Lemenkova Polina
Department of Biological, Geological and Environmental Sciences, Alma Mater Studiorum-Università di Bologna, Via Irnerio 42, 40126 Bologna, Italy.
J Imaging. 2025 May 12;11(5):153. doi: 10.3390/jimaging11050153.
This work presents the use of remote sensing data for land cover mapping with a case of Central Apennines, Italy. The data include 8 Landsat 8-9 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) satellite images in six-year period (2018-2024). The operational workflow included satellite image processing which were classified into raster maps with automatically detected 10 classes of land cover types over the tested study. The approach was implemented by using a set of modules in Geographic Resources Analysis Support System (GRASS) Geographic Information System (GIS). To classify remote sensing (RS) data, two types of approaches were carried out. The first is unsupervised classification based on the MaxLike approach and clustering which extracted Digital Numbers (DN) of landscape feature based on the spectral reflectance of signals, and the second is supervised classification performed using several methods of Machine Learning (ML), technically realised in GRASS GIS scripting software. The latter included four ML algorithms embedded from the Python's Scikit-Learn library. These classifiers have been implemented to detect subtle changes in land cover types as derived from the satellite images showing different vegetation conditions in spring and autumn periods in central Apennines, northern Italy.
这项工作展示了如何利用遥感数据进行土地覆盖制图，并以意大利亚平宁山脉中部为例。数据包括2018年至2024年六年期间的8幅陆地卫星8-9号业务陆地成像仪/热红外传感器（OLI/TIRS）卫星图像。业务工作流程包括卫星图像处理，这些图像被分类为栅格地图，在测试研究中自动检测出10种土地覆盖类型。该方法是通过使用地理资源分析支持系统（GRASS）地理信息系统（GIS）中的一组模块来实现的。为了对遥感（RS）数据进行分类，采用了两种方法。第一种是基于最大似然法和聚类的无监督分类，它根据信号的光谱反射率提取景观特征的数字值（DN），第二种是使用几种机器学习（ML）方法进行的监督分类，在GRASS GIS脚本软件中技术实现。后者包括从Python的Scikit-Learn库中嵌入的四种ML算法。这些分类器已被用于检测土地覆盖类型的细微变化，这些变化源自于显示意大利北部亚平宁山脉中部春季和秋季不同植被状况的卫星图像。
