Individual tree segmentation of airborne and UAV LiDAR point clouds based on the watershed and optimized connection center evolution clustering.

Light detection and ranging (LiDAR) data can provide 3D structural information of objects and are ideal for extracting individual tree parameters, and individual tree segmentation (ITS) is a vital step for this purpose. Various ITS methods have been emerging from airborne LiDAR scanning (ALS) or unmanned aerial vehicle LiDAR scanning (ULS) data. Here, we propose a new individual tree segmentation method, which couples the classical and efficient watershed algorithm (WS) and the newly developed connection center evolution (CCE) clustering algorithm in pattern recognition. The CCE is first used in ITS and comprehensively optimized by considering tree structure and point cloud characteristics. Firstly, the amount of data is greatly reduced by mean shift voxelization. Then, the optimal clustering scale is automatically determined by the shapes in the projection of three different directions. We select five forest plots in Saihanba, China and 14 public plots in Alpine region, Europe with ULS or ALS point cloud densities from 11 to 3295 pts/m2. Eleven ITS methods were used for comparison. The accuracy of tree top detection and tree height extraction is estimated by five and two metrics, respectively. The results show that the matching rate (R match) of tree tops is up to 0.92, the coefficient of determination (R 2) of tree height estimation is up to .94, and the minimum root mean square error (RMSE) is 0.6 m. Our method outperforms the other methods especially in the broadleaf forests plot on slopes, where the five evaluation metrics for tree top detection outperformed the other algorithms by at least 11% on average. Our ITS method is both robust and efficient and has the potential to be used especially in coniferous forests to extract the structural parameters of individual trees for forest management, carbon stock estimation, and habitat mapping.

Human Activity Changes During COVID-19 Lockdown in China-A View From Nighttime Light.

Strict lockdowns were implemented in China to fight Coronavirus Disease 2019 (COVID-19). We explored the nighttime light (NTL) of China's four cities in five stages of COVID-19 including case free period, newly appeared period, rising period, outbreak period, and stationary period. Using six categories of points of interest data ("company," "recreation," "healthcare," "residence," "shopping," and "traffic facility") and random forest models, we found that dimming light of four cities is associated with the epidemic development and human activity changes. When confirmed cases appeared, healthcare associated NTL radiance increased rapidly in Wuhan and Guangzhou, but decreased in the fourth and fifth stages. Companies in all cities were resuscitated in the fifth stage, while companies in Guangzhou was resuscitated in the fourth stage. Shopping related NTL radiance in Wuhan increased quickly in the fifth stage which indicated some resuscitation. In addition, compared to gross domestic product, the trend in electric power consumption was consistent with the trend in NTL radiance. The above findings contribute to the making of control policies for COVID-19 as well as other infectious diseases.

The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE).

The Complicate Observations and Multi-Parameter Land Information Constructions on Allied Telemetry Experiment (COMPLICATE) comprises a network of remote sensing experiments designed to enhance the dynamic analysis and modeling of remotely sensed information for complex land surfaces. Two types of experimental campaigns were established under the framework of COMPLICATE. The first was designed for continuous and elaborate experiments. The experimental strategy helps enhance our understanding of the radiative and scattering mechanisms of soil and vegetation and modeling of remotely sensed information for complex land surfaces. To validate the methodologies and models for dynamic analyses of remote sensing for complex land surfaces, the second campaign consisted of simultaneous satellite-borne, airborne, and ground-based experiments. During field campaigns, several continuous and intensive observations were obtained. Measurements were undertaken to answer key scientific issues, as follows: 1) Determine the characteristics of spatial heterogeneity and the radiative and scattering mechanisms of remote sensing on complex land surfaces. 2) Determine the mechanisms of spatial and temporal scale extensions for remote sensing on complex land surfaces. 3) Determine synergist inversion mechanisms for soil and vegetation parameters using multi-mode remote sensing on complex land surfaces. Here, we introduce the background, the objectives, the experimental designs, the observations and measurements, and the overall advances of COMPLICATE. As a result of the implementation of COMLICATE and for the next several years, we expect to contribute to quantitative remote sensing science and Earth observation techniques.

Daytime Land Surface Temperature Extraction from MODIS Thermal Infrared Data under Cirrus Clouds.

