Mohd Nazip Suratman

Amorphophallus gigas is an endemic species in Sumatra with significant ecological and economic value; however, information regarding its morphological variation remains limited. This study aimed to analyze the morphological diversity of A. gigas in several locations in North Sumatra as a basis for conservation strategies. Observations were conducted on 20 individuals per location, focusing on vegetative and generative characters, and analyzed using the UPGMA clustering method with NTSYS software. The results showed that A. gigas populations were divided into two major groups: the South Labuhanbatu, North Padang Lawas, Mandailing Natal group and the North–South Tapanuli Group. The main differences were found in floral organs, particularly the color of the appendix, spathe, and staminate flowers. Cluster analysis produced a similarity coefficient of 0.33, indicating high morphological diversity among populations. This variation reflects the influence of environmental factors on morphological expression and highlights the importance of maintaining distinct populations to support genetic conservation. These findings emphasize that morphological studies can serve as an essential foundation for sustainable management and conservation of A. gigas in North Sumatra.

Tree species classification using a combination of airborne hyperspectral and Light Detection and Ranging (LiDAR) can provide valuable and effective methods for forest management, such as planning and monitoring purposes. However, only a few studies have applied tree species classification using both combinations in the tropical forest. The research takes a comparative classification approach to examine several classifiers using airborne hyperspectral in the tropical forest. In addition, Object-Based Image Analysis (OBIA) method was applied on hyperspectral data to extract the crown of individual tree species for classification and estimation purposes. Minimum Noise Fraction Transform (MNF) was applied to reduce the data dimensionality and different training samples from the various species used in this study. The result shows that Support Vector Machine (SVM) and Random Forest (RF) achieved the highest overall accuracy above 50% compared to other classifiers in the tropical forest. Besides, LiDAR data was also used to estimate individual trees' height for all species in the study area. The multiple coefficients of determination (R²) test result between LiDAR and field observation data in eight years gaps is 0.754. Therefore, Above-Ground Biomass (AGB) and carbon stock will estimate using a combination of LiDAR, hyperspectral, and field observation data for individual tree species. This method has proven to provide the required information for forest planning and generation in a short time, especially in tree species identification, AGB, and carbon stock estimation in tropical forests.

Carbon exists as carbon dioxide (CO₂) which is one of the greenhouse gases (GHG) in the atmosphere that has an enormous influence on the impact of climate change. Therefore, the forest plays an undeniably pivotal role as a carbon sink, which absorbs carbon dioxide from the atmosphere. This research aims to develop allometric equation for above-ground live tree biomass (AGB) by combining field-based, combination of field data observation and technology (WV-3 and light detection and ranging (lidar)) and by using only technology derivation. The independent predictor was induced based on the literature review and theories, and an ordinary least square (OLS) estimator will be used to develop multiple linear regression models. During model selection, the best model fit was selected by calculating statistical parameters such as residual of the coefficient of determination (R²) selection methods, adjusted coefficient of determination (R²_adj), root mean square error, graphical analysis of the residuals, standard error (S_yx), and Akaike information criterion. An allometric equation of this research was developed using carbon stocks as dependent variables, and four of the predictor’s variables: diameter at breast height (DBH); total height observed at field (h_F); total height derived from airborne lidar (h_L); and morphometric variables of the crown projection area (CPA). Based on the statistic indicators, the most suitable model is Model 1, ln (S_c) = – β₀ + β₁ ln (h_L) + β₂ ln (DBH) + β₃ ln (CPA) for the combination of remote sensing and field observation; ln (S_c) = – β₀ + β₁ ln (h_F) + β₂ ln (DBH) for field inventory only; and ln (S_c) = – β₀ + β₁ ln (h_L) + β₂ ln (CPA) for remote sensing only. This model is reliable in forest management to estimate the AGB and carbon stock estimation using a selection of variable sources.

Estimating accurate above ground biomass (AGB) of oil palm plantation in Malaysia is crucial as it serves as an important indicator to assess the role of oil palm plantations in the global carbon cycle, particularly whether it serves as carbon source or sink. Research on oil palm AGB in Malaysia using remote sensing is almost insignificant and it has known that remote sensing provides easy, inexpensive and less time consuming over larger areas. Therefore, this study focuses on evaluating the potential of Landsat Thematic Mapper (TM) data with combination of field data survey to predict AGB estimates and mapping the oil palm plantations. The relationships of AGB with individual TM bands and various selected vegetation indices were examined. In addition, various possibilities of data transform were explored in statistical analysis. The potential models selected were obtained using backward elimination method where R², adjusted R² (R²_adj), standard error of estimate (SE_E), root mean squared error (RMSE) and Mallows’s C_p criterion were examined in model development and validation. It was found that the most promising model provides moderately good prediction of about 62% of the variability of the AGB with RMSE value of 3.68 tonnes (t) ha^-1. In conclusion, Landsat TM offers the low cost AGB estimates and mapping of oil palm plantations with moderate accuracy in Malaysia.

