Search results for " Vegetation Indices"

showing 10 items of 12 documents

Comparison between SMOS Vegetation Optical Depth products and MODIS vegetation indices over crop zones of the USA

2014

The Soil Moisture and Ocean Salinity (SMOS) mission provides multi-angular, dual-polarised brightness temperatures at 1.4 GHz, from which global soil moisture and vegetation optical depth (tau) products are retrieved. This paper presents a study of SMOS' tau product in 2010 and 2011 for crop zones of the USA. Retrieved tau values for 504 crop nodes were compared to optical/IR vegetation indices from the MODES (Moderate Resolution Imaging Spectroradiometer) satellite sensor, including the Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVE), Leaf Area Index (LAI), and a Normalised Difference Water Index (NOW!) product. tau values were observed to increase during the…

2. Zero hunger010504 meteorology & atmospheric sciences0211 other engineering and technologiesSoil ScienceGrowing seasonGeology02 engineering and technologyVegetationEnhanced vegetation index01 natural sciencesNormalized Difference Vegetation Indexvegetation optical depthLinear regressionEnvironmental scienceL-band radiometryModerate-resolution imaging spectroradiometerComputers in Earth SciencesLeaf area indexoptical vegetation indices[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingWater contentSMOS021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingRemote Sensing of Environment
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Quantifying the Robustness of Vegetation Indices through Global Sensitivity Analysis of Homogeneous and Forest Leaf-Canopy Radiative Transfer Models

2019

Vegetation indices (VIs) are widely used in optical remote sensing to estimate biophysical variables of vegetated surfaces. With the advent of spectroscopy technology, spectral bands can be combined in numerous ways to extract the desired information. This resulted in a plethora of proposed indices, designed for a diversity of applications and research purposes. However, it is not always clear whether they are sensitive to the variable of interest while at the same time, responding insensitive to confounding factors. Hence, to be able to quantify the robustness of VIs, a systematic evaluation is needed, thereby introducing a widest possible variety of biochemical and structural heterogeneit…

Canopy010504 meteorology & atmospheric sciences0211 other engineering and technologiesImaging spectrometer02 engineering and technology01 natural sciencesprosailEnMAPRadiative transferSensitivity (control systems)Leaf area indexglobal sensitivity analysis; vegetation indices; PROSAIL; INFORM; ARTMOlcsh:Science021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingartmoSpectral bandsVegetation15. Life on landinformglobal sensitivity analysisvegetation indicesGeneral Earth and Planetary SciencesEnvironmental sciencelcsh:QRemote Sensing
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High-resolution UAV imagery for field olive (Olea europaea L.) phenotyping

2021

Remote sensing techniques based on images acquired from unmanned aerial vehicles (UAVs) could represent an effective tool to speed up the data acquisition process in phenotyping trials and, consequently, to reduce the time and cost of the field work. In this study, we assessed the ability of a UAV equipped with RGB-NIR cameras in highlighting differences in geometrical and spectral canopy characteristics between eight olive cultivars planted at different planting distances in a hedgerow olive orchard. The relationships between measured and estimated canopy height, projected canopy area and canopy volume were linear regardless of the different cultivars and planting distances (RMSE of 0.12 m…

CanopyNDVIPlant ScienceHorticultureNormalized Difference Vegetation IndexSB1-1110Canopy volumeVegetation indicesYield (wine)CultivarRemote sensingbiologyFruit yieldStructure from motionHedgerow olive plantingSowinghedgerow olive plantingsPlant cultureProjected canopy areaRemote sensingbiology.organism_classificationCanopy volume; Fruit yield; Hedgerow olive plantings; NDVI; Projected canopy area; Pruning; Remote sensing; Structure from motion; Vegetation indicesPruningSettore AGR/03 - Arboricoltura Generale E Coltivazioni ArboreeOleaEnvironmental scienceOrchardPruning
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Comparison Between Fractional Vegetation Cover Retrievals from Vegetation Indices and Spectral Mixture Analysis: Case Study of PROBA/CHRIS Data Over …

2009

Abstract: In this paper we compare two different methodologies for Fractional Vegetation Cover (FVC) retrieval from Compact High Resolution Imaging Spectrometer (CHRIS) data onboard the European Space Agency (ESA) Project for On-Board Autonomy (PROBA) platform. The first methodology is based on empirical approaches using Vegetation Indices (VIs), in particular the Normalized Difference Vegetation Index (NDVI) and the Variable Atmospherically Resistant Index (VARI). The second methodology is based on the Spectral Mixture Analysis (SMA) technique, in which a Linear Spectral Unmixing model has been considered in order to retrieve the abundance of the different constituent materials within pixe…

Endmember010504 meteorology & atmospheric sciencesMean squared error0211 other engineering and technologies550 - Earth sciences02 engineering and technologyLand coverlcsh:Chemical technology01 natural sciencesBiochemistryNormalized Difference Vegetation IndexArticleCHRISAnalytical ChemistryRoot mean squareFractional Vegetation Cover; Vegetation Indices; Spectral Mixture Analysis; PROBA; CHRISPROBAlcsh:TP1-1185Electrical and Electronic EngineeringInstrumentation021101 geological & geomatics engineering0105 earth and related environmental sciencesMathematicsRemote sensingFractional Vegetation CoverPixelVegetation15. Life on landAtomic and Molecular Physics and OpticsStandard errorSpectral Mixture AnalysisVegetation Indices
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Angular Dependency of Hyperspectral Measurements over Wheat Characterized by a Novel UAV Based Goniometer

