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Harvested timber and constructed infrastructure over the logging area leave massive damage that contributes to the emission of anthropogenic gases into the atmosphere. Carbon emissions from tropical deforestation and forest degradation are the second largest source of anthropogenic emissions of greenhouse gases. Even though the emissions vary from region to region, a significant amount of carbon emissions comes mostly from timber harvesting, which is tightly linked to the selective logging intensity. This study intended to utilize a remote sensing approach to quantify carbon emissions from selective logging activities in Ulu Jelai Forest Reserve, Pahang, Malaysia. To quantify the emissions, the relevant variables from the logging's impact were identified as a predictor in the model development and were listed as stump height, stump diameter, cross-sectional area, timber volume, logging gaps, road, skid trails, and incidental damage resulting from the logging process. The predictive performance of linear regression and machine learning models, namely support vector machine (SVM), random forest, and K-nearest neighbor, were examined to assess the carbon emission from this degraded forest. To test the different methods, a combination of ground inventory plots, unmanned aerial vehicles (UAV), and satellite imagery were analyzed, and the performance in terms of root mean square error (RMSE), bias, and coefficient of correlation (R2) were calculated. Among the four models tested, the machine learning model SVM provided the best accuracy with an RMSE of 21.10% and a bias of 0.23% with an adjusted R2 of 0.80. Meanwhile, the linear model performed second with an RMSE of 22.14%, a bias of 0.72%, and an adjusted R2 of 0.75. This study demonstrates the efficacy of remotely sensed data to facilitate the conventional methods of quantifying carbon emissions from selective logging and promoting advanced assessments that are more effective, especially in massive logging areas and various forest conditions. Findings from this research will be useful in assisting the relevant authorities in optimizing logging practices to sustain forest carbon sequestration for climate change mitigation. [ABSTRACT FROM AUTHOR]

The size of Indonesia's territory poses challenges related to border security. There are still many land border points between Indonesia and Malaysia on the island of Borneo that have not been agreed upon, as well as the specific topography of the area, which requires a new pattern of border control, compared to using the traditional pattern. This study elaborates on how a new pattern in border security uses UAV's, the strength of the 51st Air Squadron. The study was conducted using qualitative methods and selecting informants with purposive sampling techniques and data processing using the NVivo 12 Plus application. The results showed that the use of UAV's in border security was not optimal. From all research dimensions, the Country and Collaboration dimensions show a pattern that is not yet in accordance with the needs, indicating that the use of UAV's in border security must start from the policies and management organizations. [ABSTRACT FROM AUTHOR]

Introduction: Given the stagnating progress in the fight against dengue in Kota Kinabalu, there is an urgent need to use other strategies to complement the existing vector control, focusing on larviciding. Unmanned aerial vehicle (UAV) technology has been used in vector control programs in many countries. The aim of this study was to determine the feasibility of using UAVs for larviciding to control Aedes mosquitoes in urban areas., Methodology: The Hexarotor Agro Drone (Polardrone Sdn Bhd, Malaysia) was used to carry out larviciding using Vectobac® manufactured by Valent BioSciences LLC, Libertyville, USA. The drone flew at a height of 10 feet and at a speed of 5 m/s. A total of 32 items with 10 larvae in each item were placed to test the effectiveness of larviciding using UAV., Results: Out of 32 items used, 4 containers had a 100% larva mortality (13.3% mortality). The drone was not able to reach the designated spraying route that had been pre-programmed. This was due to interference with the electromagnetic waves emitted from the home satellite dishes, that were in front of the houses along the route., Conclusions: This trial showed that UAVs will be more suitable for use in larviciding in an open area without electromagnetic disturbances from electric house poles and satellite TV dishes that are commonly present in urban areas., Competing Interests: No Conflict of Interest is declared, (Copyright (c) 2024 Michal Christina Steven, Prabakaran Dhanaraj Solomon, Pathman Arumugam, Rahimah Rasali, Arthur Constantine Dominic, Hiruan Md Ideris, Dina Fidelia Anna Peter Marius.)

Retrieval of biophysical properties of mangrove vegetation ( e.g. height and above ground biomass) has typically relied upon traditional forest inventory data collection methods. Recently, the availability of Unmanned Aerial Vehicles (UAV) with different types of sensors and capabilities has proliferated, opening the possibility to expand the methods to retrieve biophysical properties of vegetation. Focusing on the Matang Mangrove Forest Reserve (MMFR) in Perak Province, Malaysia, this study aimed to investigate the use of UAV imagery for retrieving structural information on mangroves. We focused on a structurally complex 90-year-old protective forest zone and a simpler 15-year-old productive forest zone that had been silviculturally managed for charcoal production. The UAV data were acquired in June 2016. In the productive zone, the median tree stand heights retrieved from the UAV and field data were, respectively, 13.7 m and 14 m (no significant difference, p -value = .375). In the protective zone, the median tree stand heights retrieved from the UAV and field data were, respectively, 25.8 and 16.5 m (significant difference, p -value = .0001) taking into account only the upper canopy. The above ground biomass (AGB) in the productive zone was estimated at 217 Mg ha −1 using UAV data and 238 Mg ha −1 using ground inventory data. In the protective zone, the AGB was estimated at 210 Mg ha −1 using UAV data and 143 Mg ha −1 using ground inventory data, taking into account only upper canopy trees in both estimations. These observations suggested that UAV data were most useful for retrieving canopy height and biomass from forests that were relatively homogeneous and with a single dominant layer. A set of guidelines for enabling the use of UAV data for local management is presented, including suggestions as to how to use these data in combination with field observations to support management activities. This approach would be applicable in other regions where mangroves occur, particularly as these are environments that are often remote, inaccessible or difficult to work in. [ABSTRACT FROM AUTHOR]

