Integration of Landscape Metrics and Object-Oriented Remote Sensing in order to determine the Crop Type and Arrangement of Agricultural Land
Subject Areas : landuseRezvan Safdary 1 , Alireza Soffianian 2 , Saeid Pourmanafi 3
1 - Ph.D. Student of Environment, Department of Environment, Faculty of Natural resources and Environment, Science and Research Branch, Islamic Azad University, Tehran, Iran. *(Corresponding Author)
2 - Associate Professor, Department of Environmental Sciences, Faculty of Natural Resources, Isfahan University of Technology, Isfahan, Iran.
3 - - Assistant Professor, Department of Environmental Sciences, Faculty of Natural Resources, Isfahan University of Technology, Isfahan, Iran.
Keywords: Segmentation, Landscape Metrics, Isfahan, Crop Type.,
Abstract :
Background and Objective: This study has been carried out to identify the crop type and spatial pattern of agricultural lands in the Segzi Hydrological Unit in Isfahan Province, Iran.
Material and Methodology: Considering the vegetative calendar and phonological cycles of the major crops in the study area including wheat, alfalfa, fruit trees and vegetables, as well as agricultural land size, it was used for the study 3 Landsat satellite images (OLI) of the year 2015. After corrections and preliminary preprocesses were used from the NDVI and multi-resolution segmentation algorithm, taking into account three criteria of color, scale and shape, to determine the agricultural land area. Then, with the development of a decision tree based on the NDVI index, major crops were identified, mapped and evaluated for their accuracy. Then, using landscape metrics including number of patch (NP), mean patch size (MPS), Mean Shape Index (MSI), Perimeter-area ratio (PARA) and Mean Nearest Neighbor (MNN) to study the structure and agricultural arrangement.
Findings: The results of this study showed that a large area of agricultural land (about 46%) in the region is dedicated to wheat cultivation and less than 8% to vegetable cultivation. The results also showed that all lands in the region have a regular geometric shape with a minimum amount of area per area.
Discussion and Conclusion: The output of this study shows the movement of agricultural land in the region towards a monoculture. Also, the lack of water resources in recent years has formed a picture of a fragmented land.
1. Godschalk, D.R. 2004. Land use planning challenges: Coping with conflicts in visions of sustainable development and livable communities. Journal of the American Planning Association, 70(1): 5-13.
2. Collier, P. M. 2015. Accounting for managers: Interpreting accounting information for decision making. John Wiley & Sons. 544 p.
3. Van Knippenberg, D., Dahlander, L., Haas, M.R. & George, G. 2015. Information, attention, and decision making. Academy of Management Journal, 58(3): 649-657.
4. Aronoff, S. 2005. Remote sensing for GIS managers. Esri Press Redlands, CA. 487p.
5. Malczewski, J. 1999. GIS and multicriteria decision analysis. John Wiley & Sons. 408p.
6. Hassan, R., Rizwan, A., Farhan, S. & Sabir, B. 2017. Comparative Study of Conventional and Satellite Based Agriculture Information System. World Academy of Science, Engineering and Technology, International Journal of Computer, Electrical, Automation, Control and Information Engineering, 11(3), 304-309.
7. Nikolaos, T. 2015. Forecasting and classifying potato yields for precision agriculture based on time series analysis of multispectral satellite imagery. 66p.
8. Asgarian, A., Soffianian, A. and Pourmanafi, S. 2016. Crop type mapping in a highly fragmented and heterogeneous agricultural landscape: A case of central Iran using multi-temporal Landsat 8 imagery. Computers and Electronics in Agriculture, 127: 531-540.
9. Ding, L., Cheng, Z., Guo, S., Zhang, R. & Huang, C. 2007. Effect of regulated deficit irrigation on water use efficiency and yield of broad bean. Journal of Gansu Agricultural University, 42: 123-126.
10. Bedada, W., Lemenih, M. & Karltun, E. 2016. Soil nutrient build-up, input interaction effects and plot level N and P balances under long-term addition of compost and NP fertilizer. Agriculture, Ecosystems & Environment, 218: 220-231.
11. Ma, X. & Ma, Y. 2017. The spatiotemporal variation analysis of virtual water for agriculture and livestock husbandry: A study for Jilin Province in China. Science of the Total Environment, 586: 1150-1161.
12. Foerster, S., Kaden, K., Foerster, M. & Itzerott, S. 2012. Crop type mapping using spectral–temporal profiles and phenological information. Computers and Electronics in Agriculture, 89: 30-40.
