scholarly journals DISCRIMINATION AND CHARACTERIZATION OF PROMINENT DESERTIC VEGETATIONS USING HYPERSPECTRAL IMAGING DATA

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 363-368 ◽  
Author(s):  
S. L. Borana ◽  
S. K. Yadav ◽  
R. T. Paturkar
Keyword(s):  
Hyperspectral Imaging ◽  
Tree Species ◽  
Vegetation Type ◽  
Spectral Band ◽  
Vegetation Indices ◽  
Hyperspectral Data ◽  
Imaging Data ◽  
Area Index ◽  
Vegetation Species

<p><strong>Abstract.</strong> Imaging Hyperspectral data are advent as potential solutions in modeling, discrimination and mapping of vegetation species. Hyperspectral remote sensing provides valuable information about vegetation type, leaf area index, chlorophyll, and leaf nutrient concentration. Estimation of these vegetation parameters has been made possible by calculating various vegetation indices (VIs), usually by ratioing, differencing, ratioing differences and combinations of suitable spectral band. This paper presents a ground-based hyperspectral imaging system for characterizing vegetation spectral features. In this study, a ground-based hyperspectral imaging data (AISA VNIR 400&amp;ndash;960&amp;thinsp;nm, Spectral Resolution @ 2.5&amp;thinsp;nm) was used for spectral vegetation discrimination and characterization of natural desertic tree species. This study assessed the utility of hyperspectral imagery of 240 narrow bands in discrimination and classification of desert tree species in Jodhpur region using ENVI software. Vegetation indices derived from hyperspectral images used in the Analysis for tree species classification discrimination study. Prominent occurring two desertic tree species, viz., Neem and Babul in Jodhpur region could be effectively discriminated. Study demonstrated the potential utility of narrow spectral bands of Hyperspectral Imaging data in discriminating vegetation species in a desertic terrain.</p>

Generalization of Vegetation Indices for Monitoring the Terrestrial Biosphere

2021 ◽  
Author(s):  
Gustau Camps-Valls ◽  
Manuel Campos-Taberner ◽  
Alvaro Moreno-Martinez ◽  
Sophia Walther ◽  
Grégory Duveiller ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Multispectral Imaging ◽  
Spectral Band ◽  
Vegetation Indices ◽  
Observation Data ◽  
Terrestrial Biosphere ◽  
Time Resolved ◽  
Area Index ◽  
Nonlinear Generalization

&lt;p&gt;Vegetation indices are the most widely used tool in remote sensing and multispectral imaging applications. This paper introduces a nonlinear generalization of the broad family of vegetation indices based on spectral band differences and ratios. The presented indices exploit all higher-order relations of the involved spectral channels, are easy to derive and use, and give some insight on problem complexity. The framework is illustrated to generalize the widely adopted Normalized Difference Vegetation Index (NDVI). Its nonlinear generalization named, kernel NDVI (kNDVI), largely improves performance over NDVI and the recent NIRv in monitoring key vegetation parameters, showing much higher correlation with independent products, such as the MODIS leaf area index (LAI), flux tower gross primary productivity (GPP), and GOME-2 sun-induced fluorescence. The family of indices constitutes a valuable choice for many applications that require spatially explicit and time-resolved analysis of Earth observation data.&lt;/p&gt;&lt;p&gt;&lt;span&gt; Reference: &lt;strong&gt;&quot;&lt;span&gt;A Unified Vegetation Index for Quantifying the Terrestrial Biosphere&lt;/span&gt;&quot;&lt;/strong&gt;, &lt;/span&gt;&lt;span&gt;Gustau Camps-Valls, Manuel Campos-Taberner, &amp;#193;lvaro Moreno-Mart&amp;#305;&amp;#769;nez, Sophia Walther, Gr&amp;#233;gory Duveiller, Alessandro Cescatti, Miguel Mahecha, Jordi Mu&amp;#241;oz-Mar&amp;#305;&amp;#769;, Francisco Javier Garc&amp;#305;&amp;#769;a-Haro, Luis Guanter, John Gamon, Martin Jung, Markus Reichstein, Steven W. Running. &lt;/span&gt;&lt;em&gt;&lt;span&gt;&lt;span&gt;Science Advances, in press&lt;/span&gt;&lt;/span&gt;&lt;span&gt;, &lt;/span&gt; &lt;span&gt;2021&lt;/span&gt; &lt;/em&gt;&lt;/p&gt;

scholarly journals An Overview of Hyperspectral Remote Sensing and its applications in various Disciplines