Tropical forest embraces a large stock of carbon in the global carbon cycle and contributes to the enormous amount of above and below ground biomass. The carbon kept in the aboveground living biomass of trees is typically the largest pool and the most directly impacted by the anthropogenic factor such as deforestation and forest degradation. However, fewer studies had been proposed to model the carbon for tropical rain forest and the quantification still remain uncertainties. A multiple linear regression (MLR) is one of the methods to define the relationship between the field inventory measurements and the statistical extracted from the remotely sensed data which is LiDAR and WorldView-3 imagery (WV-3). This paper highlight the model development from fusion of multispectral WV-3 with the LIDAR metrics to model the carbon estimation of the tropical lowland Dipterocarp forest of the study area. The result shown the over segmentation and under segmentation value for this output is 0.19 and 0.11 respectively, thus D-value for the classification is 0.19 which is 81%. Overall, this study produce a significant correlation coefficient (r) between Crown projection area (CPA) and Carbon stocks (CS); height from LiDAR (H_LDR) and Carbon stocks (CS); and Crown projection area (CPA) and height from LiDAR (H_LDR) were shown 0.671, 0.709 and 0.549 respectively. The CPA of the segmentation found to be representative spatially with higher correlation of relationship between diameter at the breast height (DBH) and carbon stocks which is Pearson Correlation p = 0.000 (p < 0.01) with correlation coefficient (r) is 0.909 which shown that there a good relationship between carbon and DBH predictors to improve the inventory estimates of carbon using multiple linear regression method. The study concluded that the integration of WV-3 imagery with the CHM raster based LiDAR were useful in order to quantify the AGB and carbon stocks for a larger sample area of the Lowland Dipterocarp forest.

Aboveground biomass (AGB) is an important source of uncertainty in the carbon estimation for the tropical forest due to the variation biodiversity of species and the complex structure of tropical rain forest. Nevertheless, the tropical rainforest holds the most extensive forest in the world with the vast diversity of tree with layered canopies. With the usage of optical sensor integrate with empirical models is a common way to assess the AGB. Using the regression, the linkage between remote sensing and a biophysical parameter of the forest may be made. Therefore, this paper exemplifies the accuracy of non-linear regression equation of quadratic function to estimate the AGB and carbon stocks for the tropical lowland Dipterocarp forest of Ayer Hitam forest reserve, Selangor. The main aim of this investigation is to obtain the relationship between biophysical parameter field plots with the remotely-sensed data using nonlinear regression model. The result showed that there is a good relationship between crown projection area (CPA) and carbon stocks (CS) with Pearson Correlation (p < 0.01), the coefficient of correlation (r) is 0.671. The study concluded that the integration of Worldview-3 imagery with the canopy height model (CHM) raster based LiDAR were useful in order to quantify the AGB and carbon stocks for a larger sample area of the lowland Dipterocarp forest.

Trees planted from agroforestry practices can become valuable resources in meeting the wood requirements of many nations. Gliricidia sepium is an exotic species introduced to the agricultural sector in Malaysia mainly for providing shade for cocoa and coffee plantations. This study investigates its wood physical properties (specific gravity and moisture content) and fibre morphology (length, lumen diameter and cell wall thickness) of G. sepium at three intervals according to age groups ( three, five and seven years of ages). Specific gravity (0.72) was significantly higher at seven years ofage as compared to five (0.41) and three (0.35) years age group with a mean of 0.43 (p<0.05). Mean moisture content was 58.3% with no significant difference existing between the tree age groups. Fibre diameter (22.4 mm) was significantly lower (p<0.05) for the trees which were three years of age when compared to five and seven years age groups (26.6 mm and 24. 7 mm), respectively. Means of fibre length, lumen diameter and cell wall thickness were 0.83 mm, 18.3 mm, and 6.2 mm, respectively, with no significant differences detected between trees in all age groups. Further calculation on the coefficient of suppleness and runkel ratio suggest that wood from G.sepium may have the potential for insulation board manufacturing and paper making. However, future studies should experiment the utilisation of this species for these products to determine its full potential.