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Papers by Narayan Kayet
Modeling Earth Systems and Environment, 2016
Land surface temperature (LST) is an important factor in global climate change studies, in estima... more Land surface temperature (LST) is an important factor in global climate change studies, in estimating radiation budgets, in heat balance studies and as a control for the climate dynamics and modelling frame. This study analyses the land surface temperature distribution in the region of Gua, Chiria, Megataburu and Kiriburu. Landsat Thematic Mapper and Enhanced Thematic Mapper Plus data of the year 1994, 2004 and 2014 are used to effects of land use/land cover changes on the surface temperature distribution. The remote sensing technique is used to detect the land use changes, its impact on the land surface temperature and variation in mean LST from these hot spots. Thermal infrared remote sensing proved its capability in monitoring temperature and affecting microclimate in urban areas. Results of the study show that the LST of different land use differs significantly. This study also indicates that the external temperature has an impact on surfaces of self-heating areas. This study demonstrates that the growth of rapid mining industrial area significantly decreases the vegetation areas, hence increased the surface temperature. This analysis demonstrates the potential applicability of the methodology for climate modelling frame.
Environmental Science and Pollution Research
A comparative study of Frequency Ratio (FR) and Analytic Hierarchy Process (AHP) models are perfo... more A comparative study of Frequency Ratio (FR) and Analytic Hierarchy Process (AHP) models are performed for forest fire risk (FFR) mapping in Melghat Tiger Reserve forest, central India. Identification of FFR depends on various hydrometeorological parameters (altitude, slope, aspect, topographic position index, normalized differential vegetation index, rainfall, air temperature, land surface temperature, wind speed, distance to settlements, and distance by road are integrated using a GIS platform. The results from FR and AHP show similar trends. The FR model was significantly higher accurate (overall accuracy of 81.3%, kappa statistic 0.78) than the AHP model (overall accuracy 79.3%, kappa statistic 0.75). The FR model total forest fire risk areas were classified into five classes:
Groundwater resources are the significant factor for maintaining life. The land use/cover (LULC) ... more Groundwater resources are the significant factor for maintaining life. The land use/cover (LULC) change and its effect on the groundwater table would enhanced land use and groundwater management for arid areas. This paper proposes a technique to LULC effects on the groundwater table. LULC extract based on time-series Landsat imagery (1988, 1993, 1998, 2003, 2007, 2013 and 2017) and its effect on the groundwater table. SVM algorithm used for classification of LULC features and its higher accuracy. In the study area, groundwater data (2001–2012) used for groundwater table change analysis. The result showed that 1.03 mbgl (Metres Below Ground Level) groundwater decreased in the study area. Analysis of the time-series climate data (1988–2014) based on the study area shows apex's for maximum temperature increased between 0.2 and 0.8 °C and 0.3 to 1.5 °C for minimum apexes. At the same time, analysis of historical rainfall data indicated that rainfall decreased by 10 mm, respectively during the years 1988–2014. The classification results showed that the SVM algorithm overall accuracy of 86.67% and the kappa coefficient of 0.82. The relationships among LULC and, climate, groundwater change values showed both positive and negative correlations. This paper highlighted the LULC change effect on the groundwater table change in arid areas.
The prime contribution of this assignment was to examine the hyperspectral remote sensing, based ... more The prime contribution of this assignment was to examine the hyperspectral remote sensing, based on iron ore minerals identification using spectral angle mapper (SAM) technique. Correlation analyses between field iron contents and environmental variables (soil, water, and vegetation) have been performed. Spectral feature fitting (SFF) and multi-range spectral feature fitting (MRSFF) methods were used for accuracy assessment in extracting iron ore minerals from Hyperion EO-1 data. Spectral inspections as a reference were used in SAM technique for image classification for iron ore minerals: Hematite (24.26%), Goethite (32.98%) and Desert (42.76). Iron ore minerals classification is justified by the United States Geological Survey (USGS) spectral library and field sample points. The regression analysis of USGS and Hyperion reflectance spectra has shown the moderate positive correlation. The regression analyses between iron ore contents and environmental parameters (soil, water, and vegetation) have shown the moderate negative correlation. The examination was significantly effectual in extracting iron ore minerals: Hematite (SFF RMSE ≤ 0.51 MRSFF RMSE ≤ 0.48), Goethite (SFF RMSE ≤ 0.047 MRSFF RMSE ≤ 0.438) and Desert (SFF RMSE ≤ 0.63 and MRSFF RMSE ≤ 0.50); and the MRSFF RMSE histograms indicate the above result likened to a conventional SFF RMSE. MRSFF RMS error result is best because multiple absorption features typically characterize spectral signatures. This analysis demonstrates the potential applicability of the methodology for iron minerals identification framework and iron minerals impact on environmental parameters.
