Pendugaan Cadangan Karbon Di Atas Permukaan Pada Hutan Rakyat Dengan Memanfaatkan Data Synthetic Aperture Radar Sentinel-1 (Studi Kasus Di Kabupaten Sukoharjo)

The high amount of carbon dioxide in the atmosphere is one of the causes of global warming. Preserve carbon stocks is an effort to reduce carbon emissions, including in the community forest which need to be recorded. Remote Sensing Data of Sentinel-1 SAR (Synthetic Aperture Radar) was used to determine the above ground carbon stocks on community forest in Sukoharjo Regency. The objectives of this reasearh are: 1) analyze the relationship between the backscatter value of Sentinel-1 SAR dual polarization data and the value of carbon stock in community forest based on the above ground green biomass value; 2) to estimate above ground carbon stock of community forest in Sukoharjo Regency by utilizing the dual-polarization SAR Sentinel-1 data; and 3) to determine the total and the spatial distribution of above ground carbon stock on community forest in Sukoharjo Regency based on Sentinel-1 SAR data. The method was used in this study is a survey method using purposive sampling to complement remote sensing data related to biomass and above ground carbon stock in community forest using allometric equations based on the the extraction result of backscatter value of each polarization VV (Vertical-Vertical), VH (Vertical-Horizontal), and band ratio VV/VH. Statistical analysis was used to generate equation for estimating carbon stocks based on the SAR data and field data. Geographic Information System (GIS) was used to represent data spatially well as information of above ground carbon stock value and used spatial data analysis approach both quantitatively and qualitatively. The results showed that: 1) there is a significant and inversely relationship between the value of VV polarization backscatter (R = -0.438 (very low)) and VH polarization (R = -0.612 (Low)) on above graound carbon stock value. 2) How to Estimate Above ground carbon stock on community forest in Sukoharjo was obtained from Sentinel-1 SAR data using VH polarization with chosen simple linear regression equation (R2 = 0.375; RMSE = 101.1648) is Y = -61.499 -493.268 + X. The Total of above ground carbon stock in Sukoharjo Regency is 228,456.36 tons of 7738.287 hectares community forest and it has a spread spatial distribution pattern at random and clustered. The largest above ground biomass carbon stock is in the community forest of Bulu Sub-district is 49540.21 tons (1782.008 ha) and least in the Gatak Sub-district is 49.50 tons (1,357 ha)

Spatial Variability in Above Ground Carbon Within an Appalachian Forest

Forests have long been recognized for their provision of air and water quality ecosystem services to society; but more recently, they have become valuable to the carbon credit markets used as a tool for mitigating climate change. Quantifying above ground carbon storage in forest ecosystems is essential for these carbon credit markets and can also provide insight into factors that control the spatial distribution of carbon in forests. The goal of this study is to assess the degree to which three factors: topography, tree species, and legacies of logging, impact the spatial variability in above ground carbon within a 400x500m Appalachian forest plot. Field work spanning 2018 to 2022 resulted in 14,932 surveyed trees with 27 unique tree species, totaling an estimated 2,107,736 kg of above ground carbon content. I used a multiple linear regression model to determine that 14% (Adjusted R2 = 0.14) of the spatial variability in above ground carbon can be explained by variables representing the three factors included in this study. Tree species, represented by their varying wood densities, explained the most variability (Std Beta = 0.39). Potential Evapotranspiration (PET), a variable summarizing the impact of topography on solar radiative plant water demand, was the second strongest (Std Beta = -0.18). Logging has a direct impact on the amount of above ground carbon content; however, the indirect effect of increased abundance of tree species with lower wood density in more recently harvested stands effectively captured this driver of forest carbon within the statistical model. This study highlights the interacting factors that simultaneously control the spatial variability of above ground carbon, and their impact on long-term carbon storage. For instance, in finding that most of the above ground carbon in the plot is in the dense wood of overstory oak species, and not in the less dense wood of the red maple-dominated understory that will likely replace the oaks over time, this study highlights how the “mesophycation” of eastern North American forests toward species like red maple may negatively affect aboveground forest carbon, and thus the carbon market value of this and similar Appalachian forests. To build on this baseline survey and spatial analysis of carbon stocks in this large Appalachian forest plot, we suggest that additional work can measure rates of aboveground carbon sequestration by repeating the tree census, and by working to identify spatial relationships with below ground carbon

