1. Introduction

2. Materials and Methods

2.1. Location

The study was carried out at Yeraba state forest. The site is located 295 km North of Addis Ababa on the way to Bahir Dar and 5km south east of Debre Markos town, in East Gojjam Zone of Amhara National Regional State, Ethiopia. This forest is located at geographical reference of 10^o 18’ 35’’N latitude and 37^o 45’ 29’’ E longitudes. The forest covers a total area of 315 hectares. There are different land uses bordering the forest namely ‘Aygereb pasture land’ in the East, ‘Abedeg pasture land’ in the West, ‘Engich got’ farm land at the North and the main asphalt road and a small village ‘Chemoga’ at the South. The land-form of the study area is characterized by undulating plain topography and dominated by gentle slopes and a localized moderate steep slopes ranging from 2-15% (Azene, 2007)

Figure 1. Map of the study area.

Figure 1. Map of the study area.

2.2. Research Methods

The researcher used a quantitative research approach which is based on the measurement of quantity or amount. It applies to phenomena that can be expressed in terms of quantity. Therefore, the quantitative design was implemented to analyze and interpret the field measured data for estimating above ground carbon stock. Besides, data was obtained from Sentinel-2 imageries and analyzed quantitatively and presented by Tables and maps.

2.2.1. Sampling Design

According to Kothari, (2004) a sample design is a definite plan to get a sample from the target population. The researcher was adopt a procedure in selecting a sample. For this study in order to select appropriate and representative sample from the study area and to make the findings more accurate, the researcher was structured the sample design that comprises of sampling techniques and sample size.

2.2.2. Sampling Technique and Sample Size

Sampling techniques are techniques used to select a sample from the target being framed. The sampling technique to be used depends on the objective of the study and the nature of the target population. Proper sampling techniques might eliminate bias in the selection process and reduce the costs or efforts in gathering data from the sample population (Kothari, 2004). Since entire forest or plantation or population cannot be measured or study therefore, researcher was followed systematic sampling technique as the population is more or less homogenous with respect to the characteristics under study area. The choice of sample plots size & shape must take into account the accuracy, precision, time and cost for measurement (Maleki & Kiviste, 2015). On top of that, the size of plot should be large enough to contain an adequate number of trees per plot to be measured (IPCC, 2006).According to Assefa et al., (2013) there are two types of plots – circular and square or rectangular plots. However, as stated by (Assefa et al., 2013) the square plots can be the most cost-efficient and they include more of within-plot heterogeneity, and thus be more representative than the circular plots of the same area (Hairiah et al., 2001) .

Therefore, for this study 17 sampling plots of square shape which have dimensions of 20 m ×20 m (400 m²) was formed (Pearson et al., 2005) within 428 meter distance and 0.25 intensity. In addition to the above plot there are also five 1 m × 1 m sub-sampling units (four at the corners and one center of main plot) was located for fallen litter and soil sampling. The sampling process was performed by using arc GIS software and create fishnet tool as indicated in the below figure 2

Living Trees above Ground Biomass (TAGB) sampling: - Trees with their plot location together were assessed in the forest. The Diameter of the individual trees was measured at an individual tree having DBH greater than 5cm using diameter tape. According to (De Cáceres et al., 2019), trees with multiple stems at 1.3 m height were selected as a single individual and DBH of the largest stems were taken. In all plots 909 total tree, 16363.03 total DBH (cm) and 11423.94 m total tree height samples were recorded. In order to identify measured tree, trees were checked off by white chalk.

Below Ground Biomass (TBGB) sampling: - The BGB carbon pool consists of the biomass contained within the live roots, the carbon sequestration potential of the forest found under the roots of the vegetation too. Below Ground Biomass Estimation: BGB was calculated by considering 26 % of the AGB (Dayathilake et al., 2020). The biomass of stock density was converted to carbon stock density by multiplying 0.47 fraction of IPCC (2006) default value.

Litter sampling: - the fallen leaves; grasses and fine branches are considered as litter. Thus, 1m*1m of plot size was estimated the biomass and carbon contents from the four corners and center of main plots. Moreover, the collected litters in the study area were weighted on the site using sensitive balance to estimate fresh weight. All samples were mixed and sub-sample taken. The sub-samples of litters are dried and the dry weight extrapolated to sub-plots.

Soil sampling: - carbon stock potential of the forest also exists under the soil. So, to estimate the carbon stock potential of the forest, soil samples taken from the forest. Thus, soil samples were taken from all parts of the study area. Before taking the soil samples from the sample areas in the forest, litter, grass and other materials on the soil surface were removed. The collection of the sample soils are categorized disturbed and undisturbed soil samples. The sampling of the soil was done 1m × 1m quadrate sub-plots at all corner and middle positions. In each sub-plot one soil sample was taken using auger at depth of 30cm, and then 85 soil samples from subplots and 17 Composite samples from each plot were collected.

2.2.3. Data Sources and Methods of Data Collection

For this study, both primary and secondary data was used to achieve the objectives of the study. However, it highly depends on the availability of input data and the quality of information. Primary data was obtained through field measurement on necessary parameters and Sentinel 2 that was used to estimate carbon stock of the study area. The secondary data sources such as published books, journals and researches, reports, website sources and other available materials used to collect reviewed information related to the objectives of the study.

2.2.4. Materials and Software

There are tools and materials and software’s that was used to collect, analyses, store and display the data for this study as listed in below table.

Table 1. Tools and software’s with their function.

Table 1. Tools and software’s with their function.

Tools and software’s with their function
Tools	Function
Garmin 72 GPS	Navigation and to indicate plot central coordinate
Clinometer	Tree height measurement
Diameter tape	To outline the plot
Caliper	DBH measurement
Soil ogre	To take soil sample
Hard plastic box	To hold the individual soil sample
Software’s	Function
SNAP	Preprocessing sentinel data
ARCGIS	Regression and correlation analysis, mapping the result
Excel	Field data organization

2.2.5. Method of Data Analysis

The collected field data was organized and recorded on the excel data sheet. The quantitative structure analysis and the collected primary and secondary data was analyzed in line with the aforementioned objectives by using tools of GIS and RS. For Sentinel 2, some pre-processing activities such as layer stacking (combining the different single bands to get colorful image), haze correction (for minimizing slight obscuration of the atmosphere, typically caused by fine suspended particles) and topographic correction (minimizing the sun angle related problems created on the bands) was done. To quantify the carbon sequestration potential of the study area tree DBH, dead woods, litter, tree root and soil data are the fundamental parameters. Thus, the way of the data analysis and interpretation have justified in detail as follows.

2.2.5.1. Aboveground Biomass Carbon Stock Estimation

Tree DBH(AGB): After the individual trees DBH in the sample plots were recorded properly, the above ground biomass of the individual tress in the sample plot by taking the consideration of the tropical forest was calculated using the following equation (Chave et al., 2014)

$$AGB=0.0559x\left(Px{D}^{2}xH\right)\dots \dots \dots \dots ...........\dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots ..\left(equ1\right)$$

where AGB is above ground biomass in kg, ‘P’ is wood Density, ‘D’ is Diameter (cm) at breast height of all woody species measured in all sample plots with DBH >=5cm at 1.3m height, ‘H’ is Height (m) of each plant species in all sample plots. There are specific wood density for each tree species as follows.

Table 2. specific wood density.

Table 2. specific wood density.

