Monitoring vegetation loss and shoreline change due to tropical cyclone Fani using Landsat imageries in Balukhand-Konark Wildlife Sanctuary, India

Mishra, Manoranjan; Santos, Celso Augusto Guimarães; Silva, Richarde Marques da; Rana, Narendra Kumar; Kar, Dipika; Parida, Nihar Ranjan

doi:10.1007/s11852-021-00840-5

Cited by 14 publications

(9 citation statements)

References 45 publications

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“…Vegetation damage severity mapping (VDSM) has been performed using pre-and postevent satellite images. NDVI is widely used for measuring vegetation density, health status, regional vegetation condition, and detecting vegetation disturbances (Xu et al, 2021;Mishra et al, 2021b;Wang et al, 2010;Yang et al, 2018). Numerous studies have shown that the NDVI is a reliable indicator of post-typhoon damage detection (Xu et al, 2021;Mishra et al, 2021a;Charrua et al, 2021;Shamsuzzoha et al, 2021;Kumar et al, 2021;Nandi et al, 2020;Wang and Xu, 2018;Konda et al, 2018;Zhang et al, 2013;Rodgers et al, 2009).…”

Section: Analyses Of Coastal Vegetation Loss and Disturbancementioning

confidence: 99%

“…The relative change in NDVI value has been used to investigate the geo-ecological impact on the forest area (Mishra et al, 2021b). The relative vegetation changes (NDVIr) after Soulik have been determined by using the following Eq.…”

Section: Analyses Of Coastal Vegetation Loss and Disturbancementioning

confidence: 99%

“…The effects of typhoons include saltwater intrusion, soil fertility depletion, reduced agricultural productivity, life losses, coastline erosion, vegetation damage, and massive economic disasters (Mishra et al, 2021b).…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have been carried out in South Asia using various techniques to map the hazard, vulnerability, risk and effects of typhoon disasters (Halder and Bandyopadhyay, 2022;Wang et al, 2021;Shamsuzzoha et al, 2021;Kumar et al, 2021;Sadik et al, 2020;Konda et al, 2018;Parida et al, 2018;Zhang et al, 2013;Yin et al, 2013;Li and Li., 2013;Rodgers et al, 2009). Remote sensing and geospatial technology played a crucial role in monitoring a variety of natural disasters (Wang and Xu, 2018;Mishra et al, 2021b;Charrua et al, 2020). The majority of studies on typhoon-induced coastal dynamics rely on passive optical remote sensing and identify natural disaster damage using changes in landuse data, vegetation https://doi.org/10.5194/nhess-2022-253 Preprint.…”

Section: Introductionmentioning

confidence: 99%

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Assessing Typhoon Soulik-induced morphodynamics over the Mokpo coast region in South Korea based on a geo-spatial approach

Yum

Song

Adhikari

2022

Preprint

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Abstract. The inner shelf and coastal region of the Yellow Sea along the Korean peninsula are frequently impacted by Typhoons. The Mokpo coastal region in South Korea has been significantly affected by typhoon Soulik in 2018, the deadliest typhoon strike to the southwestern coast, since Maemi in 2003. Typhoon Soulik overran the region, causing extensive damage to the coast, shoreline, vegetation, and coastal geomorphology. Therefore, it is important to investigate its impact on the coastal ecology, landform, erosion/accretion, suspended sediment concentration (SSC) and associated coastal changes along the Mokpo region. In this study, net shoreline movement (NSM), Normalized Difference Vegetation Index (NDVI)), coastal landform change model, Normalized Difference Suspended Sediment Index (NDSSI), and SSC-reflectance relation have been used to analyze the coastal morphodynamics over the typhoon periods. We used pre- and post-typhoon Sentinel-2B MSI images for mapping and monitoring the typhoon effect. The findings highlighted the significant impacts of typhoons on coastal dynamics, wetland vegetation and sediment resuspension along the Mokpo coast. It has been observed that typhoon-induced SSC influences shoreline and coastal morphology. The outcome of this research may provide databases to manage coastal environments and a long-term plan to restore valuable coastal habitats. In addition, the findings may be useful for post-typhoon emergency response, coastal planners, and administrators involved in the long-term development of human life.

