Estimates Of Carbon Stocks Research Articles

НЕУЧТЕННЫЕ ФАКТОРЫ, ОПРЕДЕЛЯЮЩИЕ ЗАПАСЫ УГЛЕРОДА В ЛЕСНЫХ ПОЧВАХ

The sources of uncertainties in estimating soil carbon stocks in forest ecosystems, including abiotic (mechanical composition of soil) and biotic (plants, earthworms) factors, are discussed. The results of comparative assessment of soil carbon storage in coniferous-broadleaved and taiga forests based on direct measurements and pedotransfer functions are presented. It is shown that the estimation of soil carbon stocks in forests based on both direct measurements and pedotransfer functions requires, along with other soil formation factors, taking into account the most dynamic ones, including plants and soil biota, as well as the history of nature management. The calculation of soil carbon stocks should be carried out with account of fine earth (<2 mm fraction) content.

Developing biomass estimation models of young trees in typical plantation on the Qinghai-Tibet Plateau, China.

Calculation of forest biomass is the basis for global carbon stock estimation, which has been included in national forest inventory projects. The volume-derived biomass method is generally used for trees with diameter at breast height (DBH) larger than 5 cm in most forest carbon sink measurement, which omits young trees (diameter at breast height <6 cm, height >0.3 m) and thus may underestimate ecosystem carbon sink capacity. Based on the biomass data of 137 young trees in five typical plantations on the Tibetan Plateau, independent biomass models were developed using the weighted generalized least squares method, with basic diameter as the predictor instead of DBH. Additive biomass models of controlling directly by proportion functions and controlling by the sum of equations were selected. Additive biomass models for the whole plant and each component were developed by applying weighted nonlinear seemingly uncorrelated regression. The results showed that the binary additive biomass model (R2 reached 0.90-0.99) performed better than the monadic biomass models and independent biomass models for the estimation of total biomass. For different tree species, two forms of the additive models had their own advantages, with neglectable difference in accuracy. From the perspective of forestry production, models of controlling directly by proportion functions were more practical. From the perspective of predictors extraction by remote sensing technology, suitable young tree biomass models were developed for remote sensing estimation. In this study, the additive model had high overall fitting accuracy and could accurately estimate the whole plant and component biomass of young trees in similar climatic environments.

National-scale temporal estimation of South Korean Forest carbon stocks using a machine learning-based meta model

The objective of the present study is to develop an integrated meta model, i.e. a surrogate model for process-based model for estimating forest carbon stocks in South Korea. Two meta models which cover for the process-based model were firstly developed—meta forest growth and meta forest biomass and dead organic matter carbon (FBDC) by adopting a multi-layer feedforward neural network (ML-FFNN) with a scaled conjugated gradient for training. Due to dependence of the forest carbon stocks on growth and climate variability, it was possible to interrelate the two meta models, and define a single integrated meta model. For the meta forest growth model, scientific uncertainties surrounding the driving mechanisms of tree growth in the process-based model increased model complexity, resulting in a relatively fair model performance (R2 = 0.776), compared to a near-perfect meta FBDC model performance (R2 = 0.997). The integrated meta model maintained an intermediate performance (R2 = 0.822). The integrated meta model did well to capture the spatial patterning of carbon stocks when compared to those of previous process-based models, although the former had a limited capacity to capture extreme highs associated with Quercus acutissima Carruth. As the integrated meta model was developed by using data including the impact of climate change, it is applicable to use for the forest management, for example, the implementation of Nationally Determined Contributions in the future.

Diameter growth performance and estimated carbon stock of Shorea spp. at KHDTK Haurbentes, Bogor