Species name	Specific wood density (P)	References
Cupressus lusitanica	0.414	(ICRAF Database - Wood Density, n.d.)
Acacia dicurusne	0.557	(ICRAF Database - Wood Density, n.d.)
eucallyptus globuse	0.7093	(ICRAF Database - Wood Density, n.d.)
Acacia melanoxylon	0.538	(ICRAF Database - Wood Density, n.d.)
Grevillea roubsta	0.536	(ICRAF Database - Wood Density, n.d.)
pinus patula	0.45	(ICRAF Database - Wood Density, n.d.)

Accordingly, the carbon content of tree vegetation in the study area was estimated by following (IPCC, 2006) which recommended the use of 47% (conversion factor: 0.47) for estimations of carbon concentration for aboveground biomass of tropical and subtropical forests

$$C=0.47*AGB.\dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots ..\left(equ2\right)$$

2.2.5.2. Belowground Biomass Carbon Stock Estimation

To estimate the carbon stock of belowground biomass, the methodology proposed by (IPCC, 2006), that is the application of a root to shoot ratio method was used.

The equations that was used to calculate the belowground biomass is given below:

$$BGB=AGB*0.26\dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots .\left(equ3\right)$$

where BGB is belowground biomass, AGB is aboveground biomass; 0.26 is the conversion factor (or 26% of AGB). The biomass of stock density was converted to carbon stock density by multiplying default value of 0.47 carbon fraction (IPCC, 2006)

2.2.5.3. Estimation of Carbon in the Litter Biomass

According to Pearson et al. (2005), estimation of the amount of biomass in the leaf litter can be calculated by:

$$\mathrm{L}\mathrm{B}=\frac{Wfield}{A}*\frac{Wsu{b}_{sample}\left(dry\right)}{Wsu{b}_{sample\left(fresh\right)}}*\frac{1}{10000}\dots \dots \dots \dots \dots \dots \dots \dots .\left(equ4\right)$$

where: LB = Litter (biomass of litter t ha^-1)

W field = weight of wet field sample of litter sampled within an area of size 1 m² (g);

A = size of the area in which litter were collected (ha);

W sub-sample, dry = weight of the oven-dry sub-sample of litter taken to the laboratory to determine moisture content (g), and

W sub-sample, fresh = weight of the fresh sub-sample of litter take to the laboratory to determine moisture content (g).

The percentage of organic carbon storage from the dry ash in the litter carbon pool was calculated as follows (Allan et al., 1986)

$$\%Ash=wc-wa/wb-wa100\dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots .\left(equ5\right)$$

$$\%C=(100-Ash\%)0.58\dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \left(equ6\right)$$

This is by considering 58% carbons in ash-free soil material.

Where, C = organic carbon (%), Wa = the weight of the crucible (g), Wb = the weight of oven dried grind samples and crucibles (g), Wc = the weight of ash and crucibles (g).

Finally, carbon in litter t/ha for each sample was determined.

Carbon stocks in dead litter biomass

$$CL=LB\times \%C\dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots ..\left(equ7\right)$$

where CL is total carbon stocks in the dead litter in t ha^-1, % C is carbon fraction determined in the laboratory (Pearson et al., 2005).

2.2.5.4. Estimation of Soil Organic Carbon

The carbon stock density of soil organic carbon was calculated as recommended by Pearson et al. (2005) from the volume and bulk density of the soil.

$$V=h*\pi r^2\dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \left(equ8\right)$$

where V is volume of the soil in the soil augure in cm³, h is the height of core sampler augur in cm, and r is the radius of augur in cm (Pearson et al., 2005). More over the bulk density of a soil sample can be calculated as follows:

$$BD=\frac{Wav,dry}{V}\dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots ..\left(equ9\right)$$

where BD is bulk density of the soil sample per, Wav, dry is average air-dry weight of soil sample per the quadrant, V is volume of the soil sample in the core sampler auger in cm3 (Pearson et al., 2005).

$$SOC=BD*d*\%C\dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots ..\left(equ10\right)$$

where, SOC= soil organic carbon stock per unit area (t ha^-1),

BD = soil bulk density (g cm^-3),

D = the total depth at which the sample was taken (30 cm), and

%C = Carbon concentration (%)

2.2.5.5. Total Carbon Stock Density (TCSD)

The total carbon stock density of each site was calculated by adding the carbon stock densities of the individual carbon pools using the formula (Pearson et al., 2005). In addition, it is recommended that any individual carbon pool of the given formula can be ignored if it does not contribute significantly to the total carbon stock (Subedi et al., 2010)

Carbon stock density of a study area:

$$Cdensity=CAGB+CBGB+CLit+SOC\dots \dots \dots \dots \dots \dots \dots \dots \dots ..\left(equ11\right)$$

where: C density = Carbon stock density for all pools [ton ha^-1]

C AGTB = Carbon in above -ground tree biomass [t C ha^-1]

CBGB =Carbon in below-ground biomass [t C ha^-1]

C Lit = Carbon in dead litter [t C ha^-1]

SOC =Soil organic carbon

The total carbon stocks was then converted to tons of CO2 equivalent by multiplying it by 44/12, or 3.67 (Pearson et al., 2005).

2.2.5.6. Scaling up AGB via Remote Sensing

Remote sensing facilitates the scaling-up of the plot-level AGB measurements by fine-resolution mapping to a broad spatial extent. Satellite data was used to estimate the Yeraba state forest carbon stock potential. Remote sensing based information has become an essential tool for global monitoring of natural and anthropogenic patterns, processes, and trends in continuous and repeatable observations over large areas (Chaminé et al., 2021; Pandey & Sharma, 2021; Reddy, 2021). There are so many satellites that can predict biomass but, the cost and access of the imagery restricted the usage and selection of imageries(B. Das et al., 2021; Sousa et al., 2015). Recently launched free Sentinel imagery offers a better opportunity than other freely accessed satellite imageries for forest AGB mapping and monitoring which in turn helps to estimate the carbon storage (Chen et al., 2018)

Using the ESA Sen2Cor 2.80 processor built into the Sentinel-2 satellite, the downloaded Level-1 top-of-atmosphere (TOA) data were atmospherically adjusted, radiometrically calibrated, and converted to reflectance to produce Level-2 bottom-of-atmosphere (BOA) outputs. Using the ESA Sen2Cor 2.80 processor found in the Sentinels application platform (SNAP) 7.0 software, the downloaded Sentinel-2 Level-1 Top of Atmosphere (TOA) data were atmospherically corrected, radiometrically calibrated, and converted to reflectance to produce Bottom of Atmosphere (BOA) Level 2A products. The Sentinel-2 bands (2, 3 and 8) that were initially recorded at 10 m resolution were resampled to 20 m using the closest neighbor method in order to match the resolution of the SWIR band. Sentinel-2 images were mosaicked and then cropped to fit the study area's perimeter. Five vegetation indicators were created using Sentinel-2 reflectance data in this study and compared to measurements of aboveground biomass taken on the ground to estimate AGC of the chosen sites. The information about vegetation indices chosen from the literature based on their ability to produce findings for aboveground biomass in the field that had greater coefficients of determination (R2) and higher correlation (r) values is shown in the table below

Table 3. General Information of sentinel 2A MSI level 1c spectral Vegetation indices (VI).

Table 3. General Information of sentinel 2A MSI level 1c spectral Vegetation indices (VI).

