Estimation Of Aboveground Carbon Stocks Research Articles

Aboveground Carbon Stock Estimation Based on Backpack LiDAR and UAV Multispectral Imagery at the Forest Sample Plot Scale

Aboveground carbon stocks (AGCs) in forests play an important role in understanding carbon cycle processes. The global forestry sector has been working to find fast and accurate methods to estimate forest AGCs and implement dynamic monitoring. The aim of this study was to explore the effects of backpack LiDAR and UAV multispectral imagery on AGC estimation for two tree species (Larix gmelinii and Betula platyphylla) and to emphasize the accuracy of the models used. We estimated the AGC of Larix gmelinii and B. platyphylla forests using multivariate stepwise linear regression and random forest regression models using backpack LiDAR data and multi-source remote sensing data, respectively, and compared them with measured data. This study revealed that (1) the diameter at breast height (DBH) extracted from backpack LiDAR and vegetation indices (RVI and GNDVI) extracted from UAV multispectral imagery proved to be extremely effective in modeling for estimating AGCs, significantly improving the accuracy of the model. (2) Random forest regression models estimated AGCs with higher precision (Xing’an larch R2 = 0.95, RMSE = 3.99; white birch R2 = 0.96, RMSE = 3.45) than multiple linear regression models (Xing’an larch R2 = 0.92, RMSE = 6.15; white birch R2 = 0.96, RMSE = 3.57). (3) After combining backpack LiDAR and UAV multispectral data, the estimation accuracy of AGCs for both tree species (Xing’an larch R2 = 0.95, white birch R2 = 0.96) improved by 2% compared to using backpack LiDAR alone (Xing’an larch R2 = 0.93, white birch R2 = 0.94).

Improving Pinus densata Carbon Stock Estimations through Remote Sensing in Shangri-La: A Nonlinear Mixed-Effects Model Integrating Soil Thickness and Topographic Variables

Forest carbon sinks are vital in mitigating climate change, making it crucial to have highly accurate estimates of forest carbon stocks. A method that accounts for the spatial characteristics of inventory samples is necessary for the long-term estimation of above-ground forest carbon stocks due to the spatial heterogeneity of bottom-up methods. In this study, we developed a method for analyzing space-sensing data that estimates and predicts long time series of forest carbon stock changes in an alpine region by considering the sample’s spatial characteristics. We employed a nonlinear mixed-effects model and improved the model’s accuracy by considering both static and dynamic aspects. We utilized ground sample point data from the National Forest Inventory (NFI) taken every five years, including tree and soil information. Additionally, we extracted spectral and texture information from Landsat and combined it with DEM data to obtain topographic information for the sample plots. Using static data and change data at various annual intervals, we built estimation models. We tested three non-parametric models (Random Forest, Gradient-Boosted Regression Tree, and K-Nearest Neighbor) and two parametric models (linear mixed-effects and non-linear mixed-effects) and selected the most accurate model to estimate Pinus densata’s above-ground carbon stock. The results showed the following: (1) The texture information had a significant correlation with static and dynamic above-ground carbon stock changes. The highest correlation was for large-window mean, entropy, and variance. (2) The dynamic above-ground carbon stock model outperformed the static model. Additionally, the dynamic non-parametric models and parametric models experienced improvements in prediction accuracy. (3) In the multilevel nonlinear mixed-effects models, the highest accuracy was achieved with fixed effects for aspect and two-level nested random effects for the soil and elevation categories. (4) This study found that Pinus densata’s above-ground carbon stock in Shangri-La followed a decreasing, and then, increasing trend from 1987 to 2017. The mean carbon density increased overall, from 19.575 t·hm−2 to 25.313 t·hm−2. We concluded that a dynamic model based on variability accurately reflects Pinus densata’s above-ground carbon stock changes over time. Our approach can enhance time-series estimates of above-ground carbon stocks, particularly in complex topographies, by incorporating topographic factors and soil thickness into mixed-effects models.