Shorea leprosula Miq. and Shorea selanica (Lam.) Blume are fast growing plant and they can be used as a rehabilitation plant. Plant adaptability can be seen from the growth in diameter. Furthermore, rehabilitation plants have the potential to store carbon stocks which can support Indonesia's Forestry And Other Land Use (FOLU) Net Sink 2030 program. This study aims to analyze diameter growth and estimate carbon stocks stored in 8-year-old S. leprosula and S. selanica species in unproductive forest area KHDTK Haurbentes, Bogor. Furthermore, the data collection of diameter was conducted by census while carbon stock estimation used allometric equations. The diameter distribution of S. leprosula and S. selanica was normally distributed. The normal diameter distribution is defined as the highest number of frequencies around the mean value. The results of the normality test of S. leprosula and S. selanica have normal diameter distribution data which indicate that these species have good adaptability in unproductive forest areas. Moreover, the incremental growth of S. leprosula diameter is 1.38 cm while the diameter increment of S. selanica is 1.19 cm. The carbon stock of S. leprosula is 4.72 tons/ha while S. selanica is 8.32 tons/ha. S. leprosula and S. selanica have good adaptability. Thus, they can be used as plants to rehabilitate unproductive forests and store potential carbon stocks.

Mapping of mangrove forest and carbon stock estimation of east coast Surabaya, Indonesia

Abstract. Hidayah Z, Rachman HA, As-Syakur AR. 2022. Mapping of mangrove forest and carbon stock estimation of east coast Surabaya, Indonesia. Biodiversitas 23: 4826-4837. The mangrove forests on Surabaya's east coast are an important protected area, providing habitat for a variety of marine organisms and water birds. This area is also vital for carbon storage and sequestration. The purpose of this research is to determine the changes in mangrove areas over the last 20 years and to calculate the amount of carbon stored in these mangrove forests. Changes in the area of mangrove forests and Normalized Difference Vegetation Indices (NDVI) were determined using Landsat-7 in 2000 and Landsat-8 in 2020. The vegetative carbon stock of mangrove stands was calculated using allometric measurements. The study results reveal that the mangrove forest area has increased significantly from 257.13 ha in 2000 to 753.48 ha in 2020. The results of regression models also show that NDVI has a significant influence on aboveground biomass (AGB), as indicated by the coefficient of determination (R2) = 0.7 (F test, df = 28, p-value &lt; 0.05). The estimated aboveground and belowground carbon stock is 46.35 ± 13.65 ton C ha-1 and 13.65 ± 6.74 ton C ha-1 respectively. Accordingly, it is observed that the mangrove forest of East Coast Surabaya can sequester at least a total of 192.72 tons CO2 ha-1 in above and belowground biomass.

Carbon stocks and changes in biomass of Mediterranean woody crops over a six-year period in NE Spain

Carbon sequestration and storage in biomass is one of the most important measures to mitigate climate change. Mediterranean woody crops can sequestrate carbon in the biomass of their permanent structures for decades; however, very few studies have focused on an assessment of biomass and carbon sequestration in these types of crops. This study is the first to estimate above- and belowground biomass carbon stock in Mediterranean woody crops through a bottom-up approach in the NE Iberian Peninsula in 2013. Moreover, this is the first time that an assessment of the annual changes in carbon stock in the study area over a six-year period is presented. For this purpose, eight crop- and site-specific equations relating biomass or biometric variables to crop age were calculated. Most of the data were our own measurements, but unpublished data supplied from other authors as well as data from literature were also considered. Census of Agriculture data was used to scale results from individual data up to the municipality level at the regional scale. Results show that in woody cropland in NE Spain the total biomass carbon stock in 2013 was 5.48 Tg C, with an average value of 16.44 ± 0.18 Mg C ha−1. Between 2013 and 2019, although there was a 2.8% mean annual decrease in the area covered by woody crops, the carbon stock in the biomass of these crops increased annually by 3.8% due to the growth of the remaining woody cropland. This new estimation of carbon stocks may contribute to better understand carbon balances and serve as a baseline to global inventories. It may also serve to assess and manage carbon storage as an ecosystem service provided by Mediterranean woody cropland for mitigating climate change and, in combination with adaptive strategies, for supporting a productive and resilient agro-food system.

