Lineament mapping and fractal analysis using SPOT-ASTER satellite imagery for evaluating the severity of slope weathering process

The present study describes a remote sensing approach for preparing lineament map that subsequently indicates the influence of lineament density in the severity of weathering development. In this study, SPOT-5 data, the integration of SPOT-ASTER and Digital Elevation Model (DEM) data were used and processed. The existence of an active fault system in the south of Mashhad city, NE Iran and presence of schistose rocks in this area result in the development of numerous lineament features. This region was selected for this research. Lineament features including fractures, bedding plane, cleavage, shear zones and schistosity were mapped in the study area. The results indicate that the highest concentration of lineaments occurred in the central-western and south-eastern parts of the study area, which coincide with metamorphic outcrops and NW-SE trending fault system. A comparison of lineament statistical analysis and field survey demonstrated that the structural discontinuities have a significant effect on forming and distribution of weathering profiles. It was observed that increasing the number, length and density of structural discontinuities led to strong severity in weathering, which can produce deep residual soils susceptible to landslide occurrence. The remote sensing approach developed in this study can be applicable for preparing lineament maps and evaluating the severity of weathering development in other active fault zones around the world.     

Integration of Landsat-8 and Sentinel-1 dataset to extract geological lineaments in complex formations of Tepal mountain area,Shahrood, north Iran

Recently, the detection and extraction of geological lineaments have become an essential analytical technique to find relationships between the characteristics and occurrence of hydrogeology, and tectonic studies. The use of remote sensing, with the progressive development of image enhancement techniques, provides an opportunity to produce more reliable and comprehensive lineament maps. In this paper, semi-automatic approach based on Landsat 8 and Sentinel 1 radar data is proposed for lineaments extraction and validation. The combined method of linear filtering and automatic line module ensures a high degree of accuracy resulting in a lineament map. Based on identified lineaments, Sentinel1 is more capable of detecting edges than Landsat8, but the primary orientation lineaments extracted from Landsat8 and Sentinel1 were different. So, by combining band6 of Landsat8, and VV and VH polarization of Sentinel1, the area lineaments were extracted with high accuracy. Rose diagram showed the extracted lineaments' orientation is in good compliance with the region's existing faults. Also, the formations' lineament length density has good consistent with the density of the faults in the geological map.     

Automatic lineament extraction in a heavily vegetated region using Landsat Enhanced Thematic Mapper (ETM+) imagery

Lineament extraction from satellite remotely sensed data has been one of the widely used applications of remote sensing in geology. In fact, recent advances in digital image processing allow such lineament extraction to be accomplished in semi-automatic to fully automatic approaches. However, satellite remotely sensed data acquired in heavily vegetated regions such as tropical rainforest, are vulnerable to higher inherent noise levels attributed to the resultant effects of scattering by clouds and adjacency effects of highly inhomogeneous vegetation cover within the pixel dimension. In this study, we examined the effects of noise levels to lineament extraction using a fully automatic approach, consisting of a combination of edge-line detection algorithms. Ancillary information from a digitized topographic map and image classification was used to discriminate between cultural and natural lineaments from the extracted lineaments. Adapting the combination of edge detection and a line-linking algorithm, we have found the optimal parameters for automatic lineament extraction of such complex areas using Enhanced Thematic Mapper (ETM+) data. A noise level of 30% is the maximum threshold before artifacts are generated. It is therefore concluded that the combination of edge-based and line-linking digital image processing operations with the priori local optimal parameters is crucial in lineament feature extraction in heavily vegetated regions.     

Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia

This paper presents the results of the cross-validation of a multivariate logistic regression model using remote sensing data and GIS for landslide hazard analysis on the Penang, Cameron, and Selangor areas in Malaysia. Landslide locations in the study areas were identified by interpreting aerial photographs and satellite images, supported by field surveys. SPOT 5 and Landsat TM satellite imagery were used to map landcover and vegetation index, respectively. Maps of topography, soil type, lineaments and land cover were constructed from the spatial datasets. Ten factors which influence landslide occurrence, i.e., slope, aspect, curvature, distance from drainage, lithology, distance from lineaments, soil type, landcover, rainfall precipitation, and normalized difference vegetation index (ndvi), were extracted from the spatial database and the logistic regression coefficient of each factor was computed. Then the landslide hazard was analysed using the multivariate logistic regression coefficients derived not only from the data for the respective area but also using the logistic regression coefficients calculated from each of the other two areas (nine hazard maps in all) as a cross-validation of the model. For verification of the model, the results of the analyses were then compared with the field-verified landslide locations. Among the three cases of the application of logistic regression coefficient in the same study area, the case of Selangor based on the Selangor logistic regression coefficients showed the highest accuracy (94%), where as Penang based on the Penang coefficients showed the lowest accuracy (86%). Similarly, among the six cases from the cross application of logistic regression coefficient in other two areas, the case of Selangor based on logistic coefficient of Cameron showed highest (90%) prediction accuracy where as the case of Penang based on the Selangor logistic regression coefficients showed the lowest accuracy (79%). Qualitatively, the cross application model yields reasonable results which can be used for preliminary landslide hazard mapping.     

Landsat model for groundwater exploration in Nuba Mountains,Sudan

The region of Nuba Mountains is largely dominated by Precambrian crystalline basement rocks, and often experiences acute shortage of water for domestic and irrigation purposes especially during the dry season (February – May). A Landsat model essentially based on lineament and drainage analysis is proposed to delineate potential target zones for groundwater prospecting. Target zones are indicated by the overlap of the high-intensity lineament contours and the low-intensity drainage contours, and by intersection of a stream channel and lineament structure. Most wells lying within the defined targets are successful.     

Dynamics of total suspended solid concentrations in the lower Raidak river (Himalayan foreland Basin), India

Accurate assessment of total suspended solids (TSS) is one of the most important parameters for the management of health of aquatic ecosystem. Due to the limitation of traditional TSS measurement methods, recently developed remote sensing based algorithm (Wen algorithm) has been used to measure the TSS concentration in a Himalayan foothill river like Raidak. Additionally, to examine the spatiotemporal characteristics of total suspended solids (TSS), total three consecutive years (2019, 2020 and 2021) along with different seasons have been selected. The Nechad algorithm has been used to validate the recalibrated algorithm. The result also indicates that Wen algorithm is also highly consistent with the Nechad algorithm (R² value is greater than 0.90). It is also estimated that the concentration of TSS becomes very high particularly in monsoon months (242 mg/l, 270 mg/l and 246 mg/l for the year 2019, 2020 and 2021). The Raidak river catchment is located in the foothill zone of the lofty Himalayan range and the region is totally influenced by Indian summer monsoon. In the Raidak river course, the seasonal sediment flux is highly correlated with rainfall, stream flow, cross sectional area and high anthropogenic stress. The NDTI (Normalize Difference Turbidity Index) value (Turbidity) is greater in monsoon season than pre and post monsoonal periods (0.035, 0.0851 and 0.0201 for the years 2019, 2020 and 2021 respectively). ANOVA result shows a significant difference among the TSS values in different years (p=<0.05).     

RS-GIS based morphometrical and geological multi-criteria approach to the landslide susceptibility mapping in Gish River Basin,West Bengal,India

Darjeeling Himalaya is one of the several mountainous areas of India which is often suffered from landslide hazards. In this paper, a multi criteria evaluation is applied using 16 morphometric indicators, geology and lineaments to identify the areas vulnerable in respect to drainage and relief conditions. As both drainage and relief parameters exert strong influences on landslide intensity, both the diversity maps are integrated for final landslide susceptibility mapping. The obtained results show that 20.17?sq.?km (7.61%) area within the basin is highly susceptible for landslides, where average drainage density is 3.78?km/sq.?km, relative relief is greater than 408?m and slope is greater than 12°. The validation result shows that very high landslide susceptible zone is associated with very high frequency of landslide occurrence. Beside this, ROC curve also suggests good predicted rate (86.60%) for the model. So, the proposed method can be applied for predicting landslide susceptible zone.     

