Abstract Remote sensing data and techniques are reliable tools for monitoring and studying urban land cover and land use (LCLU) change. Fine spatial resolution (FRes) commercial satellite image in conjunction with geographic object-based image change analysis (GEOBICA) methods have been used to generate detailed and accurate urban LCLU maps. The integration of a backdating approach improves LCLU change classification results for the first date of a bi-temporal image sequences. Conversely, moderate spatial resolution satellite images such as those from Landsat sensors may not allow for detailed urban land use and land cover mapping. The objective of this study is to test a new bi-temporal change identification approach that integrates image classification of fine spatial resolution satellite imagery at time-2 and moderate spatial resolution satellite imagery (Landsat) at time-1, in a backdating and GEOBICA framework for mapping urban land use change. We compare the results from this approach to those of a GEOBICA approach based on fine spatial resolution imagery in both periods. The overall accuracy of the time-1 Landsat image classification is 0.82 and that of the fine spatial resolution image is 0.87. Moreover, the overall accuracy of the areal change data estimated from the pixel-wise spatial overlay of bi-temporal FRes LCLU maps is 0.80 while that from overlaying a time-2 FRes-generated map to that from a Landsat time-1 image is 0.81. The proposed method can be used in areas that lack FRes data due to limited coverage in the early 2000s.

中文翻译：

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使用多空间分辨率数据和基于对象的图像分析进行土地覆盖和土地利用变化分析

摘要 遥感数据和技术是监测和研究城市土地覆盖和土地利用（LCLU）变化的可靠工具。精细空间分辨率 (FRes) 商业卫星图像结合基于地理对象的图像变化分析 (GEOBICA) 方法已被用于生成详细而准确的城市 LCLU 地图。回溯方法的集成改进了双时相图像序列的第一个日期的 LCLU 更改分类结果。相反，中等空间分辨率的卫星图像（例如来自 Landsat 传感器的图像）可能无法进行详细的城市土地利用和土地覆盖制图。本研究的目的是在回溯和 GEOBICA 框架中测试一种新的双时间变化识别方法，该方法将时间 2 的精细空间分辨率卫星图像和时间 1 的中等空间分辨率卫星图像 (Landsat) 的图像分类相结合用于绘制城市土地利用变化图。我们将这种方法的结果与基于两个时期的精细空间分辨率图像的 GEOBICA 方法的结果进行比较。time-1 Landsat影像分类的整体精度为0.82，精细空间分辨率影像的整体精度为0.87。此外，从双时相 FRes LCLU 地图的像素级空间叠加估计的区域变化数据的整体精度为 0.80，而从时间 2 FRes 生成的地图叠加到来自 Landsat time-1 图像的地图的整体精度为 0.81 .