Integration of remote sensing with GIS for grassland ...

Integration of remote sensing with GIS for grassland snow cover monitoring and snow disaster evaluating in Tibet Maofang Gao1, Sanchao Liu2, Zhihao Qin1,3,*, Jianjun Qiu1, Bin Xu1, Wenjuan Li1, Xiuchun Yang1, Jingjing Li3 1

Institute of Agro-Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China. 2 National Disaster Reduction Center, Ministry of Civil Affairs of China, Beijing 100053, China. 3 International Institute for Earth System Science, Nanjing University, Nanjing 210093, China. * Corresponding to Dr. Z. Qin, Email: [email protected] ABSTRACT

As an important pasture region, Tibet has about 82 million hectares of natural grassland, accounting for 68.11% of its total territory. Above 90% of Tibetan grassland belongs to the types of alpine meadow steppe and alpine steppe with highly nutritious forage plant. Animal husbandry constitutes a major part of agricultural economy in Tibet. It is believed that snow disaster become a significant threat to the development of animal husbandry in Tibet. The disaster often happens in winter and spring as a result of complicated mountainous features and mutable climatic conditions. Statistics indicates that, on average, there is a slight snow disaster for each 3-year, a medium disaster within 5 to 6 years, and a big disaster in 8-10 years. Large numbers of animals died of hungry and cold during the disaster period. Huge economic loss due to the disaster had brought giant difficulties to local herdsmen in Tibet. Accurate and timely monitoring of snow cover for snow disaster evaluating is very important to provide the required information for decision-making in anti-disaster campaigns. Remote sensing has many advantages in snow disaster monitoring hence been extensively applied as the main approach for snow cover monitoring. In this paper we present our study of snow cover monitoring and snow disaster evaluating in Tibet. An applicable approach has been developed in the study for the monitoring and evaluating. The approach is based on the normalize difference of snow index (NDSI) and DEM retrieved from MODIS and GIS data. Using the approach, we analyzed the snowstorm occurring in mid-March 2007 in southern Tibet. Results from our analysis indicated that the new approach is able to provide an accurate estimate of snow cover area and snow depth in southern Tibet. Thus we may conclude that the approach can be used as an efficient alternative for snow cover monitoring and snow disaster evaluating in Tibet. Key words: Remote sensing, GIS, Snow storm, Disaster monitoring, Damage assessment

1. INTRODUCTION Snow disaster is one of the most important natural disasters in Tibet, which has become more and more frequent in recent 20 years. There were only three big snow storms from 1961 to 1984, while serious snow disaster happened every two years after 1985 (wang et al. 2003). The most serious snow disaster happened in 1994. More than 3 million domestic animals died of cold and hungry, causing an economic loss of 500 million yuan (about 70 million dollars). It is difficult to monitor the snow disaster because heavy snowfall usually blocks all the roads and there is no enough ground observation in mountainous area of Tibet. Remote sensing has the advantages of probing large area simultaneously, having multiple bands, and possessing revisit measurements. GIS can provide spatial analysis for snow cover, land use, DEM and other basic data which can help us to know more information on the correlation of snow disaster and ground conditions. Snow cover extent is one of the most important factors for the dynamic studies and prevention of snow disasters. NASA has developed global snow cover product using a normalized difference snow index (NDSI) from Moderate Resolution Imaging Spectroradiometer (MODIS) data after the launch of Terra satellite (Hall et al. 2002). Using Landsat 30-m observations as “ground truth”, Salomonson and Appel, (2004) estimated the fraction of snow within a 500m MODIS pixel to enhance the use of the NDSI approach in monitoring snow cover. Validation of daily MODIS snow cover maps of the Upper Rio Grande River Basin showed that agreement between the MODIS and NOHRSC snow maps was high with an overall agreement of 86% over the 2000-2001 snow season (Klein and Barnett, 2003). Algorithms had also been Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VIII, edited by Ulrich Michel, Daniel L. Civco, Manfred Ehlers, Hermann J. Kaufmann, Proc. of SPIE Vol. 7110, 71100O · © 2008 SPIE · CCC code: 0277-786X/08/$18 · doi: 10.1117/12.800047 Proc. of SPIE Vol. 7110 71100O-1 2008 SPIE Digital Library -- Subscriber Archive Copy

