Monitoring Shoreline and Land Use/Land Cover Changes in Sandbanks Provincial Park Using Remote Sensing and Climate Data

Abstract

Climate change-driven forces and anthropogenic interventions have led to considerable changes in coastal zones and shoreline positions, resulting in coastal erosion or sedimentation. Shoreline change detection through cost-effective methods and easy-access data plays a key role in coastal management, where other effective parameters such as land-use/land-cover (LULC) change should be considered. This paper presents a remotely sensed shoreline monitoring in Sandbanks Provincial Park, Ontario, Canada, from 1984 to 2021. The CoastSat toolkit for Python and a multilayer perceptron (MLP) neural network classifier were used for shoreline detection, and an unsupervised change detection framework followed by a postclassification change detection method was implemented for LULC classification and change detection. The study assessed the recent coastal erosion and accretion trends in the region in association with spatiotemporal changes in the total area of the West and East Lakes, the transition between LULC classes, extreme climate events, population growth, and future climate projection scenarios. The results of the study illustrate that the accretion trend apparently can be seen in most parts of the study area since 1984 and is affected by several factors, including lake water-level changes, total annual precipitations, sand movements, and other hydrologic/climatic parameters. Furthermore, the observed LULC changes could be in line with climate change-driven forces and population growth to accelerate the detected accretion trend in the East and West Lakes. In total, the synergistic interaction of the investigated parameters would result in a greater accretion trend along with a lower groundwater table amid even a low carbon scenario. The discussed findings could be beneficial to regional/provincial authorities, policymakers, and environmental advocates for the sustainable development of coastal communities.

Geoinformation Technology in Support of Arctic Coastal Properties Characterization: State of the Art, Challenges, and Future Outlook

“…EO is increasingly used to monitor and detect coastlines as well as establish the degree of vulnerability, with many relevant studies identified in the relevant literature, e.g., [7,[17][18][19]. Furthermore, Geographical Information Systems (GISs) can capture, display, integrate, and analyze a large number of geospatial data, which explains their often-synergistic use with EO datasets [20][21][22].…”

Un-planned Urban Growth Monitoring from 1991 to 2021 of Aizawl City, North-east India by Multi- Temporal Changes and CA-ANN Model

“…Several studies were conducted based on dynamic changes of land use and land cover with land cover change detection and supervised classification (Akyürek et al 2018;Seyam et al 2023) and accuracy assessment using the kappa coefficient (Tewabe and Fentahun, 2020;Hussain and Karuppannan, 2023), and prediction by satellite image of landsat-7 and landsat-8 using cellular automated and Markov chains (Abijith and Saravanan, 2022;Wang et al 2021). Deep learning (DL)-based method (Song et al 2021), supervised classification, NDVI method (Pande et al 2021) including field verification and Google Earth Professional (Kamel, 2020), NDWI method (Raut et al 2020), MNDWI method (Bhattacharjee et al 2021), transition matrix method (Bagwan and Sopan, 2021), post classification matrix (Kouhgardi et al 2022; Márquez-Romance et al 2022; Das and Angadi, 2022), classprior object-oriented conditional random field (COCRF) method (Shi et al 2020), maximum likelihood classifier (MLC) method (Kumar and Jain, 2020; Saini et al 2019;Sarif and Gupta, 2022), Siamese global learning framework (Zhu et al 2022), preprocessing and classification and accuracy assessment (Thakur et al 2020;Mondal et al 2021;Mondal et al 2022), using various satellite imagery such as Landsat, MODIS, Sentinel and SPOT. The advantage of Landsat satellite data is the free accessibility of multi-temporal time series since 1972 (Lu et al 2019).…”

Integrating SAR and Optical Data for Aboveground Biomass Estimation of Coastal Wetlands Using Machine Learning: Multi-Scale Approach

“…There are estimates that range from 21-760 million Mg CO 2 equivalents per year that result from this land use change, which contributes to climate change [6]. The possibility of coastal wetlands mitigating climate change has received considerable attention in recent years due to their large carbon stocks, high carbon sequestration rates, and the possibility that human activities, such as conversion and degradation, could increase greenhouse gas (GHG) emissions [7,8]. Thus, several entities have expressed a desire to manage coastal wetland blue carbon [9].…”