# Remote Sensing in Urban Forestry:
## 2 Remote Sensing Applications in Urban Forests
### Theme 2: Multi-Temporal Input
#### 2.2.2 Long Series Data Input  
The majority of recent studies uses multi-temporal data to study changes in urban forests and the induced socioeconomic and environmental effects based on long series data input (16 of 26). The ability of multi-temporal remote sensing to monitor long-term changes in urban vegetation cover, urban forest structure, and urban heat island is profound. Its potential also lies in monitoring changes at various scales, from a small urban park to a metropolitan city and even to an entire region or nation. Landsat imagery has been largely used by long-term studies owing to its long and continuous time-series. MODIS imagery is also popular in urban heat island studies because it has a superior temporal resolution than Landsat. The advantages of using multi-temporal satellite images for a long-term, multi-temporal study are apparent: low cost, instant access, and short revisit period. [PERSON] et al. (2017) estimated the changes in urban greenspace coverage in 30 major Chinese cities at regional level between 1990 and 2010. Landsat-5 (TM) and Landsat-7 Enhanced Thematic Mapper Plus (ETM+) images acquired in 1990, 2000 and 2010 were used to classify land cover into five types with the SVM method. They found highly divergent trends of urban greenspace changes across different cities, which could be explained by varied developmental patterns and government policies. [PERSON] and [PERSON] (2018) explored the relationship between vegetation cover and urban heat island effect across the greater Sydney metropolitan region, in Australia. All available MODIS thermal band scenes (102 in total) with less than 5% cloud cover in the summertime between 2000 and 2011 were used as the land surface temperature index. The vegetation cover indices (e.g., foliage projective cover, fractional cover of photosynthetically active vegetation) were extracted based on Landsat imagery and ancillary data within the same time-series. All indices were resampled to 1 km\({}^{2}\) to match MODIS spatial resolution. Unlike most previous studies that employed single-phase remotely sensed data and a single mode of measuring vegetation cover, the multi-temporal satellite imagery employed in this study was capable of accounting for temporal variability and estimating the regional effects of varying density and structure of vegetation cover. [PERSON] and [PERSON] (2019) examined the spatial and temporal variations of soil organic carbon stock in a constructed urban park in Seoul, Korea, and explored how soil organic carbon stock was shaped by land use and land cover changes. In their study, the NDVI values were derived from MODIS data. Landsat-7 and -8 time-series images acquired between 2003 and 2013 were collected to measure the temporal changes in NDVI values and land use pattern. Changes in the soil organic carbon concentration of topsoil were quantified based on land use types. The results indicated that land use history of the urban park played an important role in the temporal variations of the soil organic carbon concentrations. [PERSON] et al. (2016) explored the role of urban green infrastructure in mitigating the urban heat island effect over 20 years in an urban area in sub-Saharan Africa. A total of 16 processed satellite images acquired from Landsat-5 (TM), Landsat-7 (ETM+) and Landsat-8 Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) between 1991 and 2013 were used to extract data on greenspace cover and land surface temperature. Data alignment was required for multi-temporal images from different Landsat sensors. On the one hand, the spatial resolutions of the thermal bands of the three Landsat sensors were different: 120 m for Landsat-5, 60 m for Landsat-7, and 100 m for Landsat-8. On the other hand, the thermal resolutions were also divergent: Landsat-5 and Landsat-7 had only one thermal band (Band 6), while Landsat-8 had two thermal bands (Band 10 and Band 11).