• Ecology and Resilient Infrastructure
• /
• v.10 no.4
• /
• pp.161-170
• /
• 2023

We attempted to estimate the carbon accumulation of Hibiscus hamabo and Paliurus ramosissimus, semimangroves native to Jeju Island, by remote sensing and to build an artificial intelligence model that predicts its spatial variation with climatic factors. The aboveground carbon accumulation of semi-mangroves was estimated from the aboveground biomass density (AGBD) provided by the Global Ecosystem Dynamics Investigation (GEDI) lidar upscaled using the normalized difference vegetation index (NDVI) extracted from Sentinel-2 images. In Jeju Island, carbon accumulation per unit area was 16.6 t C/ha for H. hamabo and 21.1 t C/ha for P. ramosissimus. Total carbon accumulation of semi-mangroves was estimated at 11.5 t C on the entire coast of Jeju Island. Random forest analysis was applied to predict carbon accumulation in semi-mangroves according to environmental factors. The deviation of aboveground biomass compared to the distribution area of semi-mangrove forests in Jeju Island was calculated to analyze spatial variation of biomass. The main environmental factors affecting this deviation were the precipitation of the wettest month, the maximum temperature of the warmest month, isothermality, and the mean temperature of the wettest quarter. The carbon accumulation of semi-mangroves predicted by random forest analysis in Jeju Island showed spatial variation in the range of 12.0 t C/ha - 27.6 t C/ha. The remote sensing estimation method and the artificial intelligence prediction method of carbon accumulation in this study can be used as basic data and techniques needed for the conservation and creation of mangroves as carbon sink on the Korean Peninsula.

• Journal of Forest and Environmental Science
• /
• v.31 no.3
• /
• pp.225-234
• /
• 2015

Mangroves are important coastal ecosystems and are located at the inter-tidal zones of tropical and sub-tropical belts. The global mangrove forests are declining dramatically because of the conversion of forests to shrimp farming, over-exploitation, pollution and freshwater diversion. The Bangladesh Forest Department initiated mangrove afforestation throughout the coastal belts of Bangladesh in 1966 to provide better protection for the coastal communities. Up to 1990, 120,000 ha of mangroves had been planted and it is one of the largest coastal afforestaton programs in the world. The objective of this study is to exploit the spatial extent of mangrove plantation and their dynamics of changes over the last two decades using multispectral Landsat imagery. The study area covers the coastal areas of Bangladesh that is extended over the eastern part of Sundarbans up to Teknaf, the southern tip of mainland Bangladesh. Mangrove plantations were interpreted visually on computer screen and interactive delineation of forest boundary was done. The mangrove plantation area has been estimated as 32,725, 47,636 and 43,166 ha for the year of 1989, 2000 and 2010, respectively. Mangrove deforestation by human activity has increased almost six times in the recent decade in comparison to the previous one. The mangrove forest loss due to coastal erosion has slightly declined in the 2000s. Mangroves have been lost primarily because of agricultural expansion. The result of this investigation will be helpful to understand the dynamics of mangrove plantation and the main drivers of changes in this coastal ecosystem.

• Korean Journal of Remote Sensing
• /
• v.40 no.2
• /
• pp.203-217
• /
• 2024

The Land Use, Land-Use Change and Forestry (LULUCF) sector of the National Greenhouse Gas Inventory is crucial for obtaining data on carbon sinks, necessitating accurate estimations. This study analyzes cases of countries applying the LULUCF sector at the Tier 3 level to propose enhanced methodologies for carbon sink estimation. In nations like Japan and Western Europe, satellite spatial information such as SPOT, Landsat, and Light Detection and Ranging (LiDAR)is used alongside national statistical data to estimate LULUCF. However, in Korea, the lack of land use change data and the absence of integrated management by category, measurement is predominantly conducted at the Tier 1 level, except for certain forest areas. In this study, Space-borne LiDAR Global Ecosystem Dynamics Investigation (GEDI) was used to calculate forest canopy heights based on Relative Height 100 (RH100) in the cities of Icheon, Gwangju, and Yeoju in Gyeonggi Province, Korea. These canopy heights were compared with the 1:5,000 scale forest maps used for the National Inventory Report in Korea. The GEDI data showed a maximum canopy height of 29.44 meters (m) in Gwangju, contrasting with the forest type maps that reported heights up to 34 m in Gwangju and parts of Icheon, and a minimum of 2 m in Icheon. Additionally, this study utilized Ordinary Least Squares(OLS)regression analysis to compare GEDI RH100 data with forest stand heights at the eup-myeon-dong level using ArcGIS, revealing Standard Deviations (SDs)ranging from -1.4 to 2.5, indicating significant regional variability. Areas where forest stand heights were higher than GEDI measurements showed greater variability, whereas locations with lower tree heights from forest type maps demonstrated lower SDs. The discrepancies between GEDI and actual measurements suggest the potential for improving height estimations through the application of high-resolution remote sensing techniques. To enhance future assessments of forest biomass and carbon storage at the Tier 3 level, high-resolution, reliable data are essential. These findings underscore the urgent need for integrating high-resolution, spatially explicit LiDAR data to enhance the accuracy of carbon sink calculations in Korea.