Indicators - wri/cities-cities4forests-indicators Wiki

Introduction

This project is focused on developing baseline indicators and related maps for several Cities4Forests cities. Selected indicators are in five themes related to urban greenspaces and trees:

  1. Health: heat (GRE-1)
  2. Health: air quality (GRE-2)
  3. Greenspace access (GRE-3)
  4. Flooding (GRE-4)
  5. Climate change mitigation (GRE-5)

Indicators status and notebooks

Theme Indicator core code Indicator name Related indicators Status Notebook
Heat GRE-1.1 Expected extreme heat event hazard (expected days above 35C in 2050) and trend Draft link to notebook
Heat GRE-1.2 % built land with high (3C+ above built mean) land surface temperature during hot season Draft link to notebook
Heat GRE-1.3 % built land with low (below 0.2 albedo) surface reflectivity Draft link to notebook
Heat GRE-1.4 % built land without tree cover SDG 15.1.1, SICB-11 Draft link to notebook
Air quality GRE-2.1 Level and change in air pollutant emissions by pollutant and sector SDG 3.9.1 Draft link to notebook
Air quality GRE-2.2 # of days air pollutants are above WHO standards SDG 3.9.1 Draft link to notebook
Air quality GRE-2.3 % population exposed to annual average PM 2.5 concentrations above WHO standards SDG 3.9.1 Draft link to notebook
Greenspace access GRE-3.1 % built-up area that is open space for public use SDG 11.7.1 Draft link to notebook
Greenspace access GRE-3.2 % population with access to public open space within walking distance (400m) Draft link to notebook
Greenspace access GRE-3.3 % population with threshold level (10%+) of tree cover within walking distance (400m) Draft link to notebook
Flooding GRE-4.1 % built land cover exposed to coastal or riverine flooding and trend Draft link to notebook
Flooding GRE-4.2 Expected extreme precipitation event hazard (expected maximum mm precipitation in one day in 2050) and trend Draft link to notebook
Flooding GRE-4.3 % built land cover within less than 10m height above nearest drainage Draft link to notebook
Flooding GRE-4.4 % built land cover with impervious surfaces Draft link to notebook
Flooding GRE-4.5 % built land cover without vegetation cover SDG 6.6.1 Draft link to notebook
Flooding GRE-4.6 % riparian zone with vegetation cover Draft link to notebook
Flooding GRE-4.7 % steep slopes without vegetation cover Draft link to notebook
Climate mitigation GRE-5.1 Level and share of GHG emissions by pollutant and sector Draft link to notebook
Climate mitigation GRE-5.2 Carbon flux from trees as percent of all emissions/sinks

Indicator definition, importance and methods

GRE-1: Health - heat

GRE-1.1: Extreme heat hazard

Definition

Expected extreme heat event hazard (expected days above 35C in 2050) and trend (% change between 2020 and 2050 in number of days meeting 35C threshold )

Importance

Understanding the hazard presented by extreme heat and the likely future change of that hazard can inform the importance of planning for an extreme heat event and investing in infrastructure--like trees--that can help mitigate its impacts.

Methods

This indicator uses data from the NEX-GDDP climate projections retrieved from Earth Engine, a given location, and two selected years to calculate the expected number of days that have a high temperature of 35 Celsius or greater for each year and the percentage change between years. Methods are further documented in the Data Portal hazards repo.

GRE-1.2: Land surface temperature

Definition

Percent of built land with high land surface temperature during hot season (3C+ above mean for built land)

Importance

Land surface temperature is closely correlated with near surface air temperature, which is how people experience heat. This metric can identify areas of the city exposed to above average heat. Such areas may be good candidates for heat mitigation measures such as tree planting or solar reflective surfaces.

Methods

This indicator uses land surface temperature (LST) for each pixel in the area of interest calculated using the methods from Ermida et al. 2020 and Landsat imagery as retrieved from Earth Engine. Mean LST is calculated from a mosaic of cloud masked Landsat images from years 2013-2022 selected from the month of the year that had the hottest day during this period as determined from ERA5 DAILY. Average and pixelwise LST is then retrieved for built land cover areas using the built-up class from ESA WorldCover as a mask. Finally, areas that are 3 degrees Celsius or more above the area average are masked to calculate the percent of built area with high land surface temperature.