Estimation of Aboveground Carbon Stock in The Pertamina Hulu Rokan (PHR) Forest in Pinggir District, Bengkalis Regency, Riau Province

Extreme climate change affects human life. One of the causes of climate change is the increase of CO2 gas in the air. Forests can absorb CO2 from the air and store it in the form of tree biomass through the process of photosynthesis. One of the forests that can absorb and store carbon is Pertamina Hulu Rokan (PHR) forest, therefore it is necessary to conduct research on estimating the carbon content of PHR Forest area. The methods to be used in this research are non-destructive sampling and destructive sampling methods. The method with harvesting is only for seedlings, undergrowth and litter, while for saplings, poles, trees and necromass with the method without harvesting. Aboveground carbon storage in PHR forest was found to be 95.37 tonnes/ha. The largest carbon stocks were from trees (68.96 tonnes/ha), poles (13.06 tonnes/ha), necromas (5.70 tonnes/ha), litter (3.72 tonnes/ha), saplings (3.54 tonnes/ha) and the smallest carbon stocks were seedlings and understorey (0.39 tonnes/ha).

Estimation of aboveground carbon stock based on mangrove zones in Ijo River Estuary, Ayah Village, Kebumen, Indonesia

Abstract. Ningtyas SRA, Mu’ali AYF, Putri DNAR, Silaningtyas NW, Tsabita FA, Aprianto MK, Sari SP, Fauziyyah MD, Nugraheni RS, Juniati SD, Isa MN, Nofitasari H, Sutarno, Sugiyarto, Yap CK, Budiharta S, Setyawan AD. 2023. Estimation of aboveground carbon stock based on mangrove zones in Ijo River Estuary, Ayah Village, Kebumen, Indonesia. Indo Pac J Ocean Life 7: 148-155. The increasing concentration of greenhouse gases, including carbon dioxide in the atmosphere is the driver of global warming which triggers climate change. One strategy to mitigate climate change is reducing carbon emissions and increasing the carbon stock. Mangrove forest is recognized as the largest carbon sink on the Earth, therefore, the conservation and restoration of mangrove forest are increasingly promoted as an effective way to tackle climate change. While many studies assess the carbon stock of mangrove forests, a context-specific assessment is needed to enrich the existing studies in this field. This study aimed to investigate the aboveground carbon stored in vegetation occurring in three mangrove zones (i.e., seaward, middle and landward zones) in Ijo River Estuary, Ayah Village, Kebumen, Central Java, Indonesia. Purposive sampling on each mangrove zone was conducted with vegetation data at tree and pole stages collected using nested plot methods. Aboveground biomass was calculated using an allometric equation while carbon stock was estimated using the method of the National Standardization Agency. In total, there were 11 mangrove species in the observation plots, consisting of seven true mangrove species and four mangrove associates. The seaward zone had five species and an amount of aboveground biomass (29324.07 MgB/ha), while the middle zone consisted of five species and had biomass of 52776.62 MgB/ha, and the landward zone was composed of five species and had biomass of 6428.74 MgB/ha. The carbon stock in the seaward, middle and landward zones were 43.06, 197.42 and 186.00 MgC/ha, respectively. In total, the Ayah Mangrove Forest contained aboveground biomass of 88529.43 MgB/ha, equal to 1143.31 MgC/ha carbon stock. The findings of this study reiterate the importance of conserving mangrove forests as an effective way to reduce carbon emissions which are the major cause of global warming.

Above-ground carbon stock estimation based on vegetation index classes of succession burnt peatland forest area in Jambi Province

KPHP Unit XIII Muaro Jambi as the forest management unit at site level has the potential as a large enough carbon sink. Given the area that reaches 107,839 hectares and has a type of peat soil, it is necessary to analyze carbon stocks in this forest area. Estimation of carbon stocks above ground level with non-destructive methods can be carried out using remote sensing technology by connecting the results of measurements in the field. Calculation of carbon stocks was calculated from biomass measurement in the field using allometric equations. The variables measured in this study were biomass stocks in sapling, pole, and tree stages. The data taken include data on diameter at breast height (DBH). The results showed that number of carbon stocks in 2022 in the sample plot based on the vegetation index (NDVI) class in KPHP Unit XIII Muaro Jambi from field measurements as follows: for sparse/low-density class, the average biomass is 23.27 tons/ha, the medium density class is 25.70 tons/ha. The last NDVI class, for the high-density class, the biomass stock is 74.62 tons/ha. Average biomass for all classes is 123.59 tons/ha and average of carbon content is 61.80 tons/ha.

