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Tamilnadu is a State that is deprived of perennial water sources like major rivers and streams. Agricultural activities in Tamilnadu are mainly dependent on seasonal rainfall and ground water. The shortages of water resources drive many coastal farmers to convert their fertile agricultural lands to aquaculture. Groundwater suitability for human consumption is determined by its physical, chemical and bacteriological properties. Irrational conversion of agricultural lands to aquaculture is found to be unsustainable leading to wastage of sparse natural resource fertile land and groundwater. The aquaculture practices started very extensively since year 2012 where fertile agriculture land, land with scrub and wasteland area have been converted for the aquaculture ponds. The use of temporal remote sensing helps in understanding the patterns of land use conversion from agricultural practice to aquaculture. In addition, indiscriminate use of fertilizers and chemicals in aquaculture farming has led to soil salinization and ground water pollution in many parts of study area. Use of salinity indices was tried in identification of salinity levels in lands in and around the current and abandoned aquaculture ponds. The study indicates that the use of Remote Sensing and GIS has helped in identification of landuse conversion and its adverse effects on characteristics of land due to extensive aquaculture activities in Ponneri Taluk of Tamilnadu. Using satellite data of Landsat OLI 8 (2015), IRS LISS IV, Sentinel II 2017, the changes have been estimated. The water quality parameters include pH, TDS, cations such as Ca, Mg, Na and K, anions such as Cl, SO4, HCO3, NO3 and PO4. Surface and groundwater quality results are cross-checked at the field areas which clearly indicate that almost entire Ponneri Taluk groundwater is unsuitable for drinking purposes. The classification for drinking water is obtained by DWQI. The piper plot has been studied by the geochemistry methods.

Roads in a hilly region contribute to significant development of the area and may cause negative impacts on the fragile environment. Optimal road alignment in the hilly region is challenging and mandates geospatial analysis that involves topographic and environmental parameters like slope, soil stability, forest cutting and obstruction to drainage. In this study, a multi-objective approach is proposed to delineate optimal road alignment in the hilly region of Sathuragiri hills, India using Geographic Information System (GIS) based integration techniques. Analytical Hierarchy Process (AHP) was used to generate suitability surfaces based on Engineering, Environmental, Landslide and Hybrid approaches. Thematic information like geology, land use, drainage network, slope, aspect, soil texture, distance from the road etc. have been generated from Remote Sensing and Collateral datasets. The suitability surfaces were generated through standardization and integration of criteria using a weighted overlay approach in the GIS environment. Alternate alignments were generated with these suitability surfaces using Dijkstra Algorithm for finding the shortest cost path. These alignments were evaluated for critical parameters like length of road, no. of bridges/culverts, area of forest cutting, area of agriculture land affected, length of road in critical slopes. The results indicate the efficiency of the hybrid approach combining the engineering, environmental and landslide risk-based parameters in the alignment planning.

Availability of reliable information on extent and impact of flood becomes critical for faster and efficient disaster management planning. Access to near realtime satellite data from optical and microwave sensors on Google Earth Engine (GEE) platform helps in rapid flood inundation mapping. Also, integration of Optical and Synthetic Aperture Radar (SAR) data from Sentinel series of satellites helps in detection of landuse/landcover features like Built-up, Agriculture Lands and Water bodies under flooding. The GEE platform provides extensive library tools for simultaneous pre-processing of SAR images and cloud free optical images from multiple satellites. In this research, Differential Thresholding and Differential Smoothening algorithms developed in GEE have been applied on Pre and Post SAR images for automatic identification of flood inundation areas. The inundated areas thus delineated were validated with inundation extents provided by the National Database for Emergency Management (NDEM). The methodology of automation developed in this study is applied to severe floods occurred in Kerala, India during August 2018. The sensitivity of polarization on mapping accuracy is estimated using Vertical- Horizontal (VH) and Vertical-Vertical (VV) modes available with Sentinel-1 data. The result indicates that VV mode of polarization provides better accuracy of 96% than VH mode. The coding is implemented in GEE environment with automation to provide extent of flood inundation for efficient management and mitigation planning during flood disasters.

The unpredictability of Indian Summer Monsoon (ISM) remains an area of major challenge for operational forecasters. The distribution of active and break days in a monsoon season, though of utmost importance is very difficult to predict. In this regard, the geostationary satellite observations from Indian National Satellite System-3D (INSAT-3D) satellite over the Indian region provide significant insights into the propagation of ISM. In this manuscript, the correlation of satellite derived cloud microphysical parameters (CMP) to the strength and development of the ISM is studied objectively for the years 2017 and 2018. In particular, the association of the CMP to the active and break spell has also been investigated. It is found that the Cloud Particle Effective radius (CER) and the Cloud Optical Thickness (COT) bears a maximum correlation of 0.67 and 0.65 with the daily average rainfall at 1 day lag. The histogram analysis with active/break conditions show that CER exhibit a bimodal distribution (~27 µm and ~36 µm) during an active spell, while in case of break phase, smaller cloud droplets ranging in size between 15 and 25 µm is prevalent. The findings of the study will contribute towards better understanding and prediction of active and break spells of ISM.

