Assessment of the effectiveness of supervised and unsupervised methods: maximizing land-cover classification accuracy with spectral indices data

Andrea S. Brendel; Federico Ferrelli; María C. Piccolo; Gerardo M. E. Perillo

doi:10.1117/1.JRS.13.014503

14 January 2019 Assessment of the effectiveness of supervised and unsupervised methods: maximizing land-cover classification accuracy with spectral indices data

Andrea S. Brendel, Federico Ferrelli, María C. Piccolo, Gerardo M. E. Perillo

Author Affiliations +

Funded by: Centro de Investigaciones Científicas (CONICET), Universidad Nacional del Sur (UNS), Inter-American Institute for Global Change Research (IAI)

Journal of Applied Remote Sensing, Vol. 13, Issue 1, 014503 (January 2019). https://doi.org/10.1117/1.JRS.13.014503

Abstract

This study is aimed at evaluating the effectiveness of different supervised and unsupervised methods with information derived from Landsat satellite images and fieldwork in order to maximize the land cover classification accuracy in an area with geomorphologic differences and heterogeneous edaphic characteristics located in the southwest of the Pampas (Argentina). We test two datasets: bands-based and indices-based and also we analyze the spectral behavior of each land cover identified by field trips and surveys with farmers to improve the spatial samples employed in the digital processing. Complementarily, we study the spatial and temporal information about the land cover changes during 2000 to 2016. The classification based on indices widely outperforms the analyses based on bands. The best methods to classify the land cover are the Mahalanobis distance and the maximum likelihood. The values of kappa coefficient and overall accuracy obtain from these two methods allow us to realize a multitemporal study. This study provides essential information for semiarid regions with rain-fed agriculture and livestock activities worldwide. The knowledge obtained quickly and accurately about the land covers and their changes provides essential information about the past and current situations and can be used to predict likely future trends.

Citation Download Citation

Andrea S. Brendel, Federico Ferrelli, María C. Piccolo, and Gerardo M. E. Perillo "Assessment of the effectiveness of supervised and unsupervised methods: maximizing land-cover classification accuracy with spectral indices data," Journal of Applied Remote Sensing 13(1), 014503 (14 January 2019). https://doi.org/10.1117/1.JRS.13.014503

Received: 12 September 2018; Accepted: 14 December 2018; Published: 14 January 2019

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE