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Remote Sensing And Image Interpretation

応用水文 = Applied hydrology

Multi-temporal Landsat images from Landsat 5 Thematic Mapper (TM) acquired in 1993, 1998, 2003 and 2008 and Landsat 8 Operational Land Imager (OLI) from 2017, are used for analysing and predicting the spatio-temporal distributions of land use/land cover (LULC) categories in the Halgurd-Sakran Core Zone (HSCZ) of the National Park in the Kurdistan region of Iraq. The aim of this article was to explore the LULC dynamics in the HSCZ to assess where LULC changes are expected to occur under two different business-as-usual (BAU) assumptions. Two scenarios have been assumed in the present study. The first scenario, addresses the BAU assumption to show what would happen if the past trend in 1993–1998–2003 has continued until 2023 under continuing the United Nations (UN) sanctions against Iraq and particularly Kurdistan region, which extended from 1990 to 2003. Whereas, the second scenario represents the BAU assumption to show what would happen if the past trend in 2003–2008–2017 has to continue until 2023, viz. after the end of UN sanctions. Future land use changes are simulated to the year 2023 using a Cellular Automata (CA)-Markov chain model under two different scenarios (Iraq under siege and Iraq after siege). Four LULC classes were classified from Landsat using Random Forest (RF). Their accuracy was evaluated using κ and overall accuracy. The CA-Markov chain method in TerrSet is applied based on the past trends of the land use changes from 1993 to 1998 for the first scenario and from 2003 to 2008 for the second scenario. Based on this model, predicted land use maps for the 2023 are generated. Changes between two BAU scenarios under two different conditions have been quantitatively as well as spatially analysed. Overall, the results suggest a trend towards stable and homogeneous areas in the next 6 years as shown in the second scenario. This situation will have positive implication on the park.

Predicting Land Use/Land Cover Changes Using a CA-Markov Model under Two Different Scenarios

Land Use Change

Index-based groundwater vulnerability mapping models using hydrogeological settings: A critical evaluation

Land-use/land-cover (LU/LC) change is an important component of global environmental change. The need to understand LU/LC change is essential for regional development and land use management towards sustainable development. To understand LU/LC change, the different LU/LC categories and their spatial as well as temporal variability in Tiruchirappalli city has been studied over a period of eight years (1998–2006), using the analysis of Indian Remote Sensing Satellite (IRS) images. In this, an attempt was made to adopt Markov model for obtaining and understanding LU/LC dynamics. Model performance was evaluated between the empirical LU/LC map extracted from CARTOSAT-1 PAN image and the simulated LU/LC map obtained from the Markov model. The future landscape distribution in the year 2014 and 2022 was derived using a Markov model. This result shows that Markov model and geospatial technology together are able to effectively capture the spatio-temporal trend in the landscape pattern associated with urbanization ...

https://www.tandfonline.com/doi/pdf/10.1080/19475705.2013.795502

Land use change modelling using a Markov model and remote sensing

Groundwater has been treated as an important source of water supply due to its relatively low vulnerability to pollution in comparison to surface water, and its huge storage capacity. Because of the known health and economic impacts associated with groundwater contamination, steps to measure the vulnerability of groundwater must be taken for sustainable groundwater protection and management planning. Susceptibility of groundwater refers to the intrinsic characteristics that determine the sensitivity of the water to being adversely affected by an imposed contaminant load. The DRASTIC model is the most extensively used method for identifying the areas where groundwater supplies are most vulnerable to contamination. In this study the DRASTIC model is applied for a part of Kancheepuram district, Tamil Nadu, India, to generate a small-scale map of groundwater vulnerability to contamination. The whole area is classified on a scale of very low, low, moderate and high susceptibility to pollution. The model is considered in relation to groundwater quality data and results have shown a strong relationship between DRASTIC specific vulnerability index and nitrate-as-nitrogen concentrations. A groundwater vulnerability map is developed by using the DRASTIC model in a computer based Geographic Information System. The results show that the central part of the study area is classified as a high vulnerable zone and the south and northeastern parts show medium vulnerable zones, and record higher nitrate values.