Estimation of aboveground carbon stock using Sentinel-2A data and Random Forest algorithm in scrub forests of the Salt Range, Pakistan

Abstract Forest ecosystems play a vital role in the global carbon cycle as forests store ~283 Gt of carbon globally and hence help mitigate climate change. Carbon stock estimation is the key step for assessing the mitigation potential of a given forest. About 5–10 Gt CO2 equivalent emissions come from deforestation and forest degradation annually. Pakistan’s forest resources are currently deteriorating due to deforestation and degradation and resulting in sourcing carbon dioxide emissions. One forest type that has been examined little so far in this context is subtropical scrub forests. This research suggests a workflow to estimate the carbon stock from three carbon pools (aboveground, belowground and litter) in scrub forests of the Salt Range, Pakistan by incorporating remote sensing and geographic information system techniques. The study’s objectives include the estimation of biomass and carbon stocks by using field inventory data and allometric equations, quantifying CO2 sequestration by using the ‘IPCC 2006 Guidelines for National Greenhouse Gas Inventories’ and finally map biomass and carbon by utilizing satellite imagery and statistical analysis. For prediction and mapping of biomass and carbon, field plots data along with vegetation indices and spectral bands of the Sentinel-2A satellite imagery were fed into a Random Forest (RF) algorithm in the cloud computing Google Earth Engine platform. Our results of ground data suggest that the examined scrub forests harbour 243 917 t of biomass, 114 989 t of carbon and 422 009 t of CO2 equivalent in the three carbon pools of the study area with a mean biomass density of 12.04 t ha−1 (±5.31) and mean carbon density of 5.72 t ha−1 (±2.46). The RF model showed good performance with reasonable R2 (0.53) and root mean square error (3.64 t ha−1) values and predicted average biomass at 13.93 t ha−1 (±4.35) and mean carbon density of 6.55 t ha−1 (±2.05). The total predicted and field-measured biomass has a plausible difference in values while the mean values have a minimal difference. The red-edge region and short-wave infrared (SWIR) region of the Sentinel-2A spectrum showed a strong relationship with aboveground biomass estimates from the field. We conclude that the combination of Sentinel-2A data coupled with ground data is a cost-effective and reliable tool to estimate various carbon pools in the scrub forests at a regional scale and may contribute to formulate policies to manage forests sustainably, enhance forest cover and conserve biodiversity.

Estimating aboveground carbon stocks of urban trees by synergizing ICESat-2 LiDAR with GF-2 data

Accurately mapping carbon stocks of urban trees is necessary for urban managers to design strategies to mitigate climate change. However, the aboveground carbon stocks of urban trees are usually underestimated by passive remote sensing data because of the signal saturation problem. The research is the first attempt to develop a framework to map aboveground carbon density of trees in urban areas by synergizing Ice, Cloud and Land Elevation Satellite-2 (ICESat-2) LiDAR data with Gaofen-2 (GF-2) imagery. The framework consists of three key steps. First, we used a support vector machine classifier to classify GF-2 images and extracted urban tree regions. Second, we estimated the tree carbon density of ICESat-2 strips by developing a ICESat-2 photon feature-based aboveground carbon density estimation model. Third, we mapped the carbon density of urban trees by developing a synergistic model between ICESat-2 and GF-2 data based on an object-oriented method. We tested the approach for the areas within the fifth ring road of Beijing, China. The results showed that the 50th percentile height (PH50) of nighttime photons was a good predictor for estimating carbon density of urban trees, with a R2 of 0.69 and a Root Mean Square Error (RMSE) of 2.81 kg C m−2. Using the spectral features generated by GF-2 imagery, we could further extrapolate the carbon density estimated by ICESat-2 strip data to a full coverage of accurate mapping carbon density by urban trees, resulting in a R2 of 0.64 and a RMSE of 2.32 kg C m−2. The carbon stocks within the fifth ring road of Beijing were 8.28 × 108 kg in total, with the mean carbon density of 3.52 kg C m−2. Such estimations were larger than that of previous study using passive remote sensing data only, suggesting the integration of spaceborne LiDAR and spectral data could greatly reduce the underestimation of carbon stocks of urban trees. Our approach can more accurately estimate carbon stocks of urban trees and has the potential to be applicable in other cities.

