Land cover assessment and monitoring are essential for sustainable management of natural resources and environmental protection. Object-based image analysis (OBIA) for land cover classification has become an area of interest due to its superiority over the pixel-based classification method. The main objective of this paper is developing a method for land cover classification on the national and sub-national level in the Republic of North Macedonia for mapping and monitoring the land cover changes in the study area from 1988 to 2014. For that purpose, in this study, we combine OBIA with rule set semi-automated multi-segmentation classification for large-scale areas over medium-resolution satellite imagery. Thus, Landsat image collections over North Macedonia have been combined with topographic and settlement layers for land cover classification. Based on the knowledge of certain land cover features, rule-based classification has been developed using two different segmentation parameters. The results show that the overall agreement of the new semi-automatic classification method developed for North Macedonia is 83%. The most significant change in the land cover can be noticed in the forest class, with a total increase of 8% on national and 15% in the South-East region. These results confirm that this new semi-automatic, cost-effective, and accurate land cover classification method can be easily employed and adjusted for different study areas and can be used in numerous remote sensing applications.

土地覆盖评估和监测对于自然资源和环境保护的可持续管理至关重要。由于基于对象的图像分析（OBIA）优于基于像素的分类方法，因此它已成为人们关注的领域。本文的主要目的是开发一种在北马其顿共和国进行国家和国家以下各级土地覆被分类的方法，以绘制和监测1988年至2014年研究区域的土地覆被变化。在这项研究中，我们将OBIA与规则集半自动多细分分类相结合，以用于中分辨率卫星图像上的大面积区域。因此，北马其顿的Landsat影像收集已与地形和沉降层相结合，以进行土地覆盖分类。基于某些土地覆盖特征的知识，使用两个不同的分割参数开发了基于规则的分类。结果表明，为北马其顿开发的新的半自动分类方法的总体一致性为83％。在森林类别中，土地覆盖率的变化最为显着，全国总计增加了8％，东南地区总计增加了15％。这些结果证实，这种新的半自动，经济高效且准确的土地覆被分类方法可以轻松地应用于不同的研究领域并进行调整，并且可以用于众多的遥感应用中。结果表明，为北马其顿开发的新的半自动分类方法的总体一致性为83％。在森林类别中，土地覆盖率的变化最为显着，在全国范围内总计增加了8％，在东南地区总计增加了15％。这些结果证实，这种新的半自动，经济高效且准确的土地覆被分类方法可以轻松地应用于不同的研究领域并进行调整，并且可以用于众多的遥感应用中。结果表明，为北马其顿开发的新的半自动分类方法的总体一致性为83％。在森林类别中，土地覆盖率的变化最为显着，全国总计增加了8％，东南地区总计增加了15％。这些结果证实，这种新的半自动，经济高效且准确的土地覆被分类方法可以轻松地应用于不同的研究领域并进行调整，并且可以用于众多的遥感应用中。