Discrimination of common Mediterranean plant species using field spectroradiometry

Manevski, Kiril; Manakos, Ioannis; Petropoulos, George P.; Kalaitzidis, Chariton

doi:10.1016/j.jag.2011.07.001

Cited by 57 publications

(39 citation statements)

References 43 publications

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“…However, previous studies indicate that the East Mediterranean woody species are not necessarily an easy target for classification by remote sensing. Most local vegetation formations are characterized by densely-growing evergreen species, often with similar spectral properties [58][59][60], small intra-annual differences [61] and morphological similarities [62][63][64][65]. To the best of our knowledge, there are currently no studies that use high spatial resolution imagery with a limited number of bands for the classification of East Mediterranean common species.…”

Section: Classification Of East Mediterranean Vegetation By Remote Sementioning

confidence: 99%

“…We did not choose to focus on ideal target species (e.g., large homogeneous canopies with visually distinct phenophases), rather, we examined the local species assembly as-is, including species with challenging spectral and morphological properties with regard to classification by remote sensing [59,60,62,65]; furthermore, after examining the preliminary classification results, we did not see fit to merge or omit species in order to increase overall accuracy.…”

Section: Overhead Data Acquisition and Species Classificationmentioning

confidence: 99%

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Optimizing the Timing of Unmanned Aerial Vehicle Image Acquisition for Applied Mapping of Woody Vegetation Species Using Feature Selection

et al. 2017

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Abstract:Most recent studies relating to the classification of vegetation species on the individual level use cutting-edge sensors and follow a data-driven approach, aimed at maximizing classification accuracy within a relatively small allocated area of optimal conditions. However, this approach does not incorporate cost-benefit considerations or the ability of applying the chosen methodology for applied mapping over larger areas with higher natural heterogeneity. In this study, we present a phenology-based cost-effective approach for optimizing the number and timing of unmanned aerial vehicle (UAV) imagery acquisition, based on a priori near-surface observations. A ground-placed camera was used in order to generate annual time series of nine spectral indices and three color conversions (red, green and blue to hue, saturation and value) in four different East Mediterranean sites that represent different environmental conditions. After outliers' removal, the time series dataset represented 1852 individuals of 12 common vegetation species and annual herbaceous patches. A feature selection process was used for identifying the optimal dates for species classification in every site. The feature selection can be designed for various objectives, e.g., optimization of overall classification, discrimination between two species, or discrimination of one species from all others. In order to evaluate the a priori findings, a UAV was flown for acquiring five overhead multiband orthomosaics (five bands in the visible-near infrared range based on the five optimal dates identified in the feature selection of the near-surface time series of the previous year. An object-based classification methodology was used for the discrimination of 976 individuals of nine species and annual herbaceous patches in the UAV imagery, and resulted in an average overall accuracy of 85% and an average Kappa coefficient of 0.82. This cost-effective approach has high potential for detailed vegetation mapping, regarding the accessibility of UAV-produced time series, compared to hyper-spectral imagery with high spatial resolution which is more expensive and involves great difficulties in implementation over large areas.

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Section: Classification Of East Mediterranean Vegetation By Remote Sementioning

confidence: 99%

Section: Overhead Data Acquisition and Species Classificationmentioning

confidence: 99%

Optimizing the Timing of Unmanned Aerial Vehicle Image Acquisition for Applied Mapping of Woody Vegetation Species Using Feature Selection

et al. 2017

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“…Landsat TM and SPOT sensors provide broadband multispectral data. An example of a narrowband hyperspectral spaceborne sensor is NASA's Earth Observing-1 Mission Hyperion, capable of capturing high resolution images of the earth surface in 220 contiguous spectral bands (Manevski et al, 2011). Broad waveband VIs usually lack diagnostic ability for recognizing a particular biophysical characteristic and thus narrowband indices were developed.…”

Section: Methods Of Vegetation Monitoring Using Remote Sensing Datamentioning

confidence: 99%

“…One of the most important applications of remote sensing consists in the classification of objects at the earth surface and, in fact, plant communities have been classified and mapped using a wide variety of remote sensors (Manevski et al, 2011). Crop classification is based on the differential spectral behavior of the various crops present in the study area (Xie et al, 2008).…”

Section: Crop Mappingmentioning

confidence: 99%

Remote sensing in food production and #8211; a review

Calvão

Pessoa

2015

Emir. J. Food Agric

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FAO's most recent assessments indicate that, globally, in 2011-13, about one in eight people in the world are likely to have suffered from chronic hunger, not having adequate food supplies for an active and healthy life. Food security crises are now caused, almost exclusively, by problems in access to food, not absolute food availability, but, monitoring agricultural production remains fundamental. Traditional ground-based systems of production estimation have many limitations which have restricted their use. However, remotely sensed satellite data offer timely, objective, economical, and synoptic information for crop monitoring. The objective of this paper is to review the contribution of remote sensing techniques in the classification, monitoring of crop phenology and condition and estimation of production.

