Critical Analysis of Urban Vegetation Mapping by Satellite

Multispectral and Airborne Hyperspectral Imagery

Sébastien Gadal

, Walid Ouerghemmi

, Romain Barlatier

and Gintautas Mozgeris

Aix-Marseille Univ, CNRS, ESPACE UMR 7300, Univ. Nice Sophia Antipolis, Avignon Univ,

13545 Aix-en-Provence, France

Aleksandras Stulginskis University, LT-53361, Akademija, Kaunas r., Lithuania

Keywords: Multi-temporal, Satellite Imagery, Airborne Hyperspectral Imagery, Vegetation Species, NDVI.

Abstract: The monitoring and management of urban vegetation is an important issue nowadays due to the multiple

benefits of vegetation for people well-being and for maintaining the balance of ecosystem. In that context, the

following study explore to what extent remote sensing imagery could be used to detect and to characterize

urban vegetation. Two types of imagery were tested which are low-resolution satellite (i.e. Sentinel 2 and

Landsat 8 OLI) and high resolution airborne (i.e. Rikola hyperspectral sensor), the study assessed the

detectability of vegetation species over Kaunas city (Lithuania) for different seasonal acquisitions. Satellite

imagery showed accurate detection of 3 coarse classes of vegetation with overall accuracies (O.A.) superior

to 90%, and airborne hyperspectral imagery showed decent detection of 13 fine classes of vegetation with

O.A. of up to 73%.

1 INTRODUCTION

Urban vegetation mapping using remote sensing

imagery is an emerging branch, indeed, the interest of

studying, mapping, and managing green spaces is of

capital importance for several actors including

agronomists, urban architects, and environmental

scientists. The availability of satellites imagery

including Sentinel and Landsat programs permits

several possibilities in terms of green areas detection,

including temporal monitoring, extraction of specific

species depending on the season of acquisition, and

extraction of useful green areas maps for urban

architects and cities actors. Nevertheless, due to the

limited spatial and spectral resolutions of these data,

recognition of trees species will be not feasible.

The recognition if tree species is a complex

procedure, which requires high spectral and spatial

resolution imagery. Indeed, 1) if pixels size are not

small enough, misclassifications could occur due to

mixed-pixels phenomenon and 2) intra-class and

inter-class spectral variabilities of vegetation species

could affect badly the classification performance (i.e.

appearance of salt and pepper effect within classes).

In this context, several studies have explored

vegetation species mapping, and the obtained

performance is mixed. In (Brabant et al., 2018), 19

vegetation species were mapped using 4m and 8m

airborne hyperspectral imagery (i.e. up to 192 bands).

The authors showed that a band reduction using

Minimum Noise Fraction (MNF) (Green et al, 1988),

or using a number of uncorrelated spectral indices

permits an increase in term of identification accuracy

(i.e. best Overall accuracy (O.A.) equal to 55%). In

(Ouerghemmi et al., 2018a), 8 vegetation species

were mapped using airborne hyperspectral data (i.e.

up to 64 bands at 0.5m). The authors compared a

distance based and a machine learning classifier using

fixed training samples number, best results were

obtained with machine learning classifier with MNF

transform, with best O.A. equal to 46%. In (Mozgeris

et al., 2018), the authors used an objects-based

method for 6 trees species identification over 0.5m

hyperspectral and 0.2m color infrared images.

Several classifiers were trained using the segmented

objects, best accuracies were obtained using

hyperspectral images and Multilayer Perceptron with

O.A. of 62%. Several studies claimed superiority of

objet-based approach over pixel-based approach for

vegetation species identification (e.g. Kamal and

Phinn, 2011; Ballanti et al., 2016). Nevertheless, such

approaches are more time consuming and final result

Gadal, S., Ouerghemmi, W., Barlatier, R. and Mozgeris, G.

Critical Analysis of Urban Vegetation Mapping by Satellite Multispectral and Airborne Hyperspectral Imagery.

DOI: 10.5220/0007721400970104

In Proceedings of the 5th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2019), pages 97-104

ISBN: 978-989-758-371-1

will depends on the accuracy of the used

segmentation methods.

In this study, a critical analysis on the use of

different types of remote sensing imagery to identify

vegetation species was carried. The goal was to first

define the classes of interest in accordance to the

image spatial resolution, and then to evaluate the

accuracy performance for each type of imagery and

for different seasonal acquisition when available.

