Nitrogen (N) is important for the growth of crops. Estimating leaf nitrogen concentration (LNC) accurately and nondestructively is important for precision agriculture, reduces environmental pollution, and helps model global carbon and N cycles. Leaf reflectance, especially in the visible and near-infrared regions, has been identified as a useful indicator of LNC. Except reflectance passively acquired by spectrometers, the newly developed multispectral LiDAR and hyperspectral LiDAR provide possibilities for measuring leaf spectra actively. The regression relationship between leaf reflectance spectra and rice (Oryza sativa) LNC relies greatly on the algorithm adopted. It would be preferable to find one algorithm that performs well with respect to passive and active leaf spectra. Thus, this study assesses the influence of six popular linear and nonlinear methods on rice LNC retrieval, namely, partial least-square regression, least squares boosting, bagging, random forest, back-propagation neural network (BPNN), and support vector regression of different types/kernels/parameter values. The R², root mean square error and relative error in rice LNC estimation using these different methods were compared through the passive and active spectral measurements of rice leaves of different varieties at different locations and time (Yongyou 4949, Suizhou, 2014, Yangliangyou 6, Wuhan, 2015). Results demonstrate that BPNN provided generally satisfactory performance in estimating rice LNC using the three kinds of passive and active reflectance spectra.

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.

Urban trees, as a characteristic element of the urban ecosystem, exert significant influences on climate supervision. Therefore, the extraction of individual trees in urban areas holds significant research value. However, the complexity of features in urban areas poses challenges to existing single tree segmentation algorithms, as they may be influenced by other non-tree features. In this study, to reduce the influence of non-tree categories, enhance identification of edge features between adjacent tree crowns and achieve precise delineation results of single urban tree, an improved multi-stage method was proposed for tree points extraction and individual tree segmentation in urban scenes using multispectral LiDAR. Firstly, the original three single-channel point clouds were pre-processed by intensity interpolation to generate three-wavelength multispectral point cloud. Secondly, the Point Transformer deep learning network was employed for extracting urban tree points. Thirdly, an improved tree mapping algorithm was introduced for individual tree segmentation in urban scenes, utilizing the extracted tree points. Finally, manual individual tree labeling and the high-resolution Digital Orthophoto Map (DOM) of the region was incorporated to measure the delineation precision of individual tree. It shows that the IoU of tree category in urban scene reaches 96.0%. Moreover, the F1-score for overall individual tree segmentation attains 92.8%. And, a comparison with existing algorithms reveals that the proposed method outperforms traditional raster-based watershed method or point cloud clustering-based layer-stacking approach in urban scene, improving the overall accuracy of single tree segmentation by 21.9% and 16.0%, respectively. These results highlight the enhanced applicability of the proposed multi-stage algorithm for urban scenes.

Target classification techniques using spectral imagery and light detection and ranging (LiDAR) are widely used in many disciplines. However, none of the existing methods can directly capture spectral and 3D spatial information simultaneously. Multispectral LiDAR was proposed to solve this problem as its data combines spectral and 3D spatial information. Point-based classification experiments have been conducted with the use of multispectral LiDAR; however, the low signal to noise ratio creates salt and pepper noise in the spectral-only classification, thus lowering overall classification accuracy. In our study, a two-step classification approach is proposed to eliminate this noise during target classification: routine classification based on spectral information using spectral reflectance or a vegetation index, followed by neighborhood spatial reclassification. In an experiment, a point cloud was first classified with a routine classifier using spectral information and then reclassified with the k-nearest neighbors (k-NN) algorithm using neighborhood spatial information. Next, a vegetation index (VI) was introduced for the classification of healthy and withered leaves. Experimental results show that our proposed two-step classification method is feasible if the first spectral classification accuracy is reasonable. After the reclassification based on the k-NN algorithm was combined with neighborhood spatial information, accuracies increased by 1.50–11.06%. Regarding identification of withered leaves, VI performed much better than raw spectral reflectance, with producer accuracy increasing from 23.272% to 70.507%.