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Aquaculture is one of the main drivers of mangrove loss across Southeast Asian countries. The conversion of mangroves to aquaculture generates substantial loss of carbon stocks and reduces carbon storage capacity. Here, we present total ecosystem carbon stocks (TECS), carbon dioxide (CO2) and methane (CH4) effluxes obtained from mangrove forests (fringe and interior mangroves), silvofishery aquaculture ponds (dense and sparse mangroves), and non-silvofishery aquaculture ponds in Sawah Luhur, Banten, Indonesia. We found no significant difference in TECS across five land uses, ranging from 261 ± 14 Mg C ha-1 in non-silvofishery ponds to 574 ± 119 Mg C ha-1 in fringe mangroves. Most of these stocks were found in the soil carbon pool (87%) in fringe and interior mangroves. However, the conversion of mangroves to aquaculture ponds resulted in soil carbon loss from -6% to 60%. The highest soil CO2 effluxes during dry and wet seasons were observed in interior mangroves (151 ± 12 mg CO2 m-2 h-1). The highest soil CH4 effluxes were found in fringe mangroves with 0.13 ± 0.04 mg CH4 m-2 h-1. The highest aquatic CO2 and CH4 effluxes were found in dense silvofishery ponds, at 118 ±7mg CO2 m-2 h-1 and 0.38 ± 0.04 mg CH4 m-2 h-1, respectively. Our findings suggest that land use that includes mangroves (i.e., mangrove forest and/or silvofishery ponds) tends to have higher carbon stocks, soil, and aquatic CO2 and CH4 effluxes, compared to aquaculture ponds without mangroves. It is therefore crucial to maintain mangroves for natural carbon capture and storage through carbon stock enhancement. [ABSTRACT FROM AUTHOR]

Indonesia has the largest tropical peatlands in the Asia-Pacific region and the world's second largest tropical peatlands. Growing concerns on peatland conservation and restoration emphasise the importance of these ecosystems for climate change mitigation and adaptation. Although Indonesia's peatlands are well-studied compared with other tropical countries, there are still uncertainties regarding its peat volume and carbon stock estimations, and how important it is locally and globally. To solve these uncertainties, this study estimated peat volume and carbon stock in Indonesia using the national peatlands data published by the Ministry of Agriculture, Republic of Indonesia and analysed greenhouse gas emissions among the major industrial sectors in the country and global emissions by peatland degradation. Our study posited that anthropogenic disturbance degraded Indonesian peatlands and that the country could become the largest greenhouse gas emitter in the world. Therefore, successful conservation and restoration of Indonesia's peatlands could significantly contribute to global climate change mitigation and adaptation. [ABSTRACT FROM AUTHOR]

Vegetation absorbs carbon dioxide (CO2) emissions during photosynthesis. Covering more areas with trees will increase the CO2 absorption capacity more substantially than other vegetation like bushes, grasses, or rice fields. Trees convert the CO2 captured during photosynthesis into organic carbon to be stored in biomass. Woody trees account for approximately 60% of the total aboveground tree biomass, and trunks, where food reserves produced in photosynthesis are stored, have relatively large biomass compared to other parts of the tree. The biomass of a vegetation stand determines the optimization of air pollutant absorption in urban areas. Yogyakarta City is the center for tourism, education, and cultural activities in Indonesia, which is vulnerable to land-use conversion, a factor of the shrinking green space. This study aimed to estimate carbon stock from vegetation biomass in Yogyakarta City using the remote sensing product SPOT-7 imagery. To calculate the vegetation biomass, the diameter at breast height (DBH) of stands was measured in the field. Then, statistical analyses were performed to determine the correlation and regression between the actual or observed biomass and the Normalized Difference Vegetation Index (NDVI) value derived from the SPOT-7 image. The regression model used was y = 1.4277x - 0.0849. The total biomass produced in Yogyakarta City was estimated at 1,399,487.1 tonnes, which contained 643,764.1 tonnes of carbon stock. [ABSTRACT FROM AUTHOR]

