Keywords: Forest mapping, ILU image ERS SAR Tandem, Interferometric coherence

Abstract
In order to evaluate the feasibility of ERS SAR Tandem data for mapping forest/non-forest cover, a case study was carried out over Zhengcheng County in the South China. Digital Land Use Map of this county was used as ground truth to collect signature of different land use types and to evaluate forest and non-forest classification accuracy. A hierarchical classification tree was established for land use types classification by careful analysis of histograms of several land use types. The classification result was analyzed by comparison with digital ground truth data pixel by pixel. It was shown that there was a considerably good agreement between the ground truth forest map and the obtained forest and non-forest map. An accuracy of 75% has been achieved.

Introduction
Due to small incidence angle (23°) and low penetration depth in forest covers at C-band, ERS SAR data is some limited for forest application^[1]. However, applications can be broadened significantly when interferometric coherence derived from repeat-pass ERS INSAR data are considered^[2], in addition to the usual backscatter information. The interferometric coherence, which is an indicator of the temporal stability of the target in terms of geometric and dielectric properties, proved to be a good discrimination in cultivated areas and forested landscapes^{[3, 4]}. Furthermore, ERS SAR tandem data is proved to be extremely useful for discriminating forest from non-forest in many areas of the world^[5]. It also provides unique information over regions permanently covered by clouds, such as many areas in China. So a case study for the evaluation of the feasibility of mapping forest/non-forest using ERS SAR tandem data has been carried out and some promising results have been achieved. The primary validation results over the experiment size, Zengcheng County in the South China, will be presented in detail.

Test Site and ERS Tandem Data

Test Site Selection
In order to evaluate the feasibility of forest and no-forest mapping using ESR Tandem data, an ideal test site should be carefully selected. Selection of test site was based on the following criteria:

• availability of ground truth data in digital format;
• existence of other satellite data (Landsat) as support for the evaluation of the results;
• possibility to carry out field work, if necessary, for checking ERS results in particular points;
• availability, in the ESA archives, of an ERS-SAR tandem pair acquired during the dry season over this region (i.e. from November to April).

As a result, the Zengcheng County was selected for performing the test-case exercise, which locates in the South China, 23°06' ~ 23°37' N, 113°29' ~ 113°59' E, the coverage of this county is about 2800 km². The ERS INSAR data pair used for the ILU image generation is showed in the table 1.
Table 1 ERS-1 and ERS-2 Tandem INSAR data used

Items	Date	Track	Frame
ERS-1	1996-03-02	24211	3141
ERS-2	1996-03-03	4538	3141

ERS SAR Data Processing
The selected ERS SAR tandem pair was processed with the IQL (Interferometric Quick Look) Processor at ESRIN. This processor is capable to produce multiple outputs with selectable characteristics. In this case, the following images were obtained:

• the intensity image corresponding to the ERS-1 SAR acquisition;
• the intensity image corresponding to the ERS-2 SAR acquisition; and
• the interferometric coherence.

Based on these three image, a color composite RGB image combining coherence (in Red channel), mean intensity (in green channel) and change of intensity (in Blue channel), known as Interferometric Land Use (ILU) image were generated, which would be used to do land use classification. The ILU image was in byte format and in ground range projection with pixel spacing of 40 m.

Clasification Methodlolgy
Using the geo-referenced ILU image, a classification methodology based in the behavior of intensity, coherence and change of intensity for different surfaces has been defined. The key idea for establishing a classification procedure is that surfaces presenting similar radar intensity values may present very different values of coherence. Therefore, the appropriate combination of intensity and coherence values will make possible to distinguish between surfaces which are hardly discriminated if only SAR intensity is used.

Fig. 1 (a)Histograms of Intensity Mean



Fig. 1 (b)Histograms of Intensity Mean