Planting contexts affect urban tree species classification using airborne hyperspectral and LiDAR imagery
Bibliographic Data
| ID | 21468892 |
|---|---|
| Authors | Dengkai Chi (0000-0003-4424-6689, CREATe Centre, corresponding author), Jingli Yan (0000-0001-7907-0068, Shanghai Jiao Tong University), Kang Yu (0009-0001-7315-3345, Technical University of Munich), Felix Morsdorf (0000-0001-6713-1599, University of Zurich), Ben Somers (0000-0002-7875-107X, KU Leuven) |
| Year | 2025 |
| Volume | 257 |
| Pages | 105316 |
| Publication date | 2025-05-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Landscape and Urban Planning (JOURNAL) |
| Journal identifiers | ISSN: 0169-2046 • E-ISSN: 1872-6062 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.landurbplan.2025.105316 |
| OpenAlex | W4407146263 |
| Language | EN |
| Citations received | 1 |
| References cited | 38 |
Intraspecific variations were found in trees in different urban planting contexts. • These variations can affect remote sensing-based tree species classification. • We proposed a planting context-specific modelling approach. • The proposed approach significantly improved tree species classification accuracy. • The approach will facilitate more effective urban forest management. Different urban planting contexts, such as streets and parks, can lead to significant intraspecific biochemical and structural variations in trees. These variations present challenges for remote sensing-based tree species classification and effective urban forest management. However, few studies have explored how planting contexts influence the accuracy of remote sensing-based tree species identification in urban environments. This study introduced a planting context-specific modelling approach (i.e., models trained for street trees and park trees separately) for classifying seven dominant broadleaved tree species in the Brussels Capital Region, Belgium using airborne hyperspectral and leaf-on LiDAR data. This approach was compared to a traditional general modelling approach. Linear discriminant analysis with principal component analysis was employed to classify tree species at the individual tree level using different feature sets. Our results showed that a planting context-specific modelling approach with combined hyperspectral and LiDAR features achieved an overall accuracy (OA) of 84.2%. It improved the OA of LiDAR-based classifications by 7.6 and 8.9 percentage points for street trees and park trees respectively and of hyperspectral-based street tree species classification by 4.2 percentage points. The decreased discriminatory power of features in general models can be partly attributed to their sensitivity to planting context. We concluded that a planting context-specific modeling approach can enhance urban tree species classification, ultimately supporting improved urban forest management
Agronomy · Biology · Geography · Hyperspectral imaging · Lidar · Remote sensing · Sowing · Environmental Science · Land Use and Ecosystem Services · Remote Sensing and LiDAR Applications · Remote Sensing in Agriculture · Forestry
Principal component analysis
Keys to better planning and integrating urban tree planting initiatives
Using climate analogues and vulnerability metrics to inform urban tree species selection in a changing climate
Surface paving more important than species in determining the physiology, growth and cooling effects of urban trees
An evaluation of species distribution models to estimate tree diversity at genus level in a heterogeneous urban-rural landscape
Tree tilt monitoring in rural and urban landscapes of Hong Kong using smart sensing technology
| Unique citing works | 1 |
|---|---|
| Citations per year | 1 |
| Citation span | 2026 - 2026 (1) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 1 |