Object-Based on Land Cover Classification on LAPAN-A3 Satellite Imagery Using Tree Algorithm (Case Study: Rote Island)

Agus Herawan, Atriyon Julzarika, Patria Rachman Hakim, Ega Asti Anggari


LAPAN became serious about making a remote sensing satellite on its third-generation satellite. Launched a year after LAPAN-A2, the third-generation satellite, LAPAN-A3, brought LISAT as the main payload. LISAT is a multispectral camera with 4 bands (Red, Green, Blue, NIR) that can be used for land classification, agriculture monitoring, drought monitoring, and land use changing. LAPAN-A3 is the third generation of micro-satellite developed by Satellite Technology Center – LAPAN. This satellite carries a multispectral push-broom sensor that can record the earth's surface at the visible and near-infrared spectrum. This paper aims to determine object-based land cover classification in Rote Island using the LAPAN-A3 satellite image using the tree method algorithm. This classification technique is expected to increase the accuracy of land cover classification. This classification used the LAPAN-A3 satellite imagery of Rote Island. The first process was determined the segmentation with scale parameter 60, shape 0.5, and compactness 0.5.  The result shows that OBIA classification on Rote Island, the area of the open land class is 233.67 km2, the area of the settlement is 11.57 km2, the body of water is 2006.21 km2, the area of low vegetation is 525.93 km2, the area of high vegetation is 437.5 km2, and there is no data (cloud and cloud shadows) on the LAPAN-A3 image of 45.78 km2. The accuracy values obtained were producer 86.67%, KIA 83.02%, Helden 92.86%, Short 86.7%, KIA per class 82.72%, and 85.96%. This object-based classification can meet international and national land cover classification standards, namely at 80%.


LAPAN-A3; obia; segmentation; tree algorithm; Rote.

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P. R. Hakim, R. Permala, and A. P. S. Jayani, “Acquisition performance of LAPAN-A3/IPB multispectral imager in real-time mode of operation,†in IOP Conference Series: Earth and Environmental Science, 2018, vol. 149, no. 1, doi: 10.1088/1755-1315/149/1/012061.

P. R. Hakim, A. Hadi Syafrudin, S. Salaswati, S. Utama, and W. Hasbi, “Development of systematic image pre-processing of LAPAN-A3/IPB multispectral images,†International Journal Of Advanced Studies In Computer Science In Engineering IJASCSE, Vol 7, pp.9-18, 2018.

A. B. Imran and S. Ahmed, “Potential of Landsat-8 spectral indices to estimate forest biomass,†Int. J. Hum. Cap. Urban Manag., vol. 3, no. 4, 2018.

L. C. Alatorre et al., “Temporal changes of NDVI for qualitative environmental assessment of mangroves: Shrimp farming impact on the health decline of the arid mangroves in the Gulf of California (1990-2010),†J. Arid Environ., vol. 125, 2016, doi: 10.1016/j.jaridenv.2015.10.010.

K. G. Abrantes, M. Sheaves, and J. Fries, “Estimating the value of tropical coastal wetland habitats to fisheries: Caveats and assumptions,†PLoS One, vol. 14, no. 4, 2019, doi: 10.1371/journal.pone.0215350.

D. Phiri, J. Morgenroth, C. Xu, and T. Hermosilla, “Effects of pre-processing methods on Landsat OLI-8 land cover classification using OBIA and random forests classifier,†Int. J. Appl. Earth Obs. Geoinf., vol. 73, 2018, doi: 10.1016/j.jag.2018.06.014.

I. L. Sari and S. Fildes, “Land cover classification using Object-Based Image Analysis of SPOT-6 imagery for land cover and forest monitoring in Nagan Raya, Aceh - Indonesia,†Int. J. Adv. Sci. Eng. Inf. Technol., vol. 7, no. 6, 2017, doi: 10.18517/ijaseit.7.6.3426.

E. Lichtblau and C. J. Oswald, “Classification of impervious land-use features using object-based image analysis and data fusion,†Comput. Environ. Urban Syst., vol. 75, 2019, doi: 10.1016/j.compenvurbsys.2019.01.007.

