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Agarwood is a valuable aromatic resinous wood that is biosynthesised when a fungal attack injures the healthy wood tissue of the Aquilaria tree. The magnitude of infection related to sap flow (SF) is one of the most critical functional traits to evaluate the tree’s response to various adverse conditions. Therefore, the objective of this study was to investigate the reliability of sonic tomography (SoT) and sap flow meter (SFM) in studying the influence of inoculation fungi Pichia kudriavzevii Boidin, Pignal and Besson, and Paecilomyces niveus Stolk and Samson, on deteriorated wood (Dt) and SF rate in Aquilaria malaccensis Lam. A. malaccensis trees with small, medium, and large diameters were inoculated with each fungus separately at the bottom, middle, and top positions of the tree and the area of sapwood was measured after 6, 12, and 24 months to stimulate the agarwood formation. Furthermore, the SF rate was assessed using SFM in the position of the selected trees. There was a significant difference (p ≤ 0.05) in Dt% and SF rate between inoculated and uninoculated trees. The Dt percentage in trees inoculated with P. kudriavzevii, P. niveus, and control trees was 25.6%, 25.7%, and 15.0%, respectively. The SF rate was lower in P. kudriavzevii, with 207.7 cm3/h, than in the control trees, with 312.9 cm3/h in the small-diameter class. In summary, the results of this study emphasise the importance of inoculation duration (24 months) and the effects of water conductivity, especially tree diameter class (small), on the biosynthetic response of resinous substance.

Various classification methods have been applied for low resolution of the entire Earth’s surface from recorded satellite images, but insufficient study has determined which method, for which satellite data, is economically viable for tropical forest land use mapping. This study employed Iterative Self Organizing Data Analysis Techniques (ISODATA) and K-Means classification techniques to classified Moderate Resolution Imaging Spectroradiometer (MODIS) Surface Reflectance satellite image into forests, oil palm groves, rubber plantations, mixed horticulture, mixed oil palm and rubber and mixed forest and rubber. Even though frequent cloud cover has been a challenge for mapping tropical forests, our MODIS land use classification map found that 2008 ISODATA-1 performed well with overall accuracy of 94%, with the highest Producer’s Accuracy of Forest with 86%, and were consistent with MODIS Land Cover 2008 (MOD12Q1), respectively. The MODIS land use classification was able to distinguish young oil palm groves from open areas, rubber and mature oil palm plantations, on the Advanced Land Observing Satellite (ALOS) map, whereas rubber was more easily distinguished from an open area than from mixed rubber and forest. This study provides insight on the potential for integrating regional databases and temporal MODIS data, in order to map land use in tropical forest regions.

Oil palms are an important crop for Malaysia as the main crop cultivated from agricultural lands for economic purposes. The livelihood of small growers is, in fact, very dependent on that industry. The present study employs the application of remote sensing of higher resolution to assess the biophysical characteristics of oil palms stands for a plantation in Lenggeng, Negeri Sembilan, Malay Peninsula. Band combination with the use of natural, red, blue bands and red-edge spectrum was employed to obtain early information on the oil palm stands at the site. We subsequently employed fish eye camera to collect information on leaf area index at the field. The study also measured the height and diameter at breast height of all plots established in the site. Finally, correlation was performed to establish the relationships between height-to-leaf area index relations. Diameter at breast height measuring points was scattered at the upper part of the line that formed negative relationships (R2 = -0.0313). Height was positively associated with leaf area index, a bit weaker (R2 = 0.2323). Interpolation found plots at varying elevation level. Maximum height of the trees was recorded at the highest elevation in the site, presumably due to the higher solar radiation that enhances photosynthesis. Our study demonstrates the usefulness of the finding for implementation elsewhere in assessing the biophysical characteristics of oil palm trees. The study leads to further understanding of oil palms, specifically the biophysical characteristics associated with plant productivity assessment. [ABSTRACT FROM AUTHOR]

Development of forest plantation is mainly intended to provide income opportunity and to ensure the improvement of the socioeconomics of society. Sabah Softwood Berhad (SSB) is one of the private companies that pioneered the development and commercialization of fast growing timber species and forest plantation. The Eucalyptus plantation in SSB was initiated in response to the emergence of fungal disease that implicated the environment. Nevertheless, the impacts of plantation to the environment regarding water use, soil erosion, nutrient loss, pest and disease, biodiversity, soil quality and hydrology are reviewed. The disputes among various scientists over the physiological and plantation aspects of Eucalyptus, especially their implication towards the environment are also discussed. This paper aims to present information on the impacts posed by the Eucalyptus plantation that will guide the planters and policy makers in facilitating a management plan to mitigate the impacts of this species to the environment. [ABSTRACT FROM AUTHOR]

