The Impact of Forest Density on Forest Height Inversion Modeling from Polarimetric InSAR Data

Abstract

Forest height is of great significance in analyzing the carbon cycle on a global or a local scale and in reconstructing the accurate forest underlying terrain. Major algorithms for estimating forest height, such as the three-stage inversion process, are depending on the random-volume-over-ground (RVoG) model. However, the RVoG model is characterized by a lot of parameters, which influence its applicability in forest height retrieval. Forest density, as an important biophysical parameter, is one of those main influencing factors. However, its influence to the RVoG model has been ignored in relating researches. For this paper, we study the applicability of the RVoG model in forest height retrieval with different forest densities, using the simulated and real Polarimetric Interferometric SAR data. P-band ESAR datasets of the European Space Agency (ESA) BioSAR 2008 campaign were selected for experiments. The test site was located in Krycklan River catchment in Northern Sweden. The experimental results show that the forest density clearly affects the inversion accuracy of forest height and ground phase. For the four selected forest stands, with the density increasing from 633 to 1827 stems/Ha, the RMSEs of inversion decrease from 4.6 m to 3.1 m. The RVoG model is not quite applicable for forest height retrieval especially in sparsely vegetated areas. We conclude that the forest stand density is positively related to the estimation accuracy of the ground phase, but negatively correlates to the ground-to-volume scattering ratio.

Estimation of Pine Forest Height and Underlying DEM Using Multi-Baseline P-Band PolInSAR Data

“…As a result, this cannot meet the RVoG assumption and may induce biases of ground phase estimated by the three-stage inversion procedure [23]. Nonetheless, the biases may be small and acceptable forest height results have been successfully extracted using the RVoG assumption at P band over the tropical and pine forests [24][25][26][27]. Hence, in this paper, the volume scattering contribution is still assumed to be polarization-independent for the GVB model.…”

A TSVD-Based Method for Forest Height Inversion from Single-Baseline PolInSAR Data

“…Compared to the six-dimensional nonlinear optimization method, this method can avoid significant biases caused by the unreliable initial values of forest parameters. Moreover, in comparison to the three-stage method, the proposed method can provide more accurate pure volume coherence because it is free from the assumption that there is one polarization whose ground-to-volume power ratio should be less than −10 dB [10], which cannot be fulfilled for the low-frequency PolInSAR data or for the sparse forest [31]. In addition, only one polarimetric observation is used to calculate the pure volume coherence in the three-stage method.…”

Understanding Forest Health with Remote Sensing-Part II—A Review of Approaches and Data Models

“…Coherence-Amplitude conversion RVoG (height), [236,238] WCM (AGB), [237] Hybrid Neural Networks (LAI), [235] Similar modelling approaches are available based on SAR remote-sensing data. SAR-based empirical modelling approaches are conceptually the same as for optical data and make use of the backscatter, coherence or polarization information of the processed microwave signal as explained in Section 3.2.…”

“…In contrast to optical RS, SAR-based physical models are more related to the vertical structure of vegetation and, hence, provide a more direct approach for modelling parameters of the global carbon budget, e.g., AGB. The most widely used microwave scattering models for forest stands are the Random Volume over Ground (RVoG, [236] and the Water Cloud Model (WCM, [237]) and a number of adaptations [238].…”