Estimation of Bamboo Forest Aboveground Carbon Using the RGLM Model Based on Object-Based Multiscale Segmentation of SPOT-6 Imagery

Remote sensing is an important tool for the quantitative estimation of forest carbon stock. This study presents a multiscale, object-based method for the estimation of aboveground carbon stock in Moso bamboo forests. The method differs from conventional pixel-based approaches and is more suitable for Chinese forest management inventory. This research indicates that the construction of a SPOT-6 multiscale hierarchy with the 30 scale as the optimal segmentation scale achieves accurate information extraction for Moso bamboo forests. The producer’s and user’s accuracy are 88.89% and 86.96%, respectively. A random generalized linear model (RGLM), constructed using the multiscale hierarchy, can accurately estimate carbon storage of the bamboo forest in the study area, with a fitting and test accuracy (R2) of 0.74 and 0.64, respectively. In contrast, pixel-based methods using the RGLM model have a fitting and prediction accuracy of 0.24 and 0.01, respectively; thus, the object-based RGLM is a major improvement. The multiscale object hierarchy correctly analyzed the multiscale correlation and responses of bamboo forest elements to carbon storage. Objects at the 30 scale responded to the microstructure of the bamboo forest and had the strongest correlation between estimated carbon storage and measured values. Objects at the 60 scale did not directly inherit the forest information, so the response to the measured carbon storage of the bamboo forest was the smallest. Objects at the 90 scale serve as super-objects containing the forest feature information and have a significant correlation with the measured carbon storage. Therefore, in this study, a carbon storage estimation model was constructed based on the multiscale characteristics of the bamboo forest so as to analyze correlations and greatly improve the fitting and prediction accuracy of carbon storage.

Estimation of Aboveground Carbon Stocks in Forests Based on LiDAR and Multispectral Images: A Case Study of Duraer Coniferous Forests

The correct estimation of forest aboveground carbon stocks (AGCs) allows for an accurate assessment of the carbon sequestration potential of forest ecosystems, which is important for in-depth studies of the regional ecological environment and global climate change. How to estimate forest AGCs quickly and accurately and realize dynamic monitoring has been a hot topic of research in the forestry field worldwide. LiDAR and remote sensing optical imagery can be used to monitor forest resources, enabling the simultaneous acquisition of forest structural properties and spectral information. A high-density LiDAR-based point cloud cannot only reveal stand-scale forest parameters but can also be used to extract single wood-scale forest parameters. However, there are multiple forest parameter estimation model problems, so it is especially important to choose appropriate variables and models to estimate forest AGCs. In this study, we used a Duraer coniferous forest as the study area and combined LiDAR, multispectral images, and measured data to establish multiple linear regression models and multiple power regression models to estimate forest AGCs. We selected the best model for accuracy evaluation and mapped the spatial distribution of AGC density. We found that (1) the highest accuracy of the multiple multiplicative power regression model was obtained for the estimated AGC (R2 = 0.903, RMSE = 10.91 Pg) based on the LiDAR-estimated DBH; the predicted AGC values were in the range of 4.1–279.12 kg C. (2) The highest accuracy of the multiple multiplicative power regression model was obtained by combining the normalized vegetation index (NDVI) with the predicted AGC based on the DBH estimated by LiDAR (R2 = 0.906, RMSE = 10.87 Pg); the predicted AGC values were in the range of 3.93–449.07 kg C. (3) The LiDAR-predicted AGC values and the combined LiDAR and optical image-predicted AGC values agreed with the field AGCs.

