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Modelling above-ground live trees biomass and carbon stock estimation of tropical lowland Dipterocarp forest: integration of field-based and remotely sensed estimates,

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Current Harumanismango farming technique in Malaysia still mostlydepends on the farmers' own expertise to monitor the crops from the attack ofpests and insects. This approach is susceptible to human errors, and thosewho do not possess this skill may not be able to detect the disease at the righttime. As leaf diseases seriously affect the crop's growth and the quality of theyield, this study aims to develop a recognition system that detects thepresence of disease in the mango leaf using image processing technique.First, the image is acquired through a smartphone camera; once it has beenpre-processed, it is then segmented in which the RGB image is converted toan HSI image, then the features are extracted. Lastly, the classification ofdisease is done to determine thetype of leaf disease. The proposed systemeffectively detects and classify the disease with an accuracy of 68.89%. Thefindings of this project will contribute to farmers and society's benefit, andresearchers can use the approach to address similar issues in future works.

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The unprecedented outbreak of Coronavirus Disease 2019 (COVID-19) has impacted the whole world in every aspect including health, social life, economic activity, education, and the environment. The pandemic has led to an improvement in air quality all around the world, including in Malaysia. Lockdowns have resulted in industry shutting down and road travel decreasing which can reduce the emission of Greenhouse Gases (GHG) and air pollution. This research assesses the impact of the COVID-19 lockdown on emissions using the Air Pollution Index (API), aerosols, and GHG which is Nitrogen Dioxide (NO 2 ) in Malaysia. The data used is from Sentinel-5p and Sentinel-2A which monitor the air quality based on Ozone (O 3 ) and NO 2 concentration. Using an interpolated API Index Map comparing 2019, before the implementation of a Movement Control Order (MCO), and 2020, after the MCO period we examine the impact on pollution during and after the COVID-19 lockdown. Data used Sentinel-5p, Sentinel-2A, and Air Pollution Index of Malaysia (APIMS) to monitor the air quality that contains NO 2 concentration. The result has shown the recovery in air quality during the MCO implementation which indirectly shows anthropogenic activities towards the environmental condition. The study will help to enhance and support the policy and scope for air pollution management strategies as well as raise public awareness of the main causes that contribute to air pollution.

Various technique and application have been used in determining the hazard analysis. AHP technique was chosen for this study in effort to find the most factors cause the landslide in the study area. The aim of this study is to determine the factors of landslide hazard using satellite imagery Landsat-8 OLI and Analytic Hierarchy Technique (AHP) in Tanjung Bungah, Penang. This study embarks on three objectives which are to identify the parameter involved in landslide hazard based on surface characteristics, to derive topographical surface from satellite image Landsat 8 OLI in relation of landslide hazard and to determine the correlation of identified parameter and derived topographical information for landslide hazard using AHP technique in Tanjung Bungah, Penang. There are 6 parameters used which are slope, aspect, lithology, rainfall, land surface temperature (LST), and soil-adjusted vegetation index (SAVI). Landsat 8 has been processed to provide the secondary data used in GIS platform. All the processed data are then overlaid using weighted overlay analysis. The output of the analysis shown is spatially visualized to examine the location of the landslide hazards risk. The map produced help in better understanding of nature impact of past, current and future development decision making.

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