Multitemporal Remote Sensing for Invasive Prosopis Juliflora Plants

Mapping and Monitoring: Sharjah, UAE

Alya AlMaazmi and Rami Al-Ruzouq

University of Sharjah, University City Road, Sharjah, U.A.E.

Keywords: Prosopis Juliflora, Spatial Analysis, Multitemporal Landsat, NDVI, Support Vector Machine.

Abstract: Prosopis juliflora is one of the 'world's most invasive trees that negatively affects native species and their

ecosystems. The main obstacle for controlling Prosopis juliflora pervasion is to accurately map location as

well as the distribution pattern. Locating Prosopis juliflora is a strategic priority of countries to preserve the

invaded local environment. Recent advances in remote sensing, geographic information system (GIS), and

Machine Learning (ML) techniques provide valuable tools for producing tree distribution maps. In this

research, a supervised classification method with Support Vector Machine (SVM) supported by GIS statistical

analysis was developed to map Prosopis juliflora and their pattern analysis in Sharjah, one of the major cities

in the United Arab. More than 5000-pixel labels taken from Landsat-7 and Landsat-8 imagery were used to

train object-based Support Vector Machine to map Prosopis juliflora. The suggested algorithm resulted in

75% accuracy compared to ground truth samples. Furthermore, multi-temporal detection showed 'that's spatial

clustering pattern of the trees is changing and increasing over time. The approach adopted in this study can

be applied to any other location globally.

1 INTRODUCTION

Invasive species are organisms that are non-native

and have been introduced due to their environmental

and economic benefits, such as providing habitat for

native plants and promote biodiversity due to global

biotic environmental change (Essl et al., 2020).

Nevertheless, the introduction of new species is not

always successful. New species can establish

themselves and dramatically spread in their new

environment and become invasive (Jeschke et al.,

2014). These invasive 'aliens' often reflect negative

impacts, including reduction of grazing areas,

reduction of crop yield, and risk of threat to

biodiversity (Mwangi & Swallow, 2005).

An ideal example of invasive species is Prosopis

juliflora, which is considered one of the world's worst

woody invasive plants (Berhanu & Tesfaye, 2006).

Prosopis juliflora (also known as mesquite) is a

deciduous shrub with spreading cylindrical branches

that can grow from 3-12 meters in height. The trunk

of the tree is usually around 1.2 in diameter with

compound leaves. Florets as usual, greenish-white,

turning light yellow (Burkart, 1976). Prosopis

juliflora is an evergreen tree native to the western

hemisphere, including rangelands in South America,

Central America, and the Caribbean (López-Franco et

al., 2013), and have been introduced into several arid

and semi-arid countries of Africa, Asia, and Australia

over the last century due to their quality wood for

fuel, timber and seeds for human and animal food.

Despite their advantages, these plants are also highly

invasive in new habitats creating dense thorny

thickets (FAO, 2006).

The capability to grow under-stressed, drought

and high-temperature conditions have been made

them the most multipurpose tree in arid and semi-arid

regions (George et al., 2017). Mesquite was also has

been introduced to United Arab Emirates (UAE) in

the 1970s from Central America (Environment

Agency, 2020) on a large scale in the artificial forests

due to its faster growth, soil-binding capacity, and

desertification control. However, this invasive shrub,

locally know as (Ghweif), has aggressively invaded

the UAE environment causing severe damage. It

prevents other species from growing in their vicinity,

causing twofold-allelopathy. Moreover, there is a

noticeable accumulation of litters underneath the

Prosopis juliflora, compared with native species such

as Prosopis cineraria canopies, With a significant

negative influence on seed germination and growth of

understory species (El-Keblawy & Al-Rawai, 2007).

AlMaazmi, A. and Al-Ruzouq, R.

Multitemporal Remote Sensing for Invasive Prosopis Juliﬂora Plants Mapping and Monitoring: Sharjah, UAE.

