A Framework for Lakeshore Vegetation Assessment Using Field Spectroscopy and Airborne Hyperspectral Imagery

Stratoulias, Dimitris; Keramitsoglou, Iphigenia; Burai, Péter; Csaba, László; Zlinszky, András; Tóth, Viktor R.; Balzter, Heiko

doi:10.1002/9781118793787.ch9

Cited by 5 publications

(5 citation statements)

References 29 publications

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“…Refs. [19,20] concluded that hyperspectral techniques can offer significant potential in such applications and especially in agriculture and vegetation monitoring. Multispectral satellite data have also been used in this context, with notable examples those of [14,21] who used, in both cases, Quickbird satellite images to detect BSR disease on oil palms from satellite data.…”

Section: Of 17mentioning

confidence: 99%

Detection of Oil Palm Disease in Plantations in Krabi Province, Thailand with High Spatial Resolution Satellite Imagery

2021

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Oil palm (Elaeis guineensis) trees are an important contributor of recent economic development in Southeast Asia. The high product yield, and the consequent high profitability, has led to a widespread expansion of plantations in the greater region. However, oil palms are susceptible to diseases that can have a detrimental effect. In this study we use hyper- and multi-spectral remote sensing to detect diseased oil palm trees in Krabi province, Thailand. Proximate spectroscopic measurements were used to identify and discern differences in leaf spectral radiance; the results indicate a relatively higher radiance in visible and near-infrared for the healthy leaves in comparison to the diseased. From a total of 113 samples for which the geolocation and the hyperspectral radiance were recorded, 59 and 54 samples were healthy and diseased oil palm trees, respectively. Moreover, a WorldView-2 satellite image was used to investigate the usability of traditional vegetation indices and subsequently detecting diseased oil palm trees. The results show that the overall maximum likelihood classification accuracy is 85.98%, the Kappa coefficient 0.71 and the producer’s accuracy for healthy and diseased oil palm trees 83.33 and 78.95, respectively. We conclude that high spatial and spectral resolutions can play a vital role in monitoring diseases in oil palm plantations.

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Section: Of 17mentioning

confidence: 99%

Detection of Oil Palm Disease in Plantations in Krabi Province, Thailand with High Spatial Resolution Satellite Imagery

2021

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“…As such reed dieback areas are indicated by the fragmentation of the reed patch rather than the physiological status of the plant in this classification schemethis is why lidar proved successful in recognizing the characteristic fragmentation. For a more representative estimation of the physiological status, spectral indices can show the degree to which the area is stable (Stratoulias et al 2017). However, integrating the dieback class in a macrophyte classification scheme provides an indication of fragmented and sparse reed patches which are under unfavourable environments at the period of image acquisition and hence potentially associated with reed dieback conditions.…”

Section: Resultsmentioning

confidence: 99%

A comparison of airborne hyperspectral-based classifications of emergent wetland vegetation at Lake Balaton, Hungary

Stratoulias

Balzter

Zlinszky

et al. 2018

International Journal of Remote Sensing

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Earth observation has rapidly evolved into a state-of-the-art technology providing new capabilities and a wide variety of sensors; nevertheless, it is still a challenge for practitioners external to a specialized community of experts to select the appropriate sensor, define the imaging mode requirements, and select the optimal classifier or retrieval method for the task at hand. Especially in wetland mapping, studies have relied largely on vegetation indices and hyperspectral data to capture vegetation attributes. In this study, we investigate the capabilities of a concurrently acquired very high spatial resolution airborne hyperspectral and lidar data set at the peak of aquatic vegetation growth in a nature reserve at Lake Balaton, Hungary. The aim was to examine to what degree the different remote-sensing information sources (i.e. visible and near-infrared hyperspectral, vegetation indices and lidar) are contributing to an accurate aquatic vegetation map. The results indicate that de-noised hyperspectral information in the visible and very near-infrared bands (400-1000 nm) is performing most accurately. Inclusion of lidar information, hyperspectral infrared bands (1000-2500 nm), or extracted vegetation indices does not improve the classification accuracy. Experimental results with algorithmic comparisons show that in most cases, the Support Vector Machine classifier provides a better accuracy than the Maximum Likelihood.

