## 1 Introduction  
Land use/land cover (LULC) information is essential for a region to carry out land-use planning, dynamic land monitoring, protection and management of cultivated land resources, etc. [1, 2]. In order to improve the efficiency of land use and protect the ecological environment, it is necessary to master and update the existing land-cover information. The rapid development of remote-sensing technology and the progress of image-classification methods make large-area land cover information surveys possible. How to effectively apply massive remote-sensing information to obtain higher-precision land-cover classification has always been the direction of research efforts [3]. Land-classification research has been changing and updating with the development of data sources and extraction methods. At present, there are many remote-sensing image datasets widely used in land-cover classification and change detection. Using the appropriate data source depends on the different research areas and classification purposes. MODIS, Landsat, Sentinel-2, and other medium-resolution remote-sensing images have the characteristics of wide coverage and high repeatability. These kinds of data are suitable for large-scale macro land-coversurveys and can be used to detect large areas of ground objects (e.g., forests, cultivated land, wetlands, and deserts) [4; 5; 6; 7]. However, for some small spots that are easy to be missed, it is necessary to conduct additional surveys through field investigation. With the improvement in spatial resolution, the surface features presented by high-resolution remote-sensing images have become more detailed. The boundary of land spots can be defined more accurately. At present, the most commonly used high-resolution remote-sensing images for LULC classification include QuickBird, Worldview series, and GF series [8; 9]. However, for the LULC survey, a higher resolution is not always better. Compared with low-resolution images, high-resolution images have a narrow coverage area, more noise, and a large number of phenomena of \"different objects with the same spectral characteristics\" [10], which increase the complexity of large-scale land classification. Therefore, high-resolution images are suitable for small-area research or monitoring small targets. For urban areas with complex and varied terrain types, automatically extracting the detailed LULC information from remote-sensing images is still very difficult. Medium-resolution images obviously cannot achieve the extraction of small image spots, and super-high-resolution images have high acquisition and computation costs. Therefore, this study adopts PlanetScope satellite images with medium-high resolution. The satellite revisits any part of the globe every day with a resolution of 3 m. It has the characteristics of wide coverage and fast update speed. Moreover, corrected orthophoto images can be obtained, which are suitable for LULC monitoring [11; 12; 13; 14]. NDVI is one of the most widely used Vegetation Indexes (VIs) at present. It can eliminate the influence of most instrument calibrations, the sun angle, terrain, cloud shadow, etc., and enhance the sensitivity to vegetation, which makes it an excellent indicator of vegetation growth states and vegetation coverage. In addition, vegetation cover is easily affected by seasonal changes and human activities. Therefore, using time series NDVI data can more accurately reflect the changing trend of vegetation cover in a period and effectively distinguish the spectral characteristics of different vegetation types [15; 16]. Based on NDVI and the measured data of meteorological stations, it is a major direction of NDVI research to analyze the temporal changes of vegetation, climate, and human activities through correlation analysis, residual analysis, and other methods. For the further evaluation of the impact of climatic and anthropogenic factors on NDVI changes, the regional land-cover map is important auxiliary data [17; 18; 19].