EVects of minimum mapping unit on land cover data spatial con guration and composition

Abstract. A key issue when generating a land cover map from remotely sensed data is the selection of the minimum mapping unit (MMU) to be employed, which determines the extent of detail contained in the map. This study analyses the eVects of MMU in land cover spatial con guration and composition, by using simulated landscape thematic patterns generated by the Modi ed Random Clusters method. This approach allows a detailed control of the diVerent factors in uencing landscape metrics behaviour, as well as taking into account a wide range of land cover pattern possibilities. Land cover classes that are sparse and fragmented can be considerably misrep-resented in the nal map when increasing MMU, while the classes that occupy a large percentage of map area tend to become more dominant. Mean Patch Size and Number of Patches are very poor indicators of pattern fragmentation in this context. In contrast, Landscape Division (LD) and related indices (Splitting Index and EVective Mesh Size) are clearly suitable for comparing the fragmentation of landscape data with diVerent MMUs. We suggest that the Mean Shape Index, the most sensitive to MMU of those considered in this study, should not be used in further landscape studies if land cover data with diVerent MMU or patch size frequency distribution are to be compared. In contrast, the Area Weighted Mean Shape Index presents a very robust behaviour, which advocates the use of this index for the quanti cation of the overall irregularity of patch shapes in landscape spatial patterns. The results presented allow quantifying the biases resulting from selecting a certain MMU when generating a land cover dataset. In general, a bigger MMU implies underestimating landscape diversity and fragmentation, as well as over-estimating animal population dispersal success. Guidelines are provided for the proper use and comparison of spatial pattern indices measured in maps with diVerent MMUs