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Scale Effect of Land Cover Classification from Multi-Resolution Satellite Remote Sensing Data.
Runxiang Li1,2,3, Xiaohong Gao1,2,3,4, Feifei Shi1,2,3
1School of Geographical Sciences, Qinghai Normal University, Xining 810008, China.
Sensors (Basel, Switzerland)
|July 14, 2023
Summary
The optimal spatial resolution for land cover classification using remote sensing data is between 4-6 meters, with finer scales necessary for complex landscapes. Resampling images introduces uncertainty, highlighting the need for multi-source data analysis.
Area of Science:
- Earth System Science
- Remote Sensing
- Geospatial Analysis
Background:
- Land cover data is crucial for earth system science.
- Multi-source remote sensing images are primary data for land cover classification.
- Scale effects of spatial resolution introduce uncertainties in classification accuracy.
Purpose of the Study:
- To investigate the scale effect of spatial resolution on land cover classification using multi-source remote sensing data.
- To explore the impact of mixed pixel decomposition and spatial heterogeneity on classification accuracy.
- To determine the optimal spatial resolution for land cover classification.
Main Methods:
- Utilized multi-sensor data (GF-2, SPOT-6, Sentinel-2, Landsat-8) at varying resolutions (1m to 30m).
- Employed Gradient Boosting Decision Tree (GBDT) and Random Forest (RF) algorithms for classification.
- Analyzed scale effects through mixed pixel decomposition and spatial heterogeneity assessments.
Main Results:
- GF-2 and SPOT-6 (1m-6m) yielded the best classification results, indicating an optimal scale of 4-6m.
- Optimal scale determined by linear decomposition is study-area dependent.
- Land cover scale optimality correlates with spatial heterogeneity; complex landscapes require smaller scales.
- Resampled images showed scale insensitivity and increased classification uncertainty.
Conclusions:
- The optimal spatial resolution for land cover classification is scale-dependent and influenced by landscape complexity.
- Direct use of multi-source data at native resolutions is preferable to resampling for reducing uncertainty.
- Findings inform optimal scale selection, scale effect studies, and landscape ecology research.
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