Naing Zaw Htun

Deforestation and forest degradation as measures of Popa Mountain Park (Myanmar) effectiveness

The effectiveness of protected areas (PAs) has often been measured by comparing deforestation or land cover loss before and after PA establishment or within and outside the PA. Such evaluations ignore forest degradation or the decline in quality of the ecological values of the forest. Landsat image processing and geographical information systems were used to study forest cover changes in and around Popa Mountain Park (central Myanmar). Deforestation inside the Park was lower than outside the Park, but forest degradation, namely the net area that changed from dense forest to open forest, within the Park had grown at rates exceeding those outside the Park. The effectiveness of the PA was overestimated by adopting deforestation as the only indicator of conservation effectiveness. Forest degradation studies can be used to evaluate the effectiveness of PAs.

Changes in determinants of deforestation and forest degradation in Popa Mountain Park, Central Myanmar.

Implementing effective conservation requires an understanding of factors affecting deforestation and forest degradation. Previous studies have investigated factors affecting deforestation, while few studies have examined the determinants of both of deforestation and forest degradation for more than one period. To address this gap, this study examined factors influencing deforestation and forest degradation during 1989–2000 and 2000–2005 in the Popa Mountain Park, Myanmar. We applied multinomial logistic regression (MNL) using land cover maps derived from Landsat images as the dependent variables as well as spatial and biophysical factors as the independent variables. The MNL models revealed influences of the determinants on deforestation and forest degradation changes over time. For example, during 1989–2000, deforestation from closed forest was positively correlated to the distance from the park boundary and was negatively correlated with distance from villages, roads, the park circular road, slope, western aspect and elevation. On the other hand, during 2000–2005, deforestation of closed forest was positively correlated with distance from villages, roads, the park circular road, slope and western aspect, and negatively correlated with distance from the park boundary and elevation. Similar scenarios were observed for the deforestation of open forest and forest degradation of closed forest. The study also found most of the determinants influenced deforestation and forest degradation differently. The changes in determinants of deforestation and forest degradation over time might be attributable to the general decrease in resource availability and to the effect of conservation measures conducted by the park.

Determinants of Local People's Perceptions and Attitudes Toward a Protected Area and Its Management: A Case Study From Popa Mountain Park, Central Myanmar

The attitudes and perceptions of local people are important for long-term survival of protected areas. Some studies have shown that peoples perceptions and attitudes are shaped by knowledge about protected area goals and characteristics and related to socioeconomic factors. In this study, socioeconomic status, knowledge of protected area characteristics, perceptions of benefits and costs of protected areas, and attitudes toward two management programs were examined for people in 208 households from 14 villages around Popa Mountain Park, Central Myanmar. Approximately 50% of respondents had basic knowledge about the park; 38% perceived benefits and 45% perceived losses due to the park. Approximately half held positive attitudes toward buffer zone establishment and replacement of banana plantations. Logistic regression revealed that perceptions of benefits and positive attitudes toward management were correlated with both sociodemographic characteristics and knowledge, while perceptions of losses and negative attitudes toward management were influenced by economic concerns.

Estimating forest canopy density of tropical mixed deciduous vegetation using Landsat data: a comparison of three classification approaches

Although a number of image classification approaches are available to estimate forest canopy density (FCD) using satellite data, assessment of their relative performances with tropical mixed deciduous vegetation is lacking. This study compared three image classification approaches – maximum likelihood classification (MLC), multiple linear regression (MLR) and FCD Mapper – in estimating the FCD of mixed deciduous forest in Myanmar. The application of MLC and MLR was based on spectral reflectance of vegetation, whereas FCD Mapper was operated on integrating the biophysical indices derived from the reflectance of the vegetation. The FCD was classified into four categories: closed canopy forest (CCF; FCD ≥ 70%), medium canopy forest (MCF; 40% ≥ FCD < 70%), open canopy forest (OCF; 10% ≥ FCD < 40%) and non-forest (NF; FCD < 10%). In the three classification approaches, producers and users accuracies were higher for more homogeneous vegetation such as NF and CCF than for heterogeneous vegetation density (VD) such as OCF and MCF. FCD Mapper produced the best overall accuracy and kappa coefficient. This study revealed that only spectral reflectance is not enough to get good results in estimating FCD in tropical mixed deciduous vegetation. This study indicates that FCD Mapper, an inexpensive approach because it requires only validation data and thus saves time, can be applied to monitor tropical mixed deciduous vegetation over time at lower cost than alternative methods.

Classifying tropical deciduous vegetation: A comparison of multiple approaches in Popa mountain park, Myanmar

Although several studies have reported that rule-based methods are better than other image classification methods, no study has quantified their performance for tropical deciduous vegetation classification. We compared rule-based and maximum likelihood classification (MLC) approaches in classifying tropical deciduous vegetation in Popa Mountain Park, Myanmar. Classification was primarily based on Thematic Mapper (TM) bands of multi-season Landsat images, normalized difference vegetation indices (NDVIs), NDVI differences, mean NDVI and elevation (advanced spaceborne thermal emission and reflection radiometer digital elevation model (Aster DEM)). We used two main approaches for classification, a single-step approach in which all vegetation types were classified in one procedure, and a two-step approach in which forest and non-forest were discriminated first and then forest was classified into additional classes. Each of those approaches was conducted with and without elevation under the rule-based and MLC approaches, yielding eight separate methods. The two-step approaches generated more accurate results and all classifications improved markedly when elevation was included. The rule-based two-step with elevation approach produced the best overall accuracy and reliability.