2 MATERIALS AND METHOD

2.1 Data and Study Zone

For this study, 4 satellite images of different dates

were used; a Landsat 8 OLI image (i.e. summer: 15-

08-2015), and 4 Sentinel 2A images acquired at

different seasons (i.e. respectively spring: 12-05-

2017, summer: 28-08-2016, autumn: 17-10-2016 and

winter: 25-01-2017) were used. Landsat 8 image was

pan-sharpened to 15m with 9 bands in VIS-NIR-

SWIR domain, Sentinel-2A images were pan-

sharpened at 10m with 13 bands in VIS-NIR-SWIR

domain, pan-sharpening step was carried using

nearest-neighbour (NN) interpolation method. For

Sentinel-2A, the acquisition of four seasonal mono-

annual images was not possible due to cloud

presence, images of 2016 and 2017 were used instead.

In parallel, two summer images from airborne

hyperspectral imaging system Rikola were used, with

respectively 16 bands and 64 bands in VIS-NIR

domain at 0.7m and 0.5m of spatial resolution (i.e.

acquired respectively at July 2015 and September

2016).

The study zone concerns Kaunas city (Lithuania)

(Figure 1) which is characterized by an important

green areas inside and around the city consisting in a

heterogeneous scheme combining public parks,

individual gardens, urban forests, and free green

spaces. With such an important green area, the use of

aerial and satellite imagery could facilitate the

management and monitoring of these areas.

Ground truth validation and training points were

extracted from a tree inventory over Kaunas

(Straigytė and Vaidelys, 2012), and from google

street images. Indeed, the inventory was first released

in 2012, and slightly updated since, thereby, some

trees were missing or badly georeferenced when

comparing to our test images, 2012 inventory and

dated google street images were used in conjunction

to produce accurate ground truth samples that will be

used for classification training and for results

validation. Ground truth points were then, manually

converted to each dataset resolution, in order to fulfill

the fineness of identification scale of each dataset.

Coarser images will require less effort in ground truth

point’s definition. The gap between the inventory

data acquisition and images acquisition, will not

affect too much the ground truth accuracy since few

changes were made to the existing trees. Furthermore,

an additional verification was carried using other

available datasets (i.e. Google Street pictures, ground

pictures, and field verification).

Figure 1: True colour composition of the study zone,

Kaunas city (Lithuania), Sentinel 2A (October 2016).

2.2 Method

The proposed method is composed of three main

steps which are 1) optimal Normalized Difference

Vegetation Index (NDVI) thresholding calculated

over reflectance images, 2) vegetation species

mapping using Support Vector Machine (SVM)

classifier (Vapnik, 1995), and 3) seasonal vegetation

monitoring (Figure 2).

NDVI  Nir

 Red/Nir_I

 Red

(1)

First, the images were pre-processed if necessary,

satellite imagery were pan-sharpened to the highest

spatial resolution, no atmospheric correction was

needed since the available images were already

corrected. Airborne imagery doesn’t requires pan-

sharpening since all bands were acquired at the same

resolution, they were nevertheless converted to

reflectance using MODTRAN radiative transfer

model (Matthew et al., 2000). First step of the method

aims at defining an optimal threshold of NDVI for

vegetation pixels extraction, for that purpose, a

precise study of NDVI useful bands (i.e. red and

infrared) was carried for all the available datasets.

The goal was to test NDVI index behaviour over

vegetated pixels, for this purpose, ten pixels were

randomly chosen from a collection of ground truth

GISTAM 2019 - 5th International Conference on Geographical Information Systems Theory, Applications and Management

vegetation pixels, the bands corresponding to

minimum and maximum peaks in red and infrared

intervals were extracted for each pixel, optimal NDVI

bands will corresponds to the most recurrent bands

from the 10 test samples. Sometimes, more than one

combination of optimal NDVI bands are revealed, the

determined combination or set of combinations are

then used to calculate a threshold of NDVI, best

threshold will correspond to the narrower one.

Second step concerns vegetation species

identification at different scales depending on the

input datasets. From satellite imagery, coarse

identification of 3 vegetation classes was carried, and

from airborne hyperspectral imagery, finer

identification of 13 vegetation species was carried.

For each dataset, SVM classifier was trained using

two strategies; 1) fixed and limited amount of training

samples (i.e. 100 samples from ground truth data, per

class), 2) variable amount of training samples (i.e.

50% of ground truth data, per class). The vegetation

mapping was validated using the whole ground truth

data available.

