Temporal and Spatial Patterns of Aboveground Biomass and Its Driving Forces in a Subtropical Forest: A Case Study

Ma, Lei; Wen, Li; Shi, Nan‐Nan; Fu, Shenglei; Lian, Juyu; Ye, Wanhui

doi:10.3161/15052249pje2019.67.2.001

Cited by 4 publications

(5 citation statements)

References 35 publications

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“…Repeat census data together with biomass allometric equations can be used to quantify woody productivity and woody mortality fluxes at the stand level. Numerous studies have quantified biomass stocks and fluxes in Forest-GEO plots, and their variation over space and time (Chave et al, 2003;Chave et al, 2008;Valencia et al, 2009;Lin et al, 2012;Yuan et al, 2016Yuan et al, , 2019Zuleta et al, 2017;Ma et al, 2019;Rutishauser et al, 2019). A key advantage of ForestGEO plots for these studies is that their large size reduces sampling errors in these biomass stocks and fluxes, sampling errors which remain substantial even at the 1-ha scale (Wagner et al, 2010;Muller-Landau et al, 2014;Réjou-Méchain et al, 2014).…”

Section: Biomass and Carbon Storage And Fluxesmentioning

confidence: 99%

ForestGEO: Understanding forest diversity and dynamics through a global observatory network

Davies

Abiem

Salim

et al. 2021

Biological Conservation

Self Cite

154

117

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ForestGEO is a network of scientists and long-term forest dynamics plots (FDPs) spanning the Earth's major forest types. ForestGEO's mission is to advance understanding of the diversity and dynamics of forests and to strengthen global capacity for forest science research. ForestGEO is unique among forest plot networks in its large-scale plot dimensions, censusing of all stems ≥1 cm in diameter, inclusion of tropical, temperate and boreal forests, and investigation of additional biotic (e.g., arthropods) and abiotic (e.g., soils) drivers, which together provide a holistic view of forest functioning. The 71 FDPs in 27 countries include approximately 7.33 million living trees and about 12,000 species, representing 20% of the world's known tree diversity. With >1300 published papers, ForestGEO researchers have made significant contributions in two fundamental areas: species coexistence and diversity, and ecosystem functioning. Specifically, defining the major biotic and abiotic controls on the distribution and coexistence of species and functional types and on variation in species' demography has led to improved understanding of how the multiple dimensions of forest diversity are structured across space and time and how this diversity relates to the processes controlling the role of forests in the Earth system. Nevertheless, knowledge gaps remain that impede our ability to predict how forest diversity and function will respond to climate change and other stressors. Meeting these global research challenges requires major advances in standardizing taxonomy of tropical species, resolving the main drivers of forest dynamics, and integrating plotbased ground and remote sensing observations to scale up estimates of forest diversity and function, coupled with improved predictive models. However, they cannot be met without greater financial commitment to sustain the long-term research of ForestGEO and other forest plot networks, greatly expanded scientific capacity across the world's forested nations, and increased collaboration and integration among research networks and disciplines addressing forest science.

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Section: Biomass and Carbon Storage And Fluxesmentioning

confidence: 99%

ForestGEO: Understanding forest diversity and dynamics through a global observatory network

Davies

Abiem

Salim

et al. 2021

Biological Conservation

Self Cite

154

117

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“…Soil properties play a significant role in influencing the AGB of tropical forests [39,79,94,95]. Various soil properties, such as pH, organic matter, total nitrogen, total phosphorus, and others, are analyzed to assess their impact.…”

Section: Discussionmentioning

confidence: 99%

Driving Factors and Spatial Distribution of Aboveground Biomass in the Managed Forest in the Terai Region of Nepal

KC,

Liu,

Saud

et al. 2024

Forests

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Above-ground biomass (AGB) is affected by numerous factors, including topography, climate, land use, or tree/forest attributes. Investigating the distribution and driving factors of AGB within the managed forests in Nepal is crucial for developing effective strategies for climate change mitigation, and sustainable forest management and conservation. A total of 110 field plots (circular 0.02 ha plots with a 9 m radius), and airborne laser scanning (ALS)-light detection and ranging (LiDAR) data were collected in 2021. The random forest (RF) model was employed to predict the AGB at a 30 m × 30 m resolution based on 32 LiDAR metrics derived from ALS returns. The study assessed the relationships between the AGB distribution and nine independent variables using statistical techniques like the random forest model and partial dependence plots. Results showed that the mean value of the estimated AGB was 120 tons/ha, ranging from 0 to 446.42 tons/ha. AGB showed higher values in the northeast and southeast regions, gradually decreasing towards the northwest. Land use land cover, mean annual temperature, and mean annual precipitation were identified as the primary factors influencing the variability in AGB distribution, accounting for 64% of the variability. Elevation, slope, and distance from rivers were positively correlated with AGB, while proximity to roads had a negative correlation. The increase in precipitation and temperature contributed to the initial rise in AGB, but beyond a certain lag, these variables led to a decline in AGB. This study showed the efficiency of the random forest model and partial dependence plots in examining the relationship between the AGB and its driving factors within managed forests. The study highlights the importance of understanding the AGB driving factors and utilizing LiDAR data for informed decisions regarding the region’s sustainable forest management and climate change mitigation efforts.