Estimation of aboveground carbon stock using the 8 operation land imagery in Lemo Nakai community forest, Indonesia

Estimation of aboveground carbon stock on stands vegetation, especially in community forests in Indonesia has become an urgent issue in the effort to calculate, monitor, manage, and evaluate carbon stocks. The study aims to test the accuracy of the estimated model of aboveground carbon stocks, to ascertain the total carbon stock, and to map the spatial distribution of carbon stocks on stands vegetation in Lemo Nakai community forest lying on 3°25’59,588” - 3 27’57,982” alt. and 102°19’25,108” - 102°22’23,416” long., covering 1,053. 53 ha. The study was conducted from February to June 2022 at Baturaja Village, North Bengkulu District. Forest structure grouped classified into three classes; dense, medium and sparse stand using stratified sampling plots systematically spread out at the forest studied forest area. Above-ground biomass and carbon stock of the forest stand were estimated using allometric models. Spatial data collected from Landsat 8 OLI (operational land imager) were used to produce land-use maps of the Lemo Nakai community forest to estimate the total carbon stock, obtained from the United State Geological Survey (USGS, 2021). The result showed that the dense-, medium-, and sparse forest structure covered about 753,69 ha, 3232,41ha, and 32,49 ha, respectively. Above-ground biomass of the Lemo Nakai community forest was estimated at the dense-, medium-, and sparse structures were, respectively 389.74 tons ha-1, 278.88 tons ha-1, and 149.28 tons ha-1 with carbon stock at 179.40 tons ha-1, 128.29 tons ha-1, and 68.67 tons ha-1, respectively. Furthermore, estimated CO2eq absorption at the dense-, medium-, and sparse forest structure, respectively 657.80 tons ha-1, 470.58 tons ha-1, and 26.98 tons ha-1.

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 Stock at the Scale of Individual Trees in Subtropical Forests Using UAV LiDAR and Hyperspectral Data

Accurate estimation of aboveground carbon stock for individual trees is important for evaluating forest carbon sequestration potential and maintaining ecosystem carbon balance. Airborne light detection and ranging (LiDAR) data has been widely used to estimate tree-level carbon stock. However, few studies have explored the potential of combining LiDAR and hyperspectral data to estimate tree-level carbon stock. The objective of this study is to explore the potential of integrating unmanned aerial vehicle (UAV) LiDAR with hyperspectral data for tree-level aboveground carbon stock estimation. To achieve this goal, we first delineated individual trees by a CHM-based watershed segmentation algorithm. We then extracted structural and spectral features from UAV LiDAR and hyperspectral data respectively. Then, Pearson correlation analysis was conducted to assess the correlation between LiDAR features, hyperspectral features, and tree-level carbon stock, based on which, features were selected for model development. Finally, we developed tree-level carbon stock estimation models based on the Schumacher–Hall formula and stepwise multiple regression. Results showed that both LiDAR and hyperspectral features were strongly correlated to tree-level carbon stock. Both tree height (H, r = 0.75) and Green index (GI, r = 0.83) had the highest correlation coefficients with tree-level carbon stock in LiDAR and hyperspectral features, respectively. The best model using LiDAR features alone includes the metrics of H, the 10th height percentile of points (PH10), and mean height of points (Hmean), and can explain 74% of the variations in tree-level carbon stock. Similarly, the best model using hyperspectral data includes GI and modified normalized differential vegetation index (mNDVI), and has similar explanatory power (r2 = 0.75). The model that integrates predictors, namely, GI and the 95th height percentile of points (PH95) from hyperspectral and LiDAR data, substantially improves the explanatory power (r2 = 0.89). These results indicated that while either LiDAR data or hyperspectral data alone can estimate tree-level carbon stock with reasonable accuracy, combining LiDAR and hyperspectral features can substantially improve the explanatory power of the model. Such results suggested that tree-level carbon stock estimation can greatly benefit from the complementary nature of LiDAR-detected structural characteristics and hyperspectral-captured spectral information of vegetation.

ESTIMATION OF ABOVEGROUND CARBON STOCK USING SAR SENTINEL-1 IMAGERY IN SAMARINDA CITY

Estimation of aboveground carbon stock on stands vegetation, especially in green open space, has become an urgent issue in the effort to calculate, monitor, manage, and evaluate carbon stocks, especially in a massive urban area such as Samarinda City, Kalimantan Timur Province, Indonesia. The use of Sentinel-1 imagery was maximised to accommodate the weaknesses in its optical imagery, and combined with its ability to produce cloud-free imagery and minimal atmospheric influence. The study aims to test the accuracy of the estimated model of above-ground carbon stocks, to ascertain the total carbon stock, and to map the spatial distribution of carbon stocks on stands vegetation in Samarinda City. The methods used included empirical modelling of carbon stocks and statistical analysis comparing backscatter values and actual carbon stocks in the field using VV and VH polarisation. Model accuracy tests were performed using the standard error of estimate in independent accuracy test samples. The results show that Samarinda Utara subdistrict had the highest carbon stock of 3,765,255.9 tons in the VH exponential model. Total carbon stocks in the exponential VH models were 6,489,478.1 tons, with the highest maximum accuracy of 87.6 %, and an estimated error of 0.57 tons/pixel.