Last step concerns seasonal vegetation

monitoring, using times series imagery. Four

Sentinel-2A were used to extract a climatic optimum

for vegetation extraction, and to monitor vegetation

behaviour along different seasons, the results were

compared to a Landsat 8 OLI at coarser resolution for

summer season. Second aspect of this part concerns

the feasibility assessment of specific vegetation

species identification following specific seasonal

acquisitions.

Figure 2: Vegetation species mapping by multi-temporal

satellite imagery and airborne hyperspectral imagery

(Method).

3 RESULTS

3.1 NDVI Thresholding

For Landsat 8 OLI, bands 4 and 5 (i.e. respectively at

654nm and 864nm) have been revealed for all tested

pixels as optimal NDVI bands, once the NDVI bands

revealed, an NDVI mask of [0.08-max] was

determined for the OLI 8 summer image (e.g. Ganie

and Nusrath, 2016).

For Sentinel 2A, band 4 (i.e. at 665nm) was

revealed as red optimal band for all tested pixels, and

three bands were revealed as potentially optimal

infrared bands which are band 7, band 8 and band 8A

(i.e. respectively 783nm, 842nm and 865nm). For the

spring image, the combination of bands 4 and 8

seemed to be the most adequate with an NDVI mask

of [0.46-max]. For the summer image any of the used

bands combination resulted in the same NDVI

thresholding with a mask of [0.46-max]. For the

autumn image, the combination of bands 4 and 8

seemed to be the most adequate with an NDVI mask

of [0.46-max], the chosen mask almost fulfil the

recommendation of Sentinel Hub platform for green

vegetation extraction (i.e. from 0.4 to max-value, e.g.

Gao, 1996; Piragnolo, 2018). Finally, for the winter

image, previous mask was not enough efficient for

vegetation detection (e.g. Sicre et al., 2016), the

combination of bands 4 and 8 seemed to be the most

adequate with an NDVI mask of [0.2-max], this latter

mask was a good compromise for winter season and

permit an accurate extraction of coniferous trees.

Concerning hyperspectral images of 16 bands, the

study revealed more combination of NDVI bands for

the 10 test pixels, the best combination concerned

bands 7 and 13 (i.e. respectively at 653nm and

803nm) with an NDVI mask of [0.7-max]. The 64

bands image was more stable in terms of NDVI bands

selection, indeed, any of the used bands combination

resulted in the same NDVI thresholding with a mask

of [0.55-max]. This last result could be explained by

the increasing of bands number that compensate the

spectral variability of the test pixels. The chosen

vegetation thresholds for hyperspectral images are

slightly different from conventional thresholds of [0.4

– max] and [0.5 – max], which are commonly used

for green vegetation extraction (e.g. Ouerghemmi et

al., 2018a; Piragnolo, 2018), nevertheless, we have

checked their accuracy in preventing non-vegetation

pixels inclusion after NDVI masking.

Critical Analysis of Urban Vegetation Mapping by Satellite Multispectral and Airborne Hyperspectral Imagery

3.2 Vegetation Species Discrimination

by Satellite Imagery

In the following study, several satellite images

acquired at different seasonal intervals were used for

vegetation species discrimination over Kaunas city.

The idea was to evaluate the identification accuracy

of three main group of vegetation species that are

deciduous trees, coniferous trees and grass areas. The

classes of interest were defined in accordance to the

available spatial resolution offered by Sentinel-2A

and Landsat 8 OLI that range from 10m to 15m in

pan-sharpened mode. Given these resolution it was

not reasonable to consider identifying trees individual

species, nevertheless such resolution could be a

useful tool for large-scale species identification.

For Sentinel-2A, the identification concerned four

acquisition dates corresponding to four different

seasons. The best accuracies were obtained for spring

and summer seasons (Figure 3.a-b), with O.A.

superior to 90%, and individual accuracies superior to

66% (Table 1). Spring season seems to be slightly

accurate than summer one in terms of statistical

accuracy, and could be considered therefore as

optimum season for vegetation extraction; deciduous

trees and grass accuracies were higher in spring

season, while coniferous trees accuracy increased in

summer season. Grass accuracy decreased in summer

season due to its sensibility to drought conditions.