13. Khan, M., De Bie, C., Van Keulen, H., Smaling, E. & Real, R. 2010. Disaggregating and mapping crop statistics using hypertemporal remote sensing. International Journal of Applied Earth Observation and Geoinformation, 12 (1): 36-46.
14. Blaschke, T., Lang, S. & Hay, G. 2008. Object-based image analysis: spatial concepts for knowledge-driven remote sensing applications. Springer Science & Business Media, 817 p.
15. Johnson, D.M. 2014. An assessment of pre-and within-season remotely sensed variables for forecasting corn and soybean yields in the United States. Remote Sensing of Environment, 141: 116-128.
16. Bolton, D.K. & Friedl, M.A. 2013. Forecasting crop yield using remotely sensed vegetation indices and crop phenology metrics. Agricultural and Forest Meteorology, 173: 74-84.
17. Howitt, R.E., Medellin-Azuara, J., MacEwan, D., Lund, J.R. and Sumner, D.A. 2014. Economic Analysis of the 2014 Drought for California Agriculture. Center for Watershed Sciences, University of California, Davis, California. 28p.
18. Bandyopadhyay, S., Jaiswal, R., Hegde, V. & Jayaraman, V. 2009. Assessment of land suitability potentials for agriculture using a remote sensing and GIS based approach. International Journal of Remote Sensing, 30(4): 879-895.
19. Van der Sande, C., De Jong, S. & De Roo, A. 2003. A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment. International Journal of Applied Earth Observation and Geoinformation, 4(3): 217-229.
20. Chand, R., Prasanna, P.L. & Singh, A. 2011. Farm size and productivity: Understanding the strengths of smallholders and improving their livelihoods. Economic and Political Weekly, xlvi (26 & 27): 5-11.
21. Bojnec, Š. & Latruffe, L. 2013. Farm size, agricultural subsidies and farm performance in Slovenia. Land Use Policy, 32: 207-217.
22. Blaschke, T. 2010. Object based image analysis for remote sensing. ISPRS journal of photogrammetry and remote sensing, 65(1): 2-16.
23. Feizizadeh, B., and Helali, H. 2010. Comparison Pixel-Based, Object-Oriented Methods and Effective Parameters in Classification Land Cover/ Land Use of West Province Azerbaijan. Physical Geography Research, 42(71): 73-84. (In Persian).
24. Bihamta Toosi, N., Soffianian, A., Fakheran, S., Pourmanafi, S., Ginzler, C. & Waser, L.T. 2020. Land Cover Classification in Mangrove Ecosystems Based on VHR Satellite Data and Machine Learning_An Upscaling Approach”, Remote Sensing, 12: 1-17.
25. Li, Q., Wang, C., Zhang, B. & Lu, L. 2015. Object-based crop classification with Landsat-MODIS enhanced time-series data. Remote Sensing, 7(12): 16091-16107.
26. Aguilar, M.A., Vallario, A., Aguilar, F.J., Lorca, A.G. & Parente, C. 2015. Object-based greenhouse horticultural crop identification from multi-temporal satellite imagery: A case study in Almeria, Spain. Remote Sensing, 7(6): 7378-7401.
27. Peña, J.M., Gutiérrez, P.A., Hervás-Martínez, C., Six, J., Plant, R.E. & López-Granados, F. 2014. Object-based image classification of summer crops with machine learning methods. Remote Sensing, 6(6): 5019-5041.
28. Bozorgi, M., Moein, M., Nejadkoorki, F. & Bihamta Toosi, N. 2020. Assessing the effect of water scarcity on crop selection and spatial pattern of croplands in central Iran, Computers and Electronics in Agriculture, 178: 1-9.
29. Abraham, K. 2015. Detecting shifts in agricultural landscape patterns of Hawassa, Ethiopia: an assessment of land cover change between 1984-2014 using object-based image analysis and landscape metrics. Centre for Geo-Information Thesis Report GIRS-2015-11, Wageningen University and Research Centre, 115 p.
30. Bihamta Toosi, N., Soffianian, A. & Fakheran, S. 2014. Analysis of Land Covers Changes in the Central Part of Isfahan Using Landscape Metrics, Journal of Applied Ecology, 2: 77-87. (In Persian)
31. Daneshmand, R., Mirzaei, R. & Bihamta Toosi, N. 2018. Land cover change detection of Chahar Mahal Bakhtiari province using landscape metrics (1994-2015), Journal of Applied Ecology, 7: 17-28. (In Persian)
32. Karami, A., Feghhi, J. 2012. Investigation of Quantitative metrics to protect the landscape in land use by sustainable pattern (Case study: Kohgiluyeh and Boyer Ahmad). Journal of Environmental Studies (JES), 37(60): 79-88. (In Persian).
