Aboveground Carbon Estimates Research Articles

Multi-scale geographically weighted regression estimation of carbon storage on coniferous forests considering residual distribution using remote sensing data

The Above-Ground Carbon (AGC) is an important indicator reflecting the carbon sink function of forest ecosystems. The area of coniferous forests in China accounts for about 50 % of the national forest area. Accurately estimating the AGC of coniferous forests plays an important role in evaluating forest carbon sink function. This study takes coniferous forests in Chun’an, Zhejiang Province as an example, using Landsat 8 OLI as the remote sensing data source, and innovatively proposes a multi-scale Geographically Weighted Regression model (MGWR) combined with Co-Kriging (COK), namely MGWR-COK and Ordinary Kriging (OK), namely MGWR-OK, for AGC estimation. This method first processes Landsat8 OLI remote sensing data, extracts and screens variables, and then constructs an MGWR model with AGC survey samples to estimate AGC and calculate residuals; Afterwards, two models, COK and OK, were used to spatially estimate the residuals; Finally, the AGC estimation results are overlaid with the residual estimation results to obtain the spatial distribution of AGC in coniferous forests. The study indicates that: (1) The selected remote sensing feature variables such as Optimized Soil-Adjusted Vegetation Index (OSAVI), Normalized Difference Vegetation Index (NDVI), Entropy (B3_ent_1 and B2_ent_2), Correlation (B4_corr and B5_corr_2) of texture information, B754 with band combination and elevation have a significant impact on AGC estimation. (2) The AGC accuracy R2 estimated by MGWR-OK and MGWR-COK are 0.837 and 0.857, respectively, which are 6 % and 8 % higher than the MGWR model, and the root mean square error is also reduced by 10 % and 12 %, respectively. This indicates that the combination of MGWR and Kriging interpolation can effectively reduce its spatial estimation error. (3) The range of AGC values estimated by the MGWR-COK model is 0.189–62.591 Mg ∙ hm−2, with a mean of 28.795 Mg ∙ hm−2. The spatial distribution shows a characteristic of high in the southeast and low in the northwest, which is consistent with the actual situation in the study area.

Estimation of Above Ground Carbon Stock Using Airborne Lidar in Yellapur Forest Division of Uttara Kannada District, Karnataka, India

The study was carried out at Yellapur Forest Division, Karnataka, with 86.97% forest area, exploring aboveground carbon (AGC) variations across different forest types and their correlation with LiDAR metrics. Ten one-hectare permanent plots were established, with 3 in dry and moist deciduous each, and 4 in semievergreen forests. All trees with >30 cm girth at breast height were inventoried. AGC estimates were 308.28 mg ha–1 in semi-evergreen, 207.77 mg ha–1 in moist deciduous, and 117.88 mg ha–1 in dry deciduous forests. LiDAR data were acquired using a Nextcore Lumos XM120 UAV LiDAR system. In dry deciduous and moist deciduous forests, the 95th height percentile correlated strongly with observed AGC, yielding predicted AGC values of 118.56 mg ha–1 and 208.86 mg ha–1, with R2 values of 0.91 and 0.83, respectively. Conversely, in semi-evergreen forests, the 50th height percentile outperformed other metrics, resulting in a predicted AGC of 310.90 mg ha–1 (R2 =0.57).

Mangrove above-ground carbon estimation from Sentinel-1A (SAR) and field-based data in Tien Yen district, Quang Ninh province

Mangrove forests have been globally recognized as they play a vital role in preventing coastal erosion, mitigating the effects of wave actions, and protecting coastal habitats and adjacent shoreline land-uses from extreme coastal events. Sentinel-1 (SAR) offers a new opportunity for mangrove cover mapping and biomass estimation, especially in the tropics where mangrove deforestation and degradation are highest and cloud cover is persistent. This study used the Sentinel-1A-derived VV/VH polarizations for mangrove cover mapping with thresholds of -23.5<VH<-9.05 and -17.5<VV<-3.8. Upon using VV and VH polarizations for mangrove cover mapping compared to PlanetScope data, it has been confirmed that these polarizations are suitable for mangrove cover monitoring along the coast of Tien Yen with an overall accuracy of over 90.5% and Kappa coefficient greater than 0.78 in 2022. This study also developed mangrove AGB models based on the field survey data and SAR data for estimating the AGB of mangrove forests in Tien Yen. In fact, we evaluated the capability of using Sentinel-1A for the retrieval and predictive mapping and mangrove AGB through the conventional linear regression models. The findings show that the models based on VV and VH polarization values from Sentinel-1A can be used for mangrove AGB estimation. Overall, selected AGB Model 1 with R2=0.445 (p-value<0.001) has provided an option for carbon estimation. To have more accurate AGB models based on SAR data, this study also suggests that more research should be carried out using more advanced machine learning models based on Sentinel-1A and Sentinel-1B for carbon estimation of mangrove forests in Tien Yen.

