# Remote Sensing in Urban Forestry:
## 2 Remote Sensing Applications in Urban Forests
### Theme 1: Multi-Source Input
#### 2.1.2 Satellite Imagery and Airborne LiDAR  
Due to the strengths in providing spectral details and 3D point clouds, VHSR/HSR satellite imagery and airborne LiDAR data were suited for the estimation of urban forest structure, attributes and carbon storage. The integration of optical satellite imagery and LiDAR data allows us to identify the urban forest structure more accurately in a complex urban environment. HSR multispectral imagery is able to produce precise vegetation coverage, upon which LiDAR point-cloud data are based and can be further used to estimate tree-level structure. For example, [PERSON] et al. (2016) mapped carbon storage of urban trees in Boston City using LiDAR data coupled with VHSR satellite images. QuickBird imagery was first employed to derive the Normalised Difference Vegetation Index (NDVI) values for extracting the vegetation cover. Subsequently, 3D airborne LiDAR point-cloud data were used to delineate individual tree crowns within the vegetation coverage. LiDAR-based tree height data were further used to estimate biomass and carbon storage. The results showed that previous work using single-source remotely sensed data tended to overestimate carbon storage in Boston. The QuickBird-LiDAR approach presented in this study displayed high accuracy in estimating stereo features of urban trees, like volume and biomass. Similarly, [PERSON] et al. (2016) developed a more specific method to combine LiDAR data and VHSR satellite images to evaluate the carbon storage of urban trees in Berlin, Germany. Airborne LiDAR was first employed to generate a digital surface model (DSM) and a digital terrain model (DTM). After a vegetation mask was created based on QuickBird imagery, the DSM and DTM were used to derive a canopy height model (CHM). Using a two-step approach [60; 61], dendrometric parameters including tree location, number, height, crown width and diameter at breast height were generated from the CHM. These parameters were then used to calculate carbon storage and distribution among urban trees. [PERSON] et al. [62] used a novel data fusion strategy to identify urban tree canopy cover with WorldView-2 imagery and airborne LiDAR data. The investigators adopted an automatic co-registration method, which was proposed by [PERSON] et al. [63]. The co-registration of satellite imagery to LiDAR data is a process known as point-cloud colourisation. It attaches spectral information to the LiDAR point cloud so that each colourised LiDAR point has additional RGB and NDVI values. The NDVI values can improve the accuracy of identifying LiDAR tree points during the segmentation of trees from other urban objects. Data fusion of LiDAR point-cloud data and spectral imagery has great potential in increasing the accuracy of estimating tree structure in complex urban environments with diversified tree canopy shapes and terrains. While most recent studies agree that the combination of satellite imagery and LiDAR data can lead to higher estimation accuracy, a publication by [PERSON] et al. [64] challenges this consensus. The authors found that with the help of a random forest classification method, LiDAR-derived topography metrics could detect understory plant invasion in urban forests with the highest accuracy, exceeding IKONOS data by 17.5% and combined LiDAR and IKONOS data by 5.3%. In this case, they inferred that the temporal lag that took place between LiDAR and IKONOS data collection might have affected the accuracy of identifying the fast-growing invasive species, _Ligustrum sinensis_. The off-nadir effect of tall trees [65] would also hamper HSR satellite imagery in detecting understory features in a heterogeneous urban environment. Although some researchers argue that airborne LiDAR alone is capable of producing an accurate tree-level analysis, the abundance of spectral details provided by satellite imagery should be corroborated; managed properly it can significantly enhance estimation accuracy as opposed to using LiDAR data alone.