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Monitoring and Evaluation of Grassland Desertification in Litang County Using Multi-Temporal Remote Sensing Images

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Proceedings of 14th Youth Conference on Communication Monitoring and Evaluation of Grassland Desertification in Litang County Using Multi-temporal Remote Sensing Images DAN Shang-ming1, 2，XU Hui-xi3，DAN Bo4，HE Fei5，SHI Cheng-cang6， REN Guo-ye6 1.Key Laboratory of Atmosphere Sounding, China Meteorological Administration，Chengdu 610225, P.R.China; 2.Sichuan Province Agrimeteorological Center, Chengdu 610072, P.R.China; 3. Institute of Engineering Surveying, Sichuan College of Architectural Technology, Deyang 618000, P.R.China; 4.Sichuan Provincial Meteorological Observatory, Chengdu 610072, P.R.China; 5.Chengdu University of Information Technology，Chengdu 610225, P.R.China. 6.Institute of Remote Sensing Application，Sichuan Academy Agricultural Sciences, Chengdu 610066,P.R.China; [email protected] , [email protected] Using four Landsat TM & ETM+ images which obtained in 1989, 1994, 2000 and 2005, the nor- Abstract: malized difference vegetation index (NDVI) was calculated on the basis of the geometry correction, radio- metric calibration and atmosphere correction, and the assimilation of the NDVI images was made by estab- lishing the least squares equation of linear regression, and extracted the vegetation coverage from the NDVI and converted it into the desertification index (DI) based on the binary model, the classifiable research was made on the desertification of grassland about 103 km2 near the Litang county. The result demonstrates that: st (1) Desertification in Litang County has been deteriorating in the past 16 years (1989-2005). The 1 stage nd (1989-1994) saw decreased desertification area, the 2 stage (1994-2000) observed radical deterioration, and rd the 3 stage (2000-2005) witnessed slack in this regard. (2) Tendency of desertification has nothing to do with precipitation and wind speed. (3) Human activities e.g. overload over grassland and disorder explorations etc are considered the main causes in exacerbating desertification. (4) Expansion desertification area slowed down during the 3rd stage (2000-2005), which ecological restoration projects in China have brought about marked effects. Keywords: Landsat; Multi-temporal RS images; Litang County; Grassland desertification; Driving force county between 29º54.2´～30º0.3´ N and 100º14.4´～ 1 Introduction to the study area 100º20.1´ E, covering area of 103 km2. The region fea- Litang County, on the southeast verge of a trans- ture is hill-type plateau with altitude of 3900～4500m, versal mountain range east of the Qinghai-Tibet Plateau, belonging to the category of high-altitude meadow is located in the west of Sichuan Province and in the grassland and swamp meadow grassland. Litang City, southern hinterland of Garze Tibetan Autonomous Pre- located northwest of ROIs, is 4,014m above sea level. fecture. In recent years, little work and few reports con- The NO.318 state road crosses through the city, while the tributed to grassland desertification in hinterland of West NO.217 provincial road is connected to the NO.318 state [1] Sichuan Plateau, besides Ren Guo-ye et al and Ren road from southeast to northwest. The Wuliang River An-cai et al [2] adopting two remote sensing images goes through in the southwest. Save for river, road, and marked out the grave degree of grassland desertification township in ROIs, large amounts of light coloured deser- and temporal-spatial characteristics in Litang.This paper tification plaques are obviously visible (Figure1.). monitored and evaluated tendency of grassland desertifi- 2 Data and methodology cation in Litang County from 1989~2005, adopting four periods of Landsat satellite remote sensing images. 2.1 Remote sensing data and pretreatment The study area was spotted somewhere close to the According to comprehensive consideration, this re- This research work was financial supported by Key Laboratory of Atmosphere Sounding, China Meteoro- search adopted four periods of Landsat satellite remote logical Administration (No.KLAS200703) and the key sensing images. Its spatial resolution is 30 meter, and its project of Sichuan Environmental Protection Bureau quantity is good. Metadata of images is in Table 1. (No.2008HBY002), P.R.China. 