[Retrieval of Copper Pollution Information from Hyperspectral Satellite Data in a Vegetation Cover Mining Area].

Heavy metal mining activities have caused the complex influence on the ecological environment of the mining regions. For example, a large amount of acidic waste water containing heavy metal ions have be produced in the process of copper mining which can bring serious pollution to the ecological environment of the region. In the previous research work, bare soil is mainly taken as the research target when monitoring environmental pollution, and thus the effects of land surface vegetation have been ignored. It is well known that vegetation condition is one of the most important indictors to reflect the ecological change in a certain region and there is a significant linkage between the vegetation spectral characteristics and the heavy metal when the vegetation is effected by the heavy metal pollution. It means the vegetation is sensitive to heavy metal pollution by their physiological behaviors in response to the physiological ecology change of their growing environment. The conventional methods, which often rely on large amounts of field survey data and laboratorial chemical analysis, are time consuming and costing a lot of material resources. The spectrum analysis method using remote sensing technology can acquire the information of the heavy mental content in the vegetation without touching it. However, the retrieval of that information from the hyperspectral data is not an easy job due to the difficulty in figuring out the specific band, which is sensitive to the specific heavy metal, from a huge number of hyperspectral bands. Thus the selection of the sensitive band is the key of the spectrum analysis method. This paper proposed a statistical analysis method to find the feature band sensitive to heavy metal ion from the hyperspectral data and to then retrieve the metal content using the field survey data and the hyperspectral images from China Environment Satellite HJ-1. This method selected copper ion content in the leaves as the indicator of copper pollution level, using stepwise multiple linear regression and cross validation on the dataset which is consisting of 44 groups of copper ion content information in the polluted vegetation leaves from Dexing Copper Mine in Jiangxi Province to build up a statistical model by also incorporating the HJ-1 satellite images. This model was then used to estimate the copper content distribution over the whole research area at Dexing Copper Mine. The result has shown that there is strong statistical significance of the model which revealed the most sensitive waveband to copper ion is located at 516 nm. The distribution map illustrated that the copper ion content is generally in the range of 0-130 mg · kg⁻¹ in the vegetation covering area at Dexing Copper Mine and the most seriously polluted area is located at the South-east corner of Dexing City as well as the mining spots with a higher value between 80 and 100 mg · kg⁻¹. This result is consistent with the ground observation experiment data. The distribution map can certainly provide some important basic data on the copper pollution monitoring and treatment.

MLAOS: a multi-point linear array of optical sensors for coniferous foliage clumping index measurement.

The canopy foliage clumping effect is primarily caused by the non-random distribution of canopy foliage. Currently, measurements of clumping index (CI) by handheld instruments is typically time- and labor-intensive. We propose a low-cost and low-power automatic measurement system called Multi-point Linear Array of Optical Sensors (MLAOS), which consists of three above-canopy and nine below-canopy optical sensors that capture plant transmittance at different times of the day. Data communication between the MLAOS node is facilitated by using a ZigBee network, and the data are transmitted from the field MLAOS to a remote data server using the Internet. The choice of the electronic element and design of the MLAOS software is aimed at reducing costs and power consumption. A power consumption test showed that, when a 4000 mAH Li-ion battery is used, a maximum of 8-10 months of work can be achieved. A field experiment on a coniferous forest revealed that the CI of MLAOS may reveal a clumping effect that occurs within the canopy. In further work, measurement of the multi-scale clumping effect can be achieved by utilizing a greater number of MLAOS devices to capture the heterogeneity of the plant canopy.

Full-band, multi-angle, multi-scale, and temporal dynamic field spectral measurements in China.

Field-measured spectra are critical for remote sensing physical modelling, retrieval of structural, biophysical, and biochemical parameters, and other practical applications. We present a library of field spectra, which includes (1) portable field spectroradiometer measurements of vegetation, soil, and snow in the full-wave band, (2) multi-angle spectra measurements of desert vegetation, chernozems, and snow with consideration of the anisotropic reflectance of land surface, (3) multi-scale spectra measurements of leaf and canopy of different vegetation cover surfaces, and (4) continuous reflectance spectra time-series data revealing vegetation growth dynamics of maize, rice, wheat, rape, grassland, and so on. To the best of our knowledge, this library is unique in simultaneously providing full-band, multi-angle, multi-scale spectral measurements of the main surface elements of China covering a large spatial extent over a 10-year period. Furthermore, the 101 by 101 satellite pixels of Landsat ETM/OLI and MODIS surface reflectance centered around the field site were extracted, providing a vital linkage between ground measurements and satellite observations. The code language used for this work is Matlab 2016a.

[Quantitative retrieval of soil salinity using hyperspectral data in the region of inner Mongolia hetao irrigation district].

In the present paper, to investigate the spectral property of salinized soil and the relationship between the soil salinity and the hyperspectral data, the field soil samples were collected in the region of Hetao irrigation, Neimeng in the northwest China from the end of July to the beginning of August. The partial least squares regression (PLSR) model was established based on the statistical analysis of the soil ions and the reflectance of hyperspectra. The independent validation using data which are not included in the calibration model reveals that the proposed model can predicate the main soil components such as the content of total ions (S%), SO4(2+), PH and K+ + Na+ with higher determination coefficients (R2) Of 0.728, 0.801, 0.715 and 0.734 respectively. And the ratio of prediction to deviation (RPD) of the above predicted value is larger than 1.6, which indicates that the calibrated PLSR model can be used as a tool to retrieve soil salinity with accurate results. When the PLSR model's regression coefficients were aggregated according to the wavelength of visual (blue, green and red) and near infrared bands of LandSat Thematic Mapper(TM) sensor, some significant response values were observed, which indicates that the proposed method in this paper can be used to analyse the remotely sensed data from the space-boarded platform.

