The advent of satellite rainfall products can provide a solution to the scarcity of observed rainfall data. The present study aims to evaluate the performance of high spatial-temporal resolution satellite rainfall products (SRPs) and rain gauge data in hydrological modelling and flood inundation mapping. Four SRPs, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) - Early, - Late (IMERG-E, IMERG-L), Global Satellite Mapping of Precipitation-Near Real Time (GSMaP-NRT), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks- Cloud Classification System (PERSIANN-CCS) and rain gauge data were used as the primary input to a hydrological model, Rainfall-Runoff-Inundation (RRI) and the simulated flood level and runoff were compared with the observed data using statistical metrics. GSMaP showed the best performance in simulating hourly runoff with the lowest relative bias (RB) and the highest Nash-Sutcliffe efficiency (NSE) of 4.9% and 0.79, respectively. Meanwhile, the rain gauge data was able to produce runoff with −12.2% and 0.71 for RB and NSE, respectively. The other three SRPs showed acceptable results in daily discharge simulation (NSE value between 0.42 and 0.49, and RB value between −23.3% and −31.2%). The generated flood map also agreed with the published information. In general, the SRPs, particularly the GSMaP, showed their ability to support rapid flood forecasting required for early warning of floods.

The oil palm industry is one of the main contributors to the Malaysian national economy. However, the industry faces the challenge of Ganoderma infection, a destructive disease affecting oil palm trees that causes base stem rot (BSR). To address this issue, a novel method is proposed for detecting Ganoderma disease in oil palm trees by combining hyperspectral imaging and machine learning techniques. In this paper, four different classifiers, such as Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP), and Decision Tree (DT), were trained on a tabular dataset generated by clustering spectral signatures of each pixel in the hyperspectral image. The evaluation results show that the MLP model showed 100% accuracy, sensitivity, and specificity in distinguishing between healthy and infected trees. SVM with radial basis function (RBF) and polynomial kernels show the second highest performance, attaining 91.67% accuracy, 83.33% sensitivity, and 100% specificity. Before training on classifiers, a feature-based band alignment technique is developed to overcome the hyperspectral image misalignment issue and ensure the accuracy of the spectral data. The developed band alignment significantly improved spectral coherence across the adjacent bands, as proved by enhanced Root Mean Squared Error (RMSE), Pearson Correlation Coefficient (PCC), and Normalized Mutual Information (NMI) values. Additionally, the developed background subtraction method effectively extracted the tree pixels from the background, resulting in a high precision score of 93.99%. Future research will focus on collecting more data, incorporating temporal information for disease stage classification, and implementing a more robust machine learning (ML) model for performance enhancement. Overall, this research shows the great potential of hyperspectral imaging for accurate detection of Ganodermadisease in oil palm plantations. Doi: 10.28991/HEF-2025-06-01-05 Full Text: PDF

The current trends of floods event in many countries are alarming. Hence, managing flood and the associated risk are crucial in order to reduce the loss and to be well prepared for the combined impact of urbanization and climate changes. The best approach to manage flood activities is a risk-based approach, where the vulnerability of elements at risk is reduced to a minimum. There is a significant number of studies that use an indicator-based approach for flood vulnerability assessment with focus on the macro-scale. However, this paper assesses physical flood vulnerability of buildings at micro-scale using an indicator-based method in Kota Bharu, Malaysia. The region is one of the most flood affected regions in Malaysia. Micro-scale vulnerability assessment considers damages for individual buildings at risk, rather than in aggraded manner. In this study, the methodology adopted involve the use of 1D-2D SOBEK flood modelling, the selection and weightage of indicators, development of spatial based building index and, production of building vulnerability maps. The findings demonstrate the physical pattern of flood vulnerability of buildings at a micro-scale. The approach can assist in flood management planning and risk mitigation at a local scale

Vulnerability plays an important role in risk assessment. For flood vulnerability assessment, the map and characteristics of elements-at-risk at different scales are strongly required depending on the risk and vulnerability assessment requirements. This study proposes a methodology to classify urban structure type by combining object-based image classification and different high resolution remote sensing data. In this study, a high resolution satellite image and LiDAR have been acquired over Kota Bharu, Kelantan which consists of highly heterogeneous urban structure type (UST) classes. The first stage is data pre-processing that includes orthorectification and pansharpening of Geoeye satellite image, image resampling for normalised Digital Surface Model (nDSM) and followed by image segmentation for creating meaningful objects. The second stage comprises of derivation of image features, generation of training and testing datasets, and classification of UST. The classification was based on three types of machine learning classifiers, i.e. Random Forest (RF), Support Vector Machine (SVM) and Classification and Regression Tree (CART). The results obtained from the classification processes were compared using individual omission and commission error, overcall accuracy and Kappa coefficient. The results show that Random Forest classifier with all image features achieved the highest overall accuracy (93.5%) and Kappa coefficient (0.94). This is followed by CART classifier with overall accuracy of 93.7% and Kappa coefficient of 0.92. Finally, SVM classifier produced the lowest overall accuracy and Kappa coefficient with 88.6% and 0.86, respectively. The UST classification result can be further used to assist detailed building characterisation for large scale flood vulnerability assessment.

Climate change can significantly alter the hydrological cycle and lead to severe hydrological disasters. This study aims to determine the impact of climate change on flood hazards in the Kelantan River Basin of Malaysia. The Climate Change Factor (CCF) is used to calculate 24-hour design rainfall with 50, 100 and 200-year return periods. A distributed hydrological model, Rainfall-Runoff-Inundation (RRI), is used to simulate flood inundation under current and future climate scenarios. The results indicate an increase in 50, 100, and 200 years, design rainfall, streamflow and flood hazard. The expected design rainfall and streamflow for 50, 100, and 200 years would increase by 36.6%, 37.9%, 42.7%, and 43.2%, 32.7%, 36.5%, respectively. Flood hazard is spatially variable in the Kelantan River Basin. Tanah Merah is the town that would face a significant increase in future flooding. The findings of this study can aid relevant agencies and government departments to comprehend the current and future flood hazard situation in the Kelantan River Basin. It would also assist them in formulating appropriate flood management strategies to mitigate future severe flood hazards.