Tree-size dimension inequality shapes aboveground carbon stock across temperate forest strata along environmental gradients

2021 ◽  
Vol 496 ◽  
pp. 119482
Author(s):  
Maryam Kazempour Larsary ◽  
Hassan Pourbabaei ◽  
Anvar Sanaei ◽  
Ali Salehi ◽  
Rasoul Yousefpour ◽  
...  
Keyword(s):  
Carbon Stock ◽  
Temperate Forest ◽  
Environmental Gradients ◽  
Tree Size ◽  
Aboveground Carbon ◽  
Forest Strata ◽  
Size Dimension

scholarly journals Assessment of Soil Organic Carbon Stock of Temperate Coniferous Forests in Northern Kashmir

2015 ◽  
Vol 4 (1) ◽  
pp. 161-178
Author(s):  
Davood A. Dar ◽  
Bhawana Pathak ◽  
M. H. Fulekar
Keyword(s):  
Organic Matter ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Quality ◽  
Carbon Stock ◽  
Temperate Forest ◽  
Temperate Forests ◽  
Forest Site ◽  
Soil Organic Carbon Stock ◽  
Organic Carbon Stock

 Soil organic carbon (SOC) estimation in temperate forests of the Himalaya is important to estimate their contribution to regional, national and global carbon stocks. Physico chemical properties of soil were quantified to assess soil organic carbon density (SOC) and SOC CO2 mitigation density at two soil depths (0-10 and 10-20 cms) under temperate forest in the Northern region of Kashmir Himalayas India. The results indicate that conductance, moisture content, organic carbon and organic matter were significantly higher while as pH and bulk density were lower at Gulmarg forest site. SOC % was ranging from 2.31± 0.96 at Gulmarg meadow site to 2.31 ± 0.26 in Gulmarg forest site. SOC stocks in these temperate forests were from 36.39 ±15.40 to 50.09 ± 15.51 Mg C ha-1. The present study reveals that natural vegetation is the main contributor of soil quality as it maintained the soil organic carbon stock. In addition, organic matter is an important indicator of soil quality and environmental parameters such as soil moisture and soil biological activity change soil carbon sequestration potential in temperate forest ecosystems.DOI: http://dx.doi.org/10.3126/ije.v4i1.12186International Journal of Environment Volume-4, Issue-1, Dec-Feb 2014/15; page: 161-178

scholarly journals Wood density and tree size used as cues to locate and excavate cavities in two Colaptes woodpeckers inhabiting a threatened southern temperate forest of Argentina

2021 ◽  
Vol 502 ◽  
pp. 119723
Author(s):  
Adrián Jauregui ◽  
Sabrina Andrea Rodríguez ◽  
Lucas Nahuel González García ◽  
Exequiel Gonzalez ◽  
Luciano Noel Segura
Keyword(s):  
Wood Density ◽  
Temperate Forest ◽  
Tree Size

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

2021 ◽  
Vol 18 (1) ◽  
pp. 103
Author(s):  
Bayu Elwanto Bagus Dewanto ◽  
Retnadi Heru Jatmiko
Keyword(s):  
Carbon Stock ◽  
Open Space ◽  
Exponential Model ◽  
Carbon Stocks ◽  
Total Carbon ◽  
Empirical Modelling ◽  
Aboveground Carbon ◽  
Accuracy Test ◽  
Estimated Error ◽  
Total Carbon Stock

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.

scholarly journals ANALYSIS OF FIELD SEAGRASS PERCENT COVER AND ABOVEGROUND CARBON STOCK DATA FOR NON-DESTRUCTIVE ABOVEGROUND SEAGRASS CARBON STOCK MAPPING USING WORLDVIEW-2 IMAGE

2021 ◽  
Vol XLVI-4/W6-2021 ◽  
pp. 321-327
Author(s):  
P. Wicaksono ◽  
P. Danoedoro ◽  
U. Nehren ◽  
A. Maishella ◽  
M. Hafizt ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Carbon Stock ◽  
Stepwise Regression ◽  
Species Level ◽  
Measured Parameter ◽  
Support Vector ◽  
Percent Cover ◽  
Svm Classification ◽  
Aboveground Carbon ◽  
Non Destructive