Simulated data showed that cirrus clouds could lead to a maximum land surface temperature (LST) retrieval error of 11.0 K when using the generalized split-window (GSW) algorithm with a cirrus optical depth (COD) at 0.55 µm of 0.4 and in nadir view. A correction term in the COD linear function was added to the GSW algorithm to extend the GSW algorithm to cirrus cloudy conditions. The COD was acquired by a look up table of the isolated cirrus bidirectional reflectance at 0.55 µm. Additionally, the slope k of the linear function was expressed as a multiple linear model of the top of the atmospheric brightness temperatures of MODIS channels 31-34 and as the difference between split-window channel emissivities. The simulated data showed that the LST error could be reduced from 11.0 to 2.2 K. The sensitivity analysis indicated that the total errors from all the uncertainties of input parameters, extension algorithm accuracy, and GSW algorithm accuracy were less than 2.5 K in nadir view. Finally, the Great Lakes surface water temperatures measured by buoys showed that the retrieval accuracy of the GSW algorithm was improved by at least 1.5 K using the proposed extension algorithm for cirrus skies.

Performance evaluation of four directional emissivity analytical models with thermal SAIL model and airborne images.

Land surface emissivity is a crucial parameter in the surface status monitoring. This study aims at the evaluation of four directional emissivity models, including two bi-directional reflectance distribution function (BRDF) models and two gap-frequency-based models. Results showed that the kernel-driven BRDF model could well represent directional emissivity with an error less than 0.002, and was consequently used to retrieve emissivity with an accuracy of about 0.012 from an airborne multi-angular thermal infrared data set. Furthermore, we updated the cavity effect factor relating to multiple scattering inside canopy, which improved the performance of the gap-frequency-based models.

Determination of optimum viewing angles for the angular normalization of land surface temperature over vegetated surface.

Multi-angular observation of land surface thermal radiation is considered to be a promising method of performing the angular normalization of land surface temperature (LST) retrieved from remote sensing data. This paper focuses on an investigation of the minimum requirements of viewing angles to perform such normalizations on LST. The normally kernel-driven bi-directional reflectance distribution function (BRDF) is first extended to the thermal infrared (TIR) domain as TIR-BRDF model, and its uncertainty is shown to be less than 0.3 K when used to fit the hemispheric directional thermal radiation. A local optimum three-angle combination is found and verified using the TIR-BRDF model based on two patterns: the single-point pattern and the linear-array pattern. The TIR-BRDF is applied to an airborne multi-angular dataset to retrieve LST at nadir (Te-nadir) from different viewing directions, and the results show that this model can obtain reliable Te-nadir from 3 to 4 directional observations with large angle intervals, thus corresponding to large temperature angular variations. The Te-nadir is generally larger than temperature of the slant direction, with a difference of approximately 0.5~2.0 K for vegetated pixels and up to several Kelvins for non-vegetated pixels. The findings of this paper will facilitate the future development of multi-angular thermal infrared sensors.

Noise evaluation of early images for Landsat 8 Operational Land Imager.

This study performed an on-orbit evaluation of noise level for the Operational Land Imager (OLI) onboard Landsat 8 using early images over ground homogeneous sites. The signal-to-noise ratios (SNR) were higher than 160 of OLI nine bands at typical radiance level, while the noise equivalent radiance difference (NE∆L) and the noise equivalent reflectance difference (NE∆ρ) were respectively lower than 0.8 W/m(2)/µm/sr and 0.002. Compared to pre-launch predictions, the on-orbit low noise and high SNR almost satisfied requirements for OLI bands, and can provide a prior knowledge for uncertainty analysis of OLI images in monitoring land surface, oceanic, and atmospheric status.

[Study on multi-angular polarized spectrum characteristics of leaf based on some indoor experimental data].

In the present study, we acquired multi-angular polarized spectrum of three kinds of leaves with different surface structures, and calculated the multi-angular spectral degree of polarization DOP based on Stokes parameters to explore its variation tendency with wavelength and the relationship between the leaf polarized reflectance and its physiological parameters and the relative observational geometric condition. The results show that although the spectral degree of polarization of the 3 kinds of leaves has obvious differences in value because of the leaf surface structures, the general trend of the DOP curve keeps consistent, and this trend could be explained by analyzing the properties of both specular and diffuse components of the leaf reflectance. The peak value of the DOP appears in the specular direction of the principal plane, and grows with the increase in the incident zenith angle. The analysis of the data demonstrates that compared to the other two experimental bands, the near infrared band shows a better discrimination in showing the relationship between DOP and the observational geometric configuration for all the three kinds of leaves, but the value of the DOP is smaller. Considering the effect of variation in chlorophyll content, DOP of the red band has more stable value than the near infrared band, whereas the water content has barely influence in both of the bands.

[Retrieval of spectral characteristics of hyperspectral sensor and retrieval of reflectance spectra].