DOI: 10.5220/0010440601490156

In Proceedings of the 7th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2021), pages 149-156

ISBN: 978-989-758-503-6

149

The aggressive behavior of Prosopis juliflora has

prompted researchers to establish scientiﬁc efforts

utilizing remote sensing data and Geographic

Information System (GIS). Remote sensing data can

cover more extensive areas than single plot studies

(Joshi et al., 2016). Furthermore, remote sensing

offers significant opportunities for providing timely

information on the invasion of non-native species into

native habitats.

Multispectral satellite images aid analyzing the

spectral response of the vegetation. The analysis

pattern of high and low spectral radiances introduced

one of the most effective spectral measurement for

vegetation known as the normalized difference

vegetation index (NDVI), which have been widely

and successfully used to provide surrogate indicators

of canopy greenness (Melesse et al., 2007), in

addition to ecologically map and classify vegetation

(Mehner et al., 2004).

Remote sensing data and spectral analysis could

be beneficial to map and detect Prosopis juliflora.

Several attempts to map Prosopis juliflora in Africa

are well acknowledged. For example, Normalized

Difference Vegetation Index (NDVI) from

multispectral, multi-temporal satellite imagery,

combined supervised classification methods such as

maximum likelihood classification (Rembold et al.,

2015) and random forest classifier is used to quantify

Prosopis juliflora during dry seasons in Somaliland

(Meroni et al., 2017). Another study conducted in

Ethiopia utilized spectral indices such as NDVI and

Enhanced Vegetation Index (EVI) in addition to topo-

climatic variables as input to correlative models to

map both the current and potential distribution of P.

juliflora (Wakie et al., 2014). Other spectral indices

such as Normalized Difference Infrared Index (NDII)

is used to retrieve the mesquite tree distribution area

in Sudan through measuring foliar water content,

where native plants have more water-stressed

growing than the mesquite (Hoshino et al., 2011). In

Kenya, a Land Use Land Cover (LULC)

classification using random forest supervised

classification was used to detect Prosopis juliflora, on

10–15 years study. The study showed that Prosopis

has not stopped or slowed down (Mbaabu et al.,

2019). In Asia, similar studies using remote sensing

data were conducted as well. In India NDVI and

Support Vector Machine (SVM) supervised

classifications were used to map Prosopis juliflora

(Vidhya et al., 2017). NDVI and NDII were also used

to map Prosopis juliflora in Tirunelveli and

Palayamkottai in India (Ragavan et al., 2015). A

study in the United Arab Emirates (UAE) used

onscreen digitizing of multi-temporal aerial images

with the aid of statistical analysis to estimate percent

cover, patch density, patch size, and mean patch

shape index. The study helped to understand the

spread of Prosopis juliflora in two areas in UAE

(Filayah and Khut), where it showed a dramatic

increase during 19 years of time (Issa, S. M. et al.,

2008).

This study focuses on utilizing time serires

analysis using Landsat-8 images with support vector

machiene classification and NDVI to monitor

Prosopis juliflora in Sharjah City, UAE, to obtain

numerical analysis for the covered area every decade

from 2000-2020.

2 STUDY AREA

The UAE is situated in the Middle East, and shares

coastline with Gulf of Oman in the east and the

Arabian Gulf in west, and boarders with Oman and

Saudi Arabia (Figure 1). The UAE is located in an

arid tropical region with an average annual rainfall of

80-140 mm (Sherif et al., 2014). This study

consentrate on Sharjah city, one of the largest cities

in the UAE with an area of 2590 km

, located at

25.3463° N, 55.4209° E. The annual rainfall of

Sharjah city is approximately 106.9 mm. Large parts

of the emirate consists of desert areas, profound soil

shaped in eolian sands, in addition to agriculture

areas, and expansions of acacia woodlands. (Al-

Ruzouq et al., 2019).

Figure 1: Study Area Sharjah City – UAE.

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3 MATERIALS AND SATELLITE

DATA

Ten years of satellite imagery were collected from

Landsat 7 and Landsat 8 during the year 2010 to 2020

for yearly analysis. Furthermore, one image was

collected in 2000 for decade analysis during 2000,

2010, and 2020 (Table 1). The images were collected

during the dry season in UAE in August as the

reflection of P. Juliflora appears brighter than other

vegetation. Two scenes were required to cover the full

Sharjah city. In addition, a high-resolution Khalifa-

Sat image was acquired as well for visual inspecting

areas of vegetation.

