Responses of ground vegetation species to clear-cutting in a boreal forest: aboveground biomass and nutrient contents during the first 7 years

2005 ◽  
Vol 20 (6) ◽  
pp. 652-660 ◽  
Author(s):  
Marjo Palviainen ◽  
Leena Finér ◽  
Hannu Mannerkoski ◽  
Sirpa Piirainen ◽  
Michael Starr
Keyword(s):  
Boreal Forest ◽  
Aboveground Biomass ◽  
Ground Vegetation ◽  
Nutrient Contents ◽  
Clear Cutting ◽  
Vegetation Species ◽  
Forest Aboveground Biomass

Vegetation development in a hardwood-forest chronosequence in Nova Scotia

1994 ◽  
Vol 24 (2) ◽  
pp. 260-271 ◽  
Author(s):  
M. Crowell ◽  
B. Freedman
Keyword(s):  
Nova Scotia ◽  
Aboveground Biomass ◽  
Vascular Plants ◽  
Tree Bark ◽  
Ground Vegetation ◽  
Vegetation Development ◽  
Clear Cutting ◽  
Vigorous Growth ◽  
Mature Stands ◽  
Rate Of Accumulation

Vegetation and aboveground biomass and nutrient capital (N, P, K, Ca, and Mg) were examined in a 22-stand, 75-year chronosequence within an angiosperm-dominated forest in Nova Scotia. Stands 20 years old and younger originated with clear-cutting, whereas older stands originated with wildfire. Early successional, ruderal species of vascular plants were prominent for ca. 5 years after clear-cutting, but they occurred as a part of a diverse, species-rich community dominated by more-tolerant species, many of which survived the disturbance of clear-cutting. The rate of accumulation of aboveground biomass averaged 2.2 t•ha−1•year−1 during the first 11 years after clear-cutting, 4.7 t•ha−1•year−1 between 11 and 30 years, and then decreased to 1.5 t•ha−1•year−1 between 30 and 75 years. Foliage biomass recovered to a quantity typical of mature stands within only 3–5 years of disturbance, as a result of the vigorous growth of both ground vegetation and stump sprouts of certain tree species. The patterns of accumulation of N, P, K, and Mg were similar to that of biomass, except that initially their relative rates of accumulation were faster because of the large proportion of nutrient-rich foliage in young stands. The accumulation of Ca was relatively slower, because of its large concentration in tree bark, a tissue whose proportion in the aboveground biomass reached a maximum much later than did foliage.

Changes in the Above- and Below-ground Biomass and Nutrient Pools of Ground Vegetation After Clear-cutting of a Mixed Boreal Forest

2005 ◽  
Vol 275 (1-2) ◽  
pp. 157-167 ◽  
Author(s):  
Marjo Palviainen ◽  
Leena Finér ◽  
Hannu Mannerkoski ◽  
Sirpa Piirainen ◽  
Michael Starr
Keyword(s):  
Boreal Forest ◽  
Ground Vegetation ◽  
Nutrient Pools ◽  
Clear Cutting ◽  
Ground Biomass ◽  
Below Ground

scholarly journals New Forest Aboveground Biomass Maps of China Integrating Multiple Datasets

2021 ◽  
Vol 13 (15) ◽  
pp. 2892
Author(s):  
Zhongbing Chang ◽  
Sanaa Hobeichi ◽  
Ying-Ping Wang ◽  
Xuli Tang ◽  
Gab Abramowitz ◽  
...  
Keyword(s):  
Carbon Emissions ◽  
Aboveground Biomass ◽  
Regional Scale ◽  
Remote Sensing Data ◽  
Field Measurements ◽  
Random Forest Regression ◽  
Multiple Datasets ◽  
Optimal Weighting ◽  
Two Hybrid ◽  
Forest Aboveground Biomass

