Shalei Song’s research while affiliated with Chinese Academy of Sciences and other places

The information extracted from waveform data of full-waveform light detection and ranging (LiDAR) has been widely used in applications such as 3D urban modeling, target recognition, and classification However, the presence of weak signals is inevitable in LiDAR systems. To enhance its effective detection capability and extraction accuracy, we propose a multispectral LiDAR (MSL) weak signals extraction (MSL-WSE) method. The measurement data from our MSL system were used to evaluate the performance of the proposed method. The correlation coefficient (R ² ), root mean square error (RMSE) and effective extraction rate show that the MSL-WSE method accurately detected and extracted the waveform parameters of weak echo signals, providing the more realistic and fine-grained true color 3D point cloud.

Solar-induced chlorophyll fluorescence (SIF) has been regarded as proxy data of vegetation photosynthesis; thus, it is assimilated into the terrestrial carbon cycle modeling. The Soil Canopy Observation of Photosynthesis and Energy fluxes (SCOPE) model is one of the most utilized models of SIF simulation. However, the currently incomplete understanding of SCOPE SIF factors and the lack of exploring how SCOPE works under different vegetation types would deteriorate further carbon cycle research. Herein, this study disentangled decisive SIF factors in the SCOPE model; then, a sample SIF dataset (SynSIF), with spatial resolutions of both 0.02∘ and 0.05∘, was simulated through SCOPE model using factors above. Then this study validated how far SCOPE simulating SIF could capture GPP, compared with other SIF datasets. The results showed that: (1) There are five decisive SIF factors in SCOPE model, including plant status (leaf chlorophyll content and leaf area index) and meteorological parameters (incoming shortwave radiation, air temperature, and atmospheric vapor pressure). (2) The linear relationship of SynSIF-GPP outachieved other SIF datasets across all six vegetation types in southern South America, Asia, and Africa, improving R2 averagely by 0.33, 0.28, and 0.15, respectively. (3) SynSIF in Oceania and Europe, revealing GPP better in shrublands (with SynSIF-GPP R2 increasing by 0.15 and 0.16, respectively) and grasslands (with SynSIF-GPP coefficients increasing by 0.14 and 0.06, respectively), illustrated spatially complementary characteristics with GOSIF across varying vegetation types. Thus, we anticipate that this study could provide more complete information for SCOPE simulating SIF in different biome research when estimating the terrestrial carbon cycle.

With the rapid development of light detection and ranging (LiDAR) technology, multispectral LiDAR (MSL) can realize three-dimensional (3D) imaging of the ground object by acquiring rich spectral information. Although color restoration has been achieved on the basis of the full-waveform data of MSL, further improvement of the visual effect of color point clouds still faces many challenges. In this paper, a highlight removal method for MSL color point clouds is proposed to explore the potential of 3D visualization. First, the MSL reflection model are introduced according to radar equation and Phong model, and the restored color of the MSL point clouds is determined to comprise diffuse and specular components. Second, a data conversion method is proposed to improve the massive point cloud processing efficiency by spatial dimension reduction and data compression. Then, the visual saliency map after color denoising is used to obtain the highlight region, the unknown information of which is recovered based on the global or local color information. Finally, three representative targets are selected and evaluated by qualitative and quantitative validation, which verifies that the method can effectively recover the high-quality highlight-free point clouds of MSL.

With the rapid modernization, many remote-sensing sensors were developed for classifying urban land and environmental monitoring. Multispectral LiDAR, which serves as a new technology, has exhibited potential in remote-sensing monitoring due to the synchronous acquisition of three-dimension point cloud and spectral information. This study confirmed the potential of multispectral LiDAR for complex urban land cover classification through three comparative methods. Firstly, the Optech Titan LiDAR point cloud was pre-processed and ground filtered. Then, three methods were analyzed: (1) Channel 1, based on Titan data to simulate the classification of a single-band LiDAR; (2) three-channel information and the digital surface model (DSM); and (3) three-channel information and DSM combined with the calculated three normalized difference vegetation indices (NDVIs) for urban land classification. A decision tree was subsequently used in classification based on the combination of intensity information, elevation information, and spectral information. The overall classification accuracies of the point cloud using the single-channel classification and the multispectral LiDAR were 64.66% and 93.82%, respectively. The results show that multispectral LiDAR has excellent potential for classifying land use in complex urban areas due to the availability of spectral information and that the addition of elevation information to the classification process could boost classification accuracy.

The distribution of land cover has an important impact on climate, environment, and public policy planning. The Optech Titan multispectral LiDAR system provides new opportunities and challenges for land cover classification, but the better application of spectral and spatial information of multispectral LiDAR data is a problem to be solved. Therefore, we propose a land cover classification method based on multi-scale spatial and spectral feature selection. The public data set of Tobermory Port collected by the Optech Titan multispectral airborne laser scanner was used as research data, and the data was manually divided into eight categories. The method flow is divided into four steps: neighborhood point selection, spatial–spectral feature extraction, feature selection, and classification. First, the K-nearest neighborhood is used to select the neighborhood points for the multispectral LiDAR point cloud data. Additionally, the spatial and spectral features under the multi-scale neighborhood (K = 20, 50, 100, 150) are extracted. The Equalizer Optimization algorithm is used to perform feature selection on multi-scale neighborhood spatial–spectral features, and a feature subset is obtained. Finally, the feature subset is input into the support vector machine (SVM) classifier for training. Using only small training samples (about 0.5% of the total data) to train the SVM classifier, 91.99% overall accuracy (OA), 93.41% average accuracy (AA) and 0.89 kappa coefficient were obtained in study area. Compared with the original information’s classification result, the OA, AA and kappa coefficient increased by 15.66%, 8.7% and 0.19, respectively. The results show that the constructed spatial–spectral features and the application of the Equalizer Optimization algorithm for feature selection are effective in land cover classification with Titan multispectral LiDAR point data.