Mapping variations in weight percent silica measured from multispectral thermal infrared imagery—Examples from the Hiller Mountains, Nevada, USA and Tres Virgenes-La Reforma, Baja California Sur, Mexico

Remotely sensed multispectral thermal infrared (8-13 μm) images are increasingly being used to map variations in surface silicate mineralogy. These studies utilize the shift to longer wavelengths in the main spectral feature in minerals in this wavelength region (reststrahlen band) as the mineralogy changes from felsic to mafic. An approach is described for determining the amount of this shift and then using the shift with a reference curve, derived from laboratory data, to remotely determine the weight percent SiO₂ of the surface. The approach has broad applicability to many study areas and can also be fine-tuned to give greater accuracy in a particular study area if field samples are available. The approach was assessed using airborne multispectral thermal infrared images from the Hiller Mountains, Nevada, USA and the Tres Virgenes-La Reforma, Baja California Sur, Mexico. Results indicate the general approach slightly overestimates the weight percent SiO₂ of low silica rocks (e.g. basalt) and underestimates the weight percent SiO₂ of high silica rocks (e.g. granite). Fine tuning the general approach with measurements from field samples provided good results for both areas with errors in the recovered weight percent SiO₂ of a few percent. The map units identified by these techniques and traditional mapping at the Hiller Mountains demonstrate the continuity of the crystalline rocks from the Hiller Mountains southward to the White Hills supporting the idea that these ranges represent an essentially continuous footwall block below a regional detachment. Results from the Baja California data verify the most recent volcanism to be basaltic-andesite.     

Comparison and analysis of bare soil evaporation models combined with ASTER data in Heihe River Basin

Based on ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) remote sensing data, bare soil evaporation was estimated with the Penman-Monteith model, the Priestley-Taylor model, and the aerodynamics model. Evaporation estimated by each of the three models was compared with actual evaporation, and error sources of the three models were analyzed. The mean absolute relative error was 9% for the Penman-Monteith model, 14% for the Priestley-Taylor model, and 32% for the aerodynamics model; the Penman-Monteith model was the best of these three models for estimating bare soil evaporation. The error source of the Penman-Monteith model is the neglect of the advection estimation. The error source of the Priestley-Taylor model is the simplification of the component of aerodynamics as 0.72 times the net radiation. The error source of the aerodynamics model is the difference of vapor pressure and neglect of the radiometric component. The spatial distribution of bare soil evaporation is evident, and its main factors are soil water content and elevation.     

利用ASTERGlobalDEM和GoogleEarth卫星遥感影像辅助电力工程选址选线

介绍了ASTER Global DEM数据和Google Earth影像特点,以及阐述通过定制与检查DEM数据、卫星影像下载与纠正、数据基准转换与投影变换、等高线生成与影像叠加四个环节来制作遥感影像地图的方法.结合工程实践经验,说明该方法适合于难以获取基础地理信息的区域,能辅助电力工程的选址选线.     

Spectral characterization and ASTER-based lithological mapping of an ophiolite complex: A case study from Neyriz ophiolite, SW Iran

The Neyriz ophiolite occurs along the Zagros suture zone in SW Iran, and is part of a 3000-km obduction belt thrusting over the edge of the Arabian continent during the late Cretaceous. This complex typically consists of altered dunites and peridotites, layered and massive gabbros, sheeted dykes and pillow lavas, and a thick sequence of radiolarites. Reflectance and emittance spectra of Neyriz ophiolite rock samples were measured in the laboratory and their spectra were used as endmembers in a spectral feature fitting (SFF) algorithm. Laboratory spectral reflectance measurements of field samples showed that in the visible through shortwave infrared (VNIR-SWIR) wavelength region the ultramafic and gabbroic rocks are characterized by ferrous-iron and Fe, MgOH spectral features, and the pillow lavas and radiolarites are characterized by spectral features of ferric-iron and AlOH. The laboratory spectral emittance spectra also revealed a wide wavelength range of SiO spectral features for the ophiolite rock units. After continuum removal of the spectra, the SFF classification method was applied to the VNIR + SWIR 9-band stack, and to the 11-band data set of SWIR and TIR data sets of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor, using field spectra as training sets for evaluating the potential of these data sets in discriminating ophiolite rock units. Output results were compared with the geological map of the area and field observations, and were assessed by the use of confusion matrices. The assessment showed, in terms of kappa coefficient, that the SFF classification method with continuum removal applied to the SWIR data achieved excellent results, which were distinctively better than those obtained using VNIR + SWIR data and TIR data alone.     

