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07010417 The potential of remotely sensed spectrometer data to detect invasive non-native Rhododendron Keele University; Life Sciences: Biology. 2011. Abstract: A reliable and comprehensive technique is needed to map the extent of non-native invasive species in the UK. Interest has recently focussed on the potential of remote sensing as it is more practical and reliable. By comparison the traditional ground surveys are time consuming, costly, inefficient and prone to human error. Rhododendron, a non-native species, recently added to the list of prohibited plants on Schedule of the Wildlife and Countryside act 1981, will be used as a test case species. The study will assess the ability of radiospectrometry to differentiate between rhododendron and other plants in leaf during winter (beech, laurel and holly). Leaves were collected from the woodland habitat at Keele University and leaf morphology and leaf reflectance across a 350-2500nm range measured in a dark room. A logistic regression model was used to assess ability to differentiate 48 rhododendron readings from 144 non-rhododendron readings using reflectance data from five wavelengths and a NDVI. Data were averaged across 10 and 30nm bands to represent remote sensing acquisition from the air and satellites respectively. All models had a high prediction rate, with 99% of rhododendron correctly identified. This demonstrates the potential to use spectrometry to detect rhododendron from the air and satellites, which will have important implication for the management of this invasive species. 1. Introduction 1.1 Invasive Rhododendron in the UK Rhododendron ponticum is a non- indigenous evergreen shrub now found commonly throughout the British Isles, introduced around 1763 as an ornamental plant, popular on parks and estates. Typically growing to a height of 5m it exhibits long dark leaves and has attractive bright purple flowers, much sought after by gardeners and horticulturalists. The species is able to spread and occupy areas through both seed dispersal and stem layering. As the seeds are extremely light (0.063mg) and designed for dispersal by wind they present an effective method of rapidly colonising a local area, establishing new colonies between 10 and 500m away from ‘mother plants’ (Edwards, 2006). While currently not conclusively investigated it is speculated that the stem layering spread could account for up to a 1myear -1 expansion of rhododendron habitat. After initial introduction it was further planted by Victorians establishing hunting estates, leading to prolific use of the shrub in order to provide cover for game – often infiltrating the species into existing wooded ecosystems. Continued cultivation led to crossing of rhododendron species to generate hardy rootstocks nicknamed ‘iron-clads’ (Edwards, 2006). These resilient plants were used as vehicles for a number of other ornamental species popular in the period, however due to their hardiness the rootstock frequently produced its own shoots, managing to outcompete the cultivar and result in more rhododendron propagation. Now studies indicate that most ‘wild type’ rhododendron growing in the UK exhibit introgression with other rhododendron species. In particular it is thought that high introgression rates with R. catawbiense may confer improved cold tolerance, protecting the species in higher latitude and altitude environments that it may not have previously tolerated (Edwards, 2006). Rhododendron is now distributed throughout the United Kingdom [Figure 1] as a naturalised invasive species; its ubiquitous occurrence can be attributed to the methods of distribution via wind dispersal and stem layering, allowing it to disperse widely from sites of initial introduction. Even when cleared from areas it is able to quickly repopulate from any mature bushes neighbouring the area. Data establishes that at least 52,000 ha of land in the UK are affected by rhododendron colonization – 1

BSc Dissertation - The potential of remotely sensed spectrometer data to detect invasive non-native Rhododendron

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Page 1: BSc Dissertation - The potential of remotely sensed spectrometer data to detect invasive non-native Rhododendron

07010417

The potential of remotely sensed spectrometer data to detect invasive non-native Rhododendron

Keele University; Life Sciences: Biology. 2011.

Abstract: A reliable and comprehensive technique is needed to map the extent of non-native invasive species in the UK. Interest has recently focussed on the potential of remote sensing as it is more practical and reliable. By comparison the traditional ground surveys are time consuming, costly, inefficient and prone to human error. Rhododendron, a non-native species, recently added to the list of prohibited plants on Schedule of the Wildlife and Countryside act 1981, will be used as a test case species. The study will assess the ability of radiospectrometry to differentiate between rhododendron and other plants in leaf during winter (beech, laurel and holly). Leaves were collected from the woodland habitat at Keele University and leaf morphology and leaf reflectance across a 350-2500nm range measured in a dark room. A logistic regression model was used to assess ability to differentiate 48 rhododendron readings from 144 non-rhododendron readings using reflectance data from five wavelengths and a NDVI. Data were averaged across 10 and 30nm bands to represent remote sensing acquisition from the air and satellites respectively. All models had a high prediction rate, with 99% of rhododendron correctly identified. This demonstrates the potential to use spectrometry to detect rhododendron from the air and satellites, which will have important implication for the management of this invasive species.

1. Introduction

1.1 Invasive Rhododendron in the UKRhododendron ponticum is a non-

indigenous evergreen shrub now found commonly throughout the British Isles, introduced around 1763 as an ornamental plant, popular on parks and estates. Typically growing to a height of 5m it exhibits long dark leaves and has attractive bright purple flowers, much sought after by gardeners and horticulturalists. The species is able to spread and occupy areas through both seed dispersal and stem layering. As the seeds are extremely light (0.063mg) and designed for dispersal by wind they present an effective method of rapidly colonising a local area, establishing new colonies between 10 and 500m away from ‘mother plants’ (Edwards, 2006). While currently not conclusively investigated it is speculated that the stem layering spread could account for up to a 1myear-1

expansion of rhododendron habitat. After initial introduction it was further planted by Victorians establishing hunting estates, leading to prolific use of the shrub in order to provide cover for game – often infiltrating the species into existing wooded ecosystems. Continued cultivation led to

crossing of rhododendron species to generate hardy rootstocks nicknamed ‘iron-clads’ (Edwards, 2006). These resilient plants were used as vehicles for a number of other ornamental species popular in the period, however due to their hardiness the rootstock frequently produced its own shoots, managing to outcompete the cultivar and result in more rhododendron propagation. Now studies indicate that most ‘wild type’ rhododendron growing in the UK exhibit introgression with other rhododendron species. In particular it is thought that high introgression rates with R. catawbiense may confer improved cold tolerance, protecting the species in higher latitude and altitude environments that it may not have previously tolerated (Edwards, 2006). Rhododendron is now distributed throughout the United Kingdom [Figure 1] as a naturalised invasive species; its ubiquitous occurrence can be attributed to the methods of distribution via wind dispersal and stem layering, allowing it to disperse widely from sites of initial introduction. Even when cleared from areas it is able to quickly repopulate from any mature bushes neighbouring the area. Data establishes that at least 52,000 ha of land in the UK are affected by rhododendron colonization –

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about 0.2% of the total land area (Dehnen-Schmutz et al, 2004).