GRE-1.3: Surface reflectivity

Definition

Percent of built land with low surface reflectivity (below 0.2 albedo)

Importance

This indicator can identify areas of the city with a high share of surfaces that retain excess heat. Surfaces with Low solar reflectivity (albedo) absorb heat and transfer it to immediate surroundings. Areas with a high share of low albedo surfaces may be candidates in which to install measures--such as solar reflective roofs and pavements, or trees--that will reduce the heat retained on surfaces or otherwise cool the immediate area.

Methods

This indicator uses pixelwise albedo values derived from Sentinel-1 as retrieved from Earth Engine using the algorithms defined in Bonafoni & Sekertekin 2020. Annual mean albedo is calculated from cloud-free pixels from 2021. Values for built land cover areas only are derived using the built-up class from ESA WorldCover as a mask. Finally, pixels with albedo values below 0.2 are masked to calculate the percent of built area with low surface reflectivity.

GRE-1.4: Tree cover

Definition

Percent of built land without tree cover

Importance

This indicator can identify areas of a city without significant tree cover and therefore lacking in shade and evapotranspiration that can reduce local heat. Areas with low tree cover may be candidates in which to install measures--such as trees, other vegetation, or solar reflective roofs and pavements--that will reduce the heat retained locally or otherwise cool the immediate area.

Methods

This indicator uses 10-meter resolution tree cover data for 2020 from the Trees in Mosaic Landscapes dataset as retrieved from Earth Engine. Tree cover for each pixel of built land is derived using the built-up class from ESA WorldCover as a mask of non-zero tree cover pixels. The number of built pixels with tree cover and all built pixels are counted and divided to calculate percent of built land with tree cover. This value for percent tree cover is then inverted to derive a value for percent of built-up land without tree cover.

GRE-2: Health - air quality

GRE-2.1: Air pollutant emissions

Definition

Level of air pollutant emissions by pollutant species and economic sector

Importance

Human activity contributes to air pollution and climate change through emission of gases from fuel combustion, industrial processes, and agriculture. This indicator can help decision-makers and stakeholders identify the most important pollutants emitted locally, the activities responsible for the emissions, and, with multiple years of data, emissions trends.

Methods

This indicator is based on the CAMS Global Anthropogenic Emissions dataset of the Copernicus Atmosphere Monitoring Service (CAMS) and ECCAD (Granier et al. 2019). The dataset provides estimates of emissions from 12 sectors of human activity, on a 0.1-degree (approx. 11 km) spatial resolution. The estimates are based on simulations and historical data. The included sectors are: agriculture (livestock); agriculture (soils); agriculture (waste burning); power generation; fugitive emissions; industry; combustion in residential, commercial, and other settings; ships; solvents; solid waste and wastewater; off-road transportation; and on-road transportation.

We used Google Earth Engine for spatial subsetting against cities' administrative boundaries. We report annual, sector-disaggregated emissions for the years 2010, 2015, and 2020, of the each pollutant species. We report all emissions as tonnes/year.

GRE-2.2: High pollution days

Definition

Annual number of days air pollutants are above WHO air quality standards

Importance

Exposure to high concentrations of air pollutants increases the probability of developing serious health conditions, reduced lung function, increased susceptibility to respiratory infections, and aggravated asthma. Long-term exposure increases the probability of developing chronic conditions like stroke-susceptibility, heart disease, and cancer. This indicator can help public health officials determine which air pollutants are present at levels dangerous to human health, and for how many days each year the population is exposed to them.

Methods

Our data come from the CAMS Global Reanalysis EAC4 dataset of the Copernicus Atmosphere Monitoring Service (CAMS), which combines satellite monitoring of pollutant concentrations with atmospheric modeling to estimate concentrations near the earth's surface. The EAC4 data are provided at approximately 80 km spatial resolution.

