Discussion of Methods

5.1.1 Land use changes and tiger detection information in the study area

A land use change assessment between 2000 and 2010 was carried out using WCS (Myanmar programme). Conversion of forest areas to commercial plantations accounted for major changes outside the core zone. Township development activities (for e.g., 200,000 acres for mono crop plantation projects) straddle the south-west part from the historical Ledo road. Fortunately, in the core study area (see Figure 53), there have been no major land use changes. According to this, the habitat suitability map as one main result of this study can be used in determining the high priority areas for the future protection of the tiger and its prey species.

Figure 53: The comparison of land use changes between the year 2000 and 2010, showing that no major land use changes occurred in the core study area (yellow dashed line) (source: WCS, Myanmar programme, 2011)

Year 2010 Year 2000

0 5 10 20 30

Kilometers

Up to the year 2010, the location of the tiger tracks and signs were recorded by the tiger survey team (see Figure 54). Due to political constraints, the tiger survey team could not enter into the core zone after that time, leading to a lack of tiger information for the year 2011.

5.1.2 Major issues of data availability

Landsat image acquisition: This study was conducted for a large landscape (1,713 km²).

High spatial resolution remote sensing imagery (QuickBird and IKONOS) can provide rich spatial information regarding classification, but they would be very expensive for this large area based study. So, 30 m resolution Landsat images were ordered freely and it made this study much more cost effective. But, a single scene of Landset imagery was not available for the whole study area at the same date. Due to these issues, two scenes acquired on different dates (Oct 2002 and Feb 2003) were merged to cover the study area.

There were two main reasons to use the 2002/2003 Landsat images which hold temporal differences to the reference land use map of WCS from the year 2000. The first reason was that most of the tiger locations were detected in this period, especially in the year 2003.

Figure 54: Detection of tigers‟ tracks and signs in the core zone of HVTR for the year 2010 (Source: WCS, Myanmar programme, 2010)

0 8 16 miles

The second reason was that there was no major land use change between the year 2000 and the year 2003 (neither in the time up to the year 2010) in the core zone of the HVTR as suggested by the results of the land use changes assessment. Hence, the time difference did not cause severe problems in the study. The core area has been totally banned since 2003 up to now by threat monitoring and regular patrolling activities. Owing to this, the existing reference land use map (based on the year 2000, Landsat) was able to be used as a training data set in this study.

Species data: Camera trap survey techniques by the researchers of WCS were used to estimate the tiger population in HVTR. So, the tiger presence data for this study are in the form species presence points collected by camera traps as well as GPS points of track and sign data. Camera traps were located based on the tiger detection areas of a short reconnaissance survey. In the study area, the species data sat contained only 5 individuals and all 31 species presence points were not well distributed all over the study area because surveyors were not able to be access all areas for camera-trapping. The success rate of camera trap varies in various habitats. For e.g., capture probability in kaing grass is higher than that in evergreen forest. This is also another major shortcoming for lower detection of species presence points in the evergreen closed forest.

Environmental data: The existing reference land use map was based on pixel-based classification of Landsat imagery of the year 2000, exhibiting lots of scattered white (erroneous) pixels (Salt and Pepper effect). That‟s why it could not be directly used for the land use classification, as the Salt and Pepper phenomenon may lead to a reduction in the accuracy of spatial information. Former studies showed that object-oriented image analysis provides the capability of much smoother classification that is crucial in habitat suitability mapping. In this context, object-oriented image analysis was adopted to conduct a segmentation-based land use classification in this study.

The reference data could not provide training areas of secondary forest in the core zone of the HVTR. Therefore, during a field trip, by interview with local villagers and field experts, the historical records of secondary forests in the year 2003 were labeled on the thematic classification map. This data was plotted as polygon in a GIS technique and used as training areas for that land cover category in the classification process.

Training areas for kaing grass, scrubland, and evergreen opened forest with rattan, streambed, agriculture and evergreen opened forest were selected from the reference map.

Evergreen closed forest, bamboo and water were directly mapped from the Landsat imagery by using the spectral reflection information from a 5, 4, 3 bands combination (pseudo color band composite including infrared reflection). This was achieved in eCognition and the result was imported into ArcGIS for editing and modification.