33. Frazier, A.E. and Kedron, P. 2017. Landscape metrics: past progress and future directions. Current Landscape Ecology Reports, 2(3): 63-72.
34. Mirzayi, M., Riyahi Bakhtiyari, A., Salman Mahini, A., Gholamalifard, M. 2013. Investigating the Land Cover Changes in Mazandaran Province Using Landscape Ecology’s Metrics Between 1984 - 2010. ijae. 2(4): 37-55. (In Persian)
35. Joorabian Shooshtari, S., Shayesteh, K., Gholamalifard, M., Azari, M., López-Moreno, J.I., 2017. The Role of Landscape Metrics and Spatial Processes in Performance Evaluation of GEOMOD (Case Study: Neka River Basin). Geography and Sustainability of Environment. 7(3): 63-80. (In Persian)
36. Soffianian, A., Pourmanafi, S., Soltani, S., Homami, M., Bashari, h. & Bagheri, m. (2013) Isfahan Land-use Planning Project, land use evaluation. IN Province, G.G.o.I. (Ed).
37. Makhdoom Farkhondeh, M., 2013. Landscaping foundation. Institute of Printing and Publishing, University of Tehran. Tehran, 289 p. (In Persian)
38. Country Planning and Budget Organization. 2019. Statistical Yearbook of Isfahan Province in 1396. Deputy of Statistics and Information of Isfahan Management and Planning Organization. First turn, 876 p. (In Persian)
39. Odenweller Julie, B. & Johnson, Karen I. 1984. Crop identification using Landsat temporal-spectral profiles. Remote Sens. Environ. 14 (1): 39–54.
40. Roy, D.P., Wulder, M., Loveland, T.R., Woodcock, C., Allen, R., Anderson, M., Helder, D., Irons, J., Johnson, D. & Kennedy, R. 2014. Landsat-8: Science and product vision for terrestrial global change research. Remote Sensing of Environment, 145, 154-172.
41. Markham, B., Barsi, J., Kvaran, G., Ong, L., Kaita, E., Biggar, S., Czapla-Myers, J., Mishra, N. & Helder, D. 2014. Landsat-8 operational land imager radiometric calibration and stability. Remote Sensing, 6(12): 12275-12308.
42. Chavez, P.S. 1996. Image-based atmospheric corrections-revisited and improved. Photogrammetric engineering and remote sensing, 62(9): 1025-1035.
43. Roy, D.P., Ju, J., Kline, K., Scaramuzza, P.L., Kovalskyy, V., Hansen, M., Loveland, T.R., Vermote, E. & Zhang, C. 2010. Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States. Remote Sensing of Environment, 114(1): 35-49.
44. Walter, V. 2004. Object-based classification of remote sensing data for change detection. ISPRS journal of photogrammetry and remote sensing, 58(3): 225-238.
45. Anders, N., Seijmonsbergen, A.C. & Bouten, W. 2013. Geomorphological change detection using object-based feature extraction from multi-temporal LiDAR data. IEEE Geoscience and Remote Sensing Letters, 10(6): 1587-1591.
46. Bihamta Toosi, N., Soffianian, A., Fakheran, S. & Pourmanafi, S. 2019. Incorporating CART Algorithm and Vegetation Indices for Mapping Mangrove Using Landsat 8 Imagery”, Journal of Forest Research and Development, 5: 557-569. (In Persian).
47. Baatz, M. and Schäpe, A. 2000. Multi resolution Segmentation: an optimum approach for high quality multi scale image segmentation. In Beutrage zum AGIT-Symposium. Salzburg, Heidelberg, 12-23.
48. Möller, M., Lymburner, L. & Volk, M. 2007. The comparison index: A tool for assessing the accuracy of image segmentation. International Journal of Applied Earth Observation and Geoinformation, 9(3): 311-321.
49. Leitão, A.B., Miller, J., Ahern, J. & McGarigal, K. 2012. Measuring landscapes: A planner's handbook. Island press. 272p.
50. Chen, H., Yi, Z.F., Schmidt-Vogt, D., Ahrends, A., Beckschäfer, P., Kleinn, C., Ranjitkar, S. & Xu, J. 2016. Pushing the limits: the pattern and dynamics of rubber monoculture expansion in Xishuangbanna, SW China. PloS one, 11(2), e0150062.
51. Turner, M., Gardner, R. & O'Neill, R. 2015. Landscape ecology in theory and practice: pattern and process. Springer. New York.