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“…The steep windward northern shore's sediment consists of dolomite limestone, while the shallow southern shore is dominated by sandy sediment. On the northern shore of the lake the average and maximum depth of water that reed stand can be found is 1.5 m and 2.4 m respectively, while on the southern shore the average depth on the waterward side of reed stands is 1 m [40,41] Lake Balaton has received significant scientific attention due to the reed die-back phenomenon observed (e.g., [40][41][42][43]), the macrophytic traits and species mapping (e.g., [44][45][46][47]) and the dynamic trophic gradient of the water (e.g., [48,49]). The field data for this study were collected at the Kerekedi bay, an oligo-mesotrophic bay at the easternmost basin of Lake Balaton situated at the geographic coordinates 46 • Map of the study area in Lake Balaton (inset) and field sampling points' location in the Kerekedi Bay (main).…”

Section: Study Sitementioning

confidence: 99%

Photophysiology and Spectroscopy of Sun and Shade Leaves of Phragmites australis and the Effect on Patches of Different Densities

Stratoulias

Tóth

2020

Remote Sensing

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Remote sensing of vegetation has largely been revolving around the measurement of passive or active electromagnetic radiation of the top of the canopy. Nevertheless, plants hold a vertical structure and different processes and intensities take place within a plant organism depending on the environmental conditions. One of the main inputs for photosynthesis is photosynthetic active radiation (PAR) and a few studies have taken into account the effect of the qualitative and quantitative changes of the available PAR within the plants canopies. Mostly large plants (trees, shrubs) are affected by this phenomena, while signs of it could be observed in dense monocultures, too. Lake Balaton is a large lake with 12 km2 dense reed stands, some of which have been suffering from reed die-back; consequently, the reed density and stress condition exhibit a vertical PAR variability within the canopy due to the structure and condition of the plants but also a horizontal variability attributed to the reedbed’s heterogeneous density. In this study we investigate the expression of photosynthetic and spectroscopic parameters in different PAR conditions. We concentrate on chlorophyll fluorescence as this is an early-stage indicator of stress manifestation in plants. We first investigate how these parameters differ across leaf samples which are exposed to a higher degree of PAR variability due to their vertical position in the reed culm (sun and shade leaves). In the second part, we concentrate on how the same parameters exhibit in reed patches of different densities. We then look into hyperspectral regions through graphs of coefficient of determination and associate the former with the physiological parameters. We report on the large variability found from measurements taken at different parts of the canopy and the association with spectral regions in the visible and near-infrared domain. We find that at low irradiance plants increase their acclimation to low light conditions. Plant density at Phragmites stands affects the vertical light attenuation and consequently the photophysiological response of basal leaves. Moreover, the hyperspectral response from the sun and shade leaves has been found to differ; charts of the coefficient of determination indicate that the spectral region around the red-edge inflection point for each case of sun and shade leaves correlate strongly with ETRmax and α. When analysing the data cumulatively, independent of their vertical position within the stand, we found correlations of R2 = 0.65 (band combination 696 and 651) and R2 = 0.61 (band combination 636 and 642) for the ETRmax and α, respectively.

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A Framework for Lakeshore Vegetation Assessment Using Field Spectroscopy and Airborne Hyperspectral Imagery

Cited by 5 publications

References 29 publications

Detection of Oil Palm Disease in Plantations in Krabi Province, Thailand with High Spatial Resolution Satellite Imagery

Detection of Oil Palm Disease in Plantations in Krabi Province, Thailand with High Spatial Resolution Satellite Imagery

A comparison of airborne hyperspectral-based classifications of emergent wetland vegetation at Lake Balaton, Hungary

Photophysiology and Spectroscopy of Sun and Shade Leaves of Phragmites australis and the Effect on Patches of Different Densities

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