Remote Sensing Satellite Linear Imaging Research Articles

An improved coupled framework for Glacier classification: an integration of optical and thermal infrared remote-sensing bands

ABSTRACTThis study evaluates the applicability of optical and thermal infrared (TIR) bands by the applications of two different satellite sensors for characterizing debris covers of Zemu glacier situated in the North Sikkim Himalayas India. Differentiating supraglacial debris (SGD), clean ice, and ice-covered glaciers from periglacial debris (PGD) cover is a challenging task due to similarities between their spectra. Previous studies suggest that the difference between thermal and optical properties can be used in the separation of SGD (part of the glacier boundary) and PGD (outside the glacier boundary) accurately. Thus a novel multi-criteria approach has been constructed to characterize the glacier cover utilizing Maximum Likelihood Classifier (MLC) and hybrid classification (HC) methods. Based on the integration of optical and TIR bands from two satellite systems such as Landsat and Indian Remote Sensing Satellite Linear Imaging Spectral Scanner 3, total six different image combinations were prepared to explore glacier features under various mathematical and image processing operations. Each image band combination represents a distinctive characterization of the debris cover of Zemu glacier. The spatial-spectral profiles of glacial features (spectral signatures) were drawn to distinguish SGD and PGD. The accuracy assessment was performed to test the correctness of classified maps resulted from MLC and HC methods. The HC maps show a larger efficiency (near to 90%) than MLC maps (near to 85%). The outcomes offered better understandings in the classification of SGD and PGD by using the combined products of optical and TIR remote-sensing sensors. The ground-based observations such as GPS points, field photographs of the Zemu glacier topography, and high-resolution Google Earth images helped to reduce the misclassification and improved the classification accuracy. Furthermore, a cloud removal method was applied for TIR bands to improve the accuracy of the glacier classification. The glacier features and their area statistics have been compared to temporal time scales, illustrating annual fluctuations in glacial characteristics.

Rainfall-runoff modelling of Doddahalla watershed—an application of HEC-HMS and SCN-CN in ungauged agricultural watershed

The scarcity of reliable rainfall-runoff recorded data in Doddahalla watershed is a serious problem for the analysis of the hydrology of watersheds. Sustainable management of the available water in this area is only possible when there was sound information on the rainfall-runoff and other hydrological determinants that influence the water resource. Considering the current problem rainfall-runoff. simulation is carried out using the HEC-HMS hydrological simulation model with integrated use of remote sensing and GIS. Tropical Rainfall Measuring Mission 3 hourly and Indian Meteorological Department daily rainfall datasets are utilised. Cartosat-1 CartoDEM (30 m) was used to delineate the sub-watershed and generate the stream network. Geospatial Hydrologic Modeling Extension (HEC-GeoHMS), along with ArcHydro extension in ArcGIS 9.3 utilised to create the input file for use in HEC-HMS. Indian Remote Sensing Satellite Linear Imaging and Self Scanning sensors (LISS-III, 24 m) and Survey of India (SOI) toposheet are used to prepare the soil and land use map. All the data are georectified and reprojected to Geographic Coordinate System-World Geodetic System 1984 (GCS WGS) Universal Transverse Mercator (UTM) zone 43 North for the easy handling in GIS environment. SCS-CN loss model and SCS unit hydrograph as a transform method was applied to simulate the excess storm water to direct runoff in the watershed. The Muskingum-Cunge model used as channel routing. The model is validated by using field observation data and discharge data from the neighbouring Hoovinahole watershed. The results of simulated and observed stream flow show greater confidence and the reliability of the model. Similar procedure and calibration parameters applied to the ungauged Doddahalla watershed for estimating the rainfall-runoff. The research result gives a general idea regarding the stream flow, peak flow and velocity of the peak flow. The present study concludes that the stimulated result can be useful for the water and land resource planning and management practice in the Doddahalla watershed. The models can be best utilised in ungauged watershed and water scarce region where the monitored data are limited, and runoff estimation is mandatory to sustain the water resources.

Metric Based on Morphological Dilation for the Detection of Spatially Significant Zones

The ability to derive spatially significant zones (e.g., water bodies and zones of influence) within a cluster of zones has interesting applications in understanding commonly sharing physical mechanisms. Using a morphological dilation distance technique, we introduce geometric-based criteria that serve as indicator of the spatial significance of zones within a cluster of zones. This letter focuses on the problem of identifying zones that are “strategic” in the sense that they are the most central or important based on their proximity to other zones. We have applied this technique to a task aiming at detecting a spatially significant water body from a cluster of water bodies retrieved from Indian Remote Sensing Satellite Linear Imaging Self-scanning Sensor (IRS LISS-III) multispectral satellite data.

Evaluation of spatial upscaling algorithms for different land cover types

Upscaling of 36.25 m resolution Indian Remote Sensing Satellite Linear Imaging Self Scanning (LISS-II) sensor data using 2 × 2 kernel and correlating it with the simultaneously observed 72.5 m resolution LISS-I data gave better results for mean and fractal methods than median method for mixed crop and forest cover types; local fractal dimension method gave high correlation for shallow water and poor correlation for deep water. Use of global fractal dimension significantly improved the correlation for deep water. With 36.25 m base data, upscaling was done to 978.75 m using Mean, Median, K-average, Point Spread Function and Fractal methods. Fractal method gave consistent and better results for upscaling.