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Spatial-temporal variations of NDVI and their relationship with different land use types in Hexi region from 1999 to 2009

Authors:
  • Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences

Abstract

Based on the SPOT VEGETATION NDVI data, the spatial-temporal variations of vegetation cover in Hexi region of Gansu province were analyzed by using the MVC (Maximum Value Composites) method and the linear regression analysis during 1999 to 2009, the NDVI change trends and characteristics of different land use types were extracted. According to the results, during the last 11 years, the vegetation cover showed an increasing trend in the overall Hexi region with the rate of 3.70%/10a. The improved area and degraded area account for 24.37% and 3.80% of the total area respectively, vegetation improved region is greater than the degraded region significantly. The vegetation cover has obviously increased in the oasis agriculture region, however, the degraded area of vegetation cover are mainly distributed in the southeastern high altitude mountainous area. Viewed from the land use types, in addition to the NDVI of forest and high coverage grassland have declined, other types have increased.
Spatial-temporal Variations of NDVI and Their
Relationship with Different Land Use Types in Hexi
Region from 1999 to 2009
Hanchen Duan Tao Wang Xing Wen
Xian Xue Jian Guo
Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences
Lanzhou, China
e-mail: duanhanchen09@163.com
Abstract—Based on the SPOT VEGETATION NDVI data, the
spatial-temporal variations of vegetation cover in Hexi region of
Gansu province were analyzed by using the MVC (Maximum
Value Composites) method and the linear regression analysis
during 1999 to 2009, the NDVI change trends and characteristics
of different land use types were extracted. According to the
results, during the last 11 years, the vegetation cover showed an
increasing trend in the overall Hexi region with the rate of
3.70%/10a. The improved area and degraded area account for
24.37% and 3.80% of the total area respectively, vegetation
improved region is greater than the degraded region significantly.
The vegetation cover has obviously increased in the oasis
agriculture region, however, the degraded area of vegetation
cover are mainly distributed in the southeastern high altitude
mountainous area. Viewed from the land use types, in addition to
the NDVI of forest and high coverage grassland have declined,
other types have increased.
Keywords: Hexi region; SPOT-VGT NDVI; vegetation cover;
spatial-temporal variations
I. ᓩ㿔
ỡ㹿೼ഄ⧗㋏㒳Ёᡂⓨⴔ䞡㽕ⱘ㾦㡆ˈỡ㹿ᕅડഄ⇨㋏
㒳ⱘ㛑䞣ᑇ㸵ˈ೼⇨׭ǃ∈᭛⫳࣪ᕾ⦃Ё䍋ⴔ䞡㽕ⱘ԰
⫼ˈᰃ⇨׭Ҏ᭛಴㋴ᇍ⦃๗ᕅડⱘᬣᛳᣛ󰵼[1] DŽỡ㹿԰
Ў⫳ᗕ㋏㒳ⱘЏ㽕㒘៤䚼ߚˈᰃ⫳ᗕ㋏㒳ᄬ೼ⱘ෎⸔ˈг
ᰃ䖲᥹ೳຸǃ໻⇨∈ߚⱘ㞾✊Ā㒑ᏺāˈ೼ܼ⧗ব࣪ⷨお
Ё䍋ⴔĀᣛ⼎఼āⱘ԰⫼[2]DŽỡ㹿ᰃ㞾✊⫳ᗕ㋏㒳Ё᳔⌏
䎗ⱘ಴ᄤˈ݋᳝ᯢᰒⱘᑈ䰙ᄷ㡖ব࣪⡍ᕕˈ݊ࡼᗕব࣪
ᇍܼ⧗㛑䞣ᕾ⦃⠽䋼ᕾ⦃݋᳝䞡㽕ⱘᕅડˈ೼ᶤ⾡⿟ᑺ
Ϟҷ㸼ⴔೳഄ㽚Ⲫⱘࡼᗕব࣪[3,4]DŽ಴ℸˈỡ㹿㽚Ⲫব࣪ⱘ
ⷨお೼ܼ⧗ব࣪ⷨおЁ݋᳝䞡㽕ⱘᛣНDŽ
ᴀ᭛䞛⫼ 1999-2009 SPOT VEGETATION NDVI
᭄᥂䲚˄S10˅԰Ў᭄᥂⑤ˈ߽⫼ GIS ぎ䯈ߚᵤᡔᴃˈҢ
ᯊ䯈ぎ䯈Ϟߚᵤњ 11 ᑈᴹ⊇㽓ഄỡ㹿㽚Ⲫⱘぎ䯈ߚ
Ꮧব࣪䍟࢓ˈᑊ೼㽓࣫ഄ⇨׭㞾 1986 ᑈ䍋⬅ᱪᑆ৥ᱪ
⑓䕀ൟ[5]ǃݰϮ⌏ࡼǃ⫳ᗕᓎ䆒ㄝҎ㉏⌏ࡼϡᮁ๲ᔎⱘ㚠
᱃ϟˈ䖯ϔℹ᥶䅼њϡৠೳഄ߽⫼㉏ൟϞ NDVI ⱘব࣪䍟
࢓DŽ
II. ᭄᥂Ϣᮍ⊩
A.
