Effect of Dust Deposition on Chlorophyll Concentration Estimation in Urban Plants from Reflectance and Vegetation Indexes

Using reflectance spectroscopy to monitor vegetation pigments is a crucial method to know the nutritional status, environmental stress, and phenological phase of vegetation. Defining cities as targeted areas and common greening plants as research objects, the pigment concentrations and dust deposition amounts of the urban plants were classified to explore the spectral difference, respectively. Furthermore, according to different dust deposition levels, this study compared and discussed the prediction models of chlorophyll concentration by correlation analysis and linear regression analysis. The results showed: (1) Dust deposition had interference effects on pigment concentration, leaf reflectance, and their correlations. Dust was an essential factor that must be considered. (2) The influence of dust deposition on chlorophyll—a concentration estimation was related to the selected vegetation indexes. Different modeling indicators had different sensitivity to dust. The SR705 and CIrededge vegetation indexes based on the red edge band were more suitable for establishing chlorophyll-a prediction models. (3) The leaf chlorophyll concentration prediction can be achieved by using reflectance spectroscopy data. The effect of the chlorophyll estimation model under the levels of “Medium dust” and “Heavy dust” was worse than that of “Less dust”, which meant the accumulation of dust had interference to the estimation of chlorophyll concentration. The quantitative analysis of vegetation spectrum by reflectance spectroscopy shows excellent advantages in the research and application of vegetation remote sensing, which provides an important theoretical basis and technical support for the practical application of plant chlorophyll content prediction.

Download Full-text

COMPARISON OF CHLOROPHYLL CONCENTRATION ESTIMATION USING TWO DIFFERENT ALGORITHMS AND THE EFFECT OF COLORED DISSOLVED ORGANIC MATTER

International Journal of Remote Sensing and Earth Sciences (IJReSES) ◽

10.30536/j.ijreses.2008.v5.a1232 ◽

2010 ◽

Vol 5 (1) ◽

Author(s):

Bisman Nababan

Keyword(s):

Organic Matter ◽

Dissolved Organic Matter ◽

Chlorophyll A ◽

Mississippi River ◽

Chlorophyll Concentration ◽

Wide Field ◽

Colored Dissolved Organic Matter ◽

Mississippi River Plume ◽

Concentration Estimation ◽

Mission Specification

The effect of colored dissolved organic matter (CDOM) on the Sea-viewing Wide Field-of-view Sensor(Sea WiFS) OC4v4 and the MODIS algorithms used to estimate chlorophyll-a was studied using satellite and situ data collated during seasonal cruises in the Northeastern Gulf of Mexico between 1997 and 2000. For chlorophyll-a concentrations 50 mg m, OC4v4 generally overestimated chlorophyll-a concentration by up to 300 percent. The MODIS algorithm provided better estimates of high CDOM concentration, found typically nearshore in noterhn summer and spring. For oceanic waters where chlorophyll-a concentrations 1.0 mg m, both OC4v4 and MODIS algorithm had errors within the Sea WiFS mission specification (35 percent) during fall. The OC4v4 algorithm is more susceptible to artifacts due to CDOM absorption of light at 443 mm. Keywords: chlorophyll-a, Mississippi River Plume, Sea WiFS, upwelling, OC4v4.

Download Full-text

Optimized Method for Mapping Inorganic Pigments by Means of Multispectral Imaging Combined with Hyperspectral Spectroscopy for the Study of Vincenzo Pasqualoni’s Wall Painting at the Basilica of S. Nicola in Carcere in Rome

Minerals ◽

10.3390/min11080839 ◽

2021 ◽

Vol 11 (8) ◽

pp. 839

Author(s):

Lucilla Pronti ◽

Giuseppe Capobianco ◽

Margherita Vendittelli ◽

Anna Candida Felici ◽

Silvia Serranti ◽

...

