Prediction of landslides using ASTER imagery and data mining models

The aim of this study was to identify landslide-related factors using only remotely sensed data and to present landslide susceptibility maps using a geographic information system, data-mining models, an artificial neural network (ANN), and an adaptive neuro-fuzzy interface system (ANFIS). Landslide-related factors were identified in Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery. The slope, aspect, and curvature of topographic features were calculated from a digital elevation model that was made using the ASTER imagery. Lineaments, land-cover, and normalized difference vegetative index layers were also extracted from the imagery. Landslide-susceptible areas were analyzed and mapped based on occurrence factors using the ANN and ANFIS. The generalized bell-shaped built-in membership function of the ANFIS was applied to landslide susceptibility mapping. Analytical results were validated using landslide test location data. In the validation results, the ANN model showed 80.42% prediction accuracy and the ANFIS model showed 86.55% prediction accuracy. These results suggest that the ANFIS model has a better performance than does the ANN in predicting landslide susceptibility.     

Modelling the atmospheric boundary layer for remotely sensed estimates of daily evaporation

An energy and moisture balance model of the soil surface was used to estimate daily evaporation from wheat and barley fields in West Germany. The model was calibrated using remotely sensed surface temperature estimates. Complete atmospheric boundary layer models are difficult to use because of the number of parameters involved and a simplified model was used here. The resultant evaporation estimates were compared to eddy correlation evaporation estimates and good agreement was found.     

Approaches to detection of geochemical stress in vegetation

Work has been carried out to elucidate fundamental relationships between spectral properties of plants and geochemical stress, and a programme of field and laboratory work is in progress. The most significant results and conclusions at this stage are described and attention is focused on the new concepts for stress detection which have been generated. The applications of the research are relevant to the understanding of current remotely sensed data as well as relating to ideas for new instrumentation.     

Different atmospheric effects in remote sensing of uniform and nonuniform surfaces

The atmospheric effect on the radiance of sunlight scattered from the Earth-atmosphere system is greatly dependent on the surface reflectance pattern, the contrast between adjacent fields, and the optical properties of the atmosphere. In addition, the atmospheric effect is described by the range and magnitude of the adjacency effects, the atmospheric modulation transfer function, and the apparent spatial resolution of remotely sensed imagery. This paper discusses the atmospheric effect on classification of surface features and shows that surface nonuniformity can be used for developing procedures to remove the atmospheric effect from the satellite imagery.     

Remote sensing based continuous hydrologic modeling

There are two principal ways in which remote sensing can be used with continuous hydrological models: (1) by providing a cost effective way for obtaining input data and (2) by providing synoptic measurements of various state variables. This paper discusses existing hydrologic models and the modifications required to adapt them for using remotely sensed data that may significantly improve their simulation performance. Microwave and thermal infrared measurements show promise for use in hydrologic models because they can measure certain physical properties of the watershed (emissivity and temperature) from which a hydrologic condition can be inferred. Additional applications of remote sensing data include the use of spatial data, mechanisms for extrapolating point data and direct measurement of several hydrologic state variables such as soil moisture, surface temperature, snow water equivalent, frozen soils, and rainfall distribution. Results are presented from several aircraft flights where thermal infrared and microwave data were collected over a small research basin. These results are discussed with respect to their application in continuous hydrologic simulation models.     

