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Remote Sens. 2015, 7(12), 16274-16292; doi:10.3390/rs71215833

Land Cover Mapping of a Tropical Region by Integrating Multi-Year Data into an Annual Time Series

1
Facultad de Ingenierías, Universidad de Medellín, Carrera 87 Nro. 30–65, Medellín 050026, Colombia
2
National Commission for the Knowledge and Use of Biodiversity (CONABIO), Av. Liga Periférico-Insurgentes Sur 4903, Parques del Pedregal, Tlalpan 14010, Ciudad de México, D.F., Mexico
3
Facultad de Ingenierías, Universidad de San Buenaventura, Carrera 56C Nro. 51-90, Medellín 050010, Colombia
*
Author to whom correspondence should be addressed.
Academic Editors: Parth Sarathi Roy and Prasad S. Thenkabail
Received: 9 July 2015 / Revised: 22 October 2015 / Accepted: 5 November 2015 / Published: 3 December 2015
View Full-Text   |   Download PDF [3441 KB, uploaded 3 December 2015]   |  

Abstract

Generating annual land cover maps in the tropics based on optical data is challenging because of the large amount of invalid observations resulting from the presence of clouds and haze or high moisture content in the atmosphere. This study proposes a strategy to build an annual time series from multi-year data to fill data gaps. The approach was tested using the Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation index and spectral bands as input for land cover classification of Colombia. In a second step, selected ancillary variables, such as elevation, L-band Radar, and precipitation were added to improve overall accuracy. Decision-tree classification was used for assigning eleven land cover classes using the International Geosphere-Biosphere Programme (IGBP) legend. Maps were assessed by their spatial confidence derived from the decision tree approach and conventional accuracy measures using reference data and statistics based on the error matrix. The multi-year data integration approach drastically decreased the area covered by invalid pixels. Overall accuracy of land cover maps significantly increased from 58.36% using only optical time series of 2011 filtered for low quality observations, to 68.79% when using data for 2011 ± 2 years. Adding elevation to the feature set resulted in 70.50% accuracy. View Full-Text
Keywords: quality assessment; time series; tree classifiers; land cover quality assessment; time series; tree classifiers; land cover
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Anaya, J.A.; Colditz, R.R.; Valencia, G.M. Land Cover Mapping of a Tropical Region by Integrating Multi-Year Data into an Annual Time Series. Remote Sens. 2015, 7, 16274-16292.

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