No	Vegetation indices	Formula	Reference
1	NDII/Normalized difference VI)	(B8-B12)/(B8+B12)	(Hunt & Rock, 1989)
2	ARVI /Atmospheric resistant VI)	(B8-(B4-B2))/(B8+(B4)	Kaufman, 1992
3	GNDVI /Green normalized difference VI)	(B8-B3)/(B8+B3)	(Gitelson et al., 1996)
4	VDVI (Visible band difference VI)	(2*green-red-blue)/(2*green + red + blue)	Wang et al., 2015
5	NDVI (Normalized difference VI)	(B8-B4)/(B8+B4)	Rouse et al., 1974

2.2.5.7. Correlation and regression Statistical data analysis

Field above ground biomass and carbon stck analysis was done using excel. Vegetation indices from Sentinel-2 satellite images were calculated under ArcGIS 10.8 software using raster calculator. Field plots laid on the vegetation index values using the sample plots‟ latitude and longitude. Since forest AGB data were recorded in 20m*20m grid, mean values of Vegetation indices of each sample plot extracted using 20m*20m rectangular grid in ArcGIS 10.8 as shown in figure below.

2.2.5.8. Producing Carbon Sink and Stock Potential Map of the Forest

After the above and Below Ground SOC content of the individual plots in Yeraba state forest determined; the total carbon sink potential of the individual plots were also added to quantify the above and below carbon content. In addition, in order to produce the total carbon sink potential map of the forest, GCPs of the individual sample plots collected using GPS. Thus, the quantified total carbon sequestration potential and the GCPs of the individual ample plots were tabulated together and added into Arc map compatible data. After that the corresponding GCPs and quantified carbon sequestration potential of the whole sample plots were overlaid on the Arc map Graphic Interface by points. Finally the Inversed Distance weight (IDW) method of interpolation was used for estimation of spatial distribution of biomass by Arc map 10.8.The reason that choosing IDW interpolation is that the method provides accurate biomass estimation than kriging for estimating spatial distribution of biomass (Maleika, 2020)

Figure 3. General workflow.

Figure 3. General workflow.

3. Result and Discussion

3.1. Results

3.1.1. Field above Ground Biomass of the Study Area

Following the collection of field data, the study area's above-ground biomass and carbon stock were calculated using an allometric equation created by earlier researchers(Chave et al., 2014). The allometric formula that calculates the AGB based on the tree's needed height, its diameter at breast height (DBH), and the species' individual wood densities. The field data was translated into above-ground biomass (t/ha) and above-ground carbon (t/ha) for each plot using the values as true biomass and carbon values for each plot. Based on biomass, the carbon content of each sample plot was estimated by adding the biomass produced by each species that was present there.

Utilizing the allometric derivation of the wood density of various species, height, and DBH data gathered from 17 sample plots, the above-ground biomass is calculated. As per the IPCC recommendation, 909 trees totaling greater than or equal to 5 cm DBH were counted in this study region and measured in the field to estimate the above-ground tree biomass and carbon. Field data's AGB value ranges from 73.79973 tons per plot to 2.19183 tons per plot. The greatest and minimum AGB are found in the second and eighth sample plots, respectively, out of all the plots. In contrast to the other six plots, which are sparse, long, and thickly vegetated, the seventh plot is dense, thin, short, and youthful.

Figure 4. DBH and Tree height class distribution.

Figure 4. DBH and Tree height class distribution.

3.1.2. Comparison of Vegetation Indices for AGB/AGC Estimation

The researcher created equations for the calculation of above-ground carbon stock using data from the relationship analysis of the field survey data and Sentinel-2 data. The above-ground carbon stock (AGCS) value and the Vegetation Index value obtained from Sentinel-2 were compared in this study using Pearson's correlation. It assesses how strongly two variables are linearly related. A close correlation between them can be inferred if the coefficient was near to 1. On the basis of an identifying tool, the pixel values for each sample were retrieved. These samples' image values were retrieved, and the correlation's strength, pattern, and equation were then examined. Model of each parameter are explained by using the regression correlation analysis. Regression models that are as linear as possible are utilized in the investigation to determine the correlation pattern. When vegetation canopies are not homogeneous in terms of species, which causes complexity and variance in groundcover, evaluating many vegetation indices is important (Haslwanter, 2022). Since various ranges of 46 biomass and groundcover have distinct indices that are more sensitive, factors like terrain, background soil reflectance, and variance in internal canopy signal scattering may affect the final vegetation signal. Based on this, the sentinel 2A MSI level 1C sensor's 20/02/2023 reading of 5 (five) vegetation indices was derived as figure 5 depict

3.1.2.1. Correlation of AGB/AGC with VDVI

The correlation between AGCS values and VDVI as illustrated in Figure 10, the coefficient of correlation was approximately 0.65 which showed that VDVI has performed high as compared to other indices (NDII, NDVI, GNDVI, and ARVI). Therefore VDVI was the best vegetation index that corresponded to AGCS the regression model of VDVI can explain 65% of field data while 35% of the data variations remain unexplained. In general, the result indicates that there is a strong positive correlation linear relationship between VDVI and above ground carbon stock. This shows that the VDVI average is a good indicator of the above-ground carbon stock estimation than the remaining one.

Figure 6. AGB and VDVI relationship.

Figure 6. AGB and VDVI relationship.

3.1.2.2. Correlation of AGB/AGC with NDVI

The correlation between AGCS values and Vegetation Index (NDVI) values computed from Sentinel-2 image has been shown in Figure 11. As represented in the scatterplot; the coefficient of correlation was 0.5974 which showed that NDVI model was capable to explain 59.74% of field data while the remaining 40.26% of the data are not explained by this model. The NDVI model has performed well than others except VDVI.

Figure 7. Correlation of AGB/AGC with NDVI.

Figure 7. Correlation of AGB/AGC with NDVI.

3.1.2.3. Correlation of AGB/AGC with ARVI

The correlation between AGCS values and Vegetation Index (ARVI) values computed from Sentinel-2 image has been shown in Figure 12. As represented in the scatterplot; the coefficient of correlation was 0.5801 which showed that ARVI model was capable to explain 58.01% of field data while the remaining 40.26% of the data are not explained by this model. The ARVI model has not performed well when it compared with the two above models.

Figure 8. Correlation of AGB/AGC with ARVI.

Figure 8. Correlation of AGB/AGC with ARVI.

3.1.2.4. Correlation of AGB/AGC with NDII

The correlation between AGCS values from field data and NDII values derived from Sentinel 2 image has been shown in Figure 13. As represented in the scatterplot, the coefficient of correlation was 0.534 which showed that the relationship between NDII and AGCS is not too much strong as compared to the rest of the indices (VDVI, NDVI, and ARVI), but it performs well as compared to GNDVI. The regression NDII model can explain only 53.4% of the variation in AGCS data while 56.6 % of the data is not explained by this model.

Figure 9. Correlation of AGB/AGC with NDII.

Figure 9. Correlation of AGB/AGC with NDII.

3.1.2.5. Correlation of AGB/AGC with GNDVI

GNDVI derived from Sentinel-2 was computed for correlation with above-ground carbon stock. As a result Figure 14 shows the scatter plot of carbon stock versus GNDVI weak when it compared with the above remaining vegetation indices.

Figure 10. Correlation of AGB/AGC with GNDVI.

Figure 10. Correlation of AGB/AGC with GNDVI.

3.1.3. Modeling the relationship between AGC and VIs

Linear regression models were developed based on the integration of AGCS data and vegetation indices derived from Sentinel-2. The result provides a comparison of the best regression models identified according to a multiple vegetation index. The R-value of vegetation indices ranges from 0.76 to 0.81, and R² varied between 0.51 and 0.65. All vegetation indices showed a significant and positive correlation with AGCS. VDVI was the best vegetation index that corresponded to AGCS (r = 0.81 and R2= 0.65) followed by NDVI, ARVI, NDII, and GNDVI.

Table 4. Statistical result of the relationship between AGC and VIs.

