Assessing the Potential of LAPAN-A3 Data for Landuse/landcover Mapping

https://doi.org/10.22146/ijg.31449

Zylshal Zylshal(1*), Rachmad Wirawan(2), Dony Kushardono(3)

(1) Indonesian National Institute of Aeronautics and Space (LAPAN)
(2) Universitas Negeri Malang
(3) Indonesian National Institute of Aeronautics and Space (LAPAN)
(*) Corresponding Author

Abstract


LAPAN-A3 / LAPAN-IPB is the third generation of micro-satellite developed by Indonesian National Institute of Aeronautics and Space (LAPAN). The satellite carries a multispectral push-broom sensor that can record the earth's surface at the visible and near-infrared spectrum. Being launched in June 2016, there has no been many publications related to the use of LAPAN-A3 multispectral data for landuse/landcover (LULC) mapping. This paper aims to provide information regarding the use of LAPAN-A3 data for the LULC extraction maximum likelihood algorithm as well as neural network and then evaluate the results. The LAPAN-A3 image was geometrically corrected by using Landsat-8 OLI image as reference data. Three test areas with a size of 1200x945 pixels are then selected for pixel-based classification with the two aforementioned algorithms. For comparison, both LAPAN-A3 and Landsat-8 data were classified for 3 test areas. Accuracy assessment was performed on both datasets using manually interpreted SPOT-6 Pansharpened image as reference data. Preliminary results showed that LAPAN-A3 were able to extract  10 different LULC classes, comprises of built-up area, forest, rivers, fishponds, shrubs, wetland forests, rice fields, sea, agricultural land, and bare soil. The overall accuracy of LAPAN-A3 data is generally lower than Landsat-8, which ranges from 49.76% to 71.74%. These results illustrate the potential of LAPAN-A3 data to derive LULC information. The lack of necessary parameters to perform radiometric correction and blurring effect are several issues that need to be solved to improve the accuracy LULC. 

Keywords


LAPAN-A3;Landsat-8;LULC;Maximum Likelihood;Neural Network

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DOI: https://doi.org/10.22146/ijg.31449

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