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The current status as well as the potential absorption capability of the forest carbon sinks in terrestrial ecosystems are in urgent need of further studies. The ground observation-based methods are labor-intensive, and the resulting statistics from samples are difficult to evaluate, while inversion methods based on remote sensing data lack theoretical explanation and universality. This paper proposes a pixel-level, multi-scale, high-precision Explicit Forest carbon stock Model (EFM) that is universal and adaptive. First, four key variables were used in the construction of the EFM: remote sensing image resolution, forest canopy density, terrain slope, and forest height; Second, simulated forest scene were generated based on the growth characteristics of individual trees, and EFM parameters were solved and analyzed by these simulated pixels; Third, the EFM was tested at various scales and forest saturation levels to verify its accuracy, robustness, and applicability, the simulation and real-life experiments show that the correlation coefficient is greater than 0.98 and the relative error is about 20%. The EFM solves the problem that the existing methods are lack in theoretical interpretation and universal applicability, thus can be used to map forest carbon stocks at high resolution and large scale and even monitor forest carbon dynamics at global scale.

Full-waveform light detection and ranging (LiDAR) has become a well-established technique for remote sensing of surface topography and target characterization, offering numerous opportunities in investigation and interpretation of the structural diversity of surface coverage. However, most prevailing waveform decomposition methods employ the Gaussian function or some other probability distributions to model LiDAR waveforms of specific shapes, which cannot be universally used. Moreover, most of these waveform decomposition methods operate at a single laser wavelength and cannot be applied well to multispectral LiDAR (MSL) or hyperspectral LiDAR (HSL), which can simultaneously collect the spectral and geometric attributes with multiple transmitting laser wavelengths. In this paper, we propose a new multispectral B-spline waveform decomposition model to achieve high precision multi-target ranging. Considering both the spatial consistency of each channel and the irregular shape of the received waveform, LiDAR waveforms are modelled by B-splines rather than any other specific probability distribution. Thus, the proposed method can be extended to other FWMSL data benefiting from the flexibility of B-splines on fitting arbitrary curves. Both simulated MSL echoes and a measured dataset from a FWMSL system with three wavelengths of 556, 670, and 780 nm were used in this study. Compared with two single wavelength waveform decomposition models and a multispectral waveform decomposition model based on Gaussian function, the proposed method has excellent robustness for processing different shapes of waveforms and can improve ranging accuracy significantly for irregular waveforms.

Temporal and spatial changes of aerosols characteristics was investigated to improve understandings of haze in winters of Wuhan, based on measurements of a CIMEL sun-sky radiometer and a modified Dark Target (DT) method. A large increase in no-absorbing and fine-mode particle is the main characteristic of aerosol during winter haze periods. From no-haze to haze conditions, aerosol optical depth (AOD), fine-mode peak value of size distribution, and single scattering albedo increased respectively from 0.46, 0.47 µm3/µm2, and 0.84 to 0.93, 0.91 µm3/µm2, and 0.87. Based on above aerosol optical and microphysical properties, we re-calculated aerosol models and improved the DT method. Results showed that our improvement increased the expect error (%) from 17.9 to 70.9. Spatial distributions revealed that heavy aerosol loading often occurred in rural and suburban areas rather than city centre. With the haze developing, the AOD increased by ∼0.3 over north of Wuhan and showed a downward trend from north to south.

Precise point cloud classification can enhance lidar performance in various applications, such as land cover mapping, forestry management and autonomous driving. The development of multispectral lidar improves classification performance with rich spectral information. However, the employment of spectral information for classification is still underdeveloped. Therefore, we proposed a spectrally improved classification method for multispectral LiDAR. We conducted spectral improvement in two aspects: (1) we improved the eigenentropy-based neighbourhood selection by spectral angle match (SAM) to reform the more reliable neighbour; (2) we utilized both geometric and spectral features and compare the contributions of these features. A three-wavelength multispectral lidar and a complex indoor experimental scene were used for demonstration. The results indicate the effectiveness of our proposed spectrally improved method and the promising potential of spectral information on lidar classification.

The automated building detection in aerial images is a fundamental problem encountered in aerial and satellite images analysis. Recently, thanks to the advances in feature descriptions, Region-based CNN model (R-CNN) for object detection is receiving an increasing attention. Despite the excellent performance in object detection, it is problematic to directly leverage the features of R-CNN model for building detection in single aerial image. As we know, the single aerial image is in vertical view and the buildings possess significant directional feature. However, in R-CNN model, direction of the building is ignored and the detection results are represented by horizontal rectangles. For this reason, the detection results with horizontal rectangle cannot describe the building precisely. To address this problem, in this paper, we proposed a novel model with a key feature related to orientation, namely, Oriented R-CNN (OR-CNN). Our contributions are mainly in the following two aspects: 1) Introducing a new oriented layer network for detecting the rotation angle of building on the basis of the successful VGG-net R-CNN model; 2) the oriented rectangle is proposed to leverage the powerful R-CNN for remote-sensing building detection. In experiments, we establish a complete and bran-new data set for training our oriented R-CNN model and comprehensively evaluate the proposed method on a publicly available building detection data set. We demonstrate State-of-the-art results compared with the previous baseline methods.

Ground-based lidar, working as an effective remote sensing tool, plays an irreplaceable role in the study of atmosphere, since it has the ability to provide the atmospheric vertical profile. However, the appearance of noise in a lidar signal is unavoidable, which leads to difficulties and complexities when searching for more information. Every de-noising method has its own characteristic but with a certain limitation, since the lidar signal will vary with the atmosphere changes. In this paper, a universal de-noising algorithm is proposed to enhance the SNR of a ground-based lidar signal, which is based on signal segmentation and reconstruction. The signal segmentation serving as the keystone of the algorithm, segments the lidar signal into three different parts, which are processed by different de-noising method according to their own characteristics. The signal reconstruction is a relatively simple procedure that is to splice the signal sections end to end. Finally, a series of simulation signal tests and real dual field-of-view lidar signal shows the feasibility of the universal de-noising algorithm.

Hyperspectral LiDAR (HSL) is a novel tool in the field of active remote sensing, which has been widely used in many domains because of its advantageous ability of spectrum-gained. Especially in the precise monitoring of nitrogen in green plants, the HSL plays a dispensable role. The exiting HSL system used for nitrogen status monitoring has a multi-channel detector, which can improve the spectral resolution and receiving range, but maybe result in data redundancy, difficulty in system integration and high cost as well. Thus, it is necessary and urgent to pick out the nitrogen-sensitive feature wavelengths among the spectral range. The present study, aiming at solving this problem, assigns a feature weighting to each centre wavelength of HSL system by using matrix coefficient analysis and divergence threshold. The feature weighting is a criterion to amend the centre wavelength of the detector to accommodate different purpose, especially the estimation of leaf nitrogen content (LNC) in rice. By this way, the wavelengths high-correlated to the LNC can be ranked in a descending order, which are used to estimate rice LNC sequentially. In this paper, a HSL system which works based on a wide spectrum emission and a 32-channel detector is conducted to collect the reflectance spectra of rice leaf. These spectra collected by HSL cover a range of 538 nm – 910 nm with a resolution of 12 nm. These 32 wavelengths are strong absorbed by chlorophyll in green plant among this range. The relationship between the rice LNC and reflectance-based spectra is modeled using partial least squares (PLS) and support vector machines (SVMs) based on calibration and validation datasets respectively. The results indicate that I) wavelength selection method of HSL based on feature weighting is effective to choose the nitrogen-sensitive wavelengths, which can also be co-adapted with the hardware of HSL system friendly. II) The chosen wavelength has a high correlation with rice LNC which can be retrieved by using PLS and SVMs regression methods.

