scholarly journals Response of plant reflectance spectrum to simulated dust deposition and its estimation model

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Jiyou Zhu ◽  
Xinna Zhang ◽  
Weijun He ◽  
Xuemei Yan ◽  
Qiang Yu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Spectral Reflectance ◽  
Coal Dust ◽  
Short Wave ◽  
Soil Dust ◽  
Wave Direction ◽  
Dust Deposition ◽  
Cement Dust ◽  
Blue Edge ◽  
Red Edge

Abstract To quantitatively reflect the relationship between dust and plant spectral reflectance. Dust from different sources in the city were selected to simulate the spectral characteristics of leaf dust. Taking Euonymus japonicus as the research object. Prediction model of leaf dust deposition was established based on spectral parameters. Results showed that among the three different dust pollutants, the reflection spectrum has 6 main reflection peaks and 7 main absorption valleys in 350–2500 nm. A steep reflection platform appears in the 692–763 nm band. In 760–1400 nm, the spectral reflectance gradually decreases with the increase of leaf dust coverage, and the variation range was coal dust > cement dust > pure soil dust. The spectral reflectance in 680–740 nm gradually decreases with the increase of leaf dust coverage. In the near infrared band, the fluctuation amplitude and slope of its first derivative spectrum gradually decrease with the increase of leaf dust. The biggest amplitude of variation was cement dust. With the increase of dust retention, the red edge position generally moves towards short wave direction, and the red edge slope generally decreases. The blue edge position moved to the short wave direction first and then to the long side direction, while the blue edge slope generally shows a decreasing trend. The yellow edge position moved to the long wave direction first and then to the short wave direction (coal dust, cement dust), and generally moved to the long side direction (pure soil dust). The yellow edge slope increases first and then decreases. The R2 values of the determination coefficients of the dust deposition prediction model have reached significant levels, which indicated that there was a relatively stable correlation between the spectral reflectance and dust deposition. The best prediction model of leaf dust deposition was leaf water content index model (y = 1.5019x − 1.4791, R2 = 0.7091, RMSE = 0.9725).

scholarly journals Spectral Reflectance Characteristics and Chlorophyll Content Estimation Model of Quercus aquifolioides Leaves at Different Altitudes in Sejila Mountain

2020 ◽  
Vol 10 (10) ◽  
pp. 3636 ◽  
Author(s):  
Jiyou Zhu ◽  
Weijun He ◽  
Jiangming Yao ◽  
Qiang Yu ◽  
Chengyang Xu ◽  
...  
Keyword(s):  
Chlorophyll Content ◽  
Spectral Reflectance ◽  
Spectral Response ◽  
Ecological Status ◽  
Short Wave ◽  
Relative Content ◽  
Tibet Plateau ◽  
Wave Direction ◽  
Estimation Model ◽  
Different Altitudes

Quercus aquifolioides is one of the most representative broad-leaved plants in Qinghai-Tibet Plateau with important ecological status. So far, understanding how to quickly estimate the chlorophyll content of plants in plateau areas is still an urgent problem. Field Spec 3 spectrometer was used to measure hyperspectral reflectance data of Quercus aquifolioides leaves at different altitudes, and CCI (chlorophyll relative content) of corresponding leaves was measured by a chlorophyll meter. The correlation and univariate linear fitting analysis techniques were used to establish their relationship models. The results showed that: (1) Chlorophyll relative content of Quercus aquifolioides, under different altitude gradients, were significantly different. From 2905 m to 3500 m, chlorophyll relative content increased first and then decreased. Altitude 3300 m was the most suitable growth area. (2) In 350~550 nm, the spectral reflectance was 3500 m > 3300 m > 2905 m. In 750~1100 nm, the spectral reflectivity was 2905 m > 3500 m > 3300 m. (3) There were 4 main reflection peaks and 5 main absorption valleys in the leaf surface spectral reflection curve. While, 750~1400 nm was the sensitive range of leaf spectral response of Quercus aquifolioides. (4) The red edge position and red valley position moved to short wave direction with the increase of altitude, while the yellow edge position and green peak position moved to long wave direction first and then to short wave direction. (5) The correlation curve between the original spectrum and the CCI value was the best between the wavelengths 509~650 nm. The correlation between the first derivative spectrum and CCI value was the best and most stable at 450~500 nm. The green peak reflectance was most sensitive to the relative chlorophyll content of Quercus aquifolioides. The estimation model R2 of green peak reflectance was the highest (y = 206.98e−10.85x, R2 = 0.8523), and the prediction accuracy was 95.85%. The research results can provide some technical and theoretical support for the protection of natural Quercus aquifolioides forests in Tibet.

