Hyperspectral LiDAR Research Articles

Improving the unsupervised mapping of riparian bugweed in commercial forest plantations using hyperspectral data and LiDAR

Accurate spatial information on the location of invasive alien plants (IAPs) in riparian environments is critical to fulfilling a comprehensive weed management regime. This study aimed to automatically map the occurrence of riparian bugweed (Solanum mauritianum) using airborne AISA Eagle hyperspectral data (393 nm–994 nm) in conjunction with LiDAR derived height. Utilising an unsupervised random forest (RF) classification approach and Anselin local Moran’s I clustering, results indicate that the integration of LiDAR with minimum noise fraction (MNF) produce the best detection rate (DR) of 88%, the lowest false positive rate (FPR) of 7.14% and an overall mapping accuracy of 83% for riparian bugweed. In comparison, utilising the original hyperspectral wavebands with and without LiDAR produced lower DRs and higher FPRs with overall accuracies of 79% and 68% respectively. This research demonstrates the potential of combining spectral information with LiDAR to accurately map IAPs using an automated unsupervised RF anomaly detection framework.

A 10-nm Spectral Resolution Hyperspectral LiDAR System Based on an Acousto-Optic Tunable Filter.

Hyperspectral LiDAR (HSL) technology can obtain spectral and ranging information from targets by processing the recorded waveforms and measuring the time of flight (ToF). With the development of the supercontinuum laser (SCL), it is technically easier to develop an active hyperspectral LiDAR system that can simultaneously collect both spatial information and extensive spectral information from targets. Compared with traditional LiDAR technology, which can only obtain range and intensity information at the selected spectral wavelengths, HSL detection technology has demonstrated its potential and adaptability for various quantitative applications from its spectrally resolved waveforms. However, with most previous HSLs, the collected spectral information is discrete, and such information might be insufficient and restrict the further applicability of the HSLs. In this paper, a tunable HSL technology using an acousto-optic tunable filter (AOTF) as a spectroscopic device was proposed, designed, and tested to address this issue. Both the general range precision and the accuracy of the spectral measurement were evaluated. By tuning the spectroscopic device in the time dimension, the proposed AOTF-HSL could achieve backscattered echo with continuous coverage of the full spectrum of 500–1000 nm, which had the unique characteristics of a continuous spectrum in the visible and near infrared (VNIR) regions with 10 nm spectral resolution. Yellow and green leaves from four plants (aloe, dracaena, balata, and radermachera) were measured using the AOTF-HSL to assess its feasibility in agriculture application. The spectral profiles measured by a standard spectrometer (SVC© HR-1024) were used as a reference for evaluating the measurements of the AOTF-HSL. The difference between the spectral measurements collected from active and passive instruments was minor. The comparison results show that the AOTF-based consecutive and high spectral resolution HSL was effective for this application.

Combining hyperspectral imagery and LiDAR pseudo-waveform for predicting crop LAI, canopy height and above-ground biomass

