A comparative study of different normalized difference vegetation indices from the wide band spectral imager of Tiangong II, China

2018 ◽  
Author(s):  
Qing-sheng Liu
Keyword(s):  
Comparative Study ◽  
Vegetation Indices ◽  
Wide Band ◽  
Spectral Imager ◽  
Normalized Difference Vegetation Indices

scholarly journals Sustainable Development: Evaluating Optimal Technique for Spatial Data Forecast

Proceedings ◽  
2018 ◽  
Vol 2 (22) ◽  
pp. 1371
Author(s):  
Gaurav Kumar ◽  
Rajiv Gupta
Keyword(s):  
Information System ◽  
Spatial Data ◽  
Satellite Images ◽  
Moving Average ◽  
Vegetation Indices ◽  
Geographical Information ◽  
One Dimensional ◽  
Regression Methods ◽  
Gis Tools ◽  
Normalized Difference Vegetation Indices

This paper is an approach to forecast the spatial data in time series domain. Normally in GIS (Geographical Information System), we need raster forecasting. Moving average, exponential smoothing, and linear regression methods of forecasting are used over one-dimensional data. Present work concentrates on using these methods on satellite images applying them from pixel to pixel of historical temporal satellite data. An example set of satellite images from years 2011 to 2015 has been used to forecast the image in the year 2016. GIS tools have been developed in ArcGIS 10.1 using python to implement the methods of forecasting. Forecasted and actual images of the year 2016 have been compared by calculating the Normalized Difference Vegetation Indices (NDVI) and change detection to identify the best method.

Comparative study of wide-band ultrasonic transducers

Ultrasonics ◽  
1987 ◽  
Vol 25 (6) ◽  
pp. 315-321 ◽  
Author(s):  
R.J. Dewhurst ◽  
C.E. Edwards ◽  
A.D.W. McKie ◽  
S.B. Palmer
Keyword(s):  
Comparative Study ◽  
Wide Band ◽  
Ultrasonic Transducers

scholarly journals Ultra Wide-Band Localization and SLAM: A Comparative Study for Mobile Robot Navigation

Sensors ◽  
2011 ◽  
Vol 11 (2) ◽  
pp. 2035-2055 ◽  
Author(s):  
Marcelo Segura ◽  
Fernando Auat Cheein ◽  
Juan Toibero ◽  
Vicente Mut ◽  
Ricardo Carelli
Keyword(s):  
Comparative Study ◽  
Mobile Robot ◽  
Robot Navigation ◽  
Wide Band ◽  
Mobile Robot Navigation ◽  
Ultra Wide Band

scholarly journals Multispectral LiDAR data for land cover classification of urban areas

2021 ◽  
Author(s):  
Salem Morsy ◽  
Ahmed Shaker ◽  
Ahmed El-Rabbany
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Energy Intensity ◽  
Vegetation Indices ◽  
Land Cover Classification ◽  
Lidar Data ◽  
Radiometric Correction ◽  
Correction Model ◽  
Normalized Difference Vegetation Indices

Airborne Light Detection And Ranging (LiDAR) systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity) from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects. In this context, we aim to investigate the use of multispectral LiDAR data in land cover classification using two different techniques. The first is image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood lassifier is applied. The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs) computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass. An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively. A radiometric correction model is also applied to the intensity data in order to remove the attenuation due to the system distortion and terrain height variation. The classification process is then repeated, and the results demonstrate that there are no significant improvements achieved in the overall accuracy. Keywords: multispectral LiDAR; land cover; ground filtering; NDVI; radiometric correction

scholarly journals Multispectral LiDAR data for land cover classification of urban areas

2021 ◽  
Author(s):  
Salem Morsy ◽  
Ahmed Shaker ◽  
Ahmed El-Rabbany
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Energy Intensity ◽  
Vegetation Indices ◽  
Land Cover Classification ◽  
Lidar Data ◽  
Radiometric Correction ◽  
Correction Model ◽  
Normalized Difference Vegetation Indices

Airborne Light Detection And Ranging (LiDAR) systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity) from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects. In this context, we aim to investigate the use of multispectral LiDAR data in land cover classification using two different techniques. The first is image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood lassifier is applied. The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs) computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass. An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively. A radiometric correction model is also applied to the intensity data in order to remove the attenuation due to the system distortion and terrain height variation. The classification process is then repeated, and the results demonstrate that there are no significant improvements achieved in the overall accuracy. Keywords: multispectral LiDAR; land cover; ground filtering; NDVI; radiometric correction

scholarly journals Winter-applied Glyphosate Effects on Spring Green-up of Zoysiagrasses and ‘Yukon’ Bermudagrass in a Transition Zone

2012 ◽  
Vol 22 (1) ◽  
pp. 131-136 ◽  
Author(s):  
Filippo Rimi ◽  
Stefano Macolino ◽  
Bernd Leinauer
Keyword(s):  
Weed Control ◽  
Cynodon Dactylon ◽  
Vegetation Indices ◽  
Warm Season ◽  
Zoysia Matrella ◽  
Green Cover ◽  
Winter Annual ◽  
Annual Weeds ◽  
Normalized Difference Vegetation Indices ◽  
Warm Season Grasses

In transitional environments, turf managers and sod producers of warm-season grasses face the issue of winter annual weeds that can dominate dormant turf stands through the winter until late spring. The use of glyphosate to control weeds in dormant bermudagrass (Cynodon dactylon) has been well documented, but information is lacking about its effect on spring green-up of other warm-season grasses. A field study was conducted on two commercial sod farms in northern Italy (Expt. 1) to evaluate the effects of glyphosate applied on two different winter dates on weed control and spring green-up of ‘Zeon’ manilagrass (Zoysia matrella). A second study was carried out at the experimental agricultural farm of Padova University (Expt. 2) to assess the effects of a winter application of glyphosate on weed control and spring green-up of ‘Yukon’ bermudagrass and ‘Companion’ zoysiagrass (Zoysia japonica). Each experiment was conducted from Jan. to June 2011, and glyphosate was applied at 1.1 kg·ha−1 on 8 and 21 Feb. in Expt. 1 and on 8 Feb. in Expt. 2. Spring recovery was evaluated by periodical visual ratings of green turf cover and by collecting normalized difference vegetation indices (NDVIs). Weed injury was visually evaluated on all plots 7 weeks after the 8 Feb. glyphosate application. The visual ratings of green cover were strongly and positively correlated with NDVI measurements. Glyphosate applied in February as a single treatment effectively controlled winter weeds in ‘Zeon’ manilagrass (Expt. 1) and ‘Yukon’ bermudagrass (Expt. 2) without negatively affecting spring green-up. In contrast, spring green-up of ‘Companion’ zoysiagrass (Expt. 2) was delayed by the application of glyphosate.

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