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Research Article NDVI Variation and Its Responses to Climate Change on the Northern Loess Plateau of China from 1998 to 2012

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Research Article NDVI Variation and Its Responses to Climate Change on the Northern Loess Plateau of China from 1998 to 2012 Hindawi Publishing Corporation Advances in Meteorology Volume 2015, Article ID 725427, 10 pages http://dx.doi.org/10.1155/2015/725427 Research Article NDVI Variation and Its Responses to Climate Change on the Northern Loess Plateau of China from 1998 to 2012 Tingting Ning,1,2 Wenzhao Liu,1 Wen Lin,1,2 and Xiaoqiang Song3 1 State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Institute of Soil and Water Conservation, Chinese Academy of Sciences and Ministry of Water Resources, Yangling, Shaanxi 712100, China 2University of the Chinese Academy of Sciences, Beijing 100049, China 3Soil and Water Conservation Bureau of Shaanxi Province, Xian, Shaanxi 710004, China Correspondence should be addressed to Wenzhao Liu; [email protected] Received 30 January 2015; Revised 23 April 2015; Accepted 30 April 2015 Academic Editor: Steffen Mischke Copyright © 2015 Tingting Ning et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This study analyzed temporal and spatial changes of normalized difference vegetation index (NDVI) on the northern Loess Plateau and their correlation with climatic factors from 1998 to 2012. The possible impacts of human activities on the NDVI changes were also explored. The results showed that (1) the annual maximum NDVI showed an upward trend. The significantly increased NDVI and decreasing severe desertification areas demonstrate that the vegetation condition improved in this area. (2) Over the past decades, climate tended to be warmer and drier. However, the mean temperature significantly decreased and precipitation slightly increased from 1998 to 2012, especially in spring and summer, which was one of the major reasons for the increase in the annual maximum NDVI. Compared to temperature, vegetation was more sensitive to precipitation changes in this area. The NDVI and annual precipitation changes were highly synchronous over the first half of the year, while a 1-month time lag existed between the two variables during the second half of the year. (3) Positive human activities, including the “Grain for Green” program and successful environmental treatments at coal mining bases, were some of the other factors that improved the vegetation condition. 1. Introduction photosynthetic capacity, the leaf area index, biomass, and net primary productivity [7]. The NDVI ranges from −1to1. Vegetation is the main component of terrestrial ecosystems Higher positive NDVI values correspond to higher vegetation on earth and plays an important role in the water, energy coverage and activity [8]. Negative NDVI values indicate the exchange, and biogeochemical cycles on terrestrial surfaces presence of clouds, snow, water, or a bright, nonvegetated [1]. Linking soil, atmosphere, and moisture [2, 3], vegetation surface[9].Moreover,theNDVIhasbeenwidelyusedto plays an irreplaceable role in maintaining climate stability, assess the net primary productivity of vegetation (NPP) [10, regulating the carbon balance, and reducing global-scale 11] and crop production [12, 13] and to indicate the feedback greenhouse gases [4]. However, vegetation is sensitive to effects of vegetation on local climates [14–16]. In addition, the climate changes. Vegetation dynamics and the responses to NDVI has also been used to improve predictions and impact climate changes have been recognized as one of the core issues assessments related to disturbances, such as droughts [17] and of global change in terrestrial ecosystems [5]. floods [18]. Due to high spatial and temporal resolutions and accu- Over the past 20 years, many scholars have conducted racy, remotely sensed data can provide technical support extensive research regarding surface vegetation coverage at for monitoring vegetation dynamics at large scales. The nor- different spatial and temporal scales based on NDVI time malized difference vegetation index (NDVI) was proposed series data. Their research has shown that global vegetation by Rouse et al. based on red and near-infrared reflectance activity has been gradually increasing, especially in northern [6]. The NDVI has been frequently used for studying veg- middle-high latitude regions [19–21]. And similar results etation dynamics because it is highly correlated with the have been observed on the Tibet Plateau [22], the Loess 2 Advances in Meteorology ∘ ∘ ∘ ∘ ∘ ∘ ∘ ∘ Plateau [23, 24] and over southwestern [25], central, and 107 E 108 E 109 E 110 E 111 E 112 E 113 E 114 E northwestern China [4, 26]. Conversely, decreasing NDVI ∘ 42 N N trends have been detected in many regions, such