developed for snow cover monitoring using multiple dataset including AWiFS of RESOURCESAT-1, Millimeter-wave Imaging Radiometer (MIR) data, VEGETATION sensor data on SPOT 4 and ENVISAT optical data in high elevation area such as Himalayan region and Alpine regions (Tait et al. 1999, Kulkarni et al. 2006, Pepe et al. 2005, Xiao et al. 2002, Liang. et al, 2007). Digital Elevation Model (DEM) plays a significant role in the mapping of snow cover especially in high mountain regions (Essery, 2003, Haefner et al. 1997). The existing studies on snow remote sensing mainly focus on snow mapping algorithms and their application in different regions of the world. However, snow disaster evaluation is more complicated than snow cover mapping since not all regions with snow cover will have disasters. It is the result of multiple factors for the occurrence of snow disaster, including land use, topography, snow depth, ground temperature, seasons of snowfall, conditions of animals, and so on. Extensively analysis on the correlation of these factors should be taken for the sake of better evaluation and prevention of snow disasters which has not been discussed intensively before. The purpose of this paper is to develop an applicable algorithm integrating remote sensing with GIS for snow disaster evaluation. MODIS normalized difference snow index (NDSI), along with threshold tests, is used for snow cover mapping. Analysis on land use and DEM are also carried out using GIS for systematic understanding of Tibet. NASA MODIS cloud product is used for the elimination of cloud and estimation of snow cover probability under cloud. This approach can be used for the monitoring and evaluation of snow disaster in Tibet and other similar regions.

2. METHODOLOGY 2.1 The Study Region Tibet is administratively an important ethnic Autonomous Region locating in southwest China and geographically a grassland plateau with an area of about 1.2 million square kilometers (Figure 1). Snow cover monitoring on Tibetan Plateau plays a significant role in the global climate change study because it is the highest region in the world and has large area of glacier which will greatly impact the world’s climate. Annual average temperature is 8°C and lowest temperature from 1971 to 2000 is -37.6°C in Jan 16, 1981. Annual precipitation variation is regionally and seasonally quite great. The precipitation declines gradually from 5000mm in southeastern part to 50mm in westnorthern part. 90% of rainfall concentrates within five months from May to September. Yaluzangbo River flows across the south Tibetan plateau and forms the world’s deepest gorge. The population of Tibet is about 2.8 million in 2006. Majority of people here lived on livestock breeding which relies on climatic conditions and easily been attacked by frequent natural disasters.

Yaiigtze River

Tibet

Figure 1 Geographic location of Tibet in China

2.2 Approaches for snow cover monitoring Moderate Resolution Imaging Spectroradiometer (MODIS) data is used for the detection of snow cover area. It is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra and Aqua MODIS are viewing the entire

Proc. of SPIE Vol. 7110 71100O-2

Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands within the wavelength of 0.415 - 14.235µm. Snow surface has the feature of high reflectance in Band 4(0.545-0.565µm) and relatively low reflectance in Band 6(1.628-1.652µm). The following equation is generally used to calculate normalized difference snow index (NDSI). NDSI = (Band4-Band6) / (Band4+Band6)

(1)

Where Band4 and Band6 are at-satellite reflectance of MODIS band 4 and band 6 which can be get from DN number and header files as follows: Bandi = (DN – Offsets) * Scales

(2)

Where Bandi is reflectance of band 4 or band 6, DN is DN number from MODIS 1B data, Offsets and Scales can be obtained from the header of HDF files. Band 2(0.841-0.876µm) of MODIS data is used to eliminate the effect from cloud. Figure 2 shows the algorithm developed in the study for snow detection in Tibet. After the NDSI image was generated, we used the following criteria to identify whether a pixel is snow or not. The criteria used for the detection is as follows. A pixel will be classified as snow if the NDSI is ≥0.4 and reflectance in band 2 is >11%. However, if the MODIS band 4 reflectance is 10% Yes

