Sensitivity analysis of a forest gap model concerning current and future climate variability

2005 ◽  
pp. 273-288 ◽  
Author(s):  
Petra Lasch ◽  
Felicitas Suckow ◽  
Gerd Bürger ◽  
Marcus Lindner
Keyword(s):  
Sensitivity Analysis ◽  
Climate Variability ◽  
Future Climate ◽  
Forest Gap ◽  
Gap Model ◽  
Forest Gap Model

Simulating structural forest patterns with a forest gap model: a model evaluation

2005 ◽  
Vol 181 (2-3) ◽  
pp. 161-172 ◽  
Author(s):  
Anita C. Risch ◽  
Caroline Heiri ◽  
Harald Bugmann
Keyword(s):  
Model Evaluation ◽  
Forest Gap ◽  
Gap Model ◽  
Forest Gap Model

Improving the simulation of stand structure in a forest gap model

1997 ◽  
Vol 95 (2) ◽  
pp. 183-195 ◽  
Author(s):  
Marcus Lindner ◽  
Risto Sievänen ◽  
Hans Pretzsch
Keyword(s):  
Stand Structure ◽  
Forest Gap ◽  
Gap Model ◽  
Forest Gap Model

Validation and application of a forest gap model to the southern Rocky Mountains

2017 ◽  
Vol 351 ◽  
pp. 109-128 ◽  
Author(s):  
Adrianna C. Foster ◽  
Jacquelyn K. Shuman ◽  
Herman H. Shugart ◽  
Kathleen A. Dwire ◽  
Paula J. Fornwalt ◽  
...  
Keyword(s):  
Rocky Mountains ◽  
Forest Gap ◽  
Gap Model ◽  
Southern Rocky Mountains ◽  
Forest Gap Model

A neutral vs. non-neutral parametrizations of a physiological forest gap model

2014 ◽  
Vol 288 ◽  
pp. 94-102 ◽  
Author(s):  
Martin Kazmierczak ◽  
Thorsten Wiegand ◽  
Andreas Huth
Keyword(s):  
Forest Gap ◽  
Gap Model ◽  
Forest Gap Model

scholarly journals Lessons learned from applying a forest gap model to understand ecosystem and carbon dynamics of complex tropical forests

2016 ◽  
Vol 326 ◽  
pp. 124-133 ◽  
Author(s):  
Rico Fischer ◽  
Friedrich Bohn ◽  
Mateus Dantas de Paula ◽  
Claudia Dislich ◽  
Jürgen Groeneveld ◽  
...  
Keyword(s):  
Tropical Forests ◽  
Carbon Dynamics ◽  
Lessons Learned ◽  
Forest Gap ◽  
Gap Model ◽  
Forest Gap Model

scholarly journals Using Microwave Profile Radar to Estimate Forest Canopy Leaf Area Index: Linking 3D Radiative Transfer Model and Forest Gap Model

2021 ◽  
Vol 13 (2) ◽  
pp. 297
Author(s):  
Kai Du ◽  
Huaguo Huang ◽  
Ziyi Feng ◽  
Teemu Hakala ◽  
Yuwei Chen ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Forest Canopy ◽  
Parameters Estimation ◽  
Forest Gap ◽  
Gap Model ◽  
Area Index ◽  
Forest Gap Model ◽  
Band Profile ◽  
Ku Band

Profile radar allows direct characterization of the vertical forest structure. Short-wavelength, such as Ku or X band, microwave data provide opportunities to detect the foliage. In order to exploit the potential of radar technology in forestry applications, a helicopter-borne Ku-band profile radar system, named Tomoradar, has been developed by the Finnish Geospatial Research Institute. However, how to use the profile radar waveforms to assess forest canopy parameters remains a challenge. In this study, we proposed a method by matching Tomoradar waveforms with simulated ones to estimate forest canopy leaf area index (LAI). Simulations were conducted by linking an individual tree-based forest gap model ZELIG and a three-dimension (3D) profile radar simulation model RAPID2. The ZELIG model simulated the parameters of potential local forest succession scene, and the RAPID2 model utilized the parameters to generate 3D virtual scenes and simulate waveforms based on Tomoradar configuration. The direct comparison of simulated and collected waveforms from Tomoradar could be carried out, which enabled the derivation of possible canopy LAI distribution corresponding to the Tomoradar waveform. A 600-m stripe of Tomoradar data (HH polarization) collected in the boreal forest at Evo in Finland was used as a test, which was divided into 60 plots with an interval of 10 m along the trajectory. The average waveform of each plot was employed to estimate the canopy LAI. Good results have been found in the waveform matching and the uncertainty of canopy LAI estimation. There were 95% of the plots with the mean relative overlapping rate (RO) above 0.7. The coefficients of variation of canopy LAI estimates were less than 0.20 in 80% of the plots. Compared to lidar-derived canopy effective LAI estimation, the coefficient of determination was 0.46, and the root mean square error (RMSE) was 1.81. This study established a bridge between the Ku band profile radar waveform and the forest canopy LAI by linking the RAPID2 and ZELIG model, presenting the uncertainty of forest canopy LAI estimation using Tomoradar. It is worth noting that since the difference of backscattering contribution is caused by both canopy structure and tree species, similar waveforms may correspond to different canopy LAI, inducing the uncertainty of canopy LAI estimation, which should be noticed in forest parameters estimation with empirical methods.

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