Praveen Kumar Rai

A remote sensing aided multi-layer perceptron-Markov chain analysis for land use and land cover change prediction in Patna district (Bihar), India

Land use and land cover (LULC) changes are recognized as one of the most significant driver of environmental changes, mainly due to rapid urbanization. In this paper, an attempt has been made to appraise the ability of multi-layer perceptron-Markov chain analysis (MLP-MCA) integrated method to monitor and predict the future LULC change scenarios in Patna district, Bihar using remote sensing images. A supervised maximum likelihood classification method was applied to derive LULC maps from 1988, 2001, and 2013 Landsat Thematic Mapper (TM)/Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) images, respectively. The LULC maps of 1988 and 2001 were employed to predict the LULC scenario for 2013 using MLP-MCA method. The predicted result was compared with the observed LULC map of 2013 to validate the method using kappa index statistics. Finally, based on the results, the future LULC change prediction for 2038 and 2050 was performed. The outcomes of this study reveal the rapid growth in ​built up area results in continuous decrease in agricultural lands.

Ground Water in the City of Varanasi, India: present status and prospects

Ground Water in the City of Varanasi, India: present status and prospects The city of Varanasi is short of water. The city obtains a total of 270 million litres water from the river Ganga and tubewells. Yet every fifth citizen lacks drinking water. The ground water is polluted due to nitrate and faecal coliform. A further problem is the plan to settle the growing population in a new township nearby under the integrated development plan of Greater Varanasi, a part of the Jawajarlal Nehru Urban Renewal Mission. To fulfill the growing demand of fresh water, new water bearing horizon of the most affected part of the city i.e. southern part is to be identified. This paper reports a study of the variation in the grain size attributes of an aquifer material taken from different depths from the affected region in order to establish the generalized hydrological properties and recommend the depth of the well accordingly. From the grain size analysis and hydrological study it may be concluded that water bearing zones are mainly found in three horizons at the depths 44-56 m; 56-87 m; and 87-165 m. The third water bearing horizon (total thickness being 78 m) can act as a good potential ground water horizon for a new township. Due to its greater depth, the water would be relatively fresh being characterized by very low concentration of dissolved solids. Therefore, this horizon is strongly recommended for utilizing the water resource for the township.

Using the information value method in a geographic information system and remote sensing for malaria mapping: a case study from India.

BACKGROUND This paper explores the scope of malaria-susceptibility modelling to predict malaria occurrence in an area. OBJECTIVE An attempt has been made in Varanasi district, India, to evaluate the status of malaria disease and to develop a model by which malaria-prone zones could be predicted using five classes of relative malaria susceptibility, i.e.very low, low, moderate, high and very high categories. The information value (Info Val) method was used to assess malaria occurrence and various time-were used as the independent variables. A geographical information system (GIS) is employed to investigate associations between such variables and distribution of different mosquitoes responsible for malaria transmission. Accurate prediction of risk depends on a number of variables, such as land use, NDVI, climatic factors, population, distance to health centres, ponds, streams and roads etc., all of which have an influence on malaria transmission or reporting. Climatic factors, particularly rainfall, temperature and relative humidity, are known to have a major influence on the biology of mosquitoes. To produce a malaria-susceptibility map using this method, weightings are calculated for various classes in each group. The groups are then superimposed to prepare a Malaria Susceptibility Index (MSI) map. RESULTS We found that 3.87% of the malaria cases were found in areas with a low malaria-susceptibility level predicted from the model, whereas 39.86% and 26.29% of malaria cases were found in predicted high and very high susceptibility level areas, respectively. CONCLUSIONS Malaria susceptibility modelled using a GIS may have a role in predicting the risks of malaria and enable public health interventions to be better targeted.

Hydrological inferences through morphometric analysis of lower Kosi river basin of India for water resource management based on remote sensing data

Satellite based remote sensing technology has proven to be an effectual tool in analysis of drainage networks, study of surface morphological features and their correlation with groundwater management prospect at basin level. The present study highlights the effectiveness and advantage of remote sensing and GIS-based analysis for quantitative and qualitative assessment of flood plain region of lower Kosi river basin based on morphometric analysis. In this study, ASTER DEM is used to extract the vital hydrological parameters of lower Kosi river basin in ARC GIS software. Morphometric parameters, e.g., stream order, stream length, bifurcation ratio, drainage density, drainage frequency, drainage texture, form factor, circularity ratio, elongation ratio, etc., have been calculated for the Kosi basin and their hydrological inferences were discussed. Most of the morphometric parameters such as bifurcation ratio, drainage density, drainage frequency, drainage texture concluded that basin has good prospect for water management program for various purposes and also generated data base that can provide scientific information for site selection of water-harvesting structures and flood management activities in the basin. Land use land cover (LULC) of the basin were also prepared from Landsat data of 2005, 2010 and 2015 to assess the change in dynamic of the basin and these layers are very noteworthy for further watershed prioritization.

Knowledge-based decision tree approach for mapping spatial distribution of rice crop using C-band synthetic aperture radar-derived information

Abstract. Updated and accurate information of rice-growing areas is vital for food security and investigating the environmental impact of rice ecosystems. The intent of this work is to explore the feasibility of dual-polarimetric C-band Radar Imaging Satellite-1 (RISAT-1) data in delineating rice crop fields from other land cover features. A two polarization combination of RISAT-1 backscatter, namely ratio (HH/HV) and difference (HH−HV), significantly enhanced the backscatter difference between rice and nonrice categories. With these inputs, a QUEST decision tree (DT) classifier is successfully employed to extract the spatial distribution of rice crop areas. The results showed the optimal polarization combination to be HH along with HH/HV and HH−HV for rice crop mapping with an accuracy of 88.57%. Results were further compared with a Landsat-8 operational land imager (OLI) optical sensor-derived rice crop map. Spatial agreement of almost 90% was achieved between outputs produced from Landsat-8 OLI and RISAT-1 data. The simplicity of the approach used in this work may serve as an effective tool for rice crop mapping.