No Not snow

NDSI≥ 0.4 No

Yes

Not snow Yes

Band2>11%

Snow

No

Not snow

Figure 2 Flow chart of snow cover detection

2.3 Estimation of snow cover under cloud MODIS snow mapping algorithm uses visible and near-infrared data to detect snow cover area. There is usually too much clouds in the sky during the snow storm period. It is difficult to get enough information for snow cover detection under cloudy conditions using MODIS data. For snow disaster evaluation, we can estimate the probabilities of snow cover under the cloud. MODIS atmosphere team has developed cloud product to classify the pixels to four categories: Confident Cloudy, Probably Cloudy, Probably Clear, and Confident Clear. For pixels of Confident Clear and Probably Clear, snow cover detection algorithm will be used. Pixels of Confident Cloudy and Probably Cloudy will be used to estimate the probabilities of snow cover. Cloud is one of the main preconditions of snow falling. Therefore, it is very reasonable to estimate snow cover probabilities using the cloud information. Regions with thick clouds are more likely to have snow disaster than other region under clear sky. For snow disaster estimation, we arbitrarily assumed that the probability of snow cover in Confident Cloudy pixels is 50% and 25% for Probably Cloudy pixels. Although this assumption needs further validation for snow cover study, it is quite significant for snow disaster evaluation. 2.4 Land use in Tibet Grassland is the major land use type with an area of 0.94 million km2 in Tibet (Figure 3). Widely distributed grassland has benefited the herdsmen from gerneration to gerneration and fed thousands of flocks and herds here. Grass and water is the most precious natural resources in Tibetan Plateau. There are more than 1500 lakes with a total area of about 15400 square kilometers but most of them are salty ones. Area of crop land is only 7366 square kilometers scattered in the south part. Forest and bush mainly distribute in the east part with comparatively low elevation and rich precipitation. There are 29076 km2 of desert and gobi in the north part which is adjacent to Takelamagan big desert. White spots on the


map is permanent glacier and snow. Around the permanent snow is bare land, which account for 13.5% of total area in Tibet.

Crop-land Grassland Glade r-sn ow

1 Deserhnd Lake-reservoir bare-land

city

Figure 3 Land use in Tibet

2.5 Digital Elevation Model (DEM) in Tibet Digital Elevation Model is very important for snow cover detection and snow disaster evaluation (Essery, 2003). Tibet locates on the main body of the Qinghai-Tibet Plateau which is called the “Roof of the World” and the “Third Pole of the Earth” (Figure 4). Average altitude in Tibet is about 4700 meters with world’s highest peak Everest 8848m in the south. Only one tenth of its total area is lower than 4000 meters, which distributes in the Yaluzangbo River valley and southeast part. The area with elevation between 4000-5000m is 0.6 million km2 that account for nearly 50% of Tibet. The other 39% has the altitude of 5000-6000m. Slope angle and aspect can be directly derived from DEM. About 20% of its total area has the slope angle above 10 degree, which mainly distribute on the high mountains. More than 4000 km2 have the slope higher than 30 degree.

r

Kanpur

0-200

Xining

-

r !. -

200-1000 1000-2000 2000-2500 2500-3000 3000-3500 3500-4000 4000-4500 4500-5000 5000-5500 5500-6000 6000-8000

Figure 4 Digital Elevation Model in Tibetan Plateau. The unit for the elevation in the legend is ‘meters’.

Topography and climate are main drivers for land use distribution. More than 90% of crop land, forest and bush distribute in the regions with elevation of 3000-5000m. Precipitation and temperature are usually more suitable for the growth of crop and trees than the higher places. All the lakes and reservoirs locate in the area with elevation of 4000-6000m and 96% of them distribute within the elevation of 4000-5000m. Almost all the desert and bare land locate on the high mountains of 4000-6000m. Summits of mountains higher than 5000m are covered by permanent glacier and


snow. Grassland is the most widely distributed land use type in Tibet which covered 95% of land lower than 3000m and 77% of land within the elevation of 3000-6000m. 2.6 Evaluation of snow disaster Snow disaster evaluation is carried out on the basis of study region introduction, snow cover detection, land use and digital elevation model analysis. The approach proposed in this study integrates remote sensing with GIS for snow disaster evaluation. Figure 5 shows the process for the whole evaluation. MODIS data is used for snow cover and cloud detection. A simple estimation method will be used to calculate the probability of snow cover under cloudy conditions. Combined with snow cover detection result and estimated snow cover probabilities from cloud detection, we can get maps of snow cover area for every single scene of MODIS data. With Terra and Aqua satellites we can get one or two images for one place everyday. Sometimes only one scene couldn’t cover the whole Tibet. So composite of multiple images is necessary for precise and comprehensive snow disaster evaluation. MODIS Data Snow Cover

Cloud Detection Estimated snow under cloud

Boundaries of Tibet Snow Cover In Tibet Landuse Data Snow Cover on grassland DEM Data Snow Disater evaluation Figure 5 Flow chart of snow disaster evaluation