As such a hardy evergreen it is an aggressive coloniser and has been recognized as a threat to local ecosystems since as early as the 1970s (Fuller & Boorman, 1977). It is now widely recognized as an invasive weed species that causes damage to ecosystems, particularly mature indigenous oak and holly woodland (Manchester & Bullock, 2000) and has been added to a list of ‘highest threat’ invasive species (Edwards, 2009). Its tolerance for shade allows it to establish and generate dense thickets of vegetation under the canopy of existing deciduous forestry. The canopy of the rhododendron itself is very thick casting extremely deep shade, so much so that as it grows its own leaves die off leaving an umbrella-like structure. This dense shade prevents the establishment of native tree seedlings (Cross, 1982 and Rotherham & Read, 1988) meaning as mature canopy trees die off there is no regeneration of the forest. Additionally it has been proposed that rhododendron may employ an allelopath; either released from the roots or as a result of decomposing rhododendron leaves, in order to prevent the establishment of other species in its vicinity. This is not yet fully substantiated (Rotherham & Read, 1988), however studies with Rhododendron maximum indicate that leechate from leaf litter does indeed have an inhibitory effect upon seedling germination (Nilsen et al, 1999), but the precise chemistry of this process is still unknown. Overall this massively decreases the biodiversity of an area, resulting in almost pure rhododendron

thickets rooted in soils topped with a thick mulch of shed material that are unsuitable for regeneration. In addition to this ecological concern this kind of vegetation can also present a logistic and commercial issue: with rhododendron inhibiting other woodland activity. Particularly, the cost of forestry can be significantly increased if the presence of rhododendron is obstructing felling work and has to be managed before timber cutting. As rhododendron is extremely resilient and able to repeatedly regenerate from root sprouting, fully eradicating it requires cutting and follow up with herbicide application (Edwards, 2009); in areas where herbicide cannot be appropriately used (such as near water bodies) it is necessary to cut repeatedly, weakening rootstock, until regeneration is unlikely. The cost of clearing rhododendron is estimated at between £3,500 and £15,000 ha-1 depending on the size and maturity of the growth and the terrain in question (Edwards, 2009), meaning removal in large highly affected areas quickly presents a considerable cost; for example a rhododendron control programme in the Snowdonia National Park (Gritten, 1995) estimated that the clearing of rhododendron would cost ~£42million (at 1992 prices).

1.2 Species MappingIn the case of all ecological studies

concerning aggressive invasive species: identification of the species occurrence, range and rate of spread is the initial step in any attempt to combat its presence. As rhododendron is already well established, the area involved is up to hundreds of thousands of square kilometres, it is therefore impossible to establish a wide scale evaluation of the problem without an appropriately wide sampling technique. Traditionally vegetation surveys would have to be carried out manually, with identification of species on the ground by phenotype and a simple visual estimation of their percentage cover (Schmidt & Skitmore, 2003), a technique which is significantly prone to human error as many species are similar even at close range to an untrained eye. This is still a common method of surveying vegetation, as most other vegetation studies are undertaken on a small scale and the

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logistics of a large scale survey are complex and expensive (Bayliss, 2009). More recently, use of remote sensing has been established in the form of aerial photography, which is taken over a study area then later evaluated manually in a laboratory (Finlayson, 1989). This still falls prone to human error when evaluating the photographic evidence. The natural progression is for digital analysis of the field.

1.3 Remote Sensing and SpectrometryWhen light strikes an object its chemical

and physical properties cause a preferential absorption of certain wavelengths: the reflected light is indicative of this absorption. Within the spectral range of the human eye (typically between 390 and 750nm) this variation is what we regard as colour vision. These changes in electromagnetic reflective wavelength can however be detected by specialist instruments, even beyond the range of visible light. Spectrometry is the methodology and study of reflected light, covering both its intensity and wavelength properties. Modern spectrometers are able to detect and digitally represent light entering the lens. Reflectance refers to the ratio between the intensity of the light incident upon an object and the intensity of the light reflected from it; typically it is expressed as a percentage (Van Der Meer & De Jong, 2006). As different materials express different levels of reflectance it is possible to use spectrometry to establish a compositional diagnosis of a sample, eg. [Figure_2]. While initially limited to small scale investigations, this technology has developed,

since the 1970’s, and is now used for wide scale investigation, with aerial readings taken of the sample area (from either aircraft or satellite). With advances in technology it would appear possible to evaluate not only vegetation cover, but the type or even species of vegetation occurring on the ground with a single rapid ‘sweep’ from spectrometer based on an aircraft or satellite, making remote sensing spectrometry an attractive option for evaluating the range of invasive rhododendron the UK.

1.4 Study Aims and ObjectivesThe aim of this investigation is to establish a

statistical model to accurately differentiate rhododendron from three other woodland understory species based on spectrometry data of leaf samples gathered in the laboratory. The specific objectives are:

1. Image analysis of leaf samples to quantify leaf dimensions and condition.

2. Process ASD reflectance data and generate a master sequence for the four plant species.

3. Assess the impact of orientation relative to the light source.

4. Select biologically meaningful wavelengths and calculate NDVI for each species.

5. Develop a logistic regression model using a subsample of the data.

Successful completion of these objectives will also contribute to another study taking place at Coed y Brenin, Wales funded by the Natural Environment Research Council Airborn Research & Survey Facility (NERC ARSF) to test the potential for aerially captured hyperspectral data in order to map rhododendron (Taylor et al, 2010).

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2. Methodology

2.1 Study areaThe sampling in this study was taken

from the grounds of Keele University (52º59’55” N, 2º16’13” W) [Figure 3].

2.2 Vegetation SamplingFour species were chosen for the

sampling: beech (Fagus sylvatica), cherry laurel (Prunus laurocerasus), holly (Ilex aquifolium) and rhododendron (Rhododendron ponticum) as they are common natural vegetation throughout the UK, and therefore representative of typical ecosystems [Appendix: Figures 1-4]. Particularly these species all retain their leaves throughout the winter, making them suitably visible for

radiospectrometry when the majority of the deciduous forest canopy has fallen. For each of the species vegetation was gathered by clipping current year growth, terminal branches were removed and stripped of leaves to provide the sample range. Due to rain on the day of

sampling, leaves were air-dried over night and then stored in brown envelopes in a chill-box; all samples were processed within 48hours – in an effort to reduce leaf desiccation.

2.3 Image capture and Spectrometry of leavesThe set up for photography and

spectrometry was done in a dark room and consisted of a Field Spectroscopy Facility (FSF) spectrally flat black canvas placed under a

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100watt tungsten security light (positioned at an angle of 40º in order to mimic the sun), a light box was included isolate the sample area and minimize ambient reflectance from non-sample objects in the dark room [Figure 4]. For analysis samples were cut down to individual leaves and positioned as flat as possible. Due to the heat produced by the powerful tungsten bulb all set up was done under conventional low-power lighting, and only switched to the security light for brief periods for photograph and sampling, in a further effort to reduce potential desiccation – as previous reflectance studies have indicated that leaf turgidity affects reflectance readings (Thomas et al, 1966; Van Der Meer & De Jong, 2010).

