ORIGINAL RESEARCH article
Front. Remote Sens.
Sec. Image Analysis and Classification
Volume 6 - 2025 | doi: 10.3389/frsen.2025.1570580
Resiliency of Land Change Monitoring Efforts to Input Data Resampling
Provisionally accepted
- 1KBR, Inc. contractor to the U.S. Geological Survey, Earth Resources Observation and Science (EROS) Center, Sioux Falls, United States
- 2Earth Resources Observation and Science Center, United States Geological Survey (USGS), Sioux Falls, South Dakota, United States
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The geometric transformation of remotely sensed imagery from one map projection to another necessitates a data resampling operation which alters the recorded values. The global Landsat archive is made available in the Universal Transverse Mercator (UTM) projection system which preserves geographic shape across small area but introduces small errors in distance and area. As remote sensing-based studies develop from local scales to regional and global, they need to adopt more appropriate map projections from which accurate area measurements can be made. While effects of resampling on recorded values have been studied in the past, the impacts on higher-level results such as land cover have not been widely reported. This study investigates an approach for monitoring land cover and land change using two input datasets derived from identical source Landsat data, where one input dataset is transformed to an equal-area map projection and thereby resampled. Recorded surface reflectance values are changed through the reprojection/resampling process, and our study highlights observed differences in derived land cover from these two different input datasets throughout the various stages of deriving land cover and related characteristics. Our findings suggest that large-scale analyses of land cover will not be substantially impacted by reprojection of input data, but small-scale analyses should exercise caution when interpreting timing and magnitude of pixel-level change and classification dynamics.
Keywords: land cover, Reprojection, resampling, Classification, land cover change
Received: 03 Feb 2025; Accepted: 08 May 2025.
Copyright: © 2025 Healey, Barber, Smith, Mital, Brown and Robison. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Nathan Healey, KBR, Inc. contractor to the U.S. Geological Survey, Earth Resources Observation and Science (EROS) Center, Sioux Falls, United States
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