ⷨおὖމ
⊇㽓䍄ᒞԡѢ⫬㙗ⳕ㽓䚼ˈҟѢ䴦㮣催ݙ㩭স催
П䯈ˈϰ䍋Р䵬ኁˈ㽓㟇⫬ᮄѸ⬠໘᯳᯳ኵˈ᳝⼕䖲ቅ
Ϣ䴦⍋ⳕⳌ䚏ˈ࣫Ϣݙ㩭সⳌ䚏ˈԡѢ࣫㒀 36°46ƍ~ 42°47ƍˈ
ϰ㒣 92°44ƍ~ 104°15ƍ˄೒ 1˅DŽϰ㽓ᓊԌ㑺 1000kmˈ࣫
ᆑ᭄݀䞠㟇᭄ⱒ݀䞠ˈᘏ䴶⿃䖒 29.8 ϛkm2DŽ⊇㽓䍄ᒞ
Ў݌ൟⱘᑆᯅˈ݊Ё䚼Ўᑇ⫳ᗕˈሲѢ⏽ᏺᑆᯅ㤦
⓴ǃ㤦⓴⇨׭ˈᴀܝ⛁䌘⑤Єᆠǃ⇨⏽催ǃᑈ䕗Ꮒ໻
[6]DŽҢ䍄ᒞⱘϰ৥㽓࣫⇨׭Ꮒᓖᕜ໻ˈ䰡∈⬅ϰ䚼ⱘ
150~250mm 䰡ࠄ㽓࣫䚼ⱘ 50mm ҹϟˈ㪌থҢ 2000mm
3500mm ҹϞˈᑈഛ⇨⏽Ў 5~10ćˈ໻䰚ᗻ⇨׭ᰒ㨫˗
ܝ✻ܙ䎇ˈ᮹✻ᯊ䯈೼ 2800~3300h[7,8]DŽ⬅Ѣ䰡䲼⿔ᇥˈ
⛁ᴵӊߚᏗϡഛˈࡴПϹ䝋ⱘ⫳ᗕᴵӊˈՓᕫ䆹ඳ⫳ᗕ
⦃๗ᵕ݊㛚ᔅDŽ಴ℸˈ߽⫼ỡ㹿ᣛ᭄ NDVI ⷨお⊇㽓ഄ
ⱘỡ㹿ব࣪⡍ᕕϢ䍟࢓ˈ݋᳝䞡㽕ⱘᛣНDŽ
1 ⷨおὖމ
B.
᭄᥂ᴹ⑤ঞ乘໘⧚
ᴀⷨお߽⫼↨߽ᯊԯ㦅㡦ᡔᴃⷨお᠔ᦤկⱘ㽚ⲪϰѮ
VGT-S10 ᭄᥂䲚ˈᯊ䯈䎼ᑺЎ 1999 1᳜㟇 2009
12 ᳜ˈぎ䯈ߚ䕼⥛Ў 1km×1kmˈᘏ݅ 396 ᱃೒ڣDŽ䆹᭄
᥂䲚⬅↨߽ᯊԯ㦅㡦ᡔᴃⷨお᠔ VEGETATION ᕅڣ໘⧚
Ёᖗ㒣䕤ᇘᅮ󰵼ǃ໻⇨󰶖ℷǃѥẔ⌟޴ԩ󰶖ℷㄝ໘⧚ˈ
⫳៤䗤᮹ 1km ߚ䕼⥛ⱘܼ⧗㽚Ⲫ᭄᥂ˈ᳔ৢ⫳៤ 10d ᳔໻
෎䞥乍Ⳃ
˖೑ᆊ䞡⚍෎⸔ⷨおথሩ䅵ߦ乍Ⳃ˄973˅(2009CB421308))
1590
978-1-4244-9171-1/11/$26.00 ©2011 IEEE
࣪ড়៤ⱘ NDVI ᭄᥂DŽϢ NOAA/AVHRR Ⳍ↨ˈSPOT-4
VEGETATION Ӵᛳ఼݋᳝㑶ܝ⊶↉ᇍ৊㓓㋴ᬊᬣᛳǃ
㑶໪⊶↉ࠨ䰸њᔎ∈≑ᬊᏺぎ䯈ߚ䕼⥛催ㄝӬ࢓ˈ಴
㗠᳈ࡴ䗖ᅰѢỡ㹿ব࣪ⲥ⌟ⷨお[9]DŽ⬅Ѣ䆹᭄᥂䲚㽚Ⲫᭈ
ϾϰѮഄˈϨࣙ৿⊶↉ NDVIˈЎњᕫࠄⷨおඳ
NDVI ᭄᥂ˈ߽⫼䘹ᛳ೒ڣ໘⧚䕃ӊ ERDAS 9.1 ഄ⧚
ֵᙃ㋏㒳䕃ӊ ARCGIS 9.2ˈ SPOT-VGT NDVI ᕅڣ䖯㸠
໘⧚ˈᑊҹ⬠ᅮདⱘ⊇㽓䍄ᒞ䖍⬠ᦤপⷨおݙⱘ NDVIDŽ
㒣䖛ྟ󰶖ℷৢড়៤ⱘ NDVI ᭄᥂ҟѢ-1~1 П䯈ˈЎ
њᄬټᮍ֓ˈᇚ-1~-0.1 ⱘؐ䆒㕂Ў-0. 1 ˈݡ䗮䖛݀ᓣ
DN=(NDVI+0.1)/0.004 䕀࣪Ў 0~250 DN ؐDŽ಴ℸˈ೼
᭄᥂乘໘⧚䖛⿟Ёˈᖙ乏ᇚ DN ؐ䗮䖛݀ᓣ
NDVI=0.004*DN-0.1 䕀ᤶЎⳳᅲⱘ NDVI ؐDŽ
C.