Land covers is the main determinant of river discharge rate of a watershed. The land cover, on the other hand, also contain the carbon stock accumulated above ground over watershed. This relation is very useful in carbon stock monitoring to support Paris Agreement implementation especially for tropical countries where the MRV activities are commonly very expensive. In this research the ordinary least square models were employed at significant level of 95%. First model was the river discharge, [YD], as the function of rainfall [RAIN] (mm/year) and the acreage of land covers area (ha) namely the forest [FREST], shrub [SRHB], plantation [PLNT], up land crop [UPLND], mix farming [MIXFM], and settlement [SETL]. The second model was to express the carbon stock [YC] (ton/ha) as the function of the [YD]. Data series of [RAIN], [YD] and lands covers of 2011, 2014, 2017 covering 3 watershed (Way Pengubuan, Way Bulok, and Way Besai) lies at Lampung Province, Republic of Indonesia. We employed Minitab 16 for parameter model estimation. The simple models achieved for river discharge was [YD]i=13,612.00 +0.1192[RAIN]I -238,00[FREST]i-129,08[SRHB]i-107.46[PLNT]i-184.16[SETL]i-131.08[UPLND]i-30.97[MIXFM]i-127.01[PADY]i with P=0.000 and R.Sq (adjt) =96.6%; and the carbon stock expressed by [Yc]i=17.2237 - 0.0212 [YD]i with P=0.0006 and the Rsq(adjt)= 0.5283. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: The lakeside has an enormous sediment carbon storage potential; however, it is susceptible to various environmental changes and can easily become a source of carbon emissions. Understanding the amount of carbon storage in lakeside sediments and organic matter sources may provide information about the potential of lakeside zones in climate change mitigation, particularly for sustainable lake management. This study aims to estimate sediment organic carbon stock and the sources of organic matter in the Maninjau Lakeside-West Sumatera, Indonesia. METHODS: Sediment sampling was performed at five research sites, with a depth of 0-100 centimeters. Sediment samples were divided into 4 subsamples: 0-15; 15-30; 30-50; and 50-100 centimeters. Bulk density and total nitrogen content were analyzed, and the percentage of organic carbon was calculated from the loss of ignition. The sediment organic carbon stock was calculated based on the bulk density and organic carbon content. Carbon per nitrogen ratio was also calculated to determine temporal changes in the sources of organic matter in the lake. FINDINGS: This study demonstrated that Maninjau Lakeside has an enormous potential sedimentary organic carbon stock range between 284.23-442.59 megagrams per carbon per hectare. The highest total sediment carbon stock was found in Duo Koto (442.59 megagrams per carbon per hectare), with the lowest in Koto Kaciak (284.23 megagrams per carbon per hectare). In addition, the study's results also exhibited significant differences in sediment organic carbon stocks at each location with different land use and cover; in this case, the forest area has a higher carbon stock value than the agricultural and settlement areas. Therefore, it is essential to take initiatives for the restoration and conservation of lakeside areas because of their essential role in mitigating the climate change. The mean ratio of organic carbon and total nitrogen was between 9.96 to 16.91, indicating that phytoplankton, a mixture of floating macrophytes, and submerged vegetation were the sources of organic matter. CONCLUSION: In general, the value of sediment organic carbon stocks tends to be lower in locations with intensive agricultural settlements than in forest areas. This study emphasizes that restoring lakeside wetland is vital in increasing sediment organic carbon stocks and maintaining lake sustainability. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) is one of the greenhouse gases that cause global warming, with the highest atmospheric concentration. Mangrove forests absorb CO2 three times higher than terrestrial forests and tropical rainforests. Moreover, mangrove forests can be a source of Indonesian income in the form of a blue economy. Therefore, an accurate method is needed to investigate mangrove carbon stock. The utilisation of remote sensing data with the results of the above-ground carbon (AGC) detection model of mangrove forests based on multispectral imaging and vegetation index can be a solution to obtain fast, cheap, and accurate information related to AGC estimation. This study aimed to investigate the best model for estimating the AGC of mangroves using Sentinel-2 imagery in Benoa Bay, Bali Province. The Random Forest (RF) method was used to classify the difference between mangrove and non-mangrove by treating several parameters. Furthermore, a semi-empirical approach was used to assess and map the AGC of mangroves. Allometric equations were used to calculate and produce AGC per species. Moreover, the model was built with linear regression equations for one variable x and multiple regression equations for more than one x variable. Root Mean Square Error (RMSE) was used to assess the validation of the model results. The results of the mangrove forests area detected in the research location around 1,134.92 ha, with an Overall Accuracy (OA) of 0.984 and a kappa coefficient of 0.961. This study highlights that the best model was the combination of IRECI and TRVI vegetation indices (RMSE: 11.09 Mg/ha) for a model based on red edge bands. Meanwhile, the best results from the model that does not use the red edge band were the combination of TRVI and DVI vegetation indices (RMSE: 13.63 Mg/ha). The use of red edge and NIR bands is highly recommended in building the AGC model of mangrove forests because they can increase the accuracy value. Thus, the results of this study are highly recommended in estimating the AGC of mangrove forests because it has been proven to increase the accuracy value of previous studies using optical images. [ABSTRACT FROM AUTHOR]

