Calculation of Differential Propagation Constant Determined by Plant Morphology Using Polarimetric Measurement

The morphology of vegetation greatly impacts propagation of polarized electromagnetic wave. In order to validate this phenomenon, the mathematical relation between the differential propagation constant of forest vegetation and of its polarized echo is quantitatively derived by using backscattering power profile. The fluctuation of differential propagation constant with frequency is analyzed by combining the morphological characteristics of vegetation. The accurate copolarized data of 3–10 GHz frequency-domain of small trees are obtained by indoor wideband polarimetric measurement system. The results show that morphological characteristics of vegetation at different frequencies can be obtained by the differential propagation constant of polarized electromagnetic wave. At low frequencies, the plants with structural features presented oriented distribution. However, the plants show random distribution of the echoes at higher frequencies, which is mainly from the canopy. The research provides important information to choose the coherence models employed in the parameters retrieval of vegetations

Establishing the sensitivity of Synthetic Aperture Radar to above-ground biomass in wooded savannas

Radar for biomass estimation has been widely investigated for temperate, boreal and tropical forests, yet tropical savanna woodlands, which generally form non-continuous cover canopies or sparse woodlands, have been largely neglected in biomass studies. This thesis evaluates the capability of Synthetic Aperture Radar (SAR) for estimating the above-ground biomass of the woody vegetation in a savanna in Belize, Central America. This is achieved by evaluating (i) polarimetric Synthetic Aperture Radar (SAR) backscatter and (ii) single-pass shortwave interferometric SAR (InSAR) as indicators of above-ground biomass. Specifically, the effect on SAR backscatter of woody vegetation structure such as canopy cover, basal area, vegetation height and above-ground biomass is evaluated. Since vegetation height is often correlated to above-ground biomass, the effectiveness of vegetation height retrieval from InSAR is evaluated as an indicator of above-ground biomass. The study area, situated in Belize, is representative of Central American savannas. Radar data used are AIRSAR fully polarimetric L- and P-band SAR, and AIRSAR C-band InSAR, Intermap Technologies STAR-3i X-band InSAR, and Shuttle Radar Topography Mission (SRTM) C-band InSAR. The field data comprise accurately georeferenced three-dimensional measurements for 1,133 trees and shrubs and 75 palmetto clumps and thickets in a transect of 800 m x 60 m which spans the main savanna vegetation strata of the study area. An additional 2,464 ground points were observed. Results show that savanna woodlands present a challenge for radar remote sensing methods due to the sparse and heterogeneous nature of savanna woodlands. Long-wave SAR backscatter is dominated not only by high biomass areas, but also by areas of leafy palmetto which have low vegetative biomass. Retrieved woodland canopy heights from X- and C-band InSAR are indicative of the general patterns of tree height, although retrieved heights are underestimated. The amount of underestimation is variable across the different canopy conditions. Of these two methods, the shortwave InSAR data give a better indication of the spatial distribution of the above-ground biomass of the woody vegetation in the savannas than SAR backscatter. These results have implications for new and planned future global biomass estimation missions, such as ALOS PALSAR, ESA’s planned P-band BIOMASS and TanDEM-X. Without appropriate mediation, SAR backscatter methods might overestimate above-ground biomass of the woody vegetation of savannas while InSAR height retrieval methods might underestimate biomass estimates. Some possible mediating approaches are discussed

Radar backscatter modelling of forests using a macroecological approach

This thesis provides a new explanation for the behaviour of radar backscatter of forests using vegetation structure models from the field of macroecology. The forests modelled in this work are produced using allometry-based ecological models with backscatter derived from the parameterisation of a radiative transfer model. This work is produced as a series of papers, each portraying the importance of macroecology in defining the forest radar response. Each contribution does so by incorporating structural and dynamic effects of forest growth using one of two allometric models to expose variations in backscatter as a response to vertical and horizontal forest profiles. The major findings of these studies concern the origin of backscatter saturation effects from forest SAR surveys. In each work the importance of transition from Rayleigh to Optical scattering, combined with the scaling effects of forest structure, is emphasised. These findings are administered through evidence including the transition’s emergence as the region of dominant backscatter in a vertical profile (according to a dominant canopy scattering layer), also through the existence of a two trend backscatter relationship with volume in the shape of the typical “saturation curve” (in the absence of additional attenuating factors). The importance of scattering regime change is also demonstrated through the relationships with volume, basal area and thinning. This work’s findings are reinforced by the examination of the relationships between forest height and volume, as collective values, providing evidence to suggest the non-uniqueness of volume-toheight relationships. Each of the studies refer to growing forest communities not single trees, so that unlike typical studies of radar remote sensing of forests the impact of the macroecological structural aspects are more explicit. This study emphasises the importance of the overall forest structure in producing SAR backscatter and how backscatter is not solely influenced by electrical properties of scatteres or the singular aspects of a tree but also by the collective forest parameters defining a dynamically changing forest

Growing stock volume estimation in temperate forsted areas using a fusion approach with SAR Satellites Imagery

Forest monitoring plays a central role in the context of global warming mitigation and in the assessment of forest resources. To meet these challenges, significant efforts have been made by scientists to develop new feasible remote sensing techniques for the retrieval of forest parameters. However, much work remains to be done in this area, in particular in establishing global assessments of forest biomass. In this context, this Ph.D. Thesis presents a complete methodology for estimating Growing Stock Volume (GSV) in temperate forested areas using a fusion approach based on Synthetic-Aperture Radar (SAR) satellite imagery. The investigations which were performed focused on the Thuringian Forest, which is located in Central Germany. The satellite data used are composed of an extensive set of L-band (ALOS PALSAR) and X-band (TerraSAR-X, TanDEM-X, Cosmo-SkyMed) images, which were acquired in various sensor configurations (acquisition modes, polarisations, incidence angles). The available ground data consists of a forest inventory delivered by the local forest offices. Weather measurements and a LiDAR DEM complete the datasets. The research showed that together with the topography, the forest structure and weather conditions generally limited the sensitivity of the SAR signal to GSV. The best correlations were obtained with ALOS PALSAR (R2 = 0.61) and TanDEM-X (R2 = 0.72) interferometric coherences. These datasets were chosen for the retrieval of GSV in the Thuringian Forest and led with regressions to an root-mean-square error (RMSE) in the range of 100─200 m3ha-1. As a final achievement of this thesis, a methodology for combining the SAR information was developed. Assuming that there are sufficient and adequate remote sensing data, the proposed fusion approach may increase the biomass maps accuracy, their spatial extension and their updated frequency. These characteristics are essential for the future derivation of accurate, global and robust forest biomass maps