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Journal of Arid Land
Journal of Arid Land  2024, Vol. 16 Issue (9): 1214-1231    DOI: 10.1007/s40333-024-0027-x

CSTR: 32276.14.JAL.0240027x

Research article     
Spatiotemporal landscape pattern changes and their effects on land surface temperature in greenbelt with semi-arid climate: A case study of the Erbil City, Iraq
Suzan ISMAIL*(), Hamid MALIKI
Department of Architecture, College of Engineering, Salahaddin University, Erbil 44002, Iraq
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Abstract  

Urban expansion of cities has caused changes in land use and land cover (LULC) in addition to transformations in the spatial characteristics of landscape structure. These alterations have generated heat islands and rise of land surface temperature (LST), which consequently have caused a variety of environmental issues and threated the sustainable development of urban areas. Greenbelts are employed as an urban planning containment policy to regulate urban expansion, safeguard natural open spaces, and serve adaptation and mitigation functions. And they are regarded as a powerful measure for enhancing urban environmental sustainability. Despite the fact that, the relation between landscape structure change and variation of LST has been examined thoroughly in many studies, but there is a limitation concerning this relation in semi-arid climate and in greenbelts as well, with the lacking of comprehensive research combing both aspects. Accordingly, this study investigated the spatiotemporal changes of landscape pattern of LULC and their relationship with variation of LST within an inner greenbelt in the semi-arid Erbil City of northern Iraq. The study utilized remote sensing data to retrieve LST, classified LULC, and calculated landscape metrics for analyzing spatial changes during the study period. The results indicated that both composition and configuration of LULC had an impact on the variation of LST in the study area. The Pearson's correlation showed the significant effect of Vegetation 1 type (VH), cultivated land (CU), and bare soil (BS) on LST, as increase of LST was related to the decrease of VH and the increases of CU and BS, while, neither Vegetation 2 type (VL) nor built-up (BU) had any effects. Additionally, the spatial distribution of LULC also exhibited significant effects on LST, as LST was strongly correlated with landscape indices for VH, CU, and BS. However, for BU, only aggregation index metric affected LST, while none of VL metrics had a relation. The study provides insights for landscape planners and policymakers to not only develop more green spaces in greenbelt but also optimize the spatial landscape patterns to reduce the influence of LST on the urban environment, and further promote sustainable development and enhance well-being in the cities with semi-arid climate.

Key words： land use and land cover change      landscape pattern      land surface temperature      greenbelt      remote sensing     
Received: 13 April 2024      Published: 30 September 2024
CLC:  32276.14.JAL.0240027x  
Corresponding Authors: *Suzan ISMAIL (E-mail: suzan.ismail@su.edu.krd)
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Suzan ISMAIL, Hamid MALIKI. Spatiotemporal landscape pattern changes and their effects on land surface temperature in greenbelt with semi-arid climate: A case study of the Erbil City, Iraq. Journal of Arid Land, 2024, 16(9): 1214-1231.

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http://jal.xjegi.com/10.1007/s40333-024-0027-x     OR     http://jal.xjegi.com/Y2024/V16/I9/1214