For the autumn season (Figure 3.c), the identification

accuracy of coniferous trees and grass was not much

affected, the identification accuracy of deciduous

trees was nevertheless decreased to 66% (Table 1)

due to an important decrease in chlorophyll

concentration. O.A. decreased also slightly under

90%. Winter image gives best identification accuracy

of coniferous trees, while no detection of deciduous

nor grass was possible, due to snow coverage and

leaves loss (Figure 3.d). When comparing Landsat 8

OLI result with Sentinel-2A result of summer

acquisition; O.A. are comparable with values superior

than 95% (Table 1), nevertheless, Sentinel-2A

showed to be more efficient in individual classes’

identification thanks to more efficient spatial

resolution and spectral resolution. Landsat 8 OLI

showed an important decrease of deciduous trees

accuracy compared to Sentinel-2A due to its coarser

resolution, coniferous trees accuracy slightly

decreased, and grass was over-classified

Figure 3: Vegetation species mapping by Sentinel-2A images of a) spring (12-05-2017), b) summer (28-08-2016), c) autumn

(17-10-2016), and winter (25-01-2017) at 10m resolution.

Table 1: Vegetation species identification accuracy by satellite imagery.

Vegetation species

S2A

Spring

S2A

Summer

S2A

Autumn

S2A

Winter

Landsat 8 OLI

Summer

Deciduous trees (%) 100 98.3 66.4 0 69.4

Coniferous trees (%) 96.6 99.3 97.5 100 98.4

Grass (%) 94.7 90.5 94.7 0 100

O.A. (%)/Kappa 98.0/0.97 97.5/0.96 89.1/0.80 96.3/0.00 96.3/0.84

(a) (b)

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3.3 Individual Vegetation Species

Discrimination by Airborne

Hyperspectral Imagery

In the previous study, three vegetation classes were

identified using satellite images at 10m and 15m of

spatial resolution, given such resolution, it was not

possible to identify vegetation individual trees

species. In the following study, two images acquired

using hyperspectral camera Rikola were used, with

respectively 0.7m and 0.5m of spatial resolution, and

a number of bands equal to 16 and 64 bands. Airborne

hyperspectral imagery offers technically more

efficient images than satellite imagery in terms of

both spectral and spatial resolution, in the other hands

such solution is less cost effective but still relatively

more profitable than other airborne acquisition

solutions (Mozgeris et al., 2018).

Two training strategies were used to train the

SVM classifier; 1) using 100 fixed spectral samples

from the available samples, and 2) using 50% of the

available total samples per class. First strategy would

be useful in case of limited availability of training

samples, second strategy will ensure better classes

modeling (Zhang and Xie, 2013, Fassnacht et al.,

2014). The comparison include a 16 and 64 bands

images with respectively 0.7m and 0.5m of spatial

resolution, first part of results concerned

identification of 13 vegetation species including

deciduous trees, coniferous trees and a grass variety

(Figure 4.a-b), second part include coarser

classification of the vegetation species into 3 classes.

The identification accuracy showed an important

dispersion per species, some species are well

identified, some other are less accurately identified or

not identified at all (Table 2, 1

part). The first

training strategy seems less efficient in terms of

classification accuracy, with O.A. less than 30%, the

second training strategy showed much better

identification performance with O.A. of up to 73%

and an increase of accuracy performance of up to

59%. Second strategy permits the identification of

certain species that were not detected by first training

strategy, in the other hand, some previously detected

species were not detected using second training

strategy; when increasing the training samples, some

outliers could be added and then cause this behavior.

The second part of this study consists in

identifying 3 coarser classes which are deciduous

trees, coniferous trees and grass (Table 2, 2

part).

For the first training strategy, the identification

accuracy slightly increased compared to individual

species identification, nevertheless, the overall

performance still poor (i.e. barely superior to 30% at

best). The second strategy gives on the other hand,

more accurate identification accuracy (i.e. slightly

inferior to 80% at best), when comparing to satellite

imagery case, the identification accuracy is less

efficient, with a percentage of decrease that vary from

32% to 23% approximatively. The decrease could be

explained by the fact that the grouping of classes was

carried using individual species maps, and the

validation was carried using a grouping of ground

truth pixels of individual species. Knowing that some

individual species were not detected using

hyperspectral imagery (Table 2, 1

part), the global

accuracy after grouping classes was therefore

affected by the undetected fine species.

Figure 4: Vegetation species mapping by airborne hyperspectral images of a) 16 bands at 0.7m resolution (08-2015) and b)

64 bands at 0.5m resolution (09-2016).

(a) (b)

Critical Analysis of Urban Vegetation Mapping by Satellite Multispectral and Airborne Hyperspectral Imagery

101

Table 2: Vegetation species identification accuracy by airborne hyperspectral imagery of 16 bands and 64 bands.