ⷨおᮍ⊩
1˅NDVI ᳔໻࣪ড়៤⊩
MVC (Maximum Value Composites) ᰃⳂࠡ೑䰙Ϟ᳔䗮
⫼ⱘ᳔໻࣪ড়៤⊩[10]ˈᇚ↣᳜Ёϝᯀⱘ᭄᥂প᳔໻ؐˈ
⫼䖭Ͼ᳔໻ؐ԰Ў䆹᳜ⱘ NDVI ؐˈ䖭ḋৃҹ⍜䰸໻⇨ǃ
ѥ໾䰇催ᑺ㾦ⱘ䚼ߚᑆᡄDŽ݊䅵ㅫ݀ᓣЎ˖
NDVIi = Max (NDVIj)
݊ЁˈNDVIiᰃ㄀ i᳜ⱘ NDVI ؐˈNDVIjᰃ㄀ i᳜㄀ j
ᯀⱘ NDVI ؐDŽ⬅Ѣᑈ᳔໻࣪ NDVI ৃҹ䕗དⱘড᯴ᑈᑺ
ỡ㹿䭓࢓᳔དᄷ㡖ഄ㸼ⱘỡ㹿㽚Ⲫᑺˈ಴ℸᴀⷨおᇚ䆹ᮍ
⊩ᓊԌњϔϟˈᇚܼᑈ 12 Ͼ᳜ⱘ䘹ᛳ᭄᥂ᣝ᳔໻ؐড়៤ᕫ
ࠄ৘ᑈⱘ᳔໻ NDVIˈᑊҹℸᴹҷ㸼ỡ㹿⫳䭓᳔ᯎⲯᯊᳳ
NDVI ᳔໻ؐ[4]DŽ
2˅䍟࢓ߚᵤ
䍟࢓ߚᵤ⊩ৃҹ᳝ᬜⱘ῵ᢳ↣Ͼ󰵺Ḑⱘব࣪䍟࢓[11]ˈЎ
њ䖯ϔℹⷨお⊇㽓ഄỡ㹿㽚Ⲫⱘࡼᗕব࣪䱣ᯊ䯈ব࣪ⱘ
⿟ᑺᐙᑺˈᴀⷨおՓ⫼ϔܗ㒓ᗻಲᔦߚᵤ῵ᢳ↣Ͼ䈵ܗ
೼䖛এ 11 ᑈⱘব࣪䍟࢓DŽ݊䅵ㅫ݀ᓣЎ˖
111
22
11
Ĭ
()
nnn
ii
iii
slope nn
ii
niNDVI iNDVI
ni i


qq
q


ᓣЁˈn㸼⼎῵ᢳⱘᯊ䯈ᑣ߫䭓ᑺ˄n= 11˅ˈব䞣 i
ᯊ䯈ᑣ߫ⱘᑣোˈNDVIiᰃ㄀ iᑈⱘ NDVI ᳔໻ؐDŽ݊ব
࣪䍟࢓ড᯴њⷨおᯊ↉ݙᑈ NDVI ⱘব࣪䍟࢓ˈᶤ⚍ⱘ䍟
࢓㒓ᰃ䆹⚍ 11 ᑈݙ᳔໻࣪ NDVI ؐ㒣䖛ϔܗ㒓ᗻಲᔦ῵
ߎᴹⱘϔϾᘏⱘব࣪䍟࢓DŽ⫼ Ĭslope 㸼⼎ব࣪᳆㒓ⱘ᭰⥛ˈ
Ĭslope > 0 ᯊˈ߭䇈ᯢ NDVI ⱘব࣪䍟࢓ᰃ๲ࡴⱘˈᔧ
Ĭslope < 0 ᯊˈ߭䇈ᯢ NDVI ⱘব࣪䍟࢓ᰃޣᇥⱘDŽ
III. 㒧ᵰϢ䅼䆎
A. NDVI
ⱘᑈ䰙ব࣪
䗮䖛䅵ㅫᕫࠄ⊇㽓䍄ᒞഄ 1999~2009 ᑈᑈ᳔໻ NDVI
ⱘব࣪ᚙމ˄བ೒ 2˅DŽҢ೒Ёৃҹⳟߎˈ⊇㽓ഄỡ㹿
NDVI ব࣪ᭈԧϞ๲ࡴ䍟࢓ˈ๲ࡴⱘ䗳⥛Ў 3.70%/10aˈ
