MA Xiumei; ZHOU Kefa; WANG Jinlin; CUI Shichao; ZHOU Shuguang; WANG Shanshan; ZHANG Guanbin
Hyperspectral remote sensing technology is widely used to detect element contents because of its multiple bands, high resolution, and abundant information. Although researchers have paid considerable attention to selecting the optimal bandwidth for the hyperspectral inversion of metal element contents in rocks, the influence of bandwidth on the inversion accuracy are ignored. In this study, we collected 258 rock samples in and near the Kalatage polymetallic ore concentration area in the southwestern part of Hami City, Xinjiang Uygur Autonomous Region, China and measured the ground spectra of these samples. The original spectra were resampled with different bandwidths. A Partial Least Squares Regression (PLSR) model was used to invert Cu contents of rock samples and then the influence of different bandwidths on Cu content inversion accuracy was explored. According to the results, the PLSR model obtains the highest Cu content inversion accuracy at a bandwidth of 35 nm, with the model determination coefficient (R2) of 0.5907. The PLSR inversion accuracy is relatively unaffected by the bandwidth within 5–80 nm, but the accuracy decreases significantly at 85 nm bandwidth (R2=0.5473), and the accuracy gradually decreased at bandwidths beyond 85 nm. Hence, bandwidth has a certain impact on the inversion accuracy of Cu content in rocks using the PLSR model. This study provides an indicator argument and theoretical basis for the future design of hyperspectral sensors for rock geochemistry.
WANG Jinlin; WANG Wei; CHENG Yinyi; ZHANG Zhixin; WANG Shanshan; ZHOU Kefa; LI Pingheng
|Information on the Fe content of bare rocks is needed for implementing geochemical processes and identifying mines. However, the influence of Fe content on the spectra of bare rocks has not been thoroughly analyzed in previous studies. The Saur Mountain region within the Hoboksar of the Russell Hill depression was selected as the study area. Specifically, we analyzed six hyperspectral indices related to rock Fe content based on laboratory measurements (Dataset I) and field measurements (Dataset II). In situ field measurements were acquired to verify the laboratory measurements. Fe content of the rock samples collected from different fresh and weathered rock surfaces were divided into six levels to reveal the spatial distributions of Fe content of these samples. In addition, we clearly displayed wavelengths with obvious characteristics by analyzing the spectra of these samples. The results of this work indicated that Fe content estimation models based on the fresh rock surface measurements in the laboratory can be applied to in situ field or satellite-based measurements of Fe content of the weathered rock surfaces. It is not the best way to use only the single wavelengths reflectance at all absorption wavelengths or the depth of these absorption features to estimate Fe content. Based on sample data analysis, the comparison with other indices revealed that the performance of the modified normalized difference index is the best indicator for estimating rock Fe content, with R2 values of 0.45 and 0.40 corresponding to datasets I and II, respectively. Hence, the modified normalized difference index (the wavelengths of 2220, 2290, and 2370 nm) identified in this study could contribute considerably to improve the identification accuracy of rock Fe content in the bare rock areas. The method proposed in this study can obviously provide an efficient solution for large-scale rock Fe content measurements in the field.|
WANG Shanshan; ZHOU Kefa; ZUO Qiting; WANG Jinlin; WANG Wei
|The Tarim River is the longest inland river in China and is considered as an important river to protect the oasis economy and environment of the Tarim Basin. However, excessive exploitation and over-utilization of natural resources, particularly water resources, have triggered a series of ecological and environmental problems, such as the reduction in the volume of water in the main river, deterioration of water quality, drying up of downstream rivers, degradation of vegetation, and land desertification. In this study, the land use/land cover change (LUCC) responses to ecological water conveyance in the lower reaches of the Tarim River were investigated using ENVI (Environment for Visualizing Images) and GIS (Geographic Information System) data analysis software for the period of 1990–2018. Multi-temporal remote sensing images