Mining operations result in the generation of barren land and spoil heaps which are subject to hi... more Mining operations result in the generation of barren land and spoil heaps which are subject to high erosion rate during the rainy season. The present study uses Revised Universal Soil Loss Equation (RUSLE) and SCS-CN (Soil Conservation Service-Curve Number) process to conclude the soil loss estimation in Kiruburu and Meghahatuburu mining sites area. The geospatial model of yearly soil loss rate has been driving through integrating environmental variables parameters in a raster pixels-based GIS framework. GIS layers with, rainfall passivity or runoff erosivity (R), soil erosivity (K), slope length and steepness (LS), cover management(C) and conservation practice (P) factors were calculated to condition their special effects on yearly soil erosion in the study area. The coefficient of determination (r 2) is 0.834, which indicates a strong correlation in runoff and rainfall. Sub-watershed 5,9,10 and 2 was highly runoff. Average annual soil loss was calculated (30*30 m raster grid cell) to recognize the critical soil loss areas (Sub-watershed 9 and 5). Total soil erosion area was classified five class, slight (10,025.2 ha), moderate (3124.62), high (973.17 ha), very high (260.02 ha) and severe (52.83 ha). The resulting map shows highest soil erosion of 440 t h-1 y-1 (severe) through connection to grassland, degraded and open forestry on the erect mining side-escutcheon. The Landsat pan sharpening image and DGPS survey field data were used in the justification of soil erosion results.
Hyperspectral remote sensing is a very useful tool for forest health mapping, different forest ty... more Hyperspectral remote sensing is a very useful tool for
forest health mapping, different forest types and also for natural
resource management. In this work, the NASA-EO1 Hyperion
sensor data is used for forest type and forest health monitoring for
a case study in the Saranda forest of Jharkhand, India. Saranda
forest is covered with different kinds of valuable trees and is rich
in mineral deposits. Growing anthropogenic activities within and
near the forest lands are causing a threat to the forest health and
well-being. The forest area in the buffer zone of mining fields is
under high-stress conditions may show signs of dry or dying plant
material. Forest classification based on Hyperion data reveals the
existing forest types, their overall health, and vigor in the forested
region. Such designation helps to detect pest and blight conditions
in forest particularly for assessing areas of timber harvesting and
reforestation planning. Mining activities in the iron ore belt of the
Saranda forest of Jharkhand in the Karo and Koina river basin
have high potential to induce forestry health problem. Improper
mining of minerals is often liable to damage the forests health.
Forest regeneration activities introducing new species in the forest
region can bring certain changes in the tree patterns over the years
in a particular area in the forest.
The study involves Hyperspectral EO-1data collection, preprocessing
geometric and radiometric correction (bad band
removal, cross track illumination correction, stripe removal and
reduction of atmospheric and solar flux effects using FLAASH
Correction) and generation of spectral signature from the image
based on Minimum Noise Fraction (MNF) Pixel Purity Index (PPI)
n-dimensional visualize and techniques. To distinguish the
different forest type viz. deciduous forest, evergreen forest and
mixed forest spectral classification Spectral Angle Mapper (SAM)
technology are applied. This enables to compare the spectral
signature of the forest with the USGS spectral library files using
the software ENVI. Based on the study results final map of forest
classification and their area statistics is prepared, which indicates
the grades of forest health with acceptable accuracy. The results
were considered separately with the USGS spectral library of
forest types and forest health monitoring map. The output image
and area statistics showed the capability of the method in remotely
monitoring the health of vegetation and forest type.