Keyword(s):

Multispectral Imaging ◽

Imaging System ◽

Principal Component ◽

Reflectance Spectroscopy ◽

Wall Painting ◽

High Spectral Resolution ◽

Spectral Difference ◽

Color Palette ◽

A Minor ◽

Nir Reflectance Spectroscopy

Multispectral imaging is a preliminary screening technique for the study of paintings. Although it permits the identification of several mineral pigments by their spectral behavior, it is considered less performing concerning hyperspectral imaging, since a limited number of wavelengths are selected. In this work, we propose an optimized method to map the distribution of the mineral pigments used by Vincenzo Pasqualoni for his wall painting placed at the Basilica of S. Nicola in Carcere in Rome, combining UV/VIS/NIR reflectance spectroscopy and multispectral imaging. The first method (UV/VIS/NIR reflectance spectroscopy) allowed us to characterize pigment layers with a high spectral resolution; the second method (UV/VIS/NIR multispectral imaging) permitted the evaluation of the pigment distribution by utilizing a restricted number of wavelengths. Combining the results obtained from both devices was possible to obtain a distribution map of a pictorial layer with a high accuracy level of pigment recognition. The method involved the joint use of point-by-point hyperspectral spectroscopy and Principal Component Analysis (PCA) to identify the pigments in the color palette and evaluate the possibility to discriminate all the pigments recognized, using a minor number of wavelengths acquired through the multispectral imaging system. Finally, the distribution and the spectral difference of the different pigments recognized in the multispectral images, (in this case: red ochre, yellow ochre, orpiment, cobalt blue-based pigments, ultramarine and chrome green) were shown through PCA false-color images.

Download Full-text

Can chlorophyll-a in meso-oligotrophic shallow waters be estimated using statistical approaches and empirical models from MODIS imagery?

RBRH ◽

10.1590/2318-0331.231820170106 ◽

2018 ◽

Vol 23 (0) ◽

Author(s):

Andres Mauricio Munar ◽

José Rafael Cavalcanti ◽

Juan Martin Bravo ◽

David Manuel Lelinho Da Motta Marques ◽

Carlos Ruberto Fragoso Júnior

Keyword(s):

Linear Regression ◽

Spatial Heterogeneity ◽

Multiple Linear Regression ◽

Chlorophyll A ◽

Linear Regression Analysis ◽

Multiple Linear Regression Analysis ◽

Accurate Estimation ◽

Shallow Waters ◽

Chl A ◽

Input Variables

ABSTRACT Accurate estimation of chlorophyll-a (Chl-a) concentration in inland waters through remote-sensing techniques is complicated by local differences in the optical properties of water. In this study, we applied multiple linear regression (MLR), artificial neural network (ANN), nonparametric multiplicative regression (NPMR) and four models (Appel, Kahru, FAI and O14a) to estimate the Chl -a concentration from combinations of spectral bands from the MODIS sensor. The MLR, NPMR and ANN models were calibrated and validated using in-situ Chl -a measurements. The results showed that a simple and efficient model, developed and validated through multiple linear regression analysis, offered advantages (i.e., better performance and fewer input variables) in comparison with ANN, NPMR and four models (Appel, Kahru, FAI and O14a). In addition, we observed that in a large shallow subtropical lake, where the wind and hydrodynamics are essential factors in the spatial heterogeneity (Chl-a distribution), the MLR model adjusted using the specific point dataset, performed better than using the total dataset, which suggest that would not be appropriate to generalize a single model to estimate Chl-a in these large shallow lakes from total datasets. Our approach is a useful tool to estimate Chl -a concentration in meso-oligotrophic shallow waters and corroborates the spatial heterogeneity in these ecosystems.

Download Full-text

Low-order Spherical Harmonic HRTF Restoration using a Neural Network Approach

10.20944/preprints202007.0209.v1 ◽

2020 ◽

Author(s):

Benjamin Tsui ◽

William A. P. Smith ◽

Gavin Kearney

Keyword(s):

Neural Network ◽

Spherical Harmonic ◽

High Frequency ◽

Prediction Models ◽

Machine Learning Algorithms ◽

Network Approach ◽

Spectral Difference ◽

Neural Network Approach ◽

Frequency Representation ◽

Head Related Transfer Function

Spherical harmonic (SH) interpolation is a commonly used method to spatially up-sample sparse Head Related Transfer Function (HRTF) datasets to denser HRTF datasets. However, depending on the number of sparse HRTF measurements and SH order, this process can introduce distortions in high frequency representation of the HRTFs. This paper investigates whether it is possible to restore some of the distorted high frequency HRTF components using machine learning algorithms. A combination of Convolutional Auto-Encoder (CAE) and Denoising Auto-Encoder (DAE) models is proposed to restore the high frequency distortion in SH interpolated HRTFs. Results are evaluated using both Perceptual Spectral Difference (PSD) and localisation prediction models, both of which demonstrate significant improvement after the restoration process.

