MACHINE LEARNING-BASED MANGROVE LAND CLASSIFICATION ON WORLDVIEW-2 SATELLITE IMAGE IN NUSA LEMBONGAN ISLAND

Aulia Ilham, Marza Ihsan Marzuki

Abstract

Machine learning is an empirical approach for regressions, clustering and/or classifying (supervised or unsupervised) on a non-linear system. This method is mainly used to analyze a complex system for  wide data observation. In remote sensing, machine learning method could be  used for image data classification with software tools independence. This research aims to classify the distribution, type, and area of mangroves using Akaike Information Criterion approach for case study in Nusa Lembongan Island. This study is important because mangrove forests have an important role ecologically, economically, and socially. For example is as a green belt for protection of coastline from storm and tsunami wave. Using satellite images Worldview-2 with data resolution of 0.46 meters, this method could identify automatically land class, sea class/water, and mangroves class. Three types of mangrove have been identified namely: Rhizophora apiculata, Sonnetaria alba, and other mangrove species. The result showed that the accuracy of classification was about 68.32%.

Keywords

clustering; machine learning; remote sensing data

Full Text:

PDF

References

Ashkezari MD, Hill CN, Follett CN, (2016), Oceanic Eddy Detection and Life Time for Ecast using Machine Learning Methods. Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology.

Bracher A., Taylor MH, Taylor B., et al., (2015). Using Empirical Orthogonal Functions Derived from Remote-Sensing Reflectance for the Prediction of Phytoplankton Pigment Concentrations. Ocean Science, Germany.

Davari AA, Christlein V., Vesal S., et al., (2017), GMM Supervectors for Limited Training Datain Hyperspectral Remote Sensing Image Classication. Friedrich-Alexander-University Erlangen-Nuremberg, Erlangen, Germany.

Hosseini R., Newlands NK, Dean CB, et al., (2015), Statistical Modeling of Soil Moisture, Integrating Satellite Remote-Sensing (SAR) and Ground-Based Data. Remote Sensing Journal.

Lary DJ, Alavi AH, Gandomi AH, et al., (2015), Machine Learningin Geosciences and Remote Sensing. University of Texas.

Liu X., (2005), Supervised Classification and Unsupervised Classification, ATS 670 Class Project.

Parviainen M., Zimmermann NE, Heikkinen RK, et al., (2013), Using Unclassified Continuous Remote Sensing Data to Improve Distribution Models of Red-Listed Plant Species. Biodivers Conserv.

Rhee J., Im J., Park S., (2016), Drought Forecasting Based on Machine Learning of Remote Sensing and Long-range Forecast Data, APEC Climate Center, Republic of Korea.

Suk-ueng K., Buranapratheprat A., Gunbua V., et al., (2017), Applicationof Remote Sensing Technique for Mangrove Mapping at the Welu Estuary, Thailand. Chiangrai Rajabhat University.

Viennois G., Proisy C., Feret JB, et al., (2015), Multitemporal Analysis of High-Spatial-Resolution Optical Satellite Imagery for Mangrove Species Mappingin Bali, Indonesia. France.

Welly M., Sanjaya W., (2010), Identifikasi Flora dan Fauna Mangrove Nusa Lembongan dan Nusa Ceningan. Coral Triangle Center.

Wicaksono P., Danoedoro P., Hartono, et al., (2015), Mangrove Biomass Carbon Stock Mapping of the Karimun Jawa Islands using Multispectral Remote Sensing. ITT.

Widagti N., Triyulianti I., Manessa MDM, (2011), Changesin Density of Mangrove Forestin Nusa Lembongan, Bali. Institute for Marine Research and Observation.

DOI: http://dx.doi.org/10.30536/j.ijreses.2017.v14.a2820

Refbacks

  • There are currently no refbacks.