Table 4. Statistical result of the relationship between AGC and VIs.

No	VIs	R	R2	significance
1	ARVI	0.76	0.58	0.00
2	GNDVI	0.71	0.51	0.00
3	VDVI	0.81	0.65	0.00
4	NDII	0.73	0.53	0.00
5	NDVI	0.77	0.60	0.00

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.81
R Square	0.65
Adjusted R Square	0.63
Standard Error	12.68
Observations	17

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	3.26	4.16	0.78	0.45	-5.62	12.13	-5.62	12.13
VDVI	57.24	89.08	5.27	0.00	279.43	659.18	279.43	659.18

$$Log\left(AGB\right)=\left(VDVI*57.24\right)+3.26\dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots ..\left(equ12\right)$$

(12)

$$Log\left(AGC\right)=\left(VDVI*57.24\right)+3.26+0.47\dots \dots \dots \dots \dots \dots \dots \dots ..\left(equ13\right)$$

(13)

3.1.4. Correlation of field AGB and Predicted AGB

Predicted above ground biomass extracted from pixels at the samples location was correlated with the observed above ground biomass to decide the effectiveness of the model to predict AGB for other pixels. According to figure, the coefficient of determination (R2=0.6) and correlation coefficient (r= 0.77) shows strong relationship between the observed and predicted AGB. As a result the model generated through linear regression was used to map the final map of AGB for the study area.

Figure 11. Correlation of field AGB and predicted AGB.

Figure 11. Correlation of field AGB and predicted AGB.

3.1.5. Above-Ground Carbon Stock distribution map

According to Houghton (2005), forest has large potential for temporary and long term storage of carbon. As the study showed that, different tree species had different carbon stock potential. A large amount of carbon stock has been observed in a species which has counted long lived in the study site and also species which are densely populated in the study area. As a result this study indicates unbalanced tendency of carbon stock input in the above ground of the site. Among 6 major tree species in the study site high biomass rate has been recorded by species such as Cupressus lusitanica, Eucalyptus globuse, Acacia dicurusne, Pinus patula, Acacia melanoxylon and Grevillea roubsta.

Species-specific allometric models were applied to field allometric measurements to produce AGC ground truth estimates for each of the 17 plots and scaling up with sentential data. Based on the model AGB was predicted for the whole study sites. Finally the AGB values were converted in to AGC results. As the figure depict that the result of AGC Yeraba state forest lies between 5 ton/pixel to 39.72 ton/pixel. Based on the result which illustrated in the below figure about 15.32%, 22.76%, 39.12%,21.7% and 1.1% of the study forest has an AGC from 5.005-18.349 ton, 18.349-22.842 ton, 22.842 -26.654 ton, 26.654 -31.566 ton and 31.566-39.725 per pixel respectively. The mean value and standard deviation of carbon being stored was 23.4 and 4.396 ton. The total carbon stored by the study forest was about 183745.52 ton. As the figure showed that above ground carbon stock varied in magnitude, the highest concentration of AGC was concentrated southern part of the forest, as the area was covered with tall and long Eucalyptus trees on the other hand central part of the study forest was accounted less concentration of AGC due to the area was covered by sparse vegetation and most part of the area was occupied by grass.

Figure 12. Above-Ground Carbon Stock distribution map.

Figure 12. Above-Ground Carbon Stock distribution map.

3.1.6. Below Ground Carbon Potential

Below ground carbon is another forest carbon pool which derivate from the AGC. As a result the pattern of distribution is similar with AGC except its value dedication. Similarly, BGC also varied in terms of magnitude. The highest concentration of BGB was concentrated around northern and southern central part of the study forest on the other hand central part of the study area was less concentration of BGC. As the figure illustrated that, the result of BGC vary from 1.031 ton/pixel to 10.326 ton/pixel. The mean value, standard deviation and total carbon being stored was 6.121, 1.142 and 47637.336 ton. Based on the result which present in the below figure, about 15.6%, 22.96%, 39.14%,21.2% and 1.1% of the study forest has an AGC from 1.301-4.806 ton, 4.806 -5.974 ton, 5.974 -6.965 ton, 6.965 -8.24 ton and 8.24 -10.328 per pixel respectively.

Figure 13. Below Ground Carbon Potential.

Figure 13. Below Ground Carbon Potential.

3.1.7. Litter Carbon Stock Potential

The forest carbon is found in all living/ dead erected trees, roots, herbaceous trees and litters. All the fallen leaves, grasses and fine branches found in the study were considered as litter. The collected samples of 1m*1m size in five points of a plot and mixed to one sample resulting 17 litter samples were gathered and tested in laboratories. The highest and lowest carbon stock was observed in plot 7 and plot 14 with carbon stock of 4.58 ton and 2.7 ton respectively.

Figure 14. Litter Carbon Stock Potential.

Figure 14. Litter Carbon Stock Potential.

3.1.7. Estimation of Carbon Stocks in (SOC)

3.1.7.1. Bulk Density

The bulk density was computed on the soil profile. The bulk density of the study soil was 0.377 gcm^-3 minimum value and 0.44g cm^-3 maximum value with a mean value of 0.408 g cm^-3. Generally there is no siginicant difference between maximum and minimum bulk density.

3.1.7.2. Soil Organic Carbon

The laboratory results for the organic carbon of the sample soils are shown in (Appendix). The result showed that, the highest percentage of organic carbon was 12.63% which observed at plot 2: whereas, 8.25% is the lowest value which indicated at plot 3 and the mean percentage value of organic carbon in this pool is 9.77.

3.1.7.3. Soil Carbon Stock

In the study area the minimum and maximum carbon stock in the soil was 95.153ton/plot which locate at plot 15 and 157.978 ton/plot at plot 2 with mean total carbon stock of 119.679 ton/plot.

Figure 15. Soil Carbon Stock Map.

Figure 15. Soil Carbon Stock Map.

3.1.8. Total Carbon Stock of Yeraba state Forest

The total carbon stock of the study area was done by summing up all the carbon value of each pool (above ground and belowground carbon, litter carbon and soil organic carbon) for all plot samples of the study area by using weighted sum overlay technique. The carbon values of the study Forest ranged from a minimum of 191.93 to a maximum of 625.79 ton /hectare. The average carbon density in all carbon pool of the study area was 408.86 ton /hectare.

Figure 16. Total Carbon Stock of Yeraba state Forest.

Figure 16. Total Carbon Stock of Yeraba state Forest.

3.1.9. Influences of Environmental Factors on Carbon Stock

3.1.9.1. Variation of Carbon Stock along Altitudinal Gradient

The variety, biomass, and carbon stock in forest ecosystems are known to be significantly influenced by altitude (Alves et al., 2010; Mooser et al., 2014; Waqar Ahmed Khan et al., 2021) Although there is a slight differential in carbon stock with respect to the altitudinal gradient in this study region, there is no discernible altitudinal variation. The highest elevation class of the study region was where the largest AGC values were found, while the lowest elevation class was where the least AGC values were found. The mean value of the AGC decreased from the highest to the lowest elevation when we compared the mean values in terms of altitudinal variance. The same is true of BGC, which is an ancestor of AGC. Because a few large individuals can account for a significant amount of the quadrats above and below ground carbon, the presence of species with large individuals at high altitudes may have an impact on AGB and carbon stock (Ekoungoulou et al., 2014)

The mean carbon density in litter pool and soil pool of the present study was not showed clear pattern with altitude, but they had showed relatively an increasing trend with increasing altitude though there was no significant variation due to the elevated area of the study part was characterized by sparse, tall, and tick tress and their leaves can fall down easily towards the ground. The detail values were presented below table.