2015

In this study we present a hyperspectral flying goniometer system, based on a rotary-wing unmanned aerial vehicle (UAV) equipped with a spectrometer mounted on an active gimbal. We show that this approach may be used to collect multiangular hyperspectral data over vegetated environments. The pointing and positioning accuracy are assessed using structure from motion and vary from σ = 1° to 8° in pointing and σ = 0.7 to 0.8 m in positioning. We use a wheat dataset to investigate the influence of angular effects on the NDVI, TCARI and REIP vegetation indices. Angular effects caused significant variations on the indices: NDVI = 0.83–0.95; TCARI = 0.04–0.116; REIP = 729–735 nm. Our analysis high…

HyperspectralvegetationSciencevegetation indicesQHyperspectral; Unmanned aerial vehicle (UAV); vegetation; bidirectional reflectance distribution function (BRDF); goniometer; vegetation indicesUnmanned aerial vehicle (UAV)ddc:620bidirectional reflectance distribution function (BRDF)goniometer
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Modeling Fire Danger in Galicia and Asturias (Spain) from MODIS images

2014

Forest fires are one of the most dangerous natural hazards, especially when they are recurrent. In areas such as Galicia (Spain), forest fires are frequent and devastating. The development of fire risk models becomes a very important prevention task for these regions. Vegetation and moisture indices can be used to monitor vegetation status; however, the different indices may perform differently depending on the vegetation species. Eight different spectral indices were selected to determine the most appropriate index in Galicia. This study was extended to the adjacent region of Asturias. Six years of MODIS (Moderate Resolution Imaging Spectroradiometer) images, together with ground fire data…

Index (economics)TeledeteccióMeteorologyCiències de la terraVegetationEnhanced vegetation indexLogistic regressionforest fires; vegetation indices; fire danger; MODIS; remote sensingBoscos i silviculturaremote sensingMODISvegetation indicesNatural hazardLinear regressionIncendisforest firesGeneral Earth and Planetary SciencesEnvironmental sciencelcsh:QPhysical geographyModerate-resolution imaging spectroradiometerlcsh:ScienceFire historyfire danger
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A low-cost multispectral imaging system for the characterisation of soil and small vegetation properties using visible and near-infrared reflectance

2022

Current Proximal Sensing technologies are based on multispectral imaging systems able to capture images in a few spectral bands, usually centred in VIS and NIR regions, to derive vegetation indices. However, most of such systems lack an internal radiometric calibration to estimate the actual reflectance of the observed target, making them sensitive to the local radiative environment and requiring a per-session calibration against a reference target. To overcome such dependence, the instrument described adopts an active illumination of the target surface, allowing the monitoring of soil and low vegetation surfaces by a radiometrically pre-calibrated imaging camera. The system, driven by a mi…

Settore AGR/03 - Arboricoltura Generale E Coltivazioni ArboreeMultispectral camera Vegetation Indices NDVI Image analysis Spectral reflectanceSettore AGR/09 - Meccanica AgrariaForestryHorticultureAgronomy and Crop ScienceSettore AGR/02 - Agronomia E Coltivazioni ErbaceeComputer Science ApplicationsComputers and Electronics in Agriculture
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Metodo per il monitoraggio di superfici vegetali

2019

Si descrive un metodo da impiegare per la caratterizzazione delle coperture vegetali. A method to be used for the monitoring of vegetation surfaces is described, which includes the use of a device for measuring the spectral reflectance of vegetation using images acquired with a lightening system based on visible and infrared monoband LEDs and including devignetting, image cropping, radiometric calibration procedures and calculation of reflectance values and vegetation indices.

Settore AGR/03 - Arboricoltura Generale E Coltivazioni ArboreeMultispectral camera Vegetation Indices NDVI Image analysis Spectral reflectanceSettore AGR/09 - Meccanica AgrariaSettore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-ForestaliSettore AGR/02 - Agronomia E Coltivazioni Erbacee
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Method for monitoring vegetation ground covers

2020

A method to be used for the monitoring of vegetation surfaces is described, which includes the use of a device for measuring the spectral reflectance of vegetation using images acquired with a lightining system based on visible and infrared monoband LEDs and including devignetting, image cropping, radiometric calibration procedures and calculation of reflectance values and vegetation indices.

Settore AGR/03 - Arboricoltura Generale E Coltivazioni ArboreeMultispectral camera Vegetation Indices NDVI Image analysis Spectral reflectanceSettore AGR/09 - Meccanica AgrariaSettore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-ForestaliSettore AGR/02 - Agronomia E Coltivazioni Erbacee
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Dispositivo per il monitoraggio di superfici vegetali

2017

La presente invenzione si riferisce al settore della diagnostica ambientale poiché fornisce un dispositivo che consente il monitoraggio dello stato fisiologico dei tappeti erbosi e di altre tipologie di coperture naturali che può essere anche impiegato per una accurata caratterizzazione e gestione delle coperture vegetali quali ad esempio campi sportivi, aree verdi naturali o artificiali e il relativo metodo.

Settore AGR/03 - Arboricoltura Generale E Coltivazioni Arboreemultispectral camera Vegetation Indices NDVI image analysis spectral reflectanceSettore AGR/09 - Meccanica AgrariaSettore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-ForestaliSettore AGR/02 - Agronomia E Coltivazioni Erbacee
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