RELATIONSHIP AMONG MANGROVE STAND STRUCTURE PARAMETERS IN ESTIMATING THE COMMUNITY SCALE OF ABOVEGROUND CARBON STOCK

Mangrove merupakan ekosistem pesisir yang memiliki kemampuan sangat baik dalam menyerap dan menyimpan karbon. Struktur tegakan mangrove memberikan kontribusi signifikan terhadap estimasi simpanan karbon yang umumnya tergambarkan pada persamaan alometrik dalam skala individu. Penelitian simpanan karbon atas permukaan tanah (abovegroundada komunitas mangrove telah dilakukan di mangrove Teluk Benoa. Penelitian ini bertujuan untuk membangun model dalam mengestimasi simpanan karbon aboveground dari beberapa parameter struktur tegakan mangrove. Metode stratified purposive sampling digunakan dalam penentuan sebaran titik penelitian. Sebanyak tiga zona (1–3) diidentifikasi berdasarkan interpretasi analisis mRE-SR (modified red edge-simple ratio) dan jenis mangrove yang mendominasi. Estimasi simpanan karbon aboveground diperoleh dengan metode non-destructive menggunakan persamaan common allometric. Hasil penelitian menunjukkan struktur tegakan mangrove zona 1 cenderung berbeda signifikan dengan zona lainnya. Secara keseluruhan, rata-rata simpanan karbon aboveground sebesar 193,45±34,88 ton C/ha. Simpanan karbon aboveground tertinggi ditemukan pada zona 1 yang didominasi jenis Sonneratia alba. Analisis regresi linear dan Akaike’s Information Criterion (AIC) menunjukkan bahwa kombinasi dari tutupan kanopi, kerapatan pohon, kerapatan pancang dan diameter pohon menjadi model terbaik dalam mengestimasi simpanan karbon pada skala komunitas. Model kombinasi ini memiliki nilai koefisien regresi tertinggi dan nilai root mean squared error (RMSE) terendah dibandingkan dengan model lainnya. Hasil penelitian diharapkan dapat digunakan dalam mengestimasi simpanan karbon secara lebih efisien dan akurat dalam skala komunitas.Mangrove is one of coastal ecosystem which has a major role to sequastrate and store carbon. Mangrove stand structure delivers a significant contribution for estimating biomass carbon stock through individual scale allometric equations. On the other hand, the aboveground carbon research on the community scale was conducted in Teluk Benoa. The study aimed to establish a model for estimating mangrove aboveground carbon stock from the multiple variables of mangrove stand structure. A stratified purposive sampling method was applied for distributing quadratic samples. Three mangrove zones (1–3) were identified using mRE-SR (modified red edge-simple ratio) interpretation based on mangrove species domination. A common allometric equation was applied for estimating aboveground carbon stock. The result showed that mangrove stand structure in zone 1 was significantly different to other zones. Aboveground carbon stock was 193.45±34.88 tons C/ha on entire sites. It was found highest in zone 1 which was dominated by Sonneratia alba. The linear regression and Akaike’s Information Criterion (AIC) analysis showed that the combination of canopy cover, tree density, sapling density and tree diameter became the best model in estimating carbon stock at the community scale. The multiple model had the highest regression coefficient and the lowest root mean square error (RMSE) value. We expect that the multiple variable model could be more efficient and accurate to estimate aboveground carbon stock on community scale

Soil greenhouse gas fluxes to the atmosphere during the wet season across mangrove zones in Benoa Bay, Indonesia

Abstract Behind their role as carbon sinks, mangrove soil can also emit greenhouse gases (GHG) through microbial metabolism. GHG flux measurments of mangroves are scarce in many locations, including Indonesia, which has one of the world’s most extensive and carbon-rich mangrove forests. We measured GHG fluxes (CO2, CH4, and N2O) during the wet season in Benoa Bay, Bali, a bay with considerable anthropogenic pressures. The mangroves of this Bay are dominated by Rhizophora and Sonneratia spp and have a characteristic zonation pattern. We used closed chambers to measure GHG at the three mangrove zones within three sites. Emissions ranged from 1563.5 to 2644.7 µmol m−2 h−1 for CO2, 10.0 to 34.7 µmol m−2 h−1 for CH4, and 0.6 to 1.4 µmol m−2 h−1 for N2O. All GHG fluxes were not significantly different across zones. However, most of the GHG fluxes decreased landward to seaward. Higher soil organic carbon was associated with larger CO2 and CH4 emissions, while lower redox potential and porewater salinity were associated with larger CH4 emissions. These data suggest that soil characteristics, which are partially determined by location in the intertidal, significantly influence GHG emissions in soils of these mangroves

Mangrove biomass sequestration in Benoa Bay

Mangrove ecosystems are coastal ecosystems that can store carbon three times higher than all other forests on earth. Current conditions show a decrease in mangrove forests and damage to mangrove ecosystem conditions that impact reducing mangrove carbon sequestration. Data relating to the potential of sustainable mangrove biomass is currently lacking, so research is needed. The purpose of this study was to determine changes in the amount of mangrove biomass at permanent stations temporally. This research was conducted at 10 sample points in the Benoa Bay area using a stratified purposive sampling method with a quadrant transect measuring 10 meters x 10 meters. Data were collected by measuring DBH on each mangrove stand within the transect. Data analysis was conducted using the common allometric equation by including the wood-specific gravity per species. In general, there was an increase in the average biomass in each plot with an average of 1.315 tons/ha at six months different. This shows that the larger the diameter of the stand, the greater the biomass produced

Comparison of mangrove canopy covering accuracy using landsat 8 and landsat 9 imagery based on several vegetation indices in West Bali National Park

The remote sensing implementation is beneficial as a means of monitoring the ecosystem. Landsat imagery is a remote sensing (open access) based data source with a long and wide monitoring period with good image quality. This study compares the accuracy of Landsat 8 and Landsat 9 satellite images in detecting mangrove canopy cover using 13 different remote sensing vegetation indices in the West Bali National Park, Indonesia. The mangrove canopy cover data was collected with the hemispherical photography method. A linear regression test was conducted to determine the relationship between the remote sensing vegetation indices and the field's percentage of mangrove canopy cover. The result indicated that Landsat 8 was more accurate in detecting mangrove canopy cover than Landsat 9. Of the 13 remote sensing indices evaluated, the Chlorophyll Vegetation Index (CVI) had the highest accuracy, with R2 values of 0.86 and 0.75 for Landsat 8 and 9, respectively.