Keywords— Hyperspectral Remote sensing, FLAASH Correction,
Narrowband Vegetation indices MNF, PPI, n-dimensional
visualizer, Spectral analysis, SAM, Forest type and health
monitoring.
There are continuous changes in earth surface by a variety of natural and anthropological agent’... more There are continuous changes in earth surface by a variety of natural and anthropological agent’s
activities. These agents cut, carry away and deposit the materials on the land surface. Running
water has a higher capacity of erosion than the other geomorphologic agents do. The present
work related to “Bhagirathi River in Murshidabad district.” It is the branches off from the Gang
about 40 km southeast below Farakka at Khejurtala village in Murshidabad district (lower course
of the Ganga).Bhagirathi River channel is continuously changing due to geomorphic, climatic
agents and human performance pressure in the bordered area of the present river. This improves
identification the primary purpose of paper by positive suggestions for managing the bank
erosion and shifting of Bhagirathi River. Analysing the image of the Bhagirathi River in
Murshidabad district during the year 1979, 1991, 2000 and 2015, sinuosity, Braided Index,
Island area, left the bank and right bank shifting, total area has been measured for this analyzing
years. An attempt has been made here, to apparatus the geospatial techniques for river change
detection using traditional to head geographical information source. The satellite data are to be
implementing for obtaining 36 years changes results in river stream. The moderate effect
explains the 36 years changes in the river bank due to various natural and manmade activities
like a flood, water velocity, sand excavation, removal the vegetation cover and fertile soil
excavation for the different proposed of local surrounded region’s people.
ISCA
Land use/land cover is a significant element for the interconnection of the human activities and ... more Land use/land cover is a significant element for the interconnection of the human activities and environment a monitoring
which is useful to find out the deviations to save a maintainable environment. Remote sensing is a very useful tool for the affair
of land use or land cover monitoring, which can be helpful to decide the allocation of land use and land cover. This study
involves the assessment of land use or land cover vicissitudes beginning of the year 1992, 2005 and 2014 of the Saranda forest.
In the classification map, statistics, matrix has been performed, and the user accuracy is collected for every class. To read the
thematic maps and ground truth survey, GIS software (ArcMap) has been employedtocarry out the classification and to check
the accuracy. It is mandatory to detect carefully the land use or land cover vicissitudes for continuing a sustainable
environment for a real growth. The result of the work shows the quick expansion of built-up (mining area), wasteland, open
forest, agricultural land and lessening the dense forest area and the water bodies.
Springer
Hirakud command area, situated in the western part of Odisha, comes under Mahanadi river basin. M... more Hirakud command area, situated in the western part of Odisha, comes under Mahanadi river basin. Mahanadi basin has been identified as critical basin from climate change point of view. The study presents an analysis to identify the possible reasons of groundwater depletion and water quality values with the help of land use and land cover (LULC) change detection methods. An analytic hierarchy process based novel water quality index (WQI) model is utilized. Eight water quality parameters are considered in WQI calculation process. Unsupervised classification, Discriminant function (pixel-based) and Image differencing (pixel-based) change detection methods are used for accurate mapping of spatial and temporal changes in LULC. The change detection analysis have been performed based on satellite data, e.g., Landsat MSS (1975), Landsat TM (1989), Landsat ETM+ (2000), LISS III (2009) and Google Earth (2014). Analyses show a significant increase in built-up area compared to increase in the agricultural land over last decade. Overall analyses of groundwater levels reveal the water stress scenario in the command area. The Agricultural activities are the main reason for groundwater table fluctuation during 1995–2014. This analysis is useful for land management strategies framework helpful for decision makers.