Crop Leaf area index (LAI), canopy height and above-ground biomass (AGB) are important structural parameters. Accurate predictions of these three parameters are required for improving the applications of crop growth monitoring, health status assessment and yield prediction. Airborne Light Detection and Ranging (LiDAR) system is the most reliable technique for accurately predicting vegetation structure parameters. LiDAR technique has been broadly applied to estimate vegetation LAI, height and biomass, and reliable prediction results have been obtained. However, LiDAR data lack the spectral information of vegetation. The combination of LiDAR data and hyperspectral imagery can achieve complementary advantages of two data sources and improve the prediction accuracies of vegetation parameters. In this research, we aim to estimate maize LAI, canopy height and AGB using the combined hyperspectral imagery and LiDAR pseudo-waveforms. We constructed the LiDAR pseudo-waveforms through discrete-return point clouds and extracted pseudo-waveform variables. The prediction models of maize LAI, canopy height and AGB were established with a random forest (RF) regression algorithm using the traditional statistical variables derived from discrete-return point clouds, the pseudo-waveform variables, the combined hyperspectral vegetation indices and pseudo-waveform variables, respectively. Moreover, the comparative analyses of the three prediction models were conducted to determine the optimal prediction model and explore the potential of the combined hyperspectral vegetation indices and pseudo-waveform variables for predicting maize crop structural parameters. The results showed the strong relationships between LiDAR pseudo-waveform variables and maize LAI, height, and biomass (R2 = 0.799, 0.832 and 0.871, respectively). Moreover, the pseudo-waveform variables produced better results than the results estimated from traditional statistical variables of discrete-return LiDAR (R2 = 0.772, 0.812 and 0.811, respectively). Therefore, it is a viable method for predicting maize LAI, canopy height and AGB using the LiDAR pseudo-waveforms created from discrete-return LiDAR data. Nevertheless, we found that the combined pseudo-waveform variables and vegetation indices derived from hyperspectral imagery produced a better prediction result (R2 = 0.829, 0.892 and 0.909, respectively) when compared to LiDAR pseudo-waveform data alone, and the prediction accuracies improved by 3.8%, 7.2% and 4.4%, respectively. The combined hyperspectral imagery and LiDAR pseudo-waveform data provided complementary information and therefore improved prediction accuracies of these parameters. Although small improvements were observed, the combined data have potential for improving predictions of crop parameters. Our study will provide valuable information for predicting vegetation LAI, canopy height and AGB using the combined hyperspectral imagery and pseudo-waveform constructed from discrete-return LiDAR data.

Portable hyperspectral lidar utilizing 5 GHz multichannel full waveform digitization.

The Finnish Geospatial Research Institute hyperspectral LiDAR (FGI HSL) was one of the first multichannel terrestrial LiDARs capable of producing simultaneous 3-dimensional topography with spectral data. Supercontinuum-based HSL instruments developed so far have suffered from portability and sensitivity issues, severely restricting potential applications. Recently, we have implemented a new robust field design of the FGI HSL together with an improved pulse digitizing scheme. Small size and significantly improved measuring accuracy of this new system enable a range of novel applications that so far have been impractical for multichannel terrestrial LiDARs. Particularly, this new design has enabled us to perform measurements in underground mines and detect minute spectral differences in various rock types. In this paper, we present the design of our new LiDAR and preliminary algorithms together with a brief performance assessment of the device. In addition, we provide example measurements of typical rock samples found in a ferrochrome mine.

Tree Species Classification by Employing Multiple Features Acquired from Integrated Sensors

Explicit information of tree species composition provides valuable materials for the management of forests and urban greenness. In recent years, scholars have employed multiple features in tree species classification, so as to identify them from different perspectives. Most studies use different features to classify the target tree species in a specific growth environment and evaluate the classification results. However, the data matching problems have not been discussed; besides, the contributions of different features and the performance of different classifiers have not been systematically compared. Remote sensing technology of the integrated sensors helps to realize the purpose with high time efficiency and low cost. Benefiting from an integrated system which simultaneously acquired the hyperspectral images, LiDAR waveform, and point clouds, this study made a systematic research on different features and classifiers in pixel-wised tree species classification. We extracted the crown height model (CHM) from the airborne LiDAR device and multiple features from the hyperspectral images, including Gabor textural features, gray-level co-occurrence matrix (GLCM) textural features, and vegetation indices. Different experimental schemes were tested at two study areas with different numbers and configurations of tree species. The experimental results demonstrated the effectiveness of Gabor textural features in specific tree species classification in both homogeneous and heterogeneous growing environments. The GLCM textural features did not improve the classification accuracy of tree species when being combined with spectral features. The CHM feature made more contributions to discriminating tree species than vegetation indices. Different classifiers exhibited similar performances, and support vector machine (SVM) produced the highest overall accuracy among all the classifiers.