Download Full-text

SPATIAL-TEMPORAL DISTRIBUTION OF CHLOROPHYLL-A IN SOUTHERN PART OF THE MAKASSAR STRAIT

Jurnal Ilmu Kelautan SPERMONDE ◽

10.20956/jiks.v4i1.3804 ◽

2018 ◽

Vol 4 (1) ◽

Author(s):

Wasir Samad Daming ◽

Muhammad Anshar Amran ◽

Amir Hamzah Muhiddin ◽

Rahmadi Tambaru

Keyword(s):

Chlorophyll A ◽

Satellite Data ◽

Chlorophyll Concentration ◽

Temporal Distribution ◽

Fishing Ground ◽

Landsat 8 ◽

Trade Winds ◽

Chl A ◽

June July ◽

Potential Area

Surface chlorophyll-a (Chl-a) distribution have been analyzed with seasonal variation during southeast monsoon in southern part of Makassar Strait and Flores Sea. Satellite data of Landsat-8 is applied to this study to formulate the distribution of chlorophyll concentration during monsoonal wind period. The distribution of chlorophyll concentration was normally peaked condition in August during southeast monsoon. Satellite data showed that a slowdown in the rise of the distribution of chlorophyll in September with a lower concentration than normal is likely due to a weakening the strength of southeast trade winds during June – July – August 2016. Further analysis shows that the southern part of the Makassar strait is likely occurrence of upwelling characterized by increase in surface chlorophyll concentrations were identified as the potential area of fishing ground.

Download Full-text

Using real-time ultrasound for in vivo assessment of carcass and internal adipose depots of dairy sheep

The Journal of Agricultural Science ◽

10.1017/s0021859620000106 ◽

2019 ◽

Vol 157 (7-8) ◽

pp. 650-658

Author(s):

J. Afonso ◽

C. M. Guedes ◽

A. Teixeira ◽

V. Santos ◽

J. M. T. Azevedo ◽

...

Keyword(s):

Real Time ◽

Prediction Models ◽

Linear Regression Analysis ◽

Subcutaneous Fat ◽

Live Weight ◽

Multiple Linear Regression Analysis ◽

Fat Content ◽

Fat Reserves ◽

Intermuscular Fat

AbstractFifty-one Churra da Terra Quente ewes (4–7 years old) were used to analyse the potential of real-time ultrasound (RTU) to predict the amount of internal adipose depots, in addition to carcass fat (CF). The prediction models were developed from live weight (LW) and RTU measurements taken at eight different locations. After correlation and multiple linear regression analysis, the prediction models were evaluated by k-fold cross-validation and through the ratio of prediction to deviation (RPD). All prediction models included at least one RTU measurement as an independent variable. Prediction models for the absolute weight of the different adipose depots showed higher accuracy than prediction models for fat content per kg of LW. The former showed to be very good or excellent (2.4 ⩽ RPD ⩽ 3.8) for all adipose depots except mesenteric fat (MesF) and thoracic fat, with the model for MesF still providing useful information (RPD = 1.8). Prediction models for fat content per kg of LW were also very good or excellent for subcutaneous fat, intermuscular fat, CF and body fat (2.6 ⩽ RPD ⩽ 3.2), while the best prediction models for omental fat, kidney knob, channel fat and internal fat still provided useful information. Despite some loss in the accuracy of the estimates obtained, there was a similar pattern in terms of RPD for models developed from LW and RTU measurements taken just at the level of the 11th thoracic vertebra. In vivo RTU measurements showed the potential to monitor changes in ewe internal fat reserves as well as in CF.