A first-order approximation of floodplain soil organic carbon stocks in a river network: The South Platte River, Colorado, USA as a case study

The lack of watershed-scale estimates of floodplain carbon stocks limits recognition of the important role of floodplains and river corridor restoration in efforts to enhance carbon sequestration. We use the South Platte River watershed of Colorado, USA as a case study to illustrate spatial patterns of, and controls on, floodplain carbon stocks at the watershed scale. This case study illustrates the disproportionate importance of floodplains for soil carbon stocks relative to adjacent uplands and provides an example of how spatially explicit data can be used to prioritize floodplain restoration with regard to carbon sequestration. We use the hydrogeomorphic floodplain tool GFPLAIN to delineate the extent of 100-year floodplains in the South Platte River watershed. We distinguish elevation bands for the steppe, montane, subalpine, and alpine zones. We also differentiate bead (floodplain width/channel width ≥ 5) and string (floodplain width/channel width < 5) reaches within the montane and subalpine zones. Drawing on prior, field-based measurements of organic carbon stock in downed, dead wood and soil in these floodplain types, we estimate total floodplain organic carbon stock based on median values of stock in different floodplain types and the spatial extent of these floodplain types. This estimate includes organic carbon stocks in lake and reservoir sediments in the watershed. Soil constitutes the greatest reservoir of floodplain carbon. The total estimated area of floodplain is 2916 km2, which is 4.3 % of the total watershed area of the South Platte River. Our preferred estimate is 42.7 Tg C stock (likely range of 39.1–42.7 Tg). This equates to 11.1 % of a previously estimated overall carbon stock (above and belowground biomass and soil organic carbon) in the entire watershed of 384 Tg C. Floodplains are thus disproportionately important, relative to their surface area, in storing organic carbon in this semiarid watershed. Field measurements of floodplain soil organic carbon stocks from across the globe indicate that this finding is not unique to this watershed, with implications for prioritizing floodplain management and restoration as a means of enhancing carbon sequestration.

Quantification of blue carbon stocks associated with Posidonia oceanica seagrass meadows in Corsica (NW Mediterranean).

In the last decades, the increasing necessity to reduce atmospheric carbon dioxide (CO2) concentrations has intensified interest in quantifying the capacity of coastal ecosystems to sequester carbon, referred to commonly as 'Blue Carbon' (BC). Among coastal habitats, seagrass meadows are considered as natural carbon sinks due to their capacity to store large amounts of carbon in their sediments over long periods of time. However, the spatial heterogeneity of carbon stocks in seagrass sediments needs to be better understood to improve the accuracy of BC assessments, particularly where there is high environmental variability. In the Mediterranean, Posidonia oceanica (L.) Delile constitutes extensive meadows considered as long-term carbon sinks due to the development of an exceptional structure known as 'matte', reaching several meters in height, which can be preserved over millennia. In order to specify the role of P. oceanica meadows in climate change mitigation, an estimate of carbon stocks has been conducted along the eastern coast of Corsica (NW Mediterranean). The approach is mainly based on the biogeochemical analysis of 39 sediment cores. Organic carbon (Corg; 327 ± 150 t ha-1, mean ± SE) and inorganic carbon stocks (Cinorg; 245 ± 45 t ha-1) show a high variability related to water depth, matrix (sandy vs rocky substrate) or the depositional environment (coastal vs estuary). The isotopic signature (δ13C) revealed a substantial contribution of allochthonous inputs of organic matter (macroalgae and sestonic sources) mainly in estuarine environment and shallow areas. The carbon stocks in the first 250 cm of matte (average thickness) were estimated at 5.6-14.0 million t Corg (study site) and 14.6-36.9 million t Corg (Corsica), corresponding to 11.6-29.2 and 30.4-76.8 years of CO2 emissions from the population of Corsica.

