Abstract
Several studies have focused in the past on global land cover (LC) datasets harmonization and inter-comparison and have found significant inconsistencies. Despite the known discrepancies between existing products derived from medium resolution satellite sensor data, little emphasis has been placed on examining these disagreements to improve the overall classification accuracy of future land cover maps. This work evaluates the classification performance of a least square support vector machine (LS-SVM) algorithm with respect to areas of agreement and disagreement between two existing land cover maps. The approach involves the use of time series of Moderate-resolution Imaging Spectroradiometer (MODIS) 250-m Normalized Difference Vegetation Index (NDVI) (16-day composites) and gridded climatic indicators. LS-SVM is trained on reference samples obtained through visual interpretation of Google Earth (GE) high resolution imagery. The core of the training process is based on repeated random splits of the training dataset to select a small set of suitable support vectors optimizing class separability. A large number of independent validation samples spread over three contrasting regions in Europe (Eastern Austria, Macedonia and Southern France) are used to calculate classification accuracies for the LS-SVM NDVI-derived LC map and for two (globally available) LC products: GLC2000 and GlobCover. The LS-SVM LC map reported an overall accuracy of 70%. Classification accuracies ranged from 71% where GlobCover and GLC2000 agreed to 68% for areas of disagreement. Results indicate that existing LC products are as accurate as the LS-SVM LC map in areas of agreement (with little margin for improvements), while classification accuracy is substantially better for the LS-SVM LC map in areas of disagreement. On average, the LS-SVM LC map was 14% and 18% more accurate compared to GlobCover and GLC2000, respectively.
Highlights
Reliable and regularly updated land use/land cover (LULC) maps at medium to coarse spatial resolution are required for various modeling and monitoring purposes
The least square support vector machine (LS-SVM) LC map was 14% and 18% more accurate compared to GlobCover and Global Land Cover Map 2000 (GLC2000), respectively
The LS-SVM map was 10% and 24% more accurate compared to GlobCover and GLC2000, respectively
Summary
Reliable and regularly updated land use/land cover (LULC) maps at medium to coarse spatial resolution are required for various modeling and monitoring purposes. At continental to global scale, accurate LULC data are for example needed for modeling energy, water and carbon flux exchanges of terrestrial ecosystem components [1,2]. Prominent applications range from vegetation dynamics and land change monitoring to urbanization and policy development [3,4,5]. Available (global) LULC maps show large differences in the number and definitions of LULC classes depending on satellite data type, foreseen application as well as the specific objectives of the map developers [6]. The GlobCover 2009 map [9] (Version 2.3 available for the year 2009), hereafter
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