遥感图像云检测方法综述

光学遥感图像中云层会对地面信息进行不同程度的遮挡,造成了地表观测信息的模糊和缺失,极大地影响遥感图像的成像质量。因此,对遥感图像中云层覆盖的检测和评估是进一步分析和利用遥感图像信息的基础和关键。通过充分的调研和对比总结,梳理了20世纪90年代以来,国内外基于遥感图像的云检测方法的发展趋势和代表性工作。将基于遥感图像的云检测方法分为三类：基于光谱阈值的方法、基于经典机器学习的方法以及基于深度学习的方法。总结了当前国内外云检测公开数据集,并对比了部分代表性工作的云检测精度。此外,简要梳理了与云检测相关的云雾(霾)检测、云雪检测、云阴影检测以及云去除等方法。对当前云检测相关工作中存在的问题和未来的发展趋势进行了分析和展望。     

Anomalous increase of chlorophyll concentrations associated with earthquakes

Recent studies have shown land, ocean, atmosphere and ionospheric anomalies prior to earthquakes. The optical and microwave sensors onboard satellites are now capable of monitoring land, ocean, atmosphere and ionosphere which provide changes associated with natural hazards. In this paper, we have analyzed remote sensing data of the ocean coasts lying near the epicenters of recent four major earthquakes (Gujarat of January 26, 2001, Andaman of September 13, 2002, Algeria of May 21, 2002 and Bam, Iran earthquake of December 26, 2003), our detailed analysis shows increase of Chlorophyll-a (Chl-a) concentration associated with these recent earthquakes. The increase of Chl-a concentration is due to the change in sea surface temperature (SST) associated with the changes in stress regime in the epicentral region which is responsible for modifying the in situ thermal structure of the water and enhancing the upwelling of nutrient-rich water. The increase of Chl-a concentration also shows one to one relation with the increase of surface latent heat flux (SLHF) which is found to increase significantly prior to the earthquake events. Due to cloud cover, it has not been possible to quantify the effect of the chlorophyll concentrations associated with the earthquake events for each successive day during an event. However, the limited data from the adjacent oceanic regions provide strong evidence of the increase in Chl-a concentration. The monitoring of chlorophyll concentrations with higher spatial and temporal resolutions may provide early information about impending coastal earthquakes.     

An early warning system for coastal earthquakes

Earthquakes are very common natural hazards, and every year a few damaging events occur throughout the globe. Satellite remote sensing data are found to be useful in providing information about changes in land, ocean, atmosphere and ionosphere. Recent studies have shown that some of the parameters observed from the remote sensing data are associated with impending coastal earthquakes. An automatic system “CQuake” has been developed to carry out spatial and temporal data mining analysis in real time. CQuake performs retrospective analysis of earthquakes, and performs forecast for predefined regions of the world based on the analysis of Surface latent heat flux (SLHF) or any other geophysical parameters. Details of CQuake, and an example of the Colima earthquake of January 22, 2003, are discussed.     

Computer analysis of the lineaments of Nepal

Various information has been obtained by computer analysis of the lineaments of Nepal. The data were carefully studied and correlated to establish the relationships of lineaments with geological units, structure, mineralization, etc. The lineament map of Nepal was prepared during 1977–1978; the lineament data were digitized in 1982—using a Hewlett Packard system 45 desktop computer—separated by area (east, central and west Nepal) and also according to geological units. By trial and error and using all variables, the different preferred orientation classes were identified for east, central and west Nepal. Lineaments were grouped according to length into less than 10 km, 10–20 km, 20–30 km and more than 30 km, for the entire country and also for major geological units, i.e., Siwalik (sub-Himalaya), Lesser Himalaya (midlands) and Higher Himalaya. Various rose diagrams and histograms showing frequency and lengths of lineaments were plotted for the different geological units and the different sectors. Lineaments associated with granites of major subdivisions were plotted separately and analysed.     