ᑈ᳔໻ NDVI ϢᑈӑП䯈ⱘⳌ݇㋏᭄催䖒 0.77˄P<0.01˅ˈ
䖭ϢЁ೑㽓࣫ഄỡ㹿ব࣪䍟࢓ϔ㟈[12]DŽ䰸 1999 ᑈᑈ᳔
໻࣪ NDVI ᇣѢ 0.14 ໪ˈ݊Ҫ৘ᑈⱘ NDVI ؐഛ೼ 0.14
ҹϞˈ݊Ё NDVI ᳔໻ؐߎ⦄೼ 2008 ᑈˈ催䖒 0.188DŽҢ
NDVI ᑈ䰙ব࣪ᘏԧ䍟࢓ᴹⳟ˖1999~2007 ᑈᑈ᳔໻࣪
NDVI 㓧᜶๲ࡴⱘ䍟࢓ˈ݊๲ࡴ䗳⥛Ў 1.10%/10aˈ2007
ᑈҹৢ๲ࡴᐙᑺᗹ࠻ϞDŽ䖥 40~50 ᑈᴹˈ៥೑㽓࣫ഄ
䰡∈䞣ᯢᰒ๲ࡴⱘ䍟࢓[13,14]DŽ⊇㽓㓓⌆ⱘỡ㹿㽚Ⲫব࣪
䰸ফᔧഄⱘ⇨⏽ǃ䰡∈ᕅડ໪ˈ݊∈䌘⑤Џ㽕ᴹ⑤Ѣ⼕䖲
ቅ㵡࣪ⱘބ䲾[15]DŽ䱣ⴔܼ⧗⇨׭বᱪˈ㞾 20 Ϫ㑾 50 ᑈҷ
ҹᴹ⼕䖲ቅ࣫വ⇨⏽ᘏԧϞ⊶⢊Ϟ䍟࢓ˊ䖯ܹ 90 ᑈҷ
ҹৢࡴ䗳Ϟˈބ䲾⍜㵡䞣ࡴ໻ˈৠᯊ䰡∈䞣г๲ࡴ䍟
[16]DŽ಴ℸˈ䖥ᑈᴹ⊇㽓ഄỡ㹿㽚Ⲫᯢᰒ๲ࡴ䍟࢓DŽ
2 ⷨおᑈ᳔໻࣪ NDVI ব࣪䍟࢓
3⊇㽓ഄᑈ᳔໻ NDVI ぎ䯈ߚᏗ
B.
ᑈ᳔໻
NDVI
ⱘぎ䯈ߚᏗ⡍ᕕ
3Ў⊇㽓ഄ 1999~2009 ᑈᑈ᳔໻ NDVI ぎ䯈ߚᏗ
೒ˈড᯴њ⊇㽓ഄ 11 ᑈᴹỡ㹿㽚Ⲫⱘ෎ᴀ⡍ᕕDŽҢ೒Ё
ৃҹⳟߎˈϰ䚼ഄⱘỡ㹿⢊މᯢᰒ㽕↨㽓䚼དˈᑊϨҢ
ϰ৥㽓䗤⏤䗦ޣⱘ䍟࢓DŽ⬅Ѣফ⼕䖲ቅބ䲾㵡∈ⱘ㸹㒭ˈ
⼕䖲ቅ࣫呧໻䚼ߚ㓓⌆ඳᑈ᳔໻ NDVI ؐഛ೼ 0.60 ҹ
Ϟˈ↨䕗݋᳝ҷ㸼ᗻⱘ޴Ͼ NDVI 催ؐ݌ൟඳЎ㙗Ꮂǃ
Ј⋑ওǃ⫬Ꮂǃ⇥Фǃ∌ᯠޝᎲ޴ഫ㓓⌆DŽ݊󱂖ᰃ
ᬺ✠ǃ⪰Ꮂǃ⥝䮼ǃ䞥ศǃ⇥ࢸㄝ޴Ͼ㓓⌆ඳˈ݊ NDVI
ؐഛ೼ 0.30 ҹϞDŽ⬅Ѣ䖭ѯ㓓⌆Ў⊇㽓ഄЏ㽕ⱘݰϮ⫳
ѻ෎ഄˈ಴ℸ݊ NDVI ؐᯢᰒأ催ˈ㗠ϰ䚼㓓⌆໪ೈҹ
⊇㽓ഄ㽓࣫䚼㽓䚼໻䚼ߚඳ⬅Ѣ䰡∈⿔ᇥˈ㔎ᇥ
∈⑤㸹㒭Ϩ㪌থ䞣໻ˈỡ㹿㽚Ⲫᑺᵕ䕗Ԣˈ݊ NDVI ؐ೼
0.05~0.30 П䯈DŽNDVI ؐᇣѢ 0.05 ⱘඳ໻໮ߚᏗ೼ᵕッ
1591
ᑆᯅⱘ≭⓴ǃៜຕㄝ䲒ҹ㹿߽⫼ᓔথⱘഄDŽ
C.
