An application of GIS and Remote Sensing for Analysis of Agricultural Development-Induced Changes in Land Use: A case study in Lao PDR By Boundeth Southavilay 1) , 2) , Teruaki Nanseki 3) The 30 th APAN Meeting August 2010, in Hanoi, Vietnam 1). Graduate school of Bioresource and Bioenvironmental Science, Kyushu University, Japan. 2).Department of Planning, Ministry of Agriculture and Forestry, Lao PDR. 3). Faculty of Agriculture, Kyushu University, Japan 1 E-Mails: boundeth_jong@yahoo.com; nanseki@agr.kyushu-u.ac.jp
Contents 1. Introduction 2. Statement of problems 3. Objectives 4. Materials and Methods 5. Study area 6. Results and Discussions 7. Conclusions 2
Introduction • In the last decade, in Laos GIS and Remote sensing (RS) has not much used in countrywide, including agriculture sector did not applied this technique for their agricultural development and land use planning. • A meanwhile, in that times agriculture lands in Laos were transformed from subsistence farming in uneconomic-sized farms to commercial and market-oriented farms. These transformed sometimes happens in improperly ways and induced to change in land use and land covers by despoilment of forest covers and traditional farming system. • The problems above due to lack of an appropriate tool in terms of integrated spatial data on land use/land covers. However, recently GIS and remote sensing has been using in several types of works in both government and private agencies. As we know, GIS and remote sensing have an important role in linkage and analysis of such data, in particular for detection, interpretation, area calculation, monitoring and future estimating. Therefore, this study applied GIS and remote sensing for analysis the land use pattern changes 3
Statement of Problems • After 1999, the landscape in the study area has been changed cause of policy implementation such as rubber plantation and irrigation system were installed in the area, and than this place was changed in dynamics way of land use system • The forest area was destroyed by increasingly shifting cultivation and rubber plantation areas • Lack of an appropriate tool for decision support system in terms of land use decision Rubber plantation Shifting cultivation 4
Objectives • To illustrate the change detection of land use and land covers • To create a tool for decision support system in the watershed land use planning by created land zoning. 5
6 Materials and Methods
Materials • Satellite images: – Landsat ETM+ (25 January 1999), LIG format • Resolution – 30 m (band 1-5,7) – 15 m panchromatic – Landsat ETM+ (12 March 2004), BIL format • Resolution – 25 m (band 1-5,7) – 15 m panchromatic • GIS data bases with thematic maps (Road networks, River networks, Village points, Contour line, DEM and Ground check point- from GPS) • Topography map 1:100,000 (Schema F-47-142 and F-47-130) • Software: ArcView3.2a and Idrisi 32 7
Methods 1. Geometric correction- to georeference maps to a map coordination system – Image 1999 was registered to local topography maps with 15 ground control points. root-mean-square (RMC) error = 0.45 pixels. – Image 2004 was registered with registered of image 1999 (image to image). RMC= 0.14 pixels. 2. Change pixels size- because the pixel sizes of two images are different (30m and 25m) – Change pixel 30m of image 1999 to 25m of image 2004 NDVI image 3. NDVI compositing utility – NDVI is useful for identifying of the green leaf from other objects (water, soil…) It is expressed value -1 to 1 with 0 representing non vegetation – NDVI solve the shadow problem NDVI= (b4-b3)/(b4+b3) 8
Methods (cont.) • Images interpretation by Supervised classification Training area (AOI) • Supervised classification – Maximum likelihood method The training area from two images 345, and 2ndvi7 in the1999 and 2004 – classified to 11 classes 9 • Create zone by overlaid three physical data (Ground data, GIS data and image classification)
Study area The area is located in the northern Laos, Lat: 65º07'16" to 67º59'13" Long: 222º79'96" to 225º56'22". 43 villages 1250msl 300msl • Area 696 km2 • Watershed boundary area = 22 km2 • Elevation from 300 to 1,235 msl 10 • The lowland farms are located between 300 to 450 msl.
