Sensitivity of Bistatic TanDEM-X Data to Stand Structural Parameters in Temperate Forests

Stefan Erasmi,Michael Schlund,Malte Semmler,Peter Schall

doi:10.3390/rs11242966

Stefan Erasmi, Michael Schlund + Show 2 more

Open Access

https://doi.org/10.3390/rs11242966

Copy DOI

Abstract

Synthetic aperture radar (SAR) satellite data provide a valuable means for the large-scale and long-term monitoring of structural components of forest stands. The potential of TanDEM-X interferometric SAR (InSAR) for the assessment of forest structural properties has been widely verified. However, present studies are mostly restricted to homogeneous forests and do not account for stratification in assessing model performance. A systematic sensitivity analysis of the TanDEM-X SAR signal to forest structural parameters was carried out with emphasis on different strata of forest stands (location of the study site, forest type, and development stage). Forest structure was parameterized by forest height metrics and stem volume. Results show that X-band volume coherence is highly sensitive to the forest canopy. Volume scattering within the canopy is dependent on the vertical heterogeneity of the forest stand. In general, TanDEM-X coherence is more sensitive to forest vertical structure compared to backscatter. The relations between TanDEM-X volume coherence and forest structural properties were significant at the level of a single test site as well as across sites in temperate forests in Germany. Forest type does not affect the overall relationship between the SAR signal and the forests’ vertical structure. The prediction of forest structural parameters based on the outcome of the sensitivity analysis yielded model accuracies between 15% (relative root mean square error) for Lorey’s height and 32% for stem volume. The global database of single-polarized bistatic TanDEM-X data provides an important source for mapping structural parameters in temperate forests at large scale, irrespective of forest type.

Highlights

There is a wide agreement in the scientific community today that forests play a major role in the global carbon cycle where forest degradation and conversion act as a source of carbon to the atmosphere and forest growth is a carbon sink [1]
Knowledge about the status and changes of forest structural parameters is important for the assessment of forest carbon stocks for national forest inventories in the context of reducing emissions from deforestation and forest degradation (REDD) and as a base input for the estimation of the forest above-ground biomass (AGB) which represents a key component in global carbon cycle models
In terms of the global carbon cycle, AGB is the most important forest variable. It can be assessed from three different types of remote sensing data: passive optical, light detection and ranging (LiDAR), and synthetic aperture radar (SAR) data

Summary

Introduction

There is a wide agreement in the scientific community today that forests play a major role in the global carbon cycle where forest degradation and conversion act as a source of carbon to the atmosphere and forest (re-) growth is a carbon sink [1]. In terms of the global carbon cycle, AGB is the most important forest variable. It can be assessed from three different types of remote sensing data: passive optical, light detection and ranging (LiDAR), and synthetic aperture radar (SAR) data. AGB estimates from optical remote sensing data mostly rely on vegetation indices that parameterize the photosynthetic activity of vegetation. They imply a relationship between the foliage and the total AGB of a vegetation stand, which is further used to estimate AGB. Recent studies confirmed the general ability of optical systems like Landsat to map AGB at biomass levels around 70 Mg/ha [5]

Objectives

Methods

Results

Discussion

Conclusion