Woody species diversity and carbon stock of church forests along age gradient in Dangila district, Awi-zone, Ethiopia

Ethiopian Orthodox Tewahido Churches comprise remnant native woody species around their premises that are important for biodiversity conservation and carbon sequestration. The current study was conducted to examine woody species diversity and carbon stock of church forests along age gradient in Dangila district, Awi-zone, Ethiopia. A list of documented churches was taken from parish council and tourism that categorized into three age gradients i.e., >200yrs, 100–200yrs and ≤100yrs. Then one church was purposively selected from each age gradient. At each of the selected churches, three Gentry transect lines were laid down systematically in three cardinal directions with 120° interval. Tree height and DBH/DSH ≥5cm were measured and saplings were counted within 2m width; whereas, seedling with height <1m was counted from three plots of 2 m × 2 m size along the transect line. Woody species diversity and evenness were computed using Shannon diversity and Evenness indices and carbon stock estimation was done by allometric equation. A total of 91 woody species belong to 45 families and 77 genera were recorded in the church forests. Woody species community structure along age gradients showed an inverted J-shape. The mean Shannon diversity of old, middle and recent church age gradient was 2.85 ± 0.21, 2.74 ± 0.13 and 2.37 ± 0.49 respectively. Woody species richness is statistically significant along age classes. The mean total biomass carbon stock along old, middle and recent age gradients was 64.58 ± 23.58, 65.22 ± 63.47 and 18.65 ± 11.02 metric ton respectively. Hence, old aged churches play a better role for indigenous woody species conservation and carbon sequestration.

Estimation of above ground bole biomass and carbon stock of trees in Urhonigbe Forest Reserve, Edo State, Nigeria

Estimation of above ground bole biomass and carbon stock of trees in Urhonigbe Forest Reserve, Edo State, Nigeria

Individual tree crown delineation in high-resolution remote sensing images based on U-Net

We present a deep learning-based framework for individual tree crown delineation in aerial and satellite images. This is an important task, e.g., for forest yield or carbon stock estimation. In contrast to earlier work, the presented method creates irregular polygons instead of bounding boxes and also provides a tree cover mask for areas that are not separable. Furthermore, it is trainable with low amounts of training data and does not need 3D height information from, e.g., laser sensors. We tested the approach in two scenarios: (1) with 30 cm WorldView-3 satellite imagery from an urban region in Bengaluru, India, and (2) with 5 cm aerial imagery of a densely forested area near Gartow, Germany. The intersection over union between the reference and predicted tree cover mask is 71.2% for the satellite imagery and 81.9% for the aerial images. On the polygon level, the method reaches an accuracy of 46.3% and a recall of 63.7% in the satellite images and an accuracy of 52% and recall of 66.2% in the aerial images, which is comparable to previous works that only predicted bounding boxes. Depending on the image resolution, limitations to separate individual tree crowns occur in situations where trees are hardly separable even for human image interpreters (e.g., homogeneous canopies, very small trees). The results indicate that the presented approach can efficiently delineate individual tree crowns in high-resolution optical images. Given the high availability of such imagery, the framework provides a powerful tool for tree monitoring. The source code and pretrained weights are publicly available at https://github.com/AWF-GAUG/TreeCrownDelineation.

Duration of Climate Change Mitigation Benefits from Increasing Boreal Forest Harvest Age by 10 Years

We presented a case study and associated method for stand scale assessment of the duration of the climate change mitigation benefit provided by increasing forest harvest age (i.e., the age a stand is harvested). We used stand yield curves and newly developed equations to estimate carbon stocks in various boreal forest ecosystem pools in Ontario, Canada. The proposed method was applied to forest identified as available for harvesting in management plans for three forest management units with a combined area of more than 1900 km2. Our analysis indicated that a 10-year increase in harvest age did not provide a mitigation benefit (reduced carbon stock) in about half the available harvest area (45.5%, 61.9%, and 62.1% of the total available harvest area in the management units). Increasing the harvest age by 10 years resulted in a mitigation benefit lasting longer than 25 years for 15.1%, 16.0%, and 13.0% of the total available harvest area in the management units. The results suggest that increasing harvest age may have limited mitigation potential in Ontario’s managed boreal forests in the short-term but can reduce overall carbon stocks in the longer term.