scholarly journals Estimation of nitrate nitrogen content in cotton petioles under drip irrigation based on spectral indices and wavelet neural network

2021 ◽  
Author(s):  
Zhiqiang Dong ◽  
Yang Liu ◽  
Baoxia Ci ◽  
Ming Wen ◽  
Minghua Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Drip Irrigation ◽  
Spectral Reflectance ◽  
Vegetation Index ◽  
Regression Equation ◽  
N Content ◽  
Blue Edge ◽  
First Derivative ◽  
Red Edge

Abstract Background: Estimating nitrate nitrogen (NO3--N) content in petioles is one of the key approaches for monitoring nitrogen (N) nutrition in crops. Rapid, non-destructive, and accurate monitoring of great NO3--N content in cotton petioles under drip irrigation is of great significance.Methods: NO3--N content in cotton petioles under drip irrigation and the corresponding canopy spectral reflectance of cotton plants grown in experimental plots under various N application levels were analyzed. The correlations among ‘trilateral parameters’ and six vegetation indices, and NO3--N content in petioles were determined. A traditional regression model of NO3--N content in cotton petioles under drip irrigation was established, and a wavelet neural network (WNN) model with different index numbers was developed. The WNN model was verified using independent data, and compared with the random forest algorithm , radial basis function neural network and back propagation neural network.Results: Based on the analyses of ‘trilateral parameters’ and petiole NO3--N content, blue edge amplitude (Db) and blue edge area (SDb) of the blue edge parameters exhibited a strong positive correlation with petiole NO3--N content, and the correlation coefficients was 0.90. Among the blue edge parameters, the coefficient of determination (R2) of the Db polynomial regression equation and petiole NO3--N content was the highest (R2 = 0.89), while the root mean square error (RMSE) of the linear regression model was the lowest (RMSE = 1.04). R2 value of the traditional regression model developed using blue edge parameters and petiole NO3--N content significantly increased, while RMSE value decreased when compared with those of the red edge and yellow edge parameters. Analyses results of the vegetation index developed using original spectral reflectance data and the vegetation index developed using the first set of derivative spectral reflectance data and petiole NO3--N content, revealed that the first derivative vegetation index, normalized difference spectral index (ND705) exhibited a strong negative correlation, with a correlation coefficient of -0.90. The first derivative vegetation index, ND705 and petiole NO3--N content index regression equation had the highest coefficient of determination (R2 = 0.83), while the first derivative vegetation index, red edge model index (CIred-edge) and petiole NO3--N content linear regression equation had the lowest RMSE = 0.92. R2 value of the traditional regression equation for the first derivative vegetation index and petiole NO3--N content significantly increased, while the RMSE value decreased when compared with the original spectral vegetation index. After conducting correlation analyses and developing traditional regression models, Db and SDb of the blue edge parameters, and the first derivative vegetation index, ND705 and CIred-edge were used to develop a WNN model. The model based on blue edge parameters had R2 of 0.88, RMSE of 0.74g/L and mean absolute error (MAE) of 0.58 g/L, the R2 value was 8.6% higher than the R2 the first derivative vegetation index model, in which RMSE and MAE reduced by 18.7% and 20.5%, respectively. The model was tested using independent verification data, and which revealed that the R2 value of the model was 0.88, RMSE was 0.65g/L, and MAE was 0.47g/L based on the blue edge parameters, predicted value of WNN, and true value of the verification model, which was superior other models. The performance of the WNN model based on the blue edge parameters improved by 7.3%, and RMSE and MAE reduced by 25.2% and 30.9%, respectively when compared with those of the vegetation index model.Conclusion: The present study demonstrated that an inexpensive approach consisting of WNN algorithm and spectrum can be used to enhance the accuracy of NO3--N content estimation in cotton petioles under drip irrigation, which reflects their practical application potential.