Structural geology mapping using PALSAR data in the Bau gold mining district,Sarawak, Malaysia

The application of optical remote sensing data for geological mapping is difficult in the tropical environment. The persistent cloud coverage, dominated vegetation in the landscape and limited bedrock exposures are constraints imposed by the tropical climate. Structural geology investigations that are searching for epithermal or polymetallic vein-type ore deposits can be developed using Synthetic Aperture Radar (SAR) remote sensing data in tropical/sub-tropical regions. The Bau gold mining district in the State of Sarawak, East Malaysia, on the island of Borneo has been selected for this study. The Bau is a gold field similar to Carlin style gold deposits, but gold mineralization at Bau is much more structurally controlled. Geological analyses coupled with the Phased Array type L-band Synthetic Aperture Radar (PALSAR) remote sensing data were used to detect structural elements associated with gold mineralization. The PALSAR data were used to perform lithological-structural mapping of mineralized zones in the study area and surrounding terrain. Structural elements were detected along the SSW to NNE trend of the Tuban fault zone and Tai Parit fault that corresponds to the areas of occurrence of the gold mineralization in the Bau Limestone. Most of quartz-gold bearing veins occur in high-angle faults, fractures and joints within massive units of the Bau Limestone. The results show that four deformation events (D1–D4) in the structures of the Bau district and structurally controlled gold mineralization indicators, including faults, joints and fractures are detectable using PALSAR data at both regional and district scales. The approach used in this study can be more broadly applicable to provide preliminary information for exploration potentially interesting areas of epithermal or polymetallic vein-type mineralization using the PALSAR data in the tropical/sub-tropical regions.     

Assessment of plant vitality detection through fluorescence and reflectance imagery

Recent studies of the vegetation fluorescence show that it can be successfully used as an intrinsic indicator of plant photosynthetic activity. With respect to the vegetation spectral reflectance, the chlorophyll (Chl) fluorescence is more specific as an observable of basic biophysical processes in the plant cells. Laser induced fluorescence is widely used in near range remote sensing, but it is not suitable for the global monitoring of vegetation. Decades of active fluorometry studies have collected useful information of leaf reaction to natural and anthropogenic stress. Still the passive fluorescence, the one that could be registered from satellite orbit has still to prove its advantage over widely used reflectance signature. The weakness of the signal and the lack of experience with passive fluorescence measurements require extensive technical, theoretical and experimental studies. New imaging fluorometres are to be designed for measuring steady state fluorescence in controlled and natural conditions.

In order to compare reflectance and steady state fluorescence sensitivity to stress impact, a set of experiments have been conducted under controlled illumination conditions in a bio-chamber, designed by the author’s team. The equipment allows plant vitality to be monitored both by passive fluorescence and spectral reflectance imaging. Different types of stress factors (heat and drought stress, acid impact) were investigated to demonstrate equipments ability in monitoring changes of fluorescence signal. Selected fluorescence images of foliage illustrate an early detection of plant dysfunction and the temporal and spatial spreading of the stress impact. Analysis shows that fluorescence imaging of green plants can be developed as a highly effective early warning remote sensing method, which could have application for an ecosystems’ monitoring along with high-spectral reflectance imagery.     

基于光谱信息散度的光谱解混算法

光谱解混是高光谱遥感定量化的关键,提出了一种基于光谱信息散度和光谱混合分析的光谱解混改进算法(SID-SMA,Spectral Information Divergence-Spectral Mixed Analysis).以光谱信息散度判定最优端元子集,端元选择时采用端元的初选和二次选择来提高端元选择的精度,得到较小的丰度估计误差.通过光谱库模拟数据的结果可以看出,SID-SMA的端元选择精度和丰度估计精度要优于其他算法,当信噪比为100∶ 1时,算法端元选择正确率达到了99.8%,29个端元的丰度估计总误差小于0.1,并且算法的速度较快.     

基于卫星遥感的岛礁影像多类目标智能化提取

卫星遥感影像具有背景复杂、目标尺度不一、观测方向各异、纹理不清晰等特点，主流的深度学习目标检测算法不能直接适用于卫星遥感影像的目标检测。改进了RetinaNet，使其适用于卫星遥感影像。首先设计了一种新的特征融合方式，融合ResNet50输出的特征图，使得融合后的特征图同时具有高层语义信息和低层纹理细节信息。为了减弱遥感影像复杂背景对目标特征的影响，设计了特征感知模块，在减弱噪声对特征图影响的同时增强有用特征。挑选DOTA数据集中船只、飞机和存储罐图像进行训练和测试。改进的算法与RetinaNet相比，飞机、船只和存储罐的平均精度分别提高了41%、25%、24%。基于高分二号卫星（GF 2）真实影像数据的试验结果表明，提出的算法能够用于卫星遥感岛礁影像的多类目标智能化提取。     

