基于TM和ETM+影像数据的东沙环礁珊瑚礁监测

以东沙环礁为研究区域,选取1999年Landsat-7 ETM+影像数据和2001年、2009年Landsat-5 TM影像数据为主要数据源,应用基于统计学习理论的支持向量机（SVM）分类技术,通过选择训练时间较短的“一对一”SVM方法和RBF核函数,对3个年度的影像数据进行珊瑚礁信息提取。结果表明：2009年东沙环礁珊瑚礁面积为140.93 km²;1999－2009年,东沙环礁珊瑚礁面积减少了17.54 km²,珊瑚礁破碎化、白化现象趋于明显,珊瑚礁退化处于中期阶段。空间分辨率的提高可得到更准确详尽的珊瑚礁信息,尤其对小面积珊瑚礁的信息提取。     

Use of Landsat thermal imagery for dynamically monitoring spontaneous combustion of Datong Jurassic coalfields in China

It is highly helpful and necessary to investigate and monitor the status of coal seam. Fortunately, remote sensing has facilitated the identification and dynamical monitoring of spontaneous combustion for a large area coal mining area, especially using the time series remotely-sensed datasets. In this paper, Datong Jurassic coal mining area is used as the study area, China, and an exclusion method and a multiple-factor analysis method are jointly used to identify the spontaneous combustion, including land surface temperature (LST), burnt rocks, and land use and land cover change (LUCC). The LST is firstly retrieved using a single-window algorithm due to a thermal infrared band of Landsat-5 TM (Thematic Mapper). Burnt rocks is then extracted using a decision-tree classification method based on a high-resolution SPOT-5 image. The thermal anomaly areas are identified and refined by the spatial overlay analysis of the above affecting factors. Three-period maps of coal fire areas are obtained and dynamically analyzed in 2007, 2009 and 2010. The results show that a total of 12 coal fire areas have been identified, which account for more than 1% of the total area of the study area. In general, there is an increasing trend yearly and a total of 771,970 m\(^{2}\) is increased. The average annual increase is 257,320 m\(^{2}\), the average annual growth rate is 3.78%, and the dynamic degree is 11.29%.     

Accuracy assessment of near-shore bathymetry information retrieved from Landsat-8 imagery

The improvement in the capabilities of Landsat-8 imagery to retrieve bathymetric information in shallow coastal waters was examined. Landsat-8 images have an additional band named coastal/aerosol, Band 1: 435–451 nm in comparison with former generation of Landsat imagery. The selected Landsat-8 operational land image (OLI) was of Chabahar Bay, located in the southern part of Iran (acquired on February 22, 2014 in calm weather and relatively low turbidity). Accurate and high resolution bathymetric data from the study area, produced by field surveys using a single beam echo-sounder, were selected for calibrating the models and validating the results. Three methods, including traditional linear and ratio transform techniques, as well as a novel proposed integrated method, were used to determine depth values. All possible combinations of the three bands [coastal/aerosol (CB), blue (B), and green (G)] have been considered (11 options) using the traditional linear and ratio transform techniques, together with five model options for the integrated method. The accuracy of each model was assessed by comparing the determined bathymetric information with field measured values. The standard error of the estimates, correlation coefficients (R ² ) for both calibration and validation points, and root mean square errors (RMSE) were calculated for all cases. When compared with the ratio transform method, the method employing linear transformation with a combination of CB, B, and G bands yielded more accurate results (standard error = 1.712 m, R ² _calibration = 0.594, R ² _validation = 0.551, and RMSE =1.80 m). Adding the CB band to the ratio transform methodology also dramatically increased the accuracy of the estimated depths, whereas this increment was not statistically significant when using the linear transform methodology. The integrated transform method in form of Depth = b ₀  + b ₁ X _CB  + b ₂ X _B  + b ₅ ln(R _CB )/ln(R _G ) + b ₆ ln(R _B )/ln(R _G ) yielded the highest accuracy (standard error = 1.634 m, R ² _calibration = 0.634, R ² _validation = 0.595, and RMSE = 1.71 m), where R _i (i = CB, B, or G) refers to atmospherically corrected reflectance values in the i ^th band [X _i  = ln(R _i -R _{deep water})].     

