2014 Third International Workshop on Earth Observation and Remote Sensing Applications
Spatial and Temporal Change of Vegetation in Growing Seasons in Hebei Province Based on SPOTVGT NDVI Sha Zhang, Jinguo Yuan* Hebei Key Laboratory of Environmental Change and Ecological Construction College of Resource and Environmental Sciences Hebei Normal University Shijiazhuang, China *Corresponding author:
[email protected]
Abstract—SPOT-VGT NDVI datasets were used to analyze spatial and temporal changes of NDVI in vegetation growing seasons from 1998 to 2012 in Hebei Province, China. Vegetation types were come from MODIS product MCD12Q1 in 2004. The slope of liner regression equation was used to analyze the change trend of NDVI. The results showed that NDVIs in vegetation growing seasons showed a trend of fluctuating increase from 1998 to 2012. The lowest annual average NDVI in growing seasons was 0.49 in 1999, and the highest NDVI was 0.59 in 2012. The Pearson correlation coefficient (r) between annual average NDVI and mean precipitation was 0.893, the average NDVIs were related to precipitation more closely than to air temperature. The spatial distribution of NDVI in Hebei Province presented a trend of decrease from middle area to both sides. There were two areas with high NDVI values and two areas with low NDVI values. The highest average NDVI value 0.77 appeared in the southeast of Yanshan Mountains, especially near Wuling Mountain. The lowest NDVI value 0.01 appeared in the eastern coast of Hebei Province. Broadleaf forest had the highest average NDVI value 0.68 in growing seasons, followed by shrubland with NDVI value 0.64. The lowest NDVI 0.13 was in sparsely vegetated land or wetlands. The area of NDVI decrease in vegetation growing season accounted for 9.16%, while that of NDVI increase accounted for 90.84%, indicating that NDVI showed an increasing trend from 1998 to 2012. The areas with rapid NDVI increase were distributed in farming land of North China Plain, Taihang and Yanshan Mountainous regions. The areas with rapid NDVI decrease were mainly distributed in Bashang Plateau region, Beijing-Tianjin-Tangshan cities belt, Shijiazhuang, Xingtai and Handan cities, indicating that vegetation in these regions presented a trend of deterioration. The reasons were also analyzed. Keywords-SPOT-VGT NDVI; Hebei seasons; spatial and temporal change
I.
Province;
growing
INTRODUCTION
Vegetation is a main part of terrestrial ecosystem, and it plays an important role in global environmental change research [1]. In recent years, intense human activity aggravates global climate change, and vegetation change can reflect Supported by the Natural Science Foundation of Hebei Province (D2012205084) and Physical Geography Key Subject of Hebei Province.
978-1-4799-4184-1/14/$31.00 ©2014 IEEE
climate change to some extent. Therefore, vegetation change becomes a major research content of global environmental change [2, 3, 4, 5]. In the research of vegetation dynamic monitoring, remote sensing data is regarded as the most important data source to study the relationship between vegetation and climate, because it has the continuity in space and time, and it can realize realtime dynamic monitoring macroscopically [2, 6]. NDVI is one of the most effective parameters to monitor vegetation growth condition, vegetation cover change, biomass and ecosystem dynamically [7, 8], and it is widely used to study vegetation [9, 10]. SPOT VGT NDVI (S10) data were used to estimate forest fire carbon emission in Russian Federation and spatialtemporal variation of vegetation coverage in five provinces of Northwest China [11, 12]. Delbart used SPOT VGT NDVI and NOAA-AVHRR data to remove the snowmelt effect from phonological detection in boreal regions [13]. Tarnabsky made different scales of geostatistical analysis based on AVHRR, SPOT-VGT and MODIS global NDVI products [14]. Fensholt also compared GIMMS and MODIS global NDVI data [15]. Kross used NDVI data of different temporal resolution to study the effect on season onset dates and trends across Canadian broadleaf forests [16]. GIMMS NDVI data were used to analyze the changes of vegetation photosynthetic activity [17]. This paper analyzed the change of vegetation growth based on SPOT-VGT NDVI data from 1998 to 2012, which can provide the basis for the sustainable development and ecological environment protection in Hebei Province. II.