ᑈ᳔໻
NDVI
ⱘぎ䯈ব࣪ߚᵤ
߽⫼ϔܗ㒓ᗻಲᔦߚᵤ⊩῵ᢳњ⊇㽓ഄ 1999~2009
ᑈⱘ NDVI ব࣪䍟࢓˄೒ 4˅ˈᑊ 1Ё㒳䅵᭄᥂ⱘব
࣪ᇚ NDVI ⱘব࣪䍟࢓ߚЎ 7Ͼㄝ㑻˖ᯢᰒ䗔࣪ǃЁᑺ䗔
࣪ǃ䕏ᑺ䗔࣪ǃ෎ᴀϡবǃ䕏ᑺᬍ୘ǃЁᑺᬍ୘ᯢᰒᬍ
୘DŽ㓐ড়೒ 4㸼 1ৃҹⳟߎˈ⊇㽓ഄ 11 ᑈᴹỡ㹿㽚Ⲫ
ᭈԧϞ๲ࡴⱘ䍟࢓ˈỡ㹿ᬍ୘ඳⱘ䴶⿃Ў 72764km2ˈ
ᘏ䴶⿃ⱘ 24.37%ˈᯢᰒᬍ୘ⱘඳЏ㽕䲚Ё೼⊇㽓ഄ
৘Ͼ㓓⌆ೈˈ䕏ᑺЁᑺᬍ୘ⱘඳЏ㽕ߚᏗ೼㙗࣫㩭
সᮣ㞾⊏ওⱘ໻䚼ߚഄ㙗㺩೎ᮣ㞾⊏ওⱘ࣫䚼㽓
䚼DŽỡ㹿䗔࣪ⱘ䴶⿃Ў 11360km2ˈᘏ䴶⿃ⱘ 3.80%ˈЏ
㽕ߚᏗ೼໽⼱㮣ᮣ㞾⊏ওⱘϰ䚼স⌾ওⱘ䚼ˈ݊󱂖೼
㙗㺩೎ᮣ㞾⊏ওϰ䚼ҹঞቅЍǃ⇥Ф㙗㺩೎ᮣ㞾
⊏ওϰ䚼ߚⱘ㽓䚼ഄѸ⬠໘᳝䕏ᑺ䗔࣪DŽ݊Ҫඳ෎
ᴀ≵᳝ব࣪ˈᘏ䴶⿃ⱘ 71.82%DŽᘏԧ㗠㿔ˈỡ㹿ᬍ୘ⱘ
ඳᯢᰒ໻Ѣỡ㹿䗔࣪ⱘඳˈ⊇㽓ഄ 11 ᑈᴹỡ㹿ᭈԧ
๲ࡴⱘ䍟࢓DŽ
4 1999~2009 ᑈ⊇㽓ഄᑈ᳔໻ NDVI ࡼᗕব࣪䍟࢓ߚᏗ೒
1 1999~2009 ᑈ᳔໻࣪ NDVI ব࣪䍟࢓㒳䅵
NDVI ব࣪
䍟࢓ ব࣪ㄝ㑻 䴶⿃/km2 䴶⿃
ⱒߚ↨/%
-0.015 ᯢᰒ䗔࣪ 1109 0.37
-0.015 ~ -0.01 Ёᑺ䗔࣪ 2557 0.86
-0.01 ~ -0.005 䕏ᑺ䗔࣪ 7694 2.58
-0.005 ~ 0.005 ෎ᴀϡব 214444 71.82
0.005 ~ 0.01 䕏ᑺᬍ୘ 50870 17.04
0.01 ~ 0.015 Ёᑺᬍ୘ 14466 4.85
0.015 ᯢᰒᬍ୘ 7428 2.49
D.
