Comparative assessment of groundwater quality indices of Kannur District, Kerala, India using multivariate statistical approaches and GIS.

The aim of the study was to determine the groundwater characteristics of rural and industrial zones in the Kannur region. In 2011, 25 groundwater data were collected from the centre for water resource development management (CWRDM), and in 2019, 25 groundwater samples from rural and near-industrial areas were collected and analysed for major anions (HCO3-, CO32-, Cl-, NO3- and SO42-), and cations (TH, Ca2+, Mg2+, Na+, K+ and Fe2+) using APHA standards. To better understand the link between water quality parameters, multivariate statistical analysis approaches such as principal component analysis (PCA), hierarchical cluster analysis (HCA), correlation matrix analysis (CMA), and Pearson correlation bivariate one-tailed analysis (PCBOTA) were used to analyse the inter-relationship of data. The Inverse Distance Weighed (IDW) method was used to generate the spatial distribution of the groundwater quality index (GWQI). In 2011, the water quality index (WQI) value of groundwater samples was excellent at 24.42% and good at 54.14%, which were used for drinking purposes and moderate at 17.22% and poor at 4.22% for irrigation purposes in this study area. In 2019, excellent 21.62%, good 51.56% were used for drinking purpose, and moderate at 18.14%, and poor at 8.68% for irrigation purposes. By comparing the data with BIS and WHO standards, it is clear that groundwater in Kannur district is of good quality. In groundwater samples, the PCA eigen values were reported in 2011 (84.7%) and 2019 (73.4%) for statistical approaches. This study uses HCA and PCBOTA to analyse the elements, resulting in a better understanding of groundwater quality development. GIS based WQI maps were obtained and utilised to gain a better knowledge of the study area's past and present water quality status. We observed that the quality of groundwater in the study region's north-western portion is insufficient for drinking water.

Land use/land cover changes of Noyyal watershed in Coimbatore district, India, mapped using remote sensing techniques.

The present study undertakes to produce the land use/land cover map and to explore the change detection analysis of Noyyal watershed, Coimbatore, for a time period of 18 years. Based on the remote sensing and geographical information system for monitoring the temporal variations of land use/land cover, multi-temporal Landsat satellite 30-m spatial resolution images of Landsat 4/5 MSS and TM (1999), Landsat 7 ETM + (2008), and Landsat 8 Operational Land Imager (OLI) were obtained from the USGS website. The satellite images were geocoded into the universal transverse mercator (UTM) coordinate system zone 43 N. The unsupervised classification method was done by using an iterative self-organizing data analysis algorithm to compare the images and to classify the images into various land cover categories. Kappa statistics were used to assess the validation of the present study. The analysis suggests the total forest covered in 1999 was 22.69% and that of 2008 was 24.04% and reduced to 6.09%, in 2017. The agricultural land of 17.8% is reduced to 3.11% in 2008 and 0.86% in 2017. The settlements increased from 15.59 to 24.21% in 2008 and 27.14% in 2017. Increase in deforestation leads to increase in barren land. In 1999, the percentage of barren land was 17.2%; in 2008, it was 13.19%, and 50.93% in 2017. The overall accuracy estimation of the study is 73.19% and Kappa coefficient is 0.72. This study has proven a substantial strength of agreement for the map of 2017 from the result of validation rating criteria of Kappa statistics.