ASTER spectral sensitivity of carbonate rocks – Study in Sultanate of Oman

Remote sensing satellite data plays a vital role and capable in detecting minerals and discriminating rock types for explorations of mineral resources and geological studies. Study of spectral absorption characters of remotely sensed data are under consideration by the exploration and mining companies, and demonstrating the spectral absorption characters of carbonates on the cost-effective multispectral image (rather than the hyperspectral, Lidar image) for easy understanding of all geologists and exploration communities of carbonates is very much important. The present work is an integrated study and an outcome of recently published works on the economic important carbonate rocks, includes limestone, marl, listwaenites and carbonatites occurred in parts of the Sultanate of Oman. It demonstrates the spectral sensitivity of such rocks for simple interpretation over satellite data and describes and distinguishes them based on the absorptions of carbonate minerals in the spectral bands of advanced spaceborne thermal emission and reflection radiometer (ASTER) for mapping and exploration studies. The study results that the ASTER spectral band 8 discriminates the carbonate rocks due to the presence of predominantly occurred carbonate minerals; the ASTER band 5 distinguishes the limestones and marls (more hydroxyl clay minerals) from listwaenite (hydrothermally altered rock) due to the presence of altered minerals and the ASTER band 4 detects carbonatites (ultramafic intrusive alkaline rocks) which contain relatively more silicates. The study on the intensity of the total absorptions against the reflections of these rocks shows that the limestones and marls have low intensity in absorptions (and high reflection values) due to the presence of carbonate minerals (calcite and dolomite) occurred in different proportions. The listwaenites and carbonatites have high intensity of absorptions (low reflection values) due to the occurrence of Mn-oxide in listwaenites and carbonates in carbonatites apart the influence of major carbonate minerals that occurred predominantly in these rocks. The study of ASTER thermal infrared (TIR) spectral bands distinguished the marls have low emissivity of energy due to the presence of hydroxyl bearing alumina-silicate minerals from the other rocks such as limestones, listwaenites and carbonatites which have high emissivity due to the absence of hydroxyl bearing alumina-silicate minerals and the presence of carbonate minerals and carbonates.     

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.     

Mineral mapping in the Kap Simpson complex,central East Greenland,using HyMap and ASTER remote sensing data

This research focuses on the application of HyMap airborne hyperspectral data and ASTER satellite multispectral data to mineral exploration and lithologic mapping in the Arctic regions of central East Greenland. The study area is the Kap Simpson complex in central East Greenland. The Kap Simpson complex is one of the largest exposed Palaeogene felsic complexes of East Greenland. It has been the target of several mineral exploration projects. The analysis of the HyMap data produced a detailed picture of the spatial distribution of the alteration minerals in the Kap Simpson complex, unavailable from field-based studies in the area. The analysis of the ASTER data produced mineral maps which due to the moderate spatial and spectral resolution of the ASTER imagery can be useful for reconnaissance level mineral exploration. Colour composites of the mean normalized ASTER thermal bands display lithological information and detected a large felsic igneous intrusion that has not been shown on the recently compiled geological maps of the area. The results of this research have considerable potential to evaluate the use of hyperspectral and multispectral remote sensing for geological purposes in the Arctic regions of central East Greenland.     

Environmental degradation analysis using NOAA/AVHRR data

This paper proposes a particular approach to assess information about soil degradation, based on a methodology to calculate soil color from NOAA/AVHRR data. As erosive processes change physical and chemical properties of the soil, altering, consequently, the superficial color, monitoring the change in color over time can help to identify and analyze those processes. A relationship among the soil color (described in the Munsell Color System, i.e., in terms of Hue, Value and Chroma), vegetation indices, surface temperature and emissivity has been established, which is based on the theoretical model. The methodology has three main phases: determination of the regression models among soil color and vegetation indices, emissivity and surface temperature; generation of digital soil color models; and statistical evaluation of the estimated color. The tests showed that the methodology is efficient in determining soil color using the various vegetation indices (i.e., Normalized vegetation index NDVI, Modified soil adjusted vegetation index MSAVI). One vegetation index, i.e., Purified adjusted vegetation index (PAVI) is proposed to subsidies the effect of vegetation over the soil. Best results were obtained for the Hue color component. To further test the methodology, the estimated digital color models were compared with the characteristic color of soil classes in the test area. The results of this application confirmed the methodology’s capacity to determine the soil color from NOAA/AVHRR data. This type of study is quite helpful to know the erosion of soil as well as some abrupt change in soil due to natural hazards by space borne or air-borne sensors.     

