Monitoring 3D Changes in Urban Forests Using Landscape Metrics Analyses Based on Multi-Temporal Remote Sensing Data

Abstract

Rapid urbanization is causing changes in green spaces and ecological connectivity. So far, urban ecosystem research has mainly focused on using landscape metrics (LM) in two-dimensional (2D) space. Our study proposes three-dimensional (3D) measures of urban forests (UF) and LM calculations using LiDAR technology. First, we estimated the UF volume of Krakow (Poland) and the distribution of vegetation (low, medium, high) using a voxel-based GEOBIA approach based on the ALS LiDAR point cloud, satellite imagery, and aerial orthophotos at specific timestamps: 2006, 2012, 2017. Then, the appropriate landscape metrics were selected (NP, AREA_MN, CONTIG_MN, LPI, PARA_MN, SPLIT, MESH, PD, DIVISION, LSI) to quantify the differences between the 2D- and 3D-derived vegetation structures and detect changes in the urban landscape. The results showed that areas with low vegetation decreased due to the expansion of built-up areas, while areas with medium and high vegetation increased in Krakow between 2006, 2012, and 2017. We have shown that the lack of information on the vertical features of vegetation, i.e., 2D greenery analysis, leads to an overestimation of landscape connectivity. In the 3D vegetation classes, it was observed that low vegetation was the best connected, followed by high vegetation, while medium vegetation was dispersed in the city space. These results are particularly relevant for the urban environment, where the distribution of green space is crucial for the provision of ecosystem services.

Urban Green Connectivity Assessment: A Comparative Study of Datasets in European Cities

“…Future revisions of the Urban Atlas, in particular, should strive to provide a comprehensive and ecologically meaningful characterization of UGAs across Europe. This would involve considering not only the land cover type but also the spatial configuration, vegetation structural complexity, cultivation degree, and management regime type of the UGAs [41,53,54]. Such improved data would serve as a valuable resource for promoting more sustainable urban planning and management practices, integrating ecological considerations into decision-making processes.…”

Enhancing Tree Species Identification in Forestry and Urban Forests through Light Detection and Ranging Point Cloud Structural Features and Machine Learning

“…In urban forestry, laser scanning is used for mapping and monitoring single tree characteristics, providing a convenient tool for measuring tree attributes in cities and urban forests [12,[17][18][19][20]. ALS can be used to generate high-resolution spatially explicit maps of urban forest structure, including the detection, mapping, and characterization of individual trees [21].…”