Abstract:

【Objective】 In this paper, incorporating the idea of “object-oriented” farmland management, the conception of farmland management unit (FMU) as well as its automatic extraction technique were pat forward. The moderate-resolution remotely sensed data were taken as the data source for the first time. Its capability in application was thus evaluated and discussed. 【Method】 Two typical regions in Jiangsu Province were selected within one scene of Landsat 5TM image which acquired in 2006. The FMUs in both regions were extracted through the processes of decision tree classification and multiresolution segementation. With the help of the exact farmland boundaries that digitized from the SPOT-5 high resolution images in both regions, several parameters that corresponding to the heterogeneity of the FMUs as well as the coincidence of boundaries between FMUs and farmland parcels were calculated and analyzed. 【Result】 The total accuracy of classification in both regions was over 90%. The average standard deviation and average extreme difference of FMU which reflect the heterogeneity were lower than corresponding value for entire farmland range at over 70% and 45%, respectively. The misclassified ratio and overlapping degree of FMU which reflect the coincidence of boundaries were lower than 10% for both regions. Besides, the setting of relative parameters that involved in the process of multiresolution segmentation such as the layer weight, segmentation scale, shape factor and compactness factor had a certain impact on FMU extraction. 【Conclusion】 The automatic extracted FMUs can basically satisfy the requirement of a relatively low heterogeneity of the FMU and a high coincidence of boundaries. In addition, to attain a rather ideal extraction result, the user would be better to conduct a configuration of layer weight, segmentation scale, shape factor and compactness factor according to varied planting structures and conditions.

Key words: farmland management unit, Landsat 5TM, multiresolution segmentation, heterogeneity