Precision agriculture has always been the research hotspot around the world. And the optimization of nitrogen fertilization for crops is the core concerns. It is not only to improve the productivity of crops but also to avoid the environmental risks caused by over-fertilization. Therefore, accurate estimation of nitrogen status is crucial for determining an nitrogen recommendation. Remote sensing techniques have been widely used to monitor crops for years, and they could offer estimations for stress status diagnosis through obtaining vertical structure parameters and spectral reflectance properties of crops. As an active remote sensing technology, lidar is particularly attractive for 3-dimensional information at a high point density. It has unique edges in obtaining vertical structure parameters of crops. However, capability of spectral reflectance properties is what the current lidar technology lacks because of single wavelength detection. To solve this problem, the concept of novel hyperspectral lidar (HSL), which combines the advantages of hyperspectal reflectance with high 3-dimensional capability of lidar, was proposed in our study. The design of instrument was described in detail. A broadband laser pulse was emitted and reflectance spectrum with 32 channels could be detected. Furthermore, the experiment was carried out by the novel HSL system to testify the potential application for monitoring nitrogen stress. Rice under different levels of nitrogen fertilization in central China were selected as the object of study, and four levels of nitrogen fertilization (N1-N4) were divided. With the detection of novel lidar system, high precision structure parameters of crops could be provided. Meanwhile, spectral reflectance properties in 32 wavebands were also obtained. The high precision structure parameters could be used to evaluate the stress status of crops. And abundant spectral information in 32 wavebands could improve the capacity of lidar system significantly. The results demonstrate that it is more effective for HSL system to distinguish different levels of nitrogen fertilization. Overall, HSL allows for probing not only high precision structure parameters but also spectral reflectance properties of crops. Compared with other approaches, the novel HSL has the potential to provide more comprehensive information of crops which can be assessed remotely in the application of precision agriculture.

Differential absorption lidar (DIAL) remote sensing is a promising technology for atmospheric CO2 detection. However, stringent wavelength accuracy and stability are required in DIAL system. Accurate on-line wavelength calibration is a crucial procedure for retrieving atmospheric CO2 concentration using the DIAL, particularly when pulsed lasers are adopted in the system. Large fluctuations in the intensities of a pulsed laser pose a great challenge for accurate on-line wavelength calibration. In this paper, a wavelength calibration strategy based on multi-wavelength scanning (MWS) was proposed for accurate on-line wavelength calibration of a pulsed laser for CO2 detection. The MWS conducted segmented sampling across the CO2 absorption line with appropriate number of points and range of widths by using a tunable laser. Complete absorption line of CO2 can be obtained through a curve fitting. Then, the on-line wavelength can be easily found at the peak of the absorption line. Furthermore, another algorithm called the energy matching was introduced in the MWS to eliminate the backlash error of tunable lasers during the process of on-line wavelength calibration. Finally, a series of tests was conducted to elevate the calibration precision of MWS. Analysis of tests demonstrated that the MWS proposed in this paper could calibrate the on-line wavelength of pulsed laser accurately and steadily.

Laser-induced fluorescence (LIF) served as an active technology has been widely used in many field, and it is closely related to excitation wavelength (EW). The objective of this investigation is to discuss the performance of different EWs of LIF LiDAR in identifying plant species. In this study, the 355, 460 and 556 nm lasers were utilized to excite the leaf fluorescence and the fluorescence spectra were measured by using the LIF LiDAR system built in the laboratory. Subsequently, the principal component analysis (PCA) with the help of support vector machine (SVM) was utilized to analyse fluorescence spectra. For the three EWs, the overall identification rates of the six plant species were 80 %, 83.3 % and 90 %. Experimental results demonstrated that 556 nm excitation light source is superior to 355 and 460 nm for the classification of the plant species for the same genus in this study. Thus, an appropriate excitation wavelength should be considered when the LIF LiDAR was utilized in the field of remote sensing based on the LIF technology.

