Title of article :
Ecological site classification of semiarid rangelands: Synergistic use of Landsat and Hyperion imagery
Author/Authors :
Blanco، نويسنده , , Paula D. and del Valle، نويسنده , , Héctor F. and Bouza، نويسنده , , Pablo J. and Metternicht، نويسنده , , Graciela I. and Hardtke، نويسنده , , Leonardo A.، نويسنده ,
Issue Information :
روزنامه با شماره پیاپی سال 2014
Abstract :
Ecological sites are the basic entity used in rangeland health assessment. This study evaluates the synergistic use of multi- and hyper-spectral satellite imagery for sub-pixel classification of ecological sites in semiarid rangelands. Hyperion and Landsat enhanced thematic mapper (ETM) data are included in a two-step procedure to mapping ecological sites in Patagonian rangelands of Argentina. Firstly, mixture tuned matched filtering and logistic regression analyses are used for Hyperion data processing to obtain ecological site probability images in the area covered by hyperspectral imagery. Secondly, artificial neural networks are applied to model the relationships between the spectral response patterns of Landsat and the probability images from Hyperion, and used to map ecological sites over the entire study area. Overall classification accuracy was 81% (kappa = 0.77) with relatively high accuracies for all ecological sites demonstrating that their spectral signatures are sufficiently distinct to be detectable. Better accuracies were obtained for shrub steppes with desert pavement (producerʹs and userʹs accuracies of 89% and 84%, respectively), and shrub-grass steppes associated to tertiary calcareous outcrops (producerʹs and userʹs accuracies of 100% and 86%, respectively), while poorer accuracies resulted for shrub-grass steppes on old alluvial plains (producerʹs and userʹs accuracies of 75% and 56%, respectively). Fuzzy maps of ecological sites as presented in this research can provide rangeland managers with a tool to stratify the landscape and organize ecological information for rangeland health assessment and monitoring, prioritizing and selecting appropriate management actions, and promoting the recovery of areas degraded in these environments.
Keywords :
Hyperion , Endmember selection , logistic regression , NEURAL NETWORKS , Land management , Ecological site , Mixture Tuned Matched Filtering
Journal title :
International Journal of Applied Earth Observation and Geoinformation
Journal title :
International Journal of Applied Earth Observation and Geoinformation