Monitoring and predicting spatio-temporal dynamics of river bankline movements: a case study for land use risk management in the lower Ganga River, India.

Changing the river course in the alluvial plain region is a common phenomenon that may have disastrous consequences. The risk of river bank erosion has increased dramatically during the last few decades. As a result, assessing the river bankline alteration is necessary. The study aims to determine the changes in the bankline in the lower Ganga River. This research presents a novel approach by using the digital shoreline analysis system (DSAS) in conjunction with geospatial data to monitor and predict long-term changes in river banks from 1965 to 2017, providing a comprehensive temporal analysis that is unprecedented in this study area. The study analyzes the bankline change along the river Ganga using DSAS using during the elapsed period. An erosion and accretion zonation was conducted based on the rate of bankline change of the river Ganga in the study area. The rate of bankline shifting was quantified using the endpoint rate (EPR) and linear regression rate (LRR) statistics computed using the DSAS model. The east bank of the Ganga in the study area experienced an average erosion of - 41.17 m/year according to the LRR model. Whereas, the west bank eroded an average of - 2.32 m/year between 1965 and 2017. 90.54% of the transect lines recorded erosion at the east bank and 53.69% of the transect lines at the west bank recorded erosion computed with LRR. For the assessment of the impact of river bankline change on the LULC of the study area, the future river banklines for 2027 and 2037 were forecasted. The result shows that by 2027 and 2037 about 133.24 and 147 km2 of agricultural land and 7.19 and 11.47 km2 of the built-up area may be affected by river bank erosion respectively. By extending the applications of DSAS and geospatial analytics to encompass predictive and impact assessment capabilities, this study significantly enriches the literature on the management of riverbank erosion and associated land use risks. This research provides important insights that improve river management and planning and enable the formulation of robust strategies to mitigate erosion risks on river banks.

Monitoring built-up area expansion led by industrial transformation in Delhi using geospatial techniques.

Historically, industrialization has been a catalyst for built-up expansion generated by economic growth that transforms a landscape. In India, there is a paucity of exploration into how the economic shift transforms the cityscape. Therefore, the objective of current research work was to monitor built-up growth induced by industrialization using Landsat datasets and registered industry data. The k-means clustering technique was applied for assessing land use/land cover, Shannon entropy for sprawl, and Pearson for correlation between industrial growth and built-up expansion. The results manifest exponential trend in industrialization with 102-year registered industry record along with increase in built-up density from 0.30 in 1989 to 0.69 by 2019 and in the entire Delhi; it rose from 0.16 to 0.39. Furthermore, Shannon entropy confirmed the sprawl and the strong positive correlation was found among built-up of industrial areas and built-up of Delhi and registered industries. The striking chorological change in industrial as well as city's landscape was observed co-occurring with the dynamics of economic reforms. The outcome of current research could be utilized for the sustainable planning of industrial landscape in Delhi and cities with alike geographical conditions.