TY - GEN

T1 - Sensitivity of spatially explicit land-use logistic regression models to the errors land-use change maps

AU - Prishchepov, Alexander

AU - Müller, Daniel

AU - Butsic, Van

AU - Radeloff, Volker C.

PY - 2012

Y1 - 2012

N2 - Land-use change (LUC) is a common process around the world and LUC models help elucidate LUC. Models are commonly parameterized with LUC maps derived from satellite imagery. However, such LUC maps have errors, and it is unclear how sensitive spatially explicit LUC models are to such errors. We studied the effects of errors maps on spatially explicit LUC logistic regression models of agricultural land abandonment within one Landsat footprint in Eastern Europe that covered the part of Lithuania. The selected footprint had six matching image dates (Spring, Summer and Fall) that were important to separate land-use classes for pre- (circa 1989) and post-abandonment (circa 2000). We simulated errors maps classifying all possible 49 sub-optimal image dates combinations with non-parametric support vector machines (SVM) classifier. We assessed the sensitivity of the spatially explicit LUC logistic models that had socio-economic and environmental variables to the mapping errors for the produced 49 LUC maps. When fewer image-dates combinations were used, the spatially explicit logistic regression LUC models were prone to the mapping errors. Results suggest avoiding using the classifications lower than 80% of individual class accuracy for the spatially explicit logistic regression models of agricultural land abandonment in Eastern Europe.

AB - Land-use change (LUC) is a common process around the world and LUC models help elucidate LUC. Models are commonly parameterized with LUC maps derived from satellite imagery. However, such LUC maps have errors, and it is unclear how sensitive spatially explicit LUC models are to such errors. We studied the effects of errors maps on spatially explicit LUC logistic regression models of agricultural land abandonment within one Landsat footprint in Eastern Europe that covered the part of Lithuania. The selected footprint had six matching image dates (Spring, Summer and Fall) that were important to separate land-use classes for pre- (circa 1989) and post-abandonment (circa 2000). We simulated errors maps classifying all possible 49 sub-optimal image dates combinations with non-parametric support vector machines (SVM) classifier. We assessed the sensitivity of the spatially explicit LUC logistic models that had socio-economic and environmental variables to the mapping errors for the produced 49 LUC maps. When fewer image-dates combinations were used, the spatially explicit logistic regression LUC models were prone to the mapping errors. Results suggest avoiding using the classifications lower than 80% of individual class accuracy for the spatially explicit logistic regression models of agricultural land abandonment in Eastern Europe.

KW - Abandonment

KW - Errors

KW - Land use

KW - Logistic regressions

KW - Remote sensing

KW - Sensitivity

UR - http://www.scopus.com/inward/record.url?scp=84894170535&partnerID=8YFLogxK

M3 - Article in proceedings

AN - SCOPUS:84894170535

SN - 9788890357428

SP - 2008

EP - 2015

BT - iEMSs 2012 - Managing Resources of a Limited Planet: Proceedings of the 6th Biennial Meeting of the International Environmental Modelling and Software Society

T2 - 6th Biennial Meeting of the International Environmental Modelling and Software Society: Managing Resources of a Limited Planet, iEMSs 2012

Y2 - 1 July 2012 through 5 July 2012

ER -