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“…In addition, forest pest and disease hazards and forest fires have a greater impact on forest resources [ 4 , 19 , 35 ], but these factors were not used in this study due to the lack of relevant data. Some scholars also believe that soil properties have a more significant influence on AGB [ 5 , 21 ] for analysis (e.g., pH, organic matter, total nitrogen, total phosphorus, etc. ), but our research is on a long term scale that the soil property did not change too much.…”

Section: Discussionmentioning

confidence: 99%

“…In previous studies, most scholars have mentioned that the extent of drivers varies at different scales [ 4 , 19 , 60 ]. Most scholars have focused their work on finding the main drivers [ 5 , 61 ]. In this research, long time series data were used to analyze at multiple scales to identify changes in drivers.…”

Section: Discussionmentioning

confidence: 99%

“…The importance of forest AGB to the whole ecosystem and global carbon cycle [ 3 ] means there are many studies on the drivers of forest AGB change. Nevertheless, forest resources are often affected by various factors [ 4 , 5 ], including natural disasters [ 6 , 7 ], climate change [ 8 , 9 ], and human activities [ 10 , 11 ]. Therefore, studying the drivers of forest AGB change in the ecologically fragile area can provide scientific references for the conservation and utilization of forest resources in other regions, especially in the selection of drivers and the establishment of drivers’ relationships.…”

Section: Introductionmentioning

confidence: 99%

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Temporal and Spatial Variation of Aboveground Biomass of Pinus densata and Its Drivers in Shangri-La, CHINA

Zhang

Bao

et al. 2021

IJERPH

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Understanding the drivers of forest aboveground biomass (AGB) is essential to further understanding the forest carbon cycle. In the upper Yangtze River region, where ecosystems are incredibly fragile, the driving factors that make AGB changes differ from other regions. This study aims to investigate AGB’s spatial and temporal variation of Pinus densata in Shangri-La and decompose the direct and indirect effects of spatial attribute, climate, stand structure, and agricultural activity on AGB in Shangri-La to evaluate the degree of influence of each factor on AGB change. The continuous sample plots from National Forest Inventory (NFI) and Landsat time series were used to estimate the AGB in 1987, 1992, 1997, 2002, 2007, 2012, and 2017. The structural equation model (SEM) was used to analyze the different effects of the four factors on AGB based on five scales: entire, 1987–2002, 2007–2017, low population density, and high population density. The results are as follows: (1) The AGB of Pinus densata in Shangri-La decreased from 1987 to 2017, with the total amount falling from 9.52 million tons to 7.41 million tons, and the average AGB falling from 55.49 t/ha to 40.10 t/ha. (2) At different scales, stand structure and climate were the drivers that directly affect the AGB change. In contrast, the agricultural activity had a negative direct effect on the AGB change, and spatial attribute had a relatively small indirect effect on the AGB change. (3) Analyzing the SEM results at different scales, the change of the contribution of the agricultural activity indicates that human activity is the main negative driver of AGB change in Shangri-La, especially at the high population density region. In contrast, the change of the contribution of the stand structure and climate indicates that the loss of old trees has an important influence on the AGB change. Forest resources here and other ecologically fragile areas should be gradually restored by adhering to policies, such as strengthening forest protection, improving forest stand quality, and limiting agricultural production activities.

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Temporal and Spatial Patterns of Aboveground Biomass and Its Driving Forces in a Subtropical Forest: A Case Study

Cited by 4 publications

References 35 publications

ForestGEO: Understanding forest diversity and dynamics through a global observatory network

ForestGEO: Understanding forest diversity and dynamics through a global observatory network

Driving Factors and Spatial Distribution of Aboveground Biomass in the Managed Forest in the Terai Region of Nepal

Temporal and Spatial Variation of Aboveground Biomass of Pinus densata and Its Drivers in Shangri-La, CHINA

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