基于中、高分辨率遥感数据的铜多金属矿化信息提取与找矿预测——以西藏别若则错地区为例

基于中、高分辨率遥感数据,对西藏别若则错地区开展了蚀变信息提取与找矿预测。首先利用ASTER中等分辨率遥感数据,快速获取成矿地质背景信息及遥感蚀变信息,优选找矿有利区。其次利用World View-2高分辨率遥感数据开展找矿有利区的岩性构造解译,重点解译中酸性侵入岩体、火山机构、断裂、地层岩性等控矿要素。最后在野外查证成果基础上,圈定了3处找矿靶区,取得了良好的应用效果。     

Estimating aboveground carbon in a catchment of the Siberian forest tundra: Combining satellite imagery and field inventory

This study was part of an interdisciplinary research project on soil carbon and phytomass dynamics of boreal and arctic permafrost landscapes. The 45 ha study area was a catchment located in the forest tundra in northern Siberia, approximately 100 km north of the Arctic Circle.The objective of this study was to estimate aboveground carbon (AGC) and assess and model its spatial variability. We combined multi-spectral high resolution remote sensing imagery and sample based field inventory data by means of the k-nearest neighbor (k-NN) technique and linear regression.Field data was collected by stratified systematic sampling in August 2006 with a total sample size of n = 31 circular nested sample plots of 154 m² for trees and shrubs and 1 m² for ground vegetation. Destructive biomass samples were taken on a sub-sample for fresh weight and moisture content. Species-specific allometric biomass models were constructed to predict dry biomass from diameter at breast height (dbh) for trees and from elliptic projection areas for shrubs.Quickbird data (standard imagery product), acquired shortly before the field campaign and archived ASTER data (Level-1B product) of 2001 were geo-referenced, converted to calibrated radiances at sensor and used as carrier data. Spectral information of the pixels which were located in the inventory plots were extracted and analyzed as reference set. Stepwise multiple linear regression was applied to identify suitable predictors from the set of variables of the original satellite bands, vegetation indices and texture metrics. To produce thematic carbon maps, carbon values were predicted for all pixels of the investigated satellite scenes. For this prediction, we compared the kNN distance-weighted classifier and multiple linear regression with respect to their predictions.The estimated mean value of aboveground carbon from stratified sampling in the field is 15.3 t/ha (standard error SE = 1.50 t/ha, SE% = 9.8%). Zonal prediction from the k-NN method for the Quickbird image as carrier is 14.7 t/ha with a root mean square error RMSE = 6.42 t/ha, RMSE_r = 44%) resulting from leave-one-out cross-validation. The k-NN-approach allows mapping and analysis of the spatial variability of AGC. The results show high spatial variability with AGC predictions ranging from 4.3 t/ha to 28.8 t/ha, reflecting the highly heterogeneous conditions in those permafrost-influenced landscapes. The means and totals of linear regression and k-NN predictions revealed only small differences but some regional distinctions were recognized in the maps.     

Object-based crop identification using multiple vegetation indices, textural features and crop phenology