Results and Discussions • The result of interpretation of two images ETM+1999 and ETM+2004, it provided two land use maps of 1999 and 2004. In each map was classified into 11 categories of land use/land cover types Land covers 1999 Land covers 2004 Intensive of changed areas 11
1999 2004 Results of Maximum Likelihood land use classes Km2 % Km2 % Dark evergreen forest 199.54 28.66 148.7 21.4 Classification of two images Bright evergreen forest 173.94 24.99 134.8 19.4 1999 and 2004 Disturbed forest/fallow 164.27 23.60 305.1 43.8 Bamboo 22.98 3.30 12.4 1.8 Field crop 32.53 4.67 4.0 0.6 Wet paddy 24.08 3.46 23.6 3.4 Irrigation paddy 0 0 10.5 1.5 Bare land/Wet soil 22.56 3.24 0 0 Reservoir 0.02 0.00 6.6 0.9 Mekong 6.62 0.95 3.1 0.4 Sandy area 1.70 0.24 4.1 0.6 Shrub/other crops 47.92 6.88 43.2 6.2 Total 696.2 100.00 696.2 100.0 Changes land use classes Change rate Km2 Percentage (%) (%km 2 /year) Changed Dark evergreen forest -50.84 -25.48 -5.10 Bright evergreen forest -39.14 -22.50 -4.50 Disturbed forest/fallow 140.83 85.73 17.15 Bamboo -10.58 -46.04 -9.21 Field crop -28.53 -87.70 -17.54 Wet paddy -0.48 -1.99 -0.40 Irrigation paddy 10.5 0.00 Bare land/Wet soil -22.56 -100.00 -20.00 Reservoir 6.6 0.00 Mekong -3.52 -53.17 -10.63 Sandy area 2.4 141.18 28.24 12 Shrub/other crops -4.72 -9.85 -1.97
Cheng detection • The change detection is the process of identifying differences in the state of an object or phenomenon by observing it at different times (Singh, 1989). • The change detection of land use and land cover of the study area was analyzed by cross-classification technique- – by overlaid of two land use maps 2004 1999 + = = The change detection provides the characteristic changes of each land use type 13
Legend: DT ‐ Disturbed forest/fallow EF ‐ Evergreen forest FC ‐ Field crop BB ‐ Bamboo BL ‐ Bare land WPD ‐ Wet paddy IPD ‐ Irrigation paddy RV ‐ Reservoir 14
detection of land use/land cover by cross- classification during 1999 to 2004 No Land use change 1999-2004 Pixels Hectares Km2 % During 5 years 3 1 Disturbed > Evergreen Forest 42676 2,667.25 26.67 3.83 land use types 2 Evergreen Forest > Disturbed 215062 13,441.38 134.41 19.31 were changed to 3 Field crop > Disturbed 35353 2,209.56 22.10 3.17 shifting cultivation: 4 Bare land > Disturbed 38975 2,435.94 24.36 3.50 18,100ha 5 Disturbed > Field crop 867 54.19 0.54 0.08 6 Wet paddy > Field crop 932 58.25 0.58 0.08 7 Disturb > Wet paddy 5504 344.00 3.44 0.49 8 Bare land > Wet paddy 7702 481.38 4.81 0.69 9 Disturbed > Irrigation paddy 5883 367.69 3.68 0.53 10 Field crop > Irrigation paddy 476 29.75 0.30 0.04 11 Wet paddy > Irrigation paddy 5398 337.38 3.37 0.48 12 Disturbed > Reservoir 2627 164.19 1.64 0.24 13 Wet paddy > Reservoir 1941 121.31 1.21 0.17 14 Evergreen Forest > Bare land 3297 206.06 2.06 0.30 15 Disturbed > Bare land 20361 1,272.56 12.73 1.83 16 No changes 726786 45,424.13 454.24 65.25 Total 1113840 69615 696.15 100.00 15
Land use changed in watershed boundary May,1999 March, 2004 Dense forest Open forest Dense forest Shifting cultivation Open forest Bamboo Shifting cultivation Field crop Bamboo Wet paddy Field crop Irrigated paddy field Wet paddy Reservoir Bare land Shrub land/other Shrub land/other 1999 2004 Land use types Land use/land cover changes from 1999 to 2004 Hectares % Hectares % 14000 Irrigated paddy (dry season) 0.00 0.00 202.44 0.92 12000 Reservoir 0.00 0.00 467.82 2.14 Area (ha) 10000 Bare land/wet soil 551.38 2.51 0.00 0.00 8000 Field crop 830.69 3.79 46.44 0.21 6000 Bamboo 831.81 3.79 356.69 1.63 4000 Wet paddy (rainy season) 952.56 4.34 740.25 3.37 2000 Shrub/other crops 1596.69 7.28 1524.56 6.95 0 Mixed-deciduous forest 5286.25 24.10 2827.38 12.8 Year 1 2 Dense forest 5813.31 26.50 4112.13 18.7 Dense forest Mix-deciduous forest Bamboo Shifting cultivation 6061.94 27.64 11657.81 53.1 Shifting cultivation Field crop Wet paddy Total areas 21935.50 100.0 21935.5 100.0 Irrigation paddy Bare land/wet soil Reservoir 16 Shrub/other crops
Zonation Ground information GIS data Remote Sensing Combine land use Raster maps: Slope, Composite/NDVI type/land holding DEM maps in the villages Vector maps: river, Land use/land Population and boundary cover village location Watershed Zonation Decision Support map Land use planning • The zone was created by overlaid of three physical information (Ground data, GIS data and satellite imagery data) • The zonation can be regarded as a tool for sustainable agricultural development in the watershed area. 17
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