Sheng Nie

Estimating leaf area index of maize using airborne full-waveform lidar data

ABSTRACT The leaf area index (LAI) is a key input parameter in ecosystem models and plays a vital role in gas–vegetation exchange processes. Several studies have recently been conducted to estimate the LAI of low-stature vegetation using airborne discrete-return light detection and ranging (lidar) data. However, few studies have been carried out to estimate the LAI of low-stature vegetation using airborne full-waveform lidar data. The objective of this research is to explore the potential of airborne full-waveform lidar for LAI estimation of maize. First, waveform processing was conducted for better extraction of waveform-derived metrics for LAI estimation. A method of faint returns retrieval was also proposed to obtain ground returns. Second, the LAIs of maize were estimated based on the Beer–Lambert law. Finally, the LAI estimates were validated using field-measured LAIs in Huailai, Hebei Province of China. Results indicated that maize LAI could be successfully retrieved with high accuracy (R2 = 0.724, RMSE = 0.449) using full-waveform lidar data by the method proposed in this study.

Estimating the Biomass of Maize with Hyperspectral and LiDAR Data

The accurate estimation of crop biomass during the growing season is very important for crop growth monitoring and yield estimation. The objective of this paper was to explore the potential of hyperspectral and light detection and ranging (LiDAR) data for better estimation of the biomass of maize. First, we investigated the relationship between field-observed biomass with each metric, including vegetation indices (VIs) derived from hyperspectral data and LiDAR-derived metrics. Second, the partial least squares (PLS) regression was used to estimate the biomass of maize using VIs (only) and LiDAR-derived metrics (only), respectively. Third, the fusion of hyperspectral and LiDAR data was evaluated in estimating the biomass of maize. Finally, the biomass estimates were validated by a leave-one-out cross-validation (LOOCV) method. Results indicated that all VIs showed weak correlation with field-observed biomass and the highest correlation occurred when using the red edge-modified simple ratio index (ReMSR). Among all LiDAR-derived metrics, the strongest relationship was observed between coefficient of variation (H C V of digital terrain model (DTM) normalized point elevations with field-observed biomass. The combination of VIs through PLS regression could not improve the biomass estimation accuracy of maize due to the high correlation between VIs. In contrast, the H C V combined with H m e a n performed better than one LiDAR-derived metric alone in biomass estimation (R2 = 0.835, RMSE = 374.655 g/m2, RMSECV = 393.573 g/m2). Additionally, our findings indicated that the fusion of hyperspectral and LiDAR data can provide better biomass estimates of maize (R2 = 0.883, RMSE = 321.092 g/m2, RMSECV = 337.653 g/m2) compared with LiDAR or hyperspectral data alone.

Signal-to-noise ratio–based quality assessment method for ICESat/GLAS waveform data

Abstract. Data quality determines the accuracy of results associated with remote sensing data processing and applications. However, few effective studies have been carried out on quality assessment methods for the full-waveform light detecting and ranging data. Using the geoscience laser altimeter system (GLAS) waveform data as an example, a signal-to-noise ratio (SNR)-based waveform quality assessment method is proposed to analyze the relationship between the SNR and its controlling factors, i.e., laser type, laser using time, topographic relief, and land cover type, and study the impacts of these factors on the quality of the GLAS waveform data. Results show that the SNR-based data quality assessment method can quantitatively and effectively assess the GLAS waveform data quality. The SNR linearly attenuates with the laser using time, and the attenuation rate varies with laser type. The topographic relief is inversely correlated with the SNR of the GLAS data. As the land cover structure (especially the vertical structure) becomes more complex, the SNR of the GLAS data decreases. It was found that land cover types in descending order of the SNR values are desert, farmland, water body, grassland, city, and forest.

Application and Validation of a Model for Terrain Slope Estimation Using Space-Borne LiDAR Waveform Data

The terrain slope is one of the most important surface characteristics for quantifying the Earth surface processes. Space-borne LiDAR sensors have produced high-accuracy and large-area terrain measurement within the footprint. However, rigorous procedures are required to accurately estimate the terrain slope especially within the large footprint since the estimated slope is likely affected by footprint size, shape, orientation, and terrain aspect. Therefore, based on multiple available datasets, we explored the performance of a proposed terrain slope estimation model over several study sites and various footprint shapes. The terrain slopes were derived from the ICESAT/GLAS waveform data by the proposed method and five other methods in this study. Compared with five other methods, the proposed method considered the influence of footprint shape, orientation, and terrain aspect on the terrain slope estimation. Validation against the airborne LiDAR measurements showed that the proposed method performed better than five other methods (R2 = 0.829, increased by ~0.07, RMSE = 3.596◦, reduced by ~0.6◦, n = 858). In addition, more statistics indicated that the proposed method significantly improved the terrain slope estimation accuracy in high-relief region (RMSE = 5.180◦, reduced by ~1.8◦, n = 218) or in the footprint with a great eccentricity (RMSE = 3.421◦, reduced by ~1.1◦, n = 313). Therefore, from these experiments, we concluded that this terrain slope estimation approach was beneficial for different terrains and various footprint shapes in practice and the improvement of estimated accuracy was distinctly related with the terrain slope and footprint eccentricity.

Estimating Leaf Area Index of Maize Using Airborne Discrete-Return LiDAR Data

The leaf area index (LAI) is an important vegetation biophysical parameter, which plays a critical role in gas-vegetation exchange processes. Several studies have recently been conducted to estimate vegetation LAI using airborne discrete-return Light Detection and Ranging (LiDAR) data. However, few studies have been carried out to estimate the LAI of low-statue vegetation, such as the maize. The objective of this research is to explore the potential of estimating LAI for maize using airborne discrete-return LiDAR data. The LAIs of maize were estimated by a method based on the Beer-Lambert law and a method based on the allometric relationship, respectively. In addition, a new height threshold method for separating ground returns from canopy returns was proposed to better estimate the LAI of maize. Moreover, the two LAI estimation methods were also evaluated using the leave-one-out cross-validation method. Results indicate that the new height threshold method performs better than the traditional height threshold method in separating grounds returns from LiDAR returns. The coefficient of variation of detrended return heights within a field was a good parameter to estimate the LAI of maize. In addition, results also indicate that the method based on the Beer-Lambert law (R2 = 0.849, RMSE = 0.256) was more accurate than the method based on the allometric relationship (R2 = 0.779, RMSE = 0.315) in low-LAI regions, while only the method based on the allometric relationship is suitable for estimating the LAI of maize in high-LAI regions.