Abstract. This study investigated the influences of urbanization on urban ecological and thermal environment as well as the relationships of all the biophysical parameters with each other utilizing multi-temporal datasets of CORONA (1967), Landsat TM (1992 and 2009), Landsat ETM+ (2002), IRS R2 LISS-3 (2012) and Landsat 8 (2014). The urban environmental issues related to land use and land cover, greenness, surface wetness and impervious surface were assessed using change detection, SAVI, MNDWI and IBI models respectively. The land surface temperature (LST) was also retrieved from thermal infrared band of each Landsat TM, ETM+ and Landsat 8. Based on these parameters, the urban expansion, urban heat island effect and the relationships of LSTs to other biophysical parameters were analyzed. Results indicate the area ratio of impervious surface in Pune sub-urban zone increased significantly, which grew from 1.41 % in 1967 to 8.47 % in 1992 and further to 22.45 % and 44.7 % in 2002 and 2014 respectively. Simultaneously, the intensity of urban heat island increased in observed years. A correlation analyses revealed that, the association of impervious surface to other two variables i.e. greenness and land surface wetness is negatively correlated (R2 = 0.616 and 0.607 respectively). Whereas, LST possessed a strong positive correlation with impervious surfaces (R2 = 0.658). The present study provided an integrated research approach and the outcome of the study is very useful in environmental modelling and sustainable development of urban areas and natural resources conservation.
Abstract. The present study focuses on the dynamics of conversion of agricultural land to aquaculture over a decade from 1995 to 2013 in Chinna Cherukuru Village (Thotapalligudur Mandal) in Nellore District of Andhra Pradesh State, India. Multi temporal satellite data from 1995's medium resolution to high resolution IRS LISS IV & Cartosat of 2013 time frame was analysed and mapped using RS & GIS techniques to monitor the dynamics of land transformation from agriculture to aquaculture (1995's) and conversion back to agriculture in 2013. It was observed that, in 1995 aquaculture was practiced to an extent of 62.35 hectares which accounts to 9.48 % of the Total Geographic Area (TGA) of the village (658.01 hectares), whereas in 2001 there is a major conversion from agricultural land to aquaculture accounting to an extent of 237.01 hectares or 36.01 % of total village area . However, thereafter there was a significant conversion back to agriculture accounting to an extent of 27.23 hectares or 4.13 % of TGA in 2013. The study tries to understand the underlying reasons for conversion back to agriculture which were due to several factors that include outbreak of diseases in aquatic fauna, natural calamities, variation in production cost / selling cost and non-availability of infrastructure facilities like cold storages etc. The present village level study on LUCC database provides an answer key question about socio-economic issues, land use and cropping pattern which form important input for environmental management.
Abstract. An attempt has been made to compare the multispectral Resourcesat-2 LISS III and Hyperion image for the selected area at sub class level classes of major land use/ land cover. On-screen interpretation of LISS III (resolution 23.5 m) was compared with Spectral Angle Mapping (SAM) classification of Hyperion (resolution 30m). Results of the preliminary interpretation of both images showed that features like fallow, built up and wasteland classes in Hyperion image are clearer than LISS-III and Hyperion is comparable with any high resolution data. Even canopy types of vegetation classes, aquatic vegetation and aquatic systems are distinct in Hyperion data. Accuracy assessment of SAM classification of Hyperion compared with the common classification systems followed for LISS III there was no much significant difference between the two. However, more number of vegetation classes could be classified in SAM. There is a misinterpretation of built up and fallow classes in SAM. The advantages of Hyperion over visual interpretation are the differentiation of the type of crop canopy and also crop stage could be confirmed with the spectral signature. The Red edge phenomenon was found for different canopy type of the study area and it clearly differentiated the stage of vegetation, which was verified with high resolution image. Hyperion image for a specific area is on par with high resolution data along with LISS III data.
