19 research outputs found
Linking Local Knowledge and Satellite-Derived Land-Use/Land-Cover Change Information In Krabi Province, Thailand
No full textThis study was designed to gain an understanding of the linkage between development and sustainability of mangrove forest conversion in three coastal communities in Thailand. It presents a methodology that could potentially aid coastal communities in determining sustainable land use conversion approaches by considering the viewpoint of villagers. A remote sensing analysis of Landsat satellite images from 1989, 2001 and 2007 showed the results of a moderate, but sustained shrimp farming industry that only partially exploited mangrove forests. The three villages experienced a range of changes in mangrove forest area. The villagers\u27 perceptions (collected through field surveys) did not match the results from the remote sensing analysis and varied significantly. A logit multiple regression model was utilized to study the factors influencing whether villagers\u27 estimates agreed or disagreed with the remote sensing analysis. Results showed that the only variables statistically significant at the 0.10 level were age, occupation, and proximity to the mangrove resource. There is a widespread belief that one of the main negative effects of the development of shrimp farms is the pollution of water and, as a consequence, the reduction of wild catch. In this study, a majority of fishing households reported a reduction in wild catch, with nearly all attributing it to shrimp farms. A relatively small number of households noted positive effects from shrimp farming and listed these as an increase of income as a result of working at shrimp farms. The most common negative effects identified by the locals were water pollution, followed by a decrease in wild catch, and an increase in the number of mosquitoes. Although shrimp farm developers promised many benefits from this enterprise, very few were realized by the villagers. Integrating information from household surveys with data on land-cover change derived from remote sensing improves our understanding of the causes and processes of land cover change, and the perceptions of such changes. Integrating these two data sources illustrated that while shrimp farms did not have very many positive effects on the villagers, they were not as directly harmful to the mangrove forests as many believed
Assessment of capacity-building activities for forest measurement, reporting, and verification, 2011–15
No full text
Carbon stock losses and recovery observed for a mangrove ecosystem following a major hurricane in Southwest Florida
No full textStudies integrating mangrove in-situ observations and remote sensing analysis for specific sites often lack precise estimates of carbon stocks over time frames that include disturbance events. This study quantifies change in mangrove area from 1985 to 2018 with Landsat time series analysis, estimates above and belowground stored carbon using field data, and evaluates aboveground carbon stock changes after the 2004 Category 4, Hurricane Charley, in J.N. “Ding” Darling National Wildlife Refuge. Two allometric equation methods yielding similar results were used to estimate aboveground carbon content in three mangrove species found in the refuge. Aboveground carbon contained 67 (SE = 2) MgC ha−1 with a total refuge estimate of 74,504 MgC in 2018. Sediment contained 259 (SE = 28) MgC ha−1 for a total of 288,008 MgC in the refuge. The initial reduction in mangrove area caused by Hurricane Charley was between 0.6% and 5.3%, equivalent to between 427 MgC and 3,599 MgC under three different scenarios of carbon loss. As a result of the hurricane, approximately 61 ha of mangroves were disturbed, of which 24 ha had recovered by 2018, with 37 ha (~3% of the pre-hurricane mangrove area) still not recovered 14 years after the event. The 37 ha of mangroves that have not recovered are located in a tidally restricted area of the refuge. A longer recovery time in this area will likely result in a greater loss of carbon storage than in the rest of the refuge
Carbon stock of mangrove species.
No full textBar graphs showing mean and standard error of standing tree carbon stock (MgC ha-1) by dominant mangrove species on: A) Pohnpei Island-wide, B) island sides (windward vs leeward), and C) island zones (seaward, interior, vs landward). Island sides are shown in Fig 1 and zones are discussed in the text.</p
Carbon stock by location on Pohnpei Island.
No full textBar graphs of mean and standard error of standing tree carbon (STC) and downed wood carbon (DWC) stock (MgC ha-1) by location of the field plots on the island of Pohnpei. Island sides are shown in Fig 1 and zones are discussed in the text.</p
Mangrove carbon stock from various locations world-wide.
No full textMangrove carbon stock from various locations world-wide.</p
Dominant mangrove species.
No full textDominant mangrove species and associated standing tree carbon (STC) stock (MgC ha-1) in 5 by 5 meter cells mapped using the k-nearest neighbor method, overlaid on Esri’s topographic basemap of the island of Pohnpei [15]. Municipality names and windward and leeward island sides (translucent white and grey, respectively) are also shown on the map.</p