ϡৠೳഄ߽⫼㉏ൟⱘỡ㹿ব࣪䍟࢓
ϡৠೳഄ߽⫼㉏ൟⱘỡ㹿ফࠄϡৠ⿟ᑺⱘ㞾✊಴㋴
ҎЎ಴㋴ᕅડ,݊ỡ㹿㽚Ⲫгህᄬ೼ϡৠⱘᯊぎবᓖ⡍ᕕ
[17]DŽ㞾✊ỡ㹿ব࣪᳈໻⿟ᑺϞড᯴њ⇨׭ব࣪ᇍỡ㹿ⱘᕅ
ડˈ㗠ҎᎹỡ㹿ব࣪᳈໮ԧ⦄ⱘᰃҎ㉏⌏ࡼⱘ԰⫼[18]DŽ಴
ℸˈᴀ᭛߽⫼ 2000 1:10 ϛೳഄ߽⫼᭄᥂ᑧˈҹ㒓ᗻಲ
ᔦ㋏᭄ΊșslopeΊ>0.005 ԰Ў NDVI ব࣪ⱘ߸߿󰵼ޚˈ䖯㸠
ϡৠೳഄ߽⫼㉏ൟỡ㹿㽚㹿ব࣪㾘ᕟⷨおDŽЎњ֓Ѣ㒳䅵
ߚᵤˈ䗝পⷨおᘏ䴶⿃ 96%15 ⾡ೳഄ߽⫼㉏ൟDŽ
Ң㸼 2Ёⱘ㒳䅵㒧ᵰৃҹⳟߎˈ⊇㽓ഄỡ㹿㽚Ⲫҹᬍ
୘ЎЏˈ䰸ᵫഄǃϬ䱉ᯅഄ催㽚Ⲫᑺ㤝ഄ䗔࣪䴶⿃໻Ѣ
ᬍ୘䴶⿃໪ˈ݊Ҫೳഄ߽⫼㉏ൟϞⱘỡ㹿㽚Ⲫഛ᳝᠔ᬍ୘DŽ
ᬍ୘↨䞡 24.37%䖰໻Ѣ䗔࣪↨䞡 3.80%ˈ݊Ёቅᯅഄǃ
ᑇᯅഄᓎ䆒⫼ഄỡ㹿 NDVI ๲ᐙ↨䕗ᰒ㨫ˈ䖭Џ㽕ᰃ
⬅Ѣ䖥ᑈᴹݰϮ⫳ѻ∈ᑇⱘᦤ催Ҏ㉏⌏ࡼ㣗ೈⱘᠽ໻᠔
䗴៤ⱘDŽ催㽚Ⲫᑺ㤝ഄᵫഄỡ㹿 NDVI ⱘޣᇣЏ㽕ᰃ
ѢⒹⷡⒹӤ䖛ᑺᬒ⠻᠔ᇐ㟈ⱘˈ㗠⬅Ѣ䖥ᑈᴹ䗔㗩䖬ᵫ
䖬㤝ᬓㄪⱘᅲᮑҹঞᇍ㤝ഄⱘᇕ㚆ֱᡸˈՓᕫ≭ഄៜຕ
ඳⱘỡ㹿 NDVI ᕫࠄᯢᰒᬍ୘DŽℸ໪ˈ䱣ⴔܼ⧗⇨׭ব
ᱪˈ⏽ᑺϞᇐ㟈ބᎱ㵡࣪ˈҹބᎱ㵡∈㸹㒭ЎЏⱘݙ䰚
⊇ᕘ⌕䞣๲ࡴˈՓϟ␌ഄᴹ∈䞣๲໻ˈҢ㗠Փϟ␌㤦⓴
ഄⱘỡ㹿ᕫࠄᘶ໡DŽ
2 ϡৠೳഄ߽⫼㉏ൟϞỡ㹿ব࣪䍟࢓㒳䅵㸼
ೳഄ߽⫼
㉏ൟ
ᘏ䴶⿃
/km2
ᬍ୘䴶
/km2
䗔࣪䴶
/km2
ᬍ୘↨
/%
䗔࣪↨
/%
ᵫഄ 7202 985 2104 13.68 29.21
ቅᯅഄ 79 40 4 50.63 5.06
Ϭ䱉ᯅഄ 1933 180 785 9.31 40.61
ᑇᯅഄ 16079 8127 1271 50.54 7.90
催㽚Ⲫᑺ
㤝ഄ 7133 1485 1858 20.82 26.05
Ё㽚Ⲫᑺ
㤝ഄ 15677 7385 1945 47.11 12.41
Ԣ㽚Ⲫᑺ
㤝ഄ 42826 15622 1105 36.48 2.58
∈ඳ 2590 1031 462 39.79 17.83
ᓎ䆒⫼ഄ 1278 680 112 53.25 8.77
≭ഄ 35832 4310 93 12.03 0.26
ៜຕ 83287 11329 342 13.60 0.41
Ⲥ⺅ഄ 10269 3852 49 37.51 0.48
⊐⋑ഄ 658 235 27 35.71 4.10
㻌ೳഄ 4379 1057 30 24.14 0.69
㻌ች⷇⸒
58383 11273 562 19.31 0.96
݊Ҫ 10962 5173 612 47.19 5.58
ᘏ䅵 298567 72764 11361 24.37 3.80
