Mapping Land-Use and Land-Cover Changes Through the Integration of Satellite and Airborne Remote Sensing Data

The development of an integrated, remotely sensed approach to assess land-use and land-cover change (LULCC) dynamics is of paramount importance, as it has the potential to alter the natural environment. In this study, we utilized the advantage of land-cover seasonality and computed the vegetation indices from SPOT images. Through analysing monthly Normalized Difference Vegetation Index (NDVI), and Near Infrared (NIR) values, the temporal characteristics of each land type are detected and used as indices for the land type classification. A Phenology-based Classification Model (PCM) was established to classify the land into five land-cover types: forest, built-up land (including bare soil), water, agricultural land, and grassland/shrubs. Normalized Difference Built-up Index (NDBI) derived from Landsat images and airborne lidar canopy height data were then integrated into the PCM to further improve the classification accuracy. The classification results of the Taoyuan Tableland, from 2013 to 2022 demonstrate fluctuations in land types over the years. The classification results suggest a stable forest, a slight decline in agricultural land and inland water, and an increase in grassland/shrubs. The results also reveal a negative correlation (r=-0.79) in area change between grassland/shrubs and agricultural land, as well as a positive correlation (r = 0.47) between irrigation ponds and agricultural land. The event-based LULCC analysis of Taipei City shows that the number of urbanization events becomes relatively comparable to urban greening events when the spatial extent of LULCC events exceeds 1,000 m 2 , indicating a balance between urbanization and urban greening. Small-extent of urban greening events are frequently discovered and distributed throughout the metropolitan area of Taipei City. The development of PCM largely reduced the time and effort required for manual classification, and this new implementation successfully captures annual LULCCs over the past decade in our study areas.