We report the number of days each city was estimated in 2020 to have a near-surface concentration of air pollutants that exceeds the World Health Organization's 2021 standards for outdoor air pollutants. The pollutants and standards are given in this table.

Pollutant species WHO threshold
Nitrogen dioxide (NO2) 25 µg/m3 24-hour average
Sulfur dioxide (SO2) 40 µg/m3 24-hour average
Ozone (O3) 100 µg/m3 8-hour average*
Carbon monoxide (CO) 4 mg/m3 24-hour average
Fine particulate matter (PM2.5) 15 µg/m3 24-hour average
Coarse particulate matter (PM10) 45 µg/m3 24-hour average
* For the ozone calculations, we used data for the 9-hour window 6am-3pm

GRE-2.3: Fine particulate matter exposure

Definition

Percent of population exposed to annual average Fine particulate matter (PM 2.5) concentrations above WHO standards

Importance

Fine particulate matter (PM2.5) consists of very small particles that are smaller than 2.5 µm in diameter. (A typical human hair has a diameter of 50-70 µm.) PM2.5 are released in combustion (including fuel combustion in vehicles and power plants, as well as naturally occurring fires), degradation of vehicle tires during use, and some chemical processes that occur in the atmosphere. PM2.5 is a serious health concern, because the small size allows PM2.5 particles to travel deep into human lungs, enter the bloodstream, and cause direct damage to internal organs. Long-term exposure to PM2.5 leads to increased incidence of heart and lung diseases, cancers, and premature death. This indicator can help decision-makers determine how many of a city's residents face long-term exposure to dangerous levels of PM2.5, and in which neighborhoods these residents live.

Methods

We used the global surface PM2.5 V5.GL.02 dataset from the Atmospheric Composition Analysis Group at Washington University (van Donkelaar et al. 2021). This dataset combines models of atmospheric mixing and chemistry with analysis of imagery from the NASA MODIS, MISR, and SeaWIFS satellite instruments to generate estimates of PM2.5 concentrations near the earth's surface. The data are provided at a spatial resolution of 0.01 degrees, or approximately 1.1 km. Our indicator is based on annual average concentrations for 2020.

For population, we used WorldPop's 100m-resolution population estimates for 2020. To generate its population estimates, WorldPop population uses census data and models built on analysis of satellite imagery.

For each district, we used Google Earth Engine to estimate the population in areas where the average PM2.5 concentration for 2020 was above the 2021 WHO standard of 5 µg/m3 (annual average). We divided this high-exposure population by the total district population to get the portion of the population exposed to dangerous levels of PM2.5.

We also report each district's 2020 average PM2.5 concentration as a percentage of the WHO standard. (The average is the annual average, averaged spatially over the area of the district.) For example, an average concentration of 15 µg/m3 would be reported as 300% of the WHO standard.

GRE-3: Greenspace access

GRE-3.1: Open space for public use

Definition

Percent of built-up area that is open space for public use

Importance

Availability and area of public open space, such as parks, is a key indicator to assess quality of life for residents of a city. Open spaces provide recreation opportunities and can provide ecosystem services and habitat. Indicators of open space are included in the Sustainable Development Goals (Indicator 11.7.1) and the Singapore Index on Cities' Biodiversity.

Methods

This indicator uses polygon data on categories of open space as retrieved from OpenStreetMap in August 2022. The OpenStreetMap tags used to retrieve these areas are 'park', 'nature_reserve', 'common', 'playground', 'pitch', and 'track' in the 'leisure' category and 'protected_area' and 'national_park' in the 'boundary' category. Status as open space or non-open space for each pixel of built land are derived using the built-up class from ESA WorldCover as a mask. Finally, the count of masked pixels of open space is use to calculate the percent of built area that is open space.

