Prototype development and evaluation of a hyperspectral lidar optical receiving system.

As a new type of active Earth observation technology, airborne hyperspectral lidar combines the advantages of traditional lidar 3D information acquisition and passive hyperspectral imaging technology, and it can achieve integrated imaging detection with a high spatial and hyperspectral resolution. Thus, it has become an important future direction of Earth surface remote sensing technology. This article introduces the design and development of an airborne hyperspectral imaging lidar system. The hyperspectral lidar adopts a focal plane splitting method, combined with an array of 168 optical fibers, to couple wide-spectral-range laser echo signals one by one to the corresponding single tube detector, achieving efficient splitting and precise coupling of supercontinuum laser pulse echo signals. This article proposes a fast synchronous calibration method that is suitable for hyperspectral imaging lidar systems. Results show that the spectral range of the hyperspectral lidar system is 400-900 nm, and the spectral resolution of single-fiber detection is greater than 3 nm. Notably, this article focuses on analyzing the abnormal detection channels based on the calibration results. With the test results of adjacent channels combined, the reason for the abnormal spectral bandwidth of channel 17 is analyzed as an example. This research points out the direction for verifying the design parameters of the hyperspectral lidar prototype and lays an important foundation for airborne flight test of the hyperspectral lidar.

Infrared detector module for airborne hyperspectral LiDAR: design and demonstration.

Realizing the integrated acquisition and identification of the elevation information and spectral information of the observation target is at the frontier and a future trend of Earth observation technology. This study designs and develops a set of airborne hyperspectral imaging lidar optical receiving systems and investigates the detection of the infrared band echo signal of the lidar system. A set of avalanche photodiode (APD) detectors is independently designed to detect the weak echo signal of  800-900 nm band. The actual radius of the photosensitive surface of the APD detector is 0.25 mm. We design and demonstrate the optical focusing system of the APD detector in the laboratory and obtain that the image plane size of the optical fiber end faces of the APD detector from channel 47 to channel 56 is close to 0.3 mm. Results show that the optical focusing system of the self-designed APD detector is reliable. On the basis of the focal plane splitting technology of the fiber array, we couple the echo signal of  800-900 nm band to the corresponding APD detector through the fiber array and conduct a series of test experiments for the APD detector. Field test results of the ground-based platform show that the APD detectors in all channels can complete the remote sensing measurement of 500 m. The development of this APD detector solves the problem of hyperspectral imaging under weak light signals and realizes the accurate detection of ground targets in the infrared band by airborne hyperspectral imaging lidar.

Multispectral LiDAR point cloud highlight removal based on color information.

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.

Optical system design for a hyperspectral imaging lidar using supercontinuum laser and its preliminary performance.

To meet the urgent need for surveying and mapping using remote sensing instruments, a hyperspectral imaging lidar using a supercontinuum laser is proposed. This novel lidar system can solve the problem of the mismatching of the traditional lidar retrieved elevation data and hyperspectral data obtained by passive imaging instruments. The optical design of the lidar receiving system is described, developed, and tested in this study. An off-axis parabolic mirror is used as the receiving telescope of the system, and a transmissive grating is used to split the received hyperspectral light to each detection channel. A fiber array equipped with a micro-lens is used to guide the split light to the detectors. In practice, several fibers can be coupled to one detector according to the wavelength sensitivity of different objects. A reference laser is used to monitor the possible energy jitter of each transmitted laser pulse in real time. A spectrum calibration of the receiving system is accomplished in the laboratory, and radiation calibration is applied by receiving the backscattered light reflected by a standard white board. The spectral resolution of a single fiber is approximately 3 nm. An outdoor 500-m distance experiment was carried out for green and yellow leaves in day and evening settings. During the experiment, the wavelength of the laser was 460-900 nm. The reflection spectra collected by the lidar system in day and evening were consistent, indicating that the design of the optical receiving system is reliable and can be used for airborne hyperspectral imaging lidar.

The characterization of plant species using first-derivative fluorescence spectra.