F. Baker and C. Smith, “A GIS and object based image analysis approach to mapping the greenspace composition of domestic gardens in Leicester, UK,†Landsc. Urban Plan., vol. 183, 2019, doi: 10.1016/j.landurbplan.2018.12.002.

R. Comert, U. Avdan, T. Gorum, and H. A. Nefeslioglu, “Mapping of shallow landslides with object-based image analysis from unmanned aerial vehicle data,†Eng. Geol., vol. 260, 2019, doi: 10.1016/j.enggeo.2019.105264.

S. Iro, “Land-Cover Removal and Gully Development in Southeast Nigeria: A 30-Year Analysis with Pixel and OBIA Approaches in Juxtaposition,†Am. J. Environ. Sci., vol. 16, no. 2, 2020, doi: 10.3844/ajessp.2020.34.47.

A. B. Harto et al., “Identification of banana plants from unmanned aerial vehicles (UAV) photos using object based image analysis (OBIA) method (a case study in Sayang Village, Jatinangor District, West Java),†HAYATI J. Biosci., vol. 26, no. 1, 2019, doi: 10.4308/hjb.26.1.7.

F. Hidayat, A. W. Rudiastuti, and N. Purwono, “GEOBIA an (Geographic) Object-Based Image Analysis for coastal mapping in Indonesia: A Review,†in IOP Conference Series: Earth and Environmental Science, 2018, vol. 162, no. 1, doi: 10.1088/1755-1315/162/1/012039.

D. Stéphane, D. Laurence, G. Raffaele, A. Valérie, and R. Eloise, “Land cover maps of Antananarivo (capital of Madagascar) produced by processing multisource satellite imagery and geospatial reference data,†Data Br., vol. 31, 2020, doi: 10.1016/j.dib.2020.105952.

B. A. Robson, T. Bolch, S. MacDonell, D. Hölbling, P. Rastner, and N. Schaffer, “Automated detection of rock glaciers using deep learning and object-based image analysis,†Remote Sens. Environ., vol. 250, 2020, doi: 10.1016/j.rse.2020.112033.

A. C. dos S. Luciano et al., “A generalized space-time OBIA classification scheme to map sugarcane areas at regional scale, using Landsat images time-series and the random forest algorithm,†Int. J. Appl. Earth Obs. Geoinf., vol. 80, 2019, doi: 10.1016/j.jag.2019.04.013.

P. Amatya, D. Kirschbaum, T. Stanley, and H. Tanyas, “Landslide mapping using object-based image analysis and open source tools,†Eng. Geol., vol. 282, 2021, doi: 10.1016/j.enggeo.2021.106000.

C. A. Soares Machado and J. A. Quintanilha, “Identification of trip generators using remote sensing and geographic information system,†Transp. Res. Interdiscip. Perspect., vol. 3, 2019, doi: 10.1016/j.trip.2019.100069.

R. M. Simanjuntak, M. Kuffer, and D. Reckien, “Object-based image analysis to map local climate zones: The case of Bandung, Indonesia,†Appl. Geogr., vol. 106, 2019, doi: 10.1016/j.apgeog.2019.04.001.

B. Feizizadeh, M. Kazemi Garajeh, T. Blaschke, and T. Lakes, “An object based image analysis applied for volcanic and glacial landforms mapping in Sahand Mountain, Iran,†Catena, vol. 198, 2021, doi: 10.1016/j.catena.2020.105073.

A. Dornik, L. Dragut, and P. Urdea, “Classification of Soil Types Using Geographic Object-Based Image Analysis and Random Forests,†Pedosphere, vol. 28, no. 6, 2018, doi: 10.1016/S1002-0160(17)60377-1.

P. R. Hakim, A. H. Syafrudin, and S. Salaswati, “Analysis of radiometric calibration on matrix imager of LAPAN-A3 satellite payload,†2016, doi: 10.1109/ICARES.2015.7429831.

N. M. N. Khamsah, S. Utama, R. H. Surayuda, and P. R. Hakim, “The development of LAPAN-A3 satellite off-nadir imaging mission,†2019, doi: 10.1109/ICARES.2019.8914347.

DOI: http://dx.doi.org/10.18517/ijaseit.11.6.14200


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