基于卫星云图的大区域云层预测方法

云层覆盖是影响对地观测卫星成像的一个重要问题,如果遥感图像中云层比例太高,或者特定目标不可见,则遥感图像就会失效。对地观测卫星能够根据云层预测信息,在多个观测目标之间进行选择。面向对地观测卫星任务规划的应用,设计了大区域范围的短期云层预测方法,首先通过光流法获取云运动矢量,然后依据云运动矢量外推获得预测的云层图像,同时引入拉普拉斯算子刻画云层运动过程中的扩散现象,利用风云二号卫星的真实云图序列数据,通过神经网络的反向传播算法优化扩散因子,以提升云层预测的效果。通过对结果进行分析,引入的拉普拉斯算子方法能够提高云层预测的精度,80%分位数的云层覆盖率误差约为11.7%,该精度的云层预测可以用于指导对地观测卫星任务规划。     

Soil moisture mapping over the Chinese Loess Plateau using ENVISAT/ASAR data

In this paper, we used the available algorithm for soil moisture estimation over LOPEX05 (the Loess Plateau land surface process Experiment (2005)) area. The available algorithm used ENVISAT/ASAR AP mode VV polarization observational data at a low incidence angle and ground measured soil moistures. The ground measurements were performed in the summer of the 2005 during the LOPEX05 field campaign. The validated results indicate that an average difference between the soil moistures estimated from the microwave remote sensing and ground measurements is less than 0.02 cm³/cm³, with a RMS error of 2.0%, and a maximum less than 0.04 cm³/cm³. The algorithm was applied to the surface soil moisture mapping later. The results show that this algorithm is suitable for monitoring soil moisture information of the agricultural fields over the Chinese Loess Plateau, when ground land cover situation and the resolution of imagery data are taken into account. However, we also find that there are large differences over the steep slope region, the edge of mesa. The results are not acceptable for surface soil moisture estimation in these regions. Thus, the surface soil moisture retrieval in the steep slope region of the Loess Plateau need to be further investigated in the future.     

The private sector: A global pool of technical talent for remote sensing training and program support

The growing global pool of highly trained, technical talent in the private sector throughout the world reflects the degree of maturation that remote sensing from air and space vehicles has undergone, especially during the past ten years. This has been further spawned by the US Government move to commercialize the Landsat system. The number of qualified technicians and scientists has grown in proportion to the amount of data that has become available for public and private use. Applications of the data have matured from research investigations to operational use in many disciplines and geographic areas. Private and public industry corporations have established their own image processing and analytical laboratories to ensure their competitive positions. This has been further enhanced by the simultaneous availability of lower cost, analytical computer systems. Training and operational project development remain prominent objectives.While these are all very positive aspects a note of caution must be raised. Concern has recently been expressed by representatives of developing nations that not enough attention is given by remote sensing specialists to spelling out the limitations of the sensing systems or qualifying the degree of accuracy of the results obtained. Such criticisms are probably justified and it is recommended that remote sensing workers be mindful of such concerns.     

Advances on Technology and Application of Ocean Color Remote Sensing in China (2004 - 2006)

China has great progress in the technology and application of ocean color remote sensing during 2004-2006. In this report, firstly, four major technical advances are displaying, including (1) the vector radiative transfer numerical model of coupled ocean-atmosphere system; (2) the atmospheric correction algorithm specialized on Chinese high turbid water; (3) systematical research of hyper-spectrum ocean color remote sensing; (4) local algorithms of oceanic parameters, like ocean color components, ocean primary productivity, water transparency, water quality parameters, etc. On the foundation of technical advances, ocean color remote sensing takes effect on ocean environment monitoring, with four major kinds of application systems, namely, (1) the automatic multi-satellites data receiving, processing and application system; (2) the shipboard satellite data receiving and processing system for fishery ground information; (3) Coastal water quality monitoring system, integrating satellite and airborne remote sensing technology and ship measurement; (4) the preliminary system of airborne ocean color remote sensing application system. Finally, the prospective development of Chinese ocean color remote sensing is brought forward. With Chinese second ocean color satellite (HY-1B) orbiting, great strides will take place in Chinese ocean color information accumulation and application.