Application of geo-informatics engineering to estimate above-ground carbon sequestration

This research aims to estimate above-ground carbon sequestration of orchards by using the data collected from Landsat 8 OLI. Regression equations are applied to study the relationship between the amount of above-ground carbon sequestration and vegetation indices from Landsat 8 OLI, in which the data was collected in 2015 in 3 methods: 1) Difference Vegetation Index (DVI), 2) Green Vegetation Index (GVI), and 3) Simple Ratio (SR). The results are as follows: 1) By DVI method, it results in the equation y = 0.3184e0.0482x and the coefficient of determination R² = 0.8457. The amount of the above-ground sequestration calcula-tion\u27s result is 213.176 tons per rai. 2) Using  the GVI method, it results in the equation y = 0.2619e0.0489x and the coefficient of determination R²=0.8763. The amount of the above-ground sequestration calculation\u27s result is 220.510 tons per rai. 3) Using the  SR method, it results in the equation y = 0.8900e0.0469x and the coefficient of determination R² = 0.7748.  The amount of the above-ground sequestration calculation\u27s result is 234.229 tons per rai

INDEKS VEGETASI DARI CITRA SATELIT ALOS UNTUK MEMPERKIRAKAN CADANGAN KARBON ATAS PERMUKAAN DI HUTAN MANGROVE

Mangrove forests sequestrate and store a lot of carbon and are important to tackle climate change. However, much of these forests have been cleared dramatically and such clearings destroyed carbon sinks and released carbon into the atmosphere as carbon dioxide. The international climate agreements highlight Reduced Emissions from Deforestation and Forest Degradation (REDD+) as a key and effective option for mitigating climate change. To help making REDD+ a reality, an alternative approach is needed to measure above ground carbon stock quickly and accurately. Here, we link ground-based data collected from field measurement using belt transect method with satellite image data of the ALOS AVNIR-2. The objectives are to identify the characteristics of mangrove vegetation, to estimate the amount of above ground carbon stock, and to examine capability of vegetation indices of NDVI, SR, dan SAVI from the ALOS AVNIR-2 to estimate the amount of above ground carbon stock in mangrove. Results showed that the mangrove vegetation in Gugus Pulau Pari was relatively diverse and this forest contained above ground carbon between 4694.35 and 42841.25 kg/m2. The statistics analyses showed that there was no significant correlation between the vegetation indices of NDVI, SR, and SAVI with the amount of above ground carbon stock in tropical mangrove forest. In short, the vegetation indices of NDVI, SR, and SAVI from the ALOS AVNIR-2 were not sufficient to estimate the amount of above ground carbon stock in tropical mangrove forest

Modelling above-ground carbon dynamics using multi-temporal airborne lidar: Insights from a Mediterranean woodland

Abstract. Woodlands represent highly significant carbon sinks globally, though could lose this function under future climatic change. Effective large-scale monitoring of these woodlands has a critical role to play in mitigating for, and adapting to, climate change. Mediterranean woodlands have low carbon densities, but represent important global carbon stocks due to their extensiveness and are particularly vulnerable because the region is predicted to become much hotter and drier over the coming century. Airborne lidar is already recognized as an excellent approach for high-fidelity carbon mapping, but few studies have used multi-temporal lidar surveys to measure carbon fluxes in forests and none have worked with Mediterranean woodlands. We use a multi-temporal (5-year interval) airborne lidar data set for a region of central Spain to estimate above-ground biomass (AGB) and carbon dynamics in typical mixed broadleaved and/or coniferous Mediterranean woodlands. Field calibration of the lidar data enabled the generation of grid-based maps of AGB for 2006 and 2011, and the resulting AGB change was estimated. There was a close agreement between the lidar-based AGB growth estimate (1.22 Mg ha−1 yr−1) and those derived from two independent sources: the Spanish National Forest Inventory, and a tree-ring based analysis (1.19 and 1.13 Mg ha−1 yr−1, respectively). We parameterised a simple simulator of forest dynamics using the lidar carbon flux measurements, and used it to explore four scenarios of fire occurrence. Under undisturbed conditions (no fire) an accelerating accumulation of biomass and carbon is evident over the next 100 years with an average carbon sequestration rate of 1.95 Mg C ha−1 yr−1. This rate reduces by almost a third when fire probability is increased to 0.01 (fire return rate of 100 years), as has been predicted under climate change. Our work shows the power of multi-temporal lidar surveying to map woodland carbon fluxes and provide parameters for carbon dynamics models. Space deployment of lidar instruments in the near future could open the way for rolling out wide-scale forest carbon stock monitoring to inform management and governance responses to future environmental change.The authors would like to acknowledge the personnel of the Airborne Research and Survey Facility (NERC). We thank the MAGRAMA for granting access to the Spanish Forest Inventory. WS was funded by FunDivEurope and the Isaac Newton Trust. PRB was supported by The International Post doc Fellowship Programme in Plant Sciences (PLANT FELLOWS).This is the final version of the article. It first appeared from Copernicus via http://dx.doi.org/10.5194/bg-13-961-201

