This page has only limited features, please log in for full access.
The accumulation of metals in soil harms human health through different channels. Therefore, it is very important to conduct fast and effective non-destructive prediction of metals in the soil. In this study, we investigate the characteristics of four metal contents, namely, Sb, Pb, Cr, and Co, in the soil of the Houzhai River Watershed in Guizhou Province, China, and establish the content prediction back propagation (BP) neural network and genetic-ant colony algorithm BP (GAACA-BP) neural network models based on hyperspectral data. Results reveal that the four metals in the soil have different degrees of accumulation in the study area, and the correlation between them is significant, indicating that their sources may be similar. The fitting effect and accuracy of the GAACA-BP model are greatly improved compared with those of the BP model. The R values are above 0.7, the MRE is reduced to between 6% and 15%, and the validation accuracy is increased by 12–64%. The prediction ability of the model of the four metals is Cr > Co > Sb > Pb. These results indicate the possibility of using hyperspectral techniques to predict metal content.
Shiqi Tian; Shijie Wang; Xiaoyong Bai; Dequan Zhou; Guangjie Luo; Jinfeng Wang; Mingming Wang; Qian Lu; Yujie Yang; Zeyin Hu; Chaojun Li; Yuanhong Deng. Hyperspectral Prediction Model of Metal Content in Soil Based on the Genetic Ant Colony Algorithm. Sustainability 2019, 11, 3197 .
AMA StyleShiqi Tian, Shijie Wang, Xiaoyong Bai, Dequan Zhou, Guangjie Luo, Jinfeng Wang, Mingming Wang, Qian Lu, Yujie Yang, Zeyin Hu, Chaojun Li, Yuanhong Deng. Hyperspectral Prediction Model of Metal Content in Soil Based on the Genetic Ant Colony Algorithm. Sustainability. 2019; 11 (11):3197.
Chicago/Turabian StyleShiqi Tian; Shijie Wang; Xiaoyong Bai; Dequan Zhou; Guangjie Luo; Jinfeng Wang; Mingming Wang; Qian Lu; Yujie Yang; Zeyin Hu; Chaojun Li; Yuanhong Deng. 2019. "Hyperspectral Prediction Model of Metal Content in Soil Based on the Genetic Ant Colony Algorithm." Sustainability 11, no. 11: 3197.
Diagnosing the evolution trends of vegetation and its drivers is necessary for ecological conservation and restoration. However, it remains unclear what the underlying distribution pattern of these trends and its correlation with some drivers at large spatial-temporal scales. Here we use the normalized difference vegetation index (NDVI) to quantify the activity of vegetation by Theil–Sen median trend analysis and the Mann–Kendall test, Pearson correlation analysis and Boosted regression trees (BRT) model. Results show that about 34% of the global continent area has experienced greening in the grid annual NDVI from 1982 to 2015. The major greening areas were observed in the Sahel, European, India and south China. Only 10% of the global continent land areas were browning, and these were observed in Canada, South America, central Africa and Central Asia. BRT model shows that rainfall is the most important factor affecting vegetation evolution (63.1%), followed by temperature (15%), land cover change (8.6%), population (6.5%), elevation (6.4%) and nightlight (0.4%). It’s about 21% of the world’s continent were affected by rainfall, mainly in arid regions such as central Asia and Australia. The main temperature-affected areas accounted for 36%, located near the equator or in high latitudes.
Yujie Yang; Shijie Wang; Xiaoyong Bai; Qiu Tan; Qin Li; Luhua Wu; Shiqi Tian; Zeyin Hu; Chaojun Li; Yuanhong Deng. Factors Affecting Long-Term Trends in Global NDVI. Forests 2019, 10, 372 .
AMA StyleYujie Yang, Shijie Wang, Xiaoyong Bai, Qiu Tan, Qin Li, Luhua Wu, Shiqi Tian, Zeyin Hu, Chaojun Li, Yuanhong Deng. Factors Affecting Long-Term Trends in Global NDVI. Forests. 2019; 10 (5):372.
Chicago/Turabian StyleYujie Yang; Shijie Wang; Xiaoyong Bai; Qiu Tan; Qin Li; Luhua Wu; Shiqi Tian; Zeyin Hu; Chaojun Li; Yuanhong Deng. 2019. "Factors Affecting Long-Term Trends in Global NDVI." Forests 10, no. 5: 372.
The rapid and accurate grasp of changes in residences is crucial for urban planning and urbanization. However, the traditional methods for extracting residences exists several problems, which lead to inaccurate extraction results. In this study, the Landsat image is used to establish a new method for extracting the residences quickly and accurately. The specific steps are as follows: 1) We calculate surface albedo to exclude the interference of waters and shadows; 2) Using single-band threshold method, we eliminate the interference of shadows; 3) NDVI (Normalised Difference Vegetation Index) is calculated to exclude the effects of vegetation; 4) Roads are removed by calculating the shape index. Verification shows that the accuracy of this extraction method is 92.81%, which is more accurate than the traditional methods and solves the problems existed in the traditional methods. This novel method is a new reference for other land cover research on the technical aspect.
Yujie Yang; Shijie Wang; Xiaoyong Bai; Qiu Tan; Chaojun Li; Qin Li; Luhua Wu; Jianyong Xiao; Qinghuan Qian; Fei Chen; Huiwen Li; Yue Cao; Mingming Wang; Jinfeng Wang; Shiqi Tian; Qian Lu. Residences information extraction from Landsat imagery using the multi-parameter decision tree method. Geocarto International 2018, 34, 1621 -1633.
AMA StyleYujie Yang, Shijie Wang, Xiaoyong Bai, Qiu Tan, Chaojun Li, Qin Li, Luhua Wu, Jianyong Xiao, Qinghuan Qian, Fei Chen, Huiwen Li, Yue Cao, Mingming Wang, Jinfeng Wang, Shiqi Tian, Qian Lu. Residences information extraction from Landsat imagery using the multi-parameter decision tree method. Geocarto International. 2018; 34 (14):1621-1633.
Chicago/Turabian StyleYujie Yang; Shijie Wang; Xiaoyong Bai; Qiu Tan; Chaojun Li; Qin Li; Luhua Wu; Jianyong Xiao; Qinghuan Qian; Fei Chen; Huiwen Li; Yue Cao; Mingming Wang; Jinfeng Wang; Shiqi Tian; Qian Lu. 2018. "Residences information extraction from Landsat imagery using the multi-parameter decision tree method." Geocarto International 34, no. 14: 1621-1633.