Select

Net Carbon Exchange of the Forest of Korean Pine and Broad Leaved Forest Trees in Changbai Mountain and Its Simulation Based on Temporal Convolutional Network

Jiandong Qi,Xinxin Tan

Scientia Silvae Sinicae 2022, 58 (2): 1-12. DOI: 10.11707/j.1001-7488.20220201

Abstract （522）

HTML （47）

PDF （3050KB）（602）

Save

Objective: Analyze the seasonal differences of net ecosystem exchange (NEE) of carbon in the study area(the forest of Korean pine and broad leaved trees in Changbai Mountain) and the response of the NEE to meteorological factors, reveal the dynamic impacts of meteorological factors on the NEE at a monthly basis, and provide a theoretical guidance for regulating the carbon budget in the study area. Meanwhile, the application of temporal convolution network (TCN) in the simulation of the NEE was studied to explore a new method of carbon flux simulation. Method: Based on 3 min data obtained at the flux observation station in temperate forest of Korean pine and broad-leaved trees in Changbai Mountain from 2007 to 2010, the seasonal differences of the NEE and five meteorological factors were analyzed, and correlation between the meteorological factors and the NEE was analyzed. The random forest model was used to calculate the importance scores of factors affecting NEE, five meteorological factors with higher scores were selected as inputs to the NEE simulation: latent heat flux, sensible heat flux, air humidity above the canopy, water vapor pressure above the canopy and net radiation; five NEE models were developed respectively based on TCN, long short term memory (LSTM), artificial neural network (ANN), support vector regression (SVR) and extreme learning machine (ELM). The prediction precision and stability of different models were examined by calculating coefficient of determination (R²), mean absolute error (MAE) and root mean square error (RMSE). Result: The annual total NEE was -74.777 3 gCO₂·m^-2a^-1 revealing an overall carbon sink for the whole year, but seasonally with a carbon sink in summer and a carbon source in winter. The NEE was extremely significantly (P < 0.000 1) negatively correlated with latent heat flux, vapor pressure over canopy, net radiation and air humidity over canopy, but not significantly correlated with sensible heat flux (0.077 0). The RMSE and the R² of TCN were respectively 0.110 5 mgCO₂·m^-2s^-1 and 0.821 4, the RMSE was decreased by 0.024 8, 0.022 4, 0.022 2, 0.006 8 mgCO₂·m^-2s^-1 and the R² was increased by 0.080 6, 0.077 7, 0.068 6, 0.022 3 compared with ELM, SVR, ANN and LSTM, respectively. According to 10 tests of the above 5 models, the standard deviation of RMSE of TCN obtained by calculation was 0.000 4 mgCO₂·m^-2s^-1, which was 0.001 4, 0.001 3, 0.000 2 mgCO₂·m^-2s^-1 lower than that of ELM, ANN and LSTM, respectively. Conclusion: The NEE of the flux observation station was a carbon sink overall, with obvious seasonal differences. The NEE was extremely significantly (P < 0.000 1) negatively correlated with latent heat flux, vapor pressure above the canopy, air humidity above the canopy and net radiation, but not significant (P=0.141 8) with the sensible heat flux. The long-term NEE prediction showed good precision and strong stability by the simulation model based on the TCN, providing a feasible basis for the application of the TCN in the field of ecological simulation. The study provides a theoretical guidance for regulating the carbon budget of the forest of Korean pine and broad-leaved trees in Changbai Mountain.

Table and Figures | Reference | Related Articles | Metrics

Select

A Method of Estimation Aboveground Biomass of Sparse Tree-Shrub Using Optical Remote Sensing

