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Northern peatlands are globally important carbon stores. With increasing fire frequency, the re-establishment of bryophytes becomes crucial for their carbon sequestration. Smoke-responsive germination is a common trait of seeds in fire-prone ecosystems but has not been demonstrated in bryophytes. To investigate the potential role of smoke in post-fire peatland recovery, we tested the germination of spores of 15 bryophyte species after treatment with smoke-water. The smoke responsiveness of spores with different laboratory storage times and burial depths/age (3-200 years) was subsequently tested. Smoke increased the germination percentage for 10 of the species and the germination speed for four of these. Smoke responsiveness increased along the fire frequency gradient from open expanse to forest margin, consistent with the theory that this selects for the maintenance of fire-adapted traits. Smoke enhanced the germinability of 1-year but not 4-year laboratory-stored spores, and considerably increased the germinability of spores naturally buried in peat for up to ~200 years. The effect of fire may be overlooked in non-fire-prone ecosystems, such as those in which wetland bryophytes dominate. Our study reveals a mechanism by which an increase in fire frequency may lead to shifts in species dominance, which may affect long-term carbon sequestration in peatlands.

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Salinity is an important environmental factor regulating the aquatic system structure of lakes and other water bodies. Changes in salinity, which can be caused by human activities, can adversely impact the life of water organisms. The refractive index, which can be directly related to water salinity, also controls the polarimetric properties of light reflected from the water surface. In this study, polarimetric measurements of smooth water surfaces with different salinity content were performed at different viewing zenith angles in the wavelength range of 450-1000 nm in the specular reflection directions. The results show that the light reflected from the water surface (defined as reflectance factor) in one measurement direction can be replaced by the reflectance factor derived from polarimetric measurements, and if the polarizer absorptance is considered, the average relative difference is less than 3%. The degree of linear polarization (DOLP) was used to retrieve the refractive indices of water with different salinities based on the Fresnel reflection coefficient. The inverted refractive indices not only have high accuracy (uncertainty from 0.9% to 1.8%) but also have a very strong relationship with the water salinity content. Our study shows the possibility of estimating the variation in water salinity using multi-angular polarimetric measurements.

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Land surface emissivity is a key parameter that affects energy exchange and represents the spectral characteristics of land cover. Large-scale mid-infrared (MIR) emissivity can be efficiently obtained using remote sensing technology, but current methods mainly rely on prior knowledge and multi-temporal or multi-angle remote sensing images, and additional errors may be introduced due to the uncertainty of external data such as atmospheric profiles and the inconsistency of multiple source data in spatial resolution, observation time, and other information. In this paper, a new practical method was proposed which can retrieve MIR emissivity with only a single image input by combining the radiance properties of TIR and MIR channels and the spatial information of remote sensing images based on the Sentinel-3 Sea and land surface temperature radiometer (SLSTR) data. Two split-window (SW) algorithms that use TIR channels only and MIR and TIR channels to retrieve land surface temperature (LST) were developed separately, and the initial values of MIR emissivity were obtained from the known LST and TIR emissivity. Under the assumption that the atmospheric conditions in the local area are constant, the radiance transfer equations for adjacent pixels are iterated to optimize the initial values to obtain stable estimation results. The experimental results based on the simulation dataset and real SLSTR images showed that the proposed method can achieve accurate MIR emissivity results. In future work, factors such as angular effects, solar radiance, and the influence of atmospheric water vapor will be further considered to improve performance.

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Chlorophyll is one of the primary pigments of plant leaves, and changes in its content can be used to characterize the physiological status of plants. Spectral indices have been devised and validated for estimating leaf chlorophyll content (LCC). However, most of the existing spectral indices do not consider the influence of angular reflection on the accuracy of the LCC estimation. In this study, the spectral reflectance factors of leaves from three plant species were measured from several observations in the principal plane. The relationship between the existing spectral indices and the LCC from different directions suggests that the directional reflection of a leaf surface impacts the accuracy of its LCC estimation. Subsequently, the ratio of reflectance differences, that is, the modified Datt index, was tested to reduce the directional reflection effect when predicting LCC. Our results indicated that the modified Datt index not only estimated LCC with high accuracy for all observation directions and plant species but also consistently predicted the LCC of each species in individual observation directions. Our method opens the possibility for optical detection of LCC using multiangular spectral reflection, which is convenient for plant science studies focused on the variation in LCC.