Table 1: Satellite Imagery Thematic Data Source.

4 METHODOLOGY

The developed methodology to map P.Juliflora trees

in Sharjah is represented in (Figure 2) throughout

three stages. First, historical satellite imagery data is

required to develop the thematic layers of the study

area. The first stage is pre-processing to prepare the

data for major processing in terms of band stacking

and area coverage. Then data analysis using NDVI

and Support Vector MAchiene. Finally mapping

using ArcGIS software to map total Prosopis juliflora

areas

4.1 Pre-processing

With LANDSAT-7, the Scan Line Corrector (SLC)

failed On May 31, 2003. The sensor has acquired and

delivered data with lined gap strips on each band

(Loveland & Irons, 2016). Therefore, nearest

neighbor (NN) resampling was used to fill the gap in

each band for images from 2010-2012 (Figure 3).

Figure 2: Proposed Methodology.

Figure 3: LandSat-7 SLC Using Nearest Neighbor (NN)

Resampling.

Figure 4: Common Pre-Processing Steps.

For each output point, the closest input detector

sample must be identified and selected as the output

image value.

Once Scan lines were corrected, both LANDSAT

7 and LANDSAT 8 were processed with common

steps, including band stacking, scene mosaicking, and

extracting the study area (Figure 4). These steps are

necessary for time series analysis, using WGS 84

Satellite Spatial Resolution

(meter)

Spectral

Resolution (micro meter)

Data

Acquisition

Year

LANDSAT

30 m multi-spectral

60 m Thermal

- Band 1 Visible Blue (0.45 - 0.52 µm)

- Band 2 Visible (0.52 - 0.60 µm)

- Band 3 Visible (0.63 - 0.69 µm)

- Band 4 Near-Infrared (0.77 - 0.90 µm)

- Band 5 Short-wave Infrared (1.55 - 1.75 µm)

- Band 6 Thermal (10.40 - 12.50 µm)

2000

2010

2011

2012

LANDSAT

30 m multi-spectral

15 m Panchromatic

- Band 1 Coastal aerosol (0.43-0.4530 µm)

- Band 2 Visible Blue (0.45-0.5130 µm)

- Band 3 Visible Green (0.53-0.5930 µm)

- Band 4 Visible Red (0.64-0.6730 µm)

- Band 5 Near Infrared (NIR) (0.85-0.8830 µm)

- Band 6 SWIR (11.57-1.6530 µm)

- Band 7 SWIR (22.11-2.2930 µm)

- Band 8 Panchromatic (0.50-0.6815)

- Band 9 Cirrus (1.36-1.3830 µm))

- Band 10 Thermal Infrared (110.6-11.19100 µm)

- Band 11 Thermal Infrared (211.50-12.51100 µm)

2013

2014

2015

2016

2017

2018

2019

2020

Khalifa-

SAT

0.7 m Panchromatic

2.98 m multi-

spectral

- Panchromatic (0. 55-0.9 µm)

- MS1: Visible Blue (0.450 – 0.520 µm)

- MS2: Visible Green (0.520 – 0.590 µm)

- MS3: Visible Red (0.630 – 0.690 µm)

- MS4: Near Infrared (NIR) (0.77 - 0.889 µm)

2019

Multitemporal Remote Sensing for Invasive Prosopis Juliﬂora Plants Mapping and Monitoring: Sharjah, UAE

151

zone 40N UTM coordinate system, where areas in

meters can be easily calculated to fulfill the need of

this study. A further step is needed for KhalifaSAT,

which includes geometric correction using 8 well-

distributed ground control points (GCP) and

projective transformation.

4.1 Data Analysis Processing

In the third stage, all the thematic layers were

processed to map vegetation and Prosopis juliflora.

Two approaches were used, Normalized Difference

Vegetation Index (NDVI) and supervised

classification using Support Vector Machine (SVM).