The Java Sea acts as a carbon source that contributes to the release of carbon dioxide into the atmosphere. The blue carbon ecosystems in the Karimunjawa Islands may absorb and store the carbon released from the Java Sea. The carbon stock in Kemujan Island (Karimunjawa Islands), one of these blue carbon ecosystems, was previously estimated using a remote sensing approach. In the present study, we investigated the carbon stock in the seagrass bed at Kemujan Island by conducting field observations and laboratory analysis. The result shows that there are only two seagrass species in the study area--'Enhalus acoroides' and 'Thalassia hemprichii'--which are categorized as nearly dense and very dense, respectively. Regarding the carbon biomass, the carbon stock below the substrate is more than that above the substrate. However, the carbon stock in the seagrass biomass is less than in the sediment. The average carbon stock in the seagrass bed at Kemujan Island is 4901.91 g C/m2, which is much more than the amount estimated by means of remote sensing. Furthermore, this amount is much larger compared with the average carbon stock of other areas in the Karimunjawa Islands. The large amount of carbon stock stored in the seagrass bed at Kemujan Island denotes the role of this seagrass bed as a carbon sink area. [ABSTRACT FROM AUTHOR]

Spatially explicit information on aboveground seagrass carbon stock (AGCseagrass) is required to understand the role of seagrass as a nature-based solution to mitigating and adapting to climate change. Remote sensing provides the most effective and efficient means to map AGCseagrass. This research aimed to assess the accuracy of multispectral images to map AGC in seagrass meadows with different characteristics. Kemujan Island in Indonesia was selected to represent patchy beds with relatively low seagrass percent cover and low water clarity, whereas Labuan Bajo of Indonesia served an example of continuous beds with a relatively high percent cover and water clarity. WorldView-2 is a high-spatial-resolution multispectral image with six visible bands, by which water can be penetrated. Several image processing techniques, including sunglint and water column correction, principal component analysis, and co-occurrence texture analysis, were applied to the atmospherically corrected WorldView-2 images. These datasets were used as inputs in the AGC empirical model using seven regression algorithms, namely, single-band linear regression, band-ratio linear regression, stepwise regression, random forest regression, support vector regression, extreme gradient boosting, and multivariate adaptive regression spline. Seagrass field data collected using photo quadrat technique were used to train the regression model and assess the accuracy of the resulting AGCseagrass maps. The results indicated that WorldView-2 can be used to map AGC in different seagrass meadows with a consistent accuracy. The most accurate AGCseagrass map for Kemujan Island had a root mean square error (RMSE) of 4.11 g C m−2 for the aboveground stock in the range of 0–28.70 g C m−2, and for Labuan Bajo, the RMSE of the map was 9.73 g C m−2, with aboveground stock range of 0.31–64.38 g C m−2. Model cross-validation revealed that the mapping model can be site-specific or robust depending on the characteristics of the field-derived AGCseagrass data used to train the regression algorithm. For example, the model developed for Labuan Bajo seagrasses, which had a higher AGC variance, can be successfully applied to Kemujan Island with its lower AGC variance, but not vice versa. This finding is a key factor in the future development of a robust AGCseagrass mapping model that is applicable to various seagrass meadows as a stepping stone to accelerating the filling of gaps in the global seagrass dataset. [ABSTRACT FROM AUTHOR]