GIS spatial analysis will be used in the further study of snow disaster. Snow cover area calculated from multiple images usually contains regions that we are not interest. A mask build from boundaries of Tibet will be applied to the snow cover estimation result. Snow cover maps in Tibet show the distribution of snow on all land use types. Summarize all the snow cover area for disaster evaluation will result in errors because no disaster will happen on bare land or permanent glacier and snow even they are covered by heavy snow. The most serious snow disaster usually happens on grassland as people here mainly lived on livestock. There are no solid buildings for them to avoid heavy snow and bitterly cold but only a simple tent. All animals eat and sleep outside in the field. No food will be available when snow storm happens because all the grass will be covered by snow. Snow disaster is not so significant in crop land, forest and bush, so only grassland is considered in the evaluation approach in this study. In order to get snow cover maps on grassland we convert the land use data from vector to raster with the same resolution to snow cover raster. In the new raster layer, value 1 will be given to grassland pixels and 0 for non-grassland pixels. Snow cover map will be multiplied by the binary grassland raster. Then we get the snow cover maps on grassland. Statistics of snow detection maps will show the variation of snow cover area in a sequence which will be a great help for the monitoring of snow disaster. Integrated analysis of snow cover and DEM using remote sensing and GIS will also be carried out. DEM image will be reclassified to 7 classes: 6000m. Snow cover statistics according to different elevation range will show us the relation between snow cover distribution and altitude which will be a great help for snow disaster evaluation.

3. RESULTS AND ANALYSIS 3.1 Landscape from satellite before the snow storm With its high altitude and complicate climate, Tibet is frequently attacked by snowstorm from mid-October to mid-April the next year. A heavy snowstorm happened in Tibet during March 10-14, 2007. From a sequence of MODIS data, we can detect the variation of snow cover area and evaluate the snow disaster. Pseudo color composite images can also be generated from MODIS visible and near infrared bands which can show the landscape in RGB color. Figure 6 shows the landscape in Tibet composite from band 4, band 5 and band 7 of MODIS data which was taken in March 8 2007 just before the big snow storm. From this picture we can see that it was a clear day and the main land use types are easily to distinguish. Dark blue points distribute in the center of the plateau are lakes and white color shows the cloud. Light blue area around the boundary and in the southeast part is permanent snow and glacier. It is difficult to see the landscape so clear in the following days because of the snowfall and thick cloud. So snow cover detection methodology was carried out for the evaluation.

Figure 6 Satellite image of Tibet in March 8 before the snow storm, it is composed from band 7, 5, and 4 of MODIS data with RGB colours in a resolution of 1000m

3.2 Snow cover variation and snow disaster evaluation According to the methodology in part 2, snow cover on grassland from March 8 to 15 in Tibet was calculated and shown in Figure 7. March 9 is not considered because it was also a clear day similar to the day before but the quality of MODIS image in March 8 is much better than that in March 9. Pixels were classified to seven types after the snow cover detection process. “Snow cover from NDSI”, “Confident Cloudy”, and “Probably Cloudy” are used for further statistics of snow disaster. Green colour shows the regions that are clear and not covered by snow. Only grassland was considered for the evaluation of snow disaster, so land use types that were not grassland will be marked using yellow colour. Blue circles show the centre of heavy cloud and red ones show the centre and scope of snow disaster. From the following figures we can also see that there is a type marked as “No data”. That is because only Terra MODIS data was used in this study and there are not enough images for the whole study region.


A (March 8)

B (March 10)

n C (March 11)


D (March 12)

E (March 13)

F (March 14)


G (March 15) Out of study area Snow Cover from NDSI Confident Cloudy Probably Cloudy Clear and no snow Not grassland No data

Figure 7 Snow cover and snow disaster evaluation of Tibet in Mid- March, figure A-H show the snow cover variation of March 8, 10, 11, 12, 13, 14 and 15

