|Table of Contents|

Impact of Different Extracted Methods of Annual NDVI on Vegetation Cover Change and Their Response to Climate Change in Qinling Mountains(PDF)

《北方园艺》[ISSN:1001-0009/CN:23-1247/S]

Issue:
2017年24
Page:
148-155
Research Field:
Publishing date:

Info

Title:
Impact of Different Extracted Methods of Annual NDVI on Vegetation Cover Change and Their Response to Climate Change in Qinling Mountains
Author(s):
WANG Tao1; 2; 3; TIAN Yang4; XIANG Ru2
1.College of Urban and Environmental Science,Northwest University,Xi′an,Shaanxi 710127；2.College of Geomatics,Xi′an University of Science and Technology,Xi′an,Shaanxi 710054；3.State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau/Institute of Water and Soil Conservation,Chinese Academy of Sciences and Ministry of Water Resources,Yangling,Shaanxi 712100；4.The People′s Government of Hengshan Town Qijiang District Chongqing Municipal,Chongqing 401460
Keywords:
MODIS NDVI; MVC; temperature; precipitation; Qinling Mountains
PACS:
-
DOI:
10.11937/bfyy.20172254
Abstract:
On the basis of the MODIS NDVI images,temperature and precipitation data from 2000 to 2014 in Qinling Mountains,the spatial and temporal variation of NDVI and the correlation between NDVI and temperature,precipitation were analyzed with 4 different extraction methods on annual NDVI value,such as AVM (Average Value Method),MVC (Maximum Value Composition),AMVC (Average-Maximum Value Composition) and RAVM method (Reconstruction-Average Value Method).This study would provide an information to accurate assessment of NDVI change in Qinling Mountains.The results showed that the RAVM was the best to extract annual value of NDVI,and the linear change trend was very significant,followed by the AMVC,while the annual value of NDVI extracted by MVC and AVM were higher and lower.Vegetation degradation was a hot issue for the monitoring of Qinling Mountains.The linear reduction trend area calculated by AMVC was the largest,followed by the MVC,RAVM and AVM.The correlation between NDVI and temperature was different to a certain extent,such as the spatial distribution of coefficient was relatively scattered (MVC and RAVM) or concentrated (AVM and AMVC),and the positive (AVM and RAVM) or negative correlation (MVC and AMVC).But the correlation between NDVI and precipitation was positive for the 4 different methods,which reflect the correlation between NDVI and precipitation was stable and unstable between NDVI and temperature.

References:

[1]GAO X J,SHI Y,ZHANG D F,et al.Climate change in China in the 21st century as simulated by a high resolution regional climate model[J].Chinese Science Bulletin,2012,57(10):1188-1195.

[2]STOKER T F,QIN G K,PLATTNER M,et al.IPCC,2013:Summary for policy makers[A].Climate Change 2013:The physical basis.Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change[R].Cambridge University Press,Cambridge,United Kingdom and New York,NY,USA.
[3]WALTHER G R,POST E,CONVEY P,et al.Ecological responses to recent climate change[J].Nature,2002,416:89-395.
[4]白红英.秦巴山区森林植被对环境变化的响应[M].北京:科学出版社,2014:1-42.
[5]GARTZIA M,CABELLO F P,BUENO C G,et al.Physiognomic and physiologic changes in mountain grassland in response to environmental and anthropogenic factors[J].Applied Geography,2016,66:1-11.
[6]LIU X,ZHU X,PAN Y,et al.Vegetation dynamics in Qinling-Daba Mountains in relation to climate factors between 2000 and 2014[J].Journal of Geographical Sciences,2016,26(1):45-58.
[7]ZHAO X,MA C,XIAO L.The vegetation history of Qinling Mountains,China[J].Quaternary International,2014,325:55-62.
[8]WESSELS K J,PRINCE S D,FROST P E,et al.Assessing the effects of human-induced land degradation in the former homelands of northern south Africa with a 1 km AVHRR NDVI time-series[J].Remote Sensing of Environment,2004,91:47-67.
[9]ZHAN Z Z,LIU H B,LI H M,et al.The relationship between NDVI and terrain factors:A case study of Chongqing[J].Procedia Environmental Science,2012,12:765-771.
[10]BOSCHETTI M,NUTINI F,BRIVIO P A,et al.Identification of environmental anomaly hot spots in west Africa from time series of NDVI and rainfall[J].ISPRS Journal of Photogrammetry and Remote Sensing,2013,78:26-40.
[11]ZHOU H,ROMPAEY A V,WANG J.Detecting the impact of the ‘Grain for Green’ program on the mean annual vegetation cover in the Shaanxi Province,China using SPOT-VGT NDVI data[J].Land Use Policy,2009,26:954-960.
[12]LI S,YANG S,LIU X,et al.NDVI-based analysis on the influence of climate change and human activities on vegetation restoration in the Shaanxi-Gansu-Ningxia region,central China[J].Remote Sensing,2015,7:11163-11182.
[13]FENSHOLT R,PROUD S R.Evaluation of earth observation based global long term vegetation trends-comparing GIMMS and MODIS global NDVI time series[J].Remote Sensing of Environment,2012,19:131-147.
[14]STEFANOV W L,NETZBAND M.Assessment of ASTER land cover and MODIS NDVI data at multiple scales for ecological characterization of an arid urban center[J].Remote Sensing of Environment,2005,99:31-43.
[15]崔晓临,白红英,王涛.秦岭地区植被NDVI海拔梯度差异及其气温响应[J].资源科学,2013,35(3):618-626.
[16]崔晓临,赵娟,白红英,等.基于MODIS NDVI的陕西省植被覆盖变化分析[J].西北农林科技大学学报(自然科学版),2013,41(5):87-93.
[17]王涛,陶辉,雷刚,等.博斯腾湖流域植被覆盖变化及驱动因素分析[J].中国农学通报,2015,31(4):228-236.
[18]王涛,杨梅焕,徐澜.陕西榆林地区植被退化与沙漠化趋势分析[J].西北师范大学学报(自然科学版),2017,53(2):104-111.
[19]徐建华.现代地理学中的数学方法[M].北京:高等教育出版社,2002:37-70.

Memo

Memo:
-
Last Update: 2017-12-29