Deep Insight on Land Use/Land Cover Geospatial Assessment through Internet-Based Validation Tool in Upper Karkheh River Basin (KRB), South-West Iran

Sina Mallah, Manouchehr Gorji (), Mohammad Reza Balali, Hossein Asadi, Naser Davatgar, Hojjat Varmazyari, Anna Maria Stellacci () and Mirko Castellini
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Sina Mallah: Department of Soil Science Engineering, University of Tehran, Karaj 77871-31587, Iran
Manouchehr Gorji: Department of Soil Science Engineering, University of Tehran, Karaj 77871-31587, Iran
Mohammad Reza Balali: Department of Soil Chemistry, Fertility and Plant Nutrition, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj 31779-93545, Iran
Hossein Asadi: Department of Soil Science Engineering, University of Tehran, Karaj 77871-31587, Iran
Naser Davatgar: Department of Soil Physics and Irrigation, Soil and Water Research Institute, Agricultural Research, Education and Extension Organization (AREEO), Karaj 31779-93545, Iran
Hojjat Varmazyari: Department of Agricultural Management and Development, University of Tehran, Karaj 31779-93545, Iran
Anna Maria Stellacci: Department of Soil, Plant and Food Sciences, University of Bari Aldo Moro, Via Amendola 165/A, 70126 Bari, Italy
Mirko Castellini: Council for Agricultural Research and Economics-Research Center for Agriculture and Environment (CREA-AA), Via C. Ulpiani 5, 70125 Bari, Italy

Land, 2023, vol. 12, issue 5, 1-22

Abstract: Recently, the demand for high-quality land use/land cover (LULC) information for near-real-time crop type mapping, in particular for multi-relief landscapes, has increased. While the LULC classes are inherently imbalanced, the statistics generally overestimate the majority classes and underestimate the minority ones. Therefore, the aim of this study was to assess the classes of the 10 m European Satellite Agency (ESA) WorldCover 2020 land use/land cover product with the support of the Google Earth Engine (GEE) in the Honam sub-basin, south-west Iran, using the LACOVAL (validation tool for regional-scale land cover and land cover change) online platform. The effect of imbalanced ground truth has also been explored. Four sampling schemes were employed on a total of 720 collected ground truth points over approximately 14,100 ha. The grassland and cropland totally canopied 94% of the study area, while barren land, shrubland, trees and built-up covered the rest. The results of the validation accuracy showed that the equalized sampling scheme was more realistically successful than the others in terms of roughly the same overall accuracy (91.6%), mean user’s accuracy (91.6%), mean producers’ accuracy (91.9%), mean partial portmanteau (91.9%) and kappa (0.9). The product was statistically improved to 93.5% ± 0.04 by the assembling approach and segmented with the help of supplementary datasets and visual interpretation. The findings confirmed that, in mapping LULC, data of classes should be balanced before accuracy assessment. It is concluded that the product is a reliable dataset for environmental modeling at the regional scale but needs some modifications for barren land and grassland classes in mountainous semi-arid regions of the globe.

Keywords: quality assessment; imbalanced dataset; classification accuracy; cropland area; map accuracy; image processing (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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