Assessment of the reliability of supervised classifications of Landsat-7, ASTER,and SPOT-5 multispectral data in rock unit discriminations of Jabal Daf-Wadi Fatima area,Saudi Arabia

Multispectral, multiresolution remotely sensed data were processed to emphasize geological interpretation of Jabal Daf-Wadi Fatima area. The investigated area is situated in the central western part of Saudi Arabia and geologically consists of igneous and metamorphosed rocks overlain by sedimentary sequence belonging to the Arabian-Nubian Shield. Three sets of digital satellite data, Landsat-7 ETM+, ASTER, and SPOT-5, were used in this study. The application of image processing techniques enables to identify and delineate the lithologic units and the structural features of the study area. The results of this study indicate that the confusion matrix of the three maximum likelihood supervised classifications of the three datasets shows that the Landsat ETM+ bands scored the best degree of average and overall accuracy (77 and 78%, respectively). This classification distinguishes most of the rock units for mapping in the investigated area. The supervised classification of ASTER and SPOT bands has lower degrees of accuracy than the classified Landsat data. The supervised classification of SPOT bands has a degree of average and overall accuracy of 66 and 67%, respectively, but it is the best for distinguishing the spectral signatures of the different members of Fatima Formation (lower, middle, and upper members). The statistical analyses of the confusion matrices of classifications and the interpretation of the produced classified thematic maps revealed that the classification accuracy does not necessary depend on the spatial resolution of satellite data. The data of the highest spatial resolution such as SPOT data are also very useful in emphasizing and classifying the rock units of a small outcrop area. The detailed geological map of Jabal Daf-Wadi Fatima area is interpreted in this work from supervised classified images of different resolutions as well as the structure map of this area. This study shows that it is preferable to use the supervised classifications of multiresolution data for rock unit discrimination in detailed field mapping.     

Identification of algal blooms based on support vector machine classification in Haizhou Bay, East China Sea

Harmful algal blooms commonly known as red tides have been observed at increasing frequencies, which are causing serious economic and ecologic problems in Haizhou Bay off the eastern coast of China. It is important to study the inducing factors of red tides including a wide variety of environmental variables and the complex interactions between them. This study explores the possibility of predicting the occurrence of red tides using support vector machine (SVM) with environmental variables. Seventeen in situ environmental variables which are known to affect the occurrence of red tides were collected between May and October of 2004–2006. Seven characteristic factors were extracted from these variables via factorial analysis to reduce computation complexity. Three of them are related to nutrients, others are contributed by temperature, oxygen depletion, pH, hydrodynamics, and precipitation, respectively. The classification models based on SVM were constructed to identify the red tides samples using the seven factors as independent variables and radial basis function as the kernel function. The model with the combination parameters of C = 10, γ = 0.7, and ζ = 0.1 has the highest accuracy of 92.06 %. It indicates that the model is highly valuable in predicting the occurrence of red tides by environmental variables in this region for its conservative threshold of surface algae concentration.     

Prairie or planted? Using time-series NDVI to determine grassland characteristics in Montana

Temperate grasslands are a highly threatened global biome. Complicating management and conservation strategy development, modern grasslands can be difficult to characterize across landscapes since they range from native and semi-native to completely non-native species compositions such as those found in heavily managed pastures. Similar to methods used to differentiate C3 and C4 grasses, we investigate the ability of using temporal variations in growth characteristics as an alternative pathway to predicting native versus introduced species composition across grassland landscapes. To do this, we conducted an exploratory analysis using a time-series of Normalized Difference Vegetation Index values as a measure of vegetation greenness with Landsat 5 TM imagery across a growing season and performed an unsupervised classification. Results from the classification were compared with field observation to determine if we can differentiate between native and introduced grassland types in the Northwest Glaciated Plains subecoregion of northeastern Montana. Our results indicated that we predicted grassland cover with 81% accuracy within our 200 km² study area and 71% accuracy in our 5000 km² secondary study area. Further extrapolation of our methodology, combined with the refinement of vegetation indices of time-series imagery, classification algorithms and the availability of data from planned Landsat and Sentinel missions, may provide the spatial detail necessary to improve grassland monitoring and rangeland management over large areas.     

