Forest Mapping with NOAA AVHRR Data
Case Study: Effect of Thermal Band for Refiing Forest Mapping
3.3.2 NDVI and other normalized difference indices for forest classification
The following three steps with specific thresholds were defined with six xelected profiles over well known forest and agricultural lands in Thailand for example Kao Yai National Park and Central Plain.
Step 1: Define threshold value of NDVI for dense vegetation (NDVI > 0.30 will be defined as forest and dense agriculture)
The normalized difference vegetation index (NDVI) is one such ratio which has been shown to be highly correlated with vegetation parameters biomass and green leaf area and hence is of considerable, value for vegetation discrimination (Curran 1980) . But one of the draw back of NDVI is that a cannot clearly distinguish between forest and dense agriculture area since it only detect the chlorophyll reflectance.
The additional information of the finding are :
- The result not exactly match the classified data from RFD (more forest land)
- The result can not differentiate between forest and active agriculture land (land in the central part of Thailand). (please see Figure 3 for the classified result with NDVI)
Figure 3 Classified result from NDVI
Step2: Define threshold value of normalized difference index NR34 for refining forest boundaries (NR34>19 will be defined as clear boundary of forest area)
Since we can not use NDVI only to classify forest land. from the experiment and analysis, normalized difference index between band 3 and band 4 can assist in the classification of the forest land because normalized difference e index between band 3 and band 4 can identify clear boundary of forest if we select appropriate threshold. The reason for the assumption came from the fact the value of band 4 (thermal band ) from forest land is less than its surrounding while the value of band 3 of forest land still high (please see graph of profile vector across forest area in Figure 2a) so the normalized difference index between band 3 and band 4 will able to assist in denitrifying forest land. From the examination, the value of greater than or equal to 0.19 of such an index can be used to assist in identifying forest boundary.
The additional information of the finding are :
- The result not exactly match the classified data from RFD (more forest land).
- The result can differentiate between forest and very active agriculture land (ex agriculture land in the central part of Thailand ). (please see figure 4 for the classified result with normalized difference index of band 3 and band 4)
Figure 4 Classified results from NR34
Step 3 Define threshold value of normalized difference index NR42 for avoiding agriculture land from forest boundary
From the study, we also found out that normalized difference index between land 4 and land 2 can also delineate active agriculture area from the classified forest land as defined in up to step 2. In this study, it was found that a range between 0.19 and 1.27 of NR 42 corresponds to active agricultural land. This assumption can be examined from the profile across the agriculture land in Thailand s central part (please see graph of profile vector across agriculture area in Figure 2b). The result of the combination of step 1 through step 3 is shown in Figure 5.
Figure 5 Results from decision tree of NDVI, NR34, and NR42 (Thailand Only)
Table 1 shows the results of the above classification procedures of individual and integrated steps.
According to the checking the areas from RED forest map (1991) shows a goad accuracy with a + 1.17 percent.
4. Conclusion: Results and Forest Mapping
4.1 Results obtained from the current study
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Combinations of visible near infrared, and
thermal infrared in themes of NDVI are definitely necessary to refine the tropical region because some agricultural lands show very high which cannot be delineated by NDVI see decision tree for forest classification . 7)
Figure 6 Forest Map of Thailand 1991
- The accuracy of forest area was high with a little higher value 27 . 81 compared with 26.64 percent (RED) the current thresholds involve very denss plantation, secondary forest, and fruit crop which are considered spectrally as same as land.
Figure 7 Decision tree for forest classification
4.2 Next topics to be further studies
- Selection of proper profiles ofr checking ground truth
- Verification of forest area with high reason data
- Sub-classification of forest land
- Change analysis of forest area
Reference
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Curran 1980 Curran P.J. 1980 Multispectral Remote Sensing of Vegetation Amount Prog. Phys Geogr 4,315
- (GRID 1992 ) GRID, Bankgkok, 1992. Document for NOAA AVHRR dateset.
- (Jaraput 1992) Jaruput, Thongchai, Analysis of Forest Situation in Thailand and the World, Royal Forest Department 1993
- Murai S., (editor) Applications of Remote Sensing in Asia and Oceania Environment Change Monitoring Asia Association on Remots Sensing 1991
- Murai S., (Editor) Remote Sensing Note, Japan Association OF Remote Sensing 1993
- Murai S., Special Course ON Advanced Remote Sensing Gis Technologies Data Fusion OF Multi- sensor Date, AIT, Bankok, Thailand 1994
