Evaluating the Performance of Global Land Cover Maps in Agricultural Land Delineation (Case Study: Lake Urmia Basin)

Document Type : Research Paper

Authors

1 Department of Water and Hydraulic Structure Engineering/ Faculty of Civil & Environmental Engineering/ Tarbiat Modares University,/Tehran/Iran

2 Department of Water Engineering,/ Faculty of Civil & Environmental Engineering/ Tarbiat Modares University/ Tehran/ Iran

3 Department of Remote Sensing/ Faculty of Humanities/ Tarbiat Modares University/ Tehran/ Iran

Abstract

Continuous monitoring of agricultural lands is imperative for managing water and soil resources in a watershed, due to its impact on ecosystem health and food security. Global Land Cover (GLC) maps can be used as a proxy for local and regional land use maps because of their availability, variety, and ease of use without complex processing. This study investigates the performance of three GLC products including MCD12Q1 LC, CGLS LC, and CCI LC against a reference land use/ land cover map of the year 2015 in the LUB. First, identical classes between the reference map and the GLC maps were determined based on the main land use/ land cover classes of the reference map of 2015 (rangeland, agricultural land, water, built-up areas, and bare land). To do so, different classes were merged accordingly to match the classes of the reference map. Subsequently, performance (Area and spatial consistency, and classification accuracy) of the GLC products was evaluated based on ground truth points. Results showed that MCD12Q1 LC and CGLS LC outperformed CCI LC in providing an overview of the surface cover of the LUB with 74% and 86% overall accuracy, respectively. Moreover, MCD12Q1 LC and CGLS LC had an acceptable performance in classifying rangeland and agriculture land as the dominant land cover types in the LUB with 81% and 92% classification accuracy, respectively. The CGLS LC can also be used to continuously monitor agriculture areas in practical applications to examine the overall trend of urbanization and agricultural development. Another important finding is that the GLC product with higher spatial resolution does not necessarily provide better classification accuracy for all types of covers. This study can also be used as a methodological reference in the performance evaluation of the GLC products at different scales and other parts of the country.

