aohuan Xi

Estimating FPAR of maize canopy using airborne discrete-return LiDAR data

The fraction of absorbed photosynthetically active radiation (FPAR) is a key parameter for ecosystem modeling, crop growth monitoring and yield prediction. Ground-based FPAR measurements are time consuming and labor intensive. Remote sensing provides an alternative method to obtain repeated, rapid and inexpensive estimates of FPAR over large areas. LiDAR is an active remote sensing technology and can be used to extract accurate canopy structure parameters. A method to estimating FPAR of maize from airborne discrete-return LiDAR data was developed and tested in this study. The raw LiDAR point clouds were processed to separate ground returns from vegetation returns using a filter method over a maize field in the Heihe River Basin, northwest China. The fractional cover (fCover) of maize canopy was computed using the ratio of canopy return counts or intensity sums to the total of returns or intensities. FPAR estimation models were established based on linear regression analysis between the LiDAR-derived fCover and the field-measured FPAR (R(2) = 0.90, RMSE = 0.032, p < 0.001). The reliability of the constructed regression model was assessed using the leave-one-out cross-validation procedure and results show that the regression model is not overfitting the data and has a good generalization capability. Finally, 15 independent field-measured FPARs were used to evaluate accuracy of the LiDAR-predicted FPARs and results show that the LiDAR-predicted FPAR has a high accuracy (R(2) = 0.89, RMSE = 0.034). In summary, this study suggests that the airborne discrete-return LiDAR data could be adopted to accurately estimate FPAR of maize.

Retrieving leaf area index using ICESat/GLAS full-waveform data

Leaf area index (LAI) is an important parameter controlling many biological and physical processes associated with vegetation on the Earths surface. In this study, an algorithm for estimating LAI from the ICESat (Ice, Cloud and land Elevation Satellite)/GLAS (Geoscience Laser Altimeter System) data was proposed and applied to a forest area in the Tibetan Plateau. First, Gaussian decomposition of the GLAS waveform was implemented to identify the ground peaks and calculate the ground and canopy return energy. Second, the ground-to-total energy ratio (E r) was computed as the ratio of the ground return energy to the total waveform return energy for each GLAS footprint. Third, a regression model between the E r and the field-measured LAI was established based on the Beer–Lambert law. The coefficient of determination (R 2) of the model was 0.81 and the root mean square error (RMSE) is 0.35 (n = 23, p < 0.001). Finally, the leave-one-out cross-validation procedure was used to assess the constructed regression model. The results indicate that the regression model is not overfitting the data and has a good generalization capability. We validated the accuracy of the GLAS-predicted LAIs using the other 15 field-measured LAIs (R 2 = 0.84), and the result shows that the accuracy of the GLAS-predicted LAI is high (RMSE = 0.31).

Wavelet Analysis for ICESat/GLAS Waveform Decomposition and Its Application in Average Tree Height Estimation

A waveform decomposition method, i.e., multiscale wavelet analysis, is proposed in this letter for light detection and ranging waveform characterization and average tree height estimation. First, the waveform decomposition was applied to ICESat/Geoscience Laser Altimeter System (GLAS) data to extract the waveform characteristics (waveform peaks) through performing Gaussian wavelet functions at five increasing scales. Then, the waveform length and average tree height were derived from the waveform decomposition information. This method was applied to the GLAS waveform data in Yunnan province, China, for average tree height estimation. Finally, the results were validated by field measurements and compared with the relevant parameters in the GLA14 product provided by NASA. This study indicates that, for simple-peak waveforms, the waveform decomposition was consistent with that of the GLA14 product, while for bimodal or multipeak waveforms, the result was more accurate and reasonable than that of the GLA14 product.

Fusion of Airborne Discrete-Return LiDAR and Hyperspectral Data for Land Cover Classification