MAPPING MANGROVE SURFACE CARBON STOCKS USING MULTISENSOR IMAGERY IN CLUNGUP MANGROVE CONSERVATION (CMC) MALANG REGENCY

Mangroves can store carbon effectively with a value of about 1,023 Mg C/Ha and become one of the richest forests that store 4-20 billion tons of blue carbon globally. Remote sensing imagery can be used to map mangrove surface carbon stocks using radar and optical image sensors. Generally, forest carbon on earth is stored in two places, namely above the surface (Above Ground Carbon, AGC) and below the surface (Below Ground Carbon, BGC). This study aims to estimate the surface carbon stock of mangroves using multisensory imagery using the Random Forest method in the Clungup Mangrove Conservation (CMC) area, Malang Regency, East Java. Four vegetation indices (IRECI, NDI45, NDVI, SAVI), single band, and VV VH polarization were used as predictive variables. Estimating the carbon stock mangrove value using Sentinel-1 imagery produced 2,126 tons of C with R² 0.11. Meanwhile, Sentinel-2 produces an estimated carbon value of 2,025 tons C with an R² of 0.22. The estimation model using Sentinel-2 shows a better evaluation value with a Root Mean Squared Error (RMSE) of 0.89 and a Mean Absolute Error (MAE) of 0.75. The IRECI vegetation index is the most important variable in estimating carbon stocks. The results of the mapping accuracy of the Sentinel-1 model show a value of 34.73% and Sentinel-2 35.03%.Keywords: Mangrove, Carbon, Sentinel-1, Sentinel-2, Random Forest

Aboveground biomass allometric models for large trees in southwestern Amazonia

Most of the basal area in Amazon forest is in large trees, many of which are species of interest for forest management. In forest management these trees are divided into the commercial bole that is harvested for wood production and the stump and crown that are left in the forest where they decompose and emit CO2 over a period of years. Part of the commercial bole is converted to wood products that store carbon according to their durability. The quantification of these components is difficult due to their size, especially in the case of the crown, which causes uncertainties in the estimates of biomass and carbon. Our study estimated the aboveground biomass and carbon of 223 trees and subsequently fit allometric equations to these estimates. Aboveground biomass was calculated from stem volume, wood density and a biomass expansion factor, while total carbon stock estimates used carbon content determined in the laboratory. Linear models (log-transformed) were tested to derive the best-fit allometric model for total aboveground biomass and carbon. The best-fit allometric models used squared tree diameter, tree height, and wood density for biomass, whereas the best carbon model also used carbon content. Our models were more efficient in estimating biomass than were frequently used regional and pan-tropical models. Our equations allow reducing the errors in estimates of forest biomass and carbon stocks, in addition to allowing estimation of the amount of carbon emitted after harvest, although the other models also had good fits and can be used according to the criteria of each researcher and the availability of data.

Biomass and Carbon Stock Quantification in Cork Oak Forest of Maamora Using a New Approach Based on the Combination of Aerial Laser Scanning Carried by Unmanned Aerial Vehicle and Terrestrial Laser Scanning Data

The Mediterranean forests, particularly Cork oak (Quercus suber L., 1927), make a major contribution to the fight against climate change through Carbon sequestration. Hence, there is a great interest in the accurate quantification of biomass and carbon stock. In this context, this study aims at assessing the performance of a new approach, based on the combination of Unmanned aerial vehicle airborne Aerial laser scanning (ALS-UAV) and Terrestrial laser scanning (TLS) data, in the determination of dendrometric parameters (Circumference at 1.30 m and Tree Height), and consequently the estimation of biomass and carbon stock, considering field data as reference. This study takes the Maamora forest in Morocco as an example of a Mediterranean Cork oak forest. The methodology consists of collecting data at three levels: the entire area level for an ALS-UAV scan, the plot and tree levels for TLS surveys, as well as field data collection. Afterwards, dendrometric parameters (Circumference at 1.30 m and the Tree height) were estimated using individual tree segmentation and biomass; the carbon stock (aboveground, belowground, and total) was estimated using allometric equations. The comparison of the estimated dendrometric parameters with those measured in the field shows a strong relationship, with a Pearson coefficient of 0.86 and 0.83, a correlation coefficient (R2) of 0.81 and 0.71, and a Root mean square error (RMSE) of 1.84 cm and 0.47 m, respectively. Concerning the biomass and carbon stock estimation, the proposed approach gives a satisfactory accuracy, with a Pearson coefficient of 0.77, an R2 of 0.83, and an RMSE of 36.40 kg for biomass and 20.24 kg for carbon stock.

