Wenjian Ni

Automated Methods for Measuring DBH and Tree Heights with a Commercial Scanning Lidar

Accurate forest structural parameters are crucial to forest inventory, and modeling of the carbon cycle and wildlife habitat. Lidar (Light Detection and Ranging) is particularly suitable to the measurement of forest structural parameters. In this paper, we describe a pilot study to extract forest structural parameters, such as tree height, diameter at breast height (DBH), and position of individual tree using a terrestrial lidar (LMS-Z360i; Riegel, Inc.). The lidar was operated to acquire both vertical and horizontal scanning in the field in order to obtain a point cloud of the whole scene. An Iterative Closet Point (ICP) algorithm was introduced to obtain the transformation matrix of each range image and to mosaic multiple range images together. Based on the mosaiced data set, a variable scale and threshold filtering method was used to separate ground from the vegetation. Meanwhile, a Digital Elevation Model (DEM) and a Canopy Height Model (CHM) were generated from the classified point cloud. A stem detection algorithm was used to extract the location of individual trees. A slice above 1.3 m from the ground was extracted and rasterized. A circle fitting algorithm combined with the Hough transform was used to retrieve the DBH based on the rasterized grid. Tree heights were calculated using the height difference between the minimum and maximum Z values within the position of each individual tree with a 1 m buffer. All of the 26 trees were detected correctly, tree height and DBH were determined with a precision of 0.76 m and 3.4 cm, respectively, comparing with those visually measured in the lidar data. Our methods and results confirm that terrestrial lidar can provide nondestructive, high-resolution, and automatic determination of parameters required in forest inventory.

Forest stand biomass estimation using ALOS PALSAR data based on LiDAR-derived prior knowledge in the Qilian Mountain, western China

Studies are needed to evaluate the ability of Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) for forest aboveground biomass (AGB) extraction in mountainous areas. In this article, forest biomass was estimated at plot and stand levels, and different biomass grades, respectively. Light detection and ranging (LiDAR) data with about one hit per m2 were first used for forest biomass estimation at the plot level, with R 2 of 0.77. Then the LiDAR-derived biomass, as prior knowledge, was used to investigate the relationship between ALOS PALSAR data and biomass. The results showed that at each biomass level, the range of the back-scatter coefficient in HH and HV polarization (where H and V represent horizontal and vertical polarizations, respectively, and the first of the two letters refers to the transmission polarization and the second to the received polarization) was very large and there was no obvious relationship between the synthetic aperture radar (SAR) back-scatter coefficient and biomass at plot level. At stand level and in different biomass grades, the back-scatter coefficient increased with the increase of forest biomass, and a logarithm equation can be used to describe the relationship. The main reason may be that forest structure is complex at the plot level, while the average value could partly decrease the influence of forest structure at stand level. Meanwhile, terrain radiometric correction (TRC) was investigated and found effective for forest biomass estimation.

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.

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.

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.

Estimation of Forest Aboveground Biomass in Changbai Mountain Region Using ICESat/GLAS and Landsat/TM Data

Mapping the magnitude and spatial distribution of forest aboveground biomass (AGB, in Mg·ha−1) is crucial to improve our understanding of the terrestrial carbon cycle. Landsat/TM (Thematic Mapper) and ICESat/GLAS (Ice, Cloud, and land Elevation Satellite, Geoscience Laser Altimeter System) data were integrated to estimate the AGB in the Changbai Mountain area. Firstly, four forest types were delineated according to TM data classification. Secondly, different models for prediction of the AGB at the GLAS footprint level were developed from GLAS waveform metrics and the AGB was derived from field observations using multiple stepwise regression. Lastly, GLAS-derived AGB, in combination with vegetation indices, leaf area index (LAI), canopy closure, and digital elevation model (DEM), were used to drive a data fusion model based on the random forest approach for extrapolating the GLAS footprint AGB to a continuous AGB map. The classification result showed that the Changbai Mountain region was characterized as forest-rich in altitudinal vegetation zones. The contribution of remote sensing variables in modeling the AGB was evaluated. Vegetation index metrics account for large amount of contribution in AGB ranges <150 Mg·ha−1, while canopy closure has the largest contribution in AGB ranges ≥150 Mg·ha−1. Our study revealed that spatial information from two sensors and DEM could be combined to estimate the AGB with an R2 of 0.72 and an RMSE of 25.24 Mg·ha−1 in validation at stand level (size varied from ~0.3 ha to ~3 ha).

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.