Abstract
In this study, tree-selection/plantation decision support systems (DSSs) were reviewed and evaluated against essential objectives in the available literature. We verified whether existing DSSs leverage multiple data sources and available online resources such as web interfaces. We compared the existing DSSs, and in this study mainly focused on five main objectives that DSSs can consider in tree selection, including (a) climate resilience, (b) infrastructure/space optimization, (c) agroforestry, (d) ecosystem services, and (e) urban sustainability. The climate resilience of tree species and urban sustainability are relatively rarely taken into account in existing systems, which can be integrated holistically in future DSS tools. Based on this review, deep neural networks (DNNs) are recommended to achieve trade-offs between complex objectives such as maximizing ecosystem services, the climate resilience of tree species, agroforestry conservation, and other benefits.
| Original language | English |
|---|---|
| Article number | 230 |
| Journal | Land |
| Volume | 13 |
| Issue number | 2 |
| DOIs | |
| State | Published - 1 Feb 2024 |
Keywords
- climate resilience
- decision support system
- deep neural networks
- ecosystem services
- sustainability
ASJC Scopus subject areas
- Global and Planetary Change
- Ecology
- Nature and Landscape Conservation
