scholarly journals Enriching Low-Density Terrain Maps from Satellite with Autonomous Robots Data

2021 ◽  
Vol 6 (1) ◽  
pp. 66
Author(s):  
Edmundo Guerra ◽  
Antoni Grau ◽  
Yolanda Bolea ◽  
Rodrigo Munguia
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Autonomous Robots ◽  
High Density ◽  
Low Density ◽  
Soil Features ◽  
Density Maps ◽  
Learning Techniques ◽  
Radar Information

Satellite imagery and remote sensoring have been used for some years in agriculture to create terrain maps for different soil features (humidity, vegetation index, etc.). Multichannel information provides lots of data, but with a big drawback: the low density of information per surface unit; that is, the multi-channeled pixels correspond to a large surface, and a fine characterization of the targeted areas is not possible. In this research, the authors propose the enrichment of such data by the use of autonomous robots that explore and sense the same targeted area of the satellite but yielding a finer detail of terrain, complementing and fusing both information sources. The sensory elements of the autonomous robots are in the visual spectrum as well as in the near-infrared spectrum, together with Lidar and radar information. This enrichment will provide a high-density map of the soil to the final user to improve crops, irrigation, seeding and other agricultural processes. The methodology to fuse data and create high-density maps will be deep learning techniques. The system will be validated in real fields with the use of real sensors to measure the data given by satellites and robots’ sensors.

Use of Near-Infrared Spectroscopy to Determine the Composition of High-Density/Low-Density Polyethylene Blend Films

1993 ◽  
Vol 47 (2) ◽  
pp. 222-228 ◽  
Author(s):  
Charles E. Miller
Keyword(s):  
Near Infrared ◽  
Principal Component Regression ◽  
Nir Spectroscopy ◽  
Principal Component ◽  
High Density ◽  
Low Density Polyethylene ◽  
Low Density ◽  
Blend Films ◽  
Polyethylene Blend

The ability of near-infrared (NIR) spectroscopy, combined with principal component regression (PCR), to nondestructively determine the blend ratio of high-density polyethylene (HDPE) and low-density polyethylene (LDPE) in extruded films is demonstrated. Results indicate that the NIR spectrum in the region 2100 to 2500 nm can be used to determine the HDPE mass percentage of 60–80- μm-thick film samples to within 2.5%, over a range of 0 to 100%. NIR spectral effects from scattering are important for the determination of the HDPE % for HDPE contents above 50%, and spectral effects from changes in the methyl group concentration and perhaps the PE crystallinity are important for the determination of the HDPE % for HDPE contents below 50%. In addition, a large variation between the spectra of replicate samples, probably caused by variations in the degree or direction of molecular orientation in the samples, was observed.

Near Infrared Spectra of Pellets and Thin Films of High-Density, Low-Density and Linear Low-Density Polyethylenes and Prediction of Their Physical Properties by Multivariate Data Analysis

2003 ◽  
Vol 11 (4) ◽  
pp. 309-321 ◽  
Author(s):  
Harumi Sato ◽  
Masahiko Shimoyama ◽  
Taeko Kamiya ◽  
Toru Amari ◽  
Slobodan Šašiç ◽  
...  
Keyword(s):  
Thin Films ◽  
Melting Point ◽  
Physical Properties ◽  
Correlation Coefficient ◽  
Spectral Resolution ◽  
Near Infrared ◽  
High Density ◽  
Low Density Polyethylene ◽  
Low Density ◽  
Calibration Models

The aim of the present study is to investigate in detail the near infrared (NIR) spectra of the three types of polyethylene, linear low-density polyethylene (LLDPE), low-density polyethylene (LDPE) and high-density polyethylene (HDPE), and to develop calibration models that predict their physical properties such as density, crystallinity and melting point. The effects of spectral resolution on the classification and the prediction of density for the three types of PE have been investigated. Furthermore, the NIR spectral differences among LLDPE, LDPE and HDPE have been explored in more detail using 2 cm−1 resolution. Principal component analysis (PCA) has been performed to differentiate the 18 samples of PE. They are classified into three groups, LLDPE, LDPE and HDPE, by a score plot of the PCA Factor 1 versus 3 based on the NIR spectra pretreated by multiplicative scatter correction (MSC). The 2 cm−1 spectral resolution yields a slightly better result for the classification. Partial least squares (PLS) regression has been applied to the NIR spectra after MSC to propose calibration models that predict the density, crystallinity and melting point of HDPE, LDPE and LLDPE. The correlation coefficient for the density was calculated to be 0.9898, 0.9928, 0.9925 and 0.9872 for the spectra obtained at 2, 4, 8 and 16 cm−1 resolutions, respectively, and the root mean square error of cross validation ( RMSECV) was found to be 0.0021, 0.0018, 0.0018 and 0.0023 g cm−3, respectively. It has been found that the correlation coefficient and RMSECV for the prediction of the density and crystallinity change little with the spectral resolution. However, for the prediction of melting point, the higher resolutions (2 and 4 cm−1 resolution) provide slightly better results than the lower resolutions. NIR transmission spectra of thin films of LLDPE, LDPE and HDPE have also been investigated, and calibration models for predicting their density have been developed for the film spectra.