2016 ◽  
Vol 5 (2) ◽  
pp. 85 ◽  
Author(s):  
Alpana Shukla ◽  
Rajsi Kot
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Imaging ◽  
Spatial Information ◽  
Coastal Zone Management ◽  
Hyperspectral Remote Sensing ◽  
Hyperspectral Data ◽  
Data Cube ◽  
Data Sets ◽  
Imaging Data ◽  
Advantages And Disadvantages

<div><p><em>Recent advances in remote sensing and geographic information has opened new directions for the development of hyperspectral sensors. Hyperspectral remote sensing, also known as imaging spectroscopy is a new technology. Hyperspectral imaging is currently being investigated by researchers and scientists for the detection and identification of vegetation, minerals, different objects and background.</em><em> Hyperspectral remote sensing combines imaging and spectroscopy in a single system which often includes large data sets and requires new processing methods. Hyperspectral data sets are generally made of about 100 to 200 spectral bands of relatively narrow bandwidths (5-10 nm), whereas, multispectral data sets are usually composed of about 5 to 10 bands of relatively large bandwidths (70-400 nm). Hyperspectral imagery is collected as a data cube with spatial information collected in the X-Y plane, and spectral information represented in the Z-direction. </em><em>Hyperspectral remote sensing is applicable in many different disciplines. It was originally developed for mining and geology; it has now spread into fields such as agriculture and forestry, ecology, coastal zone management, geology and mineral exploration. This paper presents an overview of hyperspectral imaging, data exploration and analysis, applications in various disciplines, advantages and disadvantages and future aspects of the technique.</em></p></div>

scholarly journals Laboratory and image spectroscopy for evaluating the biophysical state of meadow vegetation in the Krkonoše National Park

2014 ◽  
Vol 18 (2) ◽  
pp. 15-22 ◽  
Author(s):  
Jan Jelének ◽  
Lucie Kupková ◽  
Bogdan Zagajewski ◽  
Stanislav Březina ◽  
Adrian Ochytra ◽  
...  
Keyword(s):  
Invasive Species ◽  
National Park ◽  
Vegetation Indices ◽  
In Situ Measurements ◽  
Hyperspectral Data ◽  
Coefficient Of Determination ◽  
Area Index ◽  
Area Of Interest ◽  
Maximal Sensitivity

Abstract The paper deals with the evaluation of mountain meadow vegetation condition using in-situ measurements of the fraction of Accumulated Photosynthetically Active Radiation (fAPAR) and Leaf Area Index (LAI). The study analyses the relationship between these parameters and spectral properties of meadow vegetation and selected invasive species with the goal of finding out vegetation indices for the detection of fAPAR and LAI. The developed vegetation indices were applied on hyperspectral data from an APEX (Airborne Prism Experiment) sensor in the area of interest in the Krkonoše National Park. The results of index development on the level of the field data were quite good. The maximal sensitivity expressed by the coefficient of determination for LAI was R2 = 0.56 and R2 = 0.79 for fAPAR. However, the sensitivity of all the indices developed at the image level was quite low. The output values of in-situ measurements confirmed the condition of invasive species as better than that of the valuable original meadow vegetation, which is a serious problem for national park management.

scholarly journals Detection, Identification, and Quantification of SF6 Point-Source Emissions Using Hyper-Cam LW Airborne Platform

2021 ◽  
Vol 25 (3) ◽  
pp. 37-41
Author(s):  
Mariusz Kastek ◽  
Krzysztof Firmanty ◽  
Benjamin Saute ◽  
Philippe Gagnon ◽  
Martin Lariviere-Bastien ◽  
...  
Keyword(s):  
Greenhouse Gases ◽  
Hyperspectral Imaging ◽  
Hyperspectral Data ◽  
Gas Detection ◽  
Gas Release ◽  
Imaging Data ◽  
Regulatory Guidelines ◽  
Quantification Analysis ◽  
Processing Algorithms ◽  
Identification And Quantification