IV. 㒧䆎
ᴀ᭛߽⫼ SPOT VEGETATION NDVI ᭄᥂ߚᵤњ⊇㽓
ഄ 1999~2009 ᑈⱘỡ㹿㽚Ⲫᯊぎব࣪⡍ᕕˈᑊᇍϡৠೳ
ഄ߽⫼㉏ൟϞⱘỡ㹿ব࣪䍟࢓䖯㸠ߚᵤˈᕫࠄҹϟЏ㽕㒧
䆎˖
˄1˅䗮䖛ᇍ⫬㙗⊇㽓ഄ 11 ᑈᴹⱘỡ㹿㽚Ⲫ䖯㸠ⲥ⌟
ৃҹⳟߎˈ⊇㽓ഄỡ㹿 NDVI ᭈԧ๲ࡴⱘ䍟࢓ˈ๲ࡴ
ⱘ䗳⥛Ў 3.70%/10aDŽ2007 ᑈҹࠡ㓧᜶๲ࡴⱘ䍟࢓ˈ2007
ᑈҹৢᰒ㨫๲ࡴ䍟࢓DŽ
˄2˅Ңỡ㹿 NDVI ⱘᄷ㡖ব࣪ᴹⳟˈNDVI ݋᳝ᯢᰒ
ⱘᄷ㡖ব࣪㾘ᕟˈ໣ᄷ NDVI ᳔催ˈހᄷ NDVI ᳔ԢDŽ໣
ᄷ⾟ᄷ NDVI ⊶ࡼᐙᑺ䕗໻ˈ㗠᯹ᄷހᄷⳌᇍ↨䕗
ᅮDŽ
˄3˅ᭈϾ⊇㽓ഄϰ䚼ỡ㹿㽚Ⲫ⢊މᯢᰒདѢ㽓䚼ˈ
1592
ỡ㹿ᣛ᭄䕗催ⱘඳЏ㽕䲚Ё೼৘Ͼ㓓⌆ඳˈ㗠೼㓓⌆
໪ೈⱘ≭⓴ៜຕㄝ᮴ỡ㹿ᇥỡ㹿㽚Ⲫˈỡ㹿ᣛ᭄ᵕԢDŽ
˄4˅ỡ㹿ᬍ୘ⱘ䴶⿃䖰䖰໻Ѣỡ㹿䗔࣪ⱘ䴶⿃ˈỡ㹿
ᬍ୘ⱘඳЏ㽕ߚᏗ೼৘㓓⌆ೈˈ㗠ỡ㹿ᯢᰒ䗔࣪ⱘ
ඳЏ㽕ߚᏗ೼໽⼱㮣ᮣ㞾⊏ওⱘϰ䚼স⌾ওⱘ䚼DŽ
˄5˅Ңỡ㹿㉏ൟᴹⳟˈ⬅ѢҎ㉏ⱘϡড়⧚߽⫼ᇐ㟈ᵫ
ഄ催㽚Ⲫᑺ㤝ഄ NDVI ᳝᠔ϟ䰡ˈ㗠ফ৘⾡ֱᡸᬓㄪঞ
Ꮉ⿟ⱘᅲᮑˈ≭⓴ៜຕഄⱘỡ㹿 NDVI ഛ᳝᠔ᦤ催DŽ
ỡ㹿ব࣪ⲥ⌟ᰃϔϾ䭓ᳳⱘࡼᗕব࣪䖛⿟ˈ߽⫼䘹ᛳ
᠟↉䖯㸠໻ሎᑺǃ䭓ᯊ䯈ᑣ߫ⱘỡ㹿ব࣪ⲥ⌟ˈᇍҹৢⱘ
≭⓴࣪䰆⊏⫳ᗕ⦃๗ᬍ୘Ꮉ԰䛑݋᳝䴲ᐌ䞡㽕ⱘ⧚䆎ᣛ
ᇐᛣНDŽ
㟈䇶
ᛳ䇶೑ᆊ䞡⚍෎⸔ⷨおথሩ䅵ߦ乍Ⳃ˄&%˅
Āᑆᯅ㓓⌆࣪ǃ㤦⓴࣪䖛⿟ঞ݊ᇍҎ㉏⌏ࡼǃ⇨׭ব࣪
ⱘડᑨϢ䇗᥻āᇍᴀⷨおⱘ䌘ࡽˈᑊᛳ䇶 ,((( 䘹ᛳˈ⦃๗
ϢѸ䗮䖤䕧Ꮉ⿟೑䰙Ӯ䆂㒘ྨӮᦤկℸ󱂖Ѹ⌕ᑇৄDŽ
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1593
... Meanwhile, the Chinese Government has been implementing the "Grain for Green" program in northern China for over 20 years. This has resulted in an increase in vegetation coverage of the Hexi Corridor by 3.7% per decade since the 2000 (Duan et al., 2011). For example, the vegetated area in the Qilian Mountains increased by 10,779 km 2 from 2000 to 2011 (Deng et al., 2013). ...