A photographic catalogue of the leaves was taken using an Olympus µ1030SW 10.1 megapixel shock and waterproof camera (specifications: [Appendix: Table 1]). For the photographs the leaves were placed on graph paper, to provide a reference scale during photograph analysis.

Spectrometry data was then taken for each sample using an ASD Fieldspec Pro System radiospectrometer (for specifications see [Table A1]). This was placed directly above the sample, keeping the angle at which laboratory readings were taken consistent with those that might be taken by aircraft or satellites, which are best suited to reading straight down from their high altitude position. A white reflectance panel was used to calibrate the ASD readings, as the panel is a white lambertian surface reflector with close to perfect reflectance it is used to give a reference

point of 100% reflectance, to which other readings can be compared. The spectrometer was repeatedly calibrated between samples to ensure consistency and continued accuracy. The sample data covered the whole 350-2500nm range of which the spectrometer is capable of. As vegetation is not evenly distributed in the natural environment and certain environmental factors are inclined to promote growth in a particular direction; such as a south facing slope – which will have an abundance of leaves orientated in the south direction, whilst much less visible leaf growth from the north side, reflectance sampling was repeated for each sample at four different angles (↑: 360º, →: 90º, ↓: 180º and ←: 270º) [Figure 5] giving a reflectance for each individual leaf without any potential bias from its orientation. While extended data was collected including photograph and spectrometry for leaf top and leaf bottom, this experiment only concerns the data from leaf tops – as this is most likely to be viewed on natural plants in situe during applied spectrometry fieldwork.

2.4 Data Analysis

2.4.1 Analysis of PhotographySamples were subject to image analysis

using ImageJ – a public domain Java imaging software (Ferreira & Rasband, 2010). For each species three sets of leaves were analysed, while not keeping a constant number of leaves per species, the number of leaves per set was indicative of the plant’s terminal growth and representative of its cover in the wild. The graph paper included in the photographs allowed the software to establish an accurate physical scale; then the length and area were digitally measured and recorded for each leaf as demonstrated

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[Figure 6] to establish any physical differences between the leaf morphology of the species, that may have underlying effects on reflectance.

2.4.2 Data ManipulationThe majority of data manipulation was

conducted in Microsoft Excel. Reflectance data was averaged for each of the four orientations (mentioned above) and graphically represented, and an ANOVA test conducted on a set of 19 readings from each orientation used to establish if orientation produced significant variance in reflectance.

The entire dataset was then averaged by median across the samples for each wavelength to produce a final figure that could then be plotted and further manipulated. Median was chosen over mean to eliminate the effect of potentially anomalous readings in the large sample size. Additionally as orientation was shown to have a significant effect on reflectance

data was quadruplicate for each sample, including the four different orientations of the leaf – overall this gave a single average value per

wavelength, per species. This data could then be plotted as reflectance curves for the entire range 350-2500nm to visually identify any variation in reflectance and, if found, at what wavelengths this is most obvious. It should be noted that two specific wavelength bands have been removed from the data: 1349-1461nm and 1789-1961nm. These bandwidths relate to areas of the spectrum where water vapour present in the air interferes with reflectance data, giving false results (HITRAN, 2011). Water (and a range of other molecules) in the atmosphere have their own spectral absorbance properties. As a major greenhouse gas, water vapour is responsible for a significant absorption of solar insolation, as much as 70% of incoming radiation is affected by water vapour (Gordon et al, 2007). HITRAN maintain

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a website that lists the spectral absorbance bands for a wide range of atmospheric molecules.

A further reflectance curve was plotted to refine the model across the selected range of 350-700nm as this is the range of the visible spectrum and includes the key wavelengths relating to maximum absorption of photosynthetic pigment present in leaves [Table 1] (Van Der Meer & De Jong, 2006).

Table 1. Leaf pigment absorption wavelengths.

Pigment Maximum Absorption Wavelength(s)

Chlorophyll-a 420nm, 660nmChlorophyll-b 450nm, 640nmβ-Carotene 425nm, 480nmα-Carotene 440nm, 470nmXanthophyll 425nm, 475nmPhycoerythrin 550nm

This range also avoids interference from the water absorption bands and does not exhibit any noise or deterioration in the quality of the spectrometry results. Due to the biochemical significance of these photosynthetic pigments, these wavelengths were chosen for statistical testing in effort to create a spectrometry based model for accurately differentiating species solely based on reflectance data.

2.4.3 Statistical AnalysisAll statistical analysis was conducted in

Minitab. Normalized Differentiation Vegetation Index (NDVI) is a method of producing a comparable number to evaluate the different reflective properties of a specific vegetative medium. As the reflectance properties of vegetation are generally smaller in the visible wavelengths (400-650nm) than in the near infrared (700-1000nm), NDVI can be calculated [Equation 1] as a ratio between the highest and the lowest reflectance within this visible to near infrared range (400-1000nm) (Van Der Meer & De Jong, 2006).

Equation 1.

Effectively this enhances the spectral contribution from green reflectance (the predominant reflective colour of vegetation) and decreases the contribution of red/near infrared which are more commonly reflected from non-vegetative sources. A One-way ANOVA test was used between NDVI and Species to show any deviance that might isolate Rhododendron for identification modelling.

Due to changing bandwidth resolution between ground, air and satellite observations it is important to have a model that translates from one stage to another. Table 2 contains bandwidth information for current and some future remote sensing projects, generally the higher altitude the sensor, the wider the bandwidth.

Table 2. Current and planned near future air and satellite remote sensing projects.

While a sensor at very close range can accommodate very fine perception typically with a bandwidth of 1nm (allowing them show reflectance for individual wavelengths). Sensors at these higher altitude stages work by averaging a reflectance reading from an area across their wavelength bandwidth (only allowing say; a single reading per bandwidth of multiple nanometres). Thus a sensor with a higher bandwidth generates a broader average, and therefore has a lower resolution – being more likely to be affected by anomalous features in the area (Van Der Meer & De Jong, 2006). For this reason data was manipulated to represent observations at all three stages of observation. Ground viewing was maintained at a 1nm

NDVI = (Near Infrared: Maximum – Visible: Minimum)

(Near Infrared: Maximum + Visible: Minimum)

StageProject Name

Range (nm)

Bandwidth (nm)

AirAISA 430-900 1.63ASAS 404-1020 10.00

AVIRIS 400-2500 10.00

Satellite

Eagle495-570 620-750

750< As range

ASTER520-600630-690760-860

As range

HIRIS 400-1000 9.40MERES 390-1040 30.00PRISM 400-2400 10.00

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bandwidth. Air viewing was represented at a 10nm bandwidth, with values being mean averaged in an equal spread around the target wavelength. Satellite viewing was represented at a 30nm bandwidth, also with values being mean averaged in an equal spread around the target wavelength.

