	夏楠,塔西甫拉提?特依拜,丁建丽,依力亚斯江?努尔麦麦提,张东,&刘芳.(2016).基于多光谱数据的荒漠矿区土壤有机质估算模型.农业工程学报,32(6),263-267.
MLA	夏楠,et al."基于多光谱数据的荒漠矿区土壤有机质估算模型".农业工程学报 32.6(2016):263-267.