Allometric models to estimate above-ground biomass and carbon stocks in Rhizophora apiculata tropical managed mangrove forests (Southern Viet Nam)

Vinh, Truong Van; Marchand, Cyril; Linh, Tran Vu Khanh; Vinh, Duong Dang; Allenbach, Michel

doi:10.1016/j.foreco.2018.12.017

Cited by 39 publications

(23 citation statements)

References 42 publications

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“…In the first stage regression model, the dependent variable was AGB for the 270 plots within the Huntington area and the predictors were selected from lidar derived variables using the forward variable selection method. Like prior studies [36,37], using the natural logarithm of both dependent and predictor variables led to better performance for the first stage regression model. The second component of the analysis applied Equation (1) to develop regression models for a series of different sampling strategies (described in the next section).…”

Section: Regression and Variable Selectionmentioning

confidence: 75%

Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat Imagery

Quackenbush

2019

Remote Sensing

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Accurately estimating aboveground biomass (AGB) is important in many applications, including monitoring carbon stocks, investigating deforestation and forest degradation, and designing sustainable forest management strategies. Although lidar provides critical three-dimensional forest structure information for estimating AGB, acquiring comprehensive lidar coverage is often cost prohibitive. This research focused on developing a lidar sampling framework to support AGB estimation from Landsat images. Two sampling strategies, systematic and classification-based, were tested and compared. The proposed strategies were implemented over a temperate forest study site in northern New York State and the processes were then validated at a similar site located in central New York State. Our results demonstrated that while the inclusion of lidar data using systematic or classification-based sampling supports AGB estimation, the systematic sampling selection method was highly dependent on site conditions and had higher accuracy variability. Of the 12 systematic sampling plans, R2 values ranged from 0.14 to 0.41 and plot root mean square error (RMSE) ranged from 84.2 to 93.9 Mg ha−1. The classification-based sampling outperformed 75% of the systematic sampling strategies at the primary site with R2 of 0.26 and RMSE of 70.1 Mg ha−1. The classification-based lidar sampling strategy was relatively easy to apply and was readily transferable to a new study site. Adopting this method at the validation site, the classification-based sampling also worked effectively, with an R2 of 0.40 and an RMSE of 108.2 Mg ha−1 compared to the full lidar coverage model with an R2 of 0.58 and an RMSE of 96.0 Mg ha−1. This study evaluated different lidar sample selection methods to identify an efficient and effective approach to reduce the volume and cost of lidar acquisitions. The forest type classification-based sampling method described in this study could facilitate cost-effective lidar data collection in future studies.

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Section: Regression and Variable Selectionmentioning

confidence: 75%

Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat Imagery

Quackenbush

2019

Remote Sensing

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“…Some researchers have used a constant generic 0.50 carbon concentration value to be multiplied by a biomass value [45][46][47], in line with the recommendation of Roy et al [48] and Wenzl [49]. On the contrary, some authors prefer to multiply the biomass value with the total weighted mean carbon concentration (TWMCc) of an individual tree [50,51], while others prefer to used respective mean component carbon concentration (RMCCc) as a multiplying factor to obtain carbon stock estimates [52]. These second forms of approaches are considered a better approach since many studies have reported that carbon concentration varies greatly depending upon the type of tree species and tissues [37,53].…”

Section: Discussionmentioning

confidence: 99%

Aggregated Biomass Model Systems and Carbon Concentration Variations for Tree Carbon Quantification of Natural Mongolian Oak in Northeast China

Widagdo

Zhang

et al. 2020

Forests

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Three systems of additive biomass models were developed and the effects of tree components, tree sizes, and tree growing regions on the carbon concentration were analyzed for Mongolian oak (Quercus mongolica Fisch. ex Ledeb.) in the natural forests of Northeastern China. The nonlinear seemingly unrelated regression (NSUR) method was used to fit each of the three systems simultaneously; namely, aggregated model systems with no parameter restriction (AMS0), aggregated model systems with one parameter restriction (AMS1), and aggregated model systems with three parameter restrictions (AMS3). A unique weighting function for each biomass model was applied to address the heteroscedasticity issue. The systems assertively guarantee the additivity property, in which, the summation of the respective predicted tree components (i.e., root, stem, branch, and foliage) will match the prediction of subtotals (i.e., crown and aboveground) and total biomass. Using one-, two-, and three-predictor combinations (i.e., D (diameter at breast height), D and H (total height), and D, H, and CL (crown length)) as the general model underlying formats, three systems of additive biomass model were developed. Our results indicate that (1) all of the aggregated model systems performed well and the differences between the systems were relatively small; (2) the rank order of the three systems based on an array of statistics are as follows: AMS0 > AMS1 > AMS3; (3) the carbon concentration significantly varied depending on the types of tree tissues and growing regions; (4) the regional respective component carbon concentration and regional weighted mean carbon concentration multiplied by observed biomass value appeared to be the best approach to calculate carbon stock.

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“…Within the studies that focused on the calculation of biomass, there was a certain tendency regarding allometric models that involved measurements in the field for their use, mostly the product of correlations between the variables. Such is the case of Lozano [17], Mohd Zaki et al [52], Da Motta et al [53], Van Vinh et al [54], Simpson et al [55], and Chatting et al [56], among others, who did not use the same equations but had variables in common (see Table 2).…”

Section: Biomass Contentmentioning

confidence: 99%

A Critical Review on Mathematical Descriptions to Study Flux Processes and Environmental-Related Interactions of Mangroves

et al. 2021

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Trees are resources that provide multiple benefits, such as the conservation of fauna, both terrestrial and marine, a source of food and raw material, and offering protection in storms, which makes it practical to understand their behavior against different phenomena. Such understanding may be possible through process modeling. Studies confirm that mangrove forests can store more carbon than other forests, influencing the fight against global warming. Thus, a critical and systematic review was carried out regarding studies focusing on mangroves to collect information on the models that have been applied and the most influential variables highlighted by other authors. Applying a systematic search for the most relevant topics related to mangroves (basic as well as recent information), it is possible to group models and methods carried out by other authors to respond to certain behaviors presented by mangroves. Moreover, possible structuring of a mathematical model applied to a species of interest thanks to the analyzed references could provide justified information to the authorities on the importance of these forests and the benefits of their preservation and regeneration-recovery.

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Allometric models to estimate above-ground biomass and carbon stocks in Rhizophora apiculata tropical managed mangrove forests (Southern Viet Nam)

Cited by 39 publications

References 42 publications

Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat Imagery

Airborne Lidar Sampling Strategies to Enhance Forest Aboveground Biomass Estimation from Landsat Imagery

Aggregated Biomass Model Systems and Carbon Concentration Variations for Tree Carbon Quantification of Natural Mongolian Oak in Northeast China

A Critical Review on Mathematical Descriptions to Study Flux Processes and Environmental-Related Interactions of Mangroves

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