Accurate land cover classification information is a critical variable for many applications. This study presents a method to classify land cover using the fusion data of airborne discrete return LiDAR (Light Detection and Ranging) and CASI (Compact Airborne Spectrographic Imager) hyperspectral data. Four LiDAR-derived images (DTM, DSM, nDSM, and intensity) and CASI data (48 bands) with 1 m spatial resolution were spatially resampled to 2, 4, 8, 10, 20 and 30 m resolutions using the nearest neighbor resampling method. These data were thereafter fused using the layer stacking and principal components analysis (PCA) methods. Land cover was classified by commonly used supervised classifications in remote sensing images, i.e., the support vector machine (SVM) and maximum likelihood (MLC) classifiers. Each classifier was applied to four types of datasets (at seven different spatial resolutions): (1) the layer stacking fusion data; (2) the PCA fusion data; (3) the LiDAR data alone; and (4) the CASI data alone. In this study, the land cover category was classified into seven classes, i.e., buildings, road, water bodies, forests, grassland, cropland and barren land. A total of 56 classification results were produced, and the classification accuracies were assessed and compared. The results show that the classification accuracies produced from two fused datasets were higher than that of the single LiDAR and CASI data at all seven spatial resolutions. Moreover, we find that the layer stacking method produced higher overall classification accuracies than the PCA fusion method using both the SVM and MLC classifiers. The highest classification accuracy obtained (OA = 97.8%, kappa = 0.964) using the SVM classifier on the layer stacking fusion data at 1 m spatial resolution. Compared with the best classification results of the CASI and LiDAR data alone, the overall classification accuracies improved by 9.1% and 19.6%, respectively. Our findings also demonstrated that the SVM classifier generally performed better than the MLC when classifying multisource data; however, none of the classifiers consistently produced higher accuracies at all spatial resolutions.

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.

Effects of LiDAR point density, sampling size and height threshold on estimation accuracy of crop biophysical parameters

Vegetation leaf area index (LAI), height, and aboveground biomass are key biophysical parameters. Corn is an important and globally distributed crop, and reliable estimations of these parameters are essential for corn yield forecasting, health monitoring and ecosystem modeling. Light Detection and Ranging (LiDAR) is considered an effective technology for estimating vegetation biophysical parameters. However, the estimation accuracies of these parameters are affected by multiple factors. In this study, we first estimated corn LAI, height and biomass (R2 = 0.80, 0.874 and 0.838, respectively) using the original LiDAR data (7.32 points/m2), and the results showed that LiDAR data could accurately estimate these biophysical parameters. Second, comprehensive research was conducted on the effects of LiDAR point density, sampling size and height threshold on the estimation accuracy of LAI, height and biomass. Our findings indicated that LiDAR point density had an important effect on the estimation accuracy for vegetation biophysical parameters, however, high point density did not always produce highly accurate estimates, and reduced point density could deliver reasonable estimation results. Furthermore, the results showed that sampling size and height threshold were additional key factors that affect the estimation accuracy of biophysical parameters. Therefore, the optimal sampling size and the height threshold should be determined to improve the estimation accuracy of biophysical parameters. Our results also implied that a higher LiDAR point density, larger sampling size and height threshold were required to obtain accurate corn LAI estimation when compared with height and biomass estimations. In general, our results provide valuable guidance for LiDAR data acquisition and estimation of vegetation biophysical parameters using LiDAR data.

Building segmentation and modeling from airborne LiDAR data

Due to the high accuracy and fast acquisition speed offered by airborne Light Detection and Ranging (LiDAR) technology, airborne LiDAR point clouds have been widely used in three-dimensional building model reconstruction. This paper presents a novel approach to segment building roofs from point clouds using a Gaussian mixture model in which buildings are represented by a mixture of Gaussians (MoG). The Expectation-Maximization (EM) algorithm with the minimum description length (MDL) principle is employed to obtain the optimal parameters of the MoG model for separating building roofs. To separate complete planar building roofs, coplanar Gaussian components are merged according to their distances to the corresponding planes. In addition, shape analysis is utilized to remove nonplanar objects caused by trees and irregular artifacts. Building models are obtained by combining segmented planar roofs, topological relationships, and regularized building boundaries. Roof intersection segments and points are derived by the segmentation results, and a raster-based regularization method is employed to obtain geometrically correct and regular building models. Experimental results suggest that the segmentation method is able to separate building roofs with high accuracy while maintaining correct topological relationships among roofs.

Trend analysis of building height and total floor space in Beijing, China using ICESat/GLAS data

GLA14, one of the products of the spaceborne Light Detection and Ranging (LiDAR) sensor Geoscience Laser Altimeter System (GLAS), provides six Gaussian decomposition waveforms that represent different vertical layers of the ground target in a laser spot. In this article, we have extracted the relative height of ground targets from peak positions of the GLAS waveform, carried out the field validations, analysed the trend of building height in Beijing and then multiplied the building height and the percentage of building area within a pixel of the land-use/land-cover classification map to get the annual change of total floor space of buildings in Beijing. Based on the total floor space of buildings (TFSB) released by the National Bureau of Statistics of China (NBSC), we have established a linear regression model between the GLAS-estimated total floor space in Beijing and the data provided by NBSC. The results show that the building height and (TFSB) in Beijing increased from 2003 to 2008. The method proposed in this article expands research on urban change from a two-dimensional plane to a three-dimensional space to improve research accuracy, and is complementary to current remote-sensing methods.

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.