Xi Zhu

Foliar and woody materials discriminated using terrestrial LiDAR in a mixed natural forest

Abstract Separation of foliar and woody materials using remotely sensed data is crucial for the accurate estimation of leaf area index (LAI) and woody biomass across forest stands. In this paper, we present a new method to accurately separate foliar and woody materials using terrestrial LiDAR point clouds obtained from ten test sites in a mixed forest in Bavarian Forest National Park, Germany. Firstly, we applied and compared an adaptive radius near-neighbor search algorithm with a fixed radius near-neighbor search method in order to obtain both radiometric and geometric features derived from terrestrial LiDAR point clouds. Secondly, we used a random forest machine learning algorithm to classify foliar and woody materials and examined the impact of understory and slope on the classification accuracy. An average overall accuracy of 84.4% (Kappaxa0=xa00.75) was achieved across all experimental plots. The adaptive radius near-neighbor search method outperformed the fixed radius near-neighbor search method. The classification accuracy was significantly higher when the combination of both radiometric and geometric features was utilized. The analysis showed that increasing slope and understory coverage had a significant negative effect on the overall classification accuracy. Our results suggest that the utilization of the adaptive radius near-neighbor search method coupling both radiometric and geometric features has the potential to accurately discriminate foliar and woody materials from terrestrial LiDAR data in a mixed natural forest.

Estimation of forest leaf water content through inversion of a radiative transfer model from LiDAR and hyperspectral data

Abstract The accurate estimation of leaf water content (LWC) and knowledge about its spatial variation are important for forest and agricultural management since LWC provides key information for evaluating plant physiology. Hyperspectral data have been widely used to estimate LWC. However, the canopy reflectance can be affected by canopy structure, thereby introducing error to the retrieval of LWC from hyperspectral data alone. Radiative transfer models (RTM) provide a robust approach to combine LiDAR and hyperspectral data in order to address the confounding effects caused by the variation of canopy structure. In this study, the INFORM model was adjusted to retrieve LWC from airborne hyperspectral and LiDAR data. Two structural parameters (i.e. stem density and crown diameter) in the input of the INFORM model that affect canopy reflectance most were replaced by canopy cover which could be directly obtained from LiDAR data. The LiDAR-derived canopy cover was used to constrain in the inversion procedure to alleviate the ill-posed problem. The models were validated against field measurements obtained from 26 forest plots and then used to map LWC in the southern part of the Bavarian Forest National Park in Germany. The results show that with the introduction of prior information of canopy cover obtained from LiDAR data, LWC could be retrieved with a good accuracy (R2 = 0.87, RMSE = 0.0022 g/cm2, nRMSE = 0.13). The adjustment of the INFORM model facilitated the introduction of prior information over a large extent, as the estimation of canopy cover can be achieved from airborne LiDAR data.

Tree species classification using plant functional traits from LiDAR and hyperspectral data

Abstract Plant functional traits have been extensively used to describe, rank and discriminate species according to their variability between species in classical plant taxonomy. However, the utility of plant functional traits for tree species classification from remote sensing data in natural forests has not been clearly established. In this study, we integrated three selected plant functional traits (i.e. equivalent water thickness (Cw), leaf mass per area (Cm) and leaf chlorophyll (Cab)) retrieved from hyperspectral data with hyperspectral derived spectral features and airborne LiDAR derived metrics for mapping five tree species in a natural forest in Germany. Our results showed that when plant functional traits were combined with spectral features and LiDAR metrics, an overall accuracy of 83.7% was obtained, which was statistically significantly higher than using LiDAR (65.1%) or hyperspectral (69.3%) data alone. The results of our study demonstrate that plant functional traits retrieved from hyperspectral data using radiative transfer models can be used in conjunction with hyperspectral features and LiDAR metrics to further improve individual tree species classification in a mixed temperate forest.

Retrieval of vertical leaf water content using terrestrial full-waveform lidar

The vertical distribution of leaf water content (LWC) within plant canopy plays an important role in light penetration and scattering, thus affecting reflectance simulation with radiative transfer models. Although passive remote sensing techniques have been widely applied to estimate LWC, they are unable to retrieve the LWC vertical distribution within canopy. By providing vertical information, terrestrial LiDAR can potentially overcome this limitation. In this paper we investigated the applicability of the terrestrial full-waveform LiDAR to estimate the LWC vertical profile within the canopy of individual plants. A standard radiometric calibration was applied to convert the amplitude and the echo width to a physically well-defined radiometric quantity, namely the backscatter coefficient. However, the backscatter coefficient is strongly affected by the incidence angle between the laser beam and the leaf normal. In order to compensate for incidence angle effects, reference reflectors (Spectralon from Labsphere, Inc.) were used to build a look-up table to calibrated the backscatter coefficient. Our results showed that the backscatter coefficient had a strong correlation (R2 = 0.66) with LWC after correcting for the incidence angle effect. Good agreements were achieved between the predicted vertical profile of LWC and the measured vertical profile of LWC with a mean RMSE (root mean square error) value of 0.001 g/cm2 and a mean MAPE (mean absolute percent error) value of 4.46 %. Our study successfully demonstrated the feasibility of retrieving LWC vertical distribution within plant canopy from a terrestrial full-waveform LiDAR.