Earticle

Deforestation is a major and very critical problem faced by many tropical countries including Malaysia. Sabah is the second largest state in Malaysia and its deforestation rate has been accelerating. This study was conducted to monitor the deforestation in Sabah in the last two decades with Landsat images of 1990, 2000 and 2008. Supervised classification with maximum likelihood algorithm was conducted using the Landsat data for monitoring deforestation. In total, between 1990 and 2008, Sabah lost half of its intact forest, or more than 1.85 million ha in less than two decades. Overall, the deforestation rate for all forest types combined for the last two decades was 1.6% per year. Deforestation seemed to be accelerating because the deforestation rate between 1990 and 2000 was 0.9% per year and it had increased to 2.7% per year between 2000 and 2008. The deforestation trend seemed to follow a negative exponential from 1990 to 2008. In contrast, the agricultural areas increased rapidly with a total of increment more than 1 million ha. This confirmed that agriculture especially establishment of commercial plantation was the major factor of deforestation in Sabah for the last two decades.

Sarawak is the largest state in Malaysia that covers 37.5% of the total land area. Multitemporal satellite images of Landsat and SPOT were used to examine deforestation and forest fragmentation in Sarawak between 1990 and 2009. Supervised classification with maximum likelihood classifier was used to classify the land cover types in Sarawak. The overall accuracies of all classifications were more than 80%. Our results showed that forests were reduced at 0.62% annually during the two decades. The peat swamp forest suffered a tremendous loss of almost 50% between 1990 and 2009 especially at coastal divisions due to intensified oil palm plantation development. Fragmentation analysis revealed the loss of about 65% of the core area of intact forest during the change period. The core area of peat swamp forest had almost completely disappeared during the two decades.

Sabah has experienced a rapid decline in the extent of forest cover. The precise impact of habitat loss on the conservation status of the plants of Sabah is uncertain. In this study we use the niche modelling algorithm MAXENT to construct preliminary, revised and final ecological niche models for Dipterocarpus lamellatus and Dipterocarpus ochraceus and combined these models with data on current land-use to derive conservation assessments for each species. Preliminary models were based on herbarium data alone. Ground surveys were conducted to evaluate the performance of these preliminary models, and a revised niche model was generated from the combined herbarium and ground survey data. The final model was obtained by constraining the predictions of the revised models by filters. The range overlap between the preliminary and revised models was 0.47 for D. lamellatus and 0.39 for D. ochraceus, suggesting poor agreement between them. There was substantial variation in estimates of habitat loss for D. ochraceus, among the preliminary, revised and constrained models, and this has the potential to lead to incorrect threat assessments. From these estimates of habitat loss, the historic distribution and estimates of population size we determine that both species should be classified as Critically Endangered under IUCN Red List guidelines. Our results suggest that ground-truthing of ecological niche models is essential, especially if the models are being used for conservation decision making.

Dipterocarps or Dipterocarpaceae is a commercially important timber producing and dominant keystone tree family in the rain forests of Borneo. Borneo’s landscape is changing at an unprecedented rate in recent years which affects this important biodiversity. This paper attempts to model the natural occurrence (distribution including those areas with natural forests before being converted to other land uses as opposed to current distribution) of dipterocarp species in Sarawak which is important for forest biodiversity conservation and management. Local modeling method of Inverse Distance Weighting was compared with commonly used statistical method (Binary Logistic Regression) to build the best natural distribution models for three genera (12 species) of dipterocarps. Database of species occurrence data and pseudoabsence data were constructed and divided into two halves for model building and validation. For logistic regression modeling, climatic, topographical and edaphic parameters were used. Proxy variables were used to represent the parameters which were highly (p＞0.75) correlated to avoid over-fitting. The results show that Inverse Distance Weighting produced the best and consistent prediction with an average accuracy of over 80%. This study demonstrates that local interpolation method can be used for the modeling of natural distribution of dipterocarp species. The Inverse Distance Weighted was proven a better method and the possible reasons are discussed.

Dipterocarps (Dipterocarpaceae) is a dominant tree family of tropical rainforest in Southeast Asia. Dipterocarps have been exploited for its timber and disappearing fast in East Kalimantan. In this study, we predicted the distribution of dipterocarpus, one of the main dipterocarps genera, by evaluating its habitat suitability using logistic regression analysis with specimen collection points and environmental factors from GIS data. Current distribution of dipterocarpus was generated by combining the habitat suitability classes with an updated forest cover map. Rainfall, soil type, followed by elevation was the main factors that influence the distribution of dipterocarpus in East Kalimantan. Dipterocarpus can be found in a quarter of the current forest cover, which is highly suitable as habitat of Dipterocarpus.

This study was carried out in Gana Resettlement and Integrated Development (GRID) Project as a pioneer model for Community-Based Forest Management (CBFM) in Sabah, Malaysia. The GRID project is developed to improve community livelihood as well as to promote community involvement in CBFM and at the same time to lessen community encroachment and traditional farming practices inside the forest conservation area. The objectives of this study are: (i) to identify the dependency of local community on forest resources; (ii) to examine local community’s roles in conserving forest resources and, (iii) to analyze their views on GRID project in conserving forest. Interviews were conducted with randomly selected 130 households at the GRID project using semi structured questionnaires comprising closed and open-ended questions. The study reveals that community dependency on forest resources have lessened after the resettlement of the GRID project. The community shows positive perspective on their contribution for conserving the forest resources. However, the community’s willingness to contribute as well as their real contribution can be further improved. The study recommends the government to create more effective training and relevant activities to improve the livelihood of community as well as to increase their positive perception on forest conservation efforts. Further research to strengthen the relationship between local community and forest governance through forest conservation is also recommended.

In order not to hinder the trend of promoting participatory forest management under the REDD-plus mechanism, we propose a simple method to support local stakeholders evaluating and selecting promising national programs as REDD-plus activities prior to the launch of activities. Program evaluation is done from the viewpoint of expectations for achieving (1) ‘triple-benefit’ consisting of emission reduction from mitigating deforestation and forest degradation, biodiversity conservation, and poverty alleviation,and (2) ‘feasibility’ when the programs would be practiced on the ground. The method is applicable for other countries.