Engineering Headway Vol. 27

Abstract: Technological advancements in data acquisition using Unmanned Aerial Vehicles (UAVs) are increasingly applied in biosecurity, particularly for monitoring forest plantations like Eucalyptus pellita. This research has two objectives, i.e. to compare the challenges in obtaining accurate information on Industrial Forest Plantation plants and to provide an overview of the efforts made in solving this problem and the optimal results of the information obtained.The method used in the research is to conduct a Strength, Weakness, Opportunity, and Threat (SWOT) assessment on the utilization of UAVs in biosecurity development and focus on (1) Leaf Disease Severity Detection, (2) Crown Density Modelling, (3) CHM for Individual Tree Detection (4) DBH Estimation Modelling (5) Large Scale Acquisition Procedures. Strengths of UAVs include their ability to provide fast, efficient, and highly accurate data for early detection of plant diseases and pests. Weaknesses from UAVs’ reliance on favorable weather conditions. Opportunities exist in integrating UAV data with advanced analytical methods to improve biosecurity. Rapid technological advancements can become a threat as they compel organizations to continuously upgrade equipment, while widespread UAV use may raise legal and ethical concerns, including privacy and regulatory challenges. However, UAV can be a technological instrument for implementing biosecurity risk management.

Abstract: The development of remote sensing technology based on cloud computing is an important concern, especially in providing convenience in processing and storing images for various importance. This can be utilized in hydrographic activities in terms of determining depth values using the Satellite Derived Bathymetry (SDB) method. This research focuses on the utilization of Google Earth Engine (GEE) technology in obtaining depth values using the SDB method with Stumpf and Lyzenga algorithms. The area used in this research is on the island of Bangka Belitung which has different characteristics of waters such water depth, water clarity, and water bottom cover. The results of the research conducted were evaluated with the Indonesian National Standard (SNI) 8202 in 2015 regarding the accuracy of the base map. Depth extraction using 2,662 depth sample data. The results of this study show that the Stumpf model provides a better accuracy value compared to the Lyzenga model at 0 - 10 meters. But at 10-25 meters the Lyzenga model produces a better accuracy value than the Stumpf model. The best correlation value was obtained with the Lyzenga model, which is equal to 0.841 while the correlation value of the Stumpf model is 0.753. The best RMSE was obtained at 1.7 m with a depth range of 0 - 5 m with the Stumpf method. The results of the accuracy evaluation of the two empirical bathymetry models pass the requirements of SNI 8202 of 2015 regarding the Accuracy of the Base Map for making Lingkungan Pantai Indonesia (LPI) and Lingkungan Laut Nasional (LLN) maps at a scale of 1: 50,000 with a 20 m contour interval.

Abstract: The Eco-Resort area at Raja Ampat is frequently impacted by extreme waves, particularly during the west monsoon, increasing the risk of abrasion and damage to ecosystems and infrastructure. A detached hexareef breakwater configuration was designed to reduce wave height while maintaining coastal aesthetics, as the site is an eco-friendly tourism area. The data used included tidal observations, bathymetry and topography surveys, wind and wave data, and hexareef formation data. The configuration was designed based on the beach response index, leading to periodic tombolo formation. Four configuration alternatives were developed based on freeboard (f) values under the HWS (Highest Water Springs) condition. Numerical modeling using Delft3D-WAVE assessed wave reduction effectiveness for each alternative. Results showed wave reductions for two significant wind and wave directions - North and Northwest - as follows: 24% and 33% for Alternative 1 (f = -1.018 m), 46% and 55% for Alternative 2 (f = -0.518 m), 66% and 71% for Alternative 3 (f = -0.218 m), and 72% and 77% for Alternative 4 (f = -0.018 m). Alternative 3 was chosen because its performance was only 6% lower than Alternative 4 (the most effective); however, it has a submergence value that is more suitable for maintaining coastal aesthetics.

Abstract: Mangroves have an important role in the carbon storage cycle within coastal ecosystems. Indonesia, home to the largest mangrove forest in the world, has a potential in carbon trading particularly in blue carbon. It is essential to accurately identify the presence of mangrove to realize that potential. Remote Sensing (RS) and Machine Learning (ML) technology can be used to identify the presence of mangroves. In this research, the integration of active, passive sensors, National Digital Elevation Model (DEMNAS) and the used of the Random Forest (RF) algorithm were applied to identify the presence of mangroves. There were 30 independent variables, consisting of 4 independent variables from Sentinel-1A, 25 independent variables from Sentinel-2A and 1 independent variable from DEMNAS. The model was built from 75 sampling plots, 32 cross validation internal plot, and 25 testing plots. The optimal number of trees used for mapping mangrove in Youtefa Bay were 100, 200, and 800 with internal validation model 0.854. The results of this research show that mangrove forest area in Youtefa Bay is 189 ha with total accuracy 91 % and kappa index 0.89. DEMNAS, NDMI and MVI are the best independent variables on identifying mangroves in Youtefa Bay.