Estimation of Above-Ground Carbon Storage and Light Saturation Value in Northeastern China’s Natural Forests Using Different Spatial Regression Models

In recent years, accurate estimation and spatial mapping of above-ground carbon (AGC) storage in forests have been crucial for formulating carbon trading policies and promoting sustainable development strategies. Forest structure complexities mean that during their growth, trees may be affected by the surrounding environment, giving rise to spatial autocorrelation and heterogeneity in nearby forest segments. When estimating forest AGC through remote sensing, data saturation can arise in dense forest stands, adding to the uncertainties in AGC estimation. Our study used field-measured stand factors data from 138 forest fire risk plots located in Fenglin County in the Northeastern region, set within a series of temperate forest environments in 2021 and Sentinel-2 remote sensing image data with a spatial resolution of 10 m. Using ordinary least squares (OLS) as a baseline, we constructed and compared it against four spatial regression models, spatial lag model (SLM), spatial error model (SEM), spatial Durbin model (SDM), and geographically weighted regression (GWR), to better understand forest AGC spatial distribution. The results of local spatial analysis reveal significant spatial effects among plot data. The GWR model outperformed others with an R2 value of 0.695 and the lowest rRMSE at 0.273, considering spatial heterogeneity and extending the threshold range for AGC estimation. To address the challenge of light saturation during AGC estimation, we deployed traditional linear functions, the generalized additive model (GAM), and the quantile generalized additive model (QGAM). AGC light saturation values derived from QGAM most accurately reflect the actual conditions, with the forests in Fenglin County exhibiting a light saturation range of 108.832 to 129.894 Mg/ha. The GWR effectively alleviated the impact of data saturation, thereby reducing the uncertainty of AGC spatial distribution in Fenglin County. Overall, accurate predictions of large-scale forest carbon storage provide valuable guidance for forest management, forest conservation, and the promotion of sustainable development strategies.

Towards a co‐crediting system for carbon and biodiversity

Societal Impact StatementHumankind is facing both climate and biodiversity crises. This article proposes the foundations of a scheme that offers tradable credits for combined aboveground and soil carbon and biodiversity. Multidiversity—as estimated based on high‐throughput molecular identification of soil meiofauna, fungi, bacteria, protists, plants and other organisms shedding DNA into soil, complemented by acoustic and video analyses of aboveground macrobiota—offers a cost‐effective method that captures much of the terrestrial biodiversity. Such a voluntary crediting system would increase the quality of carbon projects and contribute funding for delivering the Kunming‐Montreal Global Biodiversity Framework.SummaryCarbon crediting and land offsets for biodiversity protection have been developed to tackle the challenges of increasing greenhouse gas emissions and the loss of global biodiversity. Unfortunately, these two mechanisms are not optimal when considered separately. Focusing solely on carbon capture—the primary goal of most carbon‐focused crediting and offsetting commitments—often results in the establishment of non‐native, fast‐growing monocultures that negatively affect biodiversity and soil‐related ecosystem services. Soil contributes a vast proportion of global biodiversity and contains traces of aboveground organisms. Here, we outline a carbon and biodiversity co‐crediting scheme based on the multi‐kingdom molecular and carbon analyses of soil samples, along with remote sensing estimation of aboveground carbon as well as video and acoustic analyses‐based monitoring of aboveground macroorganisms. Combined, such a co‐crediting scheme could help halt biodiversity loss by incentivising industry and governments to account for biodiversity in carbon sequestration projects more rigorously, explicitly and equitably than they currently do. In most cases, this would help prioritise protection before restoration and help promote more socially and environmentally sustainable land stewardship towards a ‘nature positive’ future.