GRE-3.2: Access to public open space

Definition

Percent of population with access to public open space within walking distance (400m)

Importance

Availability of open space in a city is also a function of its ease of access and who does or does not have easy access. As a result, spatial distribution of open space across the city and its alignment with the location of population, and its accessibility within walking distance (commonly defined at 400 meters) is a critical indicator to understand how many city residents are well served by open space.

Methods

This indicator makes use of gridded population at 100-meter resolution from the WorldPop project as accessed on Earth Engine. The open space polygons retrieved from OpenStreetMap are buffered by 400 meters to derive recreation catchment areas. The population within those recreation catchment areas is calculated and then converted to percent by dividing that value by the total population of the area of interest.

GRE-3.3: Access to tree cover

Definition

Percent of population with threshold level (10%+) of tree cover within walking distance (400m)

Importance

Access to tree cover is also an important indicator of quality greenspace, whether it is public or private space. Privately maintained trees also provide a variety of public benefits from clean air to heat mitigation and shade. This indicator considers all trees within walking distance (400 meters) for each resident of the city as an indicator of the quality of greenspace that they interact with in an average day.

Methods

This indicator uses 10-meter resolution tree cover data for 2020 from the Trees in Mosaic Landscapes dataset and gridded population at 100-meter resolution from the WorldPop project as retrieved from Earth Engine. A neighborhood reduction method using a circular kernel of 400 meters is applied to the tree cover layer to determine the percentage tree cover within 400 meters of each pixel in the area of interest. This results is then used to mask the population layer to only include pixels with at least 10% tree cover within 400 meters. The population of this masked population layer is then calculated and then converted to percent by dividing that value by the total population of the area of interest.

GRE-4: Flooding

GRE-4.1: Exposure to coastal and river flooding

Definition

Percent of built land exposed to coastal or riverine flooding

Importance

Most cities are built near coasts or along rivers. These natural assets for economic development can also present a hazard when they cause flooding in built-up areas. Prevalence of these floods is increasing globally as a result of sea level rise and extreme precipitation caused by climate change.

Methods

We use projections from Aqueduct Floods retrieved through Earth Engine to characterize the coastal and riverine flood hazards for each city in year 2050 in terms of meters of inundation depth. The scenario variables we use are a 100 year return period, the Business As Usual/Pessimistic (RCP8.5) climate scenario, high sea level rise, no subsidence, and the GFDL-ESM2M riverine projection model. We then compare locations of either projected coastal or riverine flooding to built-up areas in the city in 2020 as defined by the built-up class from ESA WorldCover. For each city and city district we calculate the number of built-up pixels and the number of built-up pixels projected to experience a flood inundation depth greater than zero. Then we calculate the percent of built-up pixels with flood exposure. Because this indicator considers 2020 built-up in comparison to projected 2050 inundation, it may underestimate built area exposed to the hazard as a result of future land development. Note that this is an indicator of flood hazard and it does not consider how mitigation measures such as flood protection infrastructure (levees, etc.) may impact risk of inundation from flooding under the scenario described.

GRE-4.2: Extreme precipitation hazard

Definition

Expected extreme precipitation event hazard (expected maximum mm precipitation in one day in 2050) and trend (% change between 2020 and 2050 in mm of precipitation on the most extreme day)

Importance

Understanding the hazard presented by extreme precipitation and the likely future change of that hazard can inform the importance of planning for extreme precipitation events and investing in infrastructure--like trees and other vegetation--that can help mitigate their impacts.

Methods

This indicator uses data from the NEX-GDDP climate projections retrieved from Earth Engine, a given location, and two selected years to calculate the expected millimeters of precipitation expected on the day with the largest amount of precipitation for each year and the percentage change between years. Methods are further documented in the Data Portal hazards repo.

GRE-4.3: Land near natural drainage

Definition

Percent of built land cover within 1 meter height above nearest drainage

Importance

Land near natural drainage (e.g., streams, rivers) is at risk for pluvial or flash flooding hazards. Development closer in elevation to natural drainage, especially near drainage for large land areas, is at greater risk. Risk for these type of floods can be difficult to predict, and beyond height above drainage, other factors--such as amount of precipitation, soil type, distance from natural drainage and the presence and efficacy of built drainage systems--are also important influences.

