School of Geographical Sciences, Qinghai Normal University, Xining 810008, China.
Qinghai Province Key Laboratory of Physical Geography and Environmental Process, Xining 810008, China.
Sensors (Basel). 2023 Jul 4;23(13):6136. doi: 10.3390/s23136136.
Land cover data are important basic data for earth system science and other fields. Multi-source remote sensing images have become the main data source for land cover classification. There are still many uncertainties in the scale effect of image spatial resolution on land cover classification. Since it is difficult to obtain multiple spatial resolution remote sensing images of the same area at the same time, the main current method to study the scale effect of land cover classification is to use the same image resampled to different resolutions, however errors in the resampling process lead to uncertainty in the accuracy of land cover classification. To study the land cover classification scale effect of different spatial resolutions of multi-source remote sensing data, we selected 1 m and 4 m of GF-2, 6 m of SPOT-6, 10 m of Sentinel-2, and 30 m of Landsat-8 multi-sensor data, and explored the scale effect of image spatial resolution on land cover classification from two aspects of mixed image element decomposition and spatial heterogeneity. For the study area, we compared the classification obtained from GF-2, SPOT-6, Sentinel-2, and Landsat-8 images at different spatial resolutions based on GBDT and RF. The results show that (1) GF-2 and SPOT-6 had the best classification results, and the optimal scale based on this classification accuracy was 4-6 m; (2) the optimal scale based on linear decomposition depended on the study area; (3) the optimal scale of land cover was related to spatial heterogeneity, i.e., the more fragmented and complex was the space, the smaller the scale needed; and (4) the resampled images were not sensitive to scale and increased the uncertainty of the classification. These findings have implications for land cover classification and optimal scale selection, scale effects, and landscape ecology uncertainty studies.
土地覆盖数据是地球系统科学和其他领域的重要基础数据。多源遥感图像已成为土地覆盖分类的主要数据源。图像空间分辨率对土地覆盖分类的尺度效应仍然存在许多不确定性。由于难以同时获得同一地区的多个空间分辨率遥感图像，目前研究土地覆盖分类尺度效应的主要方法是使用同一图像重采样到不同分辨率，但是重采样过程中的误差导致土地覆盖分类精度的不确定性。为了研究多源遥感数据不同空间分辨率的土地覆盖分类尺度效应，我们选择了 GF-2 的 1 m 和 4 m、SPOT-6 的 6 m、Sentinel-2 的 10 m 和 Landsat-8 的 30 m 多传感器数据，从混合像元分解和空间异质性两个方面探讨了图像空间分辨率对土地覆盖分类的尺度效应。对于研究区域，我们基于 GBDT 和 RF 比较了不同空间分辨率下 GF-2、SPOT-6、Sentinel-2 和 Landsat-8 图像的分类结果。结果表明：（1）GF-2 和 SPOT-6 的分类效果最好，基于该分类精度的最佳尺度为 4-6 m；（2）基于线性分解的最佳尺度取决于研究区域；（3）土地覆盖的最佳尺度与空间异质性有关，即空间越破碎和复杂，所需的尺度越小；（4）重采样图像对尺度不敏感，增加了分类的不确定性。这些发现对土地覆盖分类和最佳尺度选择、尺度效应以及景观生态学不确定性研究具有重要意义。