On-orbit spectral calibration of hyperspectral imaging data is a key step for quantitatively analyzing them. Like the atmospheric correction, accurate spectral calibration is very necessary for improved studies of land or ocean surface properties. Based on the previous literatures, a new method which coupled an optimization algorithm was developed to simultaneously retrieve the central wavelength and the full width at half maximum (FWHM) of the hyperspectral sensor without needing the in situ reflectance spectra. Firstly, the Hyperion data set simulated using MODTRAN4 with the Hyperion spectral specification was used to test the new method, and the results indicated that the maximum error was less than 0.1 and 0.7 nm for central wavelength and FWHM respectively when the spectral shift is 5 nm. Then the algorithm was applied to the Hyperion data acquired on May 20, 2008 over Heihe River Basin and it was iteratively performed for each detector of the two spectrometers of Hyperion. The results showed that the VNIR of Hyperion had a pronounced smile effect, and the shift in on-orbit calibration with respect to the laboratory was from -2 to +2 nm, while the SWIR has essentially no smile effect, the wavelength correction was relatively flat over all sample with an approximately constant value of 3 nm. The FWHM in VNIR could range from -0.2 to 0.5 nm as a function of sample number of the spectrometer, and in SWIR it ranged from -2 to -3 nm. So for both the VNIR and SWIR, the original spectral calibration should be updated. These results showed good agreement with previous research findings, and which also proved the feasibility of the new method. Finally, with the updated spectral calibration characteristics, the sample reflectances of desert and vegetation target in our study site were reconstructed by applying a further atmospheric correction, and as expected, the strong spikes around the typical atmospheric absorption were almost disappeared.

[Winter wheat growth spatial variation study based on temporal airborne high-spectrum images].

Precision agriculture technology is defined as an information-and technology-based agriculture management system to identify, analyze and manage crop spatial and temporal variation within fields for optimum profitability, sustainability and protection of the environment. In the present study, push-broom hyperspectral image sensor (PHI) image was used to investigate the spatial variance of winter wheat growth. The variable-rate fertilization contrast experiment was carried out on the National Experimental Station for Precision Agriculture of China during 2001-2002. Three airborne PHI images were acquired during the wheat growth season of 2002. Then contrast analysis about the wheat growth spatial variation was applied to the variable-rate fertilization area and uniformity fertilization area. The results showed that the spectral reflectance standard deviation increased significantly in red edge and short infrared wave band for all images. The wheat milky stage spectral reflectance has the maximum standard deviation in short infrared wave band, then the wheat jointing stage and wheat filling stage. Then six spectrum parameters that sensitive to wheat growth variation were defined and analyzed. The results indicate that parameters spatial variation coefficient for variable-rate experiment area was higher than that of contrast area in jointing stage. However, it decreased after the variable-rate fertilization application. The parameters spatial variation coefficient for variable-rate area was lower than that of contrast area in filling and milking stages. In addition, the yield spatial variation coefficient for variable-rate area was lower than that of contrast area. However, the yield mean value for variable-rate area was lower than that of contrast area. The study showed that the crop growth spatial variance information can be acquired through airborne remote sensing images timely and exactly. Remote sensing technology has provided powerful analytical tools for precision agriculture variable-rate management.

[Optical methods for in situ measuring leaf area index of forest canopy: a review].

Leaf area index (LAI) is an important parameter of canopy structure, because it relates to many biophysical and physiological processes of canopy, including photosynthesis, respiration, transpiration, carbon cycling, precipitation interception, and energy exchange, etc. This paper introduced the theoretical bases and mathematical models of optical methods for forest canopy LAI determination, introduced the principles, merits, and drawbacks of currently used optical methods, and summed up the main sources of the errors in LAI optical measurement, including clumping effect, non-photosynthesis components, measurement conditions, and terrain effect. The developing status of quantitatively evaluating clumping effect, non-photosynthesis components, and terrain effect was analyzed, and the promising development directions of optical methods for measuring forest canopy LAI were discussed.

Woody-to-total area ratio determination with a multispectral canopy imager.

