Total suspended sediments (TSS) are an important water quality indicator. The spatiotemporal distribution patterns of TSS concentrations in summer in the Peace-Athabasca Delta (PAD) remain unclear between 2000 and 2020. Among empirical and machine learning models, the random forest (RF) model exhibits the best performance for the PAD with a coefficient of determination, normalized root-means-square error, and relative root-mean-square error of 0.92, 7.51%, and 27.36%, respectively. Our study analyzes the spatiotemporal TSS distribution based on 21 years of Landsat remote sensing data and the RF model. The results showed that the TSS concentrations of the Peace River were elevated in 2000, 2016, 2019, and 2020. The long-term TSS concentration distribution in the PAD showed a decreasing trend from west to east, and the TSS concentrations ranged from 6 to 80 mg/L level and remained stable in Lake Athabasca during the investigated period. Moreover, 57.14% of the PAD area exhibited no significant change between 2000 and 2020. The results of our study provide detailed information for the local government on the TSS concentrations in the PAD.
Drill cores provide direct evidence for geological prospecting, and compiling drill core record information has been a vital tool in geological exploration. We design and fabricate a drill core spectral imaging system that covers a wavelength range of 400 to 2500 nm with a spectral resolution of 3 to 12 nm. A visible and near infrared and a shortwave infrared spectrometer are mounted together to cover the wavelength range. This paper describes the mechanical structure, light path structure, parameters, and software processing chain of the system. With the spectral imaging system and custom-design software, a mineral core sample is analyzed to validate system performance.
Due to weathering and external forces, solar panels are subject to fouling and defects after a certain amount of time in service. These fouling and defects have direct adverse consequences such as low-power efficiency. Because solar power plants usually have large-scale photovoltaic (PV) panels, fast detection and location of fouling and defects across large PV areas are imperative. A drone-mounted infrared thermography system was designed and developed, and its ability to detect rapid fouling on large-scale PV panel systems was investigated. The infrared images were preprocessed using the K neighbor mean filter, and the single PV module on each image was recognized and extracted. Combining the local and global detection method, suspicious sites were located precisely. The results showed the flexible drone-mounted infrared thermography system to have a strong ability to detect the presence and determine the position of PV fouling. Drone-mounted infrared thermography also has good technical feasibility and practical value in the detection of PV fouling detection.
The significance of laser return intensity has been widely verified in airborne light detection and ranging (LiDAR)-based forest canopy mapping, but this does not mean that all of its roles have been played. People still ask such questions as “Is it possible using this optical attribute of lasers to investigate individual tree-crown insides wherein laser intensity data are typically yielded in complicated echo-triggering modes?” To answer this question, this study examined the characteristics of the intensities of the laser points within 10 Quercus robur trees by fitting their peak amplitudes into default Gaussian distributions and then analyzing the resulting asymmetric tails. Exploratory data analyses showed that the laser points lying within the distribution tails can indicate primary tree branches in a sketchy way. This suggests that the question can be positively answered, and the traditional restriction of airborne LiDAR in canopy mapping at the crown level has been broken. Overall, this study found a unique way to detect primary tree branches in airborne LiDAR data and pointed out how to explore more ways this optical intensity attribute of airborne LiDAR data can measure tree organs at fine scales and further learn their properties.
Crop pests and diseases is one of major agricultural disasters, which have caused heavy losses in agricultural production each year. Hyperspectral remote sensing technology is one of the most advanced and effective method for monitoring crop pests and diseases. However, Hyperspectral facing serial problems such as low degree of automation of data processing and poor timeliness of information extraction. It resulting we cannot respond quickly to crop pests and diseases in a critical period, and missed the best time for quantitative spraying control on a fixed point. In this study, we take the crop pests and diseases as research point and breakthrough, using a self-development line scanning VNIR field imaging spectrometer. Take the advantage of the progressive obtain image characteristics of the push-broom hyperspectral remote sensor, a synchronous real-time progressive hyperspectral algorithms and models will development. Namely, the object’s information will get row by row just after the data obtained. It will greatly improve operating time and efficiency under the same detection accuracy. This may solve the poor timeliness problem when we using hyperspectral remote sensing for crop pests and diseases detection. Furthermore, this method will provide a common way for time-sensitive industrial applications, such as environment, disaster. It may providing methods and technical reserves for the development of real-time detection satellite technology.
