# Remote Sensing in Urban Forestry:
## 4 Concluding Remarks
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Multi-Source** & & **Strengths** & **Limitations** \\ \hline Single remote sensing data source & \(\bullet\) & Involes low labour and high efficiency in data processing & \(\bullet\) & Involes a trade-off between the highest quality spatial, temporal and spectral resolution available \\ \hline \multirow{4}{*}{Multiple sources of satellite imagery} & \(\bullet\) & Suitable for studying the urban heat island and greuspace (spatial) & \(\bullet\) & Image data processing is complex and requires data fusion algorithms to extract similar features from multiple images with different spectral bands \\  & \(\bullet\) & Large amount of open-access images are available & \(\bullet\) & Unsuited for studies on understory urban forests \\  & \(\bullet\) & Dataset availability across a long time span (e.g., Landsat) allows & \(\bullet\) & \(\bullet\) \\  & historical long-term studies & \(\bullet\) & \(\bullet\) & \\  & Multiple sensors (e.g., high spatial and spectral resolution, thermal band) allow a wide range of applications in urban forest studies & \(\bullet\) & \(\bullet\) \\ \hline Satellite imagery & \(\bullet\) & Suitable for urban forest inventories, free biomass and carbon storage & \(\bullet\) & Relatively high cost for LiDAR data acquisition and processing \\  & and airborne LiDAR & \(\bullet\) & Point-cloud colourisation technique allows tree-level analysis by \(\bullet\) & \(\bullet\) \\  & having 3D point clouds with spectral detail & \(\bullet\) & \(\bullet\) \\ \hline Satellite imagery & \(\bullet\) & Aerial imagery with higher spatial resolution can improve the classification of satellite images in urban forest applications & \(\bullet\) & Airborne imagery acquisition requires more resources and a careful plan and cannot be reiterated often due to costs involved in the process \\  & \(\bullet\) & Suitable for urban forest inventories, tree biomass and carbon storage and aerial imagery & \(\bullet\) & Airborne LiDAR acquisition requires more resources and a careful plan and cannot be reiterated often due to costs \\  & and & calculation accuracy at small scale & \(\bullet\) & \(\bullet\) \\ \hline
**Multi-temporal** & & & \\ \hline Short series & \(\bullet\) & Suitable to estimate short-term changes in urban heat island effects, \(\bullet\) & \(\bullet\) & Results need to be interpreted carefully, especially for the short-term assessment of urban forest ecosystem services \\  & \(\bullet\) & canopy cover, tree species and openspace spatial configuration, & \(\bullet\) & \(\bullet\) \\  & especially before and after a certain event & & \\ \hline Long series & \(\bullet\) & Suitable for long-term phenological studies on urban forest areas & \(\bullet\) & Data quality of long-series imagery is particularly dependent on weather conditions and availability of cloud-free images \\ \hline
**Multi-Scale** & & & \\ \hline Local-district-city level & \(\bullet\) & Suitable to determine the relationship between urban forest and ecosystem services at the local scale and to scale up this relationship at city level & \(\bullet\) & Multi-scale studies are complex to carry out and require the through knowledge of different sensors and algorithms for fusing multiple images \\ \hline City-regional level & \(\bullet\) & Suitable for assessing the impact of green infrastructures on large urban areas and to scale up the relative ecosystem services for the whole city region & \(\bullet\) & Mapping given infrastructures at multi-scale requires thorough knowledge of different sensors and algorithms for fusing multiple images \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Strengths and limitations of multi-source, multi-temporal and multi-scale inputs.