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The use of satellite-borne large-footprint LiDAR (light detection and ranging) systems allows for the acquisition of forest monitoring data. This paper mainly describes the design, use, operating principles, installation and data properties of the new Laser Vegetation Detecting Sensor (LVDS), a LiDAR system designed and developed at the Academy of Forest Inventory and Planning (AFIP) and the Beijing Institute of Telemetry (BIT). Data from LVDS were used to calculate the mean height of forest trees on sample plots using data collected in the Hunan province of China. The results show that the full waveform data obtained by LVDS has the ability to accurately characterize forest height. The mean absolute percentage error of mean forest height per plot in flat areas was 6.8%, with a mean absolute deviation of 0.78 m. The airborne LVDS system provides prototype data sets and a platform for instrument proof-of-concept studies for China's Terrestrial Ecosystem Carbon Monitoring (TECM) mission, which is an Earth remote sensing satellite due for launch in 2020. The information produced by LVDS allows for forest structure studies with high accuracy and coverage of large areas.

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The optical scattering property of a target is the essential signal for passive remote sensing. In this study, we performed the photopolarimetric measurements of a manmade target in the wavelength range of 400-1000 nm over the hemispherical directions using the Northeast Normal University Laboratory Goniospectrometer System (NENULGS). We discussed the influence of the absorption of a polarizer on the measured Stokes parameters, and used the BRF (bidirectional reflectance factor) and BPRF (bidirectional polarized reflectance factor) to characterize the scattering property of our sample at selected wavelengths. These measured BRFs and BPRFs were also compared with the modeled results using a semi-empirical photometric model and a semi-empirical polarimetric model, respectively. Subsequently, the modeled BRFs and BPRFs were used to simulate the degree of linear polarization (DoLP) of a man-made target, which provided a comparison with the measured DoLP. We found that (1) the I parameter reflectance factor (IpRF) can effectively represent the BRF if we considered the absorption of the polarizer, (2) the modeled photopolarimetric results of manmade target were in good agreement with the measurements, and (3) the simulated DoLP of manmade target also provided a good match with the measured DoLP, with an average relative difference of approximately 0.2 for all the selected wavelengths. Our results appeared very promising for proving that the polarimetric measurement is a very effective and useful method for remote sensing applications as well as deepening our understanding of the optical properties of reflected light from the manmade object as ours.

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The bidirectional reflectance factor (BRF) is commonly used to study the structure of a particulate surface based on photometric measurements. In this paper, we describe the bidirectional reflectance factor distribution of natural particulate surfaces with particles sizes varying from 0.15 mm to 0.9 mm. Two types of natural particulate surfaces (one with low reflectance and the other with moderate reflectance) were measured at visible and near-infrared wavelengths using the Northeast Normal University Laboratory Goniospectrometer System (NENULGS). Both the BRFs and anisotropic reflectance factors (ARFs) at selected wavelengths were compared with previously published results to verify the accuracy of our measurements, and we also quantitatively analyzed the effects of particle size on the BRF. It was found that the maximum reflectance difference, which was more distinct for the low-reflectance samples, between particulate surfaces with particle sizes of 0.15 mm and 0.9 mm occurred in the forward scattering direction for all samples, and the value of this maximum difference reached 59% for the low-reflectance samples. Then, we conducted a test of a photometric model to determine which parameters could be confidently linked to the surfaces' reflectance behavior. The inverted parameters were compared with the known physical parameters of our samples, such as the particle size. We found that the single-scattering albedo could be empirically used to determine the particle sizes of our samples when measurements of particulate surfaces with different particle sizes were performed under the same incidence conditions and with wide viewing angles. The potential applications of our results appear very promising for empirically resolving the spatial distribution of particle size within a given particulate sample as well as for deepening our understanding of the scattering properties of particulate media.

