2022IOP Conference Series Earth and Environmental ScienceOpen access

Utilization of UAV (Unmanned Aerial Vehicle) technology for mangrove species identification in Belawan, Medan City, North Sumatera, Indonesia

Achmad Siddik Thoha, O A Lubis, D L N Hulu, T Y Sari, M Ulfa, Zulfikar Mardiyadi

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Abstract

Abstract The mangrove forests in Indonesia are starting to decrease over time because there is still a lack of knowledge among people around the coast about the importance of mangroves. Kampung Nelayan, Medan Belawaan, is a coastal community area whose life is very dependent on coastal, mangrove, and water resources. Unmanned Aerial Vehicle (UAV) technology has the potential to provide a fast, cost-effective, and efficient mangrove mapping technique. It is very useful because mangrove areas are located in remote areas, where field measurements are difficult, time-consuming, and expensive. The objective of this study is to analyze mangrove species using UAV imagery with Object-Based Image Analysis (OBIA) classification. The object-based classification result for the overall accuracy is 82.94% where there are 7 classes of mangrove species based on the classification process, including: Avicennia alba, Avicennia officinialis, Avicennia Marina, Rhizopora apiculata, Nypah fruticans, Scyphipora hydrophylacea, Bruguiera gymnorriza. There are also two classes for non-mangrove, consisting of the water body and non-mangrove. The largest area of mangrove species in the research site is Avicennia Marina with a percentage of 33.86% covering an area of 7.80 Ha. The second-largest mangrove species with a percentage of 21.88% is Avicennia officinalis with an area of 5.04 Ha.

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Abstract The mangrove forests in Indonesia are starting to decrease over time because there is still a lack of knowledge among people around the coast about the importance of mangroves. Kampung Nelayan, Medan Belawaan, is a coastal community area whose life is very dependent on coastal, mangrove, and water resources. Unmanned Aerial Vehicle (UAV) technology has the potential to provide a fast, cost-effective, and efficient mangrove mapping technique. It is very useful because mangrove areas are located in remote areas, where field measurements are difficult, time-consuming, and expensive. The objective of this study is to analyze mangrove species using UAV imagery with Object-Based Image Analysis (OBIA) classification. The object-based classification result for the overall accuracy is 82.94% where there are 7 classes of mangrove species based on the classification process, including: Avicennia alba, Avicennia officinialis, Avicennia Marina, Rhizopora apiculata, Nypah fruticans, Scyphipora hydrophylacea, Bruguiera gymnorriza. There are also two classes for non-mangrove, consisting of the water body and non-mangrove. The largest area of mangrove species in the research site is Avicennia Marina with a percentage of 33.86% covering an area of 7.80 Ha. The second-largest mangrove species with a percentage of 21.88% is Avicennia officinalis with an area of 5.04 Ha.

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Available abstract

Abstract The mangrove forests in Indonesia are starting to decrease over time because there is still a lack of knowledge among people around the coast about the importance of mangroves. Kampung Nelayan, Medan Belawaan, is a coastal community area whose life is very dependent on coastal, mangrove, and water resources. Unmanned Aerial Vehicle (UAV) technology has the potential to provide a fast, cost-effective, and efficient mangrove mapping technique. It is very useful because mangrove areas are located in remote areas, where field measurements are difficult, time-consuming, and expensive. The objective of this study is to analyze mangrove species using UAV imagery with Object-Based Image Analysis (OBIA) classification. The object-based classification result for the overall accuracy is 82.94% where there are 7 classes of mangrove species based on the classification process, including: Avicennia alba, Avicennia officinialis, Avicennia Marina, Rhizopora apiculata, Nypah fruticans, Scyphipora hydrophylacea, Bruguiera gymnorriza. There are also two classes for non-mangrove, consisting of the water body and non-mangrove. The largest area of mangrove species in the research site is Avicennia Marina with a percentage of 33.86% covering an area of 7.80 Ha. The second-largest mangrove species with a percentage of 21.88% is Avicennia officinalis with an area of 5.04 Ha.

Key concepts: Mangrove, Avicennia marina, Avicennia, Bruguiera, Geography, Forestry, Environmental science, Ecology

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Utilization of UAV (Unmanned Aerial Vehicle) technology for mangrove species identification in Belawan, Medan City, North Sumatera, Indonesia — Research Paper | ScholarLens