Estimation of total carbon stock of Kakinada mangrove using normalized difference vegetation index, East Godavari, Andhra Pradesh

Mangroves hold a significant amount of carbon and play a critical role in the earth's climate system. To this date, carbon stock had never been estimated for Kakinada mangroves with NDVI especially in Coringa which has been affected by disturbances (e.g.: Gathering Fuelwood, Fish Farming, and Prawn Farming). Understanding the multi-temporal dynamics of carbon stocks over Kakinada forests is very crucial because previous land use land cover change activities have modified the land for unsustainable aquaculture. During the study, we described arobust approach to estimate the carbon content of mangroves by using Normalized Difference Vegetation Index features. Normalized Difference Vegetation Index has features based on nearinfrared spectrum absorption for the different vegetation classes. Further sliced Normalized Difference Vegetation Index values from 0.15 to 0.64 have been used to calculate the aboveground biomass stock and belowground biomass stock which was afterward concluded as total carbon stock. Normalized Difference Vegetation Index based estimation of aboveground carbon stock results as 41.311 ±13 Mg ha-1, whereas belowground carbon estimated as 71.382 ± 20 Mg ha-1. Final results for Total carbon calculated by AGC and BGC is 112.694 ± 26 Mg ha-1. Additionally, linear regression analysis shows positive Skewness between the NDVI and the carbon stock. The Pearson correlation was calculated based on the independent (Carbon values) and dependent variable (NDVI) results in a value of 0.95.

A novel methodology to characterize and quantify regional farmscape non-production perennial vegetation carbon storage and potential for loss in Southwest British Columbia

Temperate zone agriculture landscapes are often comprised of both croplands and non-production perennial vegetation (NPPV). The latter provides ecosystem services (ES), of which carbon sequestration is one. Estimation of aboveground carbon stocks in NPPV has incentivized carbon storage within the agricultural landscape. Remote sensing has significantly improved resolution of earth imaging and automated assessment of these often small, fragmented NPPV parcels. Novel methods of assessment have also been motivated by changes in land policy and concomitant loss of NPPV carbon stocks. To characterize and evaluate the carbon storage potential of agricultural NPPV in Southwestern British Columbia, we utilized a compliment of remote sensing and secondary data sources. Object- and pixel-oriented classification with RapidEye imagery enabled mapping of NPPV at 92.9% accuracy, covering 33.2% of the landscape. NPPV provided 3.34 Mt C in aboveground biomass, 38.4% of which was composed of fragments unlikely to be identified through coarser satellite imagery. Using secondary soil surveys and sub-regional characteristics, indicators of agricultural expansion suggested that small parcels of NPPV were more likely to persist on active farmland, while large forest stands (> 9 hectares) were less likely to occur alongside lands already in production and often located on lands amenable to farming. Were these large contiguous tracks of NPPV cleared for production agriculture, we estimate that 0.98–1.86 Mt of stored carbon could potentially be released. These stands represent important carbon sinks and their retention could be incentivized through ES services remuneration and conservation programs.