Land surface temperature (LST) is an important factor in global climate change studies, in estima... more Land surface temperature (LST) is an important factor in global climate change studies, in estimating radiation budgets, in heat balance studies and as a control for the climate dynamics and modelling frame. This study analyses the land surface temperature distribution in the region of Gua, Chiria, Megataburu and Kiriburu. Landsat Thematic Mapper and Enhanced Thematic Mapper Plus data of the year 1994, 2004 and 2014 are used to effects of land use/land cover changes on the surface temperature distribution. The remote sensing technique is used to detect the land use changes, its impact on the land surface temperature and variation in mean LST from these hot spots. Thermal infrared remote sensing proved its capability in monitoring temperature and affecting microclimate in urban areas. Results of the study show that the LST of different land use differs significantly. This study also indicates that the external temperature has an impact on surfaces of self-heating areas. This study demonstrates that the growth of rapid mining industrial area significantly decreases the vegetation areas, hence increased the surface temperature. This analysis demonstrates the potential applicability of the methodology for climate modelling frame.
Springer, 2017
Land surface temperature (LST), land use/land cover (LU/LC) and vegetation parameters are a subst... more Land surface temperature (LST), land use/land
cover (LU/LC) and vegetation parameters are a substantial
factor in worldwide climate change studies framework.
This study of investigating urban heat islands based on
thermal remote sensing data. Thermal infrared remote
sensing proved its capability in monitoring temperature and
affecting microclimate in urban areas. In the present study
have relationships among the multiple vegetation indices,
land use/land cover and LST using remote sensing techniques
in the Saranda forest state of Jharkhand. Normalized
difference vegetation index (NDVI), Soil-adjusted vegetation
index (SAVI), Ratio vegetation index (RVI) and
Normalized difference built-up index (NDBI) are used in
this study. The study work has been done on the correlation
of the association among the different vegetation indices,
land use/land cover, and land surface temperature. The
result shows that the external temperature an impact on
surfaces of self-heating (hot spots) areas. The relationship
between LST and NDVI result shows the negative correlation.
The NDVI proposes that the green land can deteriorate
the effect on mining, urban heat island while we
apparent the positive relationship between LST and NDBI.
This study demonstrates that the growth of the active
mining, the industrial area significantly decreases the
vegetation areas, hence grow the surface temperature. This
study also shows that the external temperature has an
impact on surfaces of self-heating (hot spots) areas.
Finally, the accuracy of proposed multiple indexes is
evaluated by using DGPS field survey points over the study
area. This analysis demonstrates the potential applicability
of the methodology for climate modeling framework.
articles by Narayan Kayet
The scope of this paper is to estimate foliar dust concentration using Hyperion (Narrow-bands dat... more The scope of this paper is to estimate foliar dust concentration using Hyperion (Narrow-bands data) and Landsat (Broad-bands data) images, with the aid of eight different vegetation indices (VIs) and field-based laboratory spectra. A PCE Instrument for measurement of dust accumulation on leaves and Spectroradiometer for spectral signatures, was also used to estimate foliar dust concentration. The result depicted a negative relationship between VIs (Hyperion and Landsat satellite imagery), and field based dust measurements. The Normalized Difference Vegetation Index (NDVI) shows an excellent negative correlation (R2= 0.89 for Hyperion and R2 = 0.81 for Landsat) as it is not much affected by the variation in vegetation types and patterns. Amongst the eight VIs, NDVI was selected as an optimal VI (RMSE = 0.06 g/m2for Hyperion and 0.11 g/m2for Landsat) based on both, the field measurement and satellite data for estimation of foliar dust concentration. Furthermore, a positive relationship between the field-based measured dust concentration (g/m2) and satellite image (by VIs) based dust concentration (g/m2) was observed. Field-based measured foliar dust concentration taken for 20 samples was plotted against their estimated dust values using the NDVI (R = 0.90 for Hyperion and R = 0.81 for Landsat). Hyperion data is considered as the reliable one as it gave better results than the Landsat data. Finally, the Hyperion data based foliar dust map was analyzed by a High-resolution Google Earth image (Geo Eye) for different locations viz., mines, transport sites as well as forests and matched with the field-based measured dust concentration. The result shows that maximum foliar dust was concentrated near the ore transportation network, surrounding mining locations, tailing ponds, and mining dumps areas. For making the environmental management effective (in the mining and allied areas), Hyperspectral remote sensing aided by field-based methods, for estimating foliar dust concentration would be very helpful.