Plants are one of the most important parts of the ecological system and demand a reliable method for accurate classification. In this study, the first-derivative fluorescence spectral curves (FDFSCs) based on laser-induced fluorescence technology were proposed for the characterization of plant species. The measurement system is mainly composed of a spectrometer, an excitation light source (the two excitation wavelengths are 460 and 556 nm, respectively), and an intensified charge-coupled device camera. FDFSCs were calculated from the deviation between the fluorescence values at each wavelength, plus and minus one band, divided by the wavelength range. Principal component analysis was utilized to analyze the FDFSCs by extracting the main attributes and reducing the dimensionality of variables. A support vector machine was used to evaluate FDFSC performance for the identification of plant species. Plant species that are difficult to distinguished by the naked eye, can be identified effectively using the proposed FDFSCs. For the 556 nm and 460 nm excitation wavelengths, the overall identification rates of the six plant species evaluated were 93.3% and 91.7%, respectively. Experimental results demonstrated that the combination of the FDFSCs with multivariate analysis could provide a simple and reliable method for the characterization of plant species.

The application of time decay characteristics of laser-induced fluorescence in the classification of vegetation.

In this study, the time decay of the chlorophyll fluorescence intensity (TDCFI) of vegetation was measured based on laser-induced fluorescence (LIF) technology with a 355 nm laser serving as the excitation light source. The pseudo-color diagram of the TDCFI (PDTDCFIs) was proposed for use as a characteristic fingerprint for the analysis of various plant species based on variations in the fluorescence intensity over time. Compared with the steady-state fluorescence spectra, two-dimensional PDTDCFIs contained more spectral information, including variations in both the shape of the laser-induced fluorescence spectra and the relative intensity. The experimental results demonstrated that the PDTDCFIs of various plant species show distinct differences, and this was successfully applied in the classification of plant species. Therefore, the PDTDCFIs of plants could provide researchers with a more reliable and useful tool for the characterization of vegetation. Copyright © 2016 John Wiley & Sons, Ltd.

Estimating the leaf nitrogen content of paddy rice by using the combined reflectance and laser-induced fluorescence spectra.

Paddy rice is one of the most important crops in China, and leaf nitrogen content (LNC) serves as a significant indictor for monitoring crop status. A reliable method is needed for precise and fast quantification of LNC. Laser-induced fluorescence (LIF) technology and reflectance spectra of crops are widely used to monitor leaf biochemical content. However, comparison between the fluorescence and reflectance spectra has been rarely investigated in the monitoring of LNC. In this study, the performance of the fluorescence and reflectance spectra for LNC estimation was discussed based on principal component analysis (PCA) and back-propagation neural network (BPNN). The combination of fluorescence and reflectance spectra was also proposed to monitor paddy rice LNC. The fluorescence and reflectance spectra exhibited a high degree of multi-collinearity. About 95.38%, and 97.76% of the total variance included in the spectra were efficiently extracted by using the first three PCs in PCA. The BPNN was implemented for LNC prediction based on new variables calculated using PCA. The experimental results demonstrated that the fluorescence spectra (R2 = 0.810, 0.804 for 2014 and 2015, respectively) are superior to the reflectance spectra (R2 = 0.721, 0.671 for 2014 and 2015, respectively) for estimating LNC based on the PCA-BPNN model. The proposed combination of fluorescence and reflectance spectra can greatly improve the accuracy of LNC estimation (R2 = 0.912, 0.890 for 2014 and 2015, respectively).

The Effect of Chlorophyll Concentration of Paddy Rice on the Fluorescence Spectrum.

In order to enhance the monitoring of paddy growth, utilize the fertilizer more efficiently, increase crop yield and improve the quality of grain, thus the system of laser-induced fluorescence (LIF) was built. The system was designed to study the relationship between the rice leaf chlorophyll content and fluorescence ratio. In this paper, the samples came from the second upper leaves of paddy in shooting stage and the cultivated area was located in Jianghan plain of China. Firstly, the Kjeldahl method combined with the formula which was described by Zivcak et al. was utilized to calculate the chlorophyll content of paddy, then the fluorescence spectrum of paddy leaf with different chlorophyll content by the instrument of laser-induced fluorescence (the wavelength of excitation 355 nm). Fluorescence spectra of paddy leaf with different chlorophyll content were collected and then a fluorescence spectra database was established. It is discussed that the relationship between the ratio of fluorescence (F740/F685 is the ratio of fluorescence intensity of 740 nm dividing that by 685 nm) and the chlorophyll content of paddy. It is found that the effect of chlorophyll content on the fluorescence spectral characteristics is evident. The results demonstrated that it has the tightly positive correlation between the fluorescence ratio (F740/F685) and chlorophyll content of paddy leaf. The determination coefficient (R2) can reach up to 0.901 3 and 0.912 5 at tillering stage and shooting stage, respectively. The experimental analysis showed that the LIF technology has the advantages of convenient, quick and nondestructive, and it has the potential for quantitative monitoring of crop growth.