Crop identification on specific parcels and the assessment of soil management practices are important for agro-ecological studies, greenhouse gas modeling, and agrarian policy development. Traditional pixel-based analysis of remotely sensed data results in inaccurate identification of some crops due to pixel heterogeneity, mixed pixels, spectral similarity, and crop pattern variability. These problems can be overcome using object-based image analysis (OBIA) techniques, which incorporate new spectral, textural and hierarchical features after segmentation of imagery. We combined OBIA and decision tree (DT) algorithms to develop a methodology, named Object-based Crop Identification and Mapping (OCIM), for a multi-seasonal assessment of a large number of crop types and field status.In our approach, we explored several vegetation indices (VIs) and textural features derived from visible, near-infrared and short-wave infrared (SWIR) bands of ASTER satellite scenes collected during three distinct growing-season periods (mid-spring, early-summer and late-summer). OCIM was developed for 13 major crops cultivated in the agricultural area of Yolo County in California, USA. The model design was built in four different scenarios (combinations of three or two periods) by using two independent training and validation datasets and the best DTs resulted in an error rate of 9% for the three-period model and between 12 and 16% for the two-period models. Next, the selected DT was used for the thematic classification of the entire cropland area and mapping was then evaluated applying the confusion matrix method to the independent testing dataset that reported 79% overall accuracy. OCIM detected intra-class variations in most crops attributed to variability from local crop calendars, tree-orchard structures and land management operations. Spectral variables (based on VIs) contributed around 90% to the models, although textural variables were necessary to discriminate between most of the permanent crop-fields (orchards, vineyard, alfalfa and meadow). Features extracted from late-summer imagery contributed around 60% in classification model development, whereas mid-spring and early-summer imagery contributed around 30 and 10%, respectively. The Normalized Difference Vegetation Index (NDVI) was used to identify the main groups of crops based on the presence and vigor of green vegetation within the fields, contributing around 50% to the models. In addition, other VIs based on SWIR bands were also crucial to crop identification because of their potential to detect field properties like moisture, vegetation vigor, non-photosynthetic vegetation and bare soil. The OCIM method was built using interpretable rules based on physical properties of the crops studied and it was successful for object-based feature selection and crop identification.     

光谱匹配技术应用于ASTER数据中提取蚀变异常

选择朱拉扎嘎金矿为试验区,ASTER数据为数据源,利用光谱匹配技术进行蚀变异常提取实验。重点阐述光谱匹配技术(SM)应用于ASTER数据提取蚀变异常工作中的两项关键技术:内部平均相对反射率法(IARR)大气校正和典型蚀变矿物波谱对应于ASTER数据波段重采样。最终通过光谱匹配技术提取了4组蚀变异常,分别为褐铁矿化蚀变异常、高岭土化蚀变异常、碳酸盐化蚀变组合异常和含OH-蚀变组合异常。     

用ASTER数据提取植被覆盖区遥感铁矿化蚀变信息

以植被覆盖度较大的山东省兰陵县凤凰山铁矿区为例,选取覆盖该区的ASTER数据作为遥感数据源,首先进行了几何精纠正、大气校正和水体、阴影等干扰去除;然后在充分了解岩石波谱特征和ASTER数据波段特征的基础上选择了提取矿物蚀变信息的最优波段组合,采用主成分分析(Principal component analysis,PCA)法对研究区的铁染蚀变和羟基蚀变信息进行了提取,并对蚀变异常强度进行了标准化等级划分;最后通过分析蚀变信息与已知矿床的关系圈定了遥感异常区,并在矿化蚀变较强的地段选取8处铁染蚀变异常点和6处羟基蚀变异常点,通过布设踏勘路线进行了采样和验证。结果表明：从ASTER数据中提取的铁染和羟基蚀变信息的分布与实际情况吻合较好,其高值区分别对应着铁矿化和高岭土化强烈的地区,验证精度分别达到87.5%、83.3%。可见,在植被覆盖度较大的地区,ASTER数据的短波红外波段内仍包含丰富的矿物蚀变信息,可为地质找矿提供重要依据。     

基于ASTER影像的高山峡谷区主要地类自动提取方法研究

针对高山峡谷地区遥感图像变异较大的特点,通过分析高山峡谷区的光谱结构特征,利用比值植被指数RVI、归一化植被指数NDVI及数字高程模型DEM等数据资料,结合综合阈值法,设定合适的阈值,得到分类单体图的二值化图像,并最终在ArcGIS9.0平台上进行单体图像的合成与输出。与传统最大似然法的分类结果相比,该方法在一定程度上克服了“同物异谱”、“异谱同物”等现象对分类结果带来的影响,提高了高山峡谷区地表覆盖分类的精度,得到了试验区较为可靠的遥感分类图像。