Figure 17. Elevation class of the study area.

Figure 17. Elevation class of the study area.

Table 5. Each Carbon pool statistical values with elevation class/ton.

Table 5. Each Carbon pool statistical values with elevation class/ton.

Each Carbon pool statistical values with elevation class/ton
Elevation Class	MIN	MAX	RANGE	MEAN	STD	SUM
AGC
2395 - 2427m	6.71	34.64	27.93	21.88	3.75	38930.67
2427 - 2460m	11.06	32.96	21.90	23.22	4.17	99346.66
2460 - 2493m	13.72	39.73	26.00	26.65	3.95	44671.82
			BGC
2395 - 2427m	1.75	9.01	7.26	5.69	0.97	10121.97
2427 - 2460m	2.88	8.57	5.69	6.04	1.09	25830.13
2460 - 2493m	3.57	10.33	6.76	6.93	1.03	11614.67
			LC
2395 - 2427m	2.75	4.30	1.55	3.38	0.34	6018.19
2427 - 2460m	2.43	4.52	2.09	3.38	0.45	14471.04
2460 - 2493m	2.54	4.58	2.04	3.57	0.47	5985.56
SOC
2395 - 2427m	95.23	151.58	56.35	119.27	11.26	212057.59
2427 - 2460m	104.15	157.98	53.83	120.28	8.03	514557.53
2460 - 2493m	99.21	153.78	54.57	121.38	10.66	203429.42
			TC
2395 - 2427m	213.78	574.40	360.62	398.42	50.10	708385.25
2427 - 2460m	262.04	540.99	278.94	416.19	53.11	1780063.00
2460 - 2493m	296.73	625.79	329.06	460.79	47.75	772279.93

3.1.9.2. Distribution of Carbon Stock along Slope Gradient

Slope is also one of the environmental factors that influence the distribution of carbon density (Zhuo et al., 2022). As indicated in (Zhu et al., 2011), the carbon partitioning among forest carbon pools along slope gradients is important in knowing possible change in carbon stock and thus carbon stock potential in response to the future climate change in mountain regions. The maximum above ground carbon was recorded at the lowest slope class (0-10%) followed by middle class (10 – 20%) and the higher class (20 – 31%) on the other hand minimum AGC was located at the middle class of slope (10 – 20%), followed by lowest slope class (0-10%) and higher class (20 – 31%) and its mean AGC carbon stock value is similar pattern as indicated in the following table. The same is true for BGC stock of the study area.

Litter carbon and soil carbon stock also insignificant variation along slope inclination, but the minimum and maximum carbon stock for litter carbon pool was found in middle class followed by lower and upper class on the other hand the maximum carbon stock for this pool also recorded at middle class followed by upper and lower class. The values of SC in this study area also have slit greater the lower part of the study area as the transported soil accumulated in flat surface. The maximum TC was recorded at the middle class next at lower and upper class.

Figure 18. Slope Class of the Study Area.

Figure 18. Slope Class of the Study Area.

	Each Carbon pool statistical values along Slope gradient
Slope Class	COUNT	MIN	MAX	RANGE	MEAN	STD	SUM
AGC
0 - 10%	4323	12.23	39.73	27.50	23.61	4.44	102052.88
10 -20%	2929	11.72	35.73	24.01	24.10	4.16	70591.83
20 -31%	164	12.45	30.72	18.26	23.46	4.26	3847.12
			BGC
0 - 10%	4323	3.18	10.33	7.15	6.14	1.15	26533.75
10 -20%	2929	3.05	9.29	6.24	6.27	1.08	18353.88
20 -31%	164	3.24	7.99	4.75	6.10	1.11	1000.25
			LC
0 - 10%	4323	2.45	4.54	2.09	3.36	0.46	14535.31
10 -20%	2929	2.43	4.58	2.15	3.51	0.41	10272.43
20 -31%	164	2.85	4.56	1.71	3.68	0.39	603.47
			SOC
0 - 10%	4323	95.23	157.98	62.75	120.75	9.32	522010.54
10 -20%	2929	96.48	156.41	59.92	119.87	9.61	351086.67
20 -31%	164	97.95	151.78	53.83	120.10	11.95	19695.93
	TC
0 - 10%	4323	274.51	625.79	351.28	421.56	57.11	1822412.12
10 -20%	2929	277.16	575.52	298.36	427.05	52.57	1250816.17
20 -31%	164	279.69	505.76	226.07	419.35	55.25	68773.12

3.1.9.3. Distribution of Carbon Stock in the Aspects (Slope Facings)

Aspect is one of environmental factors that can affect the carbon stock of forests in different carbon pools (Fang et al., 2018) and thus, it can be used as a useful variable to forecast the forest carbon stock in different carbon pools. Aspect has significant relationship with bio-mass carbon in forest areas due to the interaction between solar radiation and soil properties. As below table 6 depict that the maximum values of AGC and BGC were recorded towards northeastern aspect part of the study whereas the lowest values of the above carbon pool values were located towards western part of the study site. This may be related to the timing of sunlight that the vegetation can absorb. That means the vegetation that have been grown in the East direction receive sunlight earlier than vegetation in West and other directions.

Figure 19. Aspect Class of the Study Area.

Figure 19. Aspect Class of the Study Area.