Non-negative matrix factorization (NMF) has been introduced into the field of hyperspectral unmixing in the last ten years. Though NMF-based approaches have been widely accepted by researchers, the assumptions in them may not always fit for the characteristics of real ground objectives, which will cause the incorrect results and restrict the applications for these approaches. This paper proposes a novel semi-supervised NMF model, in which the ground truth information is introduced such as partial known endmembers from ground measurment. The relationship between the known and unknown endmembers are explored. The distance function is designed to describe the relationship and introduced into the NMF model. In this way, SSNMF could use the known endmembers to help estimating the unknown endmembers, so that accurate and robust results can be obtained. The proposed algorithm was compared with NMFupk, which also considered partial known endmembers, using extensive synthetic data and real hyperspectral data. The experiments show that the proposed algorithm can give a better performance.
In order to get high spatial resolution hyperspectral data, many studies have examined methods to combine spectral information contained in hyperspectral image with spatial information contained in multispectral/panchromatic image. This paper developed a new hyperspectral image fusion method base on the non-negative matrix factorization (NMF) theory. Data sets obtained by the Airborne Visible Infrared Imaging Spectrometer (AVIRIS) was used to evaluate the performance of the method. Experimental results show that the proposed algorithm can provide a good way to solve the problem of high spatial resolution hyperspectral data shortage.
LAI is a crucial parameter and a basic quantity indicating crop growth situation. Empirical models comprising spectral indices (SIs) and LAI have widely been applied to the retrieval of LAI. SI method already has exhibited feasibility in the estimation of vegetation LAI. However, it is largely subject to the inconsistency from different remote sensors which have varied specifications, such as spectral response features and central wavelength. To address this issue, a new vegetation index (VIUPD) based on the universal pattern decomposition method was proposed. It is expressed as a linear sum of the pattern decomposition coefficients and features in sensor-independency. The aim of this study was to evaluate the prediction accuracy and stability of VIUPD for estimating LAI, compared with three other common-used SIs. In this study, the measured spectra were resampled to simulated TM multispectral data and Hyperion hyperspectral data respectively, using the Gaussian spectral response function. The three typical SIs chosen were including NDVI, TVI and MCARI, which were constructed with the sensitive bands to the LAI. Finally, the regression equations between four selected SIs and LAI were established. The best index evaluated using the simulated TM data was VIUPD which exhibits the best correlation with LAI (R2=0.92) followed by NDVI (R2=0.80). For the simulated Hyperion data, VIUPD again ranks first with R2=0.89, followed by TVI (R2=0.63). Meanwhile, the consistence of VIUPD also was studied based on simulated TM and Hyperion sensor data and the R2 reached to 0.95. It is demonstrated that VIUPD has the best accuracy and stability to estimate LAI of winter wheat whether using simulated TM data or Hyperion data, which reaffirms that VIUPD is comparatively sensor independent.
Imaging spectrometers provide the unique combination of both spatially contiguous spectra and spectrally contiguous
images of the Earth's surface that allows spatial mapping of these minerals. One of the successful applications of imaging
spectrometers remote sensing identified was geological mapping and mineral exploration. A Light weight Airborne
Imaging Spectrometer System (LAISS) has been developed in China. The hardware of the compact LAISS include a
VNIR imaging spectrometer, a SWIR imaging spectrometer, a high resolution camera and a position and attitude device.
The weight of the system is less than 20kg. The VNIR imaging spectrometer measures incoming radiation in 344
contiguous spectral channels in the 400–1000 nm wavelength range with spectral resolution of better than 5 nm and
creates images of 464 pixels for a line of targets with a nominal instantaneous field of view (IFOV) of ~1 mrad. The
SWIR imaging spectrometer measures incoming radiation in the 1000–2500 nm wavelength range with spectral
resolution of better than 10 nm with a nominal instantaneous field of view (IFOV) of ~2 mrad. The 400 to 2500nm
spectral range provides abundant information about many important Earth-surface minerals. A ground mineral scan
experiment and an UAV carried flying experiment has been done. The experiment results show the LAISS have achieved
relative high performance levels in terms of signal to noise ratio and image quality. The potential applications for light
weight airborne imaging spectrometer system in mineral exploration are tremendous.
Soil-water resources are key components for agriculture and have great potential. Strategic and significant efforts are, therefore, required to make full use of soil-water resources, especially in dry or semidry areas. We coupled a soil-water model with remotely sensed data and associated techniques to analyze the spatial-temporal dynamics of soil-water resources in the Weihe River Basin in China. The moderate-resolution imaging spectroradiometer (MODIS), Chinese meteorological satellite precipitation estimation data (FY-2), and global land data assimilation system (GLDAS) products were used for spatial land surface characteristics interpretation and model parameters derivation. The modeling results were compared and validated using data from a nearby observation site. The average soil-water resources of the Weihe River Basin vary between 40 and 100 mm during the simulation period from January to December, with a maximum of 99 mm appearing in August and a minimum of 38 mm in December. Forest land was characterized by large soil-water resources, with an average annual rate of 1094.7 mm. Farmland and grassland exhibited low values, with average annual rates of 986.7 and 893.5 mm, respectively. The results could be taken into consideration for soil-water resources management.