The accurate estimation of deposits adhering on insulators is critical to prevent pollution flashovers which cause huge costs worldwide. The traditional evaluation method of insulator contaminations (IC) is based sparse manual in-situ measurements, resulting in insufficient spatial representativeness and poor timeliness. Filling that gap, we proposed a novel evaluation framework of IC based on remote sensing and data mining. Varieties of products derived from satellite data, such as aerosol optical depth (AOD), digital elevation model (DEM), land use and land cover and normalized difference vegetation index were obtained to estimate the severity of IC along with the necessary field investigation inventory (pollution sources, ambient atmosphere and meteorological data). Rough set theory was utilized to minimize input sets under the prerequisite that the resultant set is equivalent to the full sets in terms of the decision ability to distinguish severity levels of IC. We found that AOD, the strength of pollution source and the precipitation are the top 3 decisive factors to estimate insulator contaminations. On that basis, different classification algorithm such as mahalanobis minimum distance, support vector machine (SVM) and maximum likelihood method were utilized to estimate severity levels of IC. 10-fold cross-validation was carried out to evaluate the performances of different methods. SVM yielded the best overall accuracy among three algorithms. An overall accuracy of more than 70% was witnessed, suggesting a promising application of remote sensing in power maintenance. To our knowledge, this is the first trial to introduce remote sensing and relevant data analysis technique into the estimation of electrical insulator contaminations.

Remote Sensing plays a critical role in operational volcano monitoring due to the often remote locations of volcanic systems and the large spatial extent of potential eruption pre-cursor signals. Despite the all-weather capabilities of radar remote sensing and despite its high performance in monitoring change, the contribution of radar data to operational monitoring activities has been limited in the past. This is largely due to (1) the high data costs associated with radar data, (2) the slow data processing and delivery procedures, and (3) the limited temporal sampling provided by spaceborne radars. With this paper, we present new data processing and data integration techniques that mitigate some of the above mentioned limitations and allow for a meaningful integration of radar remote sensing data into operational volcano monitoring systems. The data integration concept presented here combines advanced data processing techniques with fast data access procedures in order to provide high quality radar-based volcano hazard information at improved temporal sampling rates. First performance analyses show that the integration of SAR can significantly improve the ability of operational systems to detect eruptive precursors. Therefore, the developed technology is expected to improve operational hazard detection, alerting, and management capabilities.

Nowadays lidar has become a leading edge technology for photogrammetry and remote sensing. A new lidar with two detection wavelengths was presented in this paper. The two-wavelength lidar can obtain spatial information and spectral information, and intensity of backscatter signals in two-wavelength can be availably used for objects identification and observation. However, the backscatter signals are weak and strongly influenced by the noise. In order to acquire accurate intensity, the signal noise has to be removed. As a useful tool for signal processing, wavelet transform was chosen. In this study, the principle of two-wavelength lidar was introduced and possible noise in the backscatter signal was analysed. Then the signal progressing was executed on the basic of wavelet de-noising. First, singular values caused by varieties of noise source have to be rejected from the signal based on three standard deviations. Second, combination of modulus maxima method and threshold method is used to make further improvement on the signal. Finally, an evaluation of the noise reduction was given to testify the ability of the method. The results show that the effect of the method based on wavelet transform is better than other classical algorithms, and the method is suitable for signal noise reduction in two-wavelength lidar system.

As an active remote sensing instrument, lidar provides a high spatial resolution vertical profile of aerosol optical properties. But the effective range and data reliability are often limited by various noises. Performing a proper denoising method will improve the quality of the signals obtained. The denoising method based on ensemble empirical mode decomposition (EEMD) is introduced, but the denoised results are difficult to evaluated. A dual field-of-view lidar for observing atmospheric aerosols is described. The backscattering signals obtained from two channels have different signal-to-noise ratios (SNR). To overcome the drawback of the simulation experiment, the performance of noise reduction can be investigated by comparing the high SNR signal and the denoised low SNR signal. With this approach, some parameters of the denoising method based on EEMD can be determined effectively. The experimental results show that the EEMD-based method with proper parameters can effectively increase the atmospheric lidar observing ability.