Article
Although the concept of ecological drought was first defined by the Science for Nature and People Partnership (SNAPP) in 2016, there remains no widely accepted ecological drought monitoring index. Therefore, this study constructed a new ecological drought index, the standardized ecological water deficit index (SEWDI). The SEWDI is based on the difference between ecological water requirement and consumption, referred to as the standardized precipitation index (SPI) method, which was used to monitor ecological drought in Northwestern China (NWRC). The performance of the SEWDI was compared with that of other widely-used drought indices, including standardized root soil moisture index (SSI), self-calibrated Palmer drought index (scPDSI), standardized precipitation-evaporation drought index (SPEI), and SPI, using the Pearson correlations between these indices and standardized normalized difference vegetation index (SNDVI) under different time scales, wetness and water use efficiencies (WUE) of vegetation. The SEWDI at a 12-month scale was decomposed in NWRC during 1982-2015 using the rotational empirical orthogonal function (REOF) in order to demarcate five ecological drought regions, including southeastern sub-region (SE), southwestern sub-region (SW), northwestern sub-region (NW), northeastern sub-region (NE), and central sub-region (CT). The characteristics of ecological drought in NWRC, such as intensity, duration, and frequency, were extracted using the run theory. The return periods of five types of drought were calculated using wavelet analysis. Results showed that the performance of SEWDI in monitoring ecological drought was the best among the drought indices under different time scales, and the 12-month-scale was largely unaffected by wetness and WUE. Results of monitoring indicated that serious ecological droughts in the NWRC mainly occurred in 1982-1986, 1990-1996, and 2005-2010, primarily in SE, SW, and CT, SW and NE, and NW, NE, and CT, respectively. Furthermore, SEWDI in the arid SW, NW and CT showed a longer return period compared to that in the humid NE and SE, and the evolution of ecological drought in arid regions was mainly influenced by meteorological drought and the scarcity of root soil moisture. This study provides ant approach for quantifying ecological drought severity across natural vegetation areas which can be employed by decision makers
Article
By using SPOT VEGETATION data with a resolution of 1 km, we analyzed the tempo-spatial dynamic of vegetation and its response to temperature in the southern regions of the Qinling Mountains from 1998 to 2007. According to the results, about 97% of the total area had a higher annual max NDVI (NDVImax) than 0.57 in the southern region of the Qinling Mountains. The area where the vegetation coverage increased was greater than that where the vegetation was degraded. However, the pixels where NDVImax significantly decreased accounted for more than 4. 45% of the total pixels, and the NDVI in the partial broad-leaved forest-distributed area reduced significantly. The most degraded vegetation was shrubs and bushes in the southern region of the Qinling Mountains. NDVImax was correlated significantly with the mean air temperature calculated from last August to July, with the equation of y=-0.004x2-0.0015x+0.7168 (p<0.05). When the mean air temperature was below (13.0±0.2)°C, the relation between NDVImax and the temperature was positive, while when the mean air temperature was above (14.4±0.2)°C, the relation was negative, especially in the southern cultivated area.
Article
Based on climatic data of 8 stations in the Qilian Mt. region, methods of regression analysis and moving average were used to study the climatic changing trend of the study area under the background of global warming. Results showed that the temperature increased after the mid-1980s and climatic warming has accelerated since the 1990s due to the rise of temperature in autumn and winter. The precipitation experienced. a changing trend, that is lower (the 1960s)-higher (the 1980s)-lower (the 1990s)-higher (after 2000). The regional difference in temperature rise is, with the main Heihe River as the divide, characterized by the fact that it is increasing from south to north in the middle-eastern Qilian Mt. region while decreasing from south to north in the middle-western. Also the precipitation presents obvious spatial change. The rising range is decreasing from east to west in the eastern Qilian Mt. region, while decreasing from south to north in the western. What's more, methods of T checking, Cramer and Mann-Kendall were employed to analyze climatic break, indicating that the abrupt change of temperature was more noticeable than that of precipitation in the study area. The abrupt change of spring and summer temperature happened in 1997 while that of autumn and winter appeared in 1985. Moreover, the temperature abrupt change in 1987 was more obvious than in 1997 because the scale of temperature rise was higher in autumn and winter than in spring and summer, and the precipitation break in the eastern, middle and western regions happened in 1980, 1972 and 1969, respectively.
Article
This paper analyzed the temporal and spatial responses of vegetation NDVI to the variations of temperature and precipitation in each ten-day period in the whole year, spring, summer and autumn covering 1998 to .2007 based on the SPOT VGT-NDVI data and daily temperature and precipitation data from 205 meteorological stations in eastern China. The results indicate that on the whole, the response of vegetation NDVI to the variation of temperature is greater than to that of precipitation in eastern China. Vegetation NDVI maximally responds to the variation of temperature with a lag of about 10 days, and it maximally responds to the variation of precipitation with a lag of about 30 days. The response of vegetation NDVI to temperature and precipitation is the greatest in autumn, and the lag time is longer in summer. Spatially, the maximum response of vegetation NDVI to the variation of temperature is greater in the northern and central parts than in the southern part of eastern China. The maximum response of vegetation NDVI to the variation of precipitation is greater in the northern part than in the central and southern parts of eastern China. There is more lag time of vegetation NDVI to the variation of temperature in the northern and southern parts, while less in the central part. The lag time of vegetation NDVI to the variation of precipitation gradually increases from the northern to the southern part according to the latitude. The response of vegetation NDVI to the variations of temperature and precipitation in eastern China is mainly consistent with other results obtained in eastern and southern China.
Article
The interannual variability and trend of above-ground,photosynthetic activity of Arctic tundra,vegetation in the 1990s is examined,for the north slope region of Alaska, based on the seasonally integrated normalized difference vegetation index (SINDVI) derived from local area coverage,(LAC) National Oceanic and Atmospheric Administration (NOAA) Advanced,Very High Resolution Radiometer,(AVHRR) data. Smaller SINDVI values occurred,during,the three years (1992–1994) following the volcanic eruption,of Mt Pinatubo. Even after implementing,corrections for this stratospheric aerosol effect and,adjusting for changes in radiometric calibration coefficients, an apparent increasing trend of SINDVI in the 1990s is evident for the entire north slope. The most,pronounced increase was observed,for the foothills physiographical,province.