GIS Based Assessment of Groundwater Vulnerability Using Drastic Model

Groundwater has been treated as an important source of water supply due to its relatively low vulnerability to pollution in comparison to surface water, and its huge storage capacity. Because of the known health and economic impacts associated with groundwater contamination, steps to measure the vulnerability of groundwater must be taken for sustainable groundwater protection and management planning. Susceptibility of groundwater refers to the intrinsic characteristics that determine the sensitivity of the water to being adversely affected by an imposed contaminant load. The SINTACS model is the most extensively used method for identifying the areas where groundwater supplies are most vulnerable to contamination. The SINTACS model uses seven environmental parameters (water table depth, effective infiltration, unsaturated conditions, soil media, aquifer hydrogeologic characteristics, hydraulic conductivity and topographic slope) to characterize the hydrogeological setting and evaluate aquifer vulnerability...

Groundwater vulnerability assessment using SINTACS model

Health care is one of the most important facility and it helps to improve the quality of life and social welfare of modern society. Recent advances in the field of health geography have greatly improved our understanding of the role played by geographic distribution of health services in population health maintenance. This study presents a two stages methodology to evaluate the existing locations of healthcare facilities in Thiruverumbur block, Tiruchirappalli district of Tamil Nadu, India and includes to identify the optimal site for new healthcare facility. Firstly, the Spatial Accessibility index to healthcare facilities are measured using Three Step Floating Catchment Area (3SFCA) in a geospatial framework, considering three variables; namely, attractiveness of healthcare facilities, travel time or distance between the locations of the service centre and the residence, and population demand for healthcare facilities. This index would define the disparity region in the healthcare accessibility in the block. Secondly, the Multi-criteria decision analysis would be developed to find optimal sites for new healthcare facility in the deprived area. The analysis also includes road network analysis, to determine the closest facility and shortest route to these healthcare facilities from the population. The study is thus, recommended as a spatial decision support system for urban policy makers regarding accessibility of healthcare facilities in the urban and rural area.

Accessibility Analysis of Health care facility using Geospatial Techniques

Flood is ranked as the deadliest natural disaster that has been experienced by the urban basins in the world. Its detrimental effects can be minimized by appropriate modeling, analysis and management methods. Such modeling and analysis techniques help in flood risk assessment predicting flood occurrence, aid in the emergency preparation for evacuation and reduce damage from the impact of floods. Numerous modeling techniques are available for analyzing flood events, of which HEC-HMS software is chosen for this explorative study because of its simplicity and as it is a freely available open-source software. The present study aims to develop a rainfall–runoff simulation model by generating peak flow and volume of the extreme rainfall event that occurred on 22 November 1999 in the ungauged Koraiyar basin located south of Tiruchirappalli City in South India. The hydrographs are generated for the basin by using specified hyetograph and frequency storm method to identify the best method to be adopted in the study. Digital elevation model processed with geographic information system (GIS) and HEC-Geo HMS, which is an extension of GIS, is used for the analysis. Using the terrain processing tools in ArcGIS, the basin delineation and parameters such as slope and river length are extracted from the basin. The data generated during the HEC-Geo HMS process are the hydrologic parameters of Koraiyar basin, and it is imported to HEC-HMS modeling for generating peak flow and volume. In the modeling process, HEC-HMS has three modules, namely transform, loss and base flow. SCS curve number and SCS unit hydrograph are used to determine the losses and transformation of rainfall into the runoff process in the present study. The SCS method is adopted because of its simplicity and requirement of limited data approach for modeling. The peak flow and volume prepared from the model are compared with the standard Nash–Sutcliffe values. The frequency storm method has a Nash value of 0.7, which is higher than the value obtained from the specified hyetograph process, and it is chosen as a better model for generating flood peak and volume for different return periods in the basin. It can therefore be adopted for other studies of similar basin conditions.

Nisha Radhakrishnan

Papers

Land use change modelling using a Markov model and remote sensing

GIS Based Assessment of Groundwater Vulnerability Using Drastic Model

Groundwater vulnerability assessment using SINTACS model

Accessibility Analysis of Health care facility using Geospatial Techniques

Simulation of extreme event-based rainfall–runoff process of an urban catchment area using HEC-HMS