Application of landscape metrics and object-oriented remote sensing to detect the spatial arrangement of agricultural land
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Keywords

crop type
segmentation
landscape metrics
Iran

How to Cite

Safdary, R., Soffianian, A., & Pourmanafi, S. (2022). Application of landscape metrics and object-oriented remote sensing to detect the spatial arrangement of agricultural land. Quaestiones Geographicae, 41(1), 25–35. https://doi.org/10.2478/quageo-2022-0002

Abstract

This study aims to investigate crop selection and spatial patterns of agricultural fields in a drought-affected region in Isfahan Province, central Iran. Based on field surveys portraying growth stages of the main crops including wheat, alfalfa, vegetables and fruit trees, three Landsat 8 operational land imager (OLI) images were acquired on March 15 (L1), June 27 (L2) and October 1 (L3), 2015. After performing radiometric and atmospheric corrections, Normalized Difference Vegetation Index (NDVI) maps of the images were produced and introduced to the Multi-Resolution Segmentation algorithm to delineate agricultural fields. An NDVI-based decision algorithm was then developed to identify crops devoted to each field. Finally, a set of landscape metrics including Number of Patches (NP), mean patch size (MPS), mean shape index (MSI), perimeter-to-area ratio (PARA) and Euclidian Nearest Neighborhood Distance (ENN) was utilized to evaluate their respective spatial formation. The results showed that nearly 46% of fields are devoted to wheat indicating that the landscape has been dramatically shifted towards wheat monoculture farming. Moreover, the farmers’ inclination to grow crops in large fields (approximate area of 1 ha) with more regular geometric shapes are considered as an effective way of optimising water use efficiency in areas experiencing significant water shortage.

https://doi.org/10.2478/quageo-2022-0002
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