Modeling Earth Systems and Environment, 2016
Land surface temperature (LST) is an important factor in global climate change studies, in estima... more Land surface temperature (LST) is an important factor in global climate change studies, in estimating radiation budgets, in heat balance studies and as a control for the climate dynamics and modelling frame. This study analyses the land surface temperature distribution in the region of Gua, Chiria, Megataburu and Kiriburu. Landsat Thematic Mapper and Enhanced Thematic Mapper Plus data of the year 1994, 2004 and 2014 are used to effects of land use/land cover changes on the surface temperature distribution. The remote sensing technique is used to detect the land use changes, its impact on the land surface temperature and variation in mean LST from these hot spots. Thermal infrared remote sensing proved its capability in monitoring temperature and affecting microclimate in urban areas. Results of the study show that the LST of different land use differs significantly. This study also indicates that the external temperature has an impact on surfaces of self-heating areas. This study demonstrates that the growth of rapid mining industrial area significantly decreases the vegetation areas, hence increased the surface temperature. This analysis demonstrates the potential applicability of the methodology for climate modelling frame.
Environmental Science and Pollution Research
A comparative study of Frequency Ratio (FR) and Analytic Hierarchy Process (AHP) models are perfo... more A comparative study of Frequency Ratio (FR) and Analytic Hierarchy Process (AHP) models are performed for forest fire risk (FFR) mapping in Melghat Tiger Reserve forest, central India. Identification of FFR depends on various hydrometeorological parameters (altitude, slope, aspect, topographic position index, normalized differential vegetation index, rainfall, air temperature, land surface temperature, wind speed, distance to settlements, and distance by road are integrated using a GIS platform. The results from FR and AHP show similar trends. The FR model was significantly higher accurate (overall accuracy of 81.3%, kappa statistic 0.78) than the AHP model (overall accuracy 79.3%, kappa statistic 0.75). The FR model total forest fire risk areas were classified into five classes:
Groundwater resources are the significant factor for maintaining life. The land use/cover (LULC) ... more Groundwater resources are the significant factor for maintaining life. The land use/cover (LULC) change and its effect on the groundwater table would enhanced land use and groundwater management for arid areas. This paper proposes a technique to LULC effects on the groundwater table. LULC extract based on time-series Landsat imagery (1988, 1993, 1998, 2003, 2007, 2013 and 2017) and its effect on the groundwater table. SVM algorithm used for classification of LULC features and its higher accuracy. In the study area, groundwater data (2001–2012) used for groundwater table change analysis. The result showed that 1.03 mbgl (Metres Below Ground Level) groundwater decreased in the study area. Analysis of the time-series climate data (1988–2014) based on the study area shows apex's for maximum temperature increased between 0.2 and 0.8 °C and 0.3 to 1.5 °C for minimum apexes. At the same time, analysis of historical rainfall data indicated that rainfall decreased by 10 mm, respectively during the years 1988–2014. The classification results showed that the SVM algorithm overall accuracy of 86.67% and the kappa coefficient of 0.82. The relationships among LULC and, climate, groundwater change values showed both positive and negative correlations. This paper highlighted the LULC change effect on the groundwater table change in arid areas.
The prime contribution of this assignment was to examine the hyperspectral remote sensing, based ... more The prime contribution of this assignment was to examine the hyperspectral remote sensing, based on iron ore minerals identification using spectral angle mapper (SAM) technique. Correlation analyses between field iron contents and environmental variables (soil, water, and vegetation) have been performed. Spectral feature fitting (SFF) and multi-range spectral feature fitting (MRSFF) methods were used for accuracy assessment in extracting iron ore minerals from Hyperion EO-1 data. Spectral inspections as a reference were used in SAM technique for image classification for iron ore minerals: Hematite (24.26%), Goethite (32.98%) and Desert (42.76). Iron ore minerals classification is justified by the United States Geological Survey (USGS) spectral library and field sample points. The regression analysis of USGS and Hyperion reflectance spectra has shown the moderate positive correlation. The regression analyses between iron ore contents and environmental parameters (soil, water, and vegetation) have shown the moderate negative correlation. The examination was significantly effectual in extracting iron ore minerals: Hematite (SFF RMSE ≤ 0.51 MRSFF RMSE ≤ 0.48), Goethite (SFF RMSE ≤ 0.047 MRSFF RMSE ≤ 0.438) and Desert (SFF RMSE ≤ 0.63 and MRSFF RMSE ≤ 0.50); and the MRSFF RMSE histograms indicate the above result likened to a conventional SFF RMSE. MRSFF RMS error result is best because multiple absorption features typically characterize spectral signatures. This analysis demonstrates the potential applicability of the methodology for iron minerals identification framework and iron minerals impact on environmental parameters.