scholarly journals Status of Phenological Research Using Sentinel-2 Data: A Review

2020 ◽  
Vol 12 (17) ◽  
pp. 2760
Author(s):  
Gourav Misra ◽  
Fiona Cawkwell ◽  
Astrid Wingler
Keyword(s):  
Vegetation Index ◽  
Temporal Frequency ◽  
Short Wave ◽  
Current State ◽  
Red Edge ◽  
Natural Grasslands ◽  
Wide Range ◽  
Normalised Difference Vegetation Index ◽  
Satellite Sensors ◽  
Sentinel 2

Remote sensing of plant phenology as an indicator of climate change and for mapping land cover has received significant scientific interest in the past two decades. The advancing of spring events, the lengthening of the growing season, the shifting of tree lines, the decreasing sensitivity to warming and the uniformity of spring across elevations are a few of the important indicators of trends in phenology. The Sentinel-2 satellite sensors launched in June 2015 (A) and March 2017 (B), with their high temporal frequency and spatial resolution for improved land mapping missions, have contributed significantly to knowledge on vegetation over the last three years. However, despite the additional red-edge and short wave infra-red (SWIR) bands available on the Sentinel-2 multispectral instruments, with improved vegetation species detection capabilities, there has been very little research on their efficacy to track vegetation cover and its phenology. For example, out of approximately every four papers that analyse normalised difference vegetation index (NDVI) or enhanced vegetation index (EVI) derived from Sentinel-2 imagery, only one mentions either SWIR or the red-edge bands. Despite the short duration that the Sentinel-2 platforms have been operational, they have proved their potential in a wide range of phenological studies of crops, forests, natural grasslands, and other vegetated areas, and in particular through fusion of the data with those from other sensors, e.g., Sentinel-1, Landsat and MODIS. This review paper discusses the current state of vegetation phenology studies based on the first five years of Sentinel-2, their advantages, limitations, and the scope for future developments.

scholarly journals The Physiological and Microbiological Characteristics of Cement Dust Polluted Soil Around Cement Industry

2021 ◽  
Author(s):  
Kehinde Oluwadamilare Sowunmi
Keyword(s):  
Heavy Metals ◽  
Microbial Population ◽  
Nutrient Status ◽  
Polluted Soil ◽  
Population Diversity ◽  
Cement Industry ◽  
Dust Deposition ◽  
Cement Dust ◽  
Bacteria And Fungi ◽  
The Impact

Abstract A study investigated impact of cement dust pollution from Ewekoro cement industry on soil microbes. pH of the soil ranged from 6.27±0.03- 6.47 and soil moisture content ranged from 15.78±2.52- 9.65±1.16. The levels of heavy metals except Mg, Zn and Na were higher within the factory than in the control. Microbial population diversity increased steadily away from the factory and this variation could be attributed to the impact of pH and heavy metals on microbial population. The lower counts of bacteria compared to fungi may be as a result of the nutrient status of the soil and the bacteria counts in polluted soil were lower than the fungal counts in control soil. The bacteria and fungi was influenced by the cement dust deposition. The study was published in the journal ‘Phenomenon: Microbes and the Cement Industry’.