DATA AND METHODS
A. Study Area The study area is Hebei Province, located in North China Plain, shown in Fig.1. Hebei Province is divided into three natural geographic divisions, including Yanshan Mountainous region, Bashang Plateau region and North China Plain region. The study area belongs to temperate continental monsoon climate. Most areas has four distinct seasons. Two rainy areas with generally higher than 600 mm annual mean precipitation
2014 Third International Workshop on Earth Observation and Remote Sensing Applications are situated on windward slope of south of Yanshan Mountains and east of Taihang Mountains [18]. The annual mean precipitation in Bashang Plateau region is generally less than 400 mm.
represent vegetation types in the study area, because vegetation growth was better in 2004. This product is classified by International Geosphere Biosphere Programme (IGBP) globe vegetation classification scheme (https://lpdaac.usgs.gov/products/modis_products_table/mcd12 q1). The vegetation classified data is merged in this paper, shown in Table Ⅰ. The images are transformed into Albers equal area conic projection using MODIS Reprojection Tool (MRT), with 1 km resolution. We also use 1: 1000000 scale vegetation map of China in 2001 provided by “Environmental & Ecological Science Data Center for West China, National Natural Science Foundation of China” （http://westdc.westgis.ac.cn）. TABLE I.
Fig. 1.
Location of the study area.
B. Data Sources and Processing 1) Remote sensing data The VEGETATION (VGT) is a sensor on board the SPOT4 and SPOT-5 satellites. It is designed to study regional and global scale vegetation cover. It has four spectral bands: blue (0.43-0.47μm), red (0.61-0.68μm), near infrared (0.780.89μm), and shortwave infrared (1.58-1.75μm). SPOT VGT NDVI products are got through Maximum Value Composite (MVC) method to remove the influence of cloud to the maximum extent. These data are widely used to study vegetation [3, 7, 9, 12, 23, 24]. 10-day, 1-km resolution VGT NDVI data from 1998 to 2012 are downloaded (http://free.vgt.vito.be/).The data are clipped using ArcGIS10.0 software to get 10-day NDVI images of the study area .Vegetation growing seasons in Hebei Province are mainly from April to September [19], so NDVI images from April to September in each year are selected to make this study. The MVC method is used to process 10-day NDVI data to get monthly NDVI data, and DN values of the images are changed into NDVI values using (1). NDVI=a × DN+b
(1)
where a is 0.004 and b is -0.1. 2) Meteorological data 10-day air temperature and precipitation data from April to September, 1998 to 2012 at 142 meteorological stations in Hebei Province are used. The monthly and yearly mean temperature in growing seasons is obtained by averaging the 10-day temperature. The monthly and yearly precipitation is the sum of 10-day precipitation. Inverse Distance Weighting (IDW) interpolation and exponential Kriging interpolation are used to obtain the distribution of temperature and precipitation, respectively. Root Mean Square Error (RMSE) of both interpolation methods are smaller by comparing many methods [20]. 3) Vegetation types data The MODIS vegetation type product (MCD12Q1, 500m, http://reverb.echo.nasa.gov/reverb/) in 2004 is selected to 978-1-4799-4184-1/14/$31.00 ©2014 IEEE
VEGETATION TYPES IN HEBEI PROVINCE
IGBP Evergreen Needleleaf forest Deciduous Needleleaf forest Evergreen Broadleaf forest Deciduous Broadleaf forest Mixed forest Closed shrublands Open shrublands Woody savannas Savannas Grasslands Permanent wetlands Water Croplands Cropland/Natural vegetation mosaic
Merged in this paper Needleleaf forest Broadleaf forest Shrublands Savannas and grasslands Water and permanent wetlands Croplands
Urban and built-up
Urban and built-up
Barren or sparsely vegetated
Barren or sparsely vegetated
C. Method 1) Trend analysis based on linear regression This method can be used to simulate the change trends of each grid and is commonly used to analyze vegetation change trend of long time series [21, 1]. This paper uses linear regression method to analyze vegetation change trend from 1998 to 2012 by calculating the average NDVI values of each pixel in 15 years. The change trend of NDVI can be analyzed by calculating the slope of regression equation (SLOPE). The SLOPE is usually calculated by least-squares method, shown in (2) [9]. If SLOPE>0, then NDVI shows an increasing trend; If SLOPE=0, then NDVI does not change; If SLOPE
