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ارزیابی اثر تغییرات NDVI در طبقات مختلف پوشش اراضی بر LST شهر یزد طی 30 سال اخیر با استفاده از تصاویر لندست و رگرسیون خطی

Authors:

Abstract

دمای سطح زمین (LST )، اثرات قابل توجهی بر ایجاد جزایر حرارتی (UHI ) و جزایر حرارتی سطحی (SUHI ) دارد. بررسی تغییرات دمای سطح زمین نقش مهمی در پایش تغییرات اقلیمی ایفا می‌کند. در همین راستا، نقش ارزیابی فراوانی و پراکندگی پوشش گیاهی در برآورد اثرات گرمایش جهانی و دمای سطح زمین نیز قابل توجه است. بدین منظور، این پژوهش با استفاده از تصاویر چند طیفی و حرارتی لندست 5 و 8 به بررسی تغییرات الگوی زمانی-مکانی و کمی مقادیر شاخص پوشش گیاهی تفاضلی نرمال شده (NDVI ) شهر یزد در ارتباط با طبقه بندی پوشش اراضی در کلاس های مناطق شهری، پوشش گیاهی و زمین های بایر طی 30 سال اخیر می‌پردازد. به منظور برآورد تغییرات پوشش گیاهی، این پژوهش از شاخص NDVI که در مطالعات گسترده ای اهمیت آن در برآورد میزان پوشش گیاه و زبری سطح زمین به اثبات رسیده است استفاده می‌نماید. نتایج این پژوهش نشان داد، مساحت پوشش مناطق شهری طی سه دوره ده ساله %91.6 (km2 31.6) افزایش یافته و نیز پوشش گیاهی و زمین های بایر به ترتیب 68.5% (km2 12.2) و 79.5% (km2 21.3) کاهش یافته اند. این پژوهش همچنین اثر افزایش هر 0.1 مقدار NDVI بر کاهش 2.2 (°C 0.7) و 2.1 (°C 0.6) درصدی LST در کلاس های منطقه شهری، پوشش گیاهی و بایر را به اثبات رسانده و همچنین نشان داد که کاهش اثر خنک کنندگی NDVI، با افزایش مناطق شهری (همبستگی %98) و کاهش مناطق پوشش گیاهی (همبستگی %74-) و زمین های بایر (همبستگی %98-) ارتباط معنی داری دارد. نتایج حاصل از این پژوهش می‌تواند به منظور برنامه ریزی توسعه و رفاه شهری، برنامه ریزی برای ایجاد مناطق سبز و جلوگیری از رشد بی رویه مناطق شهری مفید باشد.
ت 
  یا  ا و  ز
  یا  ا و  ز
۱۴ و۱۵ ه ا۱۳۹۹
 هادت   م نز
هو  ا  و ز
ت 
ت 
  یا  ا و  ز
زرا  اتا NDVI  ت رد ارا   LST د ۳0 ا ل 
زا هداو   نر و
مر  د هاد
mohammadmoghaddam@stu.yazd.ac.ir
ور نا د هاد *
irousta@yazd.ac.ir
 د هاد یر 
mh.mokhtari@yazd.ac.ir
ه
دی ز  (
LST
 تاا ،) ا د ا را (
UHI
۱
ا و ) را  (
SUHI
2
ر دراد ) تا دی
ز     رد تا ا ا  رد  زرا  ،ار اوا ا و    رد تاا دروآ
  د وی ز    ا  ا ،ر و زا هدا  و   را و ۵ و 8 
ر تا ای ز-  و د    له (
NDVI
۳
) د   طرا رد  ی
،ی  اراز و  ی  ۳0 ا ل دزاد دروآ ر  تا ، ا زا و

NDVI
هد ت رد ای ا  دروآ رد نآنا  ه ز وی ز  ر تا ه  هدا ا
 ا ،داد ن و %  هد هرود   ی  ۹۱.۶ (
2
km
۳۱.۶ و    و  اا )
ز    ی۶8.۵% (
2
km
۱2.2 و )7۹.۵% (
2
km
2۱.۳  )  اا ا  و ا ا0.۱ را
VIND

2.2 (
°C
0.7 و )2.۱ (
°C
0.۶ یرد )
LST
رد ار ساد ن  ر تا   و   ،ی  ی  د
 ا  
NDVI
% ( ی  اا  ،۹8% (     و )-) ز وی 
% (۹8- طرا ) و ا زا   دراد یراد ر  ا ،ی هر و  یر یا یر
 ر زا ی و   دا  ی  ور
هژاو  :   ،ارا  ی
NDVI
ز  ید ،
LST
د  ،رود زا  ،  ،