Keywords


Alizade Govarchin Ghale, Y., Altunkaynak, A., & Unal, A. (2018). Investigation Anthropogenic Impacts and Climate Factors on Drying up of Urmia Lake using Water Budget and Drought Analysis. In Water Resources Management (Vol. 32, Issue 1, pp. 325–337). https://doi.org/10.1007/s11269-017-1812-5
Alizade Govarchin Ghale, Y., Baykara, M., & Unal, A. (2019). Investigating the interaction between agricultural lands and Urmia Lake ecosystem using remote sensing techniques and hydro-climatic data analysis. Agricultural Water Management, 221, 566–579. https://doi.org/10.1016/j.agwat.2019.05.028
Almeida, C. A. de, Coutinho, A. C., Esquerdo, J. C. dalla M., Adami, M., Venturieri, A., Diniz, C. G., Dessay, N., Durieux, L., & Gomes, A. R. (2016). High spatial resolution land use and land cover mapping of the Brazilian legal Amazon in 2008 using Landsat-5/TM and MODIS data. Acta Amazonica, 46(3), 291–302. https://doi.org/10.1590/1809-4392201505504
Ban, Y., Gong, P., & Giri, C. (2015). Global land cover mapping using Earth observation satellite data: Recent progresses and challenges. ISPRS Journal of Photogrammetry and Remote Sensing, 103, 1–6. https://doi.org/10.1016/j.isprsjprs.2015.01.001
Belward, A. S., & Bartholomé, E. (2007). International Journal of Remote Sensing GLC2000: a new approach to global land cover mapping from Earth observation data GLC2000: a new approach to global land cover mapping from Earth observation data. Taylor & Francis, 26(9), 1959–1977. https://doi.org/10.1080/01431160412331291297
Chen, J., Chen, J., Liao, A., Cao, X., Chen, L., Chen, X., He, C., Han, G., Peng, S., Lu, M., Zhang, W., Tong, X., & Mills, J. (2015). Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS Journal of Photogrammetry and Remote Sensing, 103, 7–27. https://doi.org/10.1016/j.isprsjprs.2014.09.002
Defourny, P., & Kirches, G. (2012). Land cover CCI. Gofcgold.Wur.Nl. http:/www.gofcgold.wur.nl/documents/wageningen13/18-04/Session 8/FSeifert&OArino.pdf
FAO. (2014). Food and Agricultural Organization of the United Nations (FAO). 2014. “The State of the world’s land and water resources for food and agriculture.” Available at http://www.fao.org/docrep/ 017/i1688e/i1688e.pdf.
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., & Huang, X. (2010). MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114(1), 168–182. https://doi.org/10.1016/j.rse.2009.08.016
Giri, C., Zhu, Z., & Reed, B. (2005). A comparative analysis of the Global Land Cover 2000 and MODIS land cover data sets. Remote Sensing of Environment, 94(1), 123–132. https://doi.org/10.1016/j.rse.2004.09.005
Gong, P., Wang, J., Yu, L., Zhao, Y., Zhao, Y., Liang, L., Niu, Z., Huang, X., Fu, H., Liu, S., Li, C., Li, X., Fu, W., Liu, C., Xu, Y., Wang, X., Cheng, Q., Hu, L., Yao, W., … Chen, J. (2013). International Journal of Remote Sensing Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data) Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data. International Journal of Remote Sensing, 34(7), 2607–2654. https://doi.org/10.1080/01431161.2012.748992
Hoyos, A. P., García-Haro, J., Pérez-Hoyos, A., García-Haro, F. J., & San-Miguel-Ayanz, J. (2012). A methodology to generate a synergetic land-cover map by fusion of different: Land-cover products A methodology to generate a synergetic land-cover map by fusion of different land-cover products. Article in International Journal of Applied Earth Observation and Geoinformation, 19, 72–87. https://doi.org/10.1016/j.jag.2012.04.011
Hua, T., Zhao, W., Liu, Y., Wang, S., & Yang, S. (2018). Spatial consistency assessments for global land-cover datasets: A comparison among GLC2000, CCI LC, MCD12, GLOBCOVER and GLCNMO. Remote Sensing, 10(11). https://doi.org/10.3390/rs10111846
JICA. (2020). Data collection survey on improvement of the hydrological cycle model in Urmia Lake Basin in the Islamic Republic of Iran.Tehran.
Kang, J., Wang, Z., Sui, L., Yang, X., Ma, Y., & Wang, J. (2020). Consistency analysis of remote sensing land cover products in the tropical rainforest climate region: A case study of Indonesia. Remote Sensing, 12(9), 1410. https://doi.org/10.3390/RS12091410
Liang, L., Liu, Q., Liu, G., Li, H., & Huang, C. (2019). Accuracy Evaluation and Consistency Analysis of Four Global Land Cover Products in the Arctic Region. Remote Sensing, 11(12), 1396. https://doi.org/10.3390/rs11121396
Liang, Zuo, Y., Huang, L., Zhao, J., Teng, L., Yang, F., Stefanakis, E., Liu, Y., Kyriakidis, P., & Kainz, W. (2015). Geo-Information Evaluation of the Consistency of MODIS Land Cover Product (MCD12Q1) Based on Chinese 30 m GlobeLand30 Datasets: A Case Study in Anhui Province, China. ISPRS Int. J. Geo-Inf, 4, 2519–2541. https://doi.org/10.3390/ijgi4042519
Liu, X., Yu, L., & Zhong, L. (2018). Comparison of country-level cropland areas between ESA-CCI land cover maps and FAOSTAT data Special Issue in the journal MDPI Remote Sensing: Remote Sensing of Environmental Changes in Cold Regions View project Oil palm mapping View project. Article in International Journal of Remote Sensing, 39(20), 6631–6645. https://doi.org/10.1080/01431161.2018.1465613
Maghrebi, M., Noori, R., Bhattarai, R., Mundher Yaseen, Z., Tang, Q., Al-Ansari, N., Danandeh Mehr, A., Karbassi, A., Omidvar, J., Farnoush, H., Torabi Haghighi, A., Kløve, B., & Madani, K. (2020). Iran’s Agriculture in the Anthropocene. Earth’s Future, 8(9). https://doi.org/10.1029/2020EF001547
Pérez-Hoyos, A., Rembold, F., Kerdiles, H., & Gallego, J. (2017). Comparison of global land cover datasets for cropland monitoring. Remote Sensing, 9(11). https://doi.org/10.3390/rs9111118
Smets, B., Buchhorn, M., Bertels, L., Lesiv, M., Tsendbazar, N.-E., Linlin, L., & Masiliunas, D. (2019). Product User Manual. Moderate Dynamic Land Cover Change. Collection 100m. Africa. Version 2.1 (Release Candidate 1) (pp. 1–47). https://land.copernicus.eu/global/products/lai
Song, H., & Zhang, X. (2012). Precision analysis and validation of multi-sources landcover products derived from remote sensing in China. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 28(22), 207–214. https://doi.org/10.3969/j.issn.1002-6819.2012.22.029
Song, H., Zhang, X., Wang, Y., & Wang, M. (2012). Comparison of relative uniformity between GLOBCOVER and MODIS land cover data sets. Nongye Gongcheng Xuebao/Transactions of the Chinese Society of Agricultural Engineering, 28(15), 118–124. https://doi.org/10.3969/j.issn.1002-6819.2012.15.019
Stehman, S. V. (1997). Selecting and interpreting measures of thematic classification accuracy. Remote Sensing of Environment, 62(1), 77–89. https://doi.org/10.1016/S0034-4257(97)00083-7
Thibaut, A., Tchuenté, K., Roujean, J.-L., & De Jong, S. M. (2010). Comparison and relative quality assessment of the GLC2000, GLOBCOVER, MODIS and ECOCLIMAP land cover data sets at the African continental scale. International Journal of Applied Earth Observation and Geoinformation, 13, 207–219. https://doi.org/10.1016/j.jag.2010.11.005
WERI. (2018). Collaborative land cover mapping of the Lake Urmia Basin_Final Report. Water Engineering Research Institute of Tarbiat Modares University.
Zhang, X., Liu, L., Chen, X., Xie, S., & Gao, Y. (2019). Fine land-cover mapping in China using Landsat datacube and an operational SPECLib-based approach. Remote Sensing, 11(9). https://doi.org/10.3390/rs11091056