Yonglei Shi,Zhihui Wang,Shiming Li,Chunyi Li,Peiqing Xiao,Pan Zhang,Xiaoge Chang

Scientia Silvae Sinicae 2022, 58 (2): 13-22. DOI: 10.11707/j.1001-7488.20220202

Abstract （386）

HTML （21）

PDF （5280KB）（319）

Save

Objective: Three stratification schemes were used to construct canopy coverage-aboveground biomass model of tree-shrub based on UAV data and aboveground biomass models of tree-shrub based on spectral indices of Lansat8 OLI data. Differences among stratification schemes in the precision of the aboveground tree-shrub biomass model were investigated to provide a scientific and theoretical basis for high-precision estimation of aboveground tree-shrub biomass of plantations in dryland based on remote sensing data. Method: A total of 102 plots were surveyed in Mu Us Sandy Land, information on vegetation coverage of tree, shrub and herbaceous were obtained from the high-resolution UAV images using object-oriented machine learning algorithm, and then the canopy coverage-aboveground biomass models of tree-shrub were developed with three stratification schemes (non- stratification, stratification-based two vegetation types of tree and shrub, stratification-based five tree species). Based on landsat-8 OLI images, 6 spectral indices (NDVI, RVI, MSAVI, TCG, NDMI, NIRv) were used to interpret the herbaceous coverage in combination with UAV images. Different spectral index-aboveground tree-shrub biomass models were developed using the 3 stratification schemes (non-stratification, stratification using plots with and without herbaceous vegetation, stratification using plots with three herbaceous coverage level). Finally, the precision of estimation was compared among the models with different stratification schemes. Results: The canopy coverage-aboveground tree-shrub biomass model without stratification has the worst robustness (R²=0.22, n=102) and the lowest estimation precision (RMSE=14.98 t·hm^-2). The estimation errors were reduced by 50.32% and 61.1% for the models with stratification by 2 vegetation types of tree and shrub (RMSE=7.44 t·hm^-2) and by 5 tree species (RMSE=5.82 t·hm^-2), respectively. In the spectral index-aboveground tree-shrub biomass models, NIRv had the highest precision in estimation of aboveground tree-shrub biomass (the average RMSE of the 3 stratification schemes was 7.25 t·hm^-2), and NDVI had the lowest precision (the average RMSE of the 3 stratification schemes was 9.43 t·hm^-2). The explanatory ability of different spectral indices to variation of aboveground biomass of sparse tree-shrub was ranked as follows: NIRv > NDMI > TCG > MSAVI > RVI > NDVI. The effect of woody vegetation types on precision of spectral index-aboveground tree-shrub biomass model was less than that of the canopy coverage-aboveground tree-shrub biomass model. The RMSE of the spectral index-aboveground tree-shrub biomass models was reduced by 8.13%~16.62% considering the background of herbaceous coverage, and the sensitivity of precision to such background was ranked as follow: NIRv > TCG > NDVI > MSAVI > RVI > NDMI. Conclusion: The high spatial resolution remote sensing of UAV can be used to obtain prior knowledge of tree species types and herbaceous vegetation information for sparse tree-shrub mixed forest. The woody vegetation types have a great influence on the precision of canopy coverage-aboveground biomass model in sparse tree-shrub mixed forest, and at least the two vegetation types of tree and shrub should be distinguished to ensure the estimation precision of the method to meet the practical application requirements. The stratification model schemes of NIRv-aboveground biomass model derived from Landsat-8 OLI satellite data considering herbaceous vegetation coverage is suitable for remote sensing estimation of aboveground tree-shrub biomass in a large area.

Table and Figures | Reference | Related Articles | Metrics

Select

Biomass Allocation and Allometric Growth Models of Four Common Tree Species in Southern Subtropical Evergreen Broad-Leaved Forest