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“…Similar results were found by Kodagoda and Zhang (2010) who found that NIR and VIS had the most bands for discriminating Bidens pilosa L (cobbler's peg) from wheat plants. The NIR and VIS regions also had the lager number bands for discriminating plant species such as Ceratonia siliqua, Olea europea, Pistacia lentiscus, Calicotome villosa and Genista acanthoclada (Manevski et al 2011). Nicolai et al (2007) found that NIR bands were widely useful in measuring the quality of vegetation attributes.…”

Section: Optimal Bandsmentioning

confidence: 99%

Site-Specific Weed Management Using Remote Sensing

Che’Ya¹

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The objective of this research was to develop a method to map weeds in sorghum as the first step as a procedure to control them using site-specific weed management (SSWM). Site-specific weed management is a method to limit the application of herbicides only to areas with weeds.Accurate mapping of weeds is a pre-requisite for applying SSWM. Analysis of hyperspectral remote sensing imagery is recognized as a potentially cost effective technique for discriminating between weeds and crop plants. This research involved: i) collecting hyperspectral reflectance spectra from weeds and sorghum plants, ii) Stepwise Linear Discriminant Analysis (SLDA) to identify the most significant spectral bands, iii) Linear Discrimination Analysis (LDA) to test the accuracy of the SLDA bands for classifying weeds and sorghum, and iv) analysis of customized multispectral imagery to produce maps which detected weeds in the sorghum crop.Hyperspectral signatures of weeds and sorghum were obtained using a FieldSpec® Handheld2TM spectroradiometer with a spectral range from 325 nm to 1075 nm. Spectra were recorded for different weed species and sorghum plants for three years, 2012 to 2014. Data were collected at four different stages of plant growth each year, from week one to week four after planting.The results show that it is feasible to discriminate spectral profiles of weeds from each other weeds and from sorghum plants. Statistical Analysis Software (SAS) was used to identify the most significant spectral bands (10 nm width) from the hyperspectral reflectance data using SLDA. All weeds and sorghum were correctly classified in 2012 using LDA for week four reflectance data. In 2013, the classification accuracy increased with stage of growth (weeks one to four) from 85% to 90%. In 2014, the classification accuracy also increased with stage of growth (weeks two to four) from 90% to 100%. Combinations of spectral bands were analysed to reduce the number of potential bands identified from the SLDA results. Spectral bands centred on 930, 890, 710, 700, 560and 500 nm were common to the 20 most significant spectral bands identified by SLDA analysis each year. Six spectral bands 850, 720, 710, 680, 560 and 440 nm were subsequently selected for use in multispectral image collection. They were selected based on maximizing the differences and similarities between the 2013 weed and crop reflectance profiles. These bands were used for the band-pass filters in the Tetracam MCA 6 camera used for collecting high spatial resolution terrestrial and aerial imagery.The imagery was analysed using Object-Based Image Analysis (OBIA) and Vegetation Index Analysis (VIA) to classify weeds and sorghum plants. OBIA identified weeds more successfully than VIA. The accuracy of OBIA classification was tested using two methods.Confusion matrices were used to measure the Coefficient of Agreement (Khat), Overall Accuracy iii and Producer's and User Accuracies. Geometry matrices were used to measure under and over segmentation. The Overall accuracy and Khat for all...

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Discrimination of common Mediterranean plant species using field spectroradiometry

Cited by 57 publications

References 43 publications

Optimizing the Timing of Unmanned Aerial Vehicle Image Acquisition for Applied Mapping of Woody Vegetation Species Using Feature Selection

Optimizing the Timing of Unmanned Aerial Vehicle Image Acquisition for Applied Mapping of Woody Vegetation Species Using Feature Selection

Remote sensing in food production and #8211; a review

Site-Specific Weed Management Using Remote Sensing

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