Mapping the spatial variation of forest aboveground biomass (AGB) at the national or regional scale is important for estimating carbon emissions and removals and contributing to global stocktake and balancing the carbon budget. Recently, several gridded forest AGB products have been produced for China by integrating remote sensing data and field measurements, yet significant discrepancies remain among these products in their estimated AGB carbon, varying from 5.04 to 9.81 Pg C. To reduce this uncertainty, here, we first compiled independent, high-quality field measurements of AGB using a systematic and consistent protocol across China from 2011 to 2015. We applied two different approaches, an optimal weighting technique (WT) and a random forest regression method (RF), to develop two observationally constrained hybrid forest AGB products in China by integrating five existing AGB products. The WT method uses a linear combination of the five existing AGB products with weightings that minimize biases with respect to the field measurements, and the RF method uses decision trees to predict a hybrid AGB map by minimizing the bias and variance with respect to the field measurements. The forest AGB stock in China was 7.73 Pg C for the WT estimates and 8.13 Pg C for the RF estimates. Evaluation with the field measurements showed that the two hybrid AGB products had a lower RMSE (29.6 and 24.3 Mg/ha) and bias (−4.6 and −3.8 Mg/ha) than all five participating AGB datasets. Our study demonstrated both the WT and RF methods can be used to harmonize existing AGB maps with field measurements to improve the spatial variability and reduce the uncertainty of carbon stocks. The new spatial AGB maps of China can be used to improve estimates of carbon emissions and removals at the national and subnational scales.

scholarly journals Forest Aboveground Biomass Estimation and Mapping through High-Resolution Optical Satellite Imagery—A Literature Review

Forests ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 914
Author(s):  
Adeel Ahmad ◽  
Hammad Gilani ◽  
Sajid Rashid Ahmad
Keyword(s):  
High Resolution ◽  
Literature Review ◽  
Satellite Imagery ◽  
Aboveground Biomass ◽  
Vegetation Indices ◽  
Biomass Estimation ◽  
Independent Variables ◽  
Non Linear ◽  
Multiple Variables ◽  
Forest Aboveground Biomass

This paper provides a comprehensive literature review on forest aboveground biomass (AGB) estimation and mapping through high-resolution optical satellite imagery (≤5 m spatial resolution). Based on the literature review, 44 peer-reviewed journal articles were published in 15 years (2004–2019). Twenty-one studies were conducted across six continents in Asia, eight in North America and Africa, five in South America, and four in Europe. This review article gives a glance at the published methodologies for AGB prediction modeling and validation. The literature review suggested that, along with the integration of other sensors, QuickBird, WorldView-2, and IKONOS satellite images were most widely used for AGB estimations, with higher estimation accuracies. All studies were grouped into six satellite-derived independent variables, including tree crown, image textures, tree shadow fraction, canopy height, vegetation indices, and multiple variables. Using these satellite-derived independent variables, most of the studies used linear regression (41%), while 30% used linear (multiple regression and 18% used non-linear (machine learning) regression, while very few (11%) studies used non-linear (multiple and exponential) regression for estimating AGB. In the context of global forest AGB estimations and monitoring, the advantages, strengths, and limitations were discussed to achieve better accuracy and transparency towards the performance-based payment mechanism of the REDD+ program. Apart from technical limitations, we realized that very few studies talked about real-time monitoring of AGB or quantifying AGB change, a dimension that needs exploration.

scholarly journals Remote Sensing Estimation of Bamboo Forest Aboveground Biomass Based on Geographically Weighted Regression

2021 ◽  
Vol 13 (15) ◽  
pp. 2962
Author(s):  
Jingyi Wang ◽  
Huaqiang Du ◽  
Xuejian Li ◽  
Fangjie Mao ◽  
Meng Zhang ◽  
...  
Keyword(s):  
Aboveground Biomass ◽  
Geographically Weighted Regression ◽  
Local Variation ◽  
Zhejiang Province ◽  
Weighted Regression ◽  
Landsat 8 ◽  
Bamboo Forest ◽  
Prediction Ability ◽  
Gwr Model ◽  
Forest Aboveground Biomass

