Effects of Land Use Data Spatial Resolution on SDG Indicator 11.3.1 (Urban Expansion) Assessments: A Case Study Across Ethiopia

Orion S. E. Cardenas-Ritzert (), Jody C. Vogeler, Shahriar Shah Heydari, Patrick A. Fekety, Melinda Laituri and Melissa R. McHale
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Orion S. E. Cardenas-Ritzert: Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523-1499, USA
Jody C. Vogeler: Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523-1499, USA
Shahriar Shah Heydari: Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523-1499, USA
Patrick A. Fekety: Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80523-1499, USA
Melinda Laituri: Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO 80523-1476, USA
Melissa R. McHale: Department of Forest Resources Management, University of British Columbia, Vancouver, BC V6T 1Z4, Canada

Sustainability, 2024, vol. 16, issue 22, 1-21

Abstract: Geospatial data play a significant role in the United Nation’s Sustainable Development Goals, particularly through assessments of monitoring indicators. Sustainable Development Goal (SDG) Indicator 11.3.1 assessments utilize land and population geospatial data to monitor urban expansion, and were implemented to enhance inclusive and sustainable urbanization, and capacity for participatory, integrated and sustainable human settlement planning and management in all countries by 2030. Data-limited countries often rely on accessible, wide-coverage geospatial datasets for SDG Indicator 11.3.1 assessments which may have characteristics (e.g., coarse spatial resolution) influential to assessment outcomes. The presented work examines the effect of land use data spatial resolution on SDG Indicator 11.3.1 assessment components including urban area delineation, SDG Indicator 11.3.1 and supporting spatial metrics, spatial patterns of urban land development, and land use change patterns for urbanizing areas in Ethiopia from 2016 to 2020. A comparison was made between a single land use map at the spatial resolution in which it was originally produced, 30 m, and at a majority-resampled spatial resolution comparable to many global coverage datasets, 90 m. Analyses revealed changes in the urban areas identified, observed boundaries of urban areas, and all quantified metrics from 30 m resolution to 90 m resolution, with the decrease in resolution resulting in smaller urban areas being missed and differences in the delineated hinterland areas connected to an urban core. Statistical testing indicated significant differences in SDG Indicator 11.3.1 values, developed land use area per capita, and spatial patterns of urban development between the two spatial resolutions. The relative order of land use conversion types remained similar across both resolutions, with agricultural land experiencing the greatest conversion to developed land, followed by rangeland and forest, although the total area of each conversion type differed. This study illustrates the possible discrepancies in SDG Indicator 11.3.1 and related outputs when utilizing differing resolution datasets and the importance of data characteristic consideration when conducting SDG Indicator 11.3.1 assessments.

Keywords: urban SDG; land use change; spatial SDG (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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