as in the southwestern United States [27], northeastern Thailand [28], ∘ 41 N the upper catchments of the Yellow River [29], and the Lancang River source region [30]. ∘ 40 N Heidaigou In addition, the relationship between the NDVI and ∗ Daliuta-Huojitu∗ climatic factors has been explored by scholars at both local ∘ 39 N and regional scales since the 1980s. However, the mech- anisms of the vegetative response to climate change are ∘ 38 N uncertain [31], and the results from previous studies have varied due to different vegetation characteristics, regions, 37∘ and study methods. For example, Schultz and Halpert found N 150 km that the global-scale NDVI data are not highly correlated 36∘ with precipitation, although they did not correlate the data N with temperature [32]. Liu et al. reported that vegetation coverage in the arid, western regions of northeastern Asia Elevation (m) High: 2788 exhibits a strongly positive correlation with precipitation, Meteorological stations Rivers whilespatialchangesintheNDVI,whichareinfluenced Low: 607 bytemperatureintheregion,arelesspronounced[33].He et al. examined the seasonal and inter-annual relationships Figure 1: Location of the study area. between vegetation and climate data from 1985 to 2007 over Canadian ecosystems. Their results suggested that the relationship between the NDVI and temperature is stronger developed by the image processing and archiving than that with precipitation [34]. Clearly, the key climate center, VITO, Belgium (http://www.vgt.vito.be/): factors impacting the NDVI vary by region. Therefore, it is = × 0.004 − 0.1, essential to conduct regional-scale research. NDVI DN (1) The northern Loess Plateau (Figure 1), which is located in themiddlereachoftheYellowRiver,hasasemiaridclimate where DN is the digital count number used for with an annual mean precipitation of 300 to 500 mm and an storage. ∘ annual mean temperature of 6–9 C. This region is subject to (2) Monthly precipitation and temperature from 22 soil erosion; numerous soil conservation measures have been weather stations were obtained from the China Mete- implemented. Moreover, it is the largest coal mining region in orological Administration for the period of 1957 to China, with coal reserves accounting for 60% of the national 2012 and has been carried out quality control. A total [35]. Both erosion and mining, which can be linked to fewmissingdatawereinterpolatedbasedonvalues human activities, have seriously altered surface conditions. from neighboring stations over the same period. Therefore, due to the combined impacts of climate change The IDW (Inverse Distance Weighted) interpolation and human activities, it has become necessary to study the method was used to interpret the spatial distribution vegetation condition changes in this area in recent years. of monthly and annual precipitation and temperature. BasedontheSPOT-VEGETATIONDN,precipitation,and (3) Two land-use maps (i.e., from 1997 and 2010) were temperature data, this study investigated the spatial and tem- used in this study. They were produced from Landsat poral changes of the NDVI, precipitation and temperature Thematic Mapper (TM) images using the visual inter- over the past 15 years. We also explored the relationship pretation method. During the interpretation process, between the NDVI and climate factors. Finally, based on the practical field survey was considered, and land- two land-use maps, we analyzed the possible influence of use styles were classified into multiple categories, human activities on the NDVI. This information can be useful including farmland, forestland, grassland, water bod- when assessing the effects of ecological construction and for ies, urban and built-up land, and unused land. providing a scientific basis for corresponding policies. ∘ ∘ ∘ We also selected Heidaigou (111.22 E–111.33 E, 39.72 N– ∘ ∘ ∘ ∘ 39.82 N) and Daliuta-Huojitu (110.05 E–110.12 E, 39.17 N– ∘ 2. Data and Methods 39.37 N), which represent the largest opencast mine and well 2.1. Dataset. The data used in this study were as follows: mine bases in this area, respectively, to analyze their temporal NDVI variations as compared with those for the entire study (1)NDVIdatafrom1998to2012wereobtainedfrom area. SPOT-4 VGT-DN. The SPOT-4 satellite was launched in March 1998, having 10-day composite temporal 2 2.2. Methods resolution and 1-km spatial resolution. The stretched values range from 0 to 255. The true NDVI values 2.2.1. Monthly and Annual NDVI. The maximum value com- were restored with the following equation, which was positing (MVC) procedure, as described by Holben [36], was Advances in Meteorology 3 used to merge NDVI values from 10 consecutive days to period were 0.36 and 0.38, respectively. The greatest NDVI produce NDVI values for each month and year. The MVC values were located
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