Arun B. Inamdar

A framework for climate change and vulnerability assessment in an urbanized river basin through geospatial technologies and hydrological modeling

The article develops a framework for monitoring the climate change vulnerability in a river basin exploiting the geospatial and hydrological modeling. The entire work is planned in several stages; firstly, geospatial technology which is consisting of two sub-parts i.e. Remote Sensing and GIS; followed by Soil and Water Assessment Tool(SWAT) modeling and lastly, analysis of meteorological and climatic data to assess the vulnerability and future projection. As the meteorological predictors are the prime indicators of the climate change, so these have been analyzed at Grid Analysis and Display System (GrADS) for 65 years (1948-2012). For the assessment of the past scenario, present spread and future projections and vulnerability of hydrology and forestry, knowledge driven natural resource potential modeling and soft computing techniques need to apply in the Shivna River Basin; Maharashtra, India. The land Use Land Cover (LULC) change also has been shown in 1972 and 2013 by LANDSAT imagery.

Qualitative approaches to rapidly identify completely submerged rice due to tropical cyclone using satellite data

The objective of the present study is to identify completely submerged rice areas due to tropical cyclones using remotely sensed data. The Kendrapara district of Orissa state hit by a tropical cyclone on 30th October 1999 is considered as study area and for this area, pre event (October 11, 1999) visible- near IR image and pre (October 11, 1999) and post event (November 2, 1999 and November 4, 1999) Radarsat images were procured. The pre event IRS ID LISS III (resolution = 22 m) image of Kendrapara district was geometrically corrected and classified into several landuse and landcover classes. Supervised classification technique was used for landuse/landcover classification. This landuse/landcover map is assumed to be accurate and is used as a base map in the present study. Pre and post event Radarsat-SAR images were also geometrically corrected. Further preprocessing included speckle noise removal, data calibration and incidence angle adjustment. Based on literature, a threshold of -16.5 db (DN value =100) was chosen to classify each pixel in pre Radarsat-1 SAR image as water or non-water. The landuse/landcover map was used to identify the rice regions in the pre and post-event Radarsat images. Application of the threshold allows for the determination of the submerged rice areas. To determine the validity of a single threshold, water pixels in pre event Radarsat-1 SAR images were extracted corresponding to the base map. A histogram of these values suggests that a single value threshold approach may not be fully accurate. To overcome these limitations, two alternative approaches, namely image histogram and change in db were formulated. For both approaches, the rice pixels in pre and post event Radarsat- SAR images were extracted corresponding to base map rice pixels. In case of image approach, a histogram was plotted for the DN values of the pre and post Radarsat-1 SAR rice pixels. This allows the qualitative identification of the submerged rice areas. Using change in db approach, pixel-to-pixel change in db in pre and post event Radarsat-1 SAR images in rice pixels was calculated. Analysis of these values allows for the identification of different effects of submergence on the rice area. This type of analysis will help policy makers in determining the extent of submergence and could serve as a tool for rapid assessment of damage and help expedite release of relief funds and aid proper allocation of funds to the affected areas/people.

Identification of Completely Submerged Areas Due to Tropical Cyclone using Satellite Data: An Indian Case Study

Tropical cyclones are one of the most destructive natural disasters occurring frequently in coastal India. The socio economic impacts of these tropical cyclones are high as they result in enormous loss of life and property every year. In the present study, pre event visible-near IR images and post event Radarsat images were procured and used to identify completely submerged landcovers temporally. The methodology is developed considering a case study on the Kendrapara district of Orissa state, which was hit by a cyclone on 29-30 th October 1999. The pre event IRS 1D LISS III (resolution = 22m) image of Kendrapara district was procured geometrically corrected and classified into several landuse and landcover classes. For landuse/landcover classification, supervised classification technique was used. This georeferenced landuse/landcover map provided the baseline information for the district. Next step involved procurement of immediate temporal post-event SAR images of the cyclone-affected district. These images were geometrically corrected and cleaned for speckle noise. Deterministic approach was used to set up threshold for classifying pixel as completely submerged under water or non submerged for Radarsat SAR images i.e. Radarsat SAR images exactly delineated areas completely submerged under water due to cyclonic floods. This type of analysis will help policy makers in determining the extent of submergence and damage. This methodology would be used as a rapid tool to assess damage. Further, this will help in expediting the release of relief funds as well as aid proper allocation of funds to the affected areas/people.

Classification of rice crops based on submergence due to tropical cyclone using remotely sensed data: an Indian case study

Tropical cyclones are one of the most destructive natural disasters occurring frequently in coastal India. The socio economic impacts of these tropical cyclones are high as they result in enormous loss of life and property every year. In the present study, pre event visible-near IR images and post event Radarsat images were procured and used to identify completely submerged landcovers temporally. The methodology is developed considering a case study on the Kendrapara district of Orissa state, which was hit by a cyclone on 29-30th October 1999. The pre event IRS 1D LISS III (resolution = 22m) image of Kendrapara district was procured geometrically corrected and classified into several landuse and landcover classes. For landuse/landcover classification, supervised classification technique was used. This georeferenced landuse/landcover map provided the baseline information for the district. Next step involved procurement of immediate temporal post-event SAR images of the cyclone-affected district. These images were geometrically corrected and cleaned for speckle noise. Deterministic approach was used to set up threshold for classifying pixel as completely submerged under water or non submerged for Radarsat SAR images i.e. Radarsat SAR images exactly delineated areas completely submerged under water due to cyclonic floods. This type of analysis will help policy makers in determining the extent of submergence and damage. This methodology would be used as a rapid tool to assess damage. Further, this will help in expediting the release of relief funds as well as aid proper allocation of funds to the affected areas/people.

Comparison of internal area relative reflectance and 6SV reflectance calibration for impervious surface detection

Objective of this research is to compare performance of the Internal Areal Relative Reflectance (IAR), and Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) surface calibration methods for discriminating impervious surfaces in urban settings. We used EO1-Hyperion image of Pune city recorded on April 2013. We converted the radiance values to reflectance using IAR and 6SV and then classified image in to Vegetation, Impervious surface, and Soil (VIS) classes. Preliminary results indicated that 6SV does not provide any advantages over IAR. Average overall accuracy of classification over 8 different experiments was increased by ∼1% for IAR.

Study of Different Time Window of Synchronized Water Sample Collection on Turbidity Regression Model using Remotely Sensed Data

The Thane creek region, near Mumbai city is being used as dumping site for treated and untreated effluents by government agencies and private industries for the last several decades. This coastal water is very important from environmental point of view since it supports a vast area of mangrove forest besides a wide variety of flora and fauna. Turbidity, an important marine physical pollution parameter, affects the growth of mangroves, causes loss of swamps and poses threat to aquatic life. The work presented discusses the effect of varying time window of water sample collection synchronous to satellite passes on Turbidity model using Remotely Sensed Data. Marine water samples were collected synchronous to pass of Landsat satellite and Turbidity (NTU) was measured (During the post monsoon season of 1996/97 window of sample collection was plusmn 1 hour, which was reduced during the post monsoon season of 1997/98 to plusmn 15 minutes). The digital Landsat satellite images were corrected initially for geometric, sun angle and atmospheric errors. From the corrected remotely sensed data, DNs values were extracted and averaged. Multiple regression model was developed between water quality parameter, turbidity and averaged Digital Numbers (DNs) in 3times3 window size pixels corresponding to sampling locations. It was found that the regression coefficient improved significantly when synchronous time of window sampling was reduced from plusmn 1 hour to plusmn 15 minutes.