Investigating the Potential of Using the Spatial and Spectral Information of Multispectral LiDAR for Object Classification.

The abilities of multispectral LiDAR (MSL) as a new high-potential active instrument for remote sensing have not been fully revealed. This study demonstrates the potential of using the spectral and spatial features derived from a novel MSL to discriminate surface objects. Data acquired with the MSL include distance information and the intensities of four wavelengths at 556, 670, 700, and 780 nm channels. A support vector machine was used to classify diverse objects in the experimental scene into seven types: wall, ceramic pots, Cactaceae, carton, plastic foam block, and healthy and dead leaves of E. aureum. Different features were used during classification to compare the performance of different detection systems. The spectral backscattered reflectance of one wavelength and distance represented the features from an equivalent single-wavelength LiDAR system; reflectance of the four wavelengths represented the features from an equivalent multispectral image with four bands. Results showed that the overall accuracy of using MSL data was as high as 88.7%, this value was 9.8%-39.2% higher than those obtained using a single-wavelength LiDAR, and 4.2% higher than for multispectral image.

Evaluation of hyperspectral LiDAR for monitoring rice leaf nitrogen by comparison with multispectral LiDAR and passive spectrometer.

Fast and nondestructive assessment of leaf nitrogen concentration (LNC) is critical for crop growth diagnosis and nitrogen management guidance. In the last decade, multispectral LiDAR (MSL) systems have promoted developments in the earth and ecological sciences with the additional spectral information. With more wavelengths than MSL, the hyperspectral LiDAR (HSL) system provides greater possibilities for remote sensing crop physiological conditions. This study compared the performance of ASD FieldSpec Pro FR, MSL, and HSL for estimating rice (Oryza sativa) LNC. Spectral reflectance and biochemical composition were determined in rice leaves of different cultivars (Yongyou 4949 and Yangliangyou 6) throughout two growing seasons (2014-2015). Results demonstrated that HSL provided the best indicator for predicting rice LNC, yielding a coefficient of determination (R2) of 0.74 and a root mean square error of 2.80 mg/g with a support vector machine, similar to the performance of ASD (R2 = 0.73). Estimation of rice LNC could be significantly improved with the finer spectral resolution of HSL compared with MSL (R2 = 0.56).

Analyzing the performance of fluorescence parameters in the monitoring of leaf nitrogen content of paddy rice.

Leaf nitrogen content (LNC) is a significant factor which can be utilized to monitor the status of paddy rice and it requires a reliable approach for fast and precise quantification. This investigation aims to quantitatively analyze the correlation between fluorescence parameters and LNC based on laser-induced fluorescence (LIF) technology. The fluorescence parameters exhibited a consistent positive linear correlation with LNC in different growing years (2014 and 2015) and different rice cultivars. The R(2) of the models varied from 0.6978 to 0.9045. Support vector machine (SVM) was then utilized to verify the feasibility of the fluorescence parameters for monitoring LNC. Comparison of the fluorescence parameters indicated that F740 is the most sensitive (the R(2) of linear regression analysis of the between predicted and measured values changed from 0.8475 to 0.9226, and REs ranged from 3.52% to 4.83%) to the changes in LNC among all fluorescence parameters. Experimental results demonstrated that fluorescence parameters based on LIF technology combined with SVM is a potential method for realizing real-time, non-destructive monitoring of paddy rice LNC, which can provide guidance for the decision-making of farmers in their N fertilization strategies.