Zhiyu Zhang

Retrieval of Forest Biomass From ALOS PALSAR Data Using a Lookup Table Method

Mapping of forest biomass over large area and in higher accuracy becomes more and more important for researches on global carbon cycle and climate change. The feasibility and problems of forest biomass estimations based on lookup table (LUT) methods using ALOS PALSAR data are investigated in this study. Using of the forest structures from a forest growth model as inputs to a three dimensional radar backscattering model, a lookup table is built. Two types of searching methods (Nearest Distance (ND) and Distance Threshold (DT)) are used to find solutions from lookup table. When a simulated dataset is used to test the lookup table, the RMSE of biomass estimation are 39.133 Mg/ha (R2= 0.748) from ND and 26.699 Mg/ha (R2 = 0.886) from DT using dual-polarization data for forest with medium rough soil surface. All results show that DT is superior to ND. Comparisons of biomass from forest inventory data with that inversed from look up table using DT method over eight forest farms shows RMSE of 18.564 Mg/ha and 15.392 Mg/ha from PALSAR data with and without correction of the scattering mechanism, respectively. For the entire Lushuihe forest Bureau, the errors of the biomass estimation are - 13.8 Mg/ha (- 8.6%) and - 5.5 Mg/ha (- 3.5%) using PALSAR data with and without correction of scattering mechanisms due to terrain, respectively. The results shows that the radar image corrected data could be directly used for biomass estimation using the lookup table method.

Co-Registration of Two DEMs: Impacts on Forest Height Estimation From SRTM and NED at Mountainous Areas

The digital elevation model from the Shuttle Radar Topography Mission (SRTM) and the National Elevation Dataset (NED) have been used to estimate the forest canopy height. Most of such studies have been conducted over flat areas; the method performance has not been carefully examined over mountainous areas. This study, which is conducted over two mountainous test sites located in California and New Hampshire, reveals that the co-registration of these two digital elevation models (DEMs) is crucial to ensuring the quality of the results. The image co-registration method used in interferometric SAR processing is adapted to the co-registration of two DEMs. The forest canopy height from the Laser Vegetation Imaging Sensor (LVIS) is used as the reference data. The results showed that the misregistration between SRTM and NED was very obvious at both test sites. After the co-registration, the R2 of the correlation between the height of the C-band scattering phase center derived from SRTM minus NED and the forest canopy height derived from LVIS data was improved from 0.19 to 0.51, and RMSE was reduced from 16.4 m to 6.8 m for slope up to 55° at the California test site, while the R2 was improved from 0.39 to 0.57 and RMSE was reduced from 5.4 m to 3.6 m for slopes up to 45° at the New Hampshire test site. The influences of data resolution and terrain slopes were also investigated. The results showed that reducing the data resolution by spatial averaging could not reduce the influence of DEM misregistration.

Sensitivity of Multi-Source SAR Backscatter to Changes in Forest Aboveground Biomass

Accurate estimates of aboveground biomass (AGB) from forest after disturbance could reduce the uncertainties in carbon budget of terrestrial ecosystem and provide critical information to related carbon policy. Yet the loss of carbon from forest disturbance and the gain from post-disturbance recovery have not been well assessed. In this study, sensitivity analysis was conducted to investigate: (1) influence of factors other than the change of AGB (i.e. distortion caused by incident angle, soil moisture) on SAR backscatter; (2) feasibility of cross-image calibration between multi-temporal and multi-sensor SAR data; and (3) possibility of applying normalized backscatter to detect the post-disturbance AGB recovery. A semi-automatic empirical model was proposed to reduce the incident angle effect. Then, a cross-image normalization procedure was performed in order to remove the radiometric distortions among multi-source SAR data. The results indicate that effect of incident angle and soil moisture on SAR backscatter could be reduced by the proposed procedure, and a detection of biomass changes is possible using multi-temporal and multi-sensor SAR data.

A Heuristic Approach to Reduce Atmospheric Effects in InSAR Data for the Derivation of Digital Terrain Models or for the Characterization of Forest Vertical Structure

The differences of two digital terrain models (DTMs) derived from airborne interferometric synthetic aperture radar (InSAR) data of short and long wavelengths are utilized for the estimation of vertical forest structures. However, when the spaceborne repeat-pass InSAR data are used, atmospheric effects must be considered. A simple method for the reduction of atmospheric effects in spaceborne repeat-pass interferometry is proposed in this letter. By subtracting a simulated interferogram using the Shuttle Radar Topography Mission (SRTM) DTM from the interferogram of a pair of Phased Array Type L-Band Synthetic Aperture Radar (PALSAR) InSAR data, the remaining phase includes the phase caused by the height differences of scattering phase centers (SPC) at C- and L-bands and the phases caused by atmospheric effects and other changes during the PALSAR repeat-pass period. A low-pass spatial filtering can reveal the atmospheric effect in the phase image because of the low spatial frequency of the atmospheric effects. The proper size of the filtering window can be determined by the changes of standard deviation of filtered phase images as the window size increases. The changes of the standard deviations of the filtered phase images should be almost constant when only the atmospheric effect remains. After reducing the atmospheric effects, the difference between the SRTM-DTM and the PALSAR-DTM was reduced from 60.17 m±16.2 m to near 0 m (0.52 m±4.3 m) at bare surfaces, and the correlation (R2) between the mean forest height and the difference between the SRTM-DTM and the PALSAR-DTM was significantly increased from 0.021 to 0.608.