Methods

This indicator uses data on height above drainage channels from Global 30m Height Above the Nearest Drainage to estimate the land area 1 meter or less above nearest drainage, for drains with a flow accumulation area of at least 1 KM2. We then compare these areas to built-up areas in the city as defined by the built-up class from ESA WorldCover. For each city and city district we calculate at 10 meter scale the number of built-up pixels and the number of built-up pixels within 1 meter of nearest drainage. Then we calculate the percent of built-up pixels that are 1 meter or less above nearest drainage.

GRE-4.4: Impervious surfaces

Definition

Percent of built land that has impervious surface

Importance

Impervious surfaces (in urban areas, typically buildings, roads and other pavement) prevent water from soaking into the ground on-site and contribute to decreased overall water infiltration and increased runoff and can increase the prevalence of localized flooding. Building cities to minimize use of impervious surfaces (with alternative construction materials or intentional greening) allows for greater natural water infiltration and can enable cities to "sponge" up excess water, decrease flooding and protect groundwater resources.

Methods

We obtain the estimated extent of impervious areas in 2018 from Tsinghua Annual maps of global artificial impervious area. This 30-meter resolution dataset defines a pixel with at least 50% impervious surface as impervious. We then compare these areas to built-up areas (human settlement areas) in the city as defined by the built-up class from ESA WorldCover. For each city and city district, we calculate at 10 meter scale the number of built-up pixels and the number of impervious built-up pixels. Then we calculate the percent of built-up pixels that are impervious.

GRE-4.5: Vegetation cover in built areas

Definition

Percent of built land cover without vegetation cover

Importance

Vegetation cover provides many benefits in urban areas. Importantly for flood mitigation, greater vegetation cover can increase water infiltration into the ground, reducing water stagnation and runoff on the surface. Absence of vegetation cover can increase the prevalence of localized flooding.

Methods

To estimate vegetation cover we used an annual greenest pixel mosaic of Sentinel-2 imagery to calculate normalized difference vegetation index (NDVI) with a minimum threshold of 0.4. We then compare these areas to built-up areas in the city as defined by the built-up class from ESA WorldCover. For each city and city district, we calculate at 10 meter scale the number of built-up pixels and the number of vegetated built-up pixels. Then we calculate the percent of built-up pixels that are vegetated.

GRE-4.6: Vegetation cover in riparian zones

Definition

Percent of riparian areas without vegetation or water cover

Importance

Riparian areas--the spatial interface between land and water, particularly rivers and streams--can provide natural infrastructure for flood control. The presence of vegetation cover in the riparian zone is a primary factor in determining its flood mitigation effectiveness, as vegetation will slow down water flow, increase its absorption into land and its transpiration into the air. Riparian areas are also critical habitat to support biodiversity.

Methods

This indicator uses data on elevation and drainage channels from Global 30m Height Above the Nearest Drainage and on the location of other waterbodies from JRC Global Surface Water to estimate the location of lakes, rivers and streams. To estimate riparian zones, we buffered these waterways by 144 meters (the size of riparian areas needed to preserve bird diversity) and excluded the area of the water channel itself. To estimate vegetation and water cover we used an annual greenest pixel mosaic of Sentinel-2 imagery to calculate normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) with a minimum threshold of 0.4 and 0.3 respectively. For the buffered riparian areas in each city or sub-city unit we calculated at 30 meter scale the count of all riparian area pixels and the count of riparian area pixels with vegetation or water cover. Then we calculated the percent of riparian area pixels without vegetation or water cover.

GRE-4.7: Vulnerable steep slopes

Definition

Percent of steep hillside slopes without vegetation cover

Importance

Steep slopes are vulnerable to landslides, especially during incidence of extreme precipitation of flooding where soil can become saturated with or washed away by water. Vegetation is critical for stabilizing slopes as roots hold soil in place and plant absorb water from the soil. Steep slopes without vegetation coverage are particularly vulnerable to landslides.

