MODIS ,,, 2005, %, Vol. 30,No. 7 July,2008 RESOURCES SCIENCE : (2008) : ; :

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1 RESOURCES SCIENCE Vol. 30,No. 7 July,2008 : (2008) MODIS 1,2, 1,2, 3,4 (1., ; 2., ; 3., ; 4., ) :,,,, MOD09Q1 1 2,MOD09A1 6 MOD11A2,, NDVI NDSI LST p131r47 p131r48 2 ETM,, 2003, : ; ;MODIS; 1,,,,,,,,,,, %,,,, %, %,, %,,,,, NDVI [1 4 ],,,,, NDVI 1987, Hunt, E. R. Jr ( LWCI), LWCI NDVI,, NDVI [5], Gao Jurgens ( NDWI) [6] NDVI NDWI 2005, TsΠNDVI,, TsΠNDVI, [7 9],, ;,, : ; : :,,,, GIS RS E2mail :lvtt. ac. cn :, E2mail : ac. cn

2 : MODIS 1077,,,, MOD09Q1 1 2 MOD09A1 6 MOD11A2, m 2003 ETM , Everest Indian , , 5114, 250m 10 4 km 2, 200m %, 1 000m, 90 % 316 %,, : ( ), [11 ] 5, ( 1) 5 10, ;11 2, ;3 NDVI (, 1), 4 NDVI,, NDSI (,, 2) % % %( : 1 ), 3 % 1 3 ( Tucher et al.,1985 ;Loveland,Merchant,Ohlen,Brown,1991),,,,,,,, 311,, LPDAAC 2003 MOD09Q1 1 2,MOD09A1 6 MOD11A2,MOD09Q m, 1, 620nm 670nm, 2, 841nm 876nm MOD09A1, 6, 1 628nm 1 652nm [10 ] MOD11A2 8 1km Table 1 Thailand main forest types and spatial distribution (m) :

3 ( ) Fig11 Spectral reflectance of typical land covers, NDSI : NIR (MODIS2 ) ; RED NDVI (MODIS1 ) NDVI,,,,, NDSI NDVI NDVI,,, ( LST),, NDVI :, (1) NDVI NDSI ( 1) MODIS2 ( ) MODIS1 ( ) 1 MODIS6 ( ),,, 2, MODIS2 6, NDVI, :,, NDVI = NIR - RED 6 2 (1) NIR + RED MODIS6 MODIS2 2 MODIS 6 2 Fig12 Spectral reflectance of MODIS channel 6 and channel 2

4 : MODIS 1079,, 6 2 (Wataru Takeuchi,2004) - NDSI 0, NDSI NDVI NDSI,, NDVI [4 ] (2) LST,, 2003 MOD11A2 MOD09Q1,,2003 ( 3), NDVI ( 4) LS T = DEM r = 0173 (3) LS T = NDVI r = 0180 (4), 3 NDVI LST N NDVI2LSI ( 6), NDVI LST, N NDVI2LSI NDVI LST,, LST NDVI, 5 N LST = LS T - MIN (LS T) MAX ( LS T) - MIN (LS T) (5) N NDVI2LSI = ( NDVI - N LST )Π( N LST + NDVI) (6) LST 1km, NDVI NDSI, NDVI NDSI N NDVI2LST :, SWIR - NIR NDSI = (2), 3 SWIR + NIR NDVI 014 4a : SWIR (MODIS6) ; NIR NDVI 014 (MODIS2) ; 4b NDSI 0, NDSI > 0, ( Royal Forest, Department) , NDSI , NDSI, : NDSI,, 46 NDVI 014, 36 NDVI 0 5, 2003 ETM NDSI, NDSI c NDSI NDSI NDVI, NDVI, 3 Fig13 Flow diagram about the extraction of forest

5 NDSI NDVI Fig14 The process to derive forest using NDSI and NDVI

6 : MODIS Fig15 The distribution of forest lands NDVI , 4d NDSI NDVI 2003,, ( 5b) ETM (p131r47 p131r48) 2003, 30 % 25 % km km km 2, DEM b, km 2, km km km km 2, %, %, 600m 90 %,, NDSI DEM 600, N NDVI2LSI, %, 6, ETM N NDVI2LSI 0175, NDSI, N NDVI2LSI, ETM ETM 5a, , 412 MODIS, 2003

