改进支持向量机的高分遥感影像道路提取

针对支持向量机受分类数的限制在高分辨率遥感影像中无法直接获取高精度道路网信息的问题,该文提出一种新的混合的基于支持向量机的方法:首先,利用模糊C均值聚类方法将输入的遥感影像分为3类,以减少支持向量机的错分现象;其次,运用支持向量机将不同类别的像素分为道路类和非道路类;最后,应用马尔科夫随机场对分类结果进行噪声去除,并采用形态学进行后处理,进而得到精确道路网信息。实验结果表明:该算法不仅能够从高分辨率遥感影像中提取出道路网,而且精度优于直接使用支持向量机算法以及对比算法。     

恶劣环境下GPS/BDS混合双差模糊度固定性能分析

针对在恶劣观测环境下每个GNSS系统只观测到较少的几颗卫星,采用标准双差模型很难实现整周模糊度固定的问题,该文对GPS/BDS系统间混合双差模型进行研究,利用该模型计算出GPS/BDS系统偏差。在此基础上,利用实验分别对模拟和真实恶劣观测环境下的模糊度固定性能进行分析,实验结果表明,恶劣环境下GPS/BDS系统间混合双差模型表现良好,相较于经典的双差模型可将模糊度固定平均所需时间分别缩短43%和33%,模糊度固定成功率分别提高107%和31%。     

Temporal replication of the national land cover database using active machine learning

Regional and national level land cover datasets, such as the National Land Cover Database (NLCD) in the United States, have become an important resource in physical and social science research. Updates to the NLCD have been conducted every 5 years since 2001; however, the procedure for producing a new release is labor-intensive and time-consuming, taking 3 or 4 years to complete. Furthermore, in most countries very few, if any, such releases exist, and thus there is high demand for efficient production of land cover data at different points in time. In this paper, an active machine learning framework for temporal updating (or backcasting) of land cover data is proposed and tested for three study sites covered by the NLCD. The approach employs a maximum entropy classifier to extract information from one Landsat image using the NLCD, and then replicate the classification on a Landsat image for the same geographic extent from a different point in time to create land cover data of similar quality. Results show that this framework can effectively replicate the land cover database in the temporal domain with similar levels of overall and within class agreement when compared against high resolution reference land cover datasets. These results demonstrate that the land cover information encapsulated in the NLCD can effectively be extracted using solely Landsat imagery for replication purposes. The algorithm is fully automated and scalable for applications at landscape and regional scales for multiple points in time.     

Remote sensing and information value (IV) model for regional mapping of fluvial channels and topographic wetness in the Saudi Arabia

This study aims to map regions of near surface fluvial channels, mega-basins and topographic wetness in Saudi Arabia using remote sensing data and an information value (IV) model, which is a modified approach of weight of evidence. We used the new version of the Shuttle Radar Topographic Mission (SRTM) to delineate the fluvial channels, mega-basin, and slope. These hydrological parameters were used to index the topographic wetness of each mega-basin in the region based on IV in a Geographic Information System. We validated our method using the Space Imaging Radar-C and Landsat 8 images and compared the textural features (fluvial channels) evident from SRTM digital elevation model and to determine whether these patterns were different. Our results revealed that the region is drained by nine tributaries and that the Err Rub Al Khali and Sahba mega-basins have the highest value of the IV and topographic wetness values; the Arran and coastal mega-basins have the lowest value of the IV and topographic wetness values. An integrated approach is timely and economically effective and can be applied throughout the arid and semi-arid regions to help hydrologists and urban developers.     

An efficient application of fusion approach for hot spot detection with MODIS and PALSAR-1 data

Hot spot detection with satellite images, especially with synthetic aperture radar (SAR) images is still a challenging task. Several researchers have used TM/optical data for identification of hot spot but the use of SAR data is very limited for this type of application. The fusion of SAR data with TM/optical data may add additional information which in turn will lead for enhancement of detection capability of the hot spot. Therefore, this study explores the possibility of fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Phased Array L-band Synthetic Aperture Radar (PALSAR) satellite images for the hot spot detection. Image fusion is emerging as a powerful tool where information of various sensors can be used for obtaining better results. For this purpose, vegetation greenness and roughness information which is obtained from MODIS and PALSAR satellite images, respectively, are used for fusion, and then, a contextual-based thresholding algorithm is applied to the fused image for hot spot detection. The proposed approach comprises of two steps: (1) application of genetic algorithm-based scheme for image fusion of MODIS and PALSAR satellite images, and (2) classification of the fused image as either hot spot or non-hot spot pixels by employing a contextual thresholding technique. The algorithm is tested over the Jharia Coal Field region of India, where hot spot is one of the major problems and it is observed that the proposed thresholding technique classifies the each pixel of the fused image into two categories: hot spot and non-hot spot and the proposed approach detects the hot spot with better accuracy and less false alarm.     