Internet of Things to network smart devices for ecosystem monitoring.

Smart, real-time, low-cost, and distributed ecosystem monitoring is essential for understanding and managing rapidly changing ecosystems. However, new techniques in the big data era have rarely been introduced into operational ecosystem monitoring, particularly for fragile ecosystems in remote areas. We introduce the Internet of Things (IoT) techniques to establish a prototype ecosystem monitoring system by developing innovative smart devices and using IoT technologies for ecosystem monitoring in isolated environments. The developed smart devices include four categories: large-scale and nonintrusive instruments to measure evapotranspiration and soil moisture, in situ observing systems for CO2 and δ13C associated with soil respiration, portable and distributed devices for monitoring vegetation variables, and Bi-CMOS cameras and pressure trigger sensors for terrestrial vertebrate monitoring. These new devices outperform conventional devices and are connected to each other via wireless communication networks. The breakthroughs in the ecosystem monitoring IoT include new data loggers and long-distance wireless sensor network technology that supports the rapid transmission of data from devices to wireless networks. The applicability of this ecosystem monitoring IoT is verified in three fragile ecosystems, including a karst rocky desertification area, the National Park for Amur Tigers, and the oasis-desert ecotone in China. By integrating these devices and technologies with an ecosystem monitoring information system, a seamless data acquisition, transmission, processing, and application IoT is created. The establishment of this ecosystem monitoring IoT will serve as a new paradigm for ecosystem monitoring and therefore provide a platform for ecosystem management and decision making in the era of big data.

Urinary levels of N-nitroso compounds in relation to risk of gastric cancer: findings from the shanghai cohort study.

BACKGROUND: N-Nitroso compounds are thought to play a significant role in the development of gastric cancer. Epidemiological data, however, are sparse in examining the associations between biomarkers of exposure to N-nitroso compounds and the risk of gastric cancer. METHODS: A nested case-control study within a prospective cohort of 18,244 middle-aged and older men in Shanghai, China, was conducted to examine the association between urinary level of N-nitroso compounds and risk of gastric cancer. Information on demographics, usual dietary intake, and use of alcohol and tobacco was collected through in-person interviews at enrollment. Urinary levels of nitrate, nitrite, N-nitroso-2-methylthiazolidine-4-carboxylic acid (NMTCA), N-nitrosoproline (NPRO), N-nitrososarcosine (NSAR), N-nitrosothiazolidine-4-carboxylic acid (NTCA), as well as serum H. pylori antibodies were quantified in 191 gastric cancer cases and 569 individually matched controls. Logistic regression method was used to assess the association between urinary levels of N-nitroso compounds and risk of gastric cancer. RESULTS: Compared with controls, gastric cancer patients had overall comparable levels of urinary nitrate, nitrite, and N-nitroso compounds. Among individuals seronegative for antibodies to H. pylori, elevated levels of urinary nitrate were associated with increased risk of gastric cancer. The multivariate-adjusted odds ratios for the second and third tertiles of nitrate were 3.27 (95% confidence interval = 0.76-14.04) and 4.82 (95% confidence interval = 1.05-22.17), respectively, compared with the lowest tertile (P for trend = 0.042). There was no statistically significant association between urinary levels of nitrite or N-nitroso compounds and risk of gastric cancer. Urinary NMTCA level was significantly associated with consumption of alcohol and preserved meat and fish food items. CONCLUSION: The present study demonstrates that exposure to nitrate, a precursor of N-nitroso compounds, may increase the risk of gastric cancer among individuals without a history of H. pylori infection.

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.

Prediagnostic levels of serum micronutrients in relation to risk of gastric cancer in Shanghai, China.

Data on blood levels of specific carotenoids and vitamins in relation to gastric cancer are scarce. Little is known about the relationship between prediagnostic serum levels of carotenoids other than beta-carotene and risk of gastric cancer especially in non-Western populations. Prediagnostic serum concentrations of alpha-carotene, beta-carotene, beta-cryptoxanthin, lycopene, lutein/zeaxanthin, retinol, alpha-tocopherol, gamma-tocopherol, and vitamin C were determined on 191 cases and 570 matched controls within a cohort of 18,244 middle-aged or older men in Shanghai, China, with a follow-up of 12 years. High serum levels of alpha-carotene, beta-carotene, and lycopene were significantly associated with reduced risk of developing gastric cancer (all Ps for trend /=3 drinks of alcohol per day; the odds ratios (95% confidence intervals) for the second, third, and fourth quartile categories were 0.69 (0.28-1.70), 0.36 (0.14-0.94), and 0.39 (0.15-0.98), respectively, compared with the lowest quartile of vitamin C (P for trend = 0.02). There were no statistically significant relationships of serum levels of beta-cryptoxanthin, lutein/zeaxanthin, retinol, alpha-tocopherol, and gamma-tocopherol with gastric cancer risk. The present study implicates that dietary carotenes, lycopene, and vitamin C are potential chemopreventive agents for gastric cancer in humans.