A new waveform decomposition method for multispectral LiDAR

Information derived from waveform decomposition of full-waveform light detection and ranging (LiDAR) data has been widely used in vegetation detection and three-dimensional urban terrain modeling to investigate and interpret the structural diversity of surface coverage. Most prevailing waveform decomposition methods involve only a single wavelength, but these methods do not apply to full-waveform multispectral LiDAR (FWMSL) systems that simultaneously acquire spectral and geometric information. In this paper, we propose a new multispectral waveform decomposition (MSWD) method in order to explore the potential advantages of the FWMSL system. Both simulated data and measured data from our FWMSL system were used to evaluate the performance of the proposed method. The coefficient of determination (R2), root mean square error (RMSE), and relative error (rRMSE) metrics suggest that the decomposition results derived from MSWD exhibit a comparable overall fitting accuracy as a single wavelength waveform decomposition (SWWD) method. We also propose a new evaluation indicator, relative neighbor distance error (RNDE), to represent the relative error in the distance between adjacent targets. The simulation results present clear superiority of MSWD over SWWD in terms of discovering weak or overlapping components and retrieving accurate waveform parameters. The experimental results demonstrated a considerable improvement in RNDE (0.0100–0.0610) over the prevailing SWWD method (0.0566–0.2833). Unlike SWWD, MSWD initializes waveform components using mutually complementary wavelengths thus delivering higher completeness and accuracy. MSWD can be extended to other FWMSL or full-waveform hyperspectral LiDAR systems with additional wavelengths.

Urban classification by multi-feature fusion of hyperspectral image and LiDAR data

Urban classification by multi-feature fusion of hyperspectral image and LiDAR data

Raman lidar with for geoecological monitoring

Application of CCD array photodetector technology is shown to provide record synchronously hundreds of spectral intervals, increase essentially the relative aperture and intensity of echo-signals in the input optical path as well as to reduce the mass – dimension parameters of hyperspectral Raman LIDAR as a whole for geoecological monitoring by aerial and underwater unmanned vehicles.

Mapping and classifying green infrastructure typologies for climate-related studies based on remote sensing data

Despite the current evidence on the thermal benefits of vegetation and water bodies, further research is needed to investigate how cooling capacities are influenced by particular types, amounts, and spatial arrangements of green infrastructure (GI). However, there are no commonly agreed typologies that can be confidently used to compare and report the existing climatological effects of GI. Two previous studies were conducted to respond to this gap, and a conceptual GI typology matrix was developed according to functional, structural and configurational attributes. The present research presents a streamlined version and tests its applicability for the automated mapping, classification and thermal evaluation of GI using remote sensing data. A combination of parameters is introduced, including surface cover fractions and FRAGSTATS metrics estimated from very high-resolution hyperspectral imagery, LiDAR and cadastral data. The proposed framework can be applied at different spatial scales to analyse large urban areas rapidly and with high spatial accuracy. This paper also proposes a replicable workflow that can be implemented by researchers and practitioners to map existing vegetation conditions, prioritise greening interventions and assess thermal conditions with greater confidence. In this study, this workflow was successfully applied at local scale to classify green open spaces, tree canopies and water bodies in the city of Sydney. Evidence presented here demonstrates the applicability of the proposed system to evaluate and compare the intra- and inter-typology variability of land surface temperatures (LSTs), which can be potentially applied for performance assessment across other ecosystem service categories. Despite satisfactory results, the proposed typology is contingent on further developments and tests on a larger spatial extent and with a greater number of observations. Further statistical analysis is also required to determine the cooling capacity of typologies and quantify the influence of measured parameters on LSTs.

Feasibility Study of Ore Classification Using Active Hyperspectral LiDAR

Recently, a major effort has been made to develop methods or tools for rock characterization and mineral content mapping. Light detection and ranging (LiDAR) is an efficient active remote sensing technique for collecting geometry information about rock surfaces. However, traditional LiDAR sensors work with a single-wavelength laser source, and it is unfeasible to obtain spectral information using one LiDAR sensor. The combination of hyperspectral imaging and LiDAR techniques is an emerging method for acquiring spatial and spectral information simultaneously that allows remote mapping of high-resolution mineral content and distributions and identifies subtle chemical variations. Unfortunately, spatial and spectral data registration, which introduces additional complicated data processing, is an inevitable and essential issue for this method. In this letter, first, we investigate the feasibility of ore classification applications with hyperspectral LiDAR (HSL). HSL consists of 17 spectral channels covering the visible–shortwave infrared (SWIR) spectral range. Spatial and spectral information about seven different ore samples is obtained under a controlled laboratory environment using HSL. The standard deviation of the distance measurements is less than 1.1 cm for different spectral channels, and the classification accuracy can reach 100% if all 17 spectral measurements are used. To optimize the system design with lower cost and system complexity, a spectral band selection criterion is built based on the feature contribution degree (FCD), which is calculated using the normalized variance of the reflectance values for different ore samples at each wavelength. Two different strategies of FCD selection are tested to generate vectors: ascending sequences and descending sequences. Feature vectors with descending sequences have better classification accuracy. In addition, the results show that the classification accuracy can reach 100% with the feature vector of the seven largest FCD values compared to 59.57% for the feature vector with the seven smallest FCD values. Moreover, we find that the channels with high FCD values are primarily centered in SWIR bands. This result could be a reference for optimizing the hardware design of HSL for ore classification or mineral identification.