5.1.3 Segmentation-based land use classification and accuracy assessment

Altogether 15 land use classes were distinguished by using object-oriented image analysis techniques. The quality of the classification result was quantified an overall accuracy of 79% with a kappa index 0.76. Considering the relatively low spatial resolution of the imagery and the detailed classification scheme with many vegetation types, the achieved accuracy is acceptable. There was also an abundance of challenges to be faced in the classification process (for example, similarity of classes such as agriculture, kaing grass and scrubland, delineation of unclassified pixel clusters by a focal majority process, shifting of the reference raster map to align with the segmentation-based land use map, etc.). According to this, some classes were delineated from the surrounding features, especially in terms of water and streambeds, streambed and kaing grass, rattan and evergreen opened forest with rattan, agriculture and secondary forest.

A confusion matrix (see Table 22 on page 91) was utilized to estimate the accuracy of the segmentation-based land use classification. A closer inspection of the confusion matrix revealed that significant confusion occurred between the classes of rattan and evergreen opened forest. 124 reference pixels of rattan were improperly classified as evergreen opened forest. This matrix lead to a quite low user‟s accuracy of evergreen opened forest (39%). It is because rattan never appears alone but it grows in association with evergreen opened forest. In the satellite imagery (pseudo color) the evergreen opened forest with rattan appears in magenta color. By contrast, because in the evergreen closed forest, rattan does not contribute to the canopy reflection and hence this type of forest appears in a different way in the satellite imagery.

Another confusion occurred between kaing grass, streambed and agriculture. In HVTR, kaing grass grows along the streambed and many pixels of streambeds were improperly

classified as kaing grass. Another challenge was that kaing grass showed a similar reflection pattern like agriculture in the analysis, so that 55 pixels of agriculture were improperly included into the kaing grass, leading to unsatisfactory results for user‟s accuracy of kaing grass. The remaining unsatisfactory results of user‟s accuracy occur for the secondary forest class. It is likely to be assumed that its spectral reflection was difficult to differentiate from agriculture because secondary forest was automatically formed after shifting cultivation, resulting in possible mixtures of reflection. That‟s why the pixels of agriculture were improperly classified as secondary forest, leading to the user‟s accuracy of secondary forest of only 50%. One scene of satellite imagery was captured in winter (February) and another one was captured in the wet season (October). Mean annual rainfall is more in October than in February. It is therefore strong omission errors and commission errors occurred between streambed and water. It seems the reason that streambed will be water surface in the wet season.

5.1.4 Variable identification

This study is a pioneer study using data from Myanmar to draw a habitat suitability map for tigers. Hence, environmental variables for this study came from literature reviews and expert interviews. Besides, variable had to be determined with regard to tiger ecology.

Tigers need home ranges with sufficient large areas of suitable land cover and water surfaces to ensure long-term survival, adequate prey densities, and low disturbance rates from humans. To cover these requirements, three main groups of variables were identified as habitat suitability predictors. These were landscape compositional, topographical and human disturbance variables.

Landscape compositional variables were obligatory in statistical habitat modeling for the tiger. Therefore, 14 land use/ land cover types were identified as important for this study.

These were water, streambed, kaing grass, evergreen closed forest, evergreen opened forest, and evergreen opened with rattan forest, bamboo forest, secondary forest, scrubland, agriculture, settlement and road. The area and length of landscape features in a circular analysis window around each landscape cell were quantified as well as the features‟

distance to each landscape cell to find out preference and avoidance behavior of the tigers with regard to the landscape features in HVTR.

Rivers are distributed all over the HVTR. Access to water sources is essential for tigers and their prey species. River-1(30 m width), river-2 (90 m width) and river-3 (150 m width) were defined and distances to these classes included as variables in the model in order to get more information for future management, through controlling for the gold panning and dynamite fishing along the rivers.

Tigers used streambed and secondary forests as corridors whereas the kaing grass is used as hunting ground (field experience of tiger survey team, HVTR,). They also use kaing grass as a resting site (Khan et al., 2007). Evergreen closed and evergreen open forests comprise 70% of the whole study area, including the most important land cover types for tigers. Rattan, bamboo, scrubland, agriculture and saltlicks were selected as shelter and food resources for the prey species. The remaining landscape variables with regard to road and settlements were selected and confirmed those classes to be unfavorable variables for tigers.

Like protected areas of all over the world, HVTR has been encountered with various types of human intrusion. Minor logging, gold-mining, dynamite fishing, non-timber forest product collection and hunting and poaching were the most common disturbances in the core zone. Variables related to these activities were created and also included into the model to explore human impacts that cause tiger habitat loss and degradation.

Topographical variables were selected by examining tiger preferences for certain elevation and slope situations. Distance measure to each aspect (flat, north, east, south, west) were included to explore the aspect that the tigers mostly prefer in the study area.