The Chemical Composition and Sources of PM10 in Urban Road Dust

2011 ◽  
Vol 71-78 ◽  
pp. 2749-2752 ◽  
Author(s):  
Ya Hong Jia ◽  
Lin Peng ◽  
Ling Mu
Keyword(s):  
Chemical Composition ◽  
Fine Particles ◽  
Road Dust ◽  
Shanxi Province ◽  
Soil Dust ◽  
Cement Dust ◽  
Urban Road ◽  
Chemical Profiles

Samples of road dust were collected in Changzhi, Taiyuan and Jincheng in Shanxi Province, the characteristics of the chemical composition of fine particles (diameter ≤ 10μm)and the chemical profiles of road dust obtained from different cities were analyzed, the "double source apportion" technology was applied to identify the source of road dust . Results show that: the chemical profiles of road dust vary significantly in different regions, however, all contain a high percentage of Si, Ca, Al, TC and OC, with the highest content of Si; Road dust originates mainly from soil dust, smoke and dust from coal and cement dust, and their contributions to road dust are 50%,25% and 15% in Changzhi ,47%,26% and 20% in Taiyuan,48%,21% and 22% in Jincheng, respectively.

Effects of coal dust deposition on seedling growth ofHedysarum laeveMaxim., a dominant plant species on Ordos Plateau

2016 ◽  
Vol 36 (10) ◽  
Author(s):  
杨慧玲 YANG Huiling ◽  
魏玲玲 WEI Lingling ◽  
叶学华 YE Xuehua ◽  
刘国方 LIU Guofang ◽  
杨学军 YANG Xuejun ◽  
...  
Keyword(s):  
Plant Species ◽  
Seedling Growth ◽  
Coal Dust ◽  
Dust Deposition ◽  
Ordos Plateau ◽  
Dominant Plant Species

scholarly journals Identification of Rice Sheath Blight through Spectral Responses Using Hyperspectral Images

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6243 ◽  
Author(s):  
Fenfang Lin ◽  
Sen Guo ◽  
Changwei Tan ◽  
Xingen Zhou ◽  
Dongyan Zhang
Keyword(s):  
Leaf Blade ◽  
Near Infrared ◽  
Spectral Feature ◽  
Sheath Blight ◽  
Peak Amplitude ◽  
Blue Edge ◽  
Red Edge ◽  
Tree Classifier ◽  
Significant Difference ◽  
Spectral Responses

Sheath blight (ShB), caused by Rhizoctonia solani AG1-I, is one of the most important diseases in rice worldwide. The symptoms of ShB primarily develop on leaf sheaths and leaf blades. Hyperspectral remote sensing technology has the potential of rapid, efficient and accurate detection and monitoring of the occurrence and development of rice ShB and other crop diseases. This study evaluated the spectral responses of leaf blade fractions with different development stages of ShB symptoms to construct the spectral feature library of rice ShB based on “three-edge” parameters and narrow-band vegetation indices to identify the disease on the leaves. The spectral curves of leaf blade lesions have significant changes in the blue edge, green peak, yellow edge, red valley, red edge and near-infrared regions. The variables of the normalized index between green peak amplitude and red valley amplitude (Rg − Ro)/(Rg + Ro), the normalized index between the yellow edge area and blue edge area (SDy − SDb)/(SDy + SDb), the ratio index of green peak amplitude and red valley amplitude (Rg/Ro) and the nitrogen reflectance index (NRI) had high relevance to the disease. At the leaf scale, the importance weights of all attributes decreased with the effect of non-infected areas in a leaf by the ReliefF algorithm, with Rg/Ro being the indicator having the highest importance weight. Estimation rate of 95.5% was achieved in the decision tree classifier with the parameter of Rg/Ro. In addition, it was found that the variety degree of absorptive valley, reflection peak and reflecting steep slope was different in the blue edge, green and red edge regions, although there were similar spectral curve shapes between leaf sheath lesions and leaf blade lesions. The significant difference characteristic was the ratio index of the red edge area and green peak area (SDr/SDg) between them. These results can provide the basis for the development of a specific sensor or sensors system for detecting the ShB disease in rice.

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