Indonesia is one of the countries with the largest area of tropical peatlands in the world. These wide peatlands have a vital role in the carbon cycle and carbon storage in huge quantities, thus strict conservation in the area is necessary. One effort to carry out conservation is to understand the spatial distribution of carbon stock in peatlands. This study aims to map the spatial distribution of carbon stock based on peat thickness modeling using machine learning algorithms, i.e., Random Forest (RF), Quantile Regression Forests (QRF), and Cubist. A case study was conducted in a part of Lake Sentarum National Park, Indonesia where human interference is still limited. Digital elevation model (DEM) and synthetic aperture radar (SAR) data were included as the input variables. The results showed that RF performed the best among other models to estimate peat thickness (mean = 1.877 m) with an RMSE (root mean square error) of 0.483 m and an R2 of 0.786. DEMs are the most important parameters in our analysis compared to SAR data. Based on the best model, we estimated the total volume of 5,112,687 m3 in the study area, produced at 12.5 m resolution, which was then converted to a total of carbon stock at 0.337 ± 0.106 Mt carbon. [ABSTRACT FROM AUTHOR]

Reforestation in the tropics is highlighted as an important intervention to mitigate climate change globally because of its potential for high CO2 removal rates, ranging from 4.5 to 40.7 t CO2e ha−1 yr−1 during the first 20 years of tree growth. Reforestation is critical to meeting emissions' targets of the Paris Climate Agreement, as well as achieving Indonesia's Nationally Determined Contribution (NDC) targets. Increasing carbon stocks through forest and land rehabilitation activities (RHL) is one of the Ministry of Environment and Forestry (MoEF) five main strategies for reducing greenhouse gas (GHG) emissions from the land sector. This study focuses on reforestation opportunities to support Indonesia's NDC in reducing GHG emissions by 2030. We identified unproductive areas of land (shrub, open land) with highly degraded conditions as potential areas for reforestation. Based on Indonesian data of land cover change, we found that reforestation activities during 2019–2030 (11 years) under a realistic, ambitious and very ambitious scenario may remove carbon up to −0.25 GtCO2e (equal to −23 MtCO2e yr−1), −1.3 GtCO2e (equal to −124 MtCO2e yr−1) and −2.7 GtCO2e (equal to −247 MtCO2e yr−1), respectively. Based on international data of land cover change (Hansen et al. 2013), reforestation activities during 2019–2030, under a realistic, ambitious, and very ambitious scenario, have the opportunities to remove −17 MtCO2e yr−1, −118 MtCO2e yr−1, and −241 MtCO2e yr−1, respectively. This study demonstrates that ambitious and very ambitious scenarios of reforestation activities can significantly contribute to Indonesia's forestry-related NDC in 2030 by reducing the Indonesia Business As Usual (BAU) emissions up to 17% and 35%. [ABSTRACT FROM AUTHOR]

Mangrove ecosystem provides important services for coastal communities. Unfortunately, this ecosystem has undergone physical pressures for decades, including land conversion for shrimp aquaculture. Rehabilitation of the degraded mangroves offers an effective solution for bringing back mangrove functions, including carbon sequestration capacity. This study aims to quantify carbon stock (CS) and sequestration rate of a rehabilitated mangrove ecosystem in Indonesia, particularly in Banten Province. We compared sediment CS and carbon accumulation rate (CAR) of a rehabilitated mangrove in Ciseukeut, Panimbang, with a natural mangrove in Cilintang, the Ujung Kulon National Park. The sediment CS to a depth of 1 m in a rehabilitated mangrove in Ciseukeut was higher than in a natural mangrove in Cilintang, constituting 364 Mg C ha−1 and 126 Mg C ha−1, respectively. However, CAR showed the opposite pattern, in which a natural mangrove showed an almost two‐fold higher rate (0.50 ± 0.42 Mg C ha−1 year−1) than that of a rehabilitated mangrove (0.27 ± 0.20 Mg C ha−1 year−1). Local hydrogeomorphological conditions play a role in affecting sediment CS and CAR in both sites. Enhancing hydrological modification can promote CAR, especially in an abandoned pond like in Ciseukeut. This study provided a knowledge base for suitable mangrove rehabilitation in the future that supports the Indonesian government's commitment to rehabilitating ca. 600,000 ha of degraded mangroves in Indonesia. Furthermore, the study also provides the first investigation of potential blue carbon for the world's natural heritage of the Ujung Kulon National Park. [ABSTRACT FROM AUTHOR]