Maturity estimation of mangoes using hyperspectral imaging from a ground based mobile platform

Monitoring the maturity of fruit in commercial orchards can help growers optimise the time of harvest. Dry matter content (DM) of fruit is used as an indicator of mango maturity, measured in-field with a hand-held spectrometer. This approach is labour intensive, limiting the extent to which DM variability can be measured across an orchard block, which would enable selective harvesting. This paper proposes an alternative approach that utilises a hyperspectral camera, LIDAR sensor and navigation system mounted to a ground vehicle to predict fruit DM individually for hundreds of trees in a mango orchard block. First, the challenges faced due to tree geometry and shadows in mango orchards are addressed. Then the ability to predict DM at a distance using hyperspectral imaging (411.3–867.0 nm) was demonstrated. Two regression methods, partial least squares (PLS) and a convolutional neural network (CNN) were compared and tested against DM results from a hand-held NIR spectrometer using harvested (n=468,σ=2.32%w/w) and on-tree fruit (n=662,σ=1.79%w/w). The CNN achieved a cross validation RCV2=0.64 and RMSECV=1.08%w/w in fruit on tree, while PLS achieved RCV2=0.58 and RMSECV=1.17%w/w. In order to discriminate mango and non-mango pixels, PLS discriminant analysis (PLS-DA) and a CNN were also compared, where both methods achieved good classification performance with a mean F1>0.97. Having established mango classification and DM prediction performance, hyperspectral data were processed for a full orchard block and projected to world coordinates using AGV position and orientation as provided by the navigation system. Trees were segmented using corresponding LIDAR data, which allowed association of projected DM predictions to individual trees. Repeated scans of the orchard block over two days allowed a measure of repeatability, which was achieved with an RMSE<0.29%w/w. The results provide strong evidence that predicting maturity at a distance for all trees in an orchard is feasible using a hyperspectral camera, which will be an important management tool for growers to optimise harvest timing and yield.

Integrating airborne hyperspectral imagery and LiDAR for volcano mapping and monitoring through image classification

Optical and laser remote sensing provide resources for monitoring volcanic activity and surface hydrothermal alteration. In particular, multispectral and hyperspectral imaging can be used for detecting lithologies and mineral alterations on the surface of actively degassing volcanoes. This paper proposes a novel workflow to integrate existing optical and laser remote sensing data for geological mapping after the 2012 Te Maari eruptions (Tongariro Volcanic Complex, New Zealand). The image classification is based on layer-stacking of image features (optical and textural) generated from high-resolution airborne hyperspectral imagery, Light Detection and Ranging data (LiDAR) derived terrain models, and aerial photography. The images were classified using a Random Forest algorithm where input images were added from multiple sensors. Maximum image classification accuracy (overall accuracy = 85%) was achieved by adding textural information (e.g. mean, homogeneity and entropy) to the hyperspectral and LiDAR data. This workflow returned a total surface alteration area of ∼0.4 km2 at Te Maari, which was confirmed by field work, lab-spectroscopy and backscatter electron imaging. Hydrothermal alteration on volcanoes forms precipitation crusts on the surface that can mislead image classification. Therefore, we also applied spectral matching algorithms to discriminate between fresh, crust altered, and completely altered volcanic rocks. This workflow confidently recognized areas with only surface alteration, establishing a new tool for mapping structurally controlled hydrothermal alteration, evolving debris flow and hydrothermal eruption hazards. We show that data fusion of remotely sensed data can be automated to map volcanoes and significantly benefit the understanding of volcanic processes and their hazards.