4. Conculsions and Recommandetions

References

1. Allan, J., Mitchell, T., Harborne, N., Bohm, L., & Crane-Robinson, C. (1986). Roles of H1 domains in determining higher order chromatin structure and H1 location. Journal of Molecular Biology, 187(4), 591–601. [CrossRef]
2. Alves, L. F., Vieira, S. A., Scaranello, M. A., Camargo, P. B., Santos, F. A. M., Joly, C. A., & Martinelli, L. A. (2010). Forest structure and live aboveground biomass variation along an elevational gradient of tropical Atlantic moist forest (Brazil). Forest Ecology and Management, 260(5), 679–691. [CrossRef]
3. Anderson-Teixeira, K. J., Herrmann, V., Morgan, R. B., Bond-Lamberty, B., Cook-Patton, S. C., Ferson, A. E., Muller-Landau, H. C., & Wang, M. M. H. (2021). Carbon cycling in mature and regrowth forests globally. Environmental Research Letters, 16(5). [CrossRef]
4. Andrew, R. M. (2019, November 20). Global CO2 emissions from cement production, 1928-2018. Earth System Science Data; Copernicus GmbH. [CrossRef]
5. Assefa, G., Mengistu, T., Getu, Z., & Zewdie, S. (2013). Training manual on: Forest carbon pools and carbon stock assessment in the context of SFM and REDD+. January, 74.
6. Ayanu, Y. Z., Conrad, C., Nauss, T., Wegmann, M., & Koellner, T. (2012). Quantifying and mapping ecosystem services supplies and demands: a review of remote sensing applications. Environmental Science & Technology, 46(16), 8529–8541. [CrossRef]
7. Azene, B. tesema. (2007). Useful trees and shrubs of Ethiopia: Identification, Propagation and Management for 17 Agroclimatic Zones. Relma, 559. https://books.google.com/books/about/Useful_Trees_and_Shrubs_of_Ethiopia.html?id=15UfAQAAIAAJ.
8. Barstow, T. J. (2019). Understanding near infrared spectroscopy and its application to skeletal muscle research. Journal of Applied Physiology, 126(5), 1360–1376. [CrossRef]
9. Berhanu, M., Jabasingh, S. A., & Kifile, Z. (2017). Expanding sustenance in Ethiopia based on renewable energy resources – A comprehensive review. Renewable and Sustainable Energy Reviews, 75, 1035–1045. [CrossRef]
10. Bodo, T., Gbidum, B., Kemetonye, G., & Seomoni, J. (2021). Deforestation and Habitat Loss : Human Causes , Consequences and Possible Solutions. 04(02), 22–30.
11. Brockerhoff, E. G., Barbaro, L., Castagneyrol, B., Forrester, D. I., Gardiner, B., González-Olabarria, J. R., Lyver, P. O. B., Meurisse, N., Oxbrough, A., Taki, H., Thompson, I. D., van der Plas, F., & Jactel, H. (2017). Forest biodiversity, ecosystem functioning and the provision of ecosystem services. Biodiversity and Conservation, 26(13), 3005–3035. [CrossRef]
12. Caviglia-Harris, J. L., Toomey, M., Harris, D. W., Mullan, K., Bell, A. R., Sills, E. O., & Roberts, D. A. (2015). Detecting and interpreting secondary forest on an old Amazonian frontier. Journal of Land Use Science, 10(4), 442–465. [CrossRef]
13. Chaminé, H. I., Pereira, A. J. S. C., Teodoro, A. C., & Teixeira, J. (2021). Remote sensing and GIS applications in earth and environmental systems sciences. SN Applied Sciences, 3(12). [CrossRef]
14. Chave, J., Réjou-Méchain, M., Búrquez, A., Chidumayo, E., Colgan, M. S., Delitti, W. B. C., Duque, A., Eid, T., Fearnside, P. M., Goodman, R. C., Henry, M., Martínez-Yrízar, A., Mugasha, W. A., Muller-Landau, H. C., Mencuccini, M., Nelson, B. W., Ngomanda, A., Nogueira, E. M., Ortiz-Malavassi, E., … Vieilledent, G. (2014). Improved allometric models to estimate the aboveground biomass of tropical trees. Global Change Biology, 20(10), 3177–3190. [CrossRef]
15. Chen, L., Ren, C., Zhang, B., Wang, Z., & Xi, Y. (2018). Estimation of forest above-ground biomass by geographically weighted regression and machine learning with sentinel imagery. Forests, 9(10). [CrossRef]
16. Cui, X., Zhao, K., Zhou, Z., & Huang, P. (2021). Examining the uncertainty of carbon emission changes: A systematic approach based on peak simulation and resilience assessment. Environmental Impact Assessment Review, 91. [CrossRef]
17. Das, B., Bordoloi, R., Deka, S., Paul, A., Pandey, P. K., Singha, L. B., Tripathi, O. P., Mishra, B. P., & Mishra, M. (2021). Above ground biomass carbon assessment using field, satellite data and model based integrated approach to predict the carbon sequestration potential of major land use sector of Arunachal Himalaya, India. Carbon Management, 12(2), 201–214. [CrossRef]
18. Das, S., & Singh, T. P. (2012). Correlation analysis between biomass and spectral vegetation indices of forest ecosystem. International Journal of Engineering Research & Technology, 1(5). [CrossRef]
19. Dayathilake, D. D. T. L., Lokupitiya, E., & Wijeratne, V. P. I. S. (2020). Estimation of aboveground and belowground carbon stocks in urban freshwater wetlands of Sri Lanka. Carbon Balance and Management, 15(1). [CrossRef]
20. De Cáceres, M., Martín-Alcón, S., González-Olabarria, J. R., & Coll, L. (2019). A general method for the classification of forest stands using species composition and vertical and horizontal structure. Annals of Forest Science, 76(2). [CrossRef]
21. Domke, G. M., Perry, C. H., Walters, B. F., Woodall, C. W., Russell, M. B., & Smith, J. E. (2016). Estimating litter carbon stocks on forest land in the United States. Science of the Total Environment, 557–558, 469–478. [CrossRef]
22. Ekoungoulou, R., Liu, X., Loumeto, J. J., Ifo, S. A., Bocko, Y. E., Koula, F. E., & Niu, S. (2014). Tree Allometry in Tropical Forest of Congo for Carbon Stocks Estimation in Above-Ground Biomass. Open Journal of Forestry, 04(05), 481–491. [CrossRef]
23. Fang, H., Ji, B., Deng, X., Ying, J., Zhou, G., Shi, Y., Xu, L., Tao, J., Zhou, Y., Li, C., & Zheng, H. (2018). Effects of topographic factors and aboveground vegetation carbon stocks on soil organic carbon in Moso bamboo forests. Plant and Soil, 433(1–2), 363–376. [CrossRef]
24. Figueres, C., Schellnhuber, H. J., Whiteman, G., Rockström, J., Hobley, A., & Rahmstorf, S. (2017). Three years to safeguard our climate. Nature, 546(7660), 593–595. [CrossRef]
25. Forests, T. S. of the W. (2020). The State of the World’s Forests 2020. The State of the World’s Forests 2020. [CrossRef]
26. Gedefaw, M., Soromessa, T., & Belliethathan, S. (2014). Forest Carbon Stocks in Woody Plants of Tara Gedam Forest: Implication for Climate Change Mitigation. Science, Technology and Arts Research Journal, 3(1), 101. [CrossRef]
27. Giardino, A. P., & Lyn, M. A. (2021). Utilization Management, Case Management, and Care Coordination. Medical Quality Management, 139–175. [CrossRef]
28. Gibbs, H. K., Brown, S., Niles, J. O., & Foley, J. A. (2007). Monitoring and estimating tropical forest carbon stocks: Making REDD a reality. Environmental Research Letters, 2(4). [CrossRef]
29. Gitelson, A. A., Kaufman, Y. J., & Merzlyak, M. N. (1996). Use of a green channel in remote sensing of global vegetation from EOS- MODIS. Remote Sensing of Environment, 58(3), 289–298. [CrossRef]
30. Giweta, M. (2020). Role of litter production and its decomposition, and factors affecting the processes in a tropical forest ecosystem: A review. Journal of Ecology and Environment, 44(1). [CrossRef]
31. Gómez-Guerrero, A., & Doane, T. (2018). The Response of Forest Ecosystems to Climate Change. 35, 185–206. [CrossRef]