Species Distribution and Patterns in a Forest-savannah Ecotone: Environmental Change and Conservation Concerns

Understanding the dynamics and patterns of biodiversity in transition forests is vital in promoting conservation and addressing environmental change issues. This work focused on elucidating the diversity, structure, and carbon potentials of a forest-savannah ecosystem. To achieve this, 8 forest plots that measured 50 m × 50 m each was set up in a forest-savannah landscape and used to identify and measure tree species ≥ 10 cm diameter at breast height (DBH measured at 130 cm). Species importance value was used to summarize the biodiversity patterns and the aboveground carbon estimates were elicited with the allometric equation. 43 species within 22 families were enumerated and the diversity was generally low (ranging from 1.82-2.5). Species such as Daniellia oliveri (Rolfe) Hutch. & Dalziel, Pyrostria guinnensis Comm. ex A. Juss, Dialium guineense Willd. and Margariteria discoidea (Baill.) G.L Webster were the dominant species, and had the highest importance values of 113.06, 55.13, 28.16 and 16.95, respectively, while Allophlus africanus P. Beauv., Annona senegalensis Pers., Anthonatha macrophylla P. Beauv., Ficus capensis Thumb. and Lecaniodiscus cupanioides Planch had the least importance values of 0.16 each. Carbon estimates ranged from 16.43172-42.9298 t/Ha. Most frequent species with higher basal areas no doubt contributed much to the carbon estimates, but did not have higher capacities in storing carbon. Managing the ecosystem with more carbon-dense species was seen as a suitable strategy for addressing environmental change in the ecosystem and region.

Estimation and Mapping Above-Ground Mangrove Carbon Stock Using Sentinel-2 Data Derived Vegetation Indices in Benoa Bay of Bali Province, Indonesia

Carbon dioxide (CO2) is one of the greenhouse gases that causes global warming with the highest concentration in the atmosphere. Mangrove forests can 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 investigates mangrove carbon stock. Utilization 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 get fast, cheap, and accurate information related to AGC estimation. This study aimed to investigates 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 classified the difference between mangrove and non-mangrove with the treatment of several parameters. Furthermore, a semi-empirical approach was used to assessed and map the AGC of mangroves. Allometric equations were used to calculated and produced 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 1134.92 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 be able to increase the accuracy value of previous studies using optical images.

Effects of forest degradation classification on the uncertainty of aboveground carbon estimates in the Amazon

BackgroundTropical forests are critical for the global carbon budget, yet they have been threatened by deforestation and forest degradation by fire, selective logging, and fragmentation. Existing uncertainties on land cover classification and in biomass estimates hinder accurate attribution of carbon emissions to specific forest classes. In this study, we used textural metrics derived from PlanetScope images to implement a probabilistic classification framework to identify intact, logged and burned forests in three Amazonian sites. We also estimated biomass for these forest classes using airborne lidar and compared biomass uncertainties using the lidar-derived estimates only to biomass uncertainties considering the forest degradation classification as well.ResultsOur classification approach reached overall accuracy of 0.86, with accuracy at individual sites varying from 0.69 to 0.93. Logged forests showed variable biomass changes, while burned forests showed an average carbon loss of 35%. We found that including uncertainty in forest degradation classification significantly increased uncertainty and decreased estimates of mean carbon density in two of the three test sites.ConclusionsOur findings indicate that the attribution of biomass changes to forest degradation classes needs to account for the uncertainty in forest degradation classification. By combining very high-resolution images with lidar data, we could attribute carbon stock changes to specific pathways of forest degradation. This approach also allows quantifying uncertainties of carbon emissions associated with forest degradation through logging and fire. Both the attribution and uncertainty quantification provide critical information for national greenhouse gas inventories.

Estimation of above-ground carbon of mangrove forest using Sentinel-1 (SAR) and field inventory data: a case study in Quang Yen, Quang Ninh province