Remotely sensed thermal information applied to urban climate analysis

Urban heat island (UHI) effects on the ten most populated cities of Hungary, including the Budapest agglomeration area, were analyzed using remotely sensed data. Day- and night-time surface temperature time series observed by sensor MODIS on satellite Terra were used to determine UHI intensities. UHI intensity was defined as the difference in spatially averaged surface temperatures between urban and surrounding rural pixels. Spatial structures of UHIs were determined and compared for different seasons and macrocirculation conditions.     

An assessment of semi-analytical models based on the absorption coefficient in retrieving the chlorophyll-a concentration from a reservoir

Monitoring chlorophyll-a (Chl-a) concentrations in inland waters is crucial for water quality management, since Chl-a is a proxy for phytoplankton biomass and, thus, for ecological health of a water environment. Chl-a concentration can be retrieved through the inherent optical properties (IOPs) of a water system, which, in turn, can be remotely sensed obtained. Quasi-analytical algorithm (QAA), originally developed for ocean waters, can also retrieve IOPs for inland waters after re-parameterizations. This study is aimed at assessing the performance of sixteen schemes composed by QAA original and re-parameterized versions followed by models that use absorption coefficients as inputs for estimating Chl-a concentration in Ibitinga reservoir, located at Tietê River cascading system, São Paulo State, Brazil. It was verified that only QAA_V5 based schemes were able to obtain reasonable estimates for image data and that by four models tested presented similar and acceptable results for QAA_V5 outputs. The best model were applied to a Ocean and Land Colour Instrument (OLCI) image. Light absorption in the reservoir showed to be dominated by colored dissolved organic matter (CDOM), and wide spatial and temporal variability of optical and water quality properties was observed.     

基于免疫抗体编码机理的卫星图像云探测

针对卫星遥感图像中云与下垫面的复杂性和多样性,提出了一种新的在复杂背景下常用目标图像描述方法.通过类比生物免疫抗体特异性与其构成单元氨基酸性质的相关性,得到目标图像的免疫基元集合形式及其分类方法.借鉴生物免疫抗体编码顺序中氨基酸结合能量最小原则,统计分析出训练样本的免疫基元亲和度计算公式,实现了目标图像描述的有限多特征优化组合.对于云检测问题,最终提出云的免疫抗体编码方法,定义了云的免疫亲和度计算公式,并成功构建了云免疫抗体.经200幅IKONOS卫星图像测试,验证了该方法在识别率和运行时间效率方面的有效性.      

Built-up land mapping capabilities of the ASTER and Landsat ETM+ sensors in coastal areas of southeastern China

Worldwide urbanization has accelerated expansion of urban built-up lands and resulted in substantial negative impacts on the global environments. Precisely measuring the urban sprawl is becoming an increasing need. Among the satellite-based earth observation systems, the Landsat and ASTER data are most suitable for mesoscale measurements of urban changes. Nevertheless, to date the difference in the capability of mapping built-up land between the two sensors is not clear. Therefore, this study compared the performances of the Landsat-7 ETM+ and ASTER sensors for built-up land mapping in the coastal areas of southeastern China. The comparison was implemented on three date-coincident image pairs and achieved by using three approaches, including per-band-based, index-based, and classification-based comparisons. The index used is the Index-based Built-up Index (IBI), while the classification algorithm employed is the Support Vector Machine (SVM). Results show that in the study areas, ETM+ and ASTER have an overall similar performance in built-up land mapping but also differ in several aspects. The IBI values determined from ASTER were consistently higher than from ETM+ by up to 45.54% according to percentage difference. The ASTER also estimates more built-up land area than ETM+ by 5.9–6.3% estimated with the IBI-based approach or 3.9–6.1% with the SVM classification. The differences in the spectral response functions and spatial resolution between relative spectral bands of the two sensors are attributed to these different performances.     

Understanding plant-soil relationships using controlled environment facilities.

