# Remote Sensing in Urban Forestry:
## 3 Challenges and Future Directions  
The volume of remote sensing images continues to grow at an enormous rate due to rapid advances in sensor technology and the launch of new satellite constellations. Consequently, an increasing amount of images from various sensors have become available, including multi-resolution and multi-temporal as well as multi-spectral band images. Despite this upsurge in trends, challenges have emerged in the field of urban forestry concerning the use of remote sensing techniques and the realisation of their full potential. First, although many remote sensing data and products are available (Table 3), there are technical issues with regard to data fusion and synergy (such as fusing optical, hyperspectral imagery, synthetic aperture radar remote sensing data that operate at disparate resolutions and use diverse acquisition approaches) as well as data matching to meet urban foresters' practical needs [35]. Second, a direct link is still lacking between ecological and geographical features, which can be extracted from remote sensing, and the adequate monitoring and modelling of ecosystem services provision, which usually requires diverse data inputs and sophisticated data processing [27]. Third, although several urban forest management software programmes (e.g., i-Tree developed by the USDA Forest Service) have standardised remote sensing data inputs, they are far from being developed as internationally transferable tools due to insufficient field calibration and validation [83]. And fourth, the techniques employed for urban tree/forest recognition, classification, segmentation and change detection from remote sensing data are still in their infancy, even though the key advantage of remote sensing technology lies in its capability of integrating data from multiple sources with multi-spatial/spectral/temporal resolutions that can provide enhanced data on urban forest resources and more robust and interpretable information for assessing ecosystem services.  
In line with the aforementioned challenges, we present a set of future research directions on the application of remote sensing in an urban forest as follows:  
1. There is a need for robust algorithms to fully automate the co-registration of data captured by multiple sensors as well as an optimal combined strategy of fusion algorithms to integrate multi-source remote sensing data at the pixel, feature and landscape levels.
2. Another direction is to calibrate modelling techniques (such as processing accuracy and efficiency in dealing with spatial/temporal co-registration and high data dimensionality) to long-term time series so that past and present monitoring and analyses can evolve to innovative forecasting with regard to the dynamics of urban forests and their ecosystem services. Ultimately, temporal analysis would become a precursor to predicting the future and informing long-term urban forest strategy based on trends derived from past observations [84].
3. Owing to the complexity of natural and anthropogenic factors in urban areas, urban forest studies relying on remote sensing data at a larger scale are still at the experimental stage [27; 50]. Multi-scale analyses would help to access spatial information over a wide range of scales from local to regional and even global. Yet, scale variations should be carefully considered when using remote sensing applications to inform land-use policies and the related estimation of ecosystem services provision.