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

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.

Application of spectral indices and reflectance spectrum on leaf nitrogen content analysis derived from hyperspectral LiDAR data

Hyperspectral LiDAR (HSL) can be applied in several fields, especially in nitrogen analysis because of its ability of obtaining more abundant spectral information. Besides, spectral indices (SIs) related to leaf nitrogen content (LNC) of vegetation have also been applied in hyperspectral remote sensing widely. Thus, this paper is mainly to use SIs derived from HSL data to analyze rice LNC. The partial least squares regression (PLSR) is utilized to link the SIs to rice LNC. By combining several more wavelengths than other SIs, two integral indices derived from HSL data, including the normalized area over reflectance curve (NAOC) and reflectance integral index (RII), have better performance in terms of LNC retrieval. Meanwhile, HSL-based reflectance spectra in more wavelengths than SIs have been processed by a method of band selection, and then been used to validate the detection efficiency and flexibility of HSL system. By selecting bands, the LNC retrieval have been optimized and improved significantly (R2 > 0.7) than using SIs. It can be seen that HSL will be a great potential and flexible technology in remote sensing because of owning more spectral information about the objects.

Estimating leaf chlorophyll status using hyperspectral lidar measurements by PROSPECT model inversion

Chlorophyll (Chl) is an important indicator of photosynthetic capacity and stress of vegetation. Remote sensing provides fast and nondestructive methods for estimating leaf Chl content based on its optical characteristics in visible and near-infrared spectrum. Multispectral lidar (MSL) systems have been developed to combine spectral and spatial detection abilities. Statistical relationships of plant biochemical constituents can be established through MSL measurements. However, empirical models cannot be readily extended to independent datasets. Simultaneously, the few spectral bands of MSL limit the use of a physical model. Hence, the development of hyperspectral lidar (HSL) systems offers a wider range of spectrum. This study investigated the possibility of adopting an HSL system with 32 channels covering 539–910 nm to estimate foliar Chl through a physical model. This study aimed to (1) Determine whether reflectance at the 32 channels is sufficient to retrieve Chl content through PROSPECT model inversion and (2) Considering the difference between passively and actively measured reflectance, investigate whether HSL measurements can be applied into PROSPECT model inversion for leaf biochemical constituents. Three kinds of datasets were used: a synthetic dataset simulated by running the PROSPECT model in forward mode, a public dataset ANGERS taking the channels of the HSL system, and an experimental dataset of paddy rice measured by the HSL system. Results showed HSL measurements can be directly used to retrieve leaf Chl content through PROSPECT-4 model inversion (R2 = 0.55). These measurements also exhibit higher accuracy than that of support vector regression (threefold cross validation; 100 repetitions: median R2 = 0.47). This validation work provides basis in the determination of vegetation physiological status directly from HSL measurements through model inversion with the PROSPECT model.

Impact of Vertical Canopy Position on Leaf Spectral Properties and Traits across Multiple Species

Understanding the vertical pattern of leaf traits across plant canopies provide critical information on plant physiology, ecosystem functioning and structure and vegetation response to climate change. However, the impact of vertical canopy position on leaf spectral properties and subsequently leaf traits across the entire spectrum for multiple species is poorly understood. In this study, we examined the ability of leaf optical properties to track variability in leaf traits across the vertical canopy profile using Partial Least Square Discriminatory Analysis (PLS-DA). Leaf spectral measurements together with leaf traits (nitrogen, carbon, chlorophyll, equivalent water thickness and specific leaf area) were studied at three vertical canopy positions along the plant stem: lower, middle and upper. We observed that foliar nitrogen (N), chlorophyll (Cab), carbon (C), and equivalent water thickness (EWT) were higher in the upper canopy leaves compared with lower shaded leaves, while specific leaf area (SLA) increased from upper to lower canopy leaves. We found that leaf spectral reflectance significantly (P ≤ 0.05) shifted to longer wavelengths in the ‘red edge’ spectrum (685–701 nm) in the order of lower &gt; middle &gt; upper for the pooled dataset. We report that spectral bands that are influential in the discrimination of leaf samples into the three groups of canopy position, based on the PLS-DA variable importance projection (VIP) score, match with wavelength regions of foliar traits observed to vary across the canopy vertical profile. This observation demonstrated that both leaf traits and leaf reflectance co-vary across the vertical canopy profile in multiple species. We conclude that canopy vertical position has a significant impact on leaf spectral properties of an individual plant’s traits, and this finding holds for multiple species. These findings have important implications on field sampling protocols, upscaling leaf traits to canopy level, canopy reflectance modelling, and subsequent leaf trait retrieval, especially for studies that aimed to integrate hyperspectral measurements and LiDAR data.