 ا  ا    یدز ت ار  رد و هداد را   ار    و ا 
هد ر ار ا[1]  زرا  سا  ا  ا( ز  ید  ،LST
4
6
ل زا )1880 
2010  ،0.85  رد ا  اا دا[2]ا ید ت   ،ز  زا ز  ید ی
هد تا ار عرا و  ا[3]ا رد   ،ز  ید  ز  ید ل ا و ز   یژا
 ا [4] را ا ،ک ر ر رد ز  ید ( 
UHI
دراد در  ق و  و )[5]
را ا[6]آ ، یز [7] یر وا ی  ار ز  ید  [8]
1
Urban Heat Island
2
Surface Urban Heat Island
3
Normalized Difference Vegetation Index
Land Surface Temperature
ت 
  یا  ا و  ز
اها تر   ا و ع    تاا دروآ رد و هد اا ل رد ،ا رد یا
ا [9]ل     ( ه
NDVI
 ) زارد  و ز  یز زرا رد 
ر     [4]  ،  نا اا رد   طرا  زرا یا ا-
 ی تا  طرا رد یا- د هدا ا ا رد ز[10]  طرا ید ت 
LST
 و
NDVI
هد ر ار ا[11] ا زا ید یا  ر  رد 
NDVI
ا ه  ز  ید و
[3] ار   تو ،آ   رد 
LST
و
NDVI
ار ،د ل رد    ا ه 
رد و  ود ا   ار م ل ا ه ه [12]
تا
LST
و  رد یژا هذ و وآ   نآ ا و ی  را ه ف د  ار یدز  ،
ا [13]هزاا   ،ز یناد هار  ا ز  ید  [14]دو ، ی
س رد ز  تا جاا ر  ار راا ا زا هدا ،رود زا  یژ ش و  هد یا
ا هداد اا[15]  زا     و ز تا یا ا ر ،و ا ف
NDVI
رد
تا  ارا   طرا
LST
 د 30  ا ل   رد ،هرود ر رد تا ا ر  
 تازا   ز و   ،ی  ارا  تا یا ر
LST
   ا ا  ر
  و ند  و  یا و بآ ،ی   و د  ی ی[16] ر  ، ا
   یرو یو هر و  یرر
۱ : )ا ر  بذ8 (2020  )،د  )ب ا ر رد نا ر  )پ و را د
ت 
  یا  ا و  ز
 ن زا  نا و هد د نا و ن  ،د  ه  ناا ی ا ل   ا 
°54 و'22 ا ض و °31 و'55 ( ا ه او  1 ،د  )110 ر ، 
،1228 و عرا 
°C
20 م دراد  ید  )( ی د  رد ل ه 33  رد  و دا
31  ردآ و )داد( ئوژ ،دا راد را مود ر رد )داد( [16]
 شور
  
NDVI
ه  د  نود و   ود   زا[17]  نآ را و-1 و1 گر د ا    
 د ،   ک  ی     بآ   د و [18]
ارا  ی
ز و   ،ی    ،د  ارا    ر  شور   یتر ی ه
 و زا   5 ل یا( ی1990 ،2000 و2020  و )8 ل یا2020 ( لو1 ،)
 ،ید ا ما ر    جاا ل یا  ی2020 ل یا و ز دز زا ی2010 ،
2000 و1990  و ثرا  و زاا  و  د  رد  جاا ز او ی زا هد100
 زا هدا  و نا (  زرا ، د و  یرآ ی لو2د ی را )  1 %(100 ،)
ا ز او  و   [19]د را   1 %(100  د    د  رد )
  [20]
لو1هرا و ت :و رد  را هدا در یا
هرام و هنجنس
سنش
ب خیرتشاد ( تعسGMT
)
TM  ،
۵
LT05_L1TP_162038_19900701_20180612_01_T1
01
ی
1990
06
:
17
:
05
TM  ،
۵
LT05_L1TP_162038_20000728_20180922_01_T1
28
ی
2000
06
:
34
:
04
TM  ،
۵
LT05_L1TP_162038_20100708_20161014_01_T1
08
ی
2010
06
:
47
:
28
OLI ، 
8
LC08_L1TP_162038_20200804_20200804_01_T1
04
آ
2020
06
:
56
:
53
لو2 د زرا  : یی
)رد( 
ل
2020
2010
2000
1990
 د
95.88
83.27
85.84
98.93
 
93.46
75.74
77.25
98.39
ت 
  یا  ا و  ز
د ز  ی
( د زا هدا  ز  ید1  ) [17]:

=
/
[
1
+
ln
(
)
]
, (
1
)
،نآ رد 
،ا ی ور ید
،را  ج ل
c2
 د14388 و
e
 ی
 نر
 زا   شزا ر  ت ا شور   نر آ زا و ا ردیا تا زا
 ه   هدا    د او  ی یا ر  د  اود دروآ ندروآ
 سا  شور ا رده او د و ه شزا   ید  ،  زا  ا 
y
 (
2 )[21]
2 :ه و   رد  ،ر  هنر م ه اذ ی []
 و ث
    داد ن ،د  ارا  ی3 ،ی  ، هد هرود
2
km
33.6 (91.6% ا اد ر )
هرود رد س ا1990-2000 
2
km
18.5 (50.4% ،) هرود رد و ر 2000-2010  ا یر ،
2
km
12.9 (23.3% )
هرود رد س ا ر  ا هد  ار2010-2020 
2
km
2.2 (3.2%     س ا هد )
 ،هرود
2
km
12.2 (68.5% هرود رد س ا ر   ا   )1990-2000
2
km
7.3- (41%- هد )
هرود رد س ا ا2000-2010 ،
2
km
2.9- (27.6%- هرود رد س ا   ا هد ا  )2010-
2020  و
2
km
2- (26.3%-ز ا هد )       ور رد   ی30 ،ا ل
2
km
21.3-
(79.5%-  ) س ا   و  اهرود رد  ی1990-2000 
2
km
11.2- (41.7%- و )
2010-2020 
2
km
0.1- (1.7%- س ا ا هد ) هرود رد ار ی ر  2000-2010 
2
km
10-
(64.1%-( ا هد  )3)
لوط ا هدام قابنا
طوط هد
م یدومعداب
X =
هدک ب شپ
Y
=
خساپ
ت 
  یا  ا و  ز
۳ :د  ارا  ت  
ّ و ز تا  ط  ر
NDVI
یر  طرا رد  نآ  و ارا   ی
LST
د 
ل رد  ا نآ 1990 ن ا یازا  ، تر ،0.1 را 
NDVI
نا ،
LST
،3.3% (
°C
0.9 رد )
ا   ی% ،ی3.1 (
°C
0.8ا     س رد )% و ی3.2 (
°C
0.9    ارا رد )
ا هد ها
LST
ل رد2000 % ی  یا2.3 (
°C
0.7)، %    س رد2.4 (
°C
0.7   )
از رد و ی%   ی2.2 (
°C
0.7ا   ) ا   ی
LST
ل رد2010 % 1.8 (
°C
0.6 رد )
% ،ی 2.1 (
°C
0.7ا     س رد )% و ی1.6 (
°C
0.6ا    ارا رد )  ی
اا یازا0.1 را
NDVI
ل رد  را ا ا  2020 % ی  یا1.4 (
°C
0.5   )
%   س یا ،ی0.8 (
°C
0.3   )%  ارا یا و ی1.3 (
°C
0.6( ا هد ) 4)
 ا  ،هآ د  رآ سا   
NDVI
30  ،ی  س اا  ،ا ل
ز و     و ( دراد     ی لو3)
لو۳ ا    : NDVI س   هرود  ارا  ی۳0 
 س 
 
