Remote sensing has been recognized as the main technique to extract land cover/land use (LC/LU) data, required to address many environmental issues. Therefore, over the years, many approaches have been introduced and explored to optimize the resultant classification maps. Particularly, index-based methods have highlighted its efficiency and effectiveness in detecting LC/LU in a multitemporal and multisensors analysis perspective. Nevertheless, the developed indices are suitable to extract a specific class but not to completely classify the whole area. In this study, a new Landsat Images Classification Algorithm (LICA) is proposed to automatically detect land cover (LC) information using satellite open data provided by different Landsat missions in order to perform a multitemporal and multisensors analysis. All the steps of the proposed method were implemented within Google Earth Engine (GEE) to automatize the procedure, manage geospatial big data, and quickly extract land cover information. The algorithm was tested on the experimental site of Siponto, a historic municipality located in Apulia Region (Southern Italy) using 12 radiometrically and atmospherically corrected satellite images collected from Landsat archive (four images, one for each season, were selected from Landsat 5, 7, and 8, respectively). Those images were initially used to assess the performance of 82 traditional spectral indices. Since their classification accuracy and the number of identified LC categories were not satisfying, an analysis of the different spectral signatures existing in the study area was also performed, generating a new algorithm based on the sequential application of two new indices (SwirTirRed (STRed) index and SwiRed index). The former was based on the integration of shortwave infrared (SWIR), thermal infrared (TIR), and red bands, whereas the latter featured a combination of SWIR and red bands. The performance of LICA was preferable to those of conventional indices both in terms of accuracy and extracted classes number (water, dense and sparse vegetation, mining areas, built-up areas versus water, and dense and sparse vegetation). GEE platform allowed us to go beyond desktop system limitations, reducing acquisition and processing times for geospatial big data.

中文翻译：

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Landsat图像分类算法（LICA）在Google Earth Engine环境中自动提取土地覆盖信息

遥感已被认为是提取土地覆被/土地利用（LC / LU）数据的主要技术，解决了许多环境问题。因此，多年来，已经引入和探索了许多方法来优化所得的分类图。尤其是，基于索引的方法从多时间和多传感器分析的角度突出了其检测LC / LU的效率和有效性。但是，已开发的索引适合提取特定类别，但不适合对整个区域进行完全分类。在这项研究中，提出了一种新的Landsat图像分类算法（LICA），该算法使用不同Landsat任务提供的卫星开放数据自动检测土地覆盖（LC）信息，以执行多时相和多传感器分析。拟议方法的所有步骤均在Google Earth Engine（GEE）中实施，以自动执行该过程，管理地理空间大数据并快速提取土地覆盖信息。该算法在Siponto（位于意大利南部的普利亚大区）的历史名城的实验点上进行了测试，使用了从Landsat档案中收集的12幅经辐射和大气校正的卫星图像（从Landsat 5、7选择了四幅图像，每个季节一张） ，和8）。这些图像最初用于评估82个传统光谱指数的性能。由于其分类准确度和已识别LC类别的数量不令人满意，因此还对研究区域中存在的不同光谱特征进行了分析，基于两个新索引（SwirTirRed（STRed）索引和SwiRed索引）的顺序应用生成新算法。前者基于短波红外（SWIR），热红外（TIR）和红色波段的集成，而后者则具有SWIR和红色波段的组合。LICA的性能在准确性和提取类别数（水，茂密和稀疏的植被，矿区，相对于水的建成区以及茂密和稀疏的植被）方面均优于常规指标。GEE平台使我们超越了桌面系统的限制，减少了地理空间大数据的获取和处理时间。后者则结合了SWIR和红色波段。LICA的性能在准确性和提取类别数（水，茂密和稀疏的植被，矿区，相对于水的建成区以及茂密和稀疏的植被）方面均优于常规指标。GEE平台使我们超越了桌面系统的限制，减少了地理空间大数据的获取和处理时间。后者则结合了SWIR和红色波段。LICA的性能在准确性和提取类别数（水，茂密和稀疏的植被，矿区，相对于水的建成区以及茂密和稀疏的植被）方面均优于常规指标。GEE平台使我们超越了桌面系统的限制，减少了地理空间大数据的获取和处理时间。