Land cover mapping of a tropical region by integrating multi-year data into an annual time series

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 mult...

Full description

Autores:
Tipo de recurso:
Fecha de publicación:
2015
Institución:
Universidad de Medellín
Repositorio:
Repositorio UDEM
Idioma:
eng
OAI Identifier:
oai:repository.udem.edu.co:11407/2867
Acceso en línea:
http://hdl.handle.net/11407/2867
Palabra clave:
Land cover
Quality assessment
Time series
Tree classifiers
Decision trees
Error statistics
Image reconstruction
Radiometers
Satellite imagery
Time series
Trees (mathematics)
Decision tree classification
High moisture contents
Integration approach
Land cover
Land cover classification
Moderate resolution imaging spectroradiometer
Quality assessment
Tree classifiers
Data integration
Rights
restrictedAccess
License
http://purl.org/coar/access_right/c_16ec
Description
Summary: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.