Bamboo forests are widespread in subtropical areas and are well known for their rapid growth and great carbon sequestration ability. To recognize the potential roles and functions of bamboo forests in regional ecosystems, forest aboveground biomass (AGB)—which is closely related to forest productivity, the forest carbon cycle, and, in particular, carbon sinks in forest ecosystems—is calculated and applied as an indicator. Among the existing studies considering AGB estimation, linear or nonlinear regression models are the most frequently used; however, these methods do not take the influence of spatial heterogeneity into consideration. A geographically weighted regression (GWR) model, as a spatial local model, can solve this problem to a certain extent. Based on Landsat 8 OLI images, we use the Random Forest (RF) method to screen six variables, including TM457, TM543, B7, NDWI, NDVI, and W7B6VAR. Then, we build the GWR model to estimate the bamboo forest AGB, and the results are compared with those of the cokriging (COK) and orthogonal least squares (OLS) models. The results show the following: (1) The GWR model had high precision and strong prediction ability. The prediction accuracy (R2) of the GWR model was 0.74, 9%, and 16% higher than the COK and OLS models, respectively, while the error (RMSE) was 7% and 12% lower than the errors of the COK and OLS models, respectively. (2) The bamboo forest AGB estimated by the GWR model in Zhejiang Province had a relatively dense spatial distribution in the northwestern, southwestern, and northeastern areas. This is in line with the actual bamboo forest AGB distribution in Zhejiang Province, indicating the potential practical value of our study. (3) The optimal bandwidth of the GWR model was 156 m. By calculating the variable parameters at different positions in the bandwidth, close attention is given to the local variation law in the estimation of the results in order to reduce the model error.

Logging impacts on the biogeochemistry of boreal forest soils and nutrient export to aquatic systems: A review

2008 ◽  
Vol 16 (NA) ◽  
pp. 157-179 ◽  
Author(s):  
David P. Kreutzweiser ◽  
Paul W. Hazlett ◽  
John M. Gunn
Keyword(s):  
Boreal Forest ◽  
Nutrient Availability ◽  
Soil Nutrient ◽  
Soil Conditions ◽  
Soil Nutrient Availability ◽  
Clear Cutting ◽  
Partial Harvest ◽  
Forest Watersheds ◽  
Logging Impacts ◽  
Receiving Waters

Logging disturbances in boreal forest watersheds can alter biogeochemical processes in soils by changing forest composition, plant uptake rates, soil conditions, moisture and temperature regimes, soil microbial activity, and water fluxes. In general, these changes have often led to short-term increases in soil nutrient availability followed by increased mobility and losses by leaching to receiving waters. Among the studies we reviewed, dissolved organic carbon (DOC) exports usually increased after logging, and nitrogen (N) mineralization and nitrification often increased with resulting increased N availability and exports to receiving waters. Similar processes and responses occurred for phosphorus (P), but to a lesser extent than for N. In most cases, base cations were released and exported to receiving waters after logging. Several studies demonstrated that stem-only or partial-harvest logging reduced the impacts on nutrient release and exports in comparison to whole-tree clear-cutting. Despite these logging-induced increases in soil nutrient availability and movement to receiving waters, most studies reported little or no change in soil chemical properties. However, responses to logging were highly variable and often site specific. The likelihood, extent and magnitude of logging impacts on soil nutrient cycling and exports in boreal forest watersheds will be dependent on soil types, stand and site conditions, hydrological connectivity, post-logging weather patterns, and type and timing of harvest activities. Additionally, logging impacts can interact with, and be confounded by, atmospheric pollutant deposition and climate change. Further watershed-level empirical studies and modeling efforts are required to elucidate these interactions, to improve predictive capabilities, and to advance forest management guidelines for sustaining forest soil productivity and limiting nutrient exports.

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