Tax mapping is an essential element for efficient management of real estate property and tax management, paving the way for effective implementation of g-governance practices in India. Conventional mapping of individual buildings and their attributes involves large effort and is time-consuming. The use of high-resolution UAV imagery facilitates the process of automatic extraction of land parcel, roof area and height by providing accurate and latest building and landuse information for improved tax assessment and collection. Image segmentation has been performed on UAV imagery applied to differentiate rooftop surfaces from other features such as roads and vegetation using gray level thresholding. An example-based feature extraction algorithm was adopted to extract the land parcel and rooftop surfaces from the segmented image. Further, DSM and DTM derived from UAV imagery were used to determine the height of the buildings by extraction of terrain points and rooftop points. The extracted building height was used to estimate the numbers of floors in the building using thresholding. The number of floors and rooftop area were used to derive the total floor area of each building. The property tax to be levied for each building was calculated automatically using total floor area in efficient and scientific manner. The UAV imagery used in the study enabled rapid mapping of buildings parcels for tax assessment compared to conventional mapping methods.

The biomass content of vegetation has great significance for efficient balancing of biomass cycle. Estimation of biomass in vegetation requires quantification of tree canopy. Automatic extraction of tree canopy helps in faster and efficient estimation of the biomass content of a vegetative area in comparison to manual mapping methods. An agricultural farm at Papparapati Village in Dharmapuri, District, Tamil Nadu, with an Area of 1.2 sq.km was chosen as study area and multispectral UAV imagery at 4-cm spatial resolution with 4 Bands viz., NIR, Green, Red Edge, RED was acquired. In this study, an attempt has been made to automatically extract tree canopy using simple deep learning convolution neural network (CNN) using training sites as input from the band spectral information. The extracted tree canopy was refined using linear cluster algorithm to delineate the exact tree crown. The biomass of the tree is estimated using the tree properties (crown diameter, height) derived from UAV data and using the allometric equations. A field survey was conducted to validate the classification results achieved from UAV data of individual sample trees. It is observed that the accuracy of extracted tree properties was in agreement with the field measurements of sample trees.

This paper demarcated the most vulnerable regions within the Poiney sub-basin (Tamil Nadu state in India) with respect to the groundwater quality. An index-based vulnerability assessment was carried out by measuring the physico-chemical variables such as pH, electrical conductivity, total dissolved solids, magnesium, sodium, chloride, sulphate, bicarbonate and fluoride in the pre-monsoon and post-monsoon samples. Water quality index varied across the sub-basin due to differences in the water quality induced by anthropogenic activities related to land use practices and presence of industries. The MT3D engine coupled with visual MODFLOW identified that sulphate released from tanneries and leather factories is the main effluent contaminating the groundwater. Model reveals that both the flow and contaminant transport is towards southeast with maximum and minimum calculated head of 201.82 mg/l and 265.92 mg/l and calculated sulphate concentration of 394.40 mg/l and 46.79 mg/l respectively.

Consideration of groundwater vulnerability as a planning parameter is imperative in the current context of depleting groundwater resources for the efficient land use planning. This study aims for groundwater vulnerability assessment by modifying SINTACS model and involve dynamics of land use change in a case study of Paravanar sub-basin in the Tamil Nadu state of south India. Thematic maps of land use generated from remote sensing data and associated field investigations were the input for the SINTACS model. These maps integrated in GIS helped to derive intrinsic vulnerability into very low, low, medium and high vulnerability categories. The strongest correlation (r = 0.74) between intrinsic vulnerability index and the water quality index, estimated from field observations, suggested better efficiency of this model compared to the conventional SINTACS index. Application of the modified SINTACS led to the conclusion that 12.2%, 28.7%, 45.9%, and 13.1% of the study area categorized very low vulnerability, low vulnerability, moderate vulnerability and high vulnerability, respectively and should be considered for efficient land use planning.