Mining operations result in the generation of barren land and spoil heaps which are subject to hi... more Mining operations result in the generation of barren land and spoil heaps which are subject to high erosion rate during the rainy season. The present study uses Revised Universal Soil Loss Equation (RUSLE) and SCS-CN (Soil Conservation Service-Curve Number) process to conclude the soil loss estimation in Kiruburu and Meghahatuburu mining sites area. The geospatial model of yearly soil loss rate has been driving through integrating environmental variables parameters in a raster pixels-based GIS framework. GIS layers with, rainfall passivity or runoff erosivity (R), soil erosivity (K), slope length and steepness (LS), cover management(C) and conservation practice (P) factors were calculated to condition their special effects on yearly soil erosion in the study area. The coefficient of determination (r 2) is 0.834, which indicates a strong correlation in runoff and rainfall. Sub-watershed 5,9,10 and 2 was highly runoff. Average annual soil loss was calculated (30*30 m raster grid cell) to recognize the critical soil loss areas (Sub-watershed 9 and 5). Total soil erosion area was classified five class, slight (10,025.2 ha), moderate (3124.62), high (973.17 ha), very high (260.02 ha) and severe (52.83 ha). The resulting map shows highest soil erosion of 440 t h-1 y-1 (severe) through connection to grassland, degraded and open forestry on the erect mining side-escutcheon. The Landsat pan sharpening image and DGPS survey field data were used in the justification of soil erosion results.
Hyperspectral remote sensing is a very useful tool for forest health mapping, different forest ty... more Hyperspectral remote sensing is a very useful tool for
forest health mapping, different forest types and also for natural
resource management. In this work, the NASA-EO1 Hyperion
sensor data is used for forest type and forest health monitoring for
a case study in the Saranda forest of Jharkhand, India. Saranda
forest is covered with different kinds of valuable trees and is rich
in mineral deposits. Growing anthropogenic activities within and
near the forest lands are causing a threat to the forest health and
well-being. The forest area in the buffer zone of mining fields is
under high-stress conditions may show signs of dry or dying plant
material. Forest classification based on Hyperion data reveals the
existing forest types, their overall health, and vigor in the forested
region. Such designation helps to detect pest and blight conditions
in forest particularly for assessing areas of timber harvesting and
reforestation planning. Mining activities in the iron ore belt of the
Saranda forest of Jharkhand in the Karo and Koina river basin
have high potential to induce forestry health problem. Improper
mining of minerals is often liable to damage the forests health.
Forest regeneration activities introducing new species in the forest
region can bring certain changes in the tree patterns over the years
in a particular area in the forest.
The study involves Hyperspectral EO-1data collection, preprocessing
geometric and radiometric correction (bad band
removal, cross track illumination correction, stripe removal and
reduction of atmospheric and solar flux effects using FLAASH
Correction) and generation of spectral signature from the image
based on Minimum Noise Fraction (MNF) Pixel Purity Index (PPI)
n-dimensional visualize and techniques. To distinguish the
different forest type viz. deciduous forest, evergreen forest and
mixed forest spectral classification Spectral Angle Mapper (SAM)
technology are applied. This enables to compare the spectral
signature of the forest with the USGS spectral library files using
the software ENVI. Based on the study results final map of forest
classification and their area statistics is prepared, which indicates
the grades of forest health with acceptable accuracy. The results
were considered separately with the USGS spectral library of
forest types and forest health monitoring map. The output image
and area statistics showed the capability of the method in remotely
monitoring the health of vegetation and forest type.