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Section: Analyses Of Coastal Vegetation Loss and Disturbancementioning

confidence: 99%

Section: Analyses Of Coastal Vegetation Loss and Disturbancementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Assessing Typhoon Soulik-induced morphodynamics over the Mokpo coast region in South Korea based on a geo-spatial approach

Yum

Song

Adhikari

2022

Preprint

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“…Scientists use RS and spatial analysis technologies to map and identify LULC changes ( Macleod & Congalton, 1998 ), in rapid land cover mapping(Yan et al, 2019 ), in watershed mapping (Pande, 2022 ), in identifying urban changes (Alqahtany, 2023 ; Kamel, 2020 ), in landscape change assessment (Huang et al, 2020 ), in land degradation detection (Mohamed & El-Raey, 2019 ), in shoreline change detection (Kouhgardi et al, 2022 ), in studying the climate change effect on LULC change(Tariq et al, 2022 ) in flood monitoring (Sharifi, 2021 ), tracking changes to the shoreline and reduced vegetation (Mishra et al, 2021 ), and numerous other applications.…”

Section: Introductionmentioning

confidence: 99%

Modelling of land use land cover changes using machine learning and GIS techniques: a case study in El-Fayoum Governorate, Egypt

Atef

Ahmed

Abdel-Maguid

2023

Environ Monit Assess

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Land use/land cover (LULC) changes can occur naturally or due to human activities. In this study, the maximum likelihood algorithm (MLH) and machine learning (random forest algorithm (RF) and support vector machine (SVM)) were investigated for image classification to oversight spatio-temporal land use changes in El-Fayoum governorate, Egypt. The Google Earth Engine has been utilized to pre-process the Landsat imagery, and then upload it for classification. Each classification method was evaluated using field observations and high-resolution Google Earth imagery. LULC changes were assessed, utilizing Geographic Information System (GIS) techniques, over the last 20 years in three different periods: 2000–2012, 2012–2016, and 2016–2020. The results showed that socioeconomic changes occurred during these transitions. The SVM procedure provided the most accurate maps in terms of the kappa coefficient (0.916) compared to MLH (0.878) and RF (0.909) procedures. Therefore, the SVM technique was adopted to classify all available satellite imagery. The results of change detection showed that urban sprawl has occurred and most of the encroachments were on agricultural land. The results showed that agricultural land area decreased from 26.84% in 2000 to 26.61% in 2020 and urban area increased from 3.43% in 2000 to 5.99% in 2020. In addition, urban land expanded rapidly on account of agricultural lands by a total of 4.78% from 2012 to 2016, while it expanded slowly by a total of 3.23% from 2016 to 2020. Overall, this study offers useful insight into LULC changes that might aid shareholders and decision makers in making informed decisions.

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A methodological proposal to analyze urban sprawl, negative environmental impacts, and land degradation in the case of João Pessoa City (Brazil) between 1991 and 2018

Sousa

Silveira

Santos

et al. 2023

Environ Monit Assess

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Monitoring vegetation loss and shoreline change due to tropical cyclone Fani using Landsat imageries in Balukhand-Konark Wildlife Sanctuary, India

Cited by 14 publications

References 45 publications

Assessing Typhoon Soulik-induced morphodynamics over the Mokpo coast region in South Korea based on a geo-spatial approach

Assessing Typhoon Soulik-induced morphodynamics over the Mokpo coast region in South Korea based on a geo-spatial approach

Modelling of land use land cover changes using machine learning and GIS techniques: a case study in El-Fayoum Governorate, Egypt

A methodological proposal to analyze urban sprawl, negative environmental impacts, and land degradation in the case of João Pessoa City (Brazil) between 1991 and 2018

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