Model-Based Analysis of the Influence of Forest Structures on the Scattering Phase Center at L-Band

The estimation of forest biomass from synthetic aperture radar (SAR) data is limited by the lack of forest structure information. Interferometric synthetic aperture radar (InSAR) provides a means for the extraction of forest structure. The crucial issue in InSAR application is to parameterize forest structure and to link the parameter with InSAR observations. Model-based analysis enables exploring the theoretical linkages between InSAR observations and forest structure free from temporal decorrelation effects. In this paper, a semicoherent model (SCSR) was first developed and verified. A series of simulations at L-band was then made for both homogeneous and heterogeneous forests generated from a forest growth model. The forest structure was parameterized by four height indices. Aside from the height of scattering phase center (HSPC), the depth of scattering phase center (DSPC) was also proposed to characterize the scattering phase center of InSAR. The results showed that the behavior of homogeneous forest on InSAR data was quite different from that of heterogeneous forest. Special care was needed when the retrieval algorithms of forest biomass developed on a homogeneous forest were applied to a heterogeneous forest. Crown size-weighted height (CWH) and Loreys height were correlated with the HSPC at all polarizations and with the DSPC at copolarization in both cases of homogeneous and heterogeneous forests. These findings indicated that CWH could be an alternative biomass indicator of the Loreys height for biomass estimation, which can be derived from the combination of InSAR data and the elevation of the forest canopy top from lidar or high-resolution stereo images.

Biomass retrieval based on UAVSAR polarimetric data

Parameters of vegetation spatial structure have important effect on the carbon cycle and biodiversity of the ecosystems. How to estimate above-ground biomass is still a problem need to be worked out. In this paper we tried to use UAVSAR datasets to discuss the relation between backscattering coefficient and local incidence angle in different forest types. By the relation, a method based on scattering mechanism for correcting radiometric distortion caused by large range of incidence angle is developed. Biomass retrieval is based on incidence angle correction. The result shows good correlation between biomass and backscattering coefficient in 1 ha scale.

Semi-automatic extraction of digital surface model using ALOS/PRISM data with ENVI

Forest canopy height is an important indicator of standing biomass for management purposes as well as for the assessment of carbon storage. Theoretically, photogrammetry is one of remote sensing technologies which can be used to extract forest canopy height information. The Panchromatic Remote-sensing Instrument for Stereo Mapping (PRISM) carried by the Advanced Land-Observing Satellite was designed to generate worldwide topographic data with its high-resolution and stereoscopic observation. A semi-automatic method for the extraction of digital surface model using PRISM data with ENVI is introduced. Tie points scattered evenly over the common area are manually selected on the stereo-image pair. Their ground coordinates are calculated using the geocoding parameters delivered along with the nadir image. Their elevations are extracted from SRTM by their ground coordinates. Ground control points (GCP) files needed in the stereo-processing of PRISM data can be composed by the image coordinates, ground coordinates and elevations. With the prepared tie points and GCP files, PRISM data can be stereo-processed automatically by ENVI. The results showed that the elevation from PRISM DSM is highly correlated with that from GLAS data and forest canopy height information is explicitly exhibited on it.

Evaluation of UAV-based forest inventory system compared with LiDAR data

Forest spatial structure is essential for researches on forest ecosystem dynamics. Considering that the field measurement of forest structures over large field plot is labor-intensive and time-consuming, a forest inventory system is developed based on unmanned aerial vehicle (UAV). This study presented the initial evaluation of this system by comparisons with LiDAR data. Results showed that most trees appeared in LiDAR canopy height model (CHM) could also be detected in UAV CHM. The UAV system could measure forest height at plot level with R2=0.87 and RMSE=1.9 m taking CHM from LiDAR as reference data.

The Penetration Depth Derived from the Synthesis of ALOS/PALSAR InSAR Data and ASTER GDEM for the Mapping of Forest Biomass

The Global Digital Elevation Model produced from stereo images of Advanced Spaceborne Thermal Emission and Reflection Radiometer data (ASTER GDEM) covers land surfaces between latitudes of 83°N and 83°S. The Phased Array type L-band Synthetic Aperture Radar (PALSAR) onboard Advanced Land Observing Satellite (ALOS) collected many SAR images since it was launched on 24 January 2006. The combination of ALOS/PALSAR interferometric data and ASTER GDEM should provide the penetration depth of SAR data assuming ASTER GDEM was the elevation of vegetation canopy top. It would be correlated with forest biomass because penetration depth could be affected by forest density and forest canopy height. Their combination held great promises for the forest biomass mapping over large area. The feasibility of forest biomass mapping through the data synthesis of ALOS/PALSAR InSAR data and ASTER GDEM was investigated in this study. A procedure for the extraction of penetration depth was firstly proposed. Then three models were built for biomass estimation: (I) model only using backscattering coefficients of ALOS/PALSAR data; (II) model only using penetration depth; (III) model using both of them. The biomass estimated from Lidar data was taken as reference data to evaluate the three different models. The results showed that the combination of backscattering coefficients and penetration depth gave the best accuracy. The forest disturbance has to be considered in forest biomass estimation because of the long time span of ASTER data for generating ASTER GDEM. The spatial homogeneity could be used to improve estimation accuracy.

Simulation of Interferometric SAR Response for Characterizing Forest Successional Dynamics

The dynamics of scattering phase center height (SPCH) in a paper birch stand throughout its succession following clear cutting was studied to improve the interpretation of interferometric synthetic aperture radar (InSAR) response to forest structure changes. A forest growth model (ZELIG) and a fractal tree model (L-system) were utilized together for simulation of the three-dimensional (3-D) structure changes of the stand. Then, a high-fidelity coherent radar scattering model (CORSM) based on 3-D forest structure was employed to simulate the SPCH dynamics of the birch stand. The simulation experiment shows that correlation between L-band SPCH and forest parameters (Loreys mean height (hL) and biomass) could be noticeably affected by changing pattern of 3-D forest structure at different successional stages, which should be considered in our future exploration of SPCH for forest parameter estimation.