Object-oriented Classification and Sampling Rate of Landsat TM Data for Forest Cover Assessment. Yasumasa Hirata 1, Tomoaki Takahashi 1

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1 Object-oriented Classification and Sampling Rate of Landsat TM Data for Forest Cover Assessment Yasumasa Hirata 1, Tomoaki Takahashi 1 1 Forest Management Department, Forestry and Forest Products Research Institute, 1, Matsunosato, Tsukuba, , Japan; ; hirat09@affrc.go.jp, tomokun@affrc.go.jp Introduction Forest cover identification is very important to maintain sustainability of forests at the national level. Remote sensing survey, particularly using high resolution satellite data, is one possibility to assess forest cover spatially (Tomppo and Czaplewski, 2002). FAO has proposed the remote sensing survey by sampling method using Landsat TM and ETM+ data in FRA 2010 (Ridder, 2007). This survey is aimed at substantially improving the knowledge on land use change dynamics over time, including deforestation, afforestation and natural expansion of forests. In this remote sensing survey, object-oriented classification is used for land cover classification. While object-oriented classification is effective to segment area which consists of various land cover types into objects with extension of similar property (Lamonaca et al., 2008), parameters should be discussed to acquire suitable object size for the purpose of classification. In FRA 2010, sampling tiles with 10 km by 10 km are selected at the grids of 1 of longitude and latitude. This sampling corresponds about 1 % sampling rate. While this result is useful for global land-cover classification as training data and/or verification data, the sampling rate should be investigated for the estimation of forest cover at the national level. This study aims to investigate a suitable scale parameter in object-oriented classification to identify land-cover types from Landsat TM data and to evaluate sampling rate for estimation of forest cover at the national level. Materials and Methods Study area and sampling tiles Whole land area of Japan is target of this study. Sampling tiles were set by 1 interval grid of longitude and latitude with an area of 10 km by 10 km to investigate a suitable scale parameter in object-oriented classification to identify land-cover types from Landsat TM data. Forty-six sampling tiles were select for whole of Japan in total. Whole of Japan was divided into same size

2 of tiles with 10 km by 10 km to evaluate sampling rate for estimation of forest cover at the national level. Satellite data and aerial photographs Landsat TM data without clouds between spring and autumn around 1990 were prepared for whole land area of Japan in this study. All data were geo-registered to corresponding zones of the UTM coordinates (WGS84). The areas corresponding sampling tiles were extracted from them to investigate a suitable scale parameter in object-oriented classification to identify land-cover types from Landsat TM data. Ortho aerial photographs around 1990 were prepared for all sampling tiles with some margins to interpret land-cover class for validation of the results. Data analysis Land-cover type was interpreted from aerial photographs at points of 500 m interval grids in 46 sampling tiles, which were 441 points in each sampling tile (Figure 1). Interpretation results of alternate points (10,120 points in total) were used as training data of image classification and rest points (10,166 points in total) were used for verification of the classification. Figure 1. Interpretation of land-cover from aerial photograph. Landsat TM data corresponding sampling tiles were segmented using ecognition (Defininens Imaging, Germany) with different scale parameters to evaluate the influence of the parameter on generated object size (Figure 2). Maximum, minimum and average of object size were investigated by scale parameter in each sampling tile. For segmented objected, supervised classification was conducted using interpretation results of aerial photographs as training data, and the accuracy of the classification was verified. Using the

3 suitable scale parameter, object-oriented classification was conducted for all Landsat TM and ETM+ data for whole of Japan and the relationship between sampling rate and estimate of forest cover rate was investigated. Figure 2. Segmentation of Landsat TM data with different scale parameters. Results and discussion In the interpretations of aerial photographs, 72% of the points were interpreted as forest, other wooded land, and other land cover with tree cover, 23% of them were grassland, range, herbaceous, and agricultural crops. Rest 5% was covered with built-up, other non-vegetated area and inland water. Forest area of Japan was 68.2% in FRA1990 and this figure did not include wooded land and other land with tree cover. The relationship between scale parameter and object size in each sampling tile was shown in Figure 3. Bar shows maximun and minimum of object size and mark in the bar shows average of object size. The relationship between scale parameter and object size in total was shown in Figure 4. From these figures, it was clear that object with the size of less than 1 ha existed even when scale parameter was enough large and that large scale parameter led to big difference between object sizes. Total accuracy in object-oriented classification by scale parameter was shown in Figure 5. Total accuracy was about 75% and highest when scale parameter was 6 to 10. Therefore, these figures should be selected in object-oriented classification for Japanese landscape structure. Relationship between sampling rate and estimate of forest cover rate was shown in Figure 6. More than 25% sampling rate was required to estimate forest cover rate in Japan. This result indicated that 1% sampling was not enough to estimate forest cover rate at the national level. Futher studies are required for different area size and different forest cover rate.

4 Fig 3. Relationship between scale parameter and object size in each sampling tile. Object size (ha) Average Minimum Maximum Scale parameter Figure 4. Relationship between scale parameter and object size. Figure 5. Total accuracy of classification by scale parameter. Figrue 6. Relationship between sampling rate and estimate of forest cover rate.

5 Conclusions Remotely sensed data in forest area are influenced by the seasonality in almost cases in Japan. Because of mountainous terrain, the conditions of deciduous forests are different by elevation in spring and autumn. In addition, extent of snow area effects on land-cover classification. Implementations of forest management such as clear-cutting and re-planting also lead to misclassification. Complexity of landscape structure, which consists of small-scale forest patches and topography in Japan, is an important factor to decide suitable parameters in object-oriented classification. We investigated a suitable scale parameter in object-oriented classification to identify land-cover types from Landsat TM data and to evaluate sampling rate for estimation of forest cover at the national level. Our findings are summarized as follows: 1. Accuracy was highest when scale parameter in object-oriented classification was 6 to Suitable parameter should be investigated for required patch size by landscape type. 3. Seasonality, topography, foerst management should be considered to compare long-term interval multi-temporal satellite data. 4. More than 25 % sampling rate was required to estimate forest cover rate in Japan. Futher studies are required for different area size and different forest cover rate. Remote sensing survey by sampling method provides useful dataset at the global and national level. This study indicates that we need to pay attention for the use of sampling method to estimate land-cover rate at the national level. Literature cited Lamonoaca, A., Corona, P. and Barbati, A. (2008) Exploring forest structural complexity by multi-scale segmentation of VHR imagery. Remote Sensing of Environment 112: Ridder, R.H. (2007) Global forest resource assessment Options and recommendations for a global remote sensing survey of forests. FRA Working Paper 141, FAO, Rome. Tomppo, E. and Czaplewski, R.L. (2002) Potential for a remote-sensing-aided forest resource survey for the whole globe. Unasylva 210:16-18.