The mangrove ecological services as carbon sinks and storage are very useful in the efforts to mitigate global warming and climate change. In this study, the above and below-ground biomass, carbon stock, as well as carbon sequestration by the mangroves in Demta Bay, Papua Province, Indonesia were estimated. Allometric equations were used to determine the mangrove biomass in 36 observation plots. The biomass value was used to determine carbon stock and estimate carbon sequestration. Nine mangrove species were found in Demta Bay, with the contribution of mangrove species to biomass (AGB and BGB) in the following order: Rhizophora apiculata > Rhizophora mucronata > Bruguiera gymnorhiza > Bruguiera cylindrica > Heritiera Littoralis > Xylocarpus molucensis > Rhizophora stylosa > Avicennia marina > Sonneratia caseolaris. The average mangrove biomass was estimated at 174.20 ± 68.14 t/ha (AGB = 117.62 ± 45.68 t/ha and BGB = 56.58 ± 22.49 t/ha). The carbon stocks in mangroves at the Ambora site were higher than the Tarfia and Yougapsa sites, averaging 123.57 ± 30.49 t C/ha, 81.64 ± 25.29 t C/ha, and 56.09 ± 39.03 t C/ha, respectively. The average carbon stock in the mangrove ecosystem of Demta Bay is estimated at 87.10 ± 34.07 t C/ha or equivalent to 319.37 ± 124.92 t CO2 e/ha. The results of this study indicate that the mangrove ecosystem in Demta Bay stores quite high carbon stocks, so it is necessary to maintain it with sustainable management. Therefore, climate change mitigation is not only done by reducing the carbon emission levels but also needs to be balanced by maintaining the mangrove ecosystem services as carbon sinks and sequestration. [ABSTRACT FROM AUTHOR]

Mapping the above-ground carbon potential by using a non-destructive method has been a serious challenge for researchers in the effort to improve the performance of natural forest management in Indonesia, particularly in the ex-Mega Rice Project (MRP) area in Central Kalimantan Province. Nevertheless, the rapid and dynamic changes in secondary peat swamp forests are currently mapped effectively with the remote sensing technology using the Forest Canopy Density (FCD) model. FCD analysis as done by integrating vegetation index, soil index, temperature index and shadow index of Landsat 8 OLI images. The result was an FCD class map. In each class, parameter measurements were established for seedling, sapling, poles and tree stages. Above-ground carbon stock was calculated using three allometric equations. The results revealed that the values of carbon stock in ±16,147.26 ha dense secondary peat swamp forest, ±1,509.66 ha moderately dense scrub swamp forest, and ±632.07 ha sparse scrub swamp forest were, respectively, 79.28-122.96; 74.06-113.06; and 40.48-63.60 ton/ha. These results show that FCD application could be used to classify forest density effectively and in line with the variety of their attributes such us aboveground biomass and carbon stock potential. [ABSTRACT FROM AUTHOR]

The delivery of sustainable development goals (SDGs) through a participatory land and forest conservation initiative was evaluated in South Kalimantan Province, Indonesia. The initiative focused on the rehabilitation of a 410 ha forest that was managed by local villagers. A forest rehabilitation and management initiative was developed through participatory action, establishing a well-managed rubber plantation that provided new livelihood opportunities. Poverty reduction was promoted, evidenced by a reduction in inequality amongst the local community. Between 2010 and 2018 the Gini coefficient of inequality declined from 34.6% to 31.3%, demonstrating a contribution to SDGs 1 (No Poverty) and 10 (Reduced Inequalities). In addition, forest rehabilitation resulted in improved carbon stock and biodiversity management contributing to SDG15 (Life on Land). This was attributed to successful forest rehabilitation and the reduced incidence of forest fires. Cooperation amongst local villagers categorized as living in poor households was improved, facilitated by capacity building. This focused on rubber plantation management, cooperative action, and firefighting activities. This capacity building contributed to the delivery of SDG 17 (Partnerships for the Goals). Results from a socio-economic survey demonstrated that group activities and cooperation amongst stakeholders were essential to improve both livelihoods and forest management practices. [ABSTRACT FROM AUTHOR]