Refractivity happens due to stratification in the lower boundary layer over oceans due to variability of moisture,
temperature, wind and sea surface temperature which collectively may lead to generate evaporation duct. The
evaporation duct has a significant impact on the spread of electromagnetic waves in the atmosphere over oceans
both from the meteorological and military point of view. This ducting sometimes supports normal propagation of
radar signals and sometimes may cause distortion and attenuation of signals depending on the height of evaporation
duct. This leads to over-estimation and under-estimation of rainfall by weather radar meteorologically and for other
targets militarily. The aim of this study was not only to locate evaporation duct height but also to check the
efficiency of Weather Research and Forecasting Model (WRF) and Babin’s model so that results may be used in
applying correction measures for precise identification of targets by radar. In this study by utilizing the high vertical
resolution of WRF for the simulation of different meteorological parameters, the Babin’s method was used for
calculating the evaporation duct height over South China Sea for the two months, April and July. Very clear duct
heights were calculated at different areas over sea in different time domains. Study reveals that maximum height
existed in the month of April although July was rich with different EDHs in different regions in contrast to April. It
was found that in most of the cases EDH was higher or maximum when relative humidity was comparatively lower
and air temperature and wind speed were comparatively higher. This study paves a way for futuristic study of
evaporation duct monitoring and forecasting by assimilation of remote sensing data especially through that of Geostationary
satellites by incorporating verification measures from radar.
Gas hydrates are ice-like crystalline solids composed of water and gas, which widespread in permafrost regions and
beneath the sea in sediments of outer continental margins. It is a new kind of potential and clean energy resource, and the
dissociation of hydrate also play a great role in climate change due to their strong greenhouse effect. In this research,
monthly methane concentration of Muli area from 2003 to 2008 is firstly analyzed, where natural gas hydrate sample
was detected in 2008. It is found that monthly methane concentration of this area in December is obviously higher than
that of surrounding area. And before 2006, the monthly methane concentration of August in this area is higher than that
of other months, which is the same with the distribution of the whole country, however, the rule changes after that. The
monthly methane concentration of winter in Muli area becomes the same high with that of summer. Compared with the
timely earthquake data of this area, it is known that monthly methane concentration of March, 2007 abnormal increased
for a little earthquake of magnitude 4.2 happened February 23rd, 2007. Based on the analysis results of Muli area,
monthly methane concentration in permafrost area of China from 2003 to 2008 is analyzed to monitor the possible
methane seepages of potential gas hydrate area.
Methane is an important greenhouse gas contributing to global climate change, and its warming effect is just second to
carbon dioxide. Satellite remote sensing technique can obtain large scale distribution of trace gases, and it has been an
important tool in the field of atmospheric observation. This paper presents the annual variations of methane in China
based on the vertical columns of methane measured by the SCIAMACHY sensor on board ENVISAT. The variability of
yearly averaged CH4 concentration in China and the whole world during 2003-2009 shows that the rapid growth of CH4
in China during 2005-2006 widened the difference between China’s CH4 level and the whole world’s level. China’s
methane level has close relations with global climate change.
The objective of this paper is the description of the development and the validation, using airborne hyper-spectral imagery
data, of a non-conventional technique for the vegetation information extraction. The proposed approach namely the universal
pattern decomposition method (UPDM) is tailored for hyper-spectral imagery analysis, which can be explained using two
analysis methods: spectral mixing analysis and multivariate analysis. For the former, the UPDM expresses the spectrum of
each pixel as the linear sum of three fixed, standard spectral patterns (i.e., the patterns of water, vegetation, and soil); each
coefficient represents the ratio of spectral patterns of three components. If we think of the UPDM as multivariate analysis,
standard patterns are interpreted as an oblique coordinate system, and coefficients are thought of as the coordinates of a
pixel's reflectance. The later explanation is much more comprehensible than the former for the reason of additional
supplementary pattern presence when necessary. The vegetation index based on the UPDM (VIUPD) is expressed as a linear
sum of the pattern decomposition coefficients. Here, the VIUPD was used to examine vegetation amounts and degree of
terrestrial vegetation vigor; VIUPD results were compared with results by the normalized difference vegetation index
(NDVI), and an enhanced vegetation index (EVI). This paper described the calculation of VIUPD, using AVIRIS airborne
remotely sensed data. The results showed that the VIUPD reflects vegetation and vegetation activity more sensitively than
the NDVI and EVI.
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