Although soil is a component of terrestrial ecosystems, it is comprised of a complex web of interacting organisms, and therefore can be considered itself as an ecosystem. Soil microflora and fauna derive energy from plants and plant residues and serve important functions in maintaining soil physical and chemical properties, thereby affecting net primary productivity (NPP), and in the case of contained environments, the quality of the life support system. We have been using 3 controlled-environment facilities (CEF's) that incorporate different levels of soil biological complexity and environmental control, and differ in their resemblance to natural ecosystems, to study relationships among plant physiology, soil ecology, fluxes of minerals and nutrients, and overall ecosystem function. The simplest system utilizes growth chambers and specialized root chambers with organic-less media to study the physiology of plant-mycorrhizal associations. A second system incorporates natural soil in open-top chambers to study soil bacterial and fungal population response to stress. The most complex CEF incorporates reconstructed soil profiles in a "constructed" ecosystem, enabling close examination of the soil foodweb. Our results show that closed ecosystem research is important for understanding mechanisms of response to ecosystem stresses. In addition, responses observed at one level of biological complexity may not allow prediction of response at a different level of biological complexity. In closed life support systems, incorporating soil foodwebs will require less artificial manipulation to maintain system stability and sustainability.     

Study of changes in the lineament structure,caused by earthquakes in South America by applying the lineament analysis to the Aster (Terra) satellite data

The region between Southern Peru and Northern Chile is one of the most seismically and volcanically active regions in South America. This is caused by a constant subduction of the South American Plate, converging with the Nazca Plate in the extreme North of Chile. We used the 15 and 30 m resolution satellite images, provided by the ASTER (VNIR and SWIR) instrument onboard the Terra satellite to study changes in the geological faults close to earthquake epicenters in southern Peru. Visible and infrared spectral bands were analysed using “The Lineament Extraction and Stripes Statistic Analysis” (LESSA) software package to examine changes in the lineament features and stripe density fields caused by seismic activity. We used the satellite images 128 and 48 days before and 73 days after a 5.2 Richter scale magnitude earthquake. The fact that the seasonal variations in the South of Peru and North of Chile are very small, and the vegetation is very limited, allowed us to establish substantial changes in the lineament and the stripe density field features. We develop a methodology that allows to evaluate the seismic risk in this region for the future.     

Multi-seasonal evaluation of hybrid poplar (P. Deltoides) plantations using Worldview-3 imagery and State-Of-The-Art ensemble learning algorithms

Forest resources are the primary components of the ecosystem environment. Poplars (Populus sp.), a member of the fast-growing trees, are one of the most productive forest tree species for industrial production thanks to their desirable traits comprising rapid growth, hybridization ability, and ease of propagation. Determining poplar cultivated areas and mapping their geographical distributions is critical for planners and decision-makers to increase the ecological and economic benefits of poplar trees. Due to the biodiversity of each geographical region and seasonal vegetation variations, classification based on remotely sensed imagery is essential for cropland monitoring. The main goal of this study is to evaluate the potential of high-resolution multi-temporal (growing season and end of the growing season) Worldview-3 imagery in mapping poplar plantations in the Akyaz? district of Sakarya, Turkey. For this purpose, pixel- and object-based image analysis with up-to-date ensemble learning algorithms, namely random forest (RF), categorical boosting (CB), and extreme gradient boosting tree (XGB), were employed for mapping poplar fields. Results indicated that the object-based classification approach provided statistically significant improvements in map-level (about 4%) and class-level accuracy (e.g., approximately 7% and %2 for poplar and young poplar classes, respectively) than pixel-based classification. While the CB performed superior classification performance for the object-based approach (92.56%), the highest classification performance was obtained with the XGB algorithm for the pixel-based approach (90.42%) for the end of the growing season data. McNemar’s statistical test also confirmed that the performances of CB and XGB algorithms were statistically similar in pixel-based classification. Finally, analysis of multi-season images revealed that sensitivity of the vegetation phenology and seasonal effects considerably affect the separability of poplar tree species.     