Numerous analyses have been performed to quantitatively link carbon stock change caused by land-use change (CSC-LUC) to consumption of agricultural products, but results differ significantly, even for studies focussing on the same region or product. This is due to the different focuses and interpretations of the links between direct drivers and underlying causes of CSC-LUC, which can be translated into differences in key functions, i.e. specific methods, algorithms and parameters embedded in the analysis. Using the example of Indonesian palm oil production (often associated with CSC-LUC), this paper carries out a meta-analysis of 12 existing studies, determines the different settings for the key functions embedded in consumption-based CSC-LUC studies and discussed their implications for policymaking. It identifies the underlying reasons of adopting different settings within the eight key functions and their advantages and trade-offs. Examples are the way of determining how deforestation is linked to oil palm, and the inclusion of non-agriculture and non-productive drivers in the accounting to weight their roles in CSC-LUC in comparison to palm oil consumption. Following that, the quantitative results from the selected studies were processed and harmonised in terms of unit, allocation mechanism, allocation key and amortisation period. This resulting in ranges of 0.1–3.8 and −0.1–15.7 tCO 2 /t crude palm oil for historical and projection studies, respectively. It was observed that CSC-LUC allocated to palm oil is typically lower when propagating effects and non-agricultural or non-productive drivers were accounted for. Values also greatly differ when marginal and average allocation mechanisms were employed. Conclusively, individual analyses only answer part of the question about CSC-LUC drivers and have their own strengths and weaknesses. Since the context can be very different, using quantitative results from a single study for accounting purposes in policymaking is not recommended. Instead, insights from different studies should be combined, e.g. the relative role of logging and oil palm or the contribution to CSC-LUC in regional and global perspectives. [ABSTRACT FROM AUTHOR]

Currently, the function of seagrass community as carbon storage has been discussed in line with "blue carbon" function of that seagrass has. Seagrass bed are a very valuable coastal ecosystem, however, seagrass bed is threatened if compared to other coastal ecosystems, such as mangroves and coral reefs. The threatened seagrass experienced also contributes to its capacity in absorbing CO2 emission from greenhouse gasses such as CO2 emission. Temporal estimation shows that CO2 emission will increase in the coming decade. On the other side, efforts to decrease climate change can be influenced by the existence of seagrass. Informations about existence of seagrass as carbon storage are still very rare or limited. This study was aimed to estimate carbon storage on seagrass community in Marine Nature Tourism Park of Kotania Bay Area, Western Seram, Maluku Province. The quadrat transect method of 0.25 m² for each plot was used to collect seagrass existence. The content of carbon in the sample of dry biomass of seagrass was analyzed in the laboratory using Walkley & Black method. The results showed that total carbon stored was higher in both Osi and Burung Islands of Kotania Bay than other studied areas (Buntal and Tatumbu Islands, Marsegu Island, Barnusang Peninsula, Loupessy and Tamanjaya Village). The average carbon stored in Kotania Bay waters was 2.385 Mg C ha-1, whereas the total of carbon stored was 2054.4967 Mg C. [ABSTRACT FROM AUTHOR]

Abstract: Allometric equations can be used to estimate the biomass and carbon stock of forests. However, so far the equations for Dipterocarp forests have not been developed in sufficient detail. In this research, allometric equations are presented based on the genera of commercial species and mixed species. Separate equations are developed for the Dipterocarpus, Hopea, Palaquium and Shorea genera, and an equation of a mix of these genera represents commercial species. The mixed species is constructed from commercial and non-commercial species. The data were collected in lowland mixed Dipterocarp forests in East Kalimantan, Indonesia. The number of trees sampled in this research was 122, with diameters (1.30m or above buttresses) ranging from 6 to 200cm. Destructive sampling was used to collect the samples where diameter at breast height (DBH), commercial bole height (CBH), and wood density were used as predictors for dry weight of total above-ground biomass (TAGB). Model comparison and selection were based on Akaike Information Criterion (AIC), slope coefficient of the regression, average deviation, confidence interval (CI) of the mean, paired t-test. Based on these statistical indicators, the most suitable model is ln(TAGB)= c + αln(DBH). This model uses only a single predictor of DBH and produces a range of prediction values closer to the upper and lower limits of the observed mean. Model 1 is reliable for forest managers to estimate above-ground biomass, so the research findings can be extrapolated for managing forests related to carbon balance. Additional explanatory variables such as CBH do not really increase the indicators’ goodness of fit for the equation. An alternative model to incorporate wood density must be considered for estimating the above-ground biomass for mixed species. Comparing the presented equations to previously published data shows that these local species-specific and generic equations differ substantially from previously published equations and that site specific equations must be considered to get a better estimation of biomass. Based on the average deviation and the range of CI, the generalized equations are not sufficient to estimate the biomass for a certain type of forests, such as lowland Dipterocarp forests. The research findings are new for Dipterocarp forests, so they complement the previous research as well as the methodology of the Good Practice Guidance for Land Use and Land Use Change and Forestry (GPG-LULUCF). [Copyright &y& Elsevier]