Download Full-text

Chlorophyll concentration estimation model based on leaf spectra of salt marsh vegetation

2010 Second IITA International Conference on Geoscience and Remote Sensing ◽

10.1109/iita-grs.2010.5603016 ◽

2010 ◽

Cited By ~ 1

Author(s):

Xia Lu ◽

Fucheng Liu

Keyword(s):

Salt Marsh ◽

Chlorophyll Concentration ◽

Salt Marsh Vegetation ◽

Estimation Model ◽

Marsh Vegetation ◽

Model Based ◽

Concentration Estimation

Download Full-text

Prediction of soil organic and inorganic carbon concentrations in Tunisian samples by mid-infrared reflectance spectroscopy using a French national library

10.5194/egusphere-egu2020-13092 ◽

2020 ◽

Author(s):

Tiphaine Chevallier ◽

Cécile Gomez ◽

Patricia Moulin ◽

Imane Bouferra ◽

Kaouther Hmaidi ◽

...

Keyword(s):

Inorganic Carbon ◽

Prediction Models ◽

Reflectance Spectroscopy ◽

Soil Samples ◽

Infrared Reflectance ◽

Least Squares Regression ◽

Mid Infrared ◽

Climate Conditions ◽

Infrared Reflectance Spectroscopy ◽

Soil Database

<p>Mid-Infrared Reflectance Spectroscopy (MIRS, 4000&#8211;400 cm<sup>-1</sup>) is being considered to provide accurate estimations of soil properties, including soil organic carbon (SOC) and soil inorganic carbon (SIC) contents. This has mainly been demonstrated when datasets used to build, validate and test the prediction model originate from the same area A, with similar geopedological conditions. The objective of this study was to analyze how MIRS performed when used to predict SOC and SIC contents, from a calibration database collected over a region A, to predict over a region B, where A and B have no common area and different soil and climate conditions. This study used a French MIRS soil dataset including 2178 soil samples to calibrate SIC and SOC prediction models with partial least squares regression (PLSR), and a Tunisian MIRS soil dataset including 96 soil samples to test them. Our results showed that using the French MIRS soil database i) SOC and SIC of French samples were successfully predicted, ii) SIC of Tunisian samples was also predicted successfully, iii) local calibration significantly improved SOC prediction of Tunisian samples and iv) prediction models seemed more robust for SIC than for SOC. So in future, MIRS might replace, or at least be considered as, a conventional physico-chemical analysis technique, especially when as exhaustive as possible calibration database will become available.</p>

Download Full-text

Trends in the utilization of nuclear medicine and radiopharmaceuticals in an aging population

Nuklearmedizin ◽

10.3413/nukmed-0778-15-11 ◽

2016 ◽

Vol 55 (04) ◽

pp. 139-144 ◽

Cited By ~ 1

Author(s):

Jeng-Jong Hwang ◽

Mao-Chin Hung

Keyword(s):

Nuclear Medicine ◽

Life Expectancy ◽

Prediction Models ◽

Linear Regression Analysis ◽

Aging Population ◽

Multiple Linear Regression Analysis ◽

Whole Body ◽

Research Database ◽

Nuclear Medicine Procedures ◽

Nuclear Medicine Practice

SummaryAim: To investigate the trends in the utilization of nuclear medicine procedures and radiopharmaceuticals in an aging population and to establish the prediction models. Methods: Based on Taiwan’s National Health Insurance Research Database, a longitudinal study was conducted from 2000 to 2012. Descriptive statistics were adopted to analyze the frequencies and distributions of nuclear medicine procedures. Multiple linear regression analysis was applied to establish the prediction model for the utilization of nuclear medicine. Results: The utilization of myocardial perfusion imaging increased most significantly, i.e. 250 per million population (pmp) increment annually, followed by the whole-body bone scan (176 pmp) and wholebody PET scan (100 pmp) in Taiwan during the period of 2000-2012. The use rate of nuclear medicine procedure which the first quartile (Q1) of age at examination above 35 years fits the regression model: Use rate expected year = 0.03 Q1 of age at examination × use rate baseline year + 14797 life expectancy expected year / life expectancy baseline year – 15030. Adversely, the use rate of procedure which Q1 of age at examination below 35 years fits the model: Use rate expected year = 0.01 Q1 of age at examination × use rate baseline year – 4565 life expectancy expected year / life expectancy baseline year + 4749. In addition, the similar models were found in the applications of radiopharmaceuticals. Conclusion: This study demonstrates the age at examination and life expectancy can be used to predict the utilities of nuclear medicine procedures and radiopharmaceuticals in an aging population. Nuclear medicine practice applied in the geriatrics would increase significantly with the aging of population.

Download Full-text