Field-based landcover classification using TerraSAR-X texture analysis

The present study aims to evaluate the field-based approach for the classification of landcover using high-resolution SAR data. TerraSAR-X (TSX) strip mode imagery, coupled with digital ortho-photos (DOPs) with 20 cm spatial resolution was used for landcover classification and parcel mapping respectively. Different filtering and analysis techniques were applied to extract textural information from the TSX image in order to assess the enhancement of the classification accuracy. Several attributes of parcels were derived from the available TSX images in order to define the most suitable parameters discriminating between different landcover types. Then, these attributes were further statistically analysed in order to define separability and thresholds between different landcover types. The results showed that textural analysis resulted in high classification accuracy. Hence, this paper confirms that integrated landcover classification using the textural information of TerraSAR-X has a high potential for landcover mapping.     

Quality assessment by region in spot images fused by means dual-tree complex wavelet transform

This work is motivated in providing and evaluating a fusion algorithm of remotely sensed images, i.e. the fusion of a high spatial resolution panchromatic image with a multi-spectral image (also known as pansharpening) using the dual-tree complex wavelet transform (DT-CWT), an effective approach for conducting an analytic and oversampled wavelet transform to reduce aliasing, and in turn reduce shift dependence of the wavelet transform. The proposed scheme includes the definition of a model to establish how information will be extracted from the PAN band and how that information will be injected into the MS bands with low spatial resolution. The approach was applied to Spot 5 images where there are bands falling outside PAN’s spectrum. We propose an optional step in the quality evaluation protocol, which is to study the quality of the merger by regions, where each region represents a specific feature of the image. The results show that DT-CWT based approach offers good spatial quality while retaining the spectral information of original images, case SPOT 5. The additional step facilitates the identification of the most affected regions by the fusion process.     

Land use/land cover classification using time series Landsat 8 images in a heavily urbanized area

It is of great significance to timely, accurately, and effectively monitor land use/cover in city regions for the reasonable development and utilization of urban land resources. The remotely sensed dynamic monitoring of Land use/land cover (LULC) in rapidly developing city regions has increasingly depended on remote-sensing data at high temporal and spatial resolutions. However, due to the influence of revisiting periods and weather, it is difficult to acquire enough time-series images with high quality at both high temporal and spatial resolution from the same sensor. In this paper we used the temporal-spatial fusion model ESTARFM (Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model) to blend Landsat8 and MODIS data and obtain time-series Landsat8 images. Then, land cover information is extracted using an object-based classification method. In this study, the proposed method is validated by a case study of the Changsha City. The results show that the overall accuracy and Kappa coefficient were 94.38% and 0.88, respectively, and the user/producer accuracies of vegetation types were all over 85%. Our approach provides an accurate and efficient technical method for the effective extraction of land use/cover information in the highly heterogeneous regions.     

Multiple sensor geocoded data

The problem of integrating remotely sensed images acquired from different sensor systems is primarily associated with their registration into a common reference projection. This registration problem has to be considered not only between the image data themselves but also with other data already existing in various geographical data bases.The advent of the second generation of earth observation satellites is emphasizing this situation by providing imagery in different geometric configurations. Images will be acquired from many sensors at different spatial resolutions (LANDSAT MSS and TM, SPOT HRV MLA and PLA) on different dates and modes (panchromatic, multispectral, nadir and off-nadir).In order to provide image data in a geometric format compatible with the Canadian National Topographic System, Canada is developing a Multi-Observational Satellite Image Correction System (MOSAICS) that will provide geocoded data resampled into a common reference projection. This paper presents the characteristics of the geocoded products with examples from multiple sensors and different dates, and addresses the processing requirements of the satellite ground station. It also illustrates the use of digital processing techniques to derive digital elevation information from geocoded stereo imagery.