4.1.1 Normalized Difference Vegetation

Index (NDVI)

The Normalized Difference Vegetation Index

(NDVI) synthesizes the information from two

spectral channels: red and near-infrared. Vegetation

tends to have higher reflectance in the red band and

lower reflectance in the near-infrared band. Hence,

this index can be repressed as simple mathematical

expressions represented in equation (1) that combines

spectral measurements used to identify the presence

of vegetation in remotely sensed images (Leprieur et

al., 1996)(Kefalas et al., 2018).

𝑁𝐷𝑉𝐼 

𝑁𝐼𝑅  𝑅𝐸𝐷

𝑁𝐼𝑅  𝑅𝐸𝐷

(1)

NDVI was measured for three years (2000, 2010

and 2020), in addition to a yearly measurement from

2010-2020. Further assessment of vegetation analysis

was conducted using change detection over two

periods from 2000-2010 and 2010-2020. Change

detection analysis is conducted through equation (2).

𝐶𝐷  𝐼𝑚𝑎𝑔𝑒1  𝐼𝑚𝑎𝑔𝑒 2

(2)

4.1.2 Object-based-Support Vector Machine

Classification

The Support Vector Machine classifier is promising

in terms of vegetation classification as it can result in

up to 80% overall accuracy assessment with Kappa

0.8 comparing to Maximum Likelihood

Classification and Spectral Angular Mapper (Braun et

al., 2010). P. Juliflora training samples were taken

from the area behind Sharjah airport (ROI1) as shown

in figure 5. The area is located within latitude and

longitude ( 25° 20.61'N, 55° 31.64'E ), with a total

land area around 21.19 km

, most of the land is

vegetated with P. juliﬂora, with medium canopy sizes

(5–10 m diameters) and different densities

distribution[10], (Figure 6).

Figure 5: KhalifaSat image of ROI1 behind Sharjah

Airport.

Figure 6: P. Juliflora as seen from satellite nadir view taken

from Google Earth Image from ROI1.

Two classes were identified in the training samples.

The first one is P. juliﬂora taken from ROI1. Likewise

other vegetation types, P. juliﬂora has a high

reflectance from NIR band and low reflectance in the

red band. However, this tree has distinguished

spectral response where it has a minor peak and green

band as well. More than 1000 pixel samples were

taken from each year as training sample (Table 2).

The second class is all other vegetation types taken

from random distributed vegetated areas.

Table 2: Training samples were taken for SVM classifier.

Year Total number of Pixels Total Training Area ( Km

)

2000 3,598 3.25

2010 1,307 1.18

2020 5,386 4.86

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4 RESULTS AND DISSCUSSION

4.1 Vegetation Analysis of Sharjah City

The multi-temporal analysis of the NDVI during

(2000,2010,2020) for the vegetated areas in Sharjah

city showed that the vegetation density increased

during the past two decades. The highest density

appeared in 2020 with 67.18km

followed by the year

2010 with a total area of 56.55 km

, with increase of

0.188%. Finally, the year 2000 showed the least total

vegetated area with 42.96 km

(Figure 7).

Figure 7: Total Vegetated Areas in Sharjah City in (a) 2000,

The vegetation index during the period 2010-

2020 showed that variations in vegetated areas per

year were small as they did not exceed 0.25% of net

change. The lowest vegetated areas were found to be

in 2013 with 45.14 km

(Figure 8). The vegetation

areas were mapped in (Figure 9)

Figure 8: Bar chart of 10 Years Of Total Vegetation In

Sharjah City From 2010-2020.

Vegetation cover change detection was

implemented over two periods, from 2000-2010, and

2010-2020. In 2000-2010, vegetation cover increased

by a total area of 32.18 km

.The vegetation increase

concentrated in city center area, university city area,

and Sharjah-Ajman border farms area. In the same

period, vegetation decreased by 30.43 km

, the

decrease mostly found in the farmed eastern part of

Figure 9: Vegetated areas maps in Sharjah City in (a) 2000,

the city. In 2010-2020, vegetation cover increased

dramatically, almost double by the total area of 75.28

The vegetation increase distributed throughout

the city and concentrated in the city center area,

university city area, and Sharjah-Ajman border farms

area, in addition to the farmed eastern part of the city.