面向对象的喀斯特地区土地利用遥感分类信息提取

传统的面向像元分类方法虽然对光谱差异较为明显的遥感影像信息提取具有较好的效果,但会不可避免地产生“椒盐现象”,同时对纹理和形状信息不能充分应用,造成了大量信息损失。为了提高喀斯特地区土地利用遥感信息提取的精度,本文采用面向对象的分类方法,对贵州省毕节地区开展了土地利用遥感信息自动提取研究。首先对该地区Landsat-5 TM影像进行多尺度分割,形成影像对象层,然后综合应用基于知识决策树分类和基于样本的最邻近分类等技术对喀斯特地区进行遥感解译。结果表明,面向对象分类技术能较好地对喀斯特地区土地利用信息进行提取,同时避免了“椒盐现象”的产生,经野外采集样点数据验证,一级类分类精度为91.7 %,二级类分类精度为89.4 %,表明该方法在贵州省毕节地区应用效果良好。      

青藏高原Soumi-NPP和MODIS积雪范围产品的对比分析

Soumi-NPP（Soumi Polar-orbiting Partnership）卫星作为接替服役超期的Terra、Aqua卫星,其积雪范围产品在青藏高原的精度尚未被评价。以Soumi-NPP积雪范围产品为研究对象,利用气象台站点数据并结合更高分辨率的Landsat-8 OLI数据,评价该产品的精度,并与MODIS（Moderate Resolution Imaging Spectroradiometer）积雪范围产品进行对比分析。结果表明：使用气象台站进行数据验证时,NPP、MOD与MYD三种积雪范围产品的总精度均较高,但三者积雪漏分误差都较大,其中MYD的漏分误差最大,为64.2%;当雪深小于5 cm时,三种积雪范围产品的积雪分类精度都较低,雪深大于等于5 cm时,NPP积雪范围产品的积雪分类精度最高,为82.3%,MOD与MYD的精度分别为77.1%和69.4%;利用Landsat-8 OLI数据验证时,Soumi-NPP积雪范围产品的Kappa系数最高,其均值为0.707,为高度一致性。而MOD10A1与MYD10A1的Kappa系数较低,分别为0.476与0.557,为中等一致性;Soumi-NPP积雪范围产品的Kappa系数大多在0.6以上,精度比较稳定,而MODIS积雪范围产品的Kappa系数波动较大,精度稳定性较差。Soumi-NPP积雪范围产品相较于MODIS积雪范围产品,其精度有了较大的提升,为准确监测青藏高原积雪范围提供了一个更优的选择。     

Toxicity of the organophosphate pesticide diazinon to crayfish of differing age

Diazinon is a widely applied agricultural pesticide whose effect importantly on the environment and the possible contamination of surface waters has led to increased interest in toxicological studies. Crayfish, as an ecologically important benthic macroinvertebrate, seems to be an appropriate model organism for such assessments. Acute toxicity tests were carried out on three crayfish age groups: young-of-the-year (total length = 25.0 ± 4.9 mm), juvenile (total length = 56.5 ± 3.8 mm) and adult (total length = 83.5 ± 5.7 mm). Young-of-the-year crayfish were found to be the most sensitive to diazinon (96 h LC50 = 0.15 mg L^?1), followed by juvenile crayfish (96 h LC50 = 0.27 mg L^?1), and adults (96 h LC50 = 0.51 mg L^?1). Crayfish were highly sensitive to diazinon. A delayed effect of Diazinon 60EC on adults was detected (144 h LC50 = 0.44 mg L^?1) suggests functional damage from the use of sublethal concentrations.     

A novel hybrid integration model using support vector machines and random subspace for weather-triggered landslide susceptibility assessment in the Wuning area (China)