Deep Learning for Fusion of APEX Hyperspectral and Full-Waveform LiDAR Remote Sensing Data for Tree Species Mapping

Deep learning has been widely used to fuse multi-sensor data for classification. However, current deep learning architecture for multi-sensor data fusion might not always perform better than single data source, especially for the fusion of hyperspectral and light detection and ranging (LiDAR) remote sensing data for tree species mapping in complex, closed forest canopies. In this paper, we propose a new deep fusion framework to integrate the complementary information from hyperspectral and LiDAR data for tree species mapping. We also investigate the fusion of either “single-band” or multi-band (i.e., full-waveform) LiDAR with hyperspectral data for tree species mapping. Additionally, we provide a solution to estimate the crown size of tree species by the fusion of multi-sensor data. Experimental results on fusing real APEX hyperspectral and LiDAR data demonstrate the effectiveness of the proposed deep fusion framework. Compared to using only single data source or current deep fusion architecture, our proposed method yields improvements in overall and average classification accuracies ranging from 82.21% to 87.10% and 76.71% to 83.45%, respectively.

Inelastic hyperspectral lidar for aquatic ecosystems monitoring and landscape plant scanning test

We have developed an aquatic inelastic hyperspectral lidar with unrestricted focal-depth and enough sensitivity and spatio-temporal resolution to detect and resolve position and behavior of individual sub-millimeter aquatic organisms. We demonstrate ranging with monitoring of elastic echoes, water Raman signals and fluorescence from chlorophyllbearing phytoplankton and dye tagged organisms. The system is based on a blue CW diode laser and a Scheimpflug optical arrangement.

Estimating Rice Leaf Nitrogen Concentration: Influence of Regression Algorithms Based on Passive and Active Leaf Reflectance

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.

Mapping Spartina alterniflora Biomass Using LiDAR and Hyperspectral Data

Large-scale coastal reclamation has caused significant changes in Spartina alterniflora (S. alterniflora) distribution in coastal regions of China. However, few studies have focused on estimation of the wetland vegetation biomass, especially of S. alterniflora, in coastal regions using LiDAR and hyperspectral data. In this study, the applicability of LiDAR and hypersectral data for estimating S. alterniflora biomass and mapping its distribution in coastal regions of China was explored to attempt problems of wetland vegetation biomass estimation caused by different vegetation types and different canopy height. Results showed that the highest correlation coefficient with S. alterniflora biomass was vegetation canopy height (0.817), followed by Normalized Difference Vegetation Index (NDVI) (0.635), Atmospherically Resistant Vegetation Index (ARVI) (0.631), Visible Atmospherically Resistant Index (VARI) (0.599), and Ratio Vegetation Index (RVI) (0.520). A multivariate linear estimation model of S. alterniflora biomass using a variable backward elimination method was developed with R squared coefficient of 0.902 and the residual predictive deviation (RPD) of 2.62. The model accuracy of S. alterniflora biomass was higher than that of wetland vegetation for mixed vegetation types because it improved the estimation accuracy caused by differences in spectral features and canopy heights of different kinds of wetland vegetation. The result indicated that estimated S. alterniflora biomass was in agreement with the field survey result. Owing to its basis in the fusion of LiDAR data and hyperspectral data, the proposed method provides an advantage for S. alterniflora mapping. The integration of high spatial resolution hyperspectral imagery and LiDAR data derived canopy height had significantly improved the accuracy of mapping S. alterniflora biomass.