Houben Zhao,Guangyi Zhou,Zhaojia Li,Zhijun Qiu,Zhongmin Wu,Xu Wang

Scientia Silvae Sinicae 2022, 58 (2): 23-31. DOI: 10.11707/j.1001-7488.20220203

Abstract （429）

HTML （38）

PDF （2356KB）（430）

Save

Objective: In order to improve the accuracy of biomass estimation of southern subtropical evergreen broad-leaved forest, the biomass distribution among different organs of 4 common tree species were investigated, and the models of single plant and different biomass components(aboveground and underground) were constructed for each of the 4 species. Method: Based on the historical data of forest survey of the southern subtropical evergreen broad-leaved forest in Guangzhou and its actual species composition, 4 common tree species, Castanopsis fissa, C. chinensis, Aleurites montana and Machilus chinensis were selected to measure the biomass of single plant and different components of each tree species using the harvest method, and further to construct the biomass models of each tree species. The influences of adding tree height (H) and wood density (ρ) as second independent variables in the models on the accuracy of the models were discussed. Result: The proportion of leaf biomass of the four 4 tree species accounted for 5.34%-7.28%, and the proportion was significantly lower of M. chinensis than of C. fissa (P < 0.05). The proportion of branch biomass accounted for 16.82%-24.20%, and significantly lower of M. chinensis than of C. chinensis and C. fissa (P < 0.05). The proportion of trunk biomass accounted for 47.22%-58.05%, and significantly lower of C. chinensis than of the other three species (P < 0.05). The proportion of root biomass accounted for 14.25%-22.25%, and significantly lower of C. fissa and A. montana than of C. chinensis and M. chinensis (P < 0.05). The proportion of leaf biomass of A. montana and M. chinensis decreased significantly (P < 0.01) with the increase of DBH. With the increase of DBH, the proportion of branch biomass increased, while the proportion of trunk biomass decreased of the four species. There was no significant correlation between the proportion of root biomass and DBH. The biomass model with DBH as a single independent variable had good fitting accuracy (average R²=0.947). Adding H compounded with D as one variable (D²H) in the models decreases the model fitness of aboveground and total biomass, and slightly improves the fitness of underground biomass. Including H as an independent second variable in the models increases the multiple col-linearity of the model. Adding ρ compounded with D as one variable (D²ρ) in the models decreases the model fitness of single plant and all components, but including ρ as an independent second variable in the models slightly improves the model fitness. Conclusion: The sunlight-loving tree species, A. montana, allocated more biomass to the trunk, while the shade tolerant tree species, C. chinensis, allocated more biomass to the crowns. The shade tolerant tree species have more developed roots. We suggest that the model of D as a single independent variable be used to estimate the biomass of the four species, and H is not recommended to be included in the model as the second variable. Adding ρ as the second independent variable slightly improves the fitting ability of the model. It is suggested to make a trade-off between accuracy and difficulty according to the investigation purpose in practical applications.

Table and Figures | Reference | Related Articles | Metrics

Select

Prediction of Forest Biomass Carbon Pool and Carbon Sink Potential in China before 2060

Xiao Fu,Yuxing Zhang,Xuejun Wang

Scientia Silvae Sinicae 2022, 58 (2): 32-41. DOI: 10.11707/j.1001-7488.20220204

Abstract （673）

HTML （33）

PDF （1510KB）（742）

Save

Objective: Prediction of the potential of carbon pool and carbon sink by China's forest biomass before 2060 is carried out in order to provide an important basis for the study and development policies on emission reduction and sink enhancement, it is of significant implications for China to allow necessary CO₂ emissions and negotiations on global climate change. Method: Based on the data from the National Forest Inventory (NFI), eight to nine major dominant tree species (groups) were selected by region to fit the Richards growth equation, the relationship between volume per hectare and age of each dominant species (group) was established by dividing China into six regions. Using China's forest management plans to project forest area by period, the forest timber volume and biomass carbon pool and carbon sink potential (excluding economic forests and bamboo forests) are estimated in China by 2060. Results: By 2030, forest timber volume will reach 20 473 million m³, an increase of 7 473 million m³ compared with 2005, and by 2060, it will reach 28 645 million m³. In view of the dynamic changes in each region, the southwest and southeast regions are the places with the fastest growth of forest timber volume in China in the future, and the areas with the greatest potential for precise improvement of forest quality, accounting for 37.68% and 21.37% of the national forest timber volume in 2060 respectively. By 2060, the existing forest carbon stocks will reach 12.12 Pg C and new plantations will add another 0.92 Pg C; the total forest biomass carbon pool will reach 13.04 Pg C, an increase of 5.47 Pg C compared to 7.57 Pg C in 2018. The forest carbon density will reach 63.96 Mg C·hm^-2 in the same period. Conclusion: Given that China's forests are currently dominated by middle- and young-aged forests and the forest area is still increasing, the carbon pool and carbon sink capacity of China's forest biomass will continue to grow in the next 40 years, with average annual increase in forest carbon sink up to 0.13 Pg C·a^-1. This indicates that China's forests have a large potential for carbon sinks. For this reason, it is necessary to further strengthen the protection and management of forest resources, reduce forest carbon losses and continue to promote large-scale greening of the national territory, in order to maintain and enhance the carbon sink capacity of China's forests and help achieve the goal of "carbon neutrality".