32. Graves, R. A., Haugo, R. D., Holz, A., Nielsen-Pincus, M., Jones, A., Kellogg, B., Macdonald, C., Popper, K., & Schindel, M. (2020). Potential greenhouse gas reductions from Natural Climate Solutions in Oregon, USA. PLoS ONE, 15(4). [CrossRef]
33. Grundel, I., Christenson, N., & Dahlström, M. (2022). Identifying interests and values in forest areas through collaborative processes and landscape resource analysis. Forest Policy and Economics, 142. [CrossRef]
34. Gubena, A. F., & Teshome, S. (2017). Variations in Forest Carbon Stocks Along Environmental Gradients in Egdu Forest of Oromia Region , Variations in Forest Carbon Stocks Along Environmental Gradients in Egdu Forest of Oromia Region , Ethiopia : Im. 6(January), 1–8. [CrossRef]
35. H/MICHAEL, T. (2015). CARBON STOCK OF METTI MOUNTAIN FOREST-WECHECHA, WEST OROMIA, ETHIOPIA TSEGAYE. 1–83.
36. Hairiah, K., Sitompul, S., van Noordwijk, M., & Palm, C. (2001). Methods of sampling for sampling above and below-ground organic pools. IC-SEA Report No. 6: Modelling Global Change Impacts on the Soil Environment., 1–31.
37. Hamilton, S. E., & Friess, D. A. (2018). Global carbon stocks and potential emissions due to mangrove deforestation from 2000 to 2012. Nature Climate Change 2018 8:3, 8(3), 240–244. [CrossRef]
38. Haslwanter, T. (2022). Linear Regression Models. 229–263. [CrossRef]
39. Houghton, R. A. (2005). Aboveground Forest Biomass and the Global Carbon Balance. Global Change Biology, 11(6), 945–958. [CrossRef]
40. Hunt, E. R., & Rock, B. N. (1989). Detection of changes in leaf water content using Near- and Middle-Infrared reflectances. Remote Sensing of Environment, 30(1), 43–54. [CrossRef]
41. ICRAF Database - Wood Density. (n.d.). Retrieved June 15, 2023, from http://db.worldagroforestry.org//wd/genus/Acacia.
42. IPCC. (2006). Good Practice Guidance for Land Use, Land-Use Change and Forestry. 590.
43. Islam, M., Deb, G. P., & Rahman, M. (2017). Forest fragmentation reduced carbon storage in a moist tropical forest in Bangladesh: Implications for policy development. Land Use Policy, 65, 15–25. [CrossRef]
44. Jandl, R., Lindner, M., Vesterdal, L., Bauwens, B., Baritz, R., Hagedorn, F., Johnson, D. W., Minkkinen, K., & Byrne, K. A. (2007). How strongly can forest management influence soil carbon sequestration?: Review. Geoderma, 137(3–4), 253–268. [CrossRef]
45. Kebede, T. A. (2021). FACTORS AFFECTING LOCAL FARMERS ADOPTION OF EUCALYPTUS WOODLOT IN JAMMA DISTRICT, SOUTH WOLLO ZONE, AMHARA REGIONAL STATE OF ETHIOPIA. EPRA International Journal of Research & Development (IJRD), 251–256. [CrossRef]
46. Kindler, E. (2016). A comparison of the concepts: Ecosystem services and forest functions to improve interdisciplinary exchange. Forest Policy and Economics, 67, 52–59. [CrossRef]
47. Kothari. (2004). Research methdology method and technique. In NEW AGE INTERNATIONAL (P) LIMITED, PUBLISHERS (Vol. 4, Issue 1). NEW AGE INTERNATIONAL (P) LIMITED, PUBLISHERS.
48. Loew, A., Bell, W., Brocca, L., Bulgin, C. E., Burdanowitz, J., Calbet, X., Donner, R. V., Ghent, D., Gruber, A., Kaminski, T., Kinzel, J., Klepp, C., Lambert, J. C., Schaepman-Strub, G., Schröder, M., & Verhoelst, T. (2017). Validation practices for satellite-based Earth observation data across communities. Reviews of Geophysics, 55(3), 779–817. [CrossRef]
49. Ma, H., Mo, L., Crowther, T. W., Maynard, D. S., van den Hoogen, J., Stocker, B. D., Terrer, C., & Zohner, C. M. (2021). The global distribution and environmental drivers of aboveground versus belowground plant biomass. Nature Ecology and Evolution, 5(8), 1110–1122. [CrossRef]
50. Machado, R. R., Conceição, S. V., Leite, H. G., De Souza, A. L. D., & Wolff, E. (2015). Evaluation of forest growth and carbon stock in forestry projects by system dynamics. Journal of Cleaner Production, 96, 520–530. [CrossRef]
51. Majasalmi, T., & Rautiainen, M. (2016). The potential of Sentinel-2 data for estimating biophysical variables in a boreal forest: A simulation study. Remote Sensing Letters, 7(5), 427–436. [CrossRef]
52. Maleika, W. (2020). Inverse distance weighting method optimization in the process of digital terrain model creation based on data collected from a multibeam echosounder. Applied Geomatics, 12(4), 397–407. [CrossRef]
53. Maleki, K., & Kiviste, A. (2015). Effect of sample plot size and shape on estimates of structural indices: A case study in mature silver birch (Betula pendula Roth) dominating stand in Järvselja. Forestry Studies, 63, 130–150. [CrossRef]
54. Marigi, S. N. (2017). Climate Change Vulnerability and Impacts Analysis in Kenya. American Journal of Climate Change, 06(01), 52–74. [CrossRef]
55. MarshetTefera, & TeshomeSoromessa. (2015). Carbon Stock Potentials of Woody Plant Species in Biheretsige and Central Closed Public Parks of Addis Ababa and Its Contribution to Climate Change Mitigation. Journal of Environment and Earth Science, 5(13), 1–15.
56. McRoberts, R. E., Tomppo, E. O., Vibrans, A. C., & Freitas, J. V. de. (2013). Design considerations for tropical forest inventories. Pesquisa Florestal Brasileira, 33(74), 189–202. [CrossRef]
57. Metz, B., Meyer, L., & Bosch, P. (2007). Climate change 2007 mitigation of climate change. Climate Change 2007 Mitigation of Climate Change, 9780521880114, 1–861. [CrossRef]
58. Mitsch, W. J., Bernal, B., Nahlik, A. M., Mander, Ü., Zhang, L., Anderson, C. J., Jørgensen, S. E., & Brix, H. (2013). Wetlands, carbon, and climate change. Landscape Ecology, 28(4), 583–597. [CrossRef]
59. Mondal, I., & Singh, K. H. (2022). Fluid substitution in NMR T2 distribution and resistivity independent saturation computation using synthetic capillary pressure data. Petroleum Research. [CrossRef]
60. Mooser, A., Ulmer, S., Blaum, K., Franke, K., Kracke, H., Leiteritz, C., Quint, W., Rodegheri, C. C., Smorra, C., & Walz, J. (2014). Direct high-precision measurement of the magnetic moment of the proton. Nature 2014 509:7502, 509(7502), 596–599. [CrossRef]
61. Mucheye, G., & Yemata, G. (2020). Species composition, structure and regeneration status of woody plant species in a dry Afromontane forest, Northwestern Ethiopia. Cogent Food and Agriculture, 6(1). [CrossRef]
62. Muluken, N. B., Teshome, S., & Eyale, B. (2015). Carbon stock in Adaba-Dodola community forest of Danaba District, West-Arsi zone of Oromia Region, Ethiopia: An implication for climate change mitigation. Journal of Ecology and The Natural Environment, 7(1), 14–22. [CrossRef]
63. Nigussie, W. (2016). Carbon Stock and Its Variations along Environmental Factors in Gendo Moist Montane Forest, East Wollega Zone, Western Ethiopia.
64. Nijnik, M., & Bizikova, L. (2008). Responding to the Kyoto Protocol through forestry: A comparison of opportunities for several countries in Europe. Forest Policy and Economics, 10(4), 257–269. [CrossRef]
65. Ontl, T. A., Janowiak, M. K., Swanston, C. W., Daley, J., Handler, S., Cornett, M., Hagenbuch, S., Handrick, C., McCarthy, L., & Patch, N. (2020). Forest Management for Carbon Sequestration and Climate Adaptation. Journal of Forestry, 118(1), 86–101. [CrossRef]
