• Proceedings of the Korea Water Resources Association Conference
• /
• 2005.09a
• /
• pp.396-397
• /
• 2005

• Proceedings of the Korea Water Resources Association Conference
• /
• 2005.09b
• /
• pp.1411-1412
• /
• 2005

• Journal of Forest and Environmental Science
• /
• v.39 no.2
• /
• pp.96-104
• /
• 2023

The successful restoration program requires a comprehensive understanding of variables influencing seedling efficiency. Below-ground is hypothesized to have a major impact on seedling performance of species when planted in agriculture, and degraded areas with different types of mulching. This study investigated on Sg. Terla Forest Reserve in Cameron Highlands Pahang, Malaysia. In this study randomized complete block design (RCBD) was used. The excavation method was applied to study the root system development, above, and below ground biomass distributions under different types of mulching: coconut mulching (CM), oil palm mulching (OM), plastic mulching (PM) and control (CK). The root diameter, main root length, lateral root length, root coiling, and root direction toward to sun were recorded. The results in this study indicate that mulching had significant effect on root diameter, main root length, and root distributions among treatments while for lateral root length, root: shoot ratio, dry biomass distributions, and above and below ground biomass did not showed significant effect among treatments. The highest values for root diameter, lateral root length, main root length, root distributions, dry biomass distributions and above and below ground biomass were showed in CM treatments. However 75% of root coiling was observed in seedlings between treatments.

• Korean Journal of Remote Sensing
• /
• v.34 no.1
• /
• pp.45-74
• /
• 2018

Landslide is a natural hazard that threats lives and properties in many areas around the world. Landslides are difficult to recognize, particularly in rainforest regions. Thus, an accurate, detailed, and updated inventory map is required for landslide susceptibility, hazard, and risk analyses. The inconsistency in the results obtained using different features selection techniques in the literature has highlighted the importance of evaluating these techniques. Thus, in this study, six techniques of features selection were evaluated. Very-high-resolution LiDAR point clouds and orthophotos were acquired simultaneously in a rainforest area of Cameron Highlands, Malaysia by airborne laser scanning (LiDAR). A fuzzy-based segmentation parameter (FbSP optimizer) was used to optimize the segmentation parameters. Training samples were evaluated using a stratified random sampling method and set to 70% training samples. Two machine-learning algorithms, namely, Support Vector Machine (SVM) and Random Forest (RF), were used to evaluate the performance of each features selection algorithm. The overall accuracies of the SVM and RF models revealed that three of the six algorithms exhibited higher ranks in landslide detection. Results indicated that the classification accuracies of the RF classifier were higher than the SVM classifier using either all features or only the optimal features. The proposed techniques performed well in detecting the landslides in a rainforest area of Malaysia, and these techniques can be easily extended to similar regions.