Computer Assisted Monitoring of Vegetation Using Multl-resolution
Satellite and Geospatial Data
Mr. Surat Lertlum *, Prof. Shunji Murai **
* Doctoral Student, Asian Institute of Technology
E-mail: surat@cs.ait.ac.th
** Professor, Asian Institute of Technology
E-mail: smj@cs.ait.ac.th
Abstract
The main purpose of this paper is to illustrate: the outline of a new vegetation monitor methodology, I especially tropical forest by using remote sensing and GIS.
The emphasis is on the usage of new technology in remote sensing and GIS to monitor, I analyze, and predict the forest resource at regional level. First, a multi-resolution forest classification is I proposed, by using low resolution remotely-sensed data (NOAA AVHRR LAC 1.1 km. resolution) as the main 1 source of data for vegetation monitoring, and using high 1 resolution remotely-sensed data (Landsat TM 30 m. resolution) as the ground correction data for each subregion. Next we illustrates the usage of objected- oriented data model to handle multi-resolution, multi- temporal integration problem.
1 Introduction
In this section, we introduce some general knowledge related to proposed multi-resolution tropical forest classification:
- Main Data Source (NOAA A VHRR) :
The advantages of low resolution satellite data (NOM A VHRR) data are :
- Wide area coverage
- More frequency of obtaining input data
The disadvantages of low-resolution satellite:
- data (NOAA AVHRR) data are:
- Inaccurate classification of small area (less than 1.1 km2).
- Inaccurate classification of subregion that vegetation may have different reflect characteristics.
- Vegetation Classification:
In general, vegetation classification methods using remotely-sensed data are :
- Using NDVI (Normalized Difference \ Vegetation Index)
- Unsupervised Classification
- -Supervised Classification c
- Spatial Information System:
The current requirements for spatial information system are:
- Remote sensing stand point:
- Can handle multi-resolution data t
- Can handle multi-temporal data
- GIS stand point:
- Can handle both raster and vector data
- Can integrate raster and vector data
2 Vegetation classification from satellite data utilizing thermal bands
In this section, we introduce a new methodology for vegetation classification from low resolution satellite data (NOAA AVHRR LAC 1.1 km.) utilizing thermal bands.
From the trend study on tested data, the relationship between forest area, data from each band, and normalized difference indices from selected bands, the result shows that forest land can be classified with the combination of 3 parameters:
1 NDVI = (band2-band1)/(band2+band1 )
2 NR34 = (band3-band4)/(band3+band4)
3 NR42 = (band4-band2)/(band4+band2)
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These parameters involve data from 4 bands, which include thermal infrared band (band 4) of NOAA A VHRR. The proposed classification method utilizes the use of thermal band by using natural characteristic of forest area that the temperature of forest area should lower than the surrounding. (Lertlum and Murai, 1994) (Figure 1 Decision tree for forest classification)
Figure 1 Decision tree for forest classification
3 Vegetation classification from multi-resolution satellite data
Information derived from coarse spatial resolution sensors that have high temporal data acquisition rates (e.g. NOAA AVHRR) are required to accommodate the vast land area included in tropical surveys. Higher resolution sensors (MSS, TM, SPOT) are necessary tools to record the spectral and spatial detailed needed to link intensive ecological field of studied to the forest community and biome levels.
From previous section, a new methodology for vegetation classification from low resolution satellite data (NOAA A VHRR LAC 1.1 km.) utilizing thermal bands is introduced. In this section, we continue to apply similar methodology to high resolution satellite data (Landsat TM 30 m.).
For high resolution data (Landsat TM), the relationship between forest area and data from each band, normalized difference indices from selected bands shows that forest land can be classified with the combination of 2 parameters:
1 NDVI (NVI) (Normalized Difference Vegetation Index) = (band4-band2)1(band4+band2)
2 NR64 (Normalized Difference Index between band 6 and band 4) = (band6-band4) /(band6+band4)
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Table 2 bleows list the characteristics of NVI and NR64 for each type of data:
Table 2 Characteristics of NVI and NR64 for each type of area :
| type of area |
respond from NVI |
respond from NR64 |
| primary forest |
high |
low |
| secondary forest |
mid-range |
mid-range |
| plantation/high tree |
high |
mid-range |
| rice field |
low |
low |
| grass land/rice field after harvest |
low |
high |
| water |
low |
low |
From the proposed forest classification methods for NOAA AVHRR and Landsat TM I we can integrated both of the classified results by using the result from Landsat TM classification to calibrate the result from NOAA AVHRR for each subregion as mentioned previously. The integration can be performed by the method of defining more precise threshold for NOAA AVHRR data using classified forest sample from Landsat TM for each subregion, that will be devided by ARID index (Figure 2 Forest classification framework)
Figure 2 Multi-resolution forest classification framework