7 ,, ( References) : 6 Fig16 Scatter plot of MODIS2derived forest area and statistic data , % 5, NDVI NDSI N NDVI2LST DEM,, (1), NDVI,, (2) NDVI NDSI, NDSI , NDVI 2000,, NDVI NDSI,,,, (3) MODIS2LST, N NDVI2LST (4), 2003, , 316 % NDVI NDSI LST, [ 1 ] REED B C, VANDERZEE D, VANDERZEE D. Measuring phonological variability from satellite imagery [ J ]. Journal of Vegetation Science, 1994, 5 : [ 2 ] DeFries R, Fung I. Mapping the land surface for global atmosphere2 biosphere models : toward continuous distributions of vegetationπs functional properties [ J ]. Journal of Geophysical Research, 1995, 100 : [ 3 ] YASUOKA Y, SUGITA M, YAMAGATA Y, et al. Scaling between NOAA AVHRR data and Landsat TM data for monitoring wetland [ EBΠOL ]. http :ΠΠwww. aars2acrs. orgπacrsπproceedingπacrs1996π PapersΠGLE9624. htm, [ 4 ] Bruzzone L, Cossu R, Vernazza G. Combining parametric and non2 parametric algorithms for a partially unsupervised classification of multitemporal remote2sensing images[j ]. Information Fusion, 2002, 3 (4) : [ 5 ] H. P. SATO, TATEISHI. Land cover classification in SE Asia using near and short wave infrared bands[j ]. Remote Sensing, 2004, 25 (14) : [ 6 ] GAO B C. NDWI2a normalized difference water index for remote sensing of vegetation liquid water from space[j ]. Environment, 1996, 58 (3) : Remote Sensing of [ 7 ],,. NDVI [J ]., 2000,55 : [LI Xiao2bing, WANG Ying, LI Ke2rang. NDVI sensitivity to seasonal and interannual rainfall variations in Northern China [J ]. Acta Geographica Sinica, 2000,55 : ] [ 8 ],,,. NDVI2T - s [J ]., 2005, 29 (6) : [ YU Feng, LI Xiao2Bing, WANG Hong, et al. Land cover classification in China based on the NDVI2T - s feature space [J ]. Journal of Plant Ecology, 2005, 29 (6) : ] [ 9 ] Surat Lertlum. Vegetation classification methodology from multi2 resolution satellite data using a combination of optical and thermal bands[d]. Asian Institute of Technology (AIT), [10 ] Land Processes Distributed Active Archive Center ( LPDAAC), Global land cover characterization database [ EBΠOL ]. http :ΠΠ edcsns17. cr. usgs. govπglccπglobdoc2-0. html, [11 ] Hall CAS, Tian H, Qi Y, et al. Modeling spatial and temporal patterns of tropical land use change [J ]. Journal of Biogeography, 1995, 22 (5) :

8 : MODIS 1083 Extraction of Forest Resources in Thailand based on MODIS Data LV Ting2ting 1,2, SUN Xiao2yu 1,2, YU Bo2hua 3,4 (1. Institute of Geographic Science and Natural Resources Research, CAS, Beijing , China ; 2. Graduate School of the Chinese Academy of Sciences, Beijing , China ; 3. College of Resources Science and Technology, Beijing Normal University ; Beijing ; China ; 4. State Key Laboratory of Earth Surface Processes and Resources Ecology, Beijing Normal University, Beijing , China) Abstract: Forest monitoring using satellite data has become an important tool for investigating and managing the exploitation of forest land, especially in tropical forest regions where large areas of forest have disappeared. Thailand was once a forest2rich country, particularly tropical forest, but over the last fifty years it has suffered serious forest losses. According to official statistics from the Thailand Royal Forest Department, in the early 20th century the rate of forest cover was 75 %, but in 2004 it was only %. Thailand is a tropical country with extensive cloud cover from May to October, which presents a significant limitation to the availability of remote sensing data for operational monitoring of forest areas. In order to improve classification accuracy and effectively reduce cloud noise, a set of normalized indices including NDVI (Normalized Difference Vegetation Index), NDSI (Normalized Difference Soil Index) and N NDVI - LST (Normalized NDVI and Land Surface Temperature Index (LST) ) were introduced by expanding the idea of NDVI. The NDVI has been demonstrated to be a robust and sensitive vegetation measure and has been used widely for continental and global2scale land cover classification. But because it is very sensitive to cloud noise, the classification accuracy is poorer in tropical areas than in other areas of the world. Compared with NDVI, NDSI uses near2infrared bands and short2wave infrared bands which can greatly reduce the influence of cloud noise. LS T is also an important parameter to describe the characteristics of land surface cover, which is highly sensitive to different land cover types on a large scale. The results show that N NDVI - LST can more effectively reflect vegetation information than LST alone. By combining these three indices, Thailandπs forest boundaries can be clearly extracted. MODIS Land Products such as 82day MOD09Q1, 82day MOD09A1 and 82day MOD11A2 were used to construct NDVI, NDSI and N NDVI - LST for this study. The resulting MODIS2derived forest map was compared to national forest statistical data and the 2000 forest map provided by the Thailand Royal Forest Department. The forest boundaries extracted by MODIS are very similar to those in the forest map of Forest area estimation using MODIS data was highly correlated with the statistical data, with a correlation coefficient of Classification using Landsat ETM was also used to validate the MODIS2derived forest result. With a confusion matrix, the overall accuracy was %. Key words :Thailand ; Forest ; MODIS ; Multiple Indices ; NDVI ; NDSI