Geospatial analysis of short term shoreline change behavior between Subarnarekha and Rasulpur estuary, east coast of India using intelligent techniques (DSAS)

Nath, Anindita; Koley, Bappaditya; Saraswati, Subhajit; Choudhury, Tanupriya; Um, Jung‐Sup; Ray, Bidhan Chandra

doi:10.1007/s10708-022-10683-8

Cited by 11 publications

(2 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Topographical maps are very reliable and significant for accuracy assessment of past satellite images from 1975, 1972 and 1969, for example. The study also showed the increase in the water level in many parts of the study area, and this rise in water level has been shown in other articles, with loss and gain areas [105]. In a further study, satellite data with better resolution might be obtained for assessing LULC.…”

supporting

confidence: 72%

Assessing Coastal Land-Use and Land-Cover Change Dynamics Using Geospatial Techniques

Nath

Koley²,

Choudhury

et al. 2023

Sustainability

Self Cite

View full text Add to dashboard Cite

Geospatial techniques can be used to assess the dynamic conditions of coastal land use and land cover in order to make informed decisions about future management strategies for sustainable development through a combination of remote sensing data with field observations of shoreline characteristics along coastlines worldwide. Geospatial techniques offer an invaluable method for analyzing complex coastal systems at multiple scales. The coastal land use and land cover from the Subarnarekha (Orissa) to the Rasulpur estuaries (West Bengal) along the Bay of Bengal are dynamically modified by a complex interaction between land and sea. This is due to various dominating factors of physical and anthropogenic activities, which cause changes in the landscape. The main objective of this study was to identify the periodical transformation and changes in land-use/land-cover (LULC) features by the USGS-LULC classification method using a maximum-likelihood classifier (MLC) algorithm and satellite images for the period 1975–2018. The entire study area was divided into three ‘littoral zones’ (LZs). This will help in understanding how LULC has changed over time, as well as providing insight into human activities impacting on coastal environments. This study focused on five features selected for LULC classification, namely, built-up, vegetation, soil, sand and shallow-water areas. The purpose of this study was to investigate human encroachment near shore areas as well as the transformation of soil and sand into built-up areas over a 43-year period from 1975 to 2018 using geospatial techniques. To estimate the changes in the areas, a geodatabase was prepared for each LULC feature. Finally, statistical analysis was performed on all available datasets, which allowed the researchers to identify trends in land-cover change from 1975–2018 within each category, such as increasing deforestation and urbanization rates due to increased population growth. The results of the study show the expansion of shallow-water areas, which is one of the major factors influencing coastal erosion. Maximum shallow-water-level enhancement was observed in LZ I and LZ II. In LZ I, shallow water increased from 1 km2 to 4.55 km2. In LZ II, the initial 1.7 km2 shallow-water area increased to 13.56 km2, meaning an increase of 11.86 km2 in shallow-water zones. A positive change was noticed in vegetation area, which increased from 2.82% (4.13 km2) to 15.46% (22.07 km2). Accuracy assessment was applied for all classified images, and more than 85% accuracy was considered for the overall accuracy assessment. Finally, Kappa coefficient statistics were adopted to complete the accuracy analysis, and 80% or more than 80% accuracy was obtained for all classified images. This information can also help inform policy makers about potential environmental impacts associated with certain activities, such as coastal development and agricultural expansion, so that appropriate steps can be taken towards mitigating these impacts before it is too late.

show abstract

supporting

confidence: 72%

Assessing Coastal Land-Use and Land-Cover Change Dynamics Using Geospatial Techniques

Nath

Koley²,

Choudhury

et al. 2023

Sustainability

Self Cite

View full text Add to dashboard Cite

show abstract

“…Around 3.5-4 × 10 9 Mg of plant, residues are produced each year globally, among which 75% come from cereals (Mirzaei et al, 2021). A survey of a wide range of vegetation in different regions revealed that forests, grasslands and scrublands were most efficient in soil erosion control on 20°-30°, 0°-25°and 10°-25°slopes respectively (Wu et al, 2020).With the development of image processing technology, researchers have employed manual techniques to interpret satellite and aerial imagery for assessing alterations in land cover and land use (Hussein et al, 2020;Talukdar et al, 2020;Rousset et al, 2021;Srivastava and Chinnasamy, 2021;Aljenaid et al, 2022;Arshad et al, 2022;Baltodano et al, 2022;Sumangala and Kini, 2022;Nath et al, 2023), some scholars have conducted accuracy analysis of geographical spatial remote sensing images using the Kappa coefficient, achieving an overall accuracy of 80% or higher for all image classifications. Furthermore, other researchers have adjusted decisions on land use and land cover change (LULC) categories obtained from remote sensing images using diverse auxiliary data.…”

Section: Introductionmentioning

confidence: 99%

Objects detection theory for evaluating the city environmental quality

Liu,

Han,

Xie

et al. 2023

Front. Ecol. Evol.

View full text Add to dashboard Cite

IntroductionThe primary focus of this paper is to assess urban ecological environments by employing object detection on spatial-temporal data images within a city, in conjunction with other relevant information through data mining.MethodsFirstly, an improved YOLOv7 algorithm is applied to conduct object detection, particularly counting vehicles and pedestrians within the urban spatial-temporal data. Subsequently, the k-means superpixel segmentation algorithm is utilized to calculate vegetation coverage within the urban spatial-temporal data, allowing for the quantification of vegetation area. This approach involves the segmentation of vegetation areas based on color characteristics, providing the vegetation area’s measurements. Lastly, an ecological assessment of the current urban environment is conducted based on the gathered data on human and vehicle density, along with vegetation coverage.ResultsThe enhanced YOLOv7 algorithm employed in this study yields a one-percent improvement in mean AP (average precision) compared to the original YOLOv7 algorithm. Furthermore, the AP values for key categories of interest, namely, individuals and vehicles, have also improved in this ecological assessment.DiscussionSpecifically, the AP values for the ‘person’ and ‘pedestrian’ categories have increased by 13.9% and 9.3%, respectively, while ‘car’ and ‘van’ categories have seen AP improvements of 6.7% and 4.9%. The enhanced YOLOv7 algorithm contributes to more accurate data collection regarding individuals and vehicles in subsequent research. In the conclusion of this paper, we further validate the reliability of the urban environmental assessment results by employing the Recall-Precision curve.

show abstract

Subsurface Lithology Classification Using Well Log Data, an Application of Supervised Machine Learning

Patidar,

Singh,

Anand

2023

Algorithms for Intelligent Systems

View full text Add to dashboard Cite

Geospatial analysis of short term shoreline change behavior between Subarnarekha and Rasulpur estuary, east coast of India using intelligent techniques (DSAS)

Cited by 11 publications

References 64 publications

Assessing Coastal Land-Use and Land-Cover Change Dynamics Using Geospatial Techniques

Assessing Coastal Land-Use and Land-Cover Change Dynamics Using Geospatial Techniques

Objects detection theory for evaluating the city environmental quality

Subsurface Lithology Classification Using Well Log Data, an Application of Supervised Machine Learning

Contact Info

Product

Resources

About