Quick Aboveground Carbon Stock Estimation of Densely Planted Shrubs by Using Point Cloud Derived from Unmanned Aerial Vehicle

Carbon sink trading is an important aspect of carbon trading in China, and can have important significance in offsetting carbon emissions and improving ecological compensation. The use of unmanned aerial vehicles (UAVs) offers new opportunities for shrub carbon sink and accounts as a substitute for time-consuming and expensive plot investigations to estimate the carbon sink by using the aboveground carbon stock monitored by UAV. However, the UAV-based estimation of the aboveground carbon stock of densely planted shrubs still faces certain challenges. The specific objectives of this research are as follows: (1) to test the statistical relationship between the aboveground carbon stock and volume of a densely planted shrub belt, and (2) to develop a model to estimate aboveground carbon stock by monitoring the volume of the densely planted shrub belt using a UAV. The study showed that (i) the aboveground carbon stock would increase with the increase in the volume of the shrub belt, (ii) an estimation model of the aboveground carbon stock of the densely planted shrub belt was developed ( R 2 = 0.89 , P < 0.01 ), and (iii) the validation assessment to estimate aboveground carbon stock by using the UAV-based estimation model produced a coefficient of determination of R2 = 0.74 and an overall root mean square error of 18.79 kg CO2e. Good prediction ability of the model was determined using leave-one-out cross-validation (LOOCV). This output information is valuable for the design of operations in the framework of precise carbon-sink accounting of shrubs. In addition, a method using an UAV was developed and validated for the quick estimation of aboveground carbon stock for densely planted shrubs, thereby providing a potential alternative to time-consuming and expensive plot investigations of aboveground carbon-stock accounting, which is necessary for shrub projects in the carbon trading market in China.

ABILITY OF LANDSAT-8 OLI DERIVED TEXTURE METRICS IN ESTIMATING ABOVEGROUND CARBON STOCKS OF COPPICE OAK FORESTS

The role of forests as a reservoir for carbon has prompted the need for timely and reliable estimation of aboveground carbon stocks. Since measurement of aboveground carbon stocks of forests is a destructive, costly and time-consuming activity, aerial and satellite remote sensing techniques have gained many attentions in this field. Despite the fact that using aerial data for predicting aboveground carbon stocks has been proved as a highly accurate method, there are challenges related to high acquisition costs, small area coverage, and limited availability of these data. These challenges are more critical for non-commercial forests located in low-income countries. Landsat program provides repetitive acquisition of high-resolution multispectral data, which are freely available. The aim of this study was to assess the potential of multispectral Landsat 8 Operational Land Imager (OLI) derived texture metrics in quantifying aboveground carbon stocks of coppice Oak forests in Zagros Mountains, Iran. We used four different window sizes (3×3, 5×5, 7×7, and 9×9), and four different offsets ([0,1], [1,1], [1,0], and [1,-1]) to derive nine texture metrics (angular second moment, contrast, correlation, dissimilar, entropy, homogeneity, inverse difference, mean, and variance) from four bands (blue, green, red, and infrared). Totally, 124 sample plots in two different forests were measured and carbon was calculated using species-specific allometric models. Stepwise regression analysis was applied to estimate biomass from derived metrics. Results showed that, in general, larger size of window for deriving texture metrics resulted models with better fitting parameters. In addition, the correlation of the spectral bands for deriving texture metrics in regression models was ranked as b4>b3>b2>b5. The best offset was [1,-1]. Amongst the different metrics, mean and entropy were entered in most of the regression models. Overall, different models based on derived texture metrics were able to explain about half of the variation in aboveground carbon stocks. These results demonstrated that Landsat 8 derived texture metrics can be applied for mapping aboveground carbon stocks of coppice Oak Forests in large areas.