Customer attrition is especially an issue in industries such as retail, banking, and telecommunications where customer acquisition costs are significantly higher than the costs of retaining repeat customers. The customer lack of interest is now predictable through machine learning models, and deep learning has become instrumental in early intervention for retention. In order to assess the quality of churn prediction, the study tests six basic machine learning techniques: random forest, logistic regression, and the k-nearest neighbors method, as well as four deep learning techniques: long short term memory (LSTM), bidirectional LSTM, convolutional neural networks (CNN), and artificial neural networks (ANN). The performance of the model is then assessed via the evaluation matrices, including the accuracy, precision, recall, and F1-score from the customer's behavioral data after feature extraction from large datasets. The study reveals that DL models offer improved handling of the churn and non-churn customer classification and Random Forest as well as other ML models comparable accuracy. This research can conclude that LSTM and ANN models outshine in actual-world churn prediction circumstances, especially when long-term consumer behavior evaluation is required. To enhance the current outcomes of a given prediction model, this research focuses on data preprocessing and the utilization of bootstrapping, feature extraction, and the combination of multiple models. The implications of the study provide specific practical recommendations for firms to effectively manage customer churn and increase customer retention by employing data-dealing techniques.
Abstract. Land use/land cover (LULC) is dynamic in nature and can affect the ability of land to sustain human activities. The Indo-Gangetic plains of north Bihar in eastern India are prone to floods, which have a significant impact on land use / land cover, particularly agricultural lands and settlement areas. Satellite remote sensing techniques allow generating reliable and near-realtime information of LULC and have the potential to monitor these changes due to periodic flood. Automated methods such as object-based techniques have better potential to highlight changes through time series data analysis in comparison to pixel-based methods, since the former provides an opportunity to apply shape, context criteria in addition to spectral criteria to accurately characterise the changes. In this study, part of Kosi river flood plains in Supaul district, Bihar has been analysed to identify changes due to a flooding event in 2008. Object samples were collected from the post-flood image for a nearest neighbourhood (NN) classification in an object-based environment. Collection of sample were partially supported by the existing 2004–05 database. The feature space optimisation procedure was adopted to calculate an optimum feature combination (i.e. object property) that can provide highest classification accuracy. In the study, for classification of post-flood image, best class separation was obtained by using distance of 0.533 for 28 parameters out of 34. Results show that the Kosi flood has resulted in formation of sandy riverine areas.
This study investigated land use/land cover change (LULCC) dynamics using temporal satellite images and spatial statistical cluster analysis approaches in order to identify potential LULCC hot spots in the Pune region. LULCC hot spot classes defined as new, progressive and non-progressive were derived from Gi* scores. Results indicate that progressive hot spots have experienced high growth in terms of urban built-up areas (20.67% in 1972–1992 and 19.44% in 1992–2012), industrial areas (0.73% in 1972–1992 and 3.46% in 1992–2012) and fallow lands (4.35% in 1972–1992 and −6.38% in 1992–2012). It was also noticed that about 28.26% of areas near the city were identified as new hot spots after 1992. Hence, non-significant change areas were identified as non-progressive after 1992. The study demonstrated that LULCC hot spot mapping through the integrated spatial statistical approach was an effective approach for analysing the direction, rate, spatial pattern and spatial relationship of LULCC.
The study presents an approach to map Land Use / Land Cover Change (LULCC) at large scale and processing techniques that permit higher accuracy. IRS RESOURCESAT-2 LISS-IV images of Nellore district of Andhra Pradesh were used to apply the classification technique. In multi-scale feature extraction approach LULCC takes two forms i.e. conversion from one category of LULCC to another and modification of condition within a category. Thus, major LULCC classes were extracted using object based approach and uncertain classes were identified using onscreen knowledge based method. The results showed in 2009, the accuracy of cropland, water body and built-up segments were 99.3%, 94.79% and 89.72%, respectively, whereas, in 2013 the accuracies were 94.31%, 88.26% and 81.20%, respectively. Hence, this classification approach can be useful in different landscape structure over the time, which can be quantified and assessed to achieve a better understanding of the land cover.
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