In the same period, vegetation decreased sharply by

total area of 11.74km

. The decrease is mostly found

in farms in the eastern and southern parts of the city.

(Figure 10) shows the maps of change detection in

both periods.

Figure 10: Change detection in Sharjah city in period (a)

2000-2010, (b) 2010-2020.

4.2 Vegetation Analysis of Mapped P.

Juliflora

The multi-temporal analysis of the Support Vector

Machine (2000,2010,2020) for the vegetated areas of

P. Juliflora trees in Sharjah (Figure 11) showed that

the highest density appeared in 2020 with 14.13 km

followed by the year 2000 with a total area of 11.99

. Finally, the year 2010 showed the least total

vegetated area with 9.26km

as shown in (Figure 12).

The increase between 2010-2020 in the last decade

was 0.525%.

Multitemporal Remote Sensing for Invasive Prosopis Juliﬂora Plants Mapping and Monitoring: Sharjah, UAE

153

Figure 11: Total P. Juliflora Area In Sharjah City.

Figure 12: Total P. Juliflora Area in Sharjah City in (a)

4.3 Vegetation Accuracy Analysis of

Mapped P. Juliflora

Eight sites have been selected (Table 3) for visual

accuracy assessment from high-resolution google

earth (Figure 13). These sites are taken from the

resulted P. Juliflora classification results (Figure 14).

Table 3: Inspection sites ID and their location.

Figure 13: Inspection Sites Visually Inspected (a) 2020_1,

(g) 2020_7, (h) 2020_8.

The visual inspection showed that all vegetation in

the inspection sites identified as P. Juliflora, except

for site 2000_4 and 2000_5. The inspection sites also

suggest that P. Juliflora density is higher at locations

of lower altitude open fields sand-based areas. The

visual inspection also showed that P. Juliflora showed

minimum existence nearby other vegetation

communities.

Figure 14: Map of Inspection Sites.

5 CONCLUSIONS

Identifying locations of Prosopis Juliflora is a

significant environmental and ecological initiative for

any arid region country. In this study, an algorithm

that combines remote sensing data and GIS along

with ML was developed to locate areas of Prosopis

Juliflora. Eleven thematic layers from satellite

imagery were collected from Landsat 7 and Landsat

8. Two approached were used. The first one is using

vegetation index NDVI, and the second one is a

combination of NDVI and supervised classification.

The NDVI maps all types of vegetation in Sharjah

city using Red and NIR bands. Vegetation in Sharjah

city showed an increment in the past two decades

where it increased from 42.96 km

in 2000 to 67.19

in 2020. The second approach is using a Support

Vector Machine (SVM) over vegetated areas from

NDVI. Over 1000 pixels samples were taken to train

the supervised classification with two classes:

ID Year location correctly mapped

2020_1 2020 55.5587161°E 25.3250445°N yes

2020_2 2020 55.5713399°E 25.4544956°N yes

2020_3 2020 55.5570937°E 25.4715355°N yes

2020_4 2020 55.5529439°E 25.3038846°N no

2020_5 2020 55.9371005°E 25.1879427°N no

2020_6 2020 55.4891707°E 25.2709255°N yes

2020_7 2020 55.5530813°E 25.2568040°N Yes

2020_8 2020 55.5876650°E 25.4609975°N yes

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P.Juliflora and other vegetation. Total area increased

up to 14.13 km

in 2020 after it was 9.26 km

only in

2010. The vegetation analysis showed that P. Juliflora

increase dramaticlly with 0.525% in the last decade

between 2010 and 2020 comparing with other

vegetation which increased by 0.1888% only. The

visual inspection suggest that P. Juliflora density is

higher at locations of lower altitude open fields sand-

based areas. The visual inspection also showed that P.

Juliflora showed minimum existence nearby other

vegetation communities. Finally, the developed

technique can be generalized and utilized over new

arid-region locations.

ACKOWLEDGEMENTS

The project is funded by the University of Sharjah

(UoS) under grant research project ID:

#20020401163.

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