209 978-1-935068-01-3 © 2009 SciRes. Proceedings of 14th Youth Conference on Communication Firstly, make radiometric calibration for images in Methods to assimilate data are the following: Tak- four periods according to the equation in the reference [3]. ing the image obtained in 2000 with comparatively big Then make atmospheric correction for them according to NDVI dynamic scope as its main image, NDVI images COST model in the reference [4].Taking the remote sens- of other time phases were respectively corrected as fol- ing images obtained in 2005 with nice quality and clear lows: (1) Identifying the corresponding spots without objects as its base image, calibrate images of another basic change of geographical object in both the primary three time phases (1989, 1994, 2000) onto the base image and secondary images through visual interpretation and by means of polynomial. Control points were about 35～ man-machine interaction and gaining a number of sample 40 points of identical object displaying small spatial di- data sets x and y (data scattered between NDVI high and mension, obvious characteristic, easy identification, and low values areas);(2)Adopting the least squares equation even distribution. Root-Mean-Square Error (RMS) of to gain linear regression parameters a and b [5]; (3) Cal- control points obtained in 1989, 1994, and 2000 were 0.9, culating assimilation via the following equation: 0.67, and 0.68 pixel respectively in calibrating onto im- y=ax+b （2） age obtained in 2005. In the above equations, x and y respectively stand for the NDVI before and after assimilation. 2.3 Calculating vegetation coverage The Litang City fc, i.e. vegetation coverage of mixed pixels in re- mote sensing images can be gained as follows [6]: The NO. 318 State Road A2 fc=(NDVI－NDVIsoil)/(NDVIveg－NDVIsoil) （3） A1 In the above equation, NDVIsoil and NDVIveg respec- tively stand for remote sensing pixel under none vegeta- Racetrack tion and full vegetation coverage. Typical image pixel approach was adopted to determine NDVIsoil: Firstly, find a fairly large desertification plaque in the image, inside The NO. 217 Provincial Road of which there are image pixel without vegetation and water distribution, and then detect the pixel with Mini- mum NDVI inside of the plaque. After doing experi- The Wuliang River ments again and again, the value of NDVIsoil is 0.010583.Regarded maximum of NDVI in 2000 as NDVIveg and gaining 0.9. Figure 1. Research range and Regions of Interest (A1 & A2) 2.4 Desertifi cation classification & calculating Table 1. Metadata of images area NO. Satellite Sensor Date [7] 1 Landsat4 TM 1989-01-25 DI (Desertification Index) acted as an indicator of 2 Landsat5 TM 1994-01-15 assessing classification of desertification in Litang 3 Landsat7 ETM+ 2000-10-06 County: 4 Landsat5 TM 2005-10-12 DI=1－f （4） c 2.2 NDVI Calculation & assimilation Desertification falls into five categories. They are described in Table 2.Drawing on results of Chen Bao-rui Using: NDVI= (NIR－RED)/ (NIR+RED) （1） et al [7] and Wang Tao et al [8] of calculation results about In the above equation, NIR represents near infrared Litang, critical value of desertification can be determined, band, RED represents red band, i.e. band 4 and band 3 of and the areas of different category were gained according TM/ETM+ sensor respectively. to pixels of different category. 978-1-935068-01-3 © 2009 SciRes. 210 Proceedings of 14th Youth Conference on Communication Table 2. Classification of desertification extent NO. Desertification extent Classification criterion State description Signifies the extremely grave stage of desertification in the latter stage as the 1 Serious desertification DI＞86% primitive land surface was completely destructed. Denotes the intense development stage of desertification as primitive land- 2 Severe desertification 76%<DI≤86% scape. Stands for obvious degradation of grassland, in a development stage of deser- 3 Moderate desertification 66%<DI≤76% tification is basically destructed. Represents the preliminary stage of desertification as the vast majority of land 4 Mild desertification 46%<DI≤66% surfaces conserve primitive grassland. 5 Non-desertification DI≤46% Surface maintains the status of native grassland. In order to mitigate errors of geographical object study area respectively, and that in 1989 accounted for (such as river, river shoal) and man-made constructions 1.53%, 4.36%, 6.84% and 14.26%. (township and road) in judging desertification grassland 3.2 Analysis of tendency of desertification and analyzing tendency, two ROIs, i.e. A1 and A2 (Fig- ure 1.) were established by visual interpretation and Divided into three phases to calculate the rate of desertification area of R (%) and annual rate of increase man-machine interaction to gain area each category within ROIs, and then obtain percentage of desertifica- of Rn (%/a), and calculation results shown in Table 4. In tion area in the total area studied. A1 and A2 account for the first phase (1989～1994), all types of desertification 56.7% of the total area studied. Desertification grassland significantly reduced. The area of serious desertifica- outside of A1 and A2 was barely up to 1% of the total tion(DI＞0.86), above severe desertification(DI＞0.76), desertification area. As a result, misinterpretation of de- above moderate desertification(DI＞0.66), and above sertification outside of ROIs caused little error. moderate desertification(DI＞0.46) decreased 32.56%, 27.94%,18.67% and 16.43% respectively, and annual 3 Research results increasing rate was -7.58% 、 -6.34% 、 -4.05% and 3.1 Desertification situation -3.52% respectively. In the second phase(1994～2000), desertification situation sharply downturn.The area of Area and proportion of different grades of grassland serious desertification(DI＞0.86), above severe deserti- desertification in research area showed in Table 3.
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