This study proposed a hybrid modeling approach using two methods, support vector machines and random subspace, to create a novel model named random subspace-based support vector machines (RSSVM) for assessing landslide susceptibility. The newly developed model was then tested in the Wuning area, China, to produce a landslide susceptibility map. With the purpose of achieving the objective of the study, a spatial dataset was initially constructed that includes a landslide inventory map consisting of 445 landslide regions. Then, various landslide-influencing factors were defined, including slope angle, aspect, altitude, topographic wetness index, stream power index, sediment transport index, soil, lithology, normalized difference vegetation index, land use, rainfall, distance to roads, distance to rivers, and distance to faults. Next, the result of the RSSVM model was validated using statistical index-based evaluations and the receiver operating characteristic curve approach. Then, to evaluate the performance of the suggested RSSVM model, a comparison analysis was performed to other existing approaches such as artificial neural network, Naïve Bayes (NB) and support vector machine (SVM). In general, the performance of the RSSVM model was better than the other models for spatial prediction of landslide susceptibility. The AUC results of the applied models are as follows: RSSVM (AUC = 0.857), followed by MLP (AUC = 0.823), SVM (AUC = 0.814) and NB (AUC = 0.783). The present study indicates that RSSVM can be used for landslide susceptibility evaluation, and the results are very useful for local governments and people living in the Wuning area.     

塔里木河下游土地利用覆被MISR多角度遥感制图

通过对塔里木河下游MISR卫星多角度观测数据的不同组合构建多角度数据集,探索多角度观测与传统垂直观测对土地利用覆被遥感制图效果的影响,分别使用SVM(支持向量机)与传统的MLC(最大似然分类法)作为分类器,对分类后得到的混淆矩阵进行分析。结论证实:无论是使用传统的MLC还是SVM作为分类器,多角度观测都取得比垂直观测更高的总体分类精度;MISR近红外波段虽然分辨率较低,但依然含有丰富的信息,对地表覆被的分类有重要影响;无论使用哪一数据集,SVM法都能获得更高的分类精度;不同相机对分类结果的影响各不相同,其中C、D相机的作用更重要。     

Automated mapping of snow/ice surface temperature using Landsat-8 data in Beas River basin,India, and validation with wireless sensor network data

In this paper, an automated method for retrieval of snow surface temperature (SST) in Beas River Basin, India, using Landsat-8 thermal data is proposed. Digital number (DN) values of thermal data were converted into Top of Atmospheric (TOA) radiance. Surface radiance has been estimated from TOA radiance using a single channel method. The estimated surface radiance was then converted into SST. Cloud free Landsat-8 data for January and February 2017 has been used to estimate SST. Snow and Avalanche Study Establishment (SASE) has established a wireless sensor network (WSN) in an avalanche prone slope in Beas River Basin, India. Landsat-8 retrieved SST has been compared and validated with recorded SST at WSN stations. The retrieved SST using proposed algorithm was in good agreement with SST recorded on ground by sensor network. The mean absolute error (MAE) and root-mean-square error (RMSE) between estimated and recorded SST has been observed as ~?1.1 K and ~?1.5 K for 23 January 2017 and ~?0.7 and ~?1.6 K for 24 February 2017. Algorithm has shown a potential for automated mapping of snow and ice surface temperature using Landsat-8 data for snow cover and glaciers in Himalaya.     

Estimating leaf chlorophyll contents by combining multiple spectral indices with an artificial neural network

Estimating leaf chlorophyll contents through leaf reflectance spectra is efficient and nondestructive, but the actual dataset always based on a single or a few kinds of specific species, has a limitation and instability for a common use. To address this problem, a combination of multiple spectral indices and a model simulated dataset are proposed in this paper. Six spectral indices are selected, including Blue Green Index (BGI), Photochemical Reflectance Index (PRI_5), Triangle Vegetation Index (TVI), Chlorophyll Absorption Ratio Index (CARI), Carotenoid Reflectance Index (CRI) and the green peak reflectance (R₅₂₅). Both stepwise linear regression (SLR) and back-propagation artificial neural network (ANN) are used to combine the six spectral indices for the estimation of chlorophyll content (Cab). In addition, to overcome the limitation of actual dataset, a “big data” is applied by a within-leaf radiation transfer model (PROSPECT) to generate a large number of simulated samples with varying biochemical and biophysical parameters. 30% of the simulated dataset (SIM30) and an experimental dataset are used for validation. Compared with linear regression method, NN yields better result with R² = 0.96 and RMSE = 5.80ug.cm^?2 for Cab if validated by SIM30, while R² = 0.95 and RMSE = 6.39ug.cm^?2 for SLR. NN also gives satisfactory result with R² = 0.80 and RMSE = 5.93ug.cm^?2 for Cab if validated by LOPEX dataset, however, the SLR only gets 0.72 of R² and 12.20ug.cm^?2 of RMSE. The results indicate that integrating multiple spectral indices can improve the Cab estimating accuracy with a better stability in different kind of species and the model simulated dataset can make up the shortfall of actual measured dataset.     