Detection, identification, and quantification of greenhouse gases is essential to ensure compliance with regulatory guidelines and mitigate damage associated with anthropogenic climate change. Passive infrared hyperspectral imaging technology is among the solutions that can detect, identify and quantify multiple greenhouse gases simultaneously. The Telops Hyper-Cam Airborne Platform is an established system for aerial thermal infrared hyperspectral measurements for gas survey applications. In support of the Hypercam, is developing a suite of hyperspectral imaging data processing algorithms that allow for gas detection, identification, and quantification in real-time. In the Fall of 2020, the Hyper-Cam-LW Airborne platform was flown above a validated SF6 gas release system to collect hyperspectral data for gas quantification analysis. This measurement campaign was performed to document performance of the Hyper-Cam gas quantification capabilities against known quantities of released gas.

scholarly journals Estimación de variables biofísicas del pastizal en un ecosistema de dehesa a partir de espectro-radiometría de campo e imágenes hiperespectrales aeroportadas

2017 ◽  
pp. 13 ◽  
Author(s):  
J. R. Melendo-Vega ◽  
M. P. Martín ◽  
L. Vilar del Hoyo ◽  
J. Pacheco-Labrador ◽  
P. Echavarría ◽  
...  
Keyword(s):  
Vegetation Indices ◽  
Hyperspectral Data ◽  
Biophysical Parameters ◽  
Area Index ◽  
Linear Relationships ◽  
Spectral Vegetation Indices ◽  
Vegetation Parameters ◽  
Fuel Moisture Content ◽  
Canopy Water ◽  
Compact Airborne Spectrographic Imager

<p>The aim of this paper is the estimation of biophysical vegetation parameters from its optical properties. The variables Fuel Moisture Content (<em>FMC</em>), Canopy Water Content (<em>CWC</em>), Leaf Area Index (<em>LAI</em>), dry matter (Cm) and AboveGround Biomass (AGB) were estimated in the laboratory from vegetation samples collected simultaneously with the acquisition of spectral data from the Compact Airborne Spectrographic Imager (CASI) sensor and the field spectroradiometer ASD FieldSpec® 3. Spectral vegetation indices found in the literature were computed from hyperspectral data. Their linear relationships with the biophysical variables measured in the field were analysed. Results show consistent relationships between analysed biophysical parameters and spectral indices, mainly those using SWIR and red-egde bands which reveal the importance of these spectral regions for the estimation of biophysical variables in herbaceous covers. Determination coefficients (<em>R<sup>2</sup></em>) above 0.91 and <em>RRMSE</em> of 21.4% have been obtained for the spectral indexes calculated whit ASD data, and 0.91 <em>R<sup>2 </sup></em>and <em>RRMSE</em> of 15.5% for the spectral indexes calculated whit CASI data.</p>

Estimation of leaf area index by using multi-angular hyperspectral imaging data based on The two-layer canopy reflectance model

2013 ◽  
Vol 19 (3) ◽  
pp. 295-304 ◽  
Author(s):  
Qinhong Liao ◽  
Chunjiang Zhao ◽  
Guijun Yang ◽  
Craig Coburn ◽  
Jihua Wang ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Hyperspectral Imaging ◽  
Canopy Reflectance ◽  
Imaging Data ◽  
Area Index ◽  
Reflectance Model

scholarly journals Removing Cosmic Spikes Using a Hyperspectral Upper-Bound Spectrum Method

2016 ◽  
Vol 71 (3) ◽  
pp. 507-519 ◽  
Author(s):  
Stephen M. Anthony ◽  
Jerilyn A. Timlin
Keyword(s):  
Hyperspectral Imaging ◽  
Principal Components ◽  
Upper Bound ◽  
Cosmic Ray ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
Data Sets ◽  
Imaging Data ◽  
Data Set ◽  
Spectrum Method

Cosmic ray spikes are especially problematic for hyperspectral imaging because of the large number of spikes often present and their negative effects upon subsequent chemometric analysis. Fortunately, while the large number of spectra acquired in a hyperspectral imaging data set increases the probability and number of cosmic spikes observed, the multitude of spectra can also aid in the effective recognition and removal of the cosmic spikes. Zhang and Ben-Amotz were perhaps the first to leverage the additional spatial dimension of hyperspectral data matrices (DM). They integrated principal component analysis (PCA) into the upper bound spectrum method (UBS), resulting in a hybrid method (UBS-DM) for hyperspectral images. Here, we expand upon their use of PCA, recognizing that principal components primarily present in only a few pixels most likely correspond to cosmic spikes. Eliminating the contribution of those principal components in those pixels improves the cosmic spike removal. Both simulated and experimental hyperspectral Raman image data sets are used to test the newly developed UBS-DM-hyperspectral (UBS-DM-HS) method which extends the UBS-DM method by leveraging characteristics of hyperspectral data sets. A comparison is provided between the performance of the UBS-DM-HS method and other methods suitable for despiking hyperspectral images, evaluating both their ability to remove cosmic ray spikes and the extent to which they introduce spectral bias.