Mangrove ecosystems store large amounts of 'Blue Carbon', in particular in the sediment. Research in the past decade has emphasized the quantitative significance of carbon storage in mangrove forests in climate change mitigation, mainly by determining carbon stocks and calculating potential CO 2 emissions caused by mangrove degradation. However, while this approach focuses on the total amount of carbon that can be lost to degradation, it fails to capture the amount that is sequestered annually. Therefore, carbon accumulation in mangrove sediments also needs to be taken into account. This study (i) explains the differences between carbon stocks and carbon accumulation rates (CAR), (ii) it addresses the geographical variation of carbon storage and underlying factors and (iii) it assesses the global relevance of 'Blue Carbon' sequestration in mangrove sediments. Results indicate that reducing uncertainties in carbon storage estimates of individual systems requires a representative set of data that covers within-system variability. An example from Indonesia illustrates that a mangrove ecosystem with a high C stock can have a low CAR and vice versa. It is therefore conceivable that coastal environmental settings with high allochthonous supply of mineral sediment, organic matter and nutrients mostly have low carbon stocks, but high CARs. As these settings represent >80% of the global mangrove area they are most important in terms of long-term carbon storage. While a C stock is a measure of the "vulnerability potential" in the case of ecosystem degradation or total loss, a CAR is rather a measure of the "mitigation potential" of carbon storage in mangrove ecosystems. The global carbon storage in mangrove sediments of 32 Tg yr−1 estimated from CARs in this study is at the upper end of the range of global budgets (14.6–31.1 Tg yr−1, mean 22.9 Tg yr−1). It highlights that the mangrove carbon sink may be larger than previously thought, but the high variation in the global average CAR of 233 ± 280 g C m−2 yr−1 also indicates the need for further data. • Variation of C stocks and accumulation rates within one mangrove system can be high. • C stocks are a measure of the "vulnerability potential" upon mangrove degradation. • C accumulation rates are a measure of the "mitigation potential" of mangroves. • The mangrove carbon sink may be larger than previously thought. [ABSTRACT FROM AUTHOR]

Mangrove ecosystems store large amounts of 'Blue Carbon', in particular in the sediment. Research in the past decade has emphasized the quantitative significance of carbon storage in mangrove forests in climate change mitigation, mainly by determining carbon stocks and calculating potential CO 2 emissions caused by mangrove degradation. However, while this approach focuses on the total amount of carbon that can be lost to degradation, it fails to capture the amount that is sequestered annually. Therefore, carbon accumulation in mangrove sediments also needs to be taken into account. This study (i) explains the differences between carbon stocks and carbon accumulation rates (CAR), (ii) it addresses the geographical variation of carbon storage and underlying factors and (iii) it assesses the global relevance of 'Blue Carbon' sequestration in mangrove sediments. Results indicate that reducing uncertainties in carbon storage estimates of individual systems requires a representative set of data that covers within-system variability. An example from Indonesia illustrates that a mangrove ecosystem with a high C stock can have a low CAR and vice versa. It is therefore conceivable that coastal environmental settings with high allochthonous supply of mineral sediment, organic matter and nutrients mostly have low carbon stocks, but high CARs. As these settings represent >80% of the global mangrove area they are most important in terms of long-term carbon storage. While a C stock is a measure of the "vulnerability potential" in the case of ecosystem degradation or total loss, a CAR is rather a measure of the "mitigation potential" of carbon storage in mangrove ecosystems. The global carbon storage in mangrove sediments of 32 Tg yr−1 estimated from CARs in this study is at the upper end of the range of global budgets (14.6–31.1 Tg yr−1, mean 22.9 Tg yr−1). It highlights that the mangrove carbon sink may be larger than previously thought, but the high variation in the global average CAR of 233 ± 280 g C m−2 yr−1 also indicates the need for further data. • Variation of C stocks and accumulation rates within one mangrove system can be high. • C stocks are a measure of the "vulnerability potential" upon mangrove degradation. • C accumulation rates are a measure of the "mitigation potential" of mangroves. • The mangrove carbon sink may be larger than previously thought. [ABSTRACT FROM AUTHOR]