基于随机森林算法的大尹格庄金矿床三维成矿预测

大数据及机器学习技术在解决各行各业的复杂非线性关系问题方面已经体现出巨大的优势。本文尝试将随机森林(RF)算法引入三维成矿预测领域来开展研究,以胶东大尹格庄金矿为研究对象,在构建招平断裂(地质体)三维模型的基础上,通过各种空间分析方法提取控制矿体形成的若干控矿地质因素特征值,进而获取成矿空间中控矿地质因素分布值,最后将矿区钻孔立体单元化形成采样数据集并利用RF算法对矿区开展三维矿体定位预测,结果表明:决策树棵数M=800、属性个数K=7是最优参数,能获得总体精度97.32%和kappa系数0.6292的综合分类精度;RF算法的分类精度要优于支持向量机(SVM)算法和多层感知器(MP)算法。RF算法对大尹格庄金矿开展的三维矿体定位预测取得了较好效果,并在矿区深边部预测了7个三维找矿靶区,证明大数据技术在矿产资源定位预测方面具有巨大的应用前景。     

The effect of mineralogy on the mobility of heavy metals in mine tailings: a case study in the Samsanjeil mine,Korea

This study examined the chemical speciation and mobility of As and heavy metals in a tailings impoundment in Samsanjeil mine located in Gosung, Korea, as well as the factors affecting them. XRD, SEM, and 5-step sequential extraction were used to examine the samples at two sampling sites (NN and SN sites). The pH of the tailings decreased with increasing depth at the NN site (from 7.2 to 2.8), whereas no significant differences were observed at the SN site (8.1–8.8). The samples at the SN site showed a larger amount of calcite than those at the NN site, indicating that calcite plays an important role buffering the pH in the study sites. Jarosite was found only at the lower part of the NN site, where calcite was not found. The mineralogical observation of jarosite and calcite was also confirmed by SEM. The concentrations of As and heavy metals in the tailings were as follows: Cu > As > Zn > > Pb > Co > Cr > Ni > Cd. The total concentrations of Ni, Zn, Co, and Cd were higher at the SN site than those at the NN site. On the other hand, the concentrations of As and Cr existing as oxyanions were higher at the NN site, which can be explained by the mobility changes of those elements affected by pH variations. At the NN site, the fractions of heavy metals bound to the Fe/Mn oxides, except for As and Cr, decreased, and Cu, Zn, and Co showed an increasing fraction of exchangeable metals with increasing depth. This suggests that the pH and resulting surface charge of minerals, such as goethite and jarosite, are the dominant factors controlling the chemical speciation of metals. These results highlight the importance of mineralogy in controlling the mobility and possible bioavailability of heavy metals in tailings.     

Assessment of proposed approaches for bathymetry calculations using multispectral satellite images in shallow coastal/lake areas: a comparison of five models

Bathymetric information for shallow coastal/lake areas is essential for hydrological engineering applications such as sedimentary processes and coastal studies. Remotely sensed imagery is considered a time-effective, low-cost, and wide-coverage solution for bathymetric measurements. This study assesses the performance of three proposed empirical models for bathymetry calculations in three different areas: Alexandria port, Egypt, as an example of a low-turbidity deep water area with silt-sand bottom cover and a depth range of 10.5 m; the Lake Nubia entrance zone, Sudan, which is a highly turbid, unstable, clay bottom area with water depths to 6 m; and Shiraho, Ishigaki Island, Japan, a coral reef area with varied depths ranging up to 14 m. The proposed models are the ensemble regression tree-fitting algorithm using bagging (BAG), ensemble regression tree-fitting algorithm of least squares boosting (LSB), and support vector regression algorithm (SVR). Data from Landsat 8 and Spot 6 satellite images were used to assess the performance of the proposed models. The three models were used to obtain bathymetric maps using the reflectance of green, red, blue/red, and green/red band ratios. The results were compared with corresponding results yielded by two conventional empirical methods, the neural network (NN) and the Lyzenga generalised linear model (GLM). Compared with echosounder data, BAG, LSB, and SVR results demonstrate higher accuracy ranges from 0.04 to 0.35 m more than Lyzenga GLM. The BAG algorithm, producing the most accurate results, proved to be the preferable algorithm for bathymetry calculation.     