Tide-dominated mangroves are found along shallow shorelines with modest slope where they receive freshwater runoff and nutrients from rainfall. They have been globally recognized as they play a vital role in preventing coastal erosion, mitigating effects of wave actions, and protecting coastal habitats and adjacent shoreline land-uses from extreme coastal events. By using Sentinel-1A data, the study has constructed the spatial distribution of mangrove cover compared to PlanetScope image in Quang Yen. In fact, this study used the Sentinel-1A-derived VV/VH polarizations for mangrove cover mapping with thresholds of -12.8<VH<-3.8 and -16.65<VV<-3.3. Upon using VV and VH polarizations for mangrove cover mapping compared to PlanetScope data, it has been confirmed that these polarizations are suitable for mangrove cover monitoring along the coast of Quang Yen with overall accuracy over 92.0% and Kappa coefficient greater than 0.80 in 2022. This study also developed mangrove AGB models based on the field survey data and SAR data for estimating AGB and AGC of mangrove forests in Quang Yen. Overall, selected AGB models (AGB = 288.17 + 21.57*VV, R2=0.553, p-value<0.001) have provided promising options for carbon estimation and monitoring to the coastal areas where the cloudy cover is mostly present and optical remote sensing data is usable. To have more accurate AGB models based on SAR data, this study also suggests that further study should be carried out using more advanced machine learning models based on Sentinel-1A and Sentinel-1B for carbon estimation of mangrove forests in Quang Yen.

Carbon Storage and Environmental Determinants in a Tropical Rainforest Landscape

Tropical ecosystems sequester vast amounts of carbon but remain much varied across different landscapes. In order to provide estimates on carbon storage for the ecosystem and show the role of forest structure and environmental factors in determining aboveground and soil carbon of a rainforest landscape, forest inventory was conducted across 30 forest plots. Each of the plots measured 50 m × 50 m and was used to identify and measure tree species ≥ 10 cm diameter at breast height (DBH measured at 130cm). Soil samples were collected for up to 30 cm deep at the four edges and then the middle of each plot, bulked for analysis and tested in the laboratory. Aboveground carbon estimates ranged from 8.18 - 91.29 t/ha across the ecosystem and were similar with carbon storage in tropical landscapes. With variations in stem density, basal area and structure across the region, much of the carbon capacity across the ecosystem was much varied (F (29, 2127) = 3.794, p = 0.000). Environmental variables (mainly edaphic variables) were not positively correlated with soil carbon and did not largely determine its storage and variation. The need to reduce disturbances (which are a main driver of disparity in biomass carbon storage) across the region and across tropical ecosystems was advocated as a pathway to enhancing higher carbon sequestration.

Utilization of Sentinel-2 Imagery in Mapping the Distribution and Estimation of Mangroves' Carbon Stocks in Bengkulu City

The mangroves' aboveground biomass significantly contributes to the global carbon cycle or economic and ecological values. This makes knowledge about the spatial extent of the mangroves indispensable for policymakers. The sequence of mangroves’ condition range also requires remote sensing data to update the geographical information and synthesize carbon stock in Bengkulu. Therefore, this study aims to create a spatial distrribution of mangroves and evaluate their carbon stock in Bengkulu City using Sentinel-2 imagery. The semi-empirical method uses Sentinel-2 imagery through NDVI to appraise and picture the mangroves' aboveground carbon stock. An allometric equation was used to compute the mangroves' aboveground carbon stock from field measurements. Non-linear regression was used to establish a connection between the NDVI calculated from the Sentinel-2 imagery and the mangroves' aboveground biomass measured in the field, which was subsequently used for aboveground carbon estimation. The results showed that mangroves mapping could derive overall accuracy of 89.09%, where the high-density class existed in 135.12 Ha of total area. It was also discovered that Sentinel-2 imagery could estimate mangroves carbon stock up to 61%. The carbon stock estimation based on the imagery has a value of 16.3992 – 115.134 t C/ha, while that of field survey data ranges from 19.69 to 326.06 t C/ha. These results showed that Sentinel-2B spectral data is functional and has a good chance of being able to predict carbon stock.
 
 Keywords : Carbon; mangroves; NDVI; remote sensing; sentinel-2B
 
 Copyright (c) 2022 Geosfera Indonesia and Department of Geography Education, University of Jember
 This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

ESTIMASI SIMPANAN KARBON PADA BIOMASSA MANGROVE DI TAHURA NGURAH RAI

Mangroves contribute to mitigating climate change by storing carbon. As mangrove forests have a high rate of carbon sequestration and storage, carbon stock calculations are required to quantify the accumulation of carbon stored in a site to determine its involvement in decreasing greenhouse gas emissions. This study aims to estimate carbon storage at the Mangrove Information Center (MIC) by using an allometric calculation of the mangrove above-ground biomass data. Data collection was carried out on January 10-14, 2022 at the MIC, with an area of 322 ha. The method used is a 1000 meters line transect plot divided into 15 measuring plots in three mangrove ecological zones (front, middle, and back). Biomass data were taken in a non-destructive way based on measurements of the circumference girth at breast height (GBH) and the results were calculated using allometric. According to data analysis, the average total surface biomass of mangroves in MIC is 781.30 kg, with estimated carbon storage in MIC is 36,727 tons/ha. Overall, the total carbon stock in the stratum MIC is 11.826,253 tons. The highest estimated carbon storage is in Plot 9, which is 91,799 tons/ha, while the lowest estimated carbon storage is in Plot 2, which is 13,873 tons/ha. The high numbers of carbon storage from mangrove above-ground biomass are mostly found in the middle and front zones because of the very dense mangrove density.
 Keywords: mangrove; above-ground carbon estimation; climate change mitigation