Molecular Weight Characterization of Food Grade Polyethylenes and Extractables by Size Exclusion Chromatography

1981 ◽  
Vol 64 (4) ◽  
pp. 1008-1013
Author(s):  
Roger C Snyder ◽  
Charles V Breder
Keyword(s):  
Molecular Weight ◽  
Size Exclusion Chromatography ◽  
High Density ◽  
Size Exclusion ◽  
Low Density ◽  
Food Grade ◽  
Molecular Weights ◽  
Exclusion Chromatography ◽  
Band Spreading

Abstract Size exclusion chromatography (SEC) was used to characterize the molecular weight distribution (MWD) of 6 low density and 6 high density food grade polyethylene resins. The hexane and xylene extractable fractions of these 12 resins were also analyzed by SEC. An IBM 370/168 computer with an APL program was used to analyze the chromatograms and correct for chromatographic band spreading and skewing. Calculated weight average molecular weights (Mw) for the resins ranged from 40 000 to 200 000 and number average molecular weights (Mn) ranged from 6000 to 60 000. Median values of Mw and Mn were 1800 and 950, respectively, for hexane extractables from the low density resins, and were 310 and 290, respectively, for hexane extractables from the high density resins. Corresponding Mw and Mn values for xylene extractables were consistently larger than those for hexane extractables.

scholarly journals Caracterización espectral de Quillaja saponaria (Mol.)

2016 ◽  
pp. 65
Author(s):  
T. Acuña ◽  
C. Mattar ◽  
H. J. Hernández
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Spectral Response ◽  
Health State ◽  
High Concentration ◽  
Health States ◽  
Plant Vigour ◽  
Quillaja Saponaria ◽  
Spectral Signatures

<p align="justify">This paper presents a spectral reflectance characterization of the specie Quillaja saponaria (Mol.), endemic tree of Chile and valued by society due to its provision of several ecosystem services that gives to society and also for its high concentration of saponins in cortex widely used in the pharmacological industry. For spectral characterization a foliar spectral signatures protocol was designed which included standardized instrumental and environmental parameters. The spectral response of different individuals was measured to evaluate the spectral behaviour and degree of variability within species in the visible and near infrared ranges (VNIR; 400-990 nm) with two hyperspectral sensors (ASD HH and camera PDF-65-V10E). The resulting spectral signatures obtained with ASD HH showed a variation less than 5% of reflectance in VNIR and lesser than that in the transition zone from red to near infrared (red-edge; 680-730 nm). Additionally, two distinctive spectral features were detected for the specie, the first is related to a fast increase of reflectance in bands 450-480 nm and the second, to a marked decrease in the 920-970 nm range associated with water absorption features. At branch level, these distinctive features are maintained but with a smaller magnitude of reflectance, which could indicate that they are useful characteristic spectral patterns that can eventually be used for monitoring the physical health state of the specie using remote sensing. On the other hand, we used a PDF-65 camera for study the plant vigour from different health states (healthy, ill, died) with spectral vegetation index. The Plant Senescence Reflectance Index detected stress on leaves, and Triangular Vegetation Index allows for a gradually characterization of every state. This work provides the first spectral reference for one of the most important sclerophyll species of Chile.</p>

Chemical Characterization of the Serum Very Low Density and High Density Lipoproteins from Bullfrog, Rana catesbeiana

1976 ◽  
Vol 80 (6) ◽  
pp. 1241-1246 ◽  
Author(s):  
Norio SUZUKI ◽  
Kazuo DEGUCHI ◽  
Nobuo UETA ◽  
Hiroshi NAGANO ◽  
Ryoiti SHUKUYA
Keyword(s):  
Rana Catesbeiana ◽  
Chemical Characterization ◽  
High Density ◽  
High Density Lipoproteins ◽  
Low Density

scholarly journals Distribusi Spasial Kerapatan Mangrove Menggunakan Citra Sentinel-2A Di TAHURA Ngurah Rai Bali

2019 ◽  
Vol 5 (2) ◽  
pp. 192
Author(s):  
I Gede Merta Yoga Pratama ◽  
I Wayan Gede Astawa Karang ◽  
Yulianto Suteja
Keyword(s):  
Sampling Method ◽  
Vegetation Index ◽  
High Density ◽  
Low Density ◽  
Canopy Density ◽  
Ndvi Index ◽  
Sentinel 2A ◽  
Fish Eye ◽  
Very High ◽  
Better Than

The mangrove forest of TAHURA Ngurah Rai is one of the mangrove ecosystems in Bali that suffered damages and density changes due to natural factors and human activities. Remote sensing is one of the technology that can be used to estimate the density of mangrove canopy in TAHURA Ngurah Rai. The purpose of this study was to find the best vegetation index for estimating mangrove canopy density out and map it spatially using Sentinel-2A image. The method of this research is using vegetation index NDVI, EVI and mRE-SR to estimate mangrove canopy density. Field data was collected using Stratified Random and Proportional Sampling method by taking photo of the density of canopy using camera with Fish Eye lens on 34 plot. The results of this study show the satistic test of the linear model of the vegetation index with the mangrove canopy density value on the NDVI index (r = 0.8165, R2 = 0.6667, RMSE = ± 8.1508), EVI (r = 0.8597, R2 = 0.7390, RMSE = ± 7.8117), and mRE-SR (r = 0.9277, R2 = 0.8607, RMSE = ± 4.9571). The conclusion of this research is mRE-SR vegetation index able to map mangrove canopy density better than NDVI and EVI vegetation index with 86.07% accuracy. The mangrove spatial distribution generated from the mRE-SR model is 1002.22 Ha with 3.24 Ha categorized as very high density, 94.82 Ha categorized as high density, 333 Ha categorized as medium density, 402.38 Ha categorized as low density, and categorized as very low density is up to 168.76 Ha.

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