Vegetation species

16 bands hyperspectral Rikola 64 bands hyperspectral Rikola

100 samples 50% of total samples 100 samples 50% of total samples

Oak (%) 0 0,21 0 1,56

Silver Birch (%) 0 0 13,51 44,66

Norway Spruce (%) 71,32 43,38 64,41 0

Salix Fragilis (%) 0 0 0 0

Horse Chestnut (%) 0 2,29 12,8 12,98

Norway Maple (%) 55,18 77,9 12,06 80,39

Boxelder Maple (%) 28,85 45,45 0,57 16,38

Linden (%) 0 23,89 0 75,65

Black Locust (%) 42,13 38,07 45,66 40,98

Mountain ash (%) 4,29 6,75 16,39 15,57

Scots Pine (%) 19,18 0 46,6 0

Thuja (%) 0 14,38 9,09 29,09

Grass (%) 31,98 87,14 22,78 97,9

O.A. (%)/kappa 27,39/0.27 63,22/0.47 14,72/0.10 73,29/0.65

Deciduous (%) 28,42 39,16 15,62 69,48

Coniferous (%) 31,27 22,54 36,01 12,44

Grass (%) 31,98 87,14 22,78 97,9

O.A. (%)/Kappa 30,51/0.12 65,75/0.47 18,89/0.08 79,28/0.65

4 CONCLUSION

Satellite imagery presents a cost effective solution for

main urban vegetation classes, with accurate

identification accuracy (i.e. superior to 90%), such

solution is nevertheless not suited for trees species

identification due to not sufficient spatial resolution.

High repetitiveness satellite imagery (e.g. Sentinel

and Landsat programs) offers an interesting solution

for inter/intra-seasons vegetation monitoring, we

showed in this study, an inter-season monitoring

using Seninel-2A imagery that permits first to

determine an optimum climatic for three main

vegetation classes identification, and second to

determine an optimum climatic for coniferous

vegetation identification.

The second part of the study focused on the

individual trees species identification using high

spatial resolution airborne imagery with higher

spectral resolution of up to 64 bands. The goal was to

identify 13 vegetation species within Kaunas city, the

strategy of fixed training samples gives poor

identification accuracy (i.e. O.A.<30%), second

training strategy was more convincing, with 50% of

the total samples per each class. For the second

training strategy, SVM classifier showed accurate

identification accuracy for both images (i.e. O.A. of

up to 73%). In parallel, the 64 bands image gives

better accuracy performance than the 16 bands one.

The increase of bands number permits better

modelling of the classes of interest and therefore,

better identification of the corresponding vegetation

species (e.g. Mozgeris et al, 2018; Ouerghemmi et al;

2018b), at the price of an increase in terms of

processing time due to higher resolution and bands

number.

High temporal satellite imagery (e.g. Sentinel-

2A), showed to be an accurate solution for coarse

scale vegetation identification and monitoring, thanks

to a large spectral interval, and to a good acquisition

repetitiveness, it could be therefore useful to several

fields of interest such urban architecture, urban

planning, agronomy, forest management, etc. At the

same time, hyperspectral imagery offer more

potentialities in terms of fine vegetation species

identification and also determination of other plants

characteristics such as health condition (e.g.

Mozgeris et al., 2016), thanks to its better technical

characteristics. Such imagery could be useful for

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achieving finer studies at individual vegetation

species scale and could therefore be complementary

to satellite imagery.

The fusion of multi-sensor satellite imagery could

be an interesting perspective for identification

accuracy enhancement (e.g. Zhang and Xie, 2014;

Alonso et al., 2014; Gintautas et al., 2018). The use

of an airborne hyperspectral of 16 and 64 bands and

0.7m and 0.5m of spatial resolution permits to

identify most of the species of interest, nevertheless,

some additional investigations must be carried to

improve the identification accuracy. Ground truth

samples must be enriched and rectified for some

specific species, the integration of vegetation indices

in the classification process could be tested (e.g.

Erudel et al., 2017; Launeau et al., 2017; Brabant et

al., 2018), the use of some pre-processing steps could

be taken into consideration for optimal data

processing (e.g. MNF).

ACKNOWLEDGEMENTS

This research was funded by CNES THEIA program

(CES artificialisation urbanisation). This work was

supported by public funds received in the framework

of GEOSUD, a project (ANR-10-EQPX-20) of the

program "Investissements d'Avenir" managed by the

French National Research Agency.

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