Further testing was then conducted on the specific highlighted pigment wavelengths [Table_1] due to their biological importance. A fifth additional wavelength was added, 2000nm, to give a representation of variability in higher wavelengths outside the visible spectrum. For each of the wavelengths a reduced sample size of 48 was taken and triplicated to be represented at ground, air and satellite level – as the data for these samples was not normally distributed a Kruskal-Wallis test was used in place of an ANOVA.

2.4.4 Logistic Regression ModellingAs both NDVI and the selected

wavelengths showed strong differentiation in the ANOVA/Kruskal-Wallis tests they were suitable for use. This gave a total of seven predictor variables for a series of Binary Logistic Regressions; in an attempt to create a model to differentiate Rhododendron from the other three species at each survey level.

3. Results

3.1 Physical Properties of Leaves

Table 3 (data [Table A2-5]) shows the physical characteristics of the leaves. Most noticeably rhododendron and laurel exhibit similar leaf sizes and areas, much larger than those of beech and holly. Table 4 (data [Table A6]) summarises and ANOVA test between leaf length and species; showing that the species can be subdivided into two groups based on leaf morphology. Beech and holly share similar leaf length, while rhododendron and laurel are also very similar. There is no overlap between the two groups.

Table 3. Summary of average physical characteristics of leaves.

Species leaves/sample

leaf length (mm)

leaf area (cm2)

total area/sample (cm2)

Beech 16.6 65.7 16.6 243Laurel 6.3 124.0 51.4 325Holly 9.6 83.9 34.1 329

Rhododendron

8.3 136.5 40.1 334

Table 4. Summary of One-way ANOVA of Leaf length versus Species. (see [Table A6])

Species N Mean GroupingLaurel 19 124.04 A

Rhododendron 19 132.51 ABeech 19 61.72 BHolly 19 75.36 B

3.2 Spectral Properties of Leaves

3.2.1 Effects of OrientationFigure 7 summarises reflectance values

for different leaf orientations. It can be seen that there is a distinct difference in reflectance between samples perpendicular (↑: 360º and ↓: 180º) and those parallel (→: 90º and ←: 270º) to incident light.

B

A A

B

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

8.00

9.00

10.00

360º 180º 90º 270º

Orientation

Ref

lect

ance

(%)

Figure 7. Mean reflectance of Rhododendron for 550nm wavelength at four orientations, letters indicate pairwise significance at p0.05 for post-hue Tukey analysis of ANOVA.

Samples perpendicular to the incident light share a very similar reflectance (9.39 and 9.03 respectively), the same is also true for those samples parallel to the incident light (reflectance: 6.19 and 6.12 respectively). This demonstrates two pairs of corresponding angles, based on leaf orientation to incident illumination – with leaves

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perpendicular to the light expressing approximately 50% higher reflectance than those parallel to it.

3.2.2 Reflectance CurvesFigure 8 shows the reflectance for each

species across the full sample range of 350-2500nm, demonstrating that there is a visible variation in reflectance of the four species over a range of wavelengths. Data for both white reflectance and black reflectance targets was included so as to demonstrate the reliability of the readings. It can be seen that the data experiences jagged spikes in the low <400nm for both targets (as well as species readings). White target readings become less reliable after around 1980nm with black target readings deteriorating slightly later after around 2100nm. This suggests data in these regions is potentially unreliable, as the target constants are not expressed accurately.

Figure 9 refines the model across the selected range of 350nm-700nm. Variation in the reflectance between species can bee seen more clearly at this higher resolution: Beech shows the least reflectance ~4% in the blue (~450nm) range of the spectrum, before gradually increasing in an approximately linear fashion from 500nm onwards; out-reflecting rhododendron at 563nm, holly at 580nm and finally laurel at 613nm, becoming the highest reflector >10% in the red (~650nm) area of the spectrum. Holly and Rhododendron follow very similar curves; both exhibiting low reflectance ~5% in the blue spectrum before increasing in the green (~550nm) spectrum to 6.88% and 8.89% respectively. After this they gradually fall to ~4% reflectance in the red spectrum. Laurel exhibits the highest reflectance in the blue spectrum, ~6% and also climbs rapidly in the green spectrum to a peak of 12.97%, before gradually falling to a similar level to holly and rhododendron in the red spectrum. All species reflectance rises sharply as they approach the near-infrared (~700nm+) spectrum.

3.3 Statistics

3.3.1 Normalized Difference Vegetation IndexingFigure 10 (data: [Table A8]) summarises

one-way ANOVA testing conducted on calculated NDVI readings for each species. The test was found to be highly significant (P <0.000). Laurel, beech and holly share very similar means (~0.83) grouping them together in two pairs; laurel + beech and beech + holly, rhododendron exhibits a significantly different mean (0.78) placing it in a group alone.

C

B

A B

A

0.75

0.76

0.77

0.78

0.79

0.80

0.81

0.82

0.83

0.84

0.85

Laurel Beech Holly Rhododendron

Species

Mea

n ND

VI

Figure. 10. Mean NDVI per species, letter indicates pairwise statistical significance at p0.05 for post-hue Tukey analysis of ANOVA.

3.3.2 Variance Testing (Kruskal-Wallis)Tables 5-7 summarise the Kruskal-Wallis

variance testing (data [Tables A9-11]).

Table 5. Summary of Kruskal-Wallis testing on reflectance at specific wavelengths representative of ground level sampling.Wave length (nm)

Species Reflectance (%)Beech Laurel Holly Rhodod

endronP-value

450 4.21 5.68 4.63 4.71 <0.000550 6.06 12.24 8.89 7.25 <0.000640 10.52 7.62 5.66 4.94 <0.000660 11.45 6.22 4.89 4.52 <0.0002000 21.53 7.51 5.82 8.48 <0.000

At each stage all five wavelengths tested yielded highly significant results (P <0.00). Laurel was consistently the highest reflector at the 450 and 550nm wavelengths.

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With Beech consistently the highest reflector by approximately double the others in the 640, 660 and 2000nm wavelengths. Rhododendron consistently exhibited lowest reflectance in the 640 and 660nm wavelengths. Overall these tests statistically confirm that significant variance can be seen in all the species reflectance across all three stages of observation.

