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Application of Landscape Metrics and Object-Oriented Remote Sensing to Detect the Spatial Arrangement of Agricultural Land

Rezvan Safdary

Rezvan Safdary

Department of Environment, Faculty of Natural resources and Environment, Science and Research Branch, Islamic Azad UniversityTehran, Iran
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Safdary, Rezvan
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Alireza Soffianian

Alireza Soffianian

Department of Environmental Sciences, Faculty of Natural Resources, Isfahan University of TechnologyIsfahan, Iran
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Soffianian, Alireza
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Saeid Pourmanafi

Saeid Pourmanafi

Department of Environmental Sciences, Faculty of Natural Resources, Isfahan University of TechnologyIsfahan, Iran
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Pourmanafi, Saeid
  
31. März 2022
Application of Landscape Metrics and Object-Oriented Remote Sensing to Detect the Spatial Arrangement of Agricultural Land's Cover Image
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Online veröffentlicht: 31. März 2022
Seitenbereich: 25 - 35
Eingereicht: 12. Juli 2021
DOI: https://doi.org/10.2478/quageo-2022-0002
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© 2022 Rezvan Safdary et al., published by Sciendo
This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

The layout of the Segzi Hydrological Unit in Isfahan Province, central Iran.
The layout of the Segzi Hydrological Unit in Isfahan Province, central Iran.

Fig. 2

Crop type map produced from image classification.
Crop type map produced from image classification.

Fig. 3

The results of landscape metrics analysis for each crop type.NP – Number of patches, MSI – mean shape index, MPS – mean patch size, ENN – Euclidian Nearest Neighborhood Distance.
The results of landscape metrics analysis for each crop type.NP – Number of patches, MSI – mean shape index, MPS – mean patch size, ENN – Euclidian Nearest Neighborhood Distance.

The area of crops and the results of accuracy assessment_

Crop typeArea]Number of reference polygonsOmission errorCommission error
[ha][−][%]
Wheat21,8032146.5914.01
Alfalfa489213717.0215.59
Fruit tree55611628.439.87
Vegetable2816997.3611.11
Kappa Coefficient = 82.84%Overall Accuracy = 87.41%

Description of landscape metrics used in this research, adapted from Leitão et al_ (2012)_

Landscape metricAcronymAspect of patternRange [unit]
Number of patchesNPCompositionNP > 0, without limit [−]
Mean patch sizeMPSCompositionMPS > 0, without limit [ha]
Mean shape indexMSIStructureMSI > 1, without limit [−]
Perimeter-to-area ratioPARAStructurePARA > 1, without limit [−]
Euclidian nearest-neighbor distanceENNConfigurationENN > 0, without limit [m]

The classification scheme is used to recognise crop types_

Crop typeObject codeFinal code
L1L2L3
Wheat110110
Alfalfa111111
Fruit tree011011
Vegetable001001
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