scholarly journals Object-based image analysis (OBIA) for mapping mangrove using Unmanned Aerial Vehicle (UAV) on Tidung Kecil Island, Kepulauan Seribu, DKI Jakarta Province

2021 ◽  
Vol 944 (1) ◽  
pp. 012037
Author(s):  
R A Pasaribu ◽  
F A Aditama ◽  
P Setyabudi
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Spatial Data ◽  
Design Research ◽  
Direct Role ◽  
Object Based Image Analysis ◽  
Mangrove Ecosystems ◽  
Sensing Technology ◽  
Object Based ◽  
Good Classification ◽  
Aerial Vehicle

Abstract Tidung Kecil Island is a conservation and mangrove cultivation area. Therefore, the potential of mangrove ecosystems on Tidung Kecil Island will have a direct role in coastal ecosystems. Accurate mangrove mapping is necessary for the effective planning and management of ecosystems and resources because mangroves function as protectors of ecological systems. The utilization of remote sensing technology that is near real-time can be used as an alternative in providing spatial data effectively. Mapping earth’s surface objects method is growing especially after the development of design, research, and production of flexible Unmanned Aerial Vehicle (UAV) platforms. The use of object-based classification methods is currently an alternative in classifying an object of the Earth’s surface using both satellite and aerial photo imagery data (orthophoto) that has a high accuracy value. This research aim is to map object based mangrove ecosystems using UAV technology on Tidung Kecil Island, Kepulauan Seribu, DKI Jakarta. The K-NN algorithm result was a good classification with 81.081% overall accuracy (OA) at the optimum value of the MRS segmentation scale 300;0,1;0.7 and divided into two classes which are mangrove and non-mangrove for 0.381 ha and 20.912 ha respectively.

scholarly journals KLASIFIKASI MANGROVE BERBASIS OBJEK DAN PIKSEL MENGGUNAKAN CITRA SENTINEL-2B DI SUNGAI LIONG, BENGKALIS, PROVINSI RIAU

2018 ◽  
Vol 10 (3) ◽  
pp. 601-615
Author(s):  
. Rosmasita ◽  
Vincentius P. Siregar ◽  
Syamsul B. Agus
Keyword(s):  
Support Vector Machine ◽  
Land Cover ◽  
Unmanned Aerial Vehicle ◽  
Spatial Data ◽  
Data Availability ◽  
Support Vector ◽  
Object Based ◽  
Svm Algorithm ◽  
Aerial Vehicle ◽  
Increase In Accuracy

ABSTRAK Penelitian pemetaan mangrove di Sungai Liong, Bengkalis Provinsi Riau sangat terbatas, sehingga ketersediaan data spasial di wilayah ini masih sangat terbatas. Pemanfaatan citra satelit dapat dijadikan alternatif dalam menyediakan data spasial secara efektif dan efesien. Penelitian ini bertujuan untuk memetakan mangrove sampai tingkat komunitas menggunakan citra sentinel 2B dengan metode klasifikasi berbasis objek/OBIA dan membandingkannya dengan teknik klasifikasi berbasis piksel. Algoritma yang digunakan pada penelitian ini adalah support vector machine (SVM). Pengembangan skema klasifikasi mangrove pada penelitian ini di bagi menjadi 2 level, yaitu kelas penutup lahan di sekitar mangrove dan kelas komunitas mangrove. Data yang digunakan untuk klasifikasi kelas penutup lahan adalah data foto udara yang diperoleh dengan menggunakan pesawat tanpa awak (unmanned aerial vehicle/UAV) dan untuk klasifikasi komunitas menggunakan data transek tahun 2013. Akurasi keseluruhan  (OA) yang diperoleh untuk klafikasi penutup lahan mangrove dengan kedua teknik klasifikasi berbasis objek dan piksel berturut-turut adalah 78,7% dan 70,9%. Sedangkan akurasi keseluruhan (OA) untuk klasifikasi komunitas mangrove berbasis objek dan piksel berutru-turut yaitu 76,6% dan 75,0%. Sekitar 7,8% peningkatan akurasi pemetaan penutup lahan dan sekitar 1,6% peningkatan akurasi pemetaan komunitas mangrove yang diperoleh dengan metode klasifikasi berbasis objek. ABSTRACTResearch on mangrove mapping at the Liong River Bengkalis Riau Province was very limited, therefore the spatial data availability of mangrove in Liong River is also very limited. The use of satellite remote sensing to map mangrove has become widespread as it can provide accurate, effecient, and repeatable assessments. The purposed of this study was to map mangrove at the community level using sentinel 2B imagery based on object-based classification method (OBIA) and it compared pixel-based classification at Liong River, Bengkalis, Riau Provinc. This study was used support vector machine (SVM) algorithm. The scheme classification use is that land cover and mangrove community. The classification data of land cover was collected using unmanned aerial vehicle (UAV) and community mangrove was using transect data of 2013. The result of land cover classification and community mangrove indicated that object-based classification technique was better than pixel-based classification. The highest an overall accuracy of land cover is 78.7% versus 70.9%, whereas mangrove community is 76.6 versus 75.0%. Approximately 7.8% increase in accuracy can be achieved by object-based method of classification for land cover and 1.6% for mangrove community.

scholarly journals OBJECT BASED BUILDING EXTRACTION AND BUILDING PERIOD ESTIMATION FROM UNMANNED AERIAL VEHICLE DATA

2018 ◽  
Vol IV-3 ◽  
pp. 71-76 ◽  
Author(s):  
Resul Comert ◽  
Onur Kaplan
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Accuracy Assessment ◽  
Field Measurements ◽  
Seismic Performance Assessment ◽  
Object Based Image Analysis ◽  
Vehicle Data ◽  
Vulnerability Assessments ◽  
Object Based ◽  
Period Estimation ◽  
Aerial Vehicle

The aim of this study is to examine whether it is possible to estimate the building periods with respect to the building heights in the urban scale seismic performance assessment studies by using the building height retrieved from the unmanned aerial vehicle (UAV) data. For this purpose, a small area, which includes eight residential reinforced concrete buildings, was selected in Eskisehir (Turkey) city center. In this paper, the possibilities of obtaining the building heights that are used in the estimation of building periods from UAV based data, have been investigated. The investigations were carried out in 3 stages; (i) Building boundary extraction with Object Based Image Analysis (OBIA), (ii) height calculation for buildings of interest from nDSM and accuracy assessment with the terrestrial survey. (iii) Estimation of building period using height information. The average difference between the periods estimated according to the heights obtained from field measurements and from the UAV data is 2.86 % and the maximum difference is 13.2 %. Results of this study have shown that the building heights retrieved from the UAV data can be used in the building period estimation in the urban scale vulnerability assessments.

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