Spatial and temporal variation of snow cover can be clearly seen in a sequence of images in Figure 7. Generally speaking, estimated snow cover area shows an obvious increase from March 8 to March 15. Statistics of snow cover detection result are shown in Table 1. From Figure 7 and Table 1 we can analysis the development of snow storm and evaluate the intensity of this disaster. Figure 7-A shows the situation in March 8 before the snow fall. More than 70% of grassland was clear and not covered by snow. Proportion of Confident Cloudy and Probably Cloudy is 15% and 7%, areas with cloud mainly distribute in southeast part. Only 6.23% is covered by snow and it is acceptable for the living of herdsmen and their animals. Cloud began to accumulate in March 10 (Figure 7-B) near the west boundary and east part, which can be seen from three blue circles. In March 11, the situation is similar but small disaster centre appear in the southwest part. Thick cloud continued to accumulate throughout the west and north Tibet in March 12. More than 55% of grassland was covered by cloud during March 10-12. A heavy snow storm was brewing. A blizzard attacked west and south Tibet in the late afternoon of March 12. Average thickness of snow on the road which connects Xinjiang and Tibet was 50 cm. Snow depth along the road near Pulan County was reach to 150cm, which completely cut the transportation to that county. Electricity and electric communication were also been interrupted. Large number of animals died of cold and hungry and some were even buried by thick snow. Heavy snow storm also result in big difficulties for the human living here. The temperature fell to below -30°C and many herdsmen caught colds but not enough doctors were available to help them. The serious snow disaster continued to be aggravated in the following three days. Red circles in Figure 7 E-G shows the distribution of snow disaster centre. From Table 1 we can know that more than 70% of Tibet was covered by cloud and the estimated area of snow disaster was about 0.4 million km2 during March 13-15.


Table 1 Statistics of snow cover detection and snow disaster evaluation

Date Snow Cover from NDSI Confident Cloudy Probably Cloudy Clear and no Snow Not Grassland No Data Estimated Snow disaster Area

Unit: km2

8 58644 144958 66082 671060 267696 0

10 20025 517671 26697 373020 263889 7138

11 23440 520246 89 362397 259682 42586

12 28670 464472 23391 294875 230885 166147

13 26000 695795 0 180569 258851 47225

14 6463 798274 0 109861 262186 31656

15 64365 672087 62259 128610 256207 24912

147644

285535

283585

266754

373898

405600

415973

3.3 Snow cover with different elevation Digital Elevation Model (DEM) is essential for the evaluation of snow disaster. It can be used for the calibration of sun zenith angle which can improve the precision of snow cover detection. It can also be used to generate topography parameters and help to calculate snow depth. On the basis of snow disaster evaluation in the previous part, statistics of the estimated disaster area in different altitude ranges were carried out (Table 2). Table 2 Statistics of snow disaster area in different altitude ranges

Altitude Ranges (meter) 0-9000 0-1000 1000-2000 2000-3000 3000-4000 4000-5000 5000-6000 6000-9000

Area (km2) 474605 6871 9291 11859 24252 248487 172187 1658

Percentile 1.45 1.96 2.50 5.11 52.36 36.28 0.35

Table 2 shows the statistics of snow disaster area according to different elevation ranges within March 13-15. 88.64% of snow disaster happens in the area with elevation between 4000-6000m. Within the elevation of 4000-5000m, about 0.25 million km2 was under snow disaster, which account for 41% of its total area.

4. CONCLUSIONS Snow disaster evaluation in Tibet was generally done through integrated analysis of snow cover from remote sensing, land use data, digital elevation model (DEM) and basic GIS data. An applicable approach has been proposed in the study to evaluate snow disaster. Snow and cloud cover area are critical factors determining the level of the disaster. Analysis on the development of snow cover and cloud is essential for the monitoring and evaluation. Land use data is used to study the basic natural conditions of Tibet and find the pixels that are grassland. DEM is a great help for the calibration of snow cover detection result and analysis of snow disaster in different elevation. Integrated analysis with basic GIS data such as roads and airports will give us valuable suggestions for the preventing of snow disasters. Snow storm happened in Mid-March, 2007 was carefully studied according to the above methodology. Large area of grassland was covered by thick snow during March 13-15, which caused great economic loss to the local herdsmen. The result shows that our approach is able to monitor and evaluate the snow storm happened in Tibet. This approach is also applicable in other places after some revision of parameters. Heavy snow storm haven’t been seen in 50 years attacked 7 provinces of Central China in January, 2008, which has caused great destroy on basic facilities including power transmission, transportation and communication. The impact of this snow disaster has drawn the attention of the whole China. It is quite significant for the study of snow cover and snow disaster evaluation in China.


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ACKNOWLEDGEMENTS This study is supported by National Natural Science Foundation project (Grant No: 30571078), China MOST Public Program project (Grant No: 2005DIA3J032, 2006AA10Z242) and Special Basic Research Fund for Central Public Scientific Research Institutes (2007-3)
