| Autor: |
Julius M. Knopp, Gregor Levin, Ellen Banzhaf |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 16, Pp 6545-6555 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2151-1535 |
| DOI: |
10.1109/JSTARS.2023.3289218 |
| Popis: |
Fostering urban resilience and adaptation to climate change pose new demands on the knowledge of land use and land cover (LULC) in heterogeneous urban spaces. High-resolution urban mapping is a valuable tool, which serves to map detailed categories. Such semantic data are integrated in national and regional administration as public goods. In the light of many countries around the globe making their data publicly available, we present a method to map urban areas based on multitemporal orthophotos and LiDAR-derived digital surface model, and extract information about vegetation in an automated processing chain. This approach is threshold driven and relies on an automatic generation of spectral thresholds and existing real-world-based classifications. We included cadastral data to add land-use information for specific categories, such as agricultural land use and to assess the product's accuracy. Adding these data creates an LULC product and makes a seamless integration into urban planning routines possible. The results of the study provide a detailed LULC map for the municipality of Aarhus in 2015 with a spatial resolution of 20 cm and ten thematic classes. Depending on the reference data, we achieved thematic overall accuracies of 34% and 64% using a polygon-based approach. Our study has found that utilizing both multitemporal orthophotos and elevation data can enhance the LC mapping of urban landscapes. The methodology could be transferred to other areas in Denmark or to countries providing similar datasets, and lends itself to a repeatable LULC mapping with minimal user interaction. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
|
