Long time series urban mapping using Landsat images remains challenging due to the difficulty of collecting representative ground samples. We proposed a mapping approach integrating a sample-transferring scheme, multiple-feature fusion classification, and temporal filtering to generate annual urban land maps in Zhengzhou during 1986 to 2021 with the aid of the Google Earth Engine (GEE). The training and validation samples for each historical year were obtained by a proposed sample transfer scheme based on stable areas derived from the GlobeLand30 datasets. Thirteen variables, such as spectral indices, spectral bands, and terrain factors, were combined to form the feature vector. Annual classification was conducted yearly using the random forest algorithm on GEE to obtain annual impervious surface (ISs) maps. Finally, the IS classification maps were postprocessed to obtain the final urban land maps. Zhengzhou was selected as the study area due to its dramatic urbanization in recent decades. The results showed that the overall accuracy and Kappa coefficient of the IS classification maps ranged from 0.8888 to 0.9578 and 0.6548 to 0.8331, respectively. Compared with other 30-m land-cover products, our urban land maps showed higher accuracy and more reliable spatial details. The proposed approach can generate massive ground samples for long time series urban mapping and is helpful for updating regional and global land-cover products. Zhengzhou’s urban area increased by 1423.24 km2 with an average expansion area of 40.66 km2 / year from 1986 to 2021. The urban expansion in Zhengzhou showed significant stage characteristics and spatial variations. The expansion intensity indices were 0.204, 0.404, 0.736, and 2.018, respectively, at four time periods and varied across the 12 regions in Zhengzhou.

中文翻译：

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通过自动生成来自 Globeland30 的地面样本和 Google 地球引擎上的随机森林分类来绘制城市土地动态图†

由于难以收集具有代表性的地面样本，使用 Landsat 图像的长时间序列城市制图仍然具有挑战性。我们提出了一种结合样本转移方案、多特征融合分类和时间过滤的映射方法，在谷歌地球引擎 (GEE) 的帮助下生成郑州 1986 年至 2021 年的年度城市土地地图。每个历史年份的训练和验证样本是通过基于从 GlobeLand30 数据集派生的稳定区域提出的样本转移方案获得的。将光谱指数、光谱波段和地形因素等 13 个变量组合起来形成特征向量。每年使用 GEE 上的随机森林算法进行年度分类，以获得年度不透水表面 (IS) 图。最后，对 IS 分类图进行后处理以获得最终的城市土地图。郑州被选为研究区，因为它近几十年来的急剧城市化。结果表明，IS分类图的整体准确率和Kappa系数分别在0.8888到0.9578和0.6548到0.8331之间。与其他 30 米土地覆盖产品相比，我们的城市土地地图显示出更高的准确性和更可靠的空间细节。所提出的方法可以为长时间序列的城市制图生成大量地面样本，有助于更新区域和全球土地覆盖产品。1986年至2021年，郑州市城区面积增加1423.24平方公里，平均扩张面积40.66平方公里/年。郑州城市扩张呈现出显着的阶段性特征和空间变化。