DEM

Remote sensing data can be effectively used as a mean to build geological knowledge for poorly mapped terrains. Spectral remote sensing data from space- and air-borne sensors have been widely used to geological mapping, especially in areas of high outcrop density in arid regions. However, spectral remote sensing information by itself cannot be efficiently used for a comprehensive lithological classification of an area due to (1) diagnostic spectral response of a rock within an image pixel is conditioned by several factors including the atmospheric effects, spectral and spatial resolution of the image, sub-pixel level heterogeneity in chemical and mineralogical composition of the rock, presence of soil and vegetation cover; (2) only surface information and is therefore highly sensitive to the noise due to weathering, soil cover, and vegetation. Consequently, for efficient lithological classification, spectral remote sensing data needs to be supplemented with other remote sensing datasets that provide geomorphological and subsurface geological information, such as digital topographic model (DEM) and aeromagnetic data. Each of the datasets contain significant information about geology that, in conjunction, can potentially be used for automated lithological classification using supervised machine learning algorithms. In this study, support vector machine (SVM), which is a kernel-based supervised learning method, was applied to automated lithological classification of a study area in northwestern India using remote sensing data, namely, ASTER, DEM and aeromagnetic data. Several digital image processing techniques were used to produce derivative datasets that contained enhanced information relevant to lithological discrimination. A series of SVMs (trained using k-folder cross-validation with grid search) were tested using various combinations of input datasets selected from among 50 datasets including the original 14 ASTER bands and 36 derivative datasets (including 14 principal component bands, 14 independent component bands, 3 band ratios, 3 DEM derivatives: slope/curvatureroughness and 2 aeromagnetic derivatives: mean and variance of susceptibility) extracted from the ASTER, DEM and aeromagnetic data, in order to determine the optimal inputs that provide the highest classification accuracy. It was found that a combination of ASTER-derived independent components, principal components and band ratios, DEM-derived slope, curvature and roughness, and aeromagnetic-derived mean and variance of magnetic susceptibility provide the highest classification accuracy of 93.4% on independent test samples. A comparison of the classification results of the SVM with those of maximum likelihood (84.9%) and minimum distance (38.4%) classifiers clearly show that the SVM algorithm returns much higher classification accuracy. Therefore, the SVM method can be used to produce quick and reliable geological maps from scarce geological information, which is still the case with many under-developed frontier regions of the world.

Sand dunes encroachment is a challenge that faces land development in arid zone countries. Movement of such dunes threatens cultivated lands, roads, and urban settlements. This study modeled the potential risk of sand dunes encroachment related to their terrain characteristics in Sinai Peninsula, Egypt. The Food and Agricultural Organization’s land cover map of Egypt together with SPOT4 imageries was used to identify the sand dunes bodies from the desert of Sinai. Four parameters were derived from Shuttle Radar Topography Mission data, namely the elevation, slope, aspect, and relative moisture index. Ten-year average data for prevalent wind direction and speed were used. A weighted linear combination model was applied using the six parameters as criteria maps. The output is a sand dunes risk index map which was overlaid with some urban features such as cities, highways and airports. The result identified those urban features vulnerable to sand dunes encroachment risk. Such a risk map is essential for land use planning and environmental management.

In tropical areas, mass movements are common phenomena, especially during periods of heavy rainfall, which frequently take place in the summer season. These phenomena have caused loss of life and serious damage to infrastructure and properties. The most prominent of these phenomena are landslides that can produce debris flows. Thus, this article aims at determining affected areas using a model to predict landslide prone areas (SHALSTAB) combined with an empirical model designed to define the debris flow travel distance and area of deposition. The methodology of this work consists of the following steps: (a) elaboration of a digital elevation model (DEM), (b) application of the deterministic SHALSTAB model to locate the landslide prone areas, (c) identification of the debris flow travel distance and area of deposition, and (d) mapping of the affected areas (landslides and debris flows). This work was developed in an area in which many mass movements occurred after intense rainfall during the summer season (February 1996) in the state of Rio de Janeiro, southeast Brazil. All of the scars produced by that event were mapped, allowing for validation of the applied models. The model results show that the mapped landslide locations can adequately be simulated by the model.

Rainfall data in continuous space provide an essential input for most hydrological and water resources planning studies. Spatial distribution of rainfall is usually estimated using ground-based point rainfall data from sparsely positioned rain-gauge stations in a rain-gauge network. Kriging has become a widely used interpolation method to estimate the spatial distribution of climate variables including rainfall. The objective of this study is to evaluate three geostatistical (ordinary kriging (OK), ordinary cokriging (OCK), kriging with an external drift (KED)) and two deterministic (inverse distance weighting, radial basis function) interpolation methods for enhanced spatial interpolation of monthly rainfall in the Middle Yarra River catchment and the Ovens River catchment in Victoria, Australia. Historical rainfall records from existing rain-gauge stations of the catchments during 1980-2012 period are used for the analysis. A digital elevation model of each catchment is used as the supplementary information in addition to rainfall for the OCK and KED methods. The prediction performance of the adopted interpolation methods is assessed through cross-validation. Results indicate that the geostatistical methods outperform the deterministic methods for spatial interpolation of rainfall. Results also indicate that among the geostatistical methods, the OCK method is found to be the best interpolator for estimating spatial rainfall distribution in both the catchments with the lowest prediction error between the observed and estimated monthly rainfall. Thus, this study demonstrates that the use of elevation as an auxiliary variable in addition to rainfall data in the geostatistical framework can significantly enhance the estimation of rainfall over a catchment.

Flood is considered to be the most common natural disaster worldwide during the last decades. The study area Sipajhar Development Block of Darrang district of Assam is prone to flood and bank erosion because of its topographical and hydrological characteristics. The three main river passing through this region namely Nonoi, Barnadi and Saktola create high magnitude flood due to its high volume of water in every year. The river Brahmaputra flowing in the southern part of the area also create flood year by year which plays significant impact upon the existing socio economic conditions of the people. Through flood hazard zoning the losses can be prevented and reduced by providing reliable information to the public about the flood risk. The Flood hazard zonation map for the study area has been prepared by weighted overlay analysis method where the DEM and other layers such as drainage, soil, geology, landuse etc. are taken to analyse in GIS environment. The maximum flood inundated villages were integrated for assessing the frequency of inundation and subsequent generation of the hazard layer. The main objectives of this study are to mapping the flood hazard zone which is utmost need for management and mitigation of flood.

Managing natural resources in wide-scale areas can be highly time and resource consuming task which requires significant amount of data collection in the field and reduction of the data in the office to provide the necessary information. High performance LiDAR remote sensing technology has recently become an effective tool for use in applications of natural resources. In the field of forestry, the LiDAR measurements of the forested areas can provide high-quality data on three-dimensional characterizations of forest structures. Besides, LiDAR data can be used to provide very high quality and accurate Digital Elevation Model (DEM) for the forested areas. This study presents the progress and opportunities of using LiDAR remote sensing technology in various forestry applications. The results indicate that LiDAR based forest structure data and high-resolution DEMs can be used in wide-scale forestry activities such as stand characterizations, forest inventory and management, fire behaviour modeling, and forest operations.