Table and Figures | Reference | Related Articles | Metrics

Select

CO₂ Concentration in Phyllostachys praecox Stand Inversion Based on GA-BP Neural Network

Zhikang Hou,Songwei Zeng,Lufeng Mo,Yufeng Zhou

Scientia Silvae Sinicae 2022, 58 (2): 42-48. DOI: 10.11707/j.1001-7488.20220205

Abstract （217）

HTML （12）

PDF （2256KB）（237）

Save

Objective: The purpose of this work is to develop meteorological factor acquisition system of Phyllostachys praecox stand, to obtain the relationship between CO₂ concentration and meteorological factors (temperature and humidity, etc.), to discusses the CO₂ concentration inversion model based on GA-BP neural network (abbreviated as GA-BP model), and to provide fundamental data for carbon storage, carbon sinks and carbon sequestration capacity of P. praecox stand. Method: According to the relevant principles and methods of micrometeorology and the requirements of dynamic sensing of forest carbon flux, a remote and real-time monitoring system of forest carbon flux data based on embedded system is designed. Taking the mature P. praecox stand, stand as monitored object, this system monitored the environmental data for two months (October ~ November 2019). After analyzing these data, a CO₂ concentration inversion model based on genetic classification optimization neural network is proposed. Results: According to the inversion results of GA-BP and BP inversion model, the determinative coefficient R² of the inversion results of GA-BP inversion model is 0.86, which is 7 percentage points higher than that of BP inversion model. The mean absolute error is 8.12 mg·m^-3, which is 2.79 mg·m^-3 lower than that of BP inversion model. Compared with the BP inversion model, the GA-BP inversion model has more stable inversion performance and higher inversion accuracy. Conclusion: The P. praecox stand meteorological factor acquisition system can be used to obtain relevant meteorological data. Based on the correlation between CO₂ concentration and meteorological factors (temperature and humidity, etc.), the CO₂ concentration inversion model based on GA-BP neural network can effectively invert the CO₂ concentration data in the survey region.

Table and Figures | Reference | Related Articles | Metrics

Select

Latitudinal Distribution of Forest Soil Microbial Biomass Carbon and Its Affecting Factors in China

Sha Zhou,Huanfei Ma,Jieying Wang,Chengjie Ren,Yaoxin Guo,Jun Wang,Fazhu Zhao

Scientia Silvae Sinicae 2022, 58 (2): 49-57. DOI: 10.11707/j.1001-7488.20220206

Abstract （310）

HTML （19）

PDF （1141KB）（218）

Save

Objective: The study aims to investigate the patterns of latitudinal variation of soil microbial biomass carbon (MBC) and its driving forces, further to reveal the effect and mechanism of soil MBC content across forest ecosystems along the latitudinal gradients in China, providing a theoretical basis for understanding forest soil carbon turnover and sequestration in the context of global warming. Method: Ten typical forest ecosystems along the latitude gradients in China were selected to measure soil microbial biomass carbon (MBC) contents at 0-10 cm soil layer, and the correlation between MBC and abiotic factors (climate factors, soil physical and chemical properties) were conducted. Result: The MBC content of the forest soil decreased with decreasing latitude, ranging from (200.57 ± 13.99) mg·kg^-1 to (913.32 ± 39.62) mg·kg^-1. The soil MBC content and soil physical and chemical properties were positively correlated (r=0.51, P < 0.01). Highly significant positive correlation with the soil MBC content were identified in soil organic carbon content (r=0.64, P < 0.01), soil sand content (r=0.48, P < 0.01), soil hydrolase activity (r=0.48, P < 0.01), soil total phosphorus content (r=0.47, P < 0.01), pH value (r=0.43, P < 0.01) and soil total nitrogen content (r=0.31, P < 0.01). While silt content (r=0.15, P < 0.05) and soil oxidase activity (r=0.15, P < 0.05) were significantly positively correlated with MBC content. Both the mean annual temperature (r=0.31, P < 0.01), and the alkyl carbon content (r=0.21, P < 0.01) in the chemical structure of soil organic carbon were highly positively correlated with MBC content. Conclusion: Our results demonstrated a prominent decrease of MBC content along the latitude, with the main affecting factors being soil texture, soil nutrient, soil enzyme activities, and soil alkyl carbon content, followed by mean annual temperature.

Table and Figures | Reference | Related Articles | Metrics

Content of Frontier & Focus: Topic of forest carbon sequestration in our journal