ی 
0.98
 
0.74
-
ز ی
0.98
-
ی     ز
1990 36.7 17.8 26.8
2000 55.2 10.5 15.6
2010 68.1 7.6 5.6
2020 70.3 5.6 5.5
36.7
17.8
26.8
55.2
10.5
15.6
68.1
7.6
5.6
70.3
5.6
5.5
()
ارا  
ت 
  یا  ا و  ز
۴  را :
LST
یازا 0.۱ را اا
NDVI
یا ارا   ۳0 د  ا ل
ی
 را اا  داد ن  ا 
NDVI
 رد
LST
س زا   و   ،ی  ی
زد   یؤ ا یر  30 اا  ،ا ل0.1 س رد ، ا را  ی
% ،ی2.2 (
°C
0.7%    رد ،)2.1 (
°C
0.6ز و )%  ی2.1 (
°C
0.7 )
LST
ا ا هداد  ار
 و ارا  ر  30 ی  ،داد ن ا ل91% و   س و اا
ز%    ی68.5 % و79.5   ا  ،در ا     ا رد    
س ا زا% ( ی  اا ور  98% (    و )74-ز و )  ی
% (98-ز دراد  ار ) ا  یور   ا نو ن ا رد  ی 
NDVI
 ،
ر  نا ا ی        اد
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Article
Full-text available
Land surface temperature (LST) is an important parameter to evaluate environmental changes. In this paper, time series analysis was conducted to estimate the interannual variations in global LST from 2001 to 2016 based on moderate resolution imaging spectroradiometer (MODIS) LST, and normalized difference vegetation index (NDVI) products and fine particulate matter (PM 2.5) data from the Atmospheric Composition Analysis Group. The results showed that LST, seasonally integrated normalized difference vegetation index (SINDVI), and PM 2.5 increased by 0.17 K, 0.04, and 1.02 µg/m 3 in the period of 2001-2016, respectively. During the past 16 years, LST showed an increasing trend in most areas, with two peaks of 1.58 K and 1.85 K at 72 • N and 48 • S, respectively. Marked warming also appeared in the Arctic. On the contrary, remarkable decrease in LST occurred in Antarctic. In most parts of the world, LST was affected by the variation in vegetation cover and air pollutant, which can be detected by the satellite. In the Northern Hemisphere, positive relations between SINDVI and LST were found; however, in the Southern Hemisphere, negative correlations were detected. The impact of PM 2.5 on LST was more complex. On the whole, LST increased with a small increase in PM 2.5 concentrations but decreased with a marked increase in PM 2.5. The study provides insights on the complex relationship between vegetation cover, air pollution, and land surface temperature.
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The formation of surface urban heat islands (SUHIs) can cause significant adverse impacts on the quality of living in urban areas. Monitoring the spatial patterns and trajectories of UHI formations could be helpful to urban planners in crafting appropriate mitigation and adaptation measures. This study examined the spatial pattern of SUHI formation in the Colombo District (Sri Lanka), based on land surface temperature (LST), a normalized difference vegetation index (NDVI), a normalized difference built-up index (NDBI), and population density (PD) using a geospatial-based hot and cold spot analysis tool. Here, ‘hot spots’ refers to areas with significant spatial clustering of high variable values, while ‘cold spots’ refers to areas with significant spatial clustering of low variable values. The results indicated that between 1997 and 2017, 32.7% of the 557 divisions in the Colombo District persisted as hot spots. These hot spots were characterized by a significant clustering of high composite index values resulting from the four variables (LST, NDVI (inverted), NDBI, and PD). This study also identified newly emerging hot spots, which accounted for 49 divisions (8.8%). Large clusters of hot spots between both time points were found on the western side of the district, while cold spots were found on the eastern side of the district. The areas identified as hot spots are the more urbanized parts of the district. The emerging hot spots were in areas that had undergone landscape changes due to urbanization. Such areas are found between the persistent hot spots (western parts of the district) and persistent cold spots (eastern parts of the district). Generally, the spatial pattern of the emerging hot spots followed the pattern of urbanization in the district, which had been expanding from west to east. Overall, the findings of this study could be used as a reference in the context of sustainable landscape and urban planning for the Colombo District.
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Given the optimal situation, error matrices should be provided whenever accuracy is assessed so that the users can compute and interpret these values for themselves.-from Authors
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Master linear regression techniques with a new edition of a classic text Reviews of the Second Edition: "I found it enjoyable reading and so full of interesting material that even the well-informed reader will probably find something new . . . a necessity for all of those who do linear regression." -Technometrics, February 1987 "Overall, I feel that the book is a valuable addition to the now considerable list of texts on applied linear regression. It should be a strong contender as the leading text for a first serious course in regression analysis."-American Scientist, May-June 1987 Applied Linear Regression, Third Edition has been thoroughly updated to help students master the theory and applications of linear regression modeling. Focusing on model building, assessing fit and reliability, and drawing conclusions, the text demonstrates how to develop estimation, confidence, and testing procedures primarily through the use of least squares regression. To facilitate quick learning, the Third Edition stresses the use of graphical methods in an effort to find appropriate models and to better understand them. In that spirit, most analyses and homework problems use graphs for the discovery of structure as well as for the summarization of results. The Third Edition incorporates new material reflecting the latest advances, including: Use of smoothers to summarize a scatterplot Box-Cox and graphical methods for selecting transformations Use of the delta method for inference about complex combinations of parameters Computationally intensive methods and simulation, including the bootstrap method Expanded chapters on nonlinear and logistic regression Completely revised chapters on multiple regression, diagnostics, and generalizations of regression Readers will also find helpful pedagogical tools and learning aids, including: More than 100 exercises, most based on interesting real-world data Web primers demonstrating how to use standard statistical packages, including R, S-Plus, SPSS, SAS, and JMP, to work all the examples and exercises in the text A free online library for R and S-Plus that makes the methods discussed in the book easy to use With its focus on graphical methods and analysis, coupled with many practical examples and exercises, this is an excellent textbook for upper-level undergraduates and graduate students, who will quickly learn how to use linear regression analysis techniques to solve and gain insight into real-life problems.