2. Methodology and Results:
Figure 1: Study Area
The performance of SAR data in landcover
identification was examined in the following five cases.
Case 1: Backscatter alone by ERS
Case 2: Backscatter and Coherence by ERS
Case 3: Backscatter alone by JERS
Case 4: Backscatter and Coherence by JERS
Case 5: Backscatter by both ERS and JERS
The study was carried out for an area in the southern part
of the Sumatra island, Indonesia (see Figure 1). The area has been intensively converted from
natural forest into plantation. As shown in the previous study for this area (Stussi, et. al., 1996),
five categories of landcover (namely, forest, bare soil, deforested area, plantation type 1 and
plantation type 2) exist in this study area.
For ERS-1 and –2, the outcome of the previous research (Stussi, et. al., 1996) was used as
it was. Both backscatter and coherence data were obtained in June 1996, when ERS-1 and –2
were put into tandem operation mode. As shown in Figure 2, only two classes may be identified
with the backscatter data alone, while four classes could be delineated by adding the coherence
data.
For JERS-1, the backscatter data were obtained in June 1996. The coherence data were
calculated out of a pair of SAR data secured in June and October 1996, with return period of 132
days. As shown in Figure 3, only three to four classes may be identified with the backscatter
data alone and adding coherence data failed to improve the accuracy of classification.
Comparing figures 2 and 3, it sounds safe to assume that four to five classes may be
identified by integrating backscatter data on two bands. It represents the best results among the
five cases examined.
Figure 2: Landcover Classification by ERS-1/-2 data
[Source: Stussi, et. al., 1996]
Figure 3: Landcover Classification by JERS-1 data
3.Conclusion:
The outcome of the study suggests:
Integrated use of backscatter data on two bands by ERS and JERS is as powerful as use of
backscatter and coherence data on single band by one of these satellites.
In operational terms, difficult-to-obtain coherence data are not absolutely needed to
identify landcover.
Repeat-pass interferometry is feasible on L band with JERS SAR data in Indonesia even
with 132 days of return period, while repeat-pass interferometry is not employable on C band
with ERS data in tropics.
4. Further studies:
Further studies need to be carried out. A caveat for this study is that the training areas on
two bands are not necessarily identical. It was because only a graph (i.e. Figure 2) was available
on C band, as the outcome of a previous study. A study is now on-going in collaboration with
the National University of Singapore in order to carry out the same study but with identical
training areas.
The initial outcome of this new study has let the author believe that the conclusion
reached is robust, while it ought to be numerically verified.
References:
- Ribbes, F., Le Toan. T., Floury, N., Wasrin, U.R. (1999): Deforestation Monitoring in Tropical
Regions using Multitemporal ERS/JERS SAR and InSAR Data, JERS-1 Science Program '99 PI
Reports, NASDA, Tokyo.
- Siegert, F. and Nakayama.M. (1999): Comparison of ERS and JERS SAR data to assess the fire
disaster in Kalimantan 1997/1998, ESA/NASDA Workshop on Disaster Management, Unispace
III, 22 July 1999, Vienna.
- Stussi, N., Kwoh, L.K., Liew, S.C., Singh, K., Lim, H. (1996): ERS-1/2 Interferometry: Some
Results on Tropical Forest, ESA Workshop on Applications of ERS SAR Interferometry, 30
September to 2 October 1996, Zurich.