Morphotectonic analysis of Sheer Khadd River basin using geo-spatial tools

In the present study, a quantitative morphotectonic analysis of Sheer Khadd River basin has been carried out based on geomorphic and morphometric indices such as hypsometric integral, drainage basin asymmetry, mountain front sinuosity, basin elongation ratio, valley floor width to valley height ratio, river sinuosity and stream length gradient index using ASTER digital elevation model (DEM) and Google Earth images to understand the morphotectonics of the basin. The results indicate that Sheer Khadd River basin is tilting towards east and is elongated in shape due to active faulting and folding activity in the terrain. A moderate hypsometric integral value indicates that the basin is still under mature stage of erosion and reflecting a complexity in topography. Fluctuations in stream length gradient index over fault zones indicate irregularities in the drainage course due to the presence of fluvial knick points. The results of morphotectonic and morphometric analysis using DEM data is useful tool for morphotectonic evaluation of any complex terrain.

Application of Earth Observation Data for Estimation of Changes in Land Trajectories in Varanasi District, India

Abstract Digital change detection is the process that helps in shaping the changes associated with land use land cover (LULC) properties with reference to geo-registered multi-temporal remote sensing data. In this study different methods of analyzing satellite images are presented, with the aim to identify changes in land cover in a certain period of time (1980-2016). The methods represented in this study are vegetation indices, image differencing and supervised classification. These methods gave different results in terms of land cover area. Urban expansion has brought serious losses of agriculture land, vegetation and water bodies. The present study demonstrates changes in land trajectories of Varanasi district, India using Landsat MSS (1980), TM (1990 and 2010), ETM+ (2000) and Landsat-8 OLI data (2016). The LULC classes in the study area are divided into eight categories using supervised classification method. Normalized Difference Vegetation Index (NDVI) and Soil Adjusted Vegetation Index (SAVI) are also calculated to estimate the changes in LULC classes during these time periods. Major changes are seen from 2000 to 2016 for the built-up, agriculture land, water bodies and wasteland.

Performance evaluation of textural features in improving land use/land cover classification accuracy of heterogeneous landscape using multi-sensor remote sensing data

Texture analysis of remote sensing images has been received a substantial amount of attention as it plays a vital role in improving the classification accuracy of heterogeneous landscape. However, it is inadequately studied that how the images from different sensors with varying spatial resolutions influence the choice of textural features. This study endeavors to examine the textural features from the Landsat 8-OLI, RISAT-1, Resourcesat 2-LISS III, Sentinel-1A and Resourcesat 2-LISS IV satellite images with spatial resolution of 30, 25, 23.5, 5×20 and 5.8 m respectively, for improving land use/land cover (LULC) classification accuracy. The textural features were extracted from the aforesaid sensor data with the assistance of gray-level co-occurrence matrix (GLCM) with different moving window sizes. The best combination of textural features was recognized using standard deviations and correlation coefficients following separability analysis of LULC categories based on training samples. A supervised support vector machine (SVM) classifier was employed to perform LULC classification and the results were evaluated using ground truth information. This work demonstrates the significance of textural features in improving the classification accuracy of heterogeneous landscape and it becomes more significant as the spatial resolution improved. It is also revealed that textures are vital especially in the case of SAR data.

An Analysis of Geographical Survey for Utilization of Health Care Facilities

Utilization of health services is a complex phenomenon which, on the hand, is influenced by the awareness by an individual of the need for services thereby endorsing him to take a choice to use them and, on the other hand, by the availability, accessibility and organizational characteristics of health care services itself. The main objective of this chapter is to estimate the utilization pattern of health care services in the Varanasi district of India. Primary data pertaining to the utilization of health care facilities are collected from 800 respondents of 16 selected villages of rural Varanasi and analyzed with the SPSS statistical software. Varanasi City proper was not considered for this purpose because the presence and functioning of many private and government hospitals here meant that people were able to avail themselves of a fairly good range of healthcare facilities in comparison to people residing in the rural areas. Results of the findings revealed a high level of awareness among the local public of both the existence of the health care centres (78 %) and the type of health services they provided (75 % for vaccination; 70 % mother-child health (MCH) services; 62 % family planning; and 52 % general treatment). Despite such high levels of awareness only 25 % of them are satisfied with all the health care services provided by the primary health centres (PHC), 60 % are only partially satisfied and the remaining 14 % were not satisfied at all. These findings thus underline the geographical disparities in health facilities between urban and rural area of Varanasi.

GIS in Vector Born Disease Mapping

The representation and analysis of maps of vector born disease (VBD) and other related data is an important tool in the analysis and representation of local and regional variation in public health care system. GIS plays a variety of roles in the planning and management of the dynamic and complex healthcare system and disease mapping. Important vector born diseases like malaria, dengue fever, kala-azar etc. are discussed in this chapter. Spatial disease models study and predict the movements of people, information, and goods from one area to the other area. By accurately modeling these movements through GIS techniques, it is effortlessly to identify areas most at risk for disease transmission and thus target intervention efforts. Development block-wise report of VBD cases are mapped to recognize clusters necessitating intense attention for the control of disease. Location of dengue and kala-azar cases are identified through GPS. Important favorable indicators i.e. stream, ponds/water tanks, nalas, sewage zone, overhead tanks and slum areas in the Varanasi city also are very helpful malaria breeding sources and these indicators are extracted from remote sensing satellite data for the analysis. Outcomes of the present study recognized target variables that potentially favor mosquito breeding locations in the survey areas.