Mixed forest specific calibration of the 3-PGmix model parameters from site observations to predict post-fire forest regrowth

Lin, Simei; He, Zijing; Huang, Huaguo; Chen, Ling; Li, Linyuan

doi:10.1016/j.foreco.2022.120208

Cited by 8 publications

(4 citation statements)

References 58 publications

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“…Currently, most of the research related to optimization theory in forest canopies focuses on the utilization of resources (light, water) by trees [ 60 ]. For instance, some plot-level models have also been used to estimate the photosynthetic capacity of the entire forest canopy in order to maximize its photosynthetic efficiency [ 61 , 62 ]. Similarly, previous studies have analyzed the impact of different loquat tree structures on light interception from the perspective of radiation transfer using a 3D loquat tree structure combined with a radiative transfer model, providing insights for cultivar breeding [ 63 ].…”

Section: Discussionmentioning

confidence: 99%

Fine-Scale Quantification of Absorbed Photosynthetically Active Radiation (APAR) in Plantation Forests with 3D Radiative Transfer Modeling and LiDAR Data

Zhao,

Qi,

et al. 2024

Plant Phenomics

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Quantifying the relationship between light and stands or individual trees is of great significance in understanding tree competition, improving forest productivity, and comprehending ecological processes. However, accurately depicting the spatiotemporal variability of light under complex forest structural conditions poses a challenge, especially for precise forest management decisions that require a quantitative study of the relationship between fine-scale individual tree structure and light. 3D RTMs (3-dimensional radiative transfer models), which accurately characterize the interaction between solar radiation and detailed forest scenes, provide a reliable means for depicting such relationships. This study employs a 3D RTM and LiDAR (light detection and ranging) data to characterize the light environment of larch plantations at a fine spatiotemporal scale, further investigating the relationship between absorbed photosynthetically active radiation (APAR) and forest structures. The impact of specific tree structural parameters, such as crown diameter, crown area, crown length, crown ratio, crown volume, tree height, leaf area index, and a distance parameter assessing tree competition, on the daily-scale cumulative APAR per tree was investigated using a partial least squares regression (PLSR) model. Furthermore, variable importance in projection (VIP) was also calculated from the PLSR. The results indicate that among the individual tree structure parameters, crown volume is the most important one in explaining individual tree APAR (VIP = 4.19), while the competition from surrounding trees still plays a role in explaining individual tree APAR to some extent (VIP = 0.15), and crown ratio contributes the least (VIP = 0.03). Regarding the spatial distribution of trees, the average cumulative APAR per tree of larch plots does not increase with an increase in canopy gap fraction. Tree density and average cumulative APAR per tree were fitted using a natural exponential equation, with a coefficient of determination ( R 2 = 0.89), and a small mean absolute percentage error (MAPE = 0.03). This study demonstrates the potential of combining 3D RTM with LiDAR data to quantify fine-scale APAR in plantations, providing insights for optimizing forest structure, enhancing forest quality, and implementing precise forest management practices, such as selective breeding for superior tree species.

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Section: Discussionmentioning

confidence: 99%

Fine-Scale Quantification of Absorbed Photosynthetically Active Radiation (APAR) in Plantation Forests with 3D Radiative Transfer Modeling and LiDAR Data

Zhao,

Qi,

et al. 2024

Plant Phenomics

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“…Only in the subsequent model calibration, simulated patterns emerging at the stand scale were compared to time series of independent data to determine the values of five parameters. A common procedure for parameter selection would have been to conduct a sensitivity analysis (e.g., Huber et al, 2018;Lagarrigues et al, 2015;Lin et al, 2022). Instead, we selected parameters that we deemed important to tie the model even more strongly to empirical data but for which we were unable to make an assessment in the sense of parameterization (cf.…”

Section: Model Parameterization and Calibrationmentioning

confidence: 99%

“…During calibration, the parameter values were adjusted iteratively and simultaneously "by hand", similar to the calibration procedure by Pabst et al (2008) with ZELIG andRisch et al (2005) with ForClim. We deliberately decided against more sophisticated calibration procedures such as parameter optimization (e.g., Lin et al, 2022;Mina et al, 2016) or Bayesian approaches (e.g., Cailleret et al, 2020;Forrester et al, 2021a;Lagarrigues et al, 2015;van Oijen et al, 2013) that would arguably have led to a closer fit of model results to the data. We had only rather few stand records per stratum available for calibration, which in some cases also lacked information on certain processes relevant to calibration, such as almost no records of sycamore maple or no new regeneration appearing in the stands of the HM zone.…”

Section: Model Parameterization and Calibrationmentioning

confidence: 99%

ProForM: A simulation model for the management of mountain protection forests

Schmid

Frehner

Glatthorn

et al. 2023

Ecological Modelling

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“…By combining forest inventory data with remote sensing technology, the spatial inversion of forest carbon stock can effectively integrate the accuracy of field measurements and the spatial distribution information obtained through remote sensing [7,8]. Previous studies have demonstrated the significant influence of geographic and climatic factors on the spatial distribution of forest carbon stock [9,10], as well as the variation in carbon sequestration capabilities among different tree species and forest ages [11,12]. In addition, the landscape pattern and heterogeneity of forests are closely related to ecological functions [13].…”

Section: Introductionmentioning

confidence: 99%

A Novel Forest EcoSpatial Network for Carbon Stocking Using Complex Network Theory in the Yellow River Basin

Zhang

Lin

et al. 2023

Remote Sensing

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The Yellow River Basin serves as a crucial ecological barrier in China, emphasizing the importance of accurately examining the spatial distribution of forest carbon stocks and enhancing carbon sequestration in order to attain “carbon peaking and carbon neutrality”. Forest patches have complex interactions that impact ecosystem services. To our knowledge, very few studies have explored the connection between these interactions and carbon stock. This study addressed this gap by utilizing complex network theory to establish a forest ecospatial network (ForEcoNet) in the Yellow River Basin in which forest patches are represented as nodes (sources) and their interactions as edges (corridors). Our objective was to optimize the ForEcoNet’s structure and enhance forest carbon stocks. First, we employed downscaling technology to allocate the forest carbon stocks of the 69 cities in the study area to grid cells, generating a spatial distribution map of forest carbon density in the Yellow River Basin. Next, we conducted morphological spatial pattern analysis (MSPA) and used the minimum cumulative resistance model (MCR) to extract the ForEcoNet in the basin. Finally, we proposed optimization of the ForEcoNet based on the coupling coordination between the node carbon stock and topological structure. The results showed that: (1) the forest carbon stocks of the upper, middle, and lower reaches accounted for 42.35%, 54.28%, and 3.37% of the total, respectively, (2) the ForEcoNet exhibited characteristics of both a random network and a scale-free network and demonstrated poor network stability, and (3) through the introduction of 51 sources and 46 corridors, we optimized the network and significantly improved its robustness. These findings provide scientific recommendations for the optimization of forest allocation in the Yellow River Basin and achieving the goal of increasing the forest carbon stock.

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Mixed forest specific calibration of the 3-PGmix model parameters from site observations to predict post-fire forest regrowth

Cited by 8 publications

References 58 publications

Fine-Scale Quantification of Absorbed Photosynthetically Active Radiation (APAR) in Plantation Forests with 3D Radiative Transfer Modeling and LiDAR Data

Fine-Scale Quantification of Absorbed Photosynthetically Active Radiation (APAR) in Plantation Forests with 3D Radiative Transfer Modeling and LiDAR Data

ProForM: A simulation model for the management of mountain protection forests

A Novel Forest EcoSpatial Network for Carbon Stocking Using Complex Network Theory in the Yellow River Basin

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