Estimation Of Carbon Stocks In Young Plantation Forest Ecosystems In The Alpine Region Of Northwest China And Its Spatial Variation Characteristics

Global climate change,characterised by increasing greenhouse gas concentrations and rising temperatures,has become a hot issue for governments and scientists alike.Forest ecosystems are the mainstay of terrestrial ecosystems and the largest terrestrial carbon reservoir,and play a very important role in the global carbon cycle and mitigation of climate change,therefore,it is very important to estimate the biomass and carbon stocks of forest ecosystems quickly and accurately.China's forests are dominated by young and middle-aged forests,and young forests have a high potential for carbon sequestration.Young plantation forests are an important part of the ecosystem and their biomass modelling is the basis for estimating the carbon stock of the ecosystem.However,the current research on biomass modelling in young plantation forests is not yet complete and cannot meet the requirements for accurate carbon stock estimation.Carbon density and carbon stocks are important indicators of the carbon sequestration capacity of forest ecosystems,and there are differences in carbon stocks and their spatial characteristics between different forest ecosystems in the same region or the same forest ecosystem in different regions.In this study,the spatial distribution of young plantation forests of three typical native tree species,namely spruce(Picea crassifolia),Sabina chinensis(Sabina przewalskii)and Chinese pine(Pinus tabulaeformis),was extracted using a multi-scale segmented object-oriented feature classification method based on high-resolution remote sensing images,and a biomass model of the three species was established.The biomass and carbon stocks of the three species of plantation forests were quantified and their spatial differentiation characteristics were analysed.The results show that:(1)The largest area of spruce plantation is distributed in the central part of sample plot 1;the area of Chinese pine is smaller than that of spruce and Sabina chinensis,and is concentrated in the western part of sample plot2;Sabina chinensis plantation is mainly distributed in the eastern part of sample plot 2,with small plots in the central part of sample plot 1,and the areas of spruce,Chinese pine and Sabina chinensis plantation are 10.4km~2,1.725km~2 and 2.136km~2 respectively.According to the error matrix,the classification accuracy of sample 1 was 91.23%,with a kappa coefficient of 0.879;the classification accuracy was 94.55%,with a kappa coefficient of 0.924.The classification accuracy was high,the classification results were reliable,and the classification method was suitable for remote sensing classification and spatial information extraction of young plantation forests in the study area.(2)The multivariate weighted independent biomass models of the three tree species were better than the one-weighted independent biomass model,and the binary independent biomass model was the best independent biomass model.Among them,spruce and oleander had the best model fit and prediction accuracy when the ground diameter was used as the independent variable,and the height factor was added to spruce,and the crown width factor was added to Chinese pine as the independent variable of the binary model.The optimal independent biomass models for the three species were:spruce:M_total=0.098*D^2.048*H^0.403,Chinese pine:M_total=0.213*D^1.369*C^1.067,and Sabina chinensis:M_total=0.484*C^0.261*H^1.923;and the fitting effect of each component of the independent biomass model for spruce was mainly as follows:total biomass>aboveground>trunk>branch>leaf>root.The results of the independent biomass model for spruce were mainly:total biomass>aboveground>trunk>branches>leaves>roots;while the results of the independent biomass model for cypress and oleander were mainly:aboveground>total biomass>trunk>branches>leaves>roots.(3)The choice of modelling factors for each component of each tree species was consistent with that of the independent models,both for the total control and the component summation system compatibility models.The non-linear joint estimation of the compatibility models maintained good compatibility among the components,and the model fits were all better,with the coefficient of determination R~2 higher than 0.94.The component-additive model was slightly better overall,especially for the binary model with the best fitting effect and the leaf component with poor fitting accuracy,so the component camera system compatibility model with higher fitting accuracy for each component was selected as the best compatibility model.Under the same variable estimation algorithm,the accuracy of the two compatibility models for each component of spruce and Sabina chinensis were:total biomass>trunk>branches>leaves>roots;the accuracy of the component fit for each component of Chinese pine was:trunk>total biomass>branches>leaves>roots.(4)The distribution of biomass among young trees in the three coniferous plantations was significant,in which the above-ground biomass distribution of each component was as follows:leaves>branches>trunk,and the proportion of leaves tended to decrease as the ground diameter or tree height increased,while the proportion of branches and trunk tended to increase significantly,especially the proportion of trunk of Chinese pine.The above-ground biomass distribution of the three species was higher than the below-ground biomass,and as the ground diameter(0-11 cm)increased,the proportion of above-ground biomass of spruce increased,while the proportion of above-ground biomass of oil pine and cypress decreased slightly.(5)The carbon content of the young trees of the three species was higher than that of Chinese pine(0.509)>Spruce(0.483)>Sabina chinensis(0.442),but the performance of each component was different,and the carbon content of the roots(underground)of the three species was lower than that of the above-ground components;the soil carbon content coefficient of the 0-50 cm spruce forest in the study area was much higher than that of the young planted forests of Sabina chinensis and Chinese pine,and the lowest in the planted forests of Chinese pine.The carbon density of the plant layer of the three tree species in the young plantation was:spruce(18.97 kg/m~2)>Chinese pine(12.32 kg/m~2)>Sabina chinensis(7.93 kg/m~2),and the organic carbon density of the soil layer at the same depth was:spruce>Chinese pine>Sabina chinensis.In contrast to carbon density,the slope of young plantation forests of the three tree species was:Sabina chinensis>Chinese pine>spruce;and young plantation forests with higher carbon density(spruce and Chinese pine)were mainly distributed on shady slopes,while young plantation forests with lower carbon density(Sabina chinensis)were mainly distributed on sunny slopes.(6)The carbon stocks of plantation layers of the three tree species were as follows:spruce(19733.66t)>Chinese pine(2125.43)>Sabina chinensis(1694.09t);soil carbon density and carbon stocks were different at the same depth or at different depths of the same plantation,and the vertical distribution patterns were not the same,and the carbon stocks of each soil layer of the three tree species from shallow to deep were There is a gradual decrease in soil carbon storage in each layer from shallow to deep.In general,the carbon stocks of the three species of young plantation forests were as follows:spruce(33518.1t)>Chinese pine(3844.32t)>Sabina chinensis(3178.41t).The spatial distribution of carbon stocks in young plantation forests in the study area was mainly as follows:young spruce forests were distributed in the central and northern parts of Sample 1,with the rest scattered,and their ecosystem carbon stocks were high;young Chinese pine forests were distributed in the western part of Sample 2;Sabina chinensis forests were mainly distributed in the eastern part of Sample 2,with small patches of Sabina chinensis forests in the central part of Sample1,and their ecosystem carbon stocks were 432.8 t The sample area is higher than that of Chinese pine,but its ecosystem carbon stock is lower than that of Chinese pine.