Description

Urban green spaces make an important contribution to the liveability of cities. This indicator examine how green are urban green infrastructure using remote sensing data.

• 1- The greenest value per UGI is derived
• 2- patterns of changes in the long-term. are reported as:
  • a. % of change per decade
  • b. Balance between greening and browning areas

Greenness and temporal trends were measured within core cities and within their commuting zones focusing on:

• Densely built-up areas where artificial areas cover > than the 60% of a 2.25 km2 neighborhood
• not densely built up areas where artificial areas are mixed with urban forest, seminatural vegetation or urban fringes

Scale as linked to D2.2 framework

• National
• City
• Neighborhood

Data source [ link to data framework]

• Satellite

Data needs

• Landsat annual Top-of- Atmosphere (TOA) reflectance composites available as collections in the Google Earth Engine (GEE) platform for the period 1996–2018 
• the model can be implemented using NDVI trend data Measurement Unit:
  •  % [change in NDVI (greenest value) per decade]
  • Greening-Browning balance (difference between share of UGI where there has been a major upward and downward trend in vegetation cover)

Expected Outputs

• map

Calculation methods

• Trend analysis employed non-parametric approaches, namely Theil–Sen regressions. The slopes of the regression approach were tested for their statistical significance using the p-value of the Mann–Kendall test for slopes (Forkel et al. 2013; Corbane et al. 2018; Novillo et al. 2019). Pixels for which the p-value (Mann–Kendall) was less than 0.1 (90% confidence interval) were extracted and considered to have a significant medium-term trend. We then applied the Theil–Shen regression to obtain the Theil–Sen positive or negative slopes of all significant NDVI trend pixels from 1996 to 2018.
• From the Theil–Sen positive or negative slope we extracted the pixels that overlap areas where (at least once between 1996 and 2018) the highest-NDVI was greater than 0.4. In this way we could focus on changes which affected the urban green infrastructure, minimizing the impact of mixed pixels on the analysis (Dobbs et al. 2018). Changes were reported for densely and not densely built up areas.
  • Medium-term trend summary statistics
    • Average value - Coefficient of variation - Minimum - Maximum
  • Medium-term trend classes share (%)
• Slope was reclassified in 5 classes representing key change thresholds

• ≤-0.015 → hard browning	Downward trends (Browning) due to housing policies, development of industrial and commercial areas, new grey infrastructures
  -0.015 < x ≤ -0.0001 → light browning	Downward trends (Browning) due to housing policies, development of industrial and commercial areas, new grey infrastructures
  -0.0001 < x ≤ 0.0001 → no changes	No changes
  ≤ 0.007 → light greening	Upward trend (Greening) due to green infrastructure management; vegetation growth; climate change

Links and references

• Corbane C, Pesaresi M, Politis P, Florczyk J. A, Melchiorri M, Freire S, Schiavina M, Ehrlich D, Naumann G, Kemper T (2018) The grey-green divide: multi-temporal analysis of greenness across 10,000 urban centres derived from the Global Human Settlement Layer (GHSL). Int J Digit Earth 0(0): 1–18. doi: 10.1080/17538947.2018.1530311
• Dobbs C, Hernández-Moreno Á, Reyes-Paecke S, Miranda MD (2018) Exploring temporal dynamics of urban ecosystem services in Latin America: The case of Bogota (Colombia) and Santiago (Chile). Ecol Indic 85(November 2017): 1068–1080. doi: 10.1016/j.ecolind.2017.11.062
• Forkel M, Carvalhais N, Verbesselt J, Mahecha MD, Neigh CSR, Reichstein M (2013) Trend Change detection in NDVI time series: Effects of inter-annual variability and methodology. Remote Sens 5(5): 2113–2144. doi: 10.3390/rs5052113
• Jin J, Gergel SE, Lu Y, Coops NC, Wang C (2019) Asian Cities are Greening While Some North American Cities are Browning: Long-Term Greenspace Patterns in 16 Cities of the Pan-Pacific Region. Ecosystems. doi: 10.1007/s10021-019-00409-2
• Maes, J., Teller, A., Erhard, M., Condé, S., Vallecillo, S., et al., Mapping and Assessment of Ecosystems and their Services: An EU ecosystem assessment, EUR (where available), Pulications Office of the European Union, Ispra, 2020, ISBN 978- 92-79- XXXXX-X (where available), doi:10.2760/XXXXX (where available), JRC120383
• Novillo C, Arrogante-Funes P, Romero-Calcerrada R (2019) Recent NDVI Trends in Mainland Spain: Land-Cover and Phytoclimatic-Type Implications. ISPRS Int J Geo-Information 8(1): 43. doi: 10.3390/ijgi8010043
• Yu Z, Wang Y, Deng J, Shen Z, Wang K, Zhu J, Gan M (2017) Dynamics of Hierarchical Urban Green Space Patches and Implications for Management Policy.
• Zhu Z, Fu Y, Woodcock CE, Olofsson P, Vogelmann JE, Holden C, Wang M, Dai S, Yu Y (2016) Including land cover change in analysis of greenness trends using all available Landsat 5, 7, and 8 images: A case study from Guangzhou, China (2000– 2014). Remote Sens Environ 185: 243–257. doi: 10.1016/j.rse.2016.03.036