ABILITY OF LANDSAT-8 OLI DERIVED TEXTURE METRICS IN ESTIMATING ABOVEGROUND CARBON STOCKS OF COPPICE OAK FORESTS

Abstract. The role of forests as a reservoir for carbon has prompted the need for timely and reliable estimation of aboveground carbon stocks. Since measurement of aboveground carbon stocks of forests is a destructive, costly and time-consuming activity, aerial and satellite remote sensing techniques have gained many attentions in this field. Despite the fact that using aerial data for predicting aboveground carbon stocks has been proved as a highly accurate method, there are challenges related to high acquisition costs, small area coverage, and limited availability of these data. These challenges are more critical for non-commercial forests located in low-income countries. Landsat program provides repetitive acquisition of high-resolution multispectral data, which are freely available. The aim of this study was to assess the potential of multispectral Landsat 8 Operational Land Imager (OLI) derived texture metrics in quantifying aboveground carbon stocks of coppice Oak forests in Zagros Mountains, Iran. We used four different window sizes (3×3, 5×5, 7×7, and 9×9), and four different offsets ([0,1], [1,1], [1,0], and [1,-1]) to derive nine texture metrics (angular second moment, contrast, correlation, dissimilar, entropy, homogeneity, inverse difference, mean, and variance) from four bands (blue, green, red, and infrared). Totally, 124 sample plots in two different forests were measured and carbon was calculated using species-specific allometric models. Stepwise regression analysis was applied to estimate biomass from derived metrics. Results showed that, in general, larger size of window for deriving texture metrics resulted models with better fitting parameters. In addition, the correlation of the spectral bands for deriving texture metrics in regression models was ranked as b4>b3>b2>b5. The best offset was [1,-1]. Amongst the different metrics, mean and entropy were entered in most of the regression models. Overall, different models based on derived texture metrics were able to explain about half of the variation in aboveground carbon stocks. These results demonstrated that Landsat 8 derived texture metrics can be applied for mapping aboveground carbon stocks of coppice Oak Forests in large areas.

Estimation of Aboveground Carbon Stocks in Selected Homegardens in Five Agro Ecological Regions of the Low Country Intermediate Zone of Sri Lanka

Global warming is the most widespread problem of the new millennium. Carbon dioxide (CO2)is the most important greenhouse gas released as a result of human activities. As a consequence,global average temperature is projected to increase by 1.4 to 5.8 0C over the period of 1990 to2100. Forest based land use systems such as natural forests, forest plantations and agroforestrysystems store carbon in their biomass leading to reduction of the atmospheric Carbon dioxidelevels. Management of forests and agroforestry systems is identified as the most promisingoption to mitigate atmospheric Carbon dioxide (CO2). The objective of the present study was toinvestigate the carbon storage of homegardens located in the dry intermediate zone of Sri Lanka.Homegardens were selected based on the agro ecological regions and three homegardens eachwere taken for detailed studies in the 5 agro ecological regions (IL1), (IL2), (IL3), (IL1–L2),(IL1–L3) and the locations of the gardens were Kurunegala, Chilaw, Mahiyangana, Monergala,Wellawaya and Tangalle. In each garden transects were laid to capture the maximum diversityusing the Point Centre Quarter Method (PCQM) and a sampling intensity of around 60% wasobtained from each garden. Dbh and height were measured in each tree. Non woody trees andwoody trees having < 1cm dbh were excluded. Allometric regression equation AGB = 0.0509 xq D2H [AGB – abovegroud biomass (kg); D – dbh (cm) and H (height (m)] was used to estiamtethe aboveground biomass while Wc =W * 0.5 (Wc –carbon weight, W – above ground dryweight) was used to estimate the carbon content.According to the results, 55 woody species were recorded from the homegardens. Theaboveground carbon stock was highest in the gardens in Chilaw (345.88 t/ha) followed bygardens in Wellawaya (287.41 t/ha), gardens in Kurunegala (274.66 t/ha), gardens in Tangalle(229.57 t/ha), gardens in Mahiyangana (191.32 t/ha). The gardens in Moneragala recorded thelowest 9149.58 t/ha).

Allometric models for estimation of aboveground carbon stocks in improved fallows in eastern Zambia

This paper presents allometric functions for estimation of C stocks in aboveground tree biomass in 2-year-old improved fallows in eastern Zambia. A total of 222 individual trees representing 12 tree species were destructively harvested for C analysis by LECO CHN-1000 analyzer. Allometric models relating collar diameter (D10) and total tree height (H) to stem and total aboveground C stocks were developed using data from tree fallows. Logarithmically transformed power functions displayed a good ability to stabilize variance of aboveground C stocks and showed a good fit (84 < R2 < 99) with a bias of 0.7–3.6%. D10 alone and in combination with H explained most of the variability in total aboveground C stocks. Validation of the species-specific and generalized models with field data indicated that they accurately predicted aboveground tree C stocks. Generalized C estimation functions were also validated and described 73–97% of variability in aboveground C stocks with an average unsigned deviation of 1.5–4.9%. The C functions will serve as a vital tool for predicting and monitoring C pool sizes in long-term studies and agroforestry projects, especially where destructive sampling is not possible.