Diagnosis of Landscapes of the Province of Al-Diwaniyah (Iraq) by Using of Landsat-8 Multispectral Images
DOI:
https://doi.org/10.14529/cmse180301Keywords:
remote sensing of agrolandscapes, Al-Diwaniyah, Landsat-8 image processing, Normalized Difference Vegetation Index, phenology of cereal crops, automatic land cover classification, density slicing, classification accuracy assessmentAbstract
Remote sensing monitoring and prediction of crop yields are widely used throughout the world. However, such techniques are not universal and require testing in each landscape region. This paper deals with approaches to using multispectral satellite images to classify landscapes of Al-Diwaniyah, one of the main centers of irrigated agriculture inIraq. As a data source for the classification, we choose of Landsat-8 images received in March 2014, 2015 and 2016 with similar radiation characteristics. Snapshot dates coincided with the period of crops maximum vegetation, allowing a certain allocation of agricultural land based on Normalized Difference Vegetation Index (NDVI). The analysis of NDVI distributions demonstrate their sensitivity to changes of cereal crops areas and seasonal precipitation sum, that allow us to establish four types of regional landscapes based on NDVI thresholds: landscapes with health vegetation with moderate-to-high projective cover (NDVI > 0,34); landscapes with stressed or low projective cover vegetation (0,19 < NDVI ≤ 0,34), landscapes without vegetation (0,1 < NDVI ≤ 0,19) and water landscapes (NDVI ≤ –0,1). Comparison of classification with the traditional man-made interpretation of landscapes showed that the overall accuracy and F-measure of the proposed automatic classification is about 90 %.
References
Kleshchenko A.D., Savitskaya O.V. Assessment of Spatio-temporal Distribution of Grain Yields and Standardized Precipitation Index by Satellite and Ground-Based Information. Trudy GGO [Proceedings of Voeikov Geophysical Observatory]. 2014. no. 571. pp. 147–161 (in Russian)
Galakhova Yu.E., Menzhulin G.V. Experience of Yield Statistical Predictive Modeling Based on Satellite Information. Trudy GGO [Proceedings of Voeikov Geophysical Observatory]. 2015. no. 578. pp. 101–125. (in Russian)
Loupian E.A., Bartalev S.A., Krasheninnikova Yu.S. Observing an Abnormally Early Development of Crops in the Southern Regions of Russia in Spring 2016 Using Remote Monitoring Data. Sovremennyye problemy distantsionnogo zondirovaniya Zemli iz kosmosa [Current Problems in Remote Sensing of the Earth from Space]. 2016. vol. 13, no. 2. pp. 240–243 (in Russian) DOI: 10.21046/2070-7401-2016-13-2-240-243
Savin I.Yu., Zhang Х., Shishkonakova E.A. et.al. Map of Winter Crops of the Season 2017 Placed on the Service VEGA: Results of Selective Quality Control. Sovremennyye problemy distantsionnogo zondirovaniya Zemli iz kosmosa [Current Problems in Remote Sensing of the Earth from Space]. 2017. vol. 14, no. 4. pp. 125–131 (in Russian) DOI: 10.21046/2070-7401-2017-14-4-125-131
Muhaimeed A.S., Al-Hedny S.M. Evaluation of Long-Term Vegetation Trends for Northeastern of Iraq: Mosul, Kirkuk and Salah al-Din. IOSR Journal of Agriculture and Veterinary Science (IOSR-JAVS). 2013. vol. 5, no. 2. pp. 67–76
Muhsin I.J. Change Detection of Remotely Sensed Image Using NDVI Subtractive and Classification Methods. Iraqi Journal of Physics. 2016. vol. 14, no 29. pp. 125–137
Agriculture forestry and fishing. https://www.britannica.com/place/Iraq/Agricultureforestry-and-fishing (accessed: 17.03.2018)
Landsat 8 (L8) Data Users Handbook Version 2.0 March 29, 2016. Department of the Interior U.S. Geological Survey EROS Sioux Falls, South Dakot, LSDS-1574 Version 2.0, 98 p. Available at https://landsat.usgs.gov/landsat-8-data-users-handbook (accessed:
02.2018)
Barsi J.A., Lee K., Kvaran G., et al. The Spectral Response of the Landsat-8 Operational Land Imager. Remote Sens. 2014, no. 6. pp. 10232–10251. DOI: 10.3390/rs61010232
Campbell J.B., Wynne R.H. Introduction to remote sensing. The Guilford Press. New York. 5th ed., 2011. 718 p.
Abineh Tilahun, Bogale Teferie. Accuracy Assessment of Land Use Land Cover Classification using Google Earth. American J. of Environmental Protection. 2015. vol. 4, no. 4. pp. 193–198. DOI: 10.11648/j.ajep.20150404.14
Congalton R.G. A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data. Remote Sensing of Environment. July 1991, vol. 37, no. 1. pp. 35–46, DOI: 10.1016/0034-4257(91)90048-b
Yadav K., Congalton R.G. Issues with Large Area Thematic Accuracy Assessment for Mapping Cropland Extent: A Tale of Three Continents. Remote Sens. 2018. vol. 10, no. 1. pp. 1–27. DOI: 10.3390/rs10010053
Huntington J.L. Hegewisch K.C., Daudert B., et al. CLIMATE ENGINE: Cloud Computing and Visualization of Climate and Remote Sensing Data for Advanced Natural Resource Monitoring and Process Understanding. Bulletin Am. Met Soc. Nov. 2017. vol. 98, no. 11. pp. 2397–2410. DOI: 10.1175/BAMS-D-15-00324.1