Almacenamiento de carbono en etapas de crecimiento de Pinus patula Schiede ex Schltdl. & Cham. en la Sierra Alta Hidalguense

Introduction: Forests provide environmental goods and services associated with the forest ecosystem, such as carbon sequestration, which has market potential to contribute to profitability and sustainability of forestry activities. Objective: To estimate biomass and aboveground carbon during the development stages of a Pinus patula Schiede ex Schltdl. & Cham. forest, cultivated in the ejido Atopixco, Zacualtipán, Hidalgo, to know the potential of the species for climate change mitigation. Materials and methods: Aerial biomass was estimated by non-destructive method, using direct measurement of height and diameter at breast height. The volume estimated for the trees sampled, at each stage of development, was multiplied by the basic wood density and the carbon coefficient of 50 % was used. Results and discussion: The average biomass of mature trees was 294.8 kg. Estimates of aboveground carbon at the developmental stages of P. patula were as follows: brinzal (0.94 Mg C·ha-1), monte bravo (3.73 Mg C·ha-1), vardascal (5.05 Mg C·ha-1), latizal (18.18 Mg C·ha-1) and fustal (81.40 Mg C·ha-1). The non-destructive method allowed reliable estimates of carbon content of this species. Conclusion: Carbon storage quantification by stages of development of P. patula is useful marketing strategies of environmental services for carbon sequestration, which has imminent market potential to mitigate the impacts of climate change.

Size‐dependent intraspecific variation in wood traits has little impact on aboveground carbon estimates in a tropical forest landscape

Abstract There is increasing evidence that intraspecific trait variation plays a role in governing rates of ecosystem functioning. While wood traits such as wood specific gravity (WSG) and wood carbon concentration (WCC) are key drivers of forest aboveground carbon (AGC) stocks, the sources of intraspecific variation in these wood traits and the consequences of this variation on AGC are poorly known, especially in the tropics. Here, we investigated intraspecific variation in wood specific gravity (WSG) and wood carbon concentration (WCC) from 556 individual trees belonging to 15 species that well characterize different successional stages of seasonal evergreen forests in Southeast Asia. Specifically, we tested the contribution of individual or species characteristics (tree size, growth rate and regeneration guilds) and local environmental conditions (topographic wetness index and successional stages) to intraspecific variation in WSG and WCC, and assessed the consequences of intraspecific variation in these wood traits on AGC estimates in 14 permanent forest plots established along a successional gradient in Khao Yai National park, Thailand. We found that tree size was the main driver of intraspecific variation in WSG and WCC as tree sizes increased from 10–100 cm in diameter, WSG increased by 7.3%, while WCC increased by 2.4% in heartwood, 1.6% and 2.7% in sapwood without and with volatile carbon included. There was no effect of the topographic wetness and other local environment condition in wood traits led to a slight overestimation of AGC in young secondary forests (+0.09% to +1.29%) and a small underestimation in older forests (−0.86% to −2.87%), but overall AGC estimates (13 of 14 forest plots) remained within error margins (the 95% interval). Our study provides evidence that tree size variation translates into intraspecific variability in wood traits, whereas local environmental conditions related to topography successional stages had no effect on wood trait variability. While size‐dependent variation in WSG and WCC have largely been undocumented and thus ignored in forest carbon assessment approaches, we highlight that it has a limited impact on AGC estimates, indicating that it does not invalidate current forest carbon stock estimation approaches. Read the free Plain Language Summary for this article on the Journal blog.