Effect of sensor modelling methods on computation of 3-D coordinates from Cartosat-1 stereo data

The orbital and the rational polynomial coefficients (RPC) models are the two most commonly used models to compute a three-dimensional coordinates from an image stereo-pair. But it is still confusing that with the identical user provided inputs, which one of these two models provides more accurate digital elevation model (DEM), especially for mountainous terrain. This study aimed to find out the answer by evaluating the impact of used models on the vertical accuracy of DEM extracted from Cartosat-1 stereo data. We used high-accuracy photogrammetric DEM as the reference DEM. Apart from general variations in statistics, surprisingly in a few instances, both the DEMs provided contrasting results, thus proving the significance of this study. The computed root mean square errors and linear error at 90% (LE90) were lower in case of RPC DEM for various classes of slope, aspect and land cover, thus suggesting its better relative accuracy.     

Bacterially-mediated weathering of crystalline and amorphous Cu-slags

Two types of Cu-slags (CS: crystalline massive slag and GS: granulated amorphous slag) exhibiting a different chemical and mineral phase composition were compared with respect to their susceptibility to bacterial weathering using Pseudomonas aeruginosa (n° CIP 105094). Abiotic conditions e.g. sterile growth medium and ultrapure water were used for comparison. The experiments were extended up to 112 days with a systematic liquid phase renewal every 14 days. The results revealed significant release of elements in the bacterially mediated weathering experiments. Concentrations of elements (Si, Fe, Cu, Zn and Pb) in the biotic solutions were increased at least by 20% up to 99% compared to abiotic ones. From 3 to 77% of the leached elements were associated to the fraction >0.22 μm. Scanning electron microscope observations demonstrated greater weathering of mineral phases in biotic experiments than in abiotic ones which is in accordance with the solution chemistry exhibiting higher concentrations of elements leached in biotic set-ups. In the case of CS, glass and sulfides weathering was yet observed in abiotic experiment, whereas partial dissolution of fayalite (Fe₂SiO₄) was solely affected by the presence of bacteria. GS having a higher bulk content of metallic elements was found to be more stable than sulfide-bearing CS, while its (GS) glass matrix was found to weather easier under biotic conditions.     

Contamination,bioaccessibility and human health risk of heavy metals in exposed-lawn soils from 28 urban parks in southern China's largest city,Guangzhou

The total concentrations and oral bioaccessibility of heavy metals in surface-exposed lawn soils from 28 urban parks in Guangzhou were investigated, and the health risks posed to humans were evaluated. The descending order of total heavy metal concentrations was Fe > Mn > Pb > Zn > Cu > Cr > Ni > Cd, but Cd showed the highest percentage bioaccessibility (75.96%). Principal component analysis showed that Grouped Cd, Pb, Cr, Ni, Cu and Zn, and grouped Cr and Mn could be controlled two different types of human sources. Whereas, Ni and Fe were controlled by both anthropogenic and natural sources. The carcinogenic risk probabilities for Pb and Cr to children and adults were under the acceptable level (<1 × 10⁻⁴). Hazard Quotient value for each metal and Hazard Index values for all metals studied indicated no significant risk of non-carcinogenic effects to children and adults in Guangzhou urban park soils.     

A note on “Origin of components in Chilean thermal waters” by

Risacher et al. (2011) have presented voluminous data on thermal waters of Chile that is quite appreciable; however, their work still falls short on several counts. The most notable shortcoming of the work is the presentation and treatment of data. The interpretations are based on questionable premises (viz., extent of seawater intrusion) and considerations (viz., using average chemical composition of rock types for geochemical modeling, considering volcanic rocks as the only rock type in contact with the geothermal fluids) and assumptions not always substantiated by facts (with proper references) could have been corroborated. Use of Cl/Br ratio for discrimination purpose is unconvincing, considering the uncertainty in the measurement of low (<1 mg/L) Br concentration.     

早白垩世泥炭地净初级生产力及其控制因素--来自二连盆地吉尔嘎郎图凹陷6号煤的证据

现代泥炭地中蕴藏着巨量的碳,泥炭地生产力的高低会影响全球碳循环及全球气候变化。前人对全新世以来泥炭地生产力做了大量研究,但对前第四纪的“深时”阶段的泥炭地生产力则极少涉及,其主要原因是缺少精确的定年方法。以二连盆地吉尔嘎郎图凹陷早白垩世6号煤为例,利用地球物理测井信号进行频谱分析并获得煤层中米兰科维奇旋回周期参数（123 ka（偏心率）:38.1 ka（斜率）:22.1 ka（岁差））,将米兰科维奇旋回作为“深时”地层时间的“度量”工具,计算出6号煤层碳的聚集速率为35.1~38.9 g C/（m2·a）,进一步推算出其所代表的泥炭地的碳聚集速率为46.2~51.2 g C/（m2·a）,净初级生产力（NPP）为231~256 g C/（m2·a）。将该计算结果与全新世同一纬度带泥炭地生产力水平比较,并结合前人研究成果综合分析影响因素,得出早白垩世泥炭地生产力水平主要受温度和大气中二氧化碳含量控制,而这两种因素又与气候相关联,则泥炭地生产力的研究可能对进一步了解古气候提供帮助。