Pol-Insar Forest Height Inversion Using Tomosar Reflectivity Profiles

2021 ◽  
Author(s):  
Roman Guliaev ◽  
Jun Su Kim ◽  
Konstantinos P. Papathanassiou ◽  
Matteo Pardini
Keyword(s):  
Forest Height

scholarly journals Forest Height Estimation Based on P-Band Pol-InSAR Modeling and Multi-Baseline Inversion

2020 ◽  
Vol 12 (8) ◽  
pp. 1319
Author(s):  
Xiaofan Sun ◽  
Bingnan Wang ◽  
Maosheng Xiang ◽  
Liangjiang Zhou ◽  
Shuai Jiang
Keyword(s):  
Standard Deviation ◽  
Vertical Structure ◽  
Incidence Angle ◽  
Three Dimensional ◽  
Solution Space ◽  
Model Complexity ◽  
Two Dimensional ◽  
Dimensional Parameter ◽  
Model Inversion ◽  
Forest Height

The Gaussian vertical backscatter (GVB) model has a pivotal role in describing the forest vertical structure more accurately, which is reflected by P-band polarimetric interferometric synthetic aperture radar (Pol-InSAR) with strong penetrability. The model uses a three-dimensional parameter space (forest height, Gaussian mean representing the strongest backscattered power elevation, and the corresponding standard deviation) to interpret the forest vertical structure. This paper establishes a two-dimensional GVB model by simplifying the three-dimensional one. Specifically, the two-dimensional GVB model includes the following three cases: the Gaussian mean is located at the bottom of the canopy, the Gaussian mean is located at the top of the canopy, as well as a constant volume profile. In the first two cases, only the forest height and the Gaussian standard deviation are variable. The above approximation operation generates a two-dimensional volume only coherence solution space on the complex plane. Based on the established two-dimensional GVB model, the three-baseline inversion is achieved without the null ground-to-volume ratio assumption. The proposed method improves the performance by 18.62% compared to the three-baseline Random Volume over Ground (RVoG) model inversion. In particular, in the area where the radar incidence angle is less than 0.6 rad, the proposed method improves the inversion accuracy by 34.71%. It suggests that the two-dimensional GVB model reduces the GVB model complexity while maintaining a strong description ability.

Forest Height Estimation from Tandem-X InSAR Coherence Magnitude Towards Large Scale Applications

2020 ◽  
Author(s):  
Changhyun Choi ◽  
Roman Guliaev ◽  
Victor Cazcarra-Bes ◽  
Matteo Pardini ◽  
Konstantinos P. Papathanassiou
Keyword(s):  
Large Scale ◽  
Height Estimation ◽  
Forest Height

Validation of forest height–age models

1996 ◽  
Vol 26 (5) ◽  
pp. 810-818 ◽  
Author(s):  
Gordon D. Nigh ◽  
Vera Sit
Keyword(s):  
Forest Management ◽  
Confidence Intervals ◽  
Error Variance ◽  
Site Index ◽  
Random Coefficients ◽  
Multivariate Technique ◽  
Forest Height ◽  
Preliminary Validation ◽  
Age Model ◽  
Validation Procedure

Forest height–age models are used in forest management to estimate height and (or) site index. It is useful to know the bias and precision of these models in order to evaluate their applicability. Methods are available for validating the models; however, many problems exist with the methods because of a lack of independence in the data and nonconstant error variance across a range of ages. A validation procedure is presented that overcomes these problems by using a multivariate technique (random coefficients) to model the structure of the errors associated with the models. Confidence intervals for bias and precision can then be constructed based on the error structure. This method of validation was demonstrated on the white spruce (Piceaglauca (Moench) Voss) height–age model for British Columbia, Canada. The preliminary validation showed the model to be unbiased for estimating both height and site index; however, its precision was poor.

Forest-height inversion using repeat-pass spaceborne polInSAR data

2013 ◽  
Vol 57 (6) ◽  
pp. 1314-1324 ◽  
Author(s):  
Zhen Li ◽  
Ming Guo ◽  
ZhongQiong Wang ◽  
LiFang Zhao
Keyword(s):  
Forest Height
Sign in / Sign up

Export Citation Format

Share Document