Table 6. Summary of Kruskal-Wallis testing on reflectance at specific wavelengths representative of air level sampling.Wave length (nm)

Species Reflectance (%)Beech Laurel Holly Rhododen

dronP-value

450 4.24 5.68 4.62 4.70 <0.000550 6.05 12.18 8.87 7.25 <0.000640 10.50 7.63 5.68 4.95 <0.000660 11.43 6.25 4.91 4.53 <0.000

2000 21.49 7.48 5.82 8.42 <0.000Table 7. Summary of Kruskal-Wallis testing on reflectance at specific wavelengths representative of satellite level sampling.Wave length (nm)

Species Reflectance (%)Beech Laurel Holly Rhododen

dronP-value

450 4.22 5.64 4.61 4.69 <0.000550 6.07 11.98 8.74 7.15 <0.000640 10.49 7.49 5.60 4.91 <0.000660 11.42 6.26 4.91 4.55 <0.0002000 25.50 7.48 5.82 8.42 <0.000

3.3.3 Logistic Model of Rhododendron Identification

Tables 8-10 (see [Tables A12-14]) summarise the Logistic Regression model for ground, air and satellite observations, respectively. At each level all predictors were found to be statistically significant, except NDVI which exhibited P-values over the α-level (P~0.25). 660nm was consistently found to have the highest coefficient which increased from ground to satellite level (ground: 27.74, air: 30.90, satellite: 37.80). In each stage the 550nm predictor was found to be the most powerful effect upon binary result, consistently exhibiting the highest odds ratio. Again this was seen to rise from ground to satellite level (ground: 38.91, air: 45.13, satellite: 68.82). Goodness-of-fit tests at each level also yielded positive results, with almost all values close to 1.000. The Hosmer-Lemeshow test at satellite level [Table 10] was unusually low; however in this case the other two tests showed particularly strong results (P~1.000). The testing was therefore concluded

to be significant overall. When predicting the binary response, rhododendron vs not-rhododendron the model was extremely powerful at each stage with the vast majority of paring being concordant (ground: 6882/6912, air: 6883/6912, satellite: 6888/6912) resulting in a high percentage of accuracy when predicting rhododendron (ground: 99.6%, air: 99.6%, satellite: 99.7%). Interestingly the power of this model increases with the widening bandwidth from 1nm at ground level to 30nm at satellite level. Overall the model presented demonstrates a high reliability – suggesting that Rhododendron can be identified from the three other species, at all three observation levels.

4. Discussion

Spectrometry has been proposed as an effective method of differentiating different species of vegetation based on variable reflectance data. This however is only possible given a calibration and modelling that can translate the raw data into accurately distinguishable representation of the species present in the environment.

Both the photographic [Figures A1-4] and physical characteristics [Table 3] of the four sample species indicate that there some variation in the morphology of the leaves. Beech leaves express a different pigmentation from the other species; as while they retain their leaves in winter, they are not in fact evergreen. Rhododendron appears conspicuously larger in length per leaf than the other species; however it was yet to be established if this would translate into a visible effect when evaluated purely with reflectance data.

Interactions between plant leaves and solar radiation fall into three categories: thermal effects, photomorphogenic effects and photosynthetic effects (Van Der Meer & De Jong, 2006). Overall ~70% of insolation is absorbed by plants for thermal maintenance, keeping the plant at a functioning biological temperature, and allowing transpiration. Photomorphogenesis refers to the ability of a plant to sense and physically react to the presence of light. It can be seen that plants grow towards light sources, and even turn leaves and stems to

12

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Table 8. Summary of Logistic Regression model representative of ground level sampling.

Predictor Coefficient Z P Odds RatioConstant 2.80 0.10 0.919

NDVI 27.00 1.09 0.276 5.66E+11450nm -16.00 -3.42 0.001 0.00550nm 3.66 2.62 0.009 38.91640nm -16.00 -2.50 0.015 0.00660nm 27.74 3.48 0.001 1.12E+122000nm -3.65 -3.54 0.000 0.03

Goodness-of-Fit TestMethod Chi-squared DF P-valuePearson 150.10 185 0.972

Deviance 28.28 185 1.000Hosmer-Lemeshow 4.26 8 0.833

Pairs Number PercentConcordant 6882 99.6Discordant 29 0.4

Ties 1 0.0Total 6912 100.0

Table 9. Summary of Logistic Regression model representative of air level sampling.

Predictor Coefficient Z P Odds RatioConstant 1.45 0.05 0.96

NDVI 28.82 1.12 0.264 3.30E+12450nm -16.34 -3.40 0.001 0.00550nm 3.81 2.70 0.007 45.13640nm -18.36 -2.77 0.006 0.00660nm 30.90 3.56 0.000 2.62E+132000nm -3.93 -3.48 0.001 0.02

Goodness-of-Fit TestMethod Chi-squared DF P-valuePearson 128.33 185 0.999

Deviance 28.03 185 1.000Hosmer-Lemeshow 5.16 8 0.741

Pairs Number PercentConcordant 6883 99.6Discordant 27 0.4

Ties 2 0.0Total 6912 100.0

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Table 10. Summary of Logistic Regression model representative of satellite level sampling.

Predictor Coefficient Z P Odds RatioConstant -5.56 -0.17 0.863

NDVI 37.15 1.19 0.233 1.36E+16450nm -17.58 -3.24 0.001 0.00550nm 4.23 2.55 0.011 68.82640nm -23.52 -2.83 0.005 0.00660nm 37.80 3.45 0.001 2.62E+162000nm -4.51 -3.32 0.001 0.01

Goodness-of-Fit TestMethod Chi-squared DF P-valuePearson 98.86 185 1.000

Deviance 23.63 185 1.000Hosmer-Lemeshow 8.34 8 0.401

Pairs Number PercentConcordant 6888 99.7Discordant 21 0.3

Ties 3 0.0Total 6912 100.0

optimize insolation, a small amount of incoming solar radiation is absorbed and used for this purpose (Kendrick & Kronenberg, 1994). Photosynthetically active radiation comprises ~30% of total radiation absorbed by the plant. This radiation falls within the visible spectrum (350-700nm) and interacts with photopigments in the leaf’s structure. These photopigments absorb light of a specific energy causing electron transition within the chemical structure of the pigment – releasing free radicals that can be used for further photochemical reaction and ultimately the process of photosythensis (Van Der Meer & De Jong, 2006). While all green plants conduct photosynthesis, different species have different leaf morphology and chemistry; it is therefore proposed that they should exhibit different spectral reflectance. The experimentation shown here indicates such variation is expressed in radiospectrometry data – and additionally that a model can be successfully engineered to evaluate this variation and accurately differentiate the species.