Land reclamation involves extracting fill from some areas (which in the tropics are commonly mangrove and seagrass ecosystems) and transporting and depositing it into zones being reclaimed as land for housing or industrial development. Greenhouse gas (GHG) emissions are produced at various stages of the process (including for instance, from fuel used in dredging equipment). One substantial and site-dependent source of GHG emissions is associated with carbon stock losses resulting from the disturbance and extraction of fill from vegetated marine ecosystems. In this article we focus on these sources of GHG emissions and assess, for the first time, the potential carbon loss from mangrove ecosystems caused by land reclamation activities. To this end, we take a case study of a regionally significant, large-scale reclamation project in Jakarta Bay, Indonesia. The extent of mangroves likely to be impacted by the project was estimated by analysing land satellite (LANDSAT) imagery in the period before and after the start of reclamation activities. The carbon stored in the affected areas was then estimated using field measurements to determine total stocks (biomass and soil organic carbon). Mangroves in the study area at Serang (where sand mining is designated) and Angke (adjacent to deposition site) were estimated to store on average 203.64 Mg C ha−1, and 531.53 Mg C ha−1 respectively. The lower carbon stocks at Serang are a consequence of sand mining that occurred there in 2003. The regrown mangroves at Serang are smaller than that at Angke. The near-surface soil organic carbon (SOC) content at Serang was less than at Angke, 17% and 43% of the total respectively. Results also reveal that around 75% of total carbon stocks in both sites are in the form of SOC. We suggest that collating this sort of information about carbon stocks in sites impacted by land reclamation activities can help development planners identify opportunities to reduce GHG emissions. To illustrate, long-term carbon losses due to land reclamation in the case study area could be reduced if dredging is limited to 1 m depth and mangrove areas are actively rehabilitated. Image 1 • Jakarta Bay land reclamation is potentially destroying mangroves. • Satellite mapping shows increases of mangroves with cessation of sand mining. • Carbon stocks at the quarry can be saved (up to 71%) by limiting mining depth. • The carbon management approach is useful to foreshadow the land reclamation's impact. [ABSTRACT FROM AUTHOR]

Natural forests in South-East Asia have been extensively converted into other land-use systems in the past decades and still show high deforestation rates. Historically, lowland forests have been converted into rubber forests, but more recently, the dominant conversion is into oil palm plantations. While it is expected that the large-scale conversion has strong effects on the carbon cycle, detailed studies quantifying carbon pools and total net primary production (NPPtotal ) in above- and belowground tree biomass in land-use systems replacing rainforest (incl. oil palm plantations) are rare so far. We measured above- and belowground carbon pools in tree biomass together with NPPtotal in natural old-growth forests, 'jungle rubber' agroforests under natural tree cover, and rubber and oil palm monocultures in Sumatra. In total, 32 stands (eight plot replicates per land-use system) were studied in two different regions. Total tree biomass in the natural forest (mean: 384 Mg ha(-1) ) was more than two times higher than in jungle rubber stands (147 Mg ha(-1) ) and >four times higher than in monoculture rubber and oil palm plantations (78 and 50 Mg ha(-1) ). NPPtotal was higher in the natural forest (24 Mg ha(-1)  yr(-1) ) than in the rubber systems (20 and 15 Mg ha(-1)  yr(-1) ), but was highest in the oil palm system (33 Mg ha(-1)  yr(-1) ) due to very high fruit production (15-20 Mg ha(-1)  yr(-1) ). NPPtotal was dominated in all systems by aboveground production, but belowground productivity was significantly higher in the natural forest and jungle rubber than in plantations. We conclude that conversion of natural lowland forest into different agricultural systems leads to a strong reduction not only in the biomass carbon pool (up to 166 Mg C ha(-1) ) but also in carbon sequestration as carbon residence time (i.e. biomass-C:NPP-C) was 3-10 times higher in the natural forest than in rubber and oil palm plantations., (© 2015 John Wiley & Sons Ltd.)