Using ICESAT\u27s geoscience laser altimeter system to assess large scale forest disturbance caused by Hurricane Katrina

We assessed the use of GLAS data as a tool to quantify large-scale forest damage. GLAS data for the year prior to and following Hurricane Katrina were compared to wind speed, forest cover, and MODIS NPV maps to analyze senor sampling, and changes in mean canopy height. We detected significant losses in mean canopy height post-Katrina that increased with wind intensity, from ∼.5m in forests hit by tropical storm winds to ∼4m in forests experiencing category two force winds. Season of data acquisition was shown to influence calculations of mean canopy height. There was insufficient sampling to adequately detect changes at one degree resolution and less. We observed a strong relationship between delta NPV and post storm mean canopy heights. Changes in structure were converted into loss of standing carbon estimates using a height structured ecosystem model, yielding above ground carbon storage losses of ∼30Tg over the domain

High above-ground carbon stock of African tropical montane forests

Tropical forests store 40–50 per cent of terrestrial vegetation carbon1. However, spatial variations in aboveground live tree biomass carbon (AGC) stocks remain poorly understood, in particular in tropical montane forests2. Owing to climatic and soil changes with increasing elevation3, AGC stocks are lower in tropical montane forests compared with lowland forests2. Here we assemble and analyse a dataset of structurally intact old-growth forests (AfriMont) spanning 44 montane sites in 12 African countries. We find that montane sites in the AfriMont plot network have a mean AGC stock of 149.4 megagrams of carbon per hectare (95% confidence interval 137.1–164.2), which is comparable to lowland forests in the African Tropical Rainforest Observation Network4 and about 70 per cent and 32 per cent higher than averages from plot networks in montane2,5,6 and lowland7 forests in the Neotropics, respectively. Notably, our results are two-thirds higher than the Intergovernmental Panel on Climate Change default values for these forests in Africa8. We find that the low stem density and high abundance of large trees of African lowland forests4 is mirrored in the montane forests sampled. This carbon store is endangered: we estimate that 0.8 million hectares of old-growth African montane forest have been lost since 2000. We provide country-specific montane forest AGC stock estimates modelled from our plot network to help to guide forest conservation and reforestation interventions. Our findings highlight the need for conserving these biodiverse9,10 and carbon-rich ecosystems

Forest Structure, above-Ground Carbon Stocks, and Productivity along an Elevational Gradient in the Ecuadorian Andes

The Andean forest provides a natural laboratory for evaluating long-term interactions between forests and variation in environmental parameters along elevational gradients. In particular, the mechanisms that control above-ground carbon stocks (AGC) and natural dynamics in mountain ecosystems constitute a potentially powerful tool for understanding the function of these ecosystems and their response to current climate change scenarios or past human disturbances. The present study integrates biotic (rarefied species richness and leaf traits) and abiotic (climate, soil properties and degradation) factors as possible drivers of AGC stocks, AGC net change (AGCnt), AGC productivity (AGCp) and AGC mortality (AGCk), along an elevational gradient of ca. 3000 m in the montane forests of the Ecuadorian Andes. My findings show that AGC metrics respond to elevational gradients (climate conditions) and past human disturbances. I found that temperature constitutes the primary filter for forest structure, AGC stocks, AGCnt and AGCp along the elevational gradient, where abiotic factors such as degradation and soil properties represent the main drivers for AGCk. This study provides insight into the processes that control patterns of AGC metrics in mountainous ecosystems, where temperature is likely the most important source of AGC variation in Andean forests