Monitoring Changes in Coastal Mangrove Extents Using Multi-Temporal Satellite Data in Selected Communes, Hai Phong City, Vietnam

Abstract

Mangrove forests are important and known as one of the most productive ecosystems in the tropics. They reduce the impacts of extreme events, provide important breeding grounds for aquatic species and build the resilience of ecosystem-dependent coastal communities. On the contrary, they are also known as one of the most threatened and vulnerable ecosystems worldwide, which have experienced a dramatic decline due to extensive coastal development during the last half-century. Remote sensing techniques have demonstrated a high potential to detect, identify, map, and monitor mangrove conditions and its changes, which is reflected by a large number of scientific papers published on this topic. The aim of this study was to investigate the multi-decadal changes of mangrove forests selected communes in Hai Phong city, North Vietnam, based on using Landsat and Sentinel 2 data from 2000 to 2018. The study used these continuous steps: 1) data pre-processing; 2) image classification using Normalized Difference Vegetation Index; 3) accuracy assessments; and 4) multi-temporal change detection and spatial analysis of mangrove forests. The classification maps in comparison with the ground reference data showed the satisfactory agreement with the overall accuracy was higher than 80.0%. From 2000 to 2018, the areas of mangrove forests in the study regions increased by 584.2 ha in Dai Hop and Bang La communes (Region 1) and by 124.2 ha in Tan Thanh, Ngoc Xuyen and Ngoc Hai communes (Region 2), mainly due to the boom of mangrove planting projects and good mangrove management at the local community level.

Anthropogenic Drivers of Mangrove Loss and Associated Carbon Emissions in South Sumatra, Indonesia

“…It could generate accurate, reliable, and efficient spatial information important for mangrove monitoring and for examining the ecosystem response to anthropogenic drivers and environmental changes [26,27]. The findings from this study may be used by the subnational government for implementing low carbon development under the land sector, including conservation-based management improvement, and would also be useful to guide future restoration efforts [28]. This is in line with the current national climate policy interests, in which the Government of Indonesia will be incorporating the mangrove ecosystem as part of the nature-based climate solution from the land-use sector.…”

Estimation of Above-Ground Mangrove Biomass Using Landsat-8 Data- Derived Vegetation Indices: A Case Study in Quang Ninh Province, Vietnam

“…This coefficient indicates that there is a substantial agreement between data collected from the field survey and the results-derived Landsat-8 image classification (Malarvizhi et al, 2016;Dan et al, 2016). During accuracy assessments, mapping accuracy might be affected by possible mixed-pixel issues, tidal regime and cloud cover percentage (Hai-Hoa et al, 2020b). However, the overall accuracy assessment with 80.0% has confirmed that the land use and land cover mapping by Landsat-8 are applicable over the coasts of Hai Ha and Mong Cai, Quang Ninh Province (Hai-Hoa et al, 2020a, 2020b.…”

“…Gain bias correction scattering effects and corrections of the sun angle are necessary and mangrove cover analysis is conducted (Norjamaki and Tokola, 2007;Gupta et al, 2018;Hai-Hoa et al, 2020a). Radiometric calibration of Landsat-8 bands was applied to convert digital number values into top of the atmosphere spectral radiance (TOA), then bands were converted to TOA planetary reflectance (Chander et al, 2009;Gupta et al, 2018;Hai-Hoa et al, 2020b). Atmospheric and radiometric corrections were all employed to remove errors and increase accuracies by using the Spatial Analyst Tools in ArcGIS 10.6.…”

“…Therefore, these bands were staked into composite image, then were masked to both include areas where mangrove forests are more likely to be found, such as intertidal and low-lying areas, exclude areas where mangrove forests are not naturally present, like far inland, highlands and open ocean (Long and Giri, 2011;Hai-Hoa et al, 2020a). In this study, the aim of masking possible mangrove areas was to reduce unnecessary data volume and increase overall classification accuracy (Hai-Hoa et al, 2020b). To mask the possible mangrove areas accurately, 2019 PlanetScope (3mx3m) and Google Earth were used to delineate the boundary of possible mangrove areas from other coastal land use and land cover (Hai-Hoa et al, 2020a).…”

“…Mangrove forests are also well-known as important coastal resources in the tropics due to their provision of wood for construction and fuel, habitats of coastal marine organisms, carbon storage in biomass and soil, protection of coastal habitants from strong wind actions during typhoons, prevention of coastal erosion (Alongi, 2009;Donato et al, 2011). However, there is a large reduction of the global mangrove cover due to continuous conversion to nonmangrove land uses, such as aquaculture and shrimp farm expansion, agricultural development, and mangrove clearance for coastal human settlements (FAO, 2007;Hai-Hoa et al, 2013;Hai-Hoa, 2014;Hai-Hoa et al, 2020a;Hai-Hoa et al, 2020b). Therefore, it is very important to monitor mangrove forests against land-use change and detect mangrove deforestation and degradation.…”