Estimating Yellow Starthistle (Centaurea solstitialis) Leaf Area Index and Aboveground Biomass with the Use of Hyperspectral Data

Weed Science ◽  
2007 ◽  
Vol 55 (6) ◽  
pp. 671-678 ◽  
Author(s):  
Shaokui Ge ◽  
Ming Xu ◽  
Gerald L. Anderson ◽  
Raymond I. Carruthers
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Aboveground Biomass ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Hyperspectral Data ◽  
Weed Species ◽  
Yellow Starthistle ◽  
Invasive Weed ◽  
Area Index

Hyperspectral remote-sensed data were obtained via a Compact Airborne Spectrographic Imager-II (CASI-II) and used to estimate leaf-area index (LAI) and aboveground biomass of a highly invasive weed species, yellow starthistle (YST). In parallel, 34 ground-based field plots were used to measure aboveground biomass and LAI to develop and validate hyperspectral-based models for estimating these measures remotely. Derivatives of individual hyperspectral bands improved the correlations between imaged data and actual on-site measurements. Six derivative-based normalized difference vegetation indices (DNDVI) were developed; three of them were superior to the commonly used normalized difference vegetation index (NDVI) in estimating aboveground biomass of YST, but did not improve estimates of LAI. The locally integrated derivatives-based vegetation indices (LDVI) from adjacent bands within three different spectral regions (the blue, red, and green reflectance ranges) were used to enhance absorption characteristics. Three LDVIs outperformed NDVI in estimating LAI, but not biomass. Multiple regression models were developed to improve the estimation of LAI and aboveground biomass of YST, and explained 75% and 53% of the variance in biomass and LAI, respectively, based on validation assessments with actual ground measurements.

scholarly journals Functional Hyperspectral Imaging by High-Related Vegetation Indices to Track the Wide-Spectrum Trichoderma Biocontrol Activity Against Soil-Borne Diseases of Baby-Leaf Vegetables

2021 ◽  
Vol 12 ◽  
Author(s):  
Gelsomina Manganiello ◽  
Nicola Nicastro ◽  
Michele Caputo ◽  
Massimo Zaccardelli ◽  
Teodoro Cardi ◽  
...  
Keyword(s):  
Biological Control ◽  
Hyperspectral Imaging ◽  
Large Scale ◽  
Plant Pathogens ◽  
Disease Incidence ◽  
Vegetation Indices ◽  
Screening Method ◽  
Hyperspectral Data ◽  
Screening Methods

Research has been increasingly focusing on the selection of novel and effective biological control agents (BCAs) against soil-borne plant pathogens. The large-scale application of BCAs requires fast and robust screening methods for the evaluation of the efficacy of high numbers of candidates. In this context, the digital technologies can be applied not only for early disease detection but also for rapid performance analyses of BCAs. The present study investigates the ability of different Trichoderma spp. to contain the development of main baby-leaf vegetable pathogens and applies functional plant imaging to select the best performing antagonists against multiple pathosystems. Specifically, sixteen different Trichoderma spp. strains were characterized both in vivo and in vitro for their ability to contain R. solani, S. sclerotiorum and S. rolfsii development. All Trichoderma spp. showed, in vitro significant radial growth inhibition of the target phytopathogens. Furthermore, biocontrol trials were performed on wild rocket, green and red baby lettuces infected, respectively, with R. solani, S. sclerotiorum and S. rolfsii. The plant status was monitored by using hyperspectral imaging. Two strains, Tl35 and Ta56, belonging to T. longibrachiatum and T. atroviride species, significantly reduced disease incidence and severity (DI and DSI) in the three pathosystems. Vegetation indices, calculated on the hyperspectral data extracted from the images of plant-Trichoderma-pathogen interaction, proved to be suitable to refer about the plant health status. Four of them (OSAVI, SAVI, TSAVI and TVI) were found informative for all the pathosystems analyzed, resulting closely correlated to DSI according to significant changes in the spectral signatures among health, infected and bio-protected plants. Findings clearly indicate the possibility to promote sustainable disease management of crops by applying digital plant imaging as large-scale screening method of BCAs' effectiveness and precision biological control support.

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