Dependence of Groundwater is alarmingly increasing in Tamil Nadu due to irregular rainfall patterns and improper management of surface water systems particularly, the Ground Water resources overexploited in many blocks of Tamil Nadu for domestic and irrigation use leading to seawater intrusion, depletion of water tables and water quality degradation. This study is carried out for Kolur village of Ponneri taluk, Tiruvallur district, located near the sea coast where large number of tanks are available, yet agriculture practice depend solely on groundwater due to lack of maintenance of tanks and irrigation channels. UAV drone data in this study enables automatic Micro level mapping of waterways from tanks to the fields even at the field channel level where conventional remote sensing is unusable. The maximum storage capacity of the tank has been determined using the DEM created from the UAV data. At present, paddy is the dominant crop grown in study area with the help of groundwater. The volume of water stored in the tank as a result of the runoff generated from precipitation in the catchment area during the paddy season is calculated using HEC-HMS model. Water requirement during the paddy season in the study area is calculated using evapotranspiration derived from the MODIS-MOD16A2 data and Precipitation derived from the TRMM dataset. The present study shows that tank irrigation system can supplement efficient irrigation of paddy crops in the study area by proper maintenance of the channel system, thereby decreasing the excessive use of groundwater for irrigation purposes.

Segmentation of surface elevation points derived from high-resolution UAV imagery is used to identify terrain and non-terrain points. Grey level thresholding of radiance values associated with terrain points enables extraction of impervious surfaces like Roads and Platforms. A morphometric based algorithm along with threshold length to width ratio is used to extract more precisely the roads out of other ground features. The experimental results show that this method can detect better linear features. Some comparatively small mask and boundaries are detected with changes in the local statistics by the automatic methods. Further, the discontinuity is detected and eliminated with morphometric parameters to minimize the effects of interfering objects and shadow effects. The extracted road network is evaluated for both spatial and semantic accuracy by comparing with the existing road network. This method demonstrated better extraction of linear features like roads than conventional image classification methods.

Extensive use of groundwater and abandoned agriculture practices causing desertification and environmental degradation of ecosystem is of major concern to planners. Ecologically sensitive areas like Western Ghats in India which is identified as World Heritage Site by UNESCO is facing similar threat. Construction of suitable artificial recharge structures at appropriate location is essential to improve the groundwater resource for environment protection. Study of watershed morphology has indicated the elongated shape, low runoff, and highly permeable subsoil conditions. The study proposes integration of multi-criteria-based land suitability analysis with runoff modeling in HEC-HMS to identify suitable structures and their location for artificial recharge of ground water. Remote sensing and GIS tools were used for mapping, integration, and modeling of artificial recharge process. Results demonstrate the suitability of farm ponds along first-order streams, percolation ponds, and subsurface dykes in middle of watershed and check dams in higher-order stream network. It is concluded that improved groundwater resources facilitate better environment protection and ecological balance.

Synthetic aperture radar (SAR) has operational applications in crop mapping and monitoring in many countries due to the distinct backscatter signature at various stages of crop growth. Polarimetric analysis of SAR data from different satellites was used for information extraction from different types of scatters in imaged terrain. The scattering processes were analyzed through the received scatter matrix derived from the target decomposition of SAR data. Three decomposition techniques, namely Freeman–Durden, Cloude–Pottier and Touzi decomposition of the ALOS PALSAR-1 data, were used in this study to extract land use/cover information with a specific emphasis on agriculture. The decomposed output parameters from these techniques were classified with supervised classifier of support vector machine (SVM) using region of interest (ROI) selected land use/cover classes. An accuracy assessment for the classified output was carried out using the ROI. The Ramgarh village in Jaisalmer district of Rajasthan with the predominance of agricultural land, sand dunes and settlements was chosen as the study area. Freeman–Durden decomposition resulted in the highest overall accuracy of about 85% in the land use/cover classification among the three decomposition techniques adopted in the study. It was also observed that the accuracy of land use/cover mapping derived from Cloude–Pottier and Touzi decompositions improved with the use of eigenvalues in the SVM classification. Higher accuracies in the classification of agriculture land were noted with all the three decomposition techniques. The four parameters of Cloude–Pottier (H, A, α, β) and Touzi (α s, Φ s, ψ, τ) decompositions improved the classification accuracy for all the classes due to eigenvalues. The overall classification accuracy was above 88% for both the decomposition techniques with four parameters. The soil moisture values for agriculture land and sand dunes were validated through soil moisture maps generated using Oh 1992 and 2004 models.

Urban forest planning is important for providing better urban ecosystem services and conserve the natural carbon sinks inside the urban area. In this study, a demand based urban forest plan was developed for Chennai city by using Analytical Hierarchy Process (AHP) method. Population density, Tree cover, Air quality index and Carbon stocks are the parameters were considered in this study. Tree cover and Above Ground Biomass (AGB) layers were prepared at a resolution of 1m from airborne LiDAR and aerial photos. The ranks and weights are assigned by the spatial priority using AHP. The results show that, the actual status of the urban forest is not adequate to provide ecosystem services on spatial priority. From this perspective, we prepared a demand based plan for improving the urban ecosystem.