Allometric Models to Estimate Carbon Content in Arecaceae Based on Seven Species of Neotropical Palms

We present allometric models for estimating total carbon content and above ground carbon (AGC) for the Arecaceae family, and for seven abundant neotropical palm species: the canopy speciesSocratea exorrhiza(n= 10) andIriartea deltoidea(n= 10), the sub-canopy palmEuterpe precatoria(n= 10), and the understory speciesAsterogyne martiana(n= 15),Prestoea decurrens(n= 10),Geonoma interrupta(n= 10), andChamaedorea tepejilote(n= 22). Understanding the allometry of functional groups such as palms is critical for improving carbon stocks estimates in tropical forests and determining how allometric differences affect species functional diversity. The research was carried out in the tropical rainforests of the Caribbean slope of Costa Rica. We harvested 87 palms of a wide range of sizes, and separated them into roots, stems, and leaves, measured their fresh and dry biomass, and calculated their carbon content, tissue density, and dry mass fraction (dmf). Our general palm model estimating total carbon content based on these seven species and 87 samples accounted for 92% of the variation across species. We generated a similar model to estimate AGC and explained 91% of the variation. We compared our AGC model with two models used to estimate palm carbon content:Goodman et al. (2013)’s andChave et al. (2014)’s models and found that all three converged on the estimation of AGC although our model was the most parsimonious because it achieved the same efficiency with only two variables, stem diameter and stem height. To improve the accuracy of allometric models we need to incorporate more species, a greater diversity of growth forms, a wider range of sizes, a larger sample size, and more diversity of habitats dominated by palms. Estimating carbon content using allometric approaches could benefit from more consistency in data collection across plant groups.

Characterising the Aboveground Carbon Content of Saltmarsh in Jervis Bay, NSW, Using ArborCam and PlanetScope

Coastal ecosystems, including saltmarsh, provide important ecosystem services, including blue carbon storage, nutrient cycling, and coastal protection. The loss or degradation of saltmarsh ecosystems may undermine their capacity to provide these services and drive carbon emission increases. The accurate mapping and monitoring of the aboveground carbon content in these ecosystems supports protection and rehabilitation activities. Previous studies have used medium resolution satellites (e.g., Landsat and Sentinel-2) to characterise saltmarsh communities; however, these platforms are not well suited to the fine-scale patchiness of the saltmarsh ecosystems found in Australia. Here we explore the potential of a very high spatial resolution (0.15 m), seven-band multispectral ArborCam airborne sensor and 3 m images captured by the PlanetScope satellite constellation for mapping and monitoring the aboveground carbon content of a saltmarsh ecosystem in Jervis Bay National Park, Australia. The Normalized Difference Vegetation Index (NDVI) derived from an ArborCam image was calibrated to aboveground carbon content using field survey data. Strong linear relationships between the ArborCam NDVI and aboveground carbon content were found when survey data were partitioned by species. The mean aboveground carbon content derived from the calibrated ArborCam image was 1.32 Mg C ha−1 across the study area; however, this is likely to have been underestimated. A monthly NDVI time series derived from 12 PlanetScope images was analysed to investigate the short-term temporal variation in saltmarsh phenology, and significant intra-annual variation was found. An exploration of potential drivers for the variation found that local rainfall was a potential driver. The combination of the very high spatial resolution airborne ArborCam image and the regular 3 m capture by PlanetScope satellites was found to have potential for accurate mapping and monitoring of aboveground carbon in saltmarsh communities. Future work will focus on improving aboveground carbon estimates by including a very high spatial resolution species distribution map and investigating the influence of temporal variations in saltmarsh spectral response on these estimates.

Estimasi Stok Karbon Di Atas Permukaan Menggunakan Citra Sentinel-1A di Hutan Mangrove Karang Sewu, Bali

Karang Sewu mangrove is one of the forests in the tropics that can store a higher amount of carbon compared to other forest. The researh of carbon storage in the Karang Sewu mangrove forest is required to support government policies in mitigating blue carbon. Research about above ground and below ground carbons in the Karang Sewu mangroves has not provided spatial information, and one of the techniques that can be used is remote sensing with Synthetic Aperture Radar (SAR). This research used SAR Sentinel-1A C-Band sensor imagery to estimate above-ground carbon in mangroves because it can penetrate the canopy to branch vegetation so that the backscatter value is not disturbed by water on the surface. The purpose of this study was to determine the above-ground carbon value in the mangrove forest Karang Sewu, Bali, using Sentinel-1A imagery. Estimation of above-ground carbon in SAR images using the sigma nought Vertikal Horizontal polarization method based on carbon estimation models from field data and image data. The best carbon estimator model is a polynomial model with the equation y = 0.1055x2 + 4.1507x + 43,691, R2 = 0.3347. The Root Mean Square Error (RMSE) error rate is 0,7. Based on the RMSE value, it can be concluded that the difference between the field carbon and the image carbon results is relatively small The estimation result of the total above-ground carbon value using Sentinel-1A imagery is 59.85 tons/ha.

