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Comparing the MLC and JavaNNS Approaches in Classifying Multi-Temporal LANDSAT Satellite Imagery over an Ephemeral River Area

Author

Listed:
  • Eufemia Tarantino

    (Department of Civil, Environmental, Land, Building and Chemistry, Politecnico di Bari, Bari, Italy)

  • Antonio Novelli

    (Department of Civil, Environmental, Land, Building and Chemistry, Politecnico di Bari, Bari, Italy)

  • Mariella Aquilino

    (Department of Civil, Environmental, Land, Building and Chemistry, Politecnico di Bari, Bari, Italy)

  • Benedetto Figorito

    (ARPA Puglia, Bari, Italy)

  • Umberto Fratino

    (Department of Civil, Environmental, Land, Building and Chemistry, Politecnico di Bari, Bari, Italy)

Abstract

This paper analyzes two pixel-based classification approaches to support the analysis of land cover transformations based on multitemporal LANDSAT sensor data covering a time space of about 24 years. The research activity presented in this paper was carried out using Lama San Giorgio (Bari, Italy) catchment area as a study case, being this area prone to flooding as proved by its geological and hydrological characteristics and by the significant number of floods occurred in the past. Land cover classes were defined in accordance with on the CN method with the aim of characterizing land use based on attitude to generate runoff. Two different classifiers, i.e. Maximum Likelihood Classifier (MLC) and Java Neural Network Simulator (JavaNNS) models, were compared. The Artificial Neural Networks (ANN) approach was found to be the most reliable and efficient when lacking ground reference data and a priori knowledge on input data distribution.

Suggested Citation

  • Eufemia Tarantino & Antonio Novelli & Mariella Aquilino & Benedetto Figorito & Umberto Fratino, 2015. "Comparing the MLC and JavaNNS Approaches in Classifying Multi-Temporal LANDSAT Satellite Imagery over an Ephemeral River Area," International Journal of Agricultural and Environmental Information Systems (IJAEIS), IGI Global, vol. 6(4), pages 83-102, October.
    • Handle: RePEc:igg:jaeis0:v:6:y:2015:i:4:p:83-102
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    Cited by:

    1. Tauheed Ullah Khan & Abdul Mannan & Charlotte E. Hacker & Shahid Ahmad & Muhammad Amir Siddique & Barkat Ullah Khan & Emad Ud Din & Minhao Chen & Chao Zhang & Moazzam Nizami & Xiaofeng Luan, 2021. "Use of GIS and Remote Sensing Data to Understand the Impacts of Land Use/Land Cover Changes (LULCC) on Snow Leopard ( Panthera uncia ) Habitat in Pakistan," Sustainability, MDPI, vol. 13(7), pages 1-19, March.
    2. Ciro Apollonio & Gabriella Balacco & Antonio Novelli & Eufemia Tarantino & Alberto Ferruccio Piccinni, 2016. "Land Use Change Impact on Flooding Areas: The Case Study of Cervaro Basin (Italy)," Sustainability, MDPI, vol. 8(10), pages 1-18, October.

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