<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

Department of Environment, Faculty of Natural resources and Environment, Science and Research Branch

Department of Environmental Sciences, Faculty of Natural Resources

Quaestiones Geographicae

This work is licensed under the Creative Commons Attribution 4.0 International License.

This study aims to investigate crop selection and spatial patterns of agricultural fields in a drought-affected region in Isfahan Province, central Iran. Based...

Crop type	Area]	Number of reference polygons	Omission error	Commission error
Crop type	[ha]	[−]	[%]
Wheat	21,803	214	6.59	14.01
Alfalfa	4892	137	17.02	15.59
Fruit tree	5561	162	8.43	9.87
Vegetable	2816	99	7.36	11.11
Kappa Coefficient = 82.84%			Overall Accuracy = 87.41%

Landscape metric	Acronym	Aspect of pattern	Range [unit]
Number of patches	NP	Composition	NP > 0, without limit [−]
Mean patch size	MPS	Composition	MPS > 0, without limit [ha]
Mean shape index	MSI	Structure	MSI > 1, without limit [−]
Perimeter-to-area ratio	PARA	Structure	PARA > 1, without limit [−]
Euclidian nearest-neighbor distance	ENN	Configuration	ENN > 0, without limit [m]

Crop type	Object code			Final code
Crop type	L1	L2	L3	Final code
Wheat	1	1	0	110
Alfalfa	1	1	1	111
Fruit tree	0	1	1	011
Vegetable	0	0	1	001

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

Pubblicato online: 31 mar 2022

Pagine: 25 - 35

Ricevuto: 12 lug 2021

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

Parole chiave
crop type, segmentation, landscape metrics, Iran

© 2022 Rezvan Safdary et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

The area of crops and the results of accuracy assessment.

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

The classification scheme is used to recognise crop types.

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

Pubblicato online: 31 mar 2022

Pagine: 25 - 35

Ricevuto: 12 lug 2021

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

Parole chiavecrop type, segmentation, landscape metrics, Iran

© 2022 Rezvan Safdary et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Fig. 1

Fig. 2

Fig. 3

The area of crops and the results of accuracy assessment.

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

The classification scheme is used to recognise crop types.

Parole chiave
crop type, segmentation, landscape metrics, Iran