Did satellite imagery supersede aerial imagery? A perspective from 3D geopositioning accuracy

In this study, the geometric accuracy comparison of aerial photos and WorldView-2 satellite stereo image data is evaluated with the different number and the distribution of the ground control points (GCPs) on the basis of large scale map production. Also, the current situation of rivalry between airborne and satelliteborne imagery was mentioned. The geometric accuracy of Microsoft UltraCam X 45 cm ground sampling distance (GSD) aerial imagery and WorldView-2 data both with and without GCPs are also separately analyzed. The aerial photos without any GCP by only using global navigation satellite system (GNSS) and inertial measurement unit (IMU) data with tie points give an accuracy of ±1.17 m in planimetry and ±0.71 m in vertical that means nearly two times better accuracy than the rational polynomial coefficient (RPC) of stereo WorldView-2. Using one GCP affects the accuracies of aerial photos and WorldView-2 in different ways. While this situation distorts the aerial photo block, it corrects the shift effect of RPC in WorldView-2 and increases the accuracy. By using four or more GCPs, ½?pixel (～0.23 m) accuracy in aerial photos and 1 pixel (～0.50 m) accuracy in WorldView-2 can be achieved in horizontal. In vertical, aerial photos have 1 pixel (～0.55 m) and WorldView-2 has 1.5 pixels (～0.85 m) accuracy. These results show that Worldview-2 imagery can be used in the production of class I 1:5000 scale maps according to the ASPRS Accuracy Standards for Digital Geospatial Data in terms of geometric accuracy. It is concluded that the rivalry between aerial and satellite imagery will continue for some time in the future.     

Investigating the application of pixel-level and product-level image fusion approaches for monitoring surface water changes

The aim of this paper was to investigate the suitability of the pixel-level and product-level image fusion approaches to detect surface water changes. In doing so, firstly, the principal component analysis technique was applied to Landsat TM 2010 multispectral image to generate the PC components. Several pixel-level image fusion techniques were then performed to merge the Landsat ETM+ 2000 panchromatic with the PC1PC2PC3 band combination of Landsat TM 2010 imagery to highlight the surface water changes between the two images. The suitability of the resulting fused images for surface water change detection was evaluated quantitatively and visually. Finally, the support vector machine (SVM) technique was applied to the qualified fused images to map the highlighted changes. Furthermore, a product level fusion (PLF) approach based on various satellite-derived indices was employed to detect the surface water changes between ETM+ 2000 and TM 2010 images. The accuracy of the resulting change maps was assessed based on a reference change map produced using visual interpretation. The results demonstrated the effectiveness of the proposed approaches for surface water change detection, especially using the Gram Schmidt-SVM, PLF-NDWI, and PLF-NDVI methods which improved the accuracy of change detection over 99.70 %.     

Mapping of settlements in high-resolution satellite imagery using high performance computing

Classifying urban land cover from high-resolution satellite imagery is challenging, and those challenges are compounded when the imagery databases are very large. Accurate land cover data is a crucial component of the population distribution modeling efforts of the Oak Ridge National Laboratory’s (ORNL) LandScan Program. Currently, LandScan Program imagery analysts manually interpret high-resolution (1–5 m) imagery to augment existing satellite-derived medium (30 m) and coarse (1 km) resolution land cover datasets. For LandScan, the high-resolution image archives that require interpretation are on the order of terabytes. The goal of this research is to automate human settlement mapping by utilizing ORNL’s high performance computing capabilities. Our algorithm employs gray-level and local edge-pattern co-occurrence matrices to generate texture and edge patterns. Areas of urban land cover correlate with statistical features derived from these texture and edge patterns. We have parallelized our algorithms for implementation on a 64-node system using a single instruction multiple data programming model (SIMD) with Message Passing Interface (MPI) as the communication mode. Our parallel-configured classifier performs 30–40 times faster than stand-alone alternatives. We have tested our system on IKONOS imagery. The early results are promising, pointing towards future large-scale classification of human settlements at high-resolution.