and ecological water conveyance data from 1990 to 2018 were used. The results indicate that LUCC covered an area of 2644.34 km2 during this period, accounting for 15.79% of the total study area. From 1990 to 2018, wetland, farmland, forestland, and artificial surfaces increased by 533.42 km2 (216.77%), 446.68 km2 (123.66%), 284.55 km2 (5.67%), and 57.51 km2 (217.96%), respectively, whereas areas covered by grassland and other land use/land cover types, such as Gobi, bare soil, and deserts, decreased by 103.34 km2 (14.31%) and 1218.83 km2 (11.75%), respectively. Vegetation area decreased first and then increased, with the order of 2010<2000<1990<2018. LUCC in the overflow and stagnant areas in the lower reaches of the Tarim River was mainly characterized by fragmentation, irregularity, and complexity. By analyzing the LUCC responses to 19 rounds of ecological water conveyance in the lower reaches of the Tarim River from 2000 to the end of 2018, we proposed guidelines for the rational development and utilization of water and soil resources and formulation of strategies for the sustainable development of the lower reaches of the Tarim River. This study provides scientific guidance for optimal scheduling of water resources in the region.|
CUI Shichao; ZHOU Kefa; ZHANG Guanbin; DING Rufu; WANG Jinlin; CHENG Yinyi; JIANG Guo
|With the increase of exploration depth, it is more and more difficult to find Au deposits. Due to the limitation of time and cost, traditional geological exploration methods are becoming increasingly difficult to be effectively applied. Thus, new methods and ideas are urgently needed. This study assessed the feasibility and effectiveness of using hyperspectral technology to prospect for hidden Au deposits. For this purpose, 48 plant (Seriphidium terrae-albae) and soil (aeolian gravel desert soil) samples were first collected along a sampling line that traverses an Au mineralization alteration zone (Aketasi mining region in an arid region of China) and were used to obtain soil Au contents by a chemical analysis method and the reflectance spectra of plants obtained with an Analytical Spectral Device (ASD) FieldSpec3 spectrometer. Then, the corresponding relationship between the soil Au content anomaly and concealed Au deposits was investigated. Additionally, the characteristic bands were selected from plant spectra using four different methods, namely, genetic algorithm (GA), stepwise regression analysis (STE), competitive adaptive reweighted sampling (CARS), and correlation coefficient method (CC), and were then input into the partial least squares (PLS) method to construct a model for estimating the soil Au content. Finally, the quantitative relationship between the soil Au content and the 15 different plant transformation spectra was established using the PLS method. The results were compared with those of a model based on the full spectrum. The results obtained in this study indicate that the location of concealed Au deposits can be predicted based on soil geochemical anomaly information, and it is feasible and effective to use the full plant spectrum and PLS method to estimate the Au content in the soil. The cross-validated coefficient of determination (R2) and the ratio of the performance to deviation (RPD) between the predicted value and the measured value reached the maximum of 0.8218 and 2.37, respectively, with a minimum value of 6.56 μg/kg for the root-mean-squared error (RMSE) in the full spectrum model. However, in the process of modeling, it is crucial to select the appropriate transformation spectrum as the input parameter for the PLS method. Compared with the GA, STE, and CC methods, CARS was the superior characteristic band screening method based on the accuracy and complexity of the model. When modeling with characteristic bands, the highest accuracy, R2 of 0.8016, RMSE of 7.07 μg/kg, and RPD of 2.20 were obtained when 56 characteristic bands were selected from the transformed spectra (1/lnR)' (where it represents the first derivative of the reciprocal of the logarithmic spectrum) of sampled plants using the CARS method and were input into the PLS method to construct an inversion model of the Au content in the soil. Thus, characteristic bands can replace the full spectrum when constructing a model for estimating the soil Au content. Finally, this study proposes a method of using plant spectra to find concealed Au deposits, which may have promising application prospects because of its simplicity and rapidity.|