Keywords— Hyperspectral Remote sensing, FLAASH Correction,
Narrowband Vegetation indices MNF, PPI, n-dimensional
visualizer, Spectral analysis, SAM, Forest type and health
monitoring.
There are continuous changes in earth surface by a variety of natural and anthropological agent’... more There are continuous changes in earth surface by a variety of natural and anthropological agent’s
activities. These agents cut, carry away and deposit the materials on the land surface. Running
water has a higher capacity of erosion than the other geomorphologic agents do. The present
work related to “Bhagirathi River in Murshidabad district.” It is the branches off from the Gang
about 40 km southeast below Farakka at Khejurtala village in Murshidabad district (lower course
of the Ganga).Bhagirathi River channel is continuously changing due to geomorphic, climatic
agents and human performance pressure in the bordered area of the present river. This improves
identification the primary purpose of paper by positive suggestions for managing the bank
erosion and shifting of Bhagirathi River. Analysing the image of the Bhagirathi River in
Murshidabad district during the year 1979, 1991, 2000 and 2015, sinuosity, Braided Index,
Island area, left the bank and right bank shifting, total area has been measured for this analyzing
years. An attempt has been made here, to apparatus the geospatial techniques for river change
detection using traditional to head geographical information source. The satellite data are to be
implementing for obtaining 36 years changes results in river stream. The moderate effect
explains the 36 years changes in the river bank due to various natural and manmade activities
like a flood, water velocity, sand excavation, removal the vegetation cover and fertile soil
excavation for the different proposed of local surrounded region’s people.
ISCA
Land use/land cover is a significant element for the interconnection of the human activities and ... more Land use/land cover is a significant element for the interconnection of the human activities and environment a monitoring
which is useful to find out the deviations to save a maintainable environment. Remote sensing is a very useful tool for the affair
of land use or land cover monitoring, which can be helpful to decide the allocation of land use and land cover. This study
involves the assessment of land use or land cover vicissitudes beginning of the year 1992, 2005 and 2014 of the Saranda forest.
In the classification map, statistics, matrix has been performed, and the user accuracy is collected for every class. To read the
thematic maps and ground truth survey, GIS software (ArcMap) has been employedtocarry out the classification and to check
the accuracy. It is mandatory to detect carefully the land use or land cover vicissitudes for continuing a sustainable
environment for a real growth. The result of the work shows the quick expansion of built-up (mining area), wasteland, open
forest, agricultural land and lessening the dense forest area and the water bodies.
Springer
Hirakud command area, situated in the western part of Odisha, comes under Mahanadi river basin. M... more Hirakud command area, situated in the western part of Odisha, comes under Mahanadi river basin. Mahanadi basin has been identified as critical basin from climate change point of view. The study presents an analysis to identify the possible reasons of groundwater depletion and water quality values with the help of land use and land cover (LULC) change detection methods. An analytic hierarchy process based novel water quality index (WQI) model is utilized. Eight water quality parameters are considered in WQI calculation process. Unsupervised classification, Discriminant function (pixel-based) and Image differencing (pixel-based) change detection methods are used for accurate mapping of spatial and temporal changes in LULC. The change detection analysis have been performed based on satellite data, e.g., Landsat MSS (1975), Landsat TM (1989), Landsat ETM+ (2000), LISS III (2009) and Google Earth (2014). Analyses show a significant increase in built-up area compared to increase in the agricultural land over last decade. Overall analyses of groundwater levels reveal the water stress scenario in the command area. The Agricultural activities are the main reason for groundwater table fluctuation during 1995–2014. This analysis is useful for land management strategies framework helpful for decision makers.