Estimation and Simulation of Forest Carbon Stock in Northeast China Forestry Based on Future Climate Change and LUCC

Forest carbon sinks (FCS) play an important role in mitigating global climate change, but there is a lack of more accurate, comprehensive, and efficient forest carbon stock estimates and projections for larger regions. By combining 1980–2020 land use data from the Northeast China Forestry (NCF) and climate change data under the Shared Socioeconomic Pathway (SSP), the land use and cover change (LUCC) of NCF in 2030 and 2050 and the FCS of NCF were estimated based on the measured data of forest carbon density. In general, the forest area of NCF has not yet recovered to the level of 1980. The temporal change in the FCS experienced a U-shaped trend of sharp decline to slow increase, with the inflection point occurring in 2010. If strict ecological conservation measures are implemented, the FCS of the NCF is expected to recover to the 1980 levels by 2050. We believe that the ecological priority (EP) scenario is the most likely and suitable direction for future development of the NCF. We also advocate for more scientific and stringent management measures for NCF natural forests to unlock the huge potential for forest carbon sequestration, which is important for China to meet its carbon neutrality commitments.

Struktur Komposisi dan Estimasi Cadangan Karbon Tegakan Ekosistem Mangrove di Pulau Sangiang, Banten

The mangrove ecosystem is a coastal ecosystem that is important for humans. Mangroves absorb carbon in the atmosphere and store it in biomass or sediment. So, in other words, mangroves play a significant role in mitigating global climate change. This study aimed to examine the structure of the mangrove ecosystem composition and estimate standing carbon stocks on Sangiang Island, Banten. The data taken was for the categories of trees, saplings, and seedlings (consisting of mangrove species, DBH, and height). The study was conducted at three stations with three replications for each station. The allometric formula obtained the estimated carbon stock from the stand biomass value. The results showed that 11 species of mangrove species were found with an average density of 1266 ind/ha (trees), 3733 ind/ha (saplings), and &lt;70% (seedlings). Then the estimated average carbon stock in Sangiang Island, Banten is 271.29 tons/ha.

A Framework for Improving Wall-to-Wall Canopy Height Mapping by Integrating GEDI LiDAR

Spatially continuous canopy height is a vital input for modeling forest structures and functioning. The global ecosystem dynamics investigation (GEDI) waveform can penetrate a canopy to precisely find the ground and measure canopy height, but it is spatially discontinuous over the earth’s surface. A common method to achieve wall-to-wall canopy height mapping is to integrate a set of field-measured canopy heights and spectral bands from optical and/or microwave remote sensing data as ancillary information. However, due partly to the saturation of spectral reflectance to canopy height, the product of this method may misrepresent canopy height. As a result, neither GEDI footprints nor interpolated maps using the common method can accurately produce spatially continuous canopy height maps alone. To address this issue, this study proposes a framework of point-surface fusion for canopy height mapping (FPSF-CH) that uses GEDI data to calibrate the initial wall-to-wall canopy height map derived from a sub-model of FPSF-CH. The effectiveness of the proposed FPSF-CH was validated by comparison to canopy heights derived from (1) a high-resolution canopy height model derived from airborne discrete point cloud lidar across three test sites, (2) a global canopy height product (GDAL RH95), and (3) the results of the FPSF-CH sub-model without fusing with the GEDI canopy height. The results showed that the RMSE and rRMSE of FPSF-CH were 3.82, 4.05, and 3.48 m, and 18.77, 16.24, and 13.81% across the three test sites, respectively. The FPSF-CH achieved improvement over GDAL RH95, with reductions in RMSE values of 1.28, 2.25, and 2.23 m, and reductions in rRMSE values of 6.29, 9.01, and 8.90% across the three test sites, respectively. Additionally, the better performance of the FPSF-CH compared with its sub-model further confirmed the effectiveness of integrating GEDI data for calibrating wall-to-wall canopy height mapping. The proposed FPSF-CH integrates GEDI LiDAR data to provide a new avenue for accurate wall-to-wall canopy height mapping critical to applications, such as estimations of biomass, biodiversity, and carbon stocks.