NDVI had been used in the past to distinguish the presence of vegetation on the ground or not, vegetation cover, or a more simple evaluation of vegetation type (Carlson & Ripley,

1997; Horning et al, 2010). There is significant variation in NDVI between specific species on the ground, inhabiting the same ecosystem [Figure 10]. In a preliminary modelling test, this NDVI data was shown to be a significant variable in controlling the identification of Rhododendron from the three other species. To enhance the quality of the model, raw reflectance data was further taken into consideration. Five wavelengths were chosen, based predominantly on their biological importance as the key reflective maximums for different photosynthetic pigments; as previous studies have established that there are variations in pigment content between species (Sims & Gamon, 2002) and attempts at establishing spectral indices to evaluate photopigment content and composition have been attempted (Blackburn, 1998; Chapelle & Kim, 1992). It is therefore logical to couple these principles to attempt an identification of different species through spectral reflectance associated with specific photosynthetic pigments. For each of the wavelengths chosen, there was a statistically significant differentiation between the four species; showing that different species reflect different levels of light at different wavelengths [Tables 5-7]. This confirms that the

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leaves are structurally and chemically different, most obviously beech exhibits high reflectance around 660nm – corresponding to one of the absorption peaks for Chlorophyll-a, whereas it is has extremely low reflectance around 550nm, peak absorption for Phycoerythrin [Table 1]. This correlates with its deciduous status, showing that it has very little Chlorophyll-a and is therefore not likely to be photosynthetically active, in fact the leaves are visibly brown and dead. Conversely the evergreen plants all exhibit high levels of reflectance in the 550nm green spectrum, conveying the fact that they are alive and photosynthetically active all year round, therefore maintaining these specific pigments in winter. Figure 9 suggests that rhododendron possesses higher concentrations of the key photopigments studied than the other photosynthetically active species; this could well contribute to its highly competitive nature, with the combination of its large leaf area coupled with high photopigment concentration increasing its metabolic performance over its competitors.

The logistic regression model shows that by using differences in reflectance at key wavelengths and NDVI; rhododendron can be isolated from the other species with an extremely high level of accuracy (upwards of 99%). Interestingly it can be seen that as data is rescaled to lower resolution representative of higher altitude stages, the power of the predictors increases, creating the all-be-it marginal increase in the power of the model of 0.1% accuracy at satellite level compared to ground. While it was expected that the model would loose power at higher levels due to the lower resolution of the sensor, it appears that averaging the reflectance data in this case smoothes out any anomalous results increasing its statistical power.

4.1 Implications for Rhododendron ManagementThe reliability and power of this logistic

regression presents a model that could be easily used to evaluate the occurrence of rhododendron on other sites. Given spectrometry data gathered either manually, from an aircraft, or even satellite. Importantly existing and historical spectrometry data is held in archives by a myriad of scientific and commercial organizations worldwide (Ferwerda et al, 2006; Hueni et al,

2009; Baldridge et al, 2009). This data could be retrospectively evaluated using such a model to address questions such as the temporal/spatial introduction of invasive rhododendron, or the rate of spread of the species in a variety of environments. Unlike traditional survey methods, remote sensing can be conducted very quickly and feedback given from the model in a short space of time, allowing it to rapidly evaluate occurrence of rhododendron in a sample area allowing for swift action – which is all important in the case of controlling an infestation efficiently and economically. This is particularly true in the case of rhododendron as plants do not reach sexual maturity for ~10years (Edwards, 2006), meaning that they only spread by stem layering during this time. If rhododendron infestation can be caught before seed distribution its impact is relatively temporary and its spread easier to control.

4.2 Study LimitationsDue to restraints of time and manpower

the study presented here principally had extremely small sample sizes. While it is still considered conclusive and representative of the species in general: a much larger sample size would no doubt present more refined data and a more reliable model. This is particularly true of the physical characteristics recorded, as sampling individual leaves was very time consuming.

Assessing the limitations of the model as a whole the most significant drawback is the lack of a comprehensive model for a wider range of species. This model only identifies rhododendron from three other species, whereas in reality other species not taken into account may share reflectance properties with and emulate false positives for rhododendron.

4.3 Future StudyThere is a large potential for future work

in this area. Most significantly addressing the issue of a comprehensive model, future studies could take spectrometry readings from a wider range of species and attempt to reconfigure a logistic regression model in order to take their reflectance properties into account. Furthermore a conclusive test of the modelling used here translated to aerial or satellite level would be

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beneficial to ensure such a conversion can be made to efficient wide-scale sampling methods.

5. Conclusions

This study concluded that variation in spectral reflectance was seen between four species of woodland vegetation in the UK. From this a model was established to successfully identify (>99% probability) rhododendron from the other three species. While not comprehensive, the methodology here could be used as a paradigm from which to expand upon the basic principle – calibrating more species into reflectance models and effectively establishing a catalogue from which any particular species could be identified using spectrometry data.

This model was also shown to effectively translate to aerial and satellite levels, allowing it to be used for modern remote sensing projects. The combination of a vegetation catalogue and next generation remote sensing technology presents a powerful tool for evaluating the extent of vegetation species. On a global scale remote sensing of vegetation has applications in the commercial industries of forestry and agriculture, in addition to scientific applications (particularly evaluating the presence and spread of invasive species).

Acknowledgements

Thanks to Dr. Sarah Taylor for physical vegetation sampling and dark room spectrometry; and Field Spectroscopy Facility for providing ADS radiospectrometer.

References

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Appendix

Photography

Figure A1. Beech, situe in environment (Source: Taylor, 2010)

Figure A2. Laurel, situe in environment (Source: Taylor, 2010)

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Figure A3. Holly, situe in environment (Source: Taylor, 2010)

Figure A4. Rhododendron, prepared for spectrometry (Source: Taylor, 2010)

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Equipment

Table A1. Equipment specifications (Source: Olympus, 2011; Natural Environment Research Council, 2010)

Equipment SpecificationsCamera: Olympus µ1030SW Effective pixels

10.1 MegapixelsFilter arrayPrimary colour filter (RGB)Full resolution10.7 MegapixelsType1/2.33” CCD sensorShutter speed1-500ms (< 4s in low light levels)SensitivityISO 80-1600

Radiospectrometer: FSF ASD Fieldspec Pro System

Spectral Range350-2500 nmSampling Interval1.4 nm, 350-1000 nm2 nm, 1000-2500 nmSpectral Resolution (FWHM)3 nm @ 700 nm10 nm @ 140012 nm @ 2100nmDetectorsOne 512 element VNIR silicon photodiode array (350-1000 nm) Two separate, TE cooled, graded index SWIR InGaAs photodiodes (1000-2500 nm)Typical scan time (perfect skies)< 3 secondPower sourceMains, external NiMH rechargeable cells, or 12V batteriesSize13" x 4.5" x 16"Weight7.2 kgOther FeaturesDriftLock dark current compensation to ensure high accuracy

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Physical Data

Table A2. Physical data for Beech.

