Zhifeng Guo

National Forest Aboveground Biomass Mapping from ICESat/GLAS Data and MODIS Imagery in China

Forest aboveground biomass (AGB) was mapped throughout China using large footprint LiDAR waveform data from the Geoscience Laser Altimeter System (GLAS) onboard NASA’s Ice, Cloud, and land Elevation Satellite (ICESat), Moderate Resolution Imaging Spectro-radiometer (MODIS) imagery and forest inventory data. The entire land of China was divided into seven zones according to the geographic characteristics of the forests. The forest AGB prediction models were separately developed for different forest types in each of the seven forest zones at GLAS footprint level from GLAS waveform parameters and biomass derived from height and diameter at breast height (DBH) field observation. Some waveform parameters used in the prediction models were able to reduce the effects of slope on biomass estimation. The models of GLAS-based biomass estimates were developed by using GLAS footprints with slopes less than 20° and slopes ≥ 20°, respectively. Then, all GLAS footprint biomass and MODIS data were used to establish Random Forest regression models for extrapolating footprint AGB to a nationwide scale. The total amount of estimated AGB in Chinese forests around 2006 was about 12,622 Mt vs. 12,617 Mt derived from the seventh national forest resource inventory data. Nearly half of all provinces showed a relative error (%) of less than 20%, and 80% of total provinces had relative errors less than 50%.

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.

Forest Cover Classification With MODIS Images in Northeastern Asia

Forest ecosystem in Eastern Siberia and Northeastern China (ESNC) has been undergoing dramatic changes during the last several decades due to forest fires and massive logging. These changes affect climate dynamics, economic activity and living heritage in local region, further, to the global carbon balance and climate changes. In this paper, a 2D feature space grid split (FSGS) algorithm was developed to identify forests cover region by combined TM/ ETM+ images and MODIS datasets, due to its dark object attributes. This no-parametric algorithm was based on statistical signatures in feature space and Bayesian rule. The producer accuracy of tree cover commission can be approximately 90%, comparing with local TM/ETM+ classification results. Then, forests cover was stratified into different biomes by a decision tree classifier. and Forests cover map was respectively compared with MODIS land cover products and Global land cover 2000(GLC2000) products derived from images observed by VEGETATION (VGT) sensor on both areal and per-pixel bases.

Investigation of forest height retrieval using SRTM-DEM and ASTER-GDEM

Interferometric SAR (InSAR)data have been used to measure canopy height. Polarimetric interferometric SAR (PolInSAR) data can be used to derive canopy height without using ground surface elevation data. But in most cases, only single polarization InSAR data are available and the elevation of ground surface in the forested areas is needed to get the height of the scattering phase center. On contrary, the elevation of canopy surface is relatively easy to obtain by Stereo imagery. In this study the feasibility of the estimation of forest height using SRTM-DEM and ASTER- GDEM was investigated. The ASTER-GDEM was firstly resampled to the pixel size of SRTM-DEM (3 arc-second) and then was registered to SRTM- DEM using the points selected from their aspect maps. The results showed that the registration is necessary because the geolocation error at east-west direction is about half of the pixel size. The relationship between the forest height and the elevation difference was analyzed. The results showed that the elevation difference between registered ASTER-GDEM and SRTM-DEM is positively correlated with the forest height. Although there are some problems when the terrain is rough, it provides us a way to estimate the height of mature forest in flat terrain.

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.

The Potential of Combined Lidar and SAR Data in Retrieving Forest Parameters using Model Analysis

3D Lidar waveform and 3D radar backscatter models based on Radiative Transfer theory were used to simulate waveform and backscattering of various plots with different stand ages and structures, which were generated using forest growth model. The inversion models for estimating forest Above Ground Biomass (AGB) and Average Stand Height (ASH) were derived from the combined simulated database of large footprint Lidar waveforms and L-band polarimetric SAR backscattering using stepwise analysis method. The inversion procedures were then applied to NASA LVIS and ALOS PALSAR data to retrieve forest parameters for the study area. The study area is a 10km by 10km area located at International Papers Northern Experiments Forest, Maine, USA, where field measurements that include stem coordinate, DBH, species and canopy position were recorded within a 200m by 150 m stand. Heights and AGB of total 7956 trees were estimated by applying species-specific allometric equations to stand measurements. AGB and height were then scaled up to the area according to the LVIS footprint size and location at 149 20m*20m plots, which were used to verify the inversion model developed using simulated database. The study concludes that Lidar waveform indices and SAR backscattering are complementary for forest parameters retrieving, which improved the limitation of signature saturation for regional biomass mapping using SAR data only. The comparison between inversed forest parameters and field measurements shows good consistency.

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.

Research and application on spatial data Web service based on .Net platform

With the rapid development of Internet, GIS (Geographic Information System) application extends its area from desktop to the Internet, which provides various GIS services of analyzing and displaying the spatial data. Although there is huge geo-spatial information existing in the Internet, it is difficult to use Web browser to seamlessly access, view and exploit the vast, diverse and widely distributed geo-spatial data, which makes it hard to develop GIS application and to provide enough support for spatial decision-making. In this paper, we analyze the geo-spatial information Web Service architecture of OGC (OpenGIS Consortium), and then we design and implement a Web-based Geo-spatial Service Platform using GML (Geography Markup Language) and Microsoft .NET. This Geo-spatial Service Platform is built through three aspects. Firstly, we use GML as geo-spatial data model and data source, and then implement the GML data stored in SQL-Server. Secondly, we build a prototype Web service system based on Microsoft .NET to provide geo-spatial service. Thirdly, we use SVG (Scalable Vector Graphics) as a visualization tool for Web mapping on client. This Web service platform provides a vendor-neutral, interoperable framework for Web-based access, integration, analysis and visualization of multiple online geo-spatial data sources. Finally, using spatial data Web service interfaces, which are provided by this platform, we implement a simple GIS application system, which implements the spatial information query and map projective transform on the Internet.