Abstract: The construction of the airport triggers changes in land cover. These changes can be identified using remote sensing technology. The change analysis is conducted using Sentinel-2 imagery with a resolution of 10 meters. However, the results of the analysis are still quite general, as the imagery used cannot directly provide detailed information about the types of land use that are evolving. Therefore, based on the land cover changes, areas with the most dominant changes are selected for further analysis. These areas are then analyzed using very high-resolution imagery to determine the specific land use changes in 2022. The aim of this research is to map land cover changes in 2016-2022 using medium-resolution imagery in the YIA Strategic Area and examine the types of land use changes using high-resolution imagery in the Strategic Area of YIA based on the results of land cover changes. Land cover maps for 2016 and 2022 were obtained through multispectral classification using maximum likelihood algorithm on Sentinel-2 imagery. The overlay of both land cover maps indicates a decrease in the area for fields/fallow land, mixed gardens, ponds, rice fields, and open land. On the other hand, there was an increase in the area for residential buildings, roads, airport areas, and buildings used for industry, commerce, and offices. Based on the results of the hotspot analysis, the concentration of changes occurred around the airport, especially from non-building land cover to residential buildings, commercial industrial and offices. Detailed analysis using high-resolution satellite imagery (HRSI) show that the dominant objects that have developed include residential houses and boarding houses, residential complexes, hotels, schools, public facilities, healthcare facilities, and offices. Residential complexes, boarding houses, gas stations, hotels, schools, offices, and religious and health center were closely related to the presence of airports. Several objects were built because of the relocation of airport construction. For residential buildings, several objects were built because of the presence of the airport, and some were built because of personal interests.

Abstract: This study aims to model lahar flow from Volcano Merapi in the Krasak River following the 2010 eruption. The spatial modeling results of lahar flow are used to identify and predict lahar hazard zone. The lahar flow modeling is conducted using the Laharz toolbox, utilizing DEMNAS data, and lahar volume scenarios based on historical lahar volume data for the Krasak River from 2011. Remote sensing data, specifically Sentinel-2 imagery, is used in this study with interpretation methods to derive river hydrology information, which serves as one of the validation measures for the Krasak River flow. The model is developed based on predetermined volume scenarios: Scenario I with an initial volume of 125.000 m³, Scenario II with doubled volume of 250.000 m³, Scenario III with lahar volume of 500.000 m³, and Scenario IV with lahar volume of 1.000.000 m³. The model validation is conducted using the Mount Merapi Disaster-Prone Area Map. The resulting model is applied to predict hazard zone using a buffer method along the river, with specific distances defined. The model results indicate that as the lahar volume scenario increases, the lahar flow model can impact the prediction of lahar hazard zone.

Abstract: Poor waste handling causes problems in the form of increasing waste generation. The availability of waste treatment plants is needed to process waste before it is disposed of in landfills. In addition, rapid population growth leads to increased waste production. Therefore, the procurement of Reduce, Reuse, Recycle-based Waste Management Sites can be a solution. The procurement of Reduce, Reuse, Recyclebased Waste Management Sites must be designed by taking into account regulations and the physical appearance of the area. This needs to be done to protect the surrounding environment and not interfere with residents' activities. In planning the determination of 3R Waste Processing Sites locations, remote sensing and geographic information systems can be utilized, as well as the Analytical Hierarchy Process (AHP) approach to map the suitability of 3R Waste Processing Sites locations. In data processing, 5 parameters are used, land use, road class, distance to roads, distance to settlements, and distance to rivers. The results of the AHP analysis show that land use is the criteria with the highest weight at 53%, distance to the road is worth 22%, road class is worth 11%, distance to settlements is worth 8%, and distance to the river is 7%. The AHP model built has a Consistency Ratio (CR) value of 6.7% so it is considered valid. The final results show that the areas suitable for the provision of 3R Waste Processing Sites are bushes, vacant land, and plantations in the western part of Singosaren Village.

Abstract: This study investigates the relationship between the Urban Heat Island (UHI) phenomenon and population density on Batam Island, Indonesia, a rapidly urbanizing industrial hub. Accelerated urbanization has increased population and building density, intensifying UHI effects and challenging sustainable development. Using Landsat 8 satellite imagery from 2013, 2018, and 2022, temperature variations were analyzed to identify areas most affected by UHI. Population density was calculated using district administrative boundaries from the Geospatial Information Agency and population data from Statistics Indonesia. A model was developed to explore the correlation between UHI and population density, highlighting urbanization's impact on rising temperatures. The findings provide critical insights for local environmental policy and sustainable urban planning. Analysis of 30 data points revealed differing correlation strengths between UHI and population density (0.495) and LST and population density (0.611), indicating that while urbanization affects both UHI and LST, LST is more directly influenced by land cover changes. UHI effects, however, may be shaped by urban morphology. The study contributes to strategies for mitigating environmental impacts and promoting sustainable urban growth on Batam Island.

Abstract: The construction of Yogyakarta International Airport (YIA), driven by the limitations of land use at the former Adisutjipto Airport, is a key focal point of this research. The development of YIA has led to significant land cover changes in 2016 to 2021, transforming predominantly agricultural and fisheries land into built-up areas. Numerous studies have utilized remote sensing data to analyze land cover and land use (LULC) changes in Kulonprogo Regency, applying a range of remote sensing analytical methods. The most substantial LULC changes have been observed in Temon District, where agricultural land has sharply decreased, coinciding with the expansion of built-up areas. This study aims to further examine land use changes in Temon District, employing object-based classification techniques to enhance the accuracy of land cover analysis. In this study, OBIA classified land cover with 80% accuracy for the 2016 image (scale 100, shape 0.7, compactness 0.7) and 86% for the 2021 image (scale 100, shape 0.9, compactness 0.2). The most significant change was a 537-hectare reduction in paddy field.

Abstract: Reconstructing a three-dimensional model of a real-world object requires a quality point cloud to produce an accurate three-dimensional model. Point clouds can be produced by image-based and distance-based methods. The image-based method utilises the light reflected by the object, which is then processed using the principle of photogrammetry. This research is intended to test the image-based method with the help of the structure from motion approach to generate the quality of point clouds using open-source software including Visual SfM, MicMac, Meshroom and with the Agisoft Metashape Pro commercial software. The images are taken by a Xiaomi Yi Action camera with 2.68 mm focal length. The performance comparisons will consider Volume Density, Surface Variation and Point discrepancy. We utilize BLK 360 terrestrial laser scanner to collect reference point cloud. The results show that all software provide convincing result on each parameters compared.