8 research outputs found
Mapping tropical forest aboveground biomass using airborne SAR tomography
Ramachandran, N.; Department of Civil Engineering, India; email: [email protected] Researc
Error Propagation Analysis for Remotely Sensed Aboveground Biomass
Edited version available. Full version will remain embargoed due to copyright. AS DCAbstract
Above-Ground Biomass (AGB) assessment using remote sensing has been an active area
of research since the 1970s. However, improvements in the reported accuracy of wide
scale studies remain relatively small. Therefore, there is a need to improve error analysis
to answer the question: Why is AGB assessment accuracy still under doubt? This project
aimed to develop and implement a systematic quantitative methodology to analyse the
uncertainty of remotely sensed AGB, including all perceptible error types and reducing
the associated costs and computational effort required in comparison to conventional
methods.
An accuracy prediction tool was designed based on previous study inputs and their
outcome accuracy. The methodology used included training a neural network tool to
emulate human decision making for the optimal trade-off between cost and accuracy for
forest biomass surveys. The training samples were based on outputs from a number of
previous biomass surveys, including 64 optical data based studies, 62 Lidar data based
studies, 100 Radar data based studies, and 50 combined data studies. The tool showed
promising convergent results of medium production ability. However, it might take many
years until enough studies will be published to provide sufficient samples for accurate
predictions.
To provide field data for the next steps, 38 plots within six sites were scanned with a
Leica ScanStation P20 terrestrial laser scanner. The Terrestrial Laser Scanning (TLS) data
analysis used existing techniques such as 3D voxels and applied allometric equations,
alongside exploring new features such as non-plane voxel layers, parent-child
relationships between layers and skeletonising tree branches to speed up the overall
processing time. The results were two maps for each plot, a tree trunk map and branch
map.
An error analysis tool was designed to work on three stages. Stage 1 uses a Taylor method
to propagate errors from remote sensing data for the products that were used as direct
inputs to the biomass assessment process. Stage 2 applies a Monte Carlo method to
propagate errors from the direct remote sensing and field inputs to the mathematical
model. Stage 3 includes generating an error estimation model that is trained based on the
error behaviour of the training samples.
The tool was applied to four biomass assessment scenarios, and the results show that the
relative error of AGB represented by the RMSE of the model fitting was high (20-35%
of the AGB) in spite of the relatively high correlation coefficients. About 65% of the
RMSE is due to the remote sensing and field data errors, with the remaining 35% due to
the ill-defined relationship between the remote sensing data and AGB. The error
component that has the largest influence was the remote sensing error (50-60% of the
propagated error), with both the spatial and spectral error components having a clear
influence on the total error. The influence of field data errors was close to the remote
sensing data errors (40-50% of the propagated error) and its spatial and non-spatial
Overall, the study successfully traced the errors and applied certainty-scenarios using the
software tool designed for this purpose. The applied novel approach allowed for a
relatively fast solution when mapping errors outside the fieldwork areas.HCED iraq, Middle Technical Universit
Elevation and Deformation Extraction from TomoSAR
3D SAR tomography (TomoSAR) and 4D SAR differential tomography (Diff-TomoSAR) exploit multi-baseline SAR data stacks to provide an essential innovation of SAR Interferometry for many applications, sensing complex scenes with multiple scatterers mapped into the same SAR pixel cell. However, these are still influenced by DEM uncertainty, temporal decorrelation, orbital, tropospheric and ionospheric phase distortion and height blurring. In this thesis, these techniques are explored. As part of this exploration, the systematic procedures for DEM generation, DEM quality assessment, DEM quality improvement and DEM applications are first studied. Besides, this thesis focuses on the whole cycle of systematic methods for 3D & 4D TomoSAR imaging for height and deformation retrieval, from the problem formation phase, through the development of methods to testing on real SAR data. After DEM generation introduction from spaceborne bistatic InSAR (TanDEM-X) and airborne photogrammetry (Bluesky), a new DEM co-registration method with line feature validation (river network line, ridgeline, valley line, crater boundary feature and so on) is developed and demonstrated to assist the study of a wide area DEM data quality. This DEM co-registration method aligns two DEMs irrespective of the linear distortion model, which improves the quality of DEM vertical comparison accuracy significantly and is suitable and helpful for DEM quality assessment. A systematic TomoSAR algorithm and method have been established, tested, analysed and demonstrated for various applications (urban buildings, bridges, dams) to achieve better 3D & 4D tomographic SAR imaging results. These include applying Cosmo-Skymed X band single-polarisation data over the Zipingpu dam, Dujiangyan, Sichuan, China, to map topography; and using ALOS L band data in the San Francisco Bay region to map urban building and bridge. A new ionospheric correction method based on the tile method employing IGS TEC data, a split-spectrum and an ionospheric model via least squares are developed to correct ionospheric distortion to improve the accuracy of 3D & 4D tomographic SAR imaging. Meanwhile, a pixel by pixel orbit baseline estimation method is developed to address the research gaps of baseline estimation for 3D & 4D spaceborne SAR tomography imaging. Moreover, a SAR tomography imaging algorithm and a differential tomography four-dimensional SAR imaging algorithm based on compressive sensing, SAR interferometry phase (InSAR) calibration reference to DEM with DEM error correction, a new phase error calibration and compensation algorithm, based on PS, SVD, PGA, weighted least squares and minimum entropy, are developed to obtain accurate 3D & 4D tomographic SAR imaging results. The new baseline estimation method and consequent TomoSAR processing results showed that an accurate baseline estimation is essential to build up the TomoSAR model. After baseline estimation, phase calibration experiments (via FFT and Capon method) indicate that a phase calibration step is indispensable for TomoSAR imaging, which eventually influences the inversion results. A super-resolution reconstruction CS based study demonstrates X band data with the CS method does not fit for forest reconstruction but works for reconstruction of large civil engineering structures such as dams and urban buildings. Meanwhile, the L band data with FFT, Capon and the CS method are shown to work for the reconstruction of large manmade structures (such as bridges) and urban buildings
The SAR Handbook: Comprehensive Methodologies for Forest Monitoring and Biomass Estimation
This Synthetic Aperture Radar (SAR) handbook of applied methods for forest monitoring and biomass estimation has been developed by SERVIR in collaboration with SilvaCarbon to address pressing needs in the development of operational forest monitoring services. Despite the existence of SAR technology with all-weather capability for over 30 years, the applied use of this technology for operational purposes has proven difficult. This handbook seeks to provide understandable, easy-to-assimilate technical material to remote sensing specialists that may not have expertise on SAR but are interested in leveraging SAR technology in the forestry sector
Capabilities of BIOMASS Tomography for Investigating Tropical Forests
International audienc