Abstract. Remote sensing can make seagrass aboveground carbon stock (AGCseagrass) information spatially extensive and widely available. Therefore, it is necessary to develop a rapid approach to estimate AGCseagrass in the field to train and assess its remote sensing-based mapping. The aim of this research is to (1) analyze the Percent Cover (PCv)-AGCseagrass relationship in seagrass at the species and community levels to estimate AGCseagrass from PCv and (2) perform AGCseagrass mapping at both levels using WorldView-2 image and assess the accuracy of the resulting map. This research was conducted in Karimunjawa and Kemujan Islands, Indonesia. Support Vector Machine (SVM) classification was used to map seagrass species composition, and stepwise regression was used to model AGCseagrass using deglint, water column corrected, and principle component bands. The results were a rapid AGCseagrass estimation using an easily measured parameter, the seagrass PCv. At the community level, the AGCseagrass map had 58.79% accuracy (SEE = 5.41 g C m−2), whereas at the species level, the accuracy increased for the class Ea (64.73%, SEE = 6.86 g C m−2) and EaThCr (70.02%, SEE = 4.32 g C m−2) but decreased for ThCr (55.08%, SEE = 2.55 g C m−2). The results indicate that WorldView-2 image reflectance can accurately map AGCseagrass in the study area in the range of 15–20 g C m−2 for Ea, 10–15 g C m−2 for EaThCr, and 4–8 g C m−2 for ThCr. Based on our model, the AGCseagrass in the study area was estimated at 13.39 t C.

Integrating lidar with Landsat data for subalpine temperate forest aboveground carbon estimation

2015 ◽  
Vol 36 (23) ◽  
pp. 5767-5789
Author(s):  
Jun Du ◽  
Zhibin He ◽  
Longfei Chen ◽  
Junjun Yang ◽  
Xi Zhu ◽  
...  
Keyword(s):  
Temperate Forest ◽  
Landsat Data ◽  
Aboveground Carbon

scholarly journals Remote Estimation of Mangrove Aboveground Carbon Stock at the Species Level Using a Low-Cost Unmanned Aerial Vehicle System

2019 ◽  
Vol 11 (9) ◽  
pp. 1018 ◽  
Author(s):  
Zhen Li ◽  
Qijie Zan ◽  
Qiong Yang ◽  
Dehuang Zhu ◽  
Youjun Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Carbon Stock ◽  
Low Cost ◽  
Important Variable ◽  
Species Level ◽  
Canopy Height ◽  
Individual Species ◽  
Support Vector ◽  
Mangrove Forests ◽  
Aboveground Carbon

There is ongoing interest in developing remote sensing technology to map and monitor the spatial distribution and carbon stock of mangrove forests. Previous research has demonstrated that the relationship between remote sensing derived parameters and aboveground carbon (AGC) stock varies for different species types. However, the coarse spatial resolution of satellite images has restricted the estimated AGC accuracy, especially at the individual species level. Recently, the availability of unmanned aerial vehicles (UAVs) has provided an operationally efficient approach to map the distribution of species and accurately estimate AGC stock at a fine scale in mangrove areas. In this study, we estimated mangrove AGC in the core area of northern Shenzhen Bay, South China, using four kinds of variables, including species type, canopy height metrics, vegetation indices, and texture features, derived from a low-cost UAV system. Three machine-learning algorithm models, including Random Forest (RF), Support Vector Regression (SVR), and Artificial Neural Network (ANN), were compared in this study, where a 10-fold cross-validation was used to evaluate each model’s effectiveness. The results showed that a model that used all four type of variables, which were based on the RF algorithm, provided better AGC estimates (R2 = 0.81, relative RMSE (rRMSE) = 0.20, relative MAE (rMAE) = 0.14). The average predicted AGC from this model was 93.0 ± 24.3 Mg C ha−1, and the total estimated AGC was 7903.2 Mg for the mangrove forests. The species-based model had better performance than the considered canopy-height-based model for AGC estimation, and mangrove species was the most important variable among all the considered input variables; the mean height (Hmean) the second most important variable. Additionally, the RF algorithms showed better performance in terms of mangrove AGC estimation than the SVR and ANN algorithms. Overall, a low-cost UAV system with a digital camera has the potential to enable satisfactory predictions of AGC in areas of homogenous mangrove forests.

scholarly journals A Carbon Management Diagram for Oak-hickory Forests in Southern Ohio

2011 ◽  
Vol 28 (3) ◽  
pp. 161-165 ◽  
Author(s):  
Roger A. Williams ◽  
Yuhua Tao
Keyword(s):  
Carbon Stock ◽  
Basal Area ◽  
Site Index ◽  
Tree Size ◽  
Total Carbon ◽  
Site Quality ◽  
Oak Forests ◽  
Carbon Management ◽  
Tree Diameter ◽  
Total Carbon Stock

Abstract A carbon management diagram for use in oak-hickory forests in southern Ohio has been developed to allow easier quantification of total forest carbon stock. The total carbon stock is positively correlated to basal area and average stand diameter but poorly correlated to the number of trees per acre. The total amount of carbon stored in these forests is going to be influenced by age and site quality to the extent that age and site influence basal area and the average tree size. Accordingly, not all stands considered to be fully to overstocked store the most carbon. Rather, it is a combination of basal area and average tree size that determines the total carbon stored, with the carbon stock in the forest increasing with an increase in both basal area and average tree diameter. Examples illustrating the use of the diagram are presented for two oak forests on oak site indexes 60 and 80. Both forests are overstocked at age 100 years, but the forest on site index 60 stores 77 tons/ac of total carbon compared with 103 tons/ac on site index 80.

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