Sample Length AreaArea sum/sample Leaves/sample

Average area/sample

B1-1 55 1257 17 2436267.5 2333 1156.3 1311 1659.9 1547 14.6666666758.9 131266.3 206752.4 163152.7 96060.1 1916

58 170158.5 92868.6 158354.1 130264.9 178656.7 95258.6 135769.5 2380 26322

B2-1 86 2144.768.7 1409.962.6 146264.4 1128.883.8 1139.465.2 1388.668.5 1572.190.1 3411.367.8 2196.699.5 3978.4

63 1583.3 21415.1B3-1 60.9 1599.9

51.4 777.459.7 1276.343.4 1069.659.4 1358.778.5 1376.855.8 1403.657.1 1394

117.4 3361.990 2609

117.9 4289.656.1 1284.954.8 126449.7 802.257.5 776.845.9 705 25349.7

Mean 65.75227 1661

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Table A3. Physical data for Laurel

Sample Length AreaArea sum/sample Leaves/sample Area/sample

L1-1 144.9 4861 5 32525177.9 7686 6

150 4778 8171.3 6639 6.333333333164.1 6210 30174

L2-1 139.2 8064120.8 5254139.6 7257174.9 11106121.7 459792.6 4746 41024

L3-1 104.5 455297 335196 3824

100.8 3384105.6 426075.3 104293.5 412687.1 1839 26377

Mean 124.0 5135.5

Table A4. Physical data for Holly

Sample Length AreaArea sum/sample Leaves/Sample

Average area/sample

H1-1 63.5 2299 11 3293561.4 1769 8

60 1632 1062.9 1895 9.66666666773.4 291275.8 246264.2 188763.2 223974.1 316873.4 245464.2 1822 24537

H2-1 95.7 388895.3 373495.1 368695.6 395892.3 373491.2 309963.7 183866.9 1580 25515

H3-1 85.6 3751.5129.1 6349.6

88.6 2773.4105.6 4811.5

95.6 4825.290.5 4938.1

116.7 6376.586.2 3905.5

110.4 6537.1

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93.7 4485.7 48754.1Mean 83.92759 3407

Table A5. Physical data for Rhododendron

Sample Length AreaArea sum/sample Leaves/sample Area/sample

R1-4v 139.7 3696 11 33406149.8 4783 7151.2 5170 7145.6 4876 8.333333333157.9 5470143.7 4768144.9 4615128.8 3177

143 4595131.3 3586121.6 2981 47717

R1-5v 109.6 2753129.5 3563144.2 4608130.8 3636125.8 330873.1 86051.4 332 19059

R2-4v 195.8 7032192.6 7420156.1 4976127.1 3049133.6 3716138.6 3300145.9 3950 33442

Mean 136.464 4009Table A6. One-way ANOVA Length vs SpeciesSource DF SS MS F PSpecies 3 70245 23415 41.21 0.000Error 72 40909 568Total 75 111153

S = 23.84 R-Sq = 63.20% R-Sq(adj) = 61.66%

Individual 95% CIs For Mean Based on Pooled StDevLevel N Mean StDev +---------+---------+---------+---------1 19 61.72 8.11 (----*---)2 19 124.04 32.80 (----*---)3 19 75.36 13.92 (---*----)4 19 132.51 30.62 (---*---) +---------+---------+---------+--------- 50 75 100 125

Pooled StDev = 23.84

Grouping Information Using Tukey Method

Species N Mean Grouping4 19 132.51 A2 19 124.04 A3 19 75.36 B1 19 61.72 B

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Spectrometry

Table A7. One-way ANOVA orientation vs reflectanceSource DF SS MS F PC1 3 202.16 67.39 20.95 0.000Error 72 231.62 3.22Total 75 433.79

S = 1.794 R-Sq = 46.60% R-Sq(adj) = 44.38%

Individual 95% CIs For Mean Based on Pooled StDevLevel N Mean StDev -----+---------+---------+---------+----1 19 9.585 2.539 (-----*----)2 19 9.226 2.381 (-----*----)3 19 6.185 0.542 (----*-----)4 19 6.121 0.676 (-----*----) -----+---------+---------+---------+---- 6.0 7.5 9.0 10.5

Pooled StDev = 1.794

Grouping Information Using Tukey Method

C1 N Mean Grouping1 19 9.585 A2 19 9.226 A3 19 6.185 B4 19 6.121 B

Table A8. One-way ANOVA NDVI vs speciesSource DF SS MS F PLABEL 3 0.109019 0.036340 53.35 0.000Error 188 0.128051 0.000681Total 191 0.237071

S = 0.02610 R-Sq = 45.99% R-Sq(adj) = 45.12%

Individual 95% CIs For Mean Based on Pooled StDevLevel N Mean StDev ------+---------+---------+---------+---1.00 48 0.82727 0.02416 (---*--)2.00 48 0.83837 0.02356 (---*---)3.00 48 0.82431 0.02895 (---*---)4.00 48 0.77630 0.02734 (---*---) ------+---------+---------+---------+--- 0.780 0.800 0.820 0.840

Pooled StDev = 0.02610

Grouping Information Using Tukey Method

LABEL N Mean Grouping2.00 48 0.83837 A1.00 48 0.82727 A B3.00 48 0.82431 B4.00 48 0.77630 C

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Table A9. Kruskal-wallis tests for wavelengths at ground level

Kruskal-Wallis Test on 450nm

Species N Median Ave Rank Z1 48 4.215 64.0 -4.682 48 5.687 119.7 3.343 48 4.630 91.9 -0.664 48 4.719 110.3 1.99Overall 192 96.5

H = 28.07 DF = 3 P = 0.000

Kruskal-Wallis Test on 550nm

Species N Median Ave Rank Z1 48 6.069 45.6 -7.322 48 12.244 149.9 7.693 48 8.893 94.1 -0.354 48 7.250 96.4 -0.01Overall 192 96.5

H = 84.61 DF = 3 P = 0.000

Kruskal-Wallis Test on 640nm

Species N Median Ave Rank Z1 48 10.525 145.4 7.042 48 7.627 112.3 2.273 48 5.666 61.7 -5.014 48 4.944 66.6 -4.30Overall 192 96.5

H = 73.80 DF = 3 P = 0.000

Kruskal-Wallis Test on 660nm

Species N Median Ave Rank Z1 48 11.454 165.5 9.932 48 6.224 91.4 -0.733 48 4.896 61.3 -5.064 48 4.525 67.8 -4.13Overall 192 96.5

H = 106.35 DF = 3 P = 0.000

Kruskal-Wallis Test on 2000nm

Species N Median Ave Rank Z1 48 21.538 168.5 10.372 48 7.510 71.9 -3.553 48 5.829 41.3 -7.954 48 8.488 104.4 1.13Overall 192 96.5

H = 138.38 DF = 3 P = 0.000

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Table A10. Kruskal-wallis tests for wavelengths at air level

Kruskal-Wallis Test on 450nm_1

Species N Median Ave Rank Z1 48 4.242 64.5 -4.612 48 5.682 119.9 3.373 48 4.624 91.5 -0.724 48 4.707 110.1 1.96Overall 192 96.5

H = 27.72 DF = 3 P = 0.000

Kruskal-Wallis Test on 550nm_1

Species N Median Ave Rank Z1 48 6.052 45.5 -7.342 48 12.189 149.8 7.673 48 8.876 94.2 -0.344 48 7.251 96.5 0.01Overall 192 96.5