Mineração de dados aplicada a métodos de seleção de variáveis para a modelagem de estoque de carbono acima do solo

Abstract The objective of this work was to apply the random forest (RF) algorithm to the modelling of the aboveground carbon (AGC) stock of a tropical forest by testing three feature selection procedures – recursive removal and the uniobjective and multiobjective genetic algorithms (GAs). The used database covered 1,007 plots sampled in the Rio Grande watershed, in the state of Minas Gerais state, Brazil, and 114 environmental variables (climatic, edaphic, geographic, terrain, and spectral). The best feature selection strategy – RF with multiobjective GA – reaches the minor root-square error of 17.75 Mg ha-1 with only four spectral variables – normalized difference moisture index, normalized burnratio 2 correlation text ure, treecover, and latent heat flux –, which represents a reduction of 96.5% in the size of the database. Feature selection strategies assist in obtaining a better RF performance, by improving the accuracy and reducing the volume of the data. Although the recursive removal and multiobjective GA showed a similar performance as feature selection strategies, the latter presents the smallest subset of variables, with the highest accuracy. The findings of this study highlight the importance of using near infrared, short wavelengths, and derived vegetation indices for the remote-sense-based estimation of AGC. The MODIS products show a significant relationship with the AGC stock and should be further explored by the scientific community for the modelling of this stock.

Above ground carbon stock mapping over Coimbatore and Nilgiris Biosphere: a key source to the C sink

Mapping and quantifying above ground carbon (AGC) Stocks reflect significant dynamics in the terrestrial carbon cycle and cascade climate change. Estimation of such key driver was performed for the dominant species (Bamboo, Eucalyptus and Teak) over Coimbatore and Nilgiris Biosphere (2006 − 2018 quadruple interval) of Tamilnadu, India with the developed global stepwise multiple linear regression (SMLR) and local geographically weighted regression (GWR) models using multi-dynamic variables. Evapotranspiration (ET) developed using Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) model for the region was analysed with the best-fitted AGC estimation model to understand AGC—ET synergistic pertinence dynamics. The study compared and validated the estimation by the models and indicated that AGC estimation using SMLR exhibiting a high degree of accuracy ( ) with nominal negative bias in the estimation ranging from with amplification of GWR prediction indicted positive bias with comparatively least mean accuracy ( ). The ET—AGC reciprocity for the dominant species resulted that, bamboo with lower AGC correlated with higher ET tailed by teak with higher AGC and ET and eucalyptus with relatively higher AGC and lower ET and respectively. The analysis resulted in minimal biasness in AGC mapping using SMLR, and both the model signifies that the region can potentially be considered a long-term carbon sink.

Forest carbon trajectories: Consequences of alternative land-use scenarios in New England

The extent to which forest carbon sequestration can help mitigate climate change will be determined in large part by future land use. Here we quantify the impacts of five divergent future land-use scenarios on aboveground forest carbon stocks and fluxes throughout New England. These scenarios, four co-designed with stakeholders from throughout the region and the fifth a continuation of recent trends in land use, were simulated by coupling a land-cover change model with a mechanistic forest growth model to produce estimates of aboveground carbon over 50 years. We tracked the fate of forest carbon removed through harvesting and development using a standard carbon accounting methodology, modified to fit our modeling framework. Of the simulated changes in land use, changes in harvesting had the most profound and immediate impacts on carbon stocks and fluxes. In one land-use scenario that included a rapid expansion of harvesting for biomass energy, New England’s forests stopped serving as a net carbon sink and became a net carbon source by 2060. In an alternative scenario, relatively small reductions in harvest intensities (i.e., ~10% less biomass removed), coupled with an increased percent of wood going into longer-term storage, led to substantial reductions in net carbon emissions (909 MMtCO2eq) as compared to a continuation of recent trends in land use. However, these projected gains in carbon storage and reduction in emissions from less intense harvesting regimes can only be realized if they are paired with a reduction in the consumption of the timber products, and their replacements, that otherwise would result in additional emissions from leakage and substitution.