Combining different resolution of remote sensing satellites becomes a unique approach for vegetation and wildlife habitat assessment study. Remote sensing technology can reach land and water on the Earth's surface, and it can interpret signals from spectral responses. When these techniques are combined with Geographical Information Systems (GIS), land can be monitored in a variety of ways. Meanwhile, changes in land use led to changes in vegetation on the ground, with natural vegetation being removed from natural forests, leaving a degraded forest. This issue was not investigated for assessing habitat suitability for important plantations such as Eucalyptus plantation. Therefore, the study employed remote sensing and Geographical Information System (GIS) to model suitability of habitat to live and to survive in the Eucalyptus plantation. Normalized Difference Vegetation Index (NDVI) and Normalized Difference Water Index (NDWI) derived from a mathematical equation can demonstrate intensity of greenness of green vegetation in particular area and time, and availability of soil moisture, respectively, is very suitable to model the greenness of the area. WorldView-2 satellite image was pre-proceed, proceed, and classified to produce land use indicator in Sabah Softwoods Berhad plantation majoring Eucalyptus spp. tree planted in Tawau, Sabah. Sentinel and Landsat 8 image were used for vegetation and water stress indicator were downloaded from Land Viewer application. Net Primary Productivity (NPP) at monthly scale was also calculated and ranked the productivity for the suitability mapping. Climatic condition based on monthly precipitation and seasonality derived from ASEAN Specialized Meteorological Centre (ASMC) was employed for ranking its suitability value. In this study, natural forest and oil palm plantation is tested to developed suitability map for vegetation and wildlife habitat to live with. All indicators were ranked 10 to 40 presenting benefit and usefulness of the indicator to vegetation and wildlife in the study area. Then, final classification was made from accumulation of those indicators into 0 to 200 (Not suitable to Highly suitable). The results showed 59.9% of the area classified as moderately suitable, 36.9% highly suitable, 3.2% least suitable and no area was classified as not suitable. This type of study assisted forest managers and policymakers for better managing of their forests for better life of trees and wildlife under their management. The methodology adapted in the study is ecologically sounded and economically viable to be modified and complied in Sustainable Forest Management (SFM) in Malaysia and other tropical forest regions.

Peatland refers to the peat soil and wetland biological environment growing on the surface. However, unexpected fires in peatlands frequently have brought severe greenhouse gas emissions and transboundary haze to Southeast Asia. To alleviate this issue, this paper first establishes an Internet of Things (IoT) system for peatland monitoring and management in the Raja Musa Forest Reserve (RMFR) in Selangor, Malaysia, and proposes a more efficient and low-complexity model for calculating the Duff Moisture Code (DMC) in peatland forests using groundwater level (GWL) and relative humidity. The feasibility of the IoT system is verified by comparing its data with those published by Malaysian Meteorological Department (METMalaysia). The proposed Linear_DMC Model and Linear_Mixed_DMC Model are compared with the Canadian Fire Weather Index (FWI) model, and their performance is evaluated using IoT measurement data and actual values published by METMalaysia. The results show that the correlation between the measured data of the IoT system and the data from METMalaysia within the same duration is larger than 0.84, with a mean square error (MSE) of 2.56, and a correlation of 0.91 can be achieved between calculated DMC using the proposed model and actual values. This finding is of great significance for predicting peatland forest fires in the field and providing the basis for fire prevention and decision making to improve disaster prevention and reduction.

Eucalyptus is a diverse genus from which several species are often deployed for commercial industrial tree plantation due to their desirable wood properties for utilization in both solid wood and fiber products, as well as their growth and productivity in many environments. In this study, a method for monitoring the health status of a 22.78 ha Eucalyptus pellita plantation stand was developed using the red-green-blue channels captured using an unmanned aerial vehicle. The ortho-image was generated, and visual atmospheric resistance index (VARI) indices were developed. Herein, four classification levels of pest and disease were generated using the VARI-green algorithm. The range of normalized VARI-green indices was between −2.0 and 2.0. The results identified seven dead trees (VARI-green index −2 to 0), five trees that were severely infected (VARI-green index 0 to 0.05), 967 trees that were mildly infected (VARI-green index 0.06 to 0.16), and 10,090 trees that were considered healthy (VARI-green index 0.17 to 2.00). The VARI-green indices were verified by manual ground-truthing and by comparison with normalized difference vegetation index which showed a mean correlation of 0.73. This study has shown practical application of aerial survey of a large-scale operational area of industrial tree plantation via low-cost UAV and RGB camera, to analyze VARI-green images in the detection of pest and disease.