Satellite/Sensor Landsat-7 ETM+ Landsat-8 OLI TIRS
Image ID LE07_L1TP_169035_20000416_20200918_02_T1 LC08_L1TP_169035_20220421_
20220428_02_T1
Path/Row 169/35 169/35
Date acquired (dd/mm/yyyy) 16/04/2000 21/04/2022
Scene center time 07:31:00 AM (LST) 07:38:00 AM (LST)
Cloud cover (%) 0.00 0.14
Projection UTM Zone 38 UTM Zone 38
Ellipsoid WGS 84 WGS 84
Resolution (m) 30 30
Table 1 Satellite images and descriptions
Fig. 1 Flowchart of the methodology used in the study. DN, digital number; TOA, top of atmosphere; LULC, land use and land cover; NDVI, normalized difference vegetation index; LST, land surface temperature; LPI, largest patch index; SHAPE_MN, mean shape index; LSI, landscape shape index; DIVISION, landscape division index; NP, number of patches; PD, patch density; AI, aggregation index. The abbreviations are the same in the following figures.
Fig. 2 LULC classification of greenbelt in 2000 (a) and 2022 (b)
LULC classification Description
Built-up (BU) Settlements like built up village lands, roadways, and dispersed residential areas
Bare soil (BS) Areas characterized by the absence of vegetation with surface comprising of rock, sand, or clay
Cultivated land (CU) Areas specifically prepared for agricultural use
Vegetation 1 (VH) Many life forms of plant including crops, grains, shrubs, and dispersed trees
Vegetation 2 (VL) Areas primarily comprise grasslands
Table 2 LULC classification of greenbelt and description
Year Accuracy BU BS CU VH VL OA KC
(%)
2000 UA 90.00 90.00 96.66 90.00 93.33 92.00 0.90
PA 87.09 84.37 93.54 100.00 96.55 - -
2022 UA 90.00 93.33 93.33 100.00 96.66 94.66 0.93
PA 96.42 87.50 93.33 100.00 96.66 - -
Table 3 Accuracy of classification of LULC of greenbelt in 2000 and 2022
Landscape metric Abbreviation Description Category Unit Range
Largest patch
index		 LPI Percentage of the landscape comprised by the largest patch Dominance % 0<LPI≤100
Mean shape index SHAPE_MN Mean patch perimeter divided by the minimum perimeter of landscape class type area Shape
complexity		 Dimensionless SHAPE_
MN≥1
Landscape shape
index		 LSI Total length of edge divided by the shortest possible edge length for the area of a patch Shape
complexity		 Dimensionless LSI≥1,
without limit
Landscape
division index		 DIVISION Equals to 1 minus the area of plaque divided by the sum of squares of landscape comprised of landscape class type Fragmentation % 0≥DIVISION<
100
Number of
patches		 NP A count of the total number of patches Fragmentation Dimensionless NP≥1, without
limit
Patch density PD Number of patches of the landscape class type divided by total landscape area Fragmentation numbers/km2 PD>0
Aggregation index AI Number of similar adjacencies involving the landscape class type, divided by the maximum possible number of similar adjacencies involving the class type, multiplied by 100 Aggregation % 0≤AI≤100
Table 4 Landscape metrics of greenbelt for the study
Fig. 3 LST of greenbelt in 2000 (a) and 2022(b)
LULC classification 2000 2022
Area (km2) Percentage (%) Mean LST (°C) Area (km2) Percentage (%) Mean LST (°C)
VL 106.67 56.75 31.63 90.30 48.03 37.64
BU 1.97 1.05 30.42 14.89 7.92 37.65
BS 18.68 9.94 31.64 22.93 12.20 37.65
CU 23.18 12.33 31.76 26.66 14.18 37.87
VH 37.48 19.94 29.48 33.22 17.67 30.34
Table 5 Changes of area and mean LST of different LULC classifications of greenbelt in 2000 and 2022
LULC
classification		 LST of VH
in 2000 (°C)		 LST of VH
in 2022 (°C)		 LULC
classification		 LST of VH
in 2000 (°C)		 LST of VH
in 2022 (°C)
VL 2.15 7.30 BS 2.16 7.31
BU 0.94 7.31 CU 2.28 7.53
Table 6 Difference in LST of greenbelt between VH and the other LULC classifications
Fig. 4 Changes of LULC area and LST of greenbelt from 2000 to 2022. **, P<0.01 level.
Fig. 5 Variations of landscape metrics of different LULC classifications of greenbelt in 2000 and 2022. (a), LPI; (b), SHAPE_MN; (c), DIVISION; (d), LSI; (e), NP; (f), AI; (g), PD.
Change of landscape metrics in 2000 and 2022 Correlation VL BU BS CU VH
LPI Pearson's correlation -0.028 0.103 0.139 0.451** -0.488**
P 0.750 0.281 0.125 0.000 0.000
n 136 111 123 125 113
SHAPE_MN Pearson's correlation -0.004 0.134 0.163 0.258** -0.475**
P 0.961 0.160 0.071 0.004 0.000
n 136 111 123 125 113
LSI Pearson's correlation 0.014 0.074 0.415** 0.181* 0.023
P 0.868 0.442 0.000 0.044 0.807
n 136 111 123 125 113
DIVISION Pearson's correlation 0.086 -0.086 -0.009 -0.319** 0.374**
P 0.321 0.370 0.919 0.000 0.000
n 136 111 123 125 113
NP Pearson's correlation 0.078 0.069 0.329** 0.106 0.181
P 0.369 0.473 0.000 0.241 0.055
n 136 111 123 125 113
PD Pearson's correlation 0.090 0.064 0.320** 0.111 0.190*
P 0.300 0.504 0.000 0.217 0.043
n 136 111 123 125 113
AI Pearson's correlation -0.158 0.325** 0.199* 0.417** -0.381**
P 0.065 0.001 0.030 0.000 0.000
n 136 95 119 121 109
Table 7 Pearson's correlation between changes of landscape metrics and LST of greenbelt
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