Abstract
In order to monitor forest area in such a wide area as Southeast Asia Peninsula, high resolution data set cannot easily be used for such a task because of the high volume of the data. Low resolution data such as NOAA AVHRR data set is a solution to such a task. A forest classification technique by using band 1, 2 3, and 4 of NOAA AVHRR including thermal bands is proposed in this study for more prices tropical forest classification, The accuracy was checked with the referenced forest map produced in 1991 by Royal Forest Department (RED) in Thailand, which showed only 1.17 percent discrepancy
1. Introduction
This research study tries to utilize low resolution data (cloud free mosaic of NOAA AVHRR LAC 1.1 km resolution) to monitor forest change in regional level for the purpose of management and planning The advantages of the use of low resolution satellite data (NOAA AVHRR) data are :
The advantages of the use of low resolution satellite data (NOAA AVHRR) data are :
- Wide area coverage
- More frequency of obtaining input data
The disadvantages of the use of low resolution satellite data (NOAA AVHRR) data are
- Wide area coverage
- More frequency of obtaining input data
The disadvantages of the use of low resolution satellite data (noaa avhrr) Data are :
-
inaccurate Classification of Small Forest Area Dess than 1.1 KM2
- Inaccurate classification of sub region that forest area may have differed characteristics.
2. Data Used: NOAA Mosaic of Southeast Asia from GRID Bangkok (1991)
This relatively cloud free AVHARR mosaic, which is the primary product of grid Bagkok is used as the tested data, The mosaic covers an area extending from East 81 degree to East 120 degree, and from North 29 degree to South 10 degree. The mosaic is composted from 38 selected daily AVHRR images recorded by NOAA -11 (afternoon pas) along 10 different orbits between 12 November 1990 and 2 March 1991.
The AVHRR HRPT Level 1b data were received by the Thailand Meteorological Department receiving station on a daily basis. Each 4-band image was geo-referenced by GRID-Bangkok at 1.1 kilometer resolution (actually 1/100 oa degree) into a Plate Carrie Projection
The geometric accuracy of about 1 pixel is reached using a two step rectification algorithm (orbital model and transformation by ground control points) The cloud screening algorithm uses NDVI and the band 4 to composite the daily images. No radiometric calibration or atmospheric correction is done (grid Bangkok 1992).
3. Methodology for Forest Classification
3.1 The reference data : Forest map by RFD (1991)
The reference data (Thailand's forest area 1991) came from the interpretation of Lansat 5 data in the scale of 1:250,000 took in 1991 Jarsput 1993 Thailand has forest area left about 136698 sq. Km or 26.64 percent of the area of the whole cournty
3.2 Expected result from this stage
At this stage forest map with 2 classified classed (forest non-forest) should be able to obtain. By first, the result forest/non forest map of Thailand classified by proposed method should be compared to the referenced forest map (1991) from the Royal Forest Department to check for the accuracy of the proposed calssification method. Then forest/non forest map of the whole study area including Thailand, Myanmar, Laos, Cambodia and vetnam area is
generated.
3.3 Classification with low resolutin data
Until now, the research study has been done on multi-spectral analysis with not only Isabelle and infrared bands but also thermal bands by using mainly IDRISI software for analysis task (please see Figure 1 for forest classification framework). But the multi-resolution framework is already planned, and it will be studies d net to improve the result.
Figure 1 Forest classification framework with multispectral data sets
3.3.1. Trend study
In order to study the relationship between each parameter that may be used for the forest classification, trend study has been preformed on tested data by
Step 1 Profile checking between clearly classified forest and non-forest area to identify any relationships between tested parameters and the characteristic of forest area. (please see Figure 2 for the graph of the selected profiles)
Figure 2 Sample selected profiles across (a) clearly forest/non-forest area (Kao Yai National Park) and
(b) active agriculture area (Central Plain)
This procedure is consisted of the following :
-
Select a testing vector that pass trough the interested area in question
- Obtain pixel values along the testing vector from each band, or normalized difference index of interest.
- Plot the graph of all data obtained, and study the relationship of the parameter in question
Step 2 Visual inspection of each band and result images from tested operations overlaid by forest/non forest vector map from RFD.
This procedure is consisted of the following :
-
Display the studied data from each band or index of interested.
- Overlay with digitized forest/non forest map from RED.
- From the overlaid image, the visual inspection of the result can be performed to make to adjustment of parameter (s)
Step 3 Histogram analysis of selected sample area of clearly classified zone to identify the range of studied parameters which represents forest area.
This procedure is consisted of the following:
- Select sample area that is clearly identify as forest area from the overlaid forest map from RFD.
- Window the selected area to make the histogram of that area. Then the histogram will be studies for the statistics of the interested parameters
From the trend study of the relationship between forest area and data from each band and normalized difference indices from selected bands shows that forest land canbe classified with the combination of 3 parameters:
1 NDVI (NVI) (Normalized Difference Vegetation Index)= (band2-band1)/(band 2 +band1)
2 NR 34 (Normalized Difference index between band 3 and band 4) = (band3-band4)= / (band 3 + band4)
3 NR42 (Normalized Difference Index between band 4 and band 2) = (nand4-band2)/(band4+band2)