H = 84.52 DF = 3 P = 0.000

Kruskal-Wallis Test on 640nm_1

Species N Median Ave Rank Z1 48 10.504 144.7 6.942 48 7.639 112.6 2.323 48 5.680 61.9 -4.994 48 4.950 66.8 -4.27Overall 192 96.5

H = 72.55 DF = 3 P = 0.000

Kruskal-Wallis Test on 660nm_1

Species N Median Ave Rank Z1 48 11.431 165.3 9.912 48 6.256 91.8 -0.683 48 4.914 61.2 -5.084 48 4.537 67.7 -4.14Overall 192 96.5

H = 106.25 DF = 3 P = 0.000

Kruskal-Wallis Test on 2000nm_1

Species N Median Ave Rank Z1 48 21.499 168.5 10.372 48 7.484 71.9 -3.553 48 5.826 41.3 -7.954 48 8.429 104.4 1.13Overall 192 96.5

H = 138.36 DF = 3 P = 0.000

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Table A11. Kruskal-wallis tests for wavelengths at satellite level

Kruskal-Wallis Test on 450nm_2

Species N Median Ave Rank Z1 48 4.226 64.8 -4.562 48 5.649 119.9 3.373 48 4.612 91.4 -0.744 48 4.696 109.9 1.93Overall 192 96.5

H = 27.35 DF = 3 P = 0.000

Kruskal-Wallis Test on 550nm_2

Species N Median Ave Rank Z1 48 6.070 47.6 -7.042 48 11.989 149.3 7.603 48 8.744 93.4 -0.444 48 7.155 95.7 -0.11Overall 192 96.5

H = 80.67 DF = 3 P = 0.000

Kruskal-Wallis Test on 640nm_2

Species N Median Ave Rank Z1 48 10.492 147.0 7.262 48 7.490 110.7 2.043 48 5.605 61.4 -5.054 48 4.912 66.9 -4.26Overall 192 96.5

H = 75.39 DF = 3 P = 0.000

Kruskal-Wallis Test on 660nm_2

Species N Median Ave Rank Z1 48 11.424 165.5 9.932 48 6.269 92.2 -0.623 48 4.916 60.9 -5.124 48 4.552 67.4 -4.19Overall 192 96.5

H = 107.14 DF = 3 P = 0.000

Kruskal-Wallis Test on 2000nm_2

Species N Median Ave Rank Z1 48 21.500 168.5 10.372 48 7.480 71.9 -3.553 48 5.823 41.4 -7.934 48 8.420 104.2 1.11Overall 192 96.5

H = 138.07 DF = 3 P = 0.000

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Logistic Regression Modelling

Table A12. Binary logistic regression at ground levelPredictor Coef SE Coef Z P Odds Ratio Lower UpperConstant 2.80358 27.6264 0.10 0.919NDVI 27.0622 24.8568 1.09 0.276 5.66176E+11 0.00 8.14922E+32660nm 27.7443 7.97475 3.48 0.001 1.11999E+12 182431.31 6.87593E+18450nm -16.0010 4.68504 -3.42 0.001 0.00 0.00 0.00640nm -15.9567 6.37404 -2.50 0.012 0.00 0.00 0.03550nm 3.66115 1.39844 2.62 0.009 38.91 2.51 603.072000nm -3.64742 1.02984 -3.54 0.000 0.03 0.00 0.20

Log-Likelihood = -14.138Test that all slopes are zero: G = 187.661, DF = 6, P-Value = 0.000

Goodness-of-Fit Tests

Method Chi-Square DF PPearson 150.102 185 0.972Deviance 28.275 185 1.000Hosmer-Lemeshow 4.258 8 0.833

Measures of Association:(Between the Response Variable and Predicted Probabilities)

Pairs Number Percent Summary MeasuresConcordant 6882 99.6 Somers' D 0.99Discordant 29 0.4 Goodman-Kruskal Gamma 0.99Ties 1 0.0 Kendall's Tau-a 0.37Total 6912 100.

Table A13. Binary logistic regression at air levelPredictor Coef SE Coef Z P Odds Ratio Lower UpperConstant 1.45001 28.3046 0.05 0.959NDVI 28.8226 25.8227 1.12 0.264 3.29236E+12 0.00 3.14665E+34660nm_1 30.8961 8.67896 3.56 0.000 2.61819E+13 1072639.11 6.39071E+20450nm_1 -16.3393 4.82330 -3.39 0.001 0.00 0.00 0.00640nm_1 -18.3554 6.63393 -2.77 0.006 0.00 0.00 0.00550nm_1 3.80949 1.41267 2.70 0.007 45.13 2.83 719.292000nm_1 -3.92800 1.12974 -3.48 0.001 0.02 0.00 0.18

Log-Likelihood = -14.016Test that all slopes are zero: G = 187.904, DF = 6, P-Value = 0.000

Goodness-of-Fit Tests

Method Chi-Square DF PPearson 128.335 185 0.999Deviance 28.033 185 1.000Hosmer-Lemeshow 5.156 8 0.741

Measures of Association:(Between the Response Variable and Predicted Probabilities)

Pairs Number Percent Summary MeasuresConcordant 6883 99.6 Somers' D 0.99Discordant 27 0.4 Goodman-Kruskal Gamma 0.99Ties 2 0.0 Kendall's Tau-a 0.37Total 6912 100.0

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Table A14. Binary logistic regression at satellite levelPredictor Coef SE Coef Z P Odds Ratio Lower UpperConstant -5.56188 32.1416 -0.17 0.863NDVI 37.1521 31.1592 1.19 0.233 1.36449E+16 0.00 4.54733E+42660nm_2 37.8034 10.9722 3.45 0.001 2.61696E+16 11974775.51 5.71907E+25450nm_2 -17.5835 5.42139 -3.24 0.001 0.00 0.00 0.00640nm_2 -23.5195 8.30847 -2.83 0.005 0.00 0.00 0.00550nm_2 4.23143 1.66039 2.55 0.011 68.82 2.66 1782.432000nm_2 -4.51021 1.35790 -3.32 0.001 0.01 0.00 0.16

Log-Likelihood = -11.813Test that all slopes are zero: G = 192.312, DF = 6, P-Value = 0.000

Goodness-of-Fit Tests

Method Chi-Square DF PPearson 98.8594 185 1.000Deviance 23.6251 185 1.000Hosmer-Lemeshow 8.3430 8 0.401

Measures of Association:(Between the Response Variable and Predicted Probabilities)

Pairs Number Percent Summary MeasuresConcordant 6888 99.7 Somers' D 0.99Discordant 21 0.3 Goodman-Kruskal Gamma 0.99Ties 3 0.0 Kendall's Tau-a 0.37Total 6912 100.0

Additional Data

All raw spectrometry data and photographic sample sets were stored electronically, to request access contact Dr. Sarah L. Taylor, Natural Sciences: Life Sciences, Keele University, Staffordshire, UK.