The information on biophysical parameters—such as height, crown area, and vegetation indices such as the normalized difference vegetation index (NDVI) and normalized difference red edge index (NDRE)—are useful to monitor health conditions and the growth of oil palm trees in precision agriculture practices. The use of multispectral sensors mounted on unmanned aerial vehicles (UAV) provides high spatio-temporal resolution data to study plant health. However, the influence of UAV altitude when extracting biophysical parameters of oil palm from a multispectral sensor has not yet been well explored. Therefore, this study utilized the MicaSense RedEdge sensor mounted on a DJI Phantom–4 UAV platform for aerial photogrammetry. Three different close-range multispectral aerial images were acquired at a flight altitude of 20 m, 60 m, and 80 m above ground level (AGL) over the young oil palm plantation area in Malaysia. The images were processed using the structure from motion (SfM) technique in Pix4DMapper software and produced multispectral orthomosaic aerial images, digital surface model (DSM), and point clouds. Meanwhile, canopy height models (CHM) were generated by subtracting DSM and digital elevation models (DEM). Oil palm tree heights and crown projected area (CPA) were extracted from CHM and the orthomosaic. NDVI and NDRE were calculated using the red, red-edge, and near-infrared spectral bands of orthomosaic data. The accuracy of the extracted height and CPA were evaluated by assessing accuracy from a different altitude of UAV data with ground measured CPA and height. Correlations, root mean square deviation (RMSD), and central tendency were used to compare UAV extracted biophysical parameters with ground data. Based on our results, flying at an altitude of 60 m is the best and optimal flight altitude for estimating biophysical parameters followed by 80 m altitude. The 20 m UAV altitude showed a tendency of overestimation in biophysical parameters of young oil palm and is less consistent when extracting parameters among the others. The methodology and results are a step toward precision agriculture in the oil palm plantation area. [ABSTRACT FROM AUTHOR]

Malaysia's heritage structures are facing challenges due to rapid local development and societal challenges that threaten their cultural and artistic values. Improving conservation approaches in this context is an urgent and crucial task. The application of geo-information technologies in laser scanning, photogrammetry, and geographic information systems (GISs) has significantly improved these conservation approaches. In this study, we fused drone images and range data from a laser scanner to construct a high-resolution three-dimensional GIS city model for one traditional Malay settlement located in Malaysia. The results showed that fusing photogrammetry and laser scanning can effectively capture the architectural uniqueness of Malay buildings, including specific façade geometries on walls, roofs, and motifs. The findings show that the development of various geoinformation approaches can assist with the conservation of Malay city heritage in this region. [ABSTRACT FROM AUTHOR]

Although methods were proposed for eliminating sun glint effects from airborne and satellite images over coral reef environments, a method was not proposed previously for unmanned aerial vehicle (UAV) image data. De-glinting in UAV image analysis may improve coral distribution mapping accuracy result compared with an uncorrected image classification technique. The objective of this research was to determine accuracy of coral reef habitat classification maps based on glint correction methods proposed by Lyzenga et al., Joyce, Hedley et al., and Goodman et al. The UAV imagery collected from the coral-dominated Pulau Bidong (Peninsular Malaysia) on 20 April 2016 was analyzed in this study. Images were pre-processed with the following two strategies: Strategy-1 was the glint removal technique applied to the whole image, while Strategy-2 used only the regions impacted by glint instead of the whole image. Accuracy measures for the glint corrected images showed that the method proposed by Lyzenga et al. following Strategy-2 could eliminate glints over the branching coral—Acropora (BC), tabulate coral—Acropora + Montipora (TC), patch coral (PC), coral rubble (R), and sand (S) with greater accuracy than the other four methods using Strategy-1. Tested in two different coral environments (Site-1: Pantai Pasir Cina and Site-2: Pantai Vietnam), the glint-removed UAV imagery produced reliable maps of coral habitat distribution with finer details. The proposed strategies can potentially be used to remove glint from UAV imagery and may improve usability of glint-affected imagery, for analyzing spatiotemporal changes of coral habitats from multi-temporal UAV imagery. [ABSTRACT FROM AUTHOR]