Methods

To estimate slopes we apply the Earth Engine slope algorithm to the global NASA NASADEM 30 meter digital elevation model. The slope layer is then masked to only include high slope areas of 10 degrees or greater (the threshold at which landslide susceptibility starts to grow quickly). Vegetation cover is estimated using Sentinel-2 imagery to calculate normalized difference vegetation index (NDVI) with a minimum threshold of 0.4. For the high slope areas in each city or sub-city unit we calculated at 30 meter scale the count of all high slope pixels and the count of high slope pixels with vegetation cover. Then we calculated the percent of high slope pixels without vegetation cover.

GRE-5: Climate change mitigation

GRE-5.1: Greenhouse gas emissions

Definition

Level and share of GHG emissions by pollutant and sector

Importance

Human activity contributes to air pollution and climate change through emission of gases from fuel combustion, industrial processes, and agriculture. This indicator can help decision-makers and stakeholders identify the most important pollutants emitted locally, the activities responsible for the emissions, and, with multiple years of data, emissions trends.

Methods

Similar to GRE-2.1: Air pollutant emissions, this indicator is based on the CAMS Global Anthropogenic Emissions dataset of the Copernicus Atmosphere Monitoring Service (CAMS) and ECCAD (Granier et al. 2019). The dataset provides estimates of emissions from 12 sectors of human activity, on a 0.1-degree (approx. 11 km) spatial resolution. The estimates are based on simulations and historical data. The included sectors are: agriculture (livestock); agriculture (soils); agriculture (waste burning); power generation; fugitive emissions; industry; combustion in residential, commercial, and other settings; ships; solvents; solid waste and wastewater; off-road transportation; and on-road transportation.

We used Google Earth Engine for spatial subsetting against cities' administrative boundaries. We report annual, sector-disaggregated emissions for the years 2010, 2015, and 2020, of the each pollutant species. We also report emissions of the greenhouse gases, as total CO2e aggregated over sectors for each of the three years. We calculated CO2e based on 20-year and 100-year global warming potentials, as listed in the table. We report all emissions as tonnes/year.

Pollutant species Included as GHG GWP (20-year) GWP (100-year) GWP source
Black carbon (BC) yes 460 1504 global values reported in IPCC AR5 Table 8.A.6
Methane (CH4) yes 84 28 IPCC AR5 Table 8.A.1
Carbon monoxide (CO) yes 7.65 2.49 midpoints of global values reported in IPCC AR5 Table 8.A.4
Carbon dioxide (CO2) yes 1 1 definition of GWP
Nitrogen oxides (NOx) yes 19 -10.34 global values reported in IPCC AR5 Table 8.A.3
Sulfur dioxide (SO2) no NA NA NA
Organic carbon (OC) yes -240 -64.86 global values reported in IPCC AR5 Table 8.A.6
Ammonia (NH3) no NA NA NA
Non-methane volatile organic compounds (NMVOC) yes 14 4.23 global values reported in IPCC AR5 Table 8.A.5

GRE-5.2: Climate change impact of trees

Definition

Carbon flux from trees as percent of all emissions/sinks

Importance

Methods


References

Granier, C., S. Darras, H. Denier van der Gon, J. Doubalova, N. Elguindi, B. Galle, M. Gauss, M. Guevara, J.-P. Jalkanen, J. Kuenen, C. Liousse, B. Quack, D. Simpson, K. Sindelarova The Copernicus Atmosphere Monitoring Service global and regional emissions (April 2019 version)

Aaron van Donkelaar, Melanie S. Hammer, Liam Bindle, Michael Brauer, Jeffery R. Brook, Michael J. Garay, N. Christina Hsu, Olga V. Kalashnikova, Ralph A. Kahn, Colin Lee, Robert C. Levy, Alexei Lyapustin, Andrew M. Sayer and Randall V. Martin (2021). Monthly Global Estimates of Fine Particulate Matter and Their Uncertainty Environmental Science & Technology, 2021, doi:10.1021/acs.est.1c05309.

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