66. Pan, Y., Birdsey, R. A., Phillips, O. L., & Jackson, R. B. (2013). The structure, distribution, and biomass of the world’s forests. Annual Review of Ecology, Evolution, and Systematics. 44(1): 593-622., 44(1), 593–622. [CrossRef]
67. Pandey, P. C., & Sharma, L. K. (2021). Advances in remote sensing for natural resource monitoring. Advances in Remote Sensing for Natural Resource Monitoring, 1–480. [CrossRef]
68. Pearson, T., Walker, S., & Brown, S. (2005). Sourcebook for Land use, Land-use change and forestry projects. Winrock International and the BioCarbon Fund of the World Bank 57 (2005), 21(3), 64. http://wbcarbonfinance.org/docs/Background_LULUCF_Sourcebook_compressed.pdf.
69. Powers, J. S., Montgomery, R. A., Adair, E. C., Brearley, F. Q., Dewalt, S. J., Castanho, C. T., Chave, J., Deinert, E., Ganzhorn, J. U., Gilbert, M. E., González-Iturbe, J. A., Bunyavejchewin, S., Grau, H. R., Harms, K. E., Hiremath, A., Iriarte-Vivar, S., Manzane, E., De Oliveira, A. A., Poorter, L., … Lerdau, M. T. (2009). Decomposition in tropical forests: A pan-tropical study of the effects of litter type, litter placement and mesofaunal exclusion across a precipitation gradient. Journal of Ecology, 97(4), 801–811. [CrossRef]
70. Proistosescu, C., & Wagner, G. (2020). Uncertainties in Climate and Weather Extremes Increase the Cost of Carbon. One Earth, 2(6), 515–517. [CrossRef]
71. Pugh, T. A. M., Lindeskog, M., Smith, B., Poulter, B., Arneth, A., Haverd, V., & Calle, L. (2019). Role of forest regrowth in global carbon sink dynamics. Proceedings of the National Academy of Sciences of the United States of America, 116(10), 4382–4387. [CrossRef]
72. Reddy, C. S. (2021). Remote sensing of biodiversity: what to measure and monitor from space to species? Biodiversity and Conservation, 30(10), 2617–2631. [CrossRef]
73. Sahle, M. (2011). Estimating and Mapping of Carbon Stocks based on Remote Sensing, GIS and Ground Survey in the Menagesha Suba State Forest, Ethiopia. 128.
74. Salas, E. A. L., Subburayalu, S. K., Slater, B., Dave, R., Parekh, P., Zhao, K., & Bhattacharya, B. (2021). Assessing the effectiveness of ground truth data to capture landscape variability from an agricultural region using Gaussian simulation and geostatistical techniques. Heliyon, 7(7). [CrossRef]
75. Sha, Z., Bai, Y., Li, R., Lan, H., Zhang, X., Li, J., Liu, X., Chang, S., & Xie, Y. (2022). The global carbon sink potential of terrestrial vegetation can be increased substantially by optimal land management. Communications Earth & Environment 2022 3:1, 3(1), 1–10. [CrossRef]
76. Sibanda, M., Mutanga, O., & Rouget, M. (2015). Examining the potential of Sentinel-2 MSI spectral resolution in quantifying above ground biomass across different fertilizer treatments. ISPRS Journal of Photogrammetry and Remote Sensing, 110, 55–65. [CrossRef]
77. Smith, C. C., Healey, J. R., Berenguer, E., Young, P. J., Taylor, B., Elias, F., Espírito-Santo, F., & Barlow, J. (2021). Old-growth forest loss and secondary forest recovery across Amazonian countries. Environmental Research Letters, 16(8). [CrossRef]
78. Soromessa, T., & Eshete, A. (2022). Carbon storage of selected church forests in northern Ethiopia: Implications for climate change mitigation. European Journal of Agriculture and Forestry Research, 10(1), 25–48. [CrossRef]
79. Sousa, A. M. O., Gonçalves, A. C., Mesquita, P., & Marques da Silva, J. R. (2015). Biomass estimation with high resolution satellite images: A case study of Quercus rotundifolia. ISPRS Journal of Photogrammetry and Remote Sensing, 101, 69–79. [CrossRef]
80. Srikrishnan, V., Guan, Y., Tol, R. S. J., & Keller, K. (2022). Probabilistic projections of baseline twenty-first century CO2 emissions using a simple calibrated integrated assessment model. Climatic Change, 170(3–4). [CrossRef]
81. Stubenrauch, J., Ekardt, F., Hagemann, K., & Garske, B. (2022). Potential and Limits of Forest Ecosystems on Climate and Biodiversity Protection and Implications for the Legislative Process. 91–113. [CrossRef]
82. Subedi, B., Rana, E., Nepal, A., & Bhattarai, S. (2010). Forest Carbon Stock Measurement: Guidelines for measuring carbon stocks in community-managed forests. https://www.academia.edu/1098241/Forest_Carbon_Stock_Measurement_Guidelines_for_measuring_carbon_stocks_in_community_managed_forests.
83. Taye, F. A., Folkersen, M. V., Fleming, C. M., Buckwell, A., Mackey, B., Diwakar, K. C., Le, D., Hasan, S., & Ange, C. Saint. (2021). The economic values of global forest ecosystem services: A meta-analysis. Ecological Economics, 189. [CrossRef]
84. Tomppo, E., Gschwantner, T., Lawrence, M., & McRoberts, R. E. (2010). National Forest Inventories : pathways for common reporting. National Forest Inventories: Pathways for Common Reporting, 1–612. [CrossRef]
85. Tura, T. T., Argaw, M., & Eshetu, Z. (2013). Estimation of carbon stock in church forests: Implications for managing church forest to help with carbon emission reduction. Climate Change Management, 403–414. [CrossRef]
86. Vanino, S., Nino, P., De Michele, C., Falanga Bolognesi, S., D’Urso, G., Di Bene, C., Pennelli, B., Vuolo, F., Farina, R., Pulighe, G., & Napoli, R. (2018). Capability of Sentinel-2 data for estimating maximum evapotranspiration and irrigation requirements for tomato crop in Central Italy. Remote Sensing of Environment, 215, 452–470. [CrossRef]
87. Vasiljevic, N., & Gavrilovic, S. (2019). Cultural Ecosystem Services. 1–10. [CrossRef]
88. Verrelst, J., Schaepman, M. E., Koetz, B., & Kneubühler, M. (2008). Angular sensitivity analysis of vegetation indices derived from CHRIS/PROBA data. Remote Sensing of Environment, 112(5), 2341–2353. [CrossRef]
89. Waqar Ahmed Khan, R., Shaheen, H., & Awan, S. N. (2021). Biomass and soil carbon stocks in relation to the structure and composition of Chir Pine dominated forests in the lesser Himalayan foothills of Kashmir. Carbon Management, 12(4), 429–437. [CrossRef]
90. Wolde, B. M., Kelbessa, E., & Soromessa, T. (2014). Forest Carbon Stocks in Woody Plants of Arba Minch Ground Water Forest and its Variations along Environmental Gradients. Science, Technology and Arts Research Journal, 3(2), 141. [CrossRef]
91. Xue, J., & Su, B. (2017). Significant remote sensing vegetation indices: A review of developments and applications. Journal of Sensors, 2017. [CrossRef]
92. Yitebitu, M. (2010). Yitebitu MOGES | National REDD+ Coordinator | National REDD+ Secretariat | Research profile. https://www.researchgate.net/profile/Yitebitu-Moges.
93. Zhao, Y., Liu, Z., & Wu, J. (2020). Grassland ecosystem services: a systematic review of research advances and future directions. Landscape Ecology, 35(4), 793–814. [CrossRef]
94. Zhu, L. P., Li, L., Li, R., & Zhu, L. X. (2011). Model-free feature screening for ultrahigh-dimensional data. Journal of the American Statistical Association, 106(496), 1464–1475. [CrossRef]
95. Zhuo, Z., Chen, Q., Zhang, X., Chen, S., Gou, Y., Sun, Z., Huang, Y., & Shi, Z. (2022). Soil organic carbon storage, distribution, and influencing factors at different depths in the dryland farming regions of Northeast and North China. CATENA, 210, 105934. [CrossRef]