Integrating LiDAR Intensity and Elevation Data for Terrain Characterization in a Forested Area Cheng Wang and Nancy F. Glenn IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, VOL.6 NO.3 JULY 2009

Ⅰ. INTRODUCTION Separating ground from nonground laser returns from airborne light detection and ranging (LiDAR) data is key step in creating digital terrain models (DTMs). In this letter, bare-earth and forested surfaces are classified from LiDAR intensity data in a data set from central Idaho, U.S. and then a Gaussian fitting(GF) method is applied to determine ground elevations from LiDAR elevation data according to the land-cover information.

Ⅱ. DATA The study area is a 1-km 2 area near Redfish Lake in central Idaho, U.S. The elevation ranges between 1990 and 2180 m above sea level, and terrain slopes are as high as 45°. Three land-cover types: –Tall evergreen conifer –Bare soil –Pavement There is also a sparse amount (below 10% canopy cover) of low-height shrub beneath the forest canopy.

The LiDAR data were collected on October 8, 2005, using an Optech 50-kHz scanning LiDAR system. Ground reference observations were collected on September 18-19, 2007, and include the following: 1.Spectroradiometer reflectance measurements of soil, pavement, and vegetation (conifer trees and shrub) (ten samples each) points of land cover and spatial coordinates measured by a differentially corrected GPS.

Ⅲ. METHODS A.LiDAR Intensity Normalization and Segmentation In theory, LiDAR intensity values depend on surface reflectance, atmospheric transmission, local incidence angle, and sensor-to- object distance. owing to the negligible effects from local incidence angle and atmospheric conditions and the lack of calibration, the intensity data can be normalized with respect to the sensor-to-object distance (path length). The intensity level changes with the inverse square of the distance. B.GF of LiDAR Elevation Data For discrete LiDAR data, due to the high number of laser pulses penetrating the vegetation canopy, the returns within a local area will represent both the ground and vegetation canopy.

For a bare-earth area, LiDAR data have a Gaussian distribution if there are enough samples in a window (unit) size. [Fig. 1(a)] For a forested area, the frequency distributions of LiDAR elevations is considered to have a bimodal Gaussian shape with two overlapping single-frequency distributions. [Fig. 1(b)]

For the method developed here, we assume that F=f(z) is a frequency distribution of LiDAR elevations withing a local area (unit), and the elevation can be fit by two Gaussian distributions to represent bare ground [see (1) ] and forest [see (2)].

Since our objective is to estimate the ground elevation, (1) and (2) are simplified to b g = ground elevation a g and c g = related to the standard deviation of the ground- characteristic Gaussian function and are representative of the ground slope. a v, b v, and c v refer to the forest canopy.

A 4m × 4m analysis unit was used with approximately 130 laser returns in this letter (8 points/m 2 ). The GF algorithm was applied to each unit, and the Gaussian parameters were computed from the frequency distribution. In addition to the GF, two limitations were applied to the date processing to obtain reliable estimations: 1)Only units with a ground Gaussian distribution with standard deviation < 1m were retained, which roughly corresponds to the largest ground slope (45°) 2)Only the units with the difference of the two derived mean values > 1m were retained in order to remove units with complex vertical structure.

Ⅳ. RESULTS A.LiDAR Intensity Classification Map The original LiDAR intensities were normalized with a 700-m flight altitude to obtain a normalized intensity image [Fig. 2(a)]. A linear relationship was found between the normalized LiDAR intensity and the field-measured spectral reflectance at 1064 nm [Fig.2(b)]. The mean intensity value of vegetation and nonvegetation was applied to the normalized intensity data to produce a binary classification map [Fig. 2(c)].

B. DTM Fig. 3(a) is the map of identified ground elevations at the study site. Fig. 3(b) shows the derived DTM with 4-m spatial resolution that is produced by applying the inverse distance-weighting interpolation algorithm on the identified ground elevations. We calculated the DTM error between the LiDAR-derived and field- measured ground elevations for 182 sample points.

Ⅴ. DISCUSSION Previous studies have developed methods to create DTMs in forested areas with errors from tens of centimeters to several meters. Other studies indicate that the terrain slope is an important factor to LiDAR-derived DTM accuracy. However, the DTM error by the GF method is not related to the terrain slope.

Fig. 4 shows the relationship between terrain slope and error based on 37 field points along a range of slopes (from 0° to 45°)

Although our results indicate that the GF method can generate a relatively higher DTM accuracy in forested areas with less influence from terrain slope, our study used a higher point density (8 points/m 2 )

Although complex vertical structures are sparse at the study site, the two additional limitations of the GF method can effectively remove these units.

A complex vertical structure may reduce estimation accuracy for any method to identify ground returns because of its lower laser penetration rate and because of multiple returns in the understory.

Ⅵ. CONCLUSION AND FUTURE WORK In this letter we utilized the LiDAR spatial and spectral information to identify the ground elevations by a GF method in a forested area. The GF method is simple to implement in data analysis tools. In addition, it is flexible and adaptable for different terrain and canopy conditions. Although we considered simple land-cover conditions, GF can be extended to more complicated land cover by increasing the number of Gaussian distributions.