Hyperspectral Technology in Vegetation Analysis

Jung, András; Kardeván, Péter; Tőkei, László

doi:10.1556/progress.2.2006.1.5

Cited by 10 publications

(9 citation statements)

References 13 publications

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“…Yellow Edge Position (YEP) was also created and calculated based on REP (Jung et al, 2006). YEP is narrow band index which shows the inflection point of the sigmoid curve.…”

Section: Resultsmentioning

confidence: 99%

Colour and water content detection of sweet cherry by portable spectrometer

Nagy

Tamás

Soltész

et al. 2011

Int. j. hortic. sci.

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Based on the most recent data, the average amount of sweet cherry produced in Hungary is around 10-12 thousand tons. Therefore fast and effective method is important for sweet cherry fruit quality analyses. The aim of the study was to examine the applicability of reflectance measurements for sweet cherry fruit quality analyses. In our experiment five cherry species (Vera, Cristalina, Germersdorfi, Noir de Mechet, Canada Giant) were examined in order to measure the spectral differences between species. Further more, spectral alteration was examined between different health and maturity status of the fruits in the case of a specified, the Germesdorfi species. The four new indices are appropriate tools for cherry quality analysis. Thus reflectance measurements can also support more precise and automated fruit selections. The methods for the differentiation of species could also be viable at a concerned habitat; however, the climate, habitat and soil conditions strongly affect the yield quality. Concerning the fast determination of water content, WBI could be a reliable method for the assessment

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“…Yellow Edge Position (YEP) was also created and calculated based on REP (Jung et al, 2006). YEP is narrow band index which shows the inflection point of the sigmoid curve.…”

Section: Resultsmentioning

confidence: 99%

Colour and water content detection of sweet cherry by portable spectrometer

Nagy

Tamás

Soltész

et al. 2011

Int. j. hortic. sci.

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“…An advantage of Sentinel-2 is its inclusion of spectral bands in the red-edge region of the electromagnetic spectrum (Smith et al 2004). The red-edge describes the spike in vegetation reflectivity between visible red and near infrared wavelengths (approximately 700-790 nm), and indices calculated using the red-edge region have been found to be more sensitive to changes in chlorophyll content (Sims & Gamon 2002;Jung et al 2006;Peng & Gitelson 2011;Schuster et al 2012;Clevers & Gitelson 2013;Hawryło et al 2018;Morley et al 2020). Consequently, Sentinel-2 provides additional potential to resolve subtle changes in chlorophyll content which may lead to an improved early assessment of premature leaf discolouration and senescence in European beech forests.…”

Section: Discussionmentioning

confidence: 99%

Satellite data track spatial and temporal declines in European beech forest canopy characteristics associated with intense drought events in the Rhön Biosphere Reserve, central Germany

et al. 2022

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The increasing intensity and frequency of droughts under climate change demands effective ways to monitor drought impacts. We sought to determine how different satellite remote sensing sources influence our ability to identify temporal and spatial impacts on European beech forest canopy health during intense drought events.• Imagery from three satellite series (MODIS, Landsat and Sentinel-2) was used to observe changes in canopy health during the intense droughts of 2003 and 2018 in the Rh€ on Biosphere Reserve, central Germany. Monthly normalized difference vegetation index (NDVI) anomalies were calculated for each satellite between 2000-2020 and compared against temperature, precipitation and the standardized precipitation evapotranspiration index (SPEI).• Severe canopy impacts in 2003 and 2018 were associated with low NDVI in August and September. At the stand-scale, Sentinel-2 data allowed a spatially detailed understanding of canopy-level impacts, while MODIS provided the clearest temporal progression of the drought's impacts on the forest canopy. Low NDVI values were not exclusively associated with extremes of either temperature and precipitation individually; however, low canopy NDVI in August was associated with SPEI values below À1.5.• Although the intense drought of 2018, as defined by meteorological parameters, peaked in July, canopy NDVI did not decline until August, highlighting that our ability to detect canopy impact during drought events is sensitive to the timing of image acquisition. No single satellite sensor affords a full picture of the temporal or spatial progression of drought impacts. Consequently, using sensors in tandem provides the best possible representation of canopy health during intense drought events.

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“…For the problem of binary segmentation of soil and vegetation, two vegetation indices were considered: NDVI (normalized difference vegetation index) [13] and EVI (enhanced vegetation index) [14]. These indexes are used to find photosynthetically active biomass:…”

Section: Hyperspectral Data Processingmentioning

confidence: 99%

Spectral-spatial neural network classification of hyperspectral vegetation images

Подлипнов

Firsov²,

Ivliev³

et al. 2023

IOP Conf. Ser.: Earth Environ. Sci.

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The paper presents the results of a study of the method of classification of vegetation types based on hyperspectral survey data. A hyperspectrometer based on the Offner scheme was used as a shooting equipment. The spatial-spectral architecture of a convolutional neural network is used as a classifier. The experiments performed show the effectiveness of the proposed approach for the classification of vegetation types.

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Hyperspectral Technology in Vegetation Analysis

Cited by 10 publications

References 13 publications

Colour and water content detection of sweet cherry by portable spectrometer

Colour and water content detection of sweet cherry by portable spectrometer

Satellite data track spatial and temporal declines in European beech forest canopy characteristics associated with intense drought events in the Rhön Biosphere Reserve, central Germany

Spectral-spatial neural network classification of hyperspectral vegetation images

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