Land surface temperature (LST) is an important factor in global climate change studies, in estima... more Land surface temperature (LST) is an important factor in global climate change studies, in estimating radiation budgets, in heat balance studies and as a control for the climate dynamics and modelling frame. This study analyses the land surface temperature distribution in the region of Gua, Chiria, Megataburu and Kiriburu. Landsat Thematic Mapper and Enhanced Thematic Mapper Plus data of the year 1994, 2004 and 2014 are used to effects of land use/land cover changes on the surface temperature distribution. The remote sensing technique is used to detect the land use changes, its impact on the land surface temperature and variation in mean LST from these hot spots. Thermal infrared remote sensing proved its capability in monitoring temperature and affecting microclimate in urban areas. Results of the study show that the LST of different land use differs significantly. This study also indicates that the external temperature has an impact on surfaces of self-heating areas. This study demonstrates that the growth of rapid mining industrial area significantly decreases the vegetation areas, hence increased the surface temperature. This analysis demonstrates the potential applicability of the methodology for climate modelling frame.
Springer, 2017
Land surface temperature (LST), land use/land cover (LU/LC) and vegetation parameters are a subst... more Land surface temperature (LST), land use/land
cover (LU/LC) and vegetation parameters are a substantial
factor in worldwide climate change studies framework.
This study of investigating urban heat islands based on
thermal remote sensing data. Thermal infrared remote
sensing proved its capability in monitoring temperature and
affecting microclimate in urban areas. In the present study
have relationships among the multiple vegetation indices,
land use/land cover and LST using remote sensing techniques
in the Saranda forest state of Jharkhand. Normalized
difference vegetation index (NDVI), Soil-adjusted vegetation
index (SAVI), Ratio vegetation index (RVI) and
Normalized difference built-up index (NDBI) are used in
this study. The study work has been done on the correlation
of the association among the different vegetation indices,
land use/land cover, and land surface temperature. The
result shows that the external temperature an impact on
surfaces of self-heating (hot spots) areas. The relationship
between LST and NDVI result shows the negative correlation.
The NDVI proposes that the green land can deteriorate
the effect on mining, urban heat island while we
apparent the positive relationship between LST and NDBI.
This study demonstrates that the growth of the active
mining, the industrial area significantly decreases the
vegetation areas, hence grow the surface temperature. This
study also shows that the external temperature has an
impact on surfaces of self-heating (hot spots) areas.
Finally, the accuracy of proposed multiple indexes is
evaluated by using DGPS field survey points over the study
area. This analysis demonstrates the potential applicability
of the methodology for climate modeling framework.
The scope of this paper is to estimate foliar dust concentration using Hyperion (Narrow-bands dat... more The scope of this paper is to estimate foliar dust concentration using Hyperion (Narrow-bands data) and Landsat (Broad-bands data) images, with the aid of eight different vegetation indices (VIs) and field-based laboratory spectra. A PCE Instrument for measurement of dust accumulation on leaves and Spectroradiometer for spectral signatures, was also used to estimate foliar dust concentration. The result depicted a negative relationship between VIs (Hyperion and Landsat satellite imagery), and field based dust measurements. The Normalized Difference Vegetation Index (NDVI) shows an excellent negative correlation (R2= 0.89 for Hyperion and R2 = 0.81 for Landsat) as it is not much affected by the variation in vegetation types and patterns. Amongst the eight VIs, NDVI was selected as an optimal VI (RMSE = 0.06 g/m2for Hyperion and 0.11 g/m2for Landsat) based on both, the field measurement and satellite data for estimation of foliar dust concentration. Furthermore, a positive relationship between the field-based measured dust concentration (g/m2) and satellite image (by VIs) based dust concentration (g/m2) was observed. Field-based measured foliar dust concentration taken for 20 samples was plotted against their estimated dust values using the NDVI (R = 0.90 for Hyperion and R = 0.81 for Landsat). Hyperion data is considered as the reliable one as it gave better results than the Landsat data. Finally, the Hyperion data based foliar dust map was analyzed by a High-resolution Google Earth image (Geo Eye) for different locations viz., mines, transport sites as well as forests and matched with the field-based measured dust concentration. The result shows that maximum foliar dust was concentrated near the ore transportation network, surrounding mining locations, tailing ponds, and mining dumps areas. For making the environmental management effective (in the mining and allied areas), Hyperspectral remote sensing aided by field-based methods, for estimating foliar dust concentration would be very helpful.