Leaf area index (LAI) - defined as one half of the total green leaf area per unit ground surface area - can be determined by direct or indirect methods. Three major sources of errors exist in indirect LAI measurements: within-shoot clumping, beyond-shoot clumping and non-photosynthetic components. The effect of non-photosynthetic components on LAI measurements can be described by the woody-to-total area ratio, alpha; however, no convenient and efficient indirect methods have been developed to estimate alpha, especially the variations in alpha with zenith angle , alpha(theta). We describe the development and use of a multispectral canopy imager (MCI) to estimate alpha and alpha(theta) by considering the effects of non-random distributions of canopy elements and woody components and the overestimation of needle-to-shoot area ratio on woody components. The MCI, which mainly comprises a near-infrared band camera (Fujifilm IS-1), two visible band cameras (Canon 40D), filters and a pan tilt, was developed to measure clumping index, woody-to-total area ratio and geometric parameters of isolated trees. Two typical sampling plots (Plots 1 and 5) chosen from among 16 permanent forest experiment plots were selected for the estimation of alpha and alpha(theta). The non-random distributions of canopy elements and woody components were estimated separately at eight zenith angles (from 0 degrees to 70 degrees in increments of 10 degrees) using MCI images based on the gap size distribution theory. The visible/near-infrared image pairs captured by the MCI were able to discriminate among sky, leaves, cloud and woody components. Based on three methods of estimation, we obtained woody-to-total area ratios of 0.24, 0.19, 0.19 for Plot 1 and 0.23, 0.18, 0.17 for Plot 5. If clumping effects were ignored, alpha values were overestimated by as much as 21% and 24% at Plots 1 and 5, respectively. We demonstrated that alpha(theta) varied with the zenith angle, with variations in the range of 3-33% at Plot 1 and 2-65% at Plot 5. A new formula for the precise determination of LAI is proposed.

A review of current methodologies for regional evapotranspiration estimation from remotely sensed data.

An overview of the commonly applied evapotranspiration (ET) models using remotely sensed data is given to provide insight into the estimation of ET on a regional scale from satellite data. Generally, these models vary greatly in inputs, main assumptions and accuracy of results, etc. Besides the generally used remotely sensed multi-spectral data from visible to thermal infrared bands, most remotely sensed ET models, from simplified equations models to the more complex physically based two-source energy balance models, must rely to a certain degree on ground-based auxiliary measurements in order to derive the turbulent heat fluxes on a regional scale. We discuss the main inputs, assumptions, theories, advantages and drawbacks of each model. Moreover, approaches to the extrapolation of instantaneous ET to the daily values are also briefly presented. In the final part, both associated problems and future trends regarding these remotely sensed ET models were analyzed to objectively show the limitations and promising aspects of the estimation of regional ET based on remotely sensed data and ground-based measurements.

A Contextual Fire Detection Algorithm for Simulated HJ-1B Imagery.

The HJ-1B satellite, which was launched on September 6, 2008, is one of the small ones placed in the constellation for disaster prediction and monitoring. HJ-1B imagery was simulated in this paper, which contains fires of various sizes and temperatures in a wide range of terrestrial biomes and climates, including RED, NIR, MIR and TIR channels. Based on the MODIS version 4 contextual algorithm and the characteristics of HJ-1B sensor, a contextual fire detection algorithm was proposed and tested using simulated HJ-1B data. It was evaluated by the probability of fire detection and false alarm as functions of fire temperature and fire area. Results indicate that when the simulated fire area is larger than 45 m(2) and the simulated fire temperature is larger than 800 K, the algorithm has a higher probability of detection. But if the simulated fire area is smaller than 10 m(2), only when the simulated fire temperature is larger than 900 K, may the fire be detected. For fire areas about 100 m(2), the proposed algorithm has a higher detection probability than that of the MODIS product. Finally, the omission and commission error were evaluated which are important factors to affect the performance of this algorithm. It has been demonstrated that HJ-1B satellite data are much sensitive to smaller and cooler fires than MODIS or AVHRR data and the improved capabilities of HJ-1B data will offer a fine opportunity for the fire detection.

Regularized inversion method for retrieval of aerosol particle size distribution function in W(1,2) space.

A determination of the aerosol particle size distribution function by using the particle spectrum extinction equation is an ill-posed integral equation of the first kind. To overcome this, we must incorporate regularization techniques. Most of the literature focuses on the Phillips-Twomey regularization or its variations. However, there are drawbacks for some applications in which the real aerosol distributions have large oscillations in a Junge-type distribution. The reason for this is that the scale matrix based on the norm of the second differences in the Phillips-Twomey regularization is too ill- conditioned to filter the large perturbations induced by the small algebraic spectrum of the kernel matrix and the additive noise. Therefore we reexamine the aerosol particle size distribution function retrieval problem and solve it in W1,2 space. This setting is based on Sobolev's embedding theorem in which the approximate solution best simulates the true particle size distribution functions. For choosing the regularization parameters, we also develop an a posteriori parameter choice method, which is based on the discrepancy principle. Our numerical results are based on the remote sensing data measured by the CE318 sunphotometer in Jia Xiang County, Shan Dong Province, China, and are performed to show the feasibility of the proposed algorithms.