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In the context of remote sensing, the reflectance of snow is a key factor for accurate inversion for snow properties, such as snow grain size, albedo, because of it is influenced by the change of snow properties. The polarized reflectance is a general phenomenon during the reflected progress in natural incident light In this paper, based on the correct measurements for the multiple-angle reflected property of snow field in visible and near infrared wavelength (from 350 to 2,500 nm), the influence of snow grain size and wet snow on the bidirectional polarized property of snow was measured and analyzed. Combining the results measured in the field and previous conclusions confirms that the relation between polarization and snow grain size is obvious in infrared wavelength (at about 1,500 nm), which means the degree of polarization increasing with an increase of snow grain size in the forward scattering direction, it is because the strong absorption of ice near 1,500 nm leads to the single scattering light contributes to the reflection information obtained by the sensor; in other word, the larger grain size, the more absorption accompanying the larger polarization in forward scattering direction; we can illustrate that the change from dry snow to wet snow also influences the polarization property of snow, because of the water on the surface of snow particle adheres the adjacent particles, that means the wet snow grain size is larger than the dry snow grain size. Therefore, combining the multiple-angle polarization with reflectance will provide solid method and theoretical basis for inversion of snow properties.

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The corn in the grain filling stage fell over in the central region of Jilin province by the Typhoon Bolaven influence. In order to determine the impact of falling over corn canopy on the reflected information, the hyperspectral reflectance was detected at different viewing zenith angles, at the same time, the polarized reflection was also measured. The results from the analysis by combining the reflection and polarization from corn canopy showed that the reflection of falling over corn is low in visible, while increases in the near infrared wavelength. The reflection from falling over corn canopy was more anisotropic than stand-up corn canopy. The reflected light was highly polarized, the polarization of corn canopy provided the probability for distinguishing between falling over corn and stand-up corn. This research provides a basis for estimating the disaster area and lost units.

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In order to extract more reflection characteristic information of oil film on water surface by using remote sensing technology, the authors measured and analyzed the multi-angle hyperspectral polarized reflection information of oil film based on the traditional remote sensing researches. The authors used polarization as the indicator of quantitative study, adopted a three-factor-three-level orthogonal test to analyze the incident angle, oil film thickness, band, and the influence of oil film on water surface from their interaction between each other. The results of variance analysis of this orthogonal test show that: the three factors and their interaction between each other all have an influence on the polarization of oil film; the interaction between incident angle and thickness of oil slick, and the interaction between band and thickness of oil slick both show a significant impact on polarization. That is to say, the research on oil film by using hyperspectral multi-angle polarized reflectance needs both the influence on polarization from each factor and their interaction.

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Due to the need of snow monitoring and the impact of the global change on the snow, on the basis of the traditional research on snow, starting from the perspective of multi-angle polarized reflectance, we analyzed the influencing factors of snow from the incidence zenith angles, the detection zenith angles, the detection azimuth angles, polarized angles, the density of snow, the degree of pollution, and the background of the undersurface. It was found that these factors affected the spectral reflectance values of the snow, and the effect of some factors on the polarization hyperspectral reflectance observation is more evident than in the vertical observation. Among these influencing factors, the pollution of snow leads to an obvious change in the snow reflectance spectrum curve, while other factors have little effect on the shape of the snow reflectance spectrum curve and mainly impact the reflection ratio of the snow. Snow reflectance polarization information has not only important theoretical significance, but also wide application prospect, and provides new ideas and methods for the quantitative research on snow using the remote sensing technology.

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Objective. To investigate the effect of eccentric strengthening (EC) exercise on muscular structure, plasma CK and biomechanical behavior under different mechanical loadings and to study the mechanical factors involved in sports muscle injury. Method. We developed an apparatus to do eccentric strength training with male SD rats under different mechanical loadings and to measure the biomechanical behavior. Result. No significant difference (P>0.05) of muscular structure, plasma CK and biomechanical behavior were found between high and low force groups. Conclusion. Skeletal muscle injury after cyclic EC with different mechanical loadings suggested that muscle damage is not simply a function of peak muscle force.

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