Since 1991 we have been working for sustainable development, fulfilling the mission of the United Nations Environment Programme (UNEP) in Poland being a part of the GRID network, embedded in the Science Division of the UN Environment Programme.

This topic is in line with many strategies
and other policy documents, such as:

• European Green Deal
• EU Biodiversity Strategy for 2030
• Green Infrastructure (GI) - Enhancing Europe’s Natural Capital
• SPA 2020 - a strategic adaptation plan for sectors and areas sensitive to climate changes by 2020, with an outlook by 2030
• National Urban Policy 2023
• The 2030 National Environmental Policy

The most important proposed objectives are:

• Adopting a binding climate neutrality target by 2050
• Revision of the short-term target

It provides an action plan to:

• Boost the efficient use of resources
• Restore biodiversity and reduce pollution

It assumes the synergy of actions to combat the climate crisis, among others through actions to protect biodiversity, presented in the EU Biodiversity strategy until 2030.

EU Nature Restoration Plan (key commitments by 2030) includes Urban Greening Plan for cities with at least 20,000 inhabitants.

50% of dry weight of a tree comes from the carbon present in the atmosphere as carbon dioxide

When trees die the carbon goes back to the atmosphere

Urban trees act as "carbon sink”

However, carbon sequestration (climate mitigation) is not the key ecosystem service of urban trees

Urban Heat Island (UHI) occurs when the city proper records are much higher temperatures than nearby rural areas

Trees reduce the UHI in the summer period by:

• Evapotranspiration
• Deflection of solar radiation

Trees provide shade and block wind

Potential energy conservation results:

• Heating savings (MBTU and Kwh)
• Cooling savings (Kwh)

Impervious surfaces are the main contributor excess stormwater runoff

The trees can help to manage the runoff by:

• Retention of precipitation water in canopies
• Water interception and uptake by roots

Challenges:

• Expensive
• Time consuming
• Limited access to private areas
• Lack of coherence in data collection
• Quickly outdated

Source: portalkomunalny.pl, K. Babicki

• 1957 - Sputnik-1
• 1972 - Landsat-1 (NASA & USGS)
• 1986 - SPOT
• 1998 - GMES Programme
• 2014 - COPERNICUS Programme
• 2014/16 - Sentiel-1
• 2015/17 - Sentiel-2
• 2016/18 - Sentiel-3
• new missions planned
• Selected commercial satellites (spatial resolution less than 1m):
  IKONOS, QuickBird, WorldView GeoEye, Pleidades, KOMPSAT

Satellites around the Earth (Source: ESA/Rex Features)

Spatial resolution - the size of one pixel on the ground.

Spatial resolution is a measure of the smallest object that can be resolved by the sensor, or the ground area imaged for the instantaneous field of view (IFOV) of the sensor, or the linear dimension on the ground represented by each pixel.
(Advanced Remote Sensing, 2012)

(Source: Radiant Earth Foundation)

Radiometric resolution - the sensitivity to the magnitude of the electromagnetic energy. It describes the actual information content in an image, expressed in units of bits.

The finer the radiometric resolution, the more sensitive it is to detect small differences in reflected or emitted energy.

(Source: Research Gate)

Sentinel-2 NDVI image (Source: EO Browser Sentinel-Hub)

Sentinel-2 Global Land Cover (Source: Creodias Browser)

Spatial extent:
EU27, EFTA, West Balkans, Turkey, UK

Classification:
17 urban classes with MMU 0.25ha
10 rural classes with MMU 1ha,
MMW 10m

Products:

→ Land Cover/Land Use - edition 2018

→ 2012-2018 change product

→ Revised 2012 edition of Urban Atlas

→ Street Tree Layer (STL)

Extent of Urban Atlas 2018 (Source: Land Monitoring Service)

The Street Tree Layer (STL) is a separate layer from the Urban Atlas 2012 LU/LC layer produced within the level 1 urban mask.

It includes contiguous rows or a patches of trees covering 500 m² or more and with a minimum width of
10 m over "Artificial surfaces" (nomenclature class 1) inside FUA (i.e. rows of trees along the road network outside urban areas or forest adjacent to urban areas should not be included).

Street Tree Layer 2012 (Source: Land Monitoring Service)

Land cover change detection:

• increase/decrease of green areas in the city
• determination of change trend

(A) Land cover in Warsaw - Urban Atlas 2018, (B) Land cover changes in Warsaw between 2012 and 2018 (Source: Publication Warszawa z kosmosu)

Change of biologically functional surface - comparison of the situation in 2006 and 2014 (Source: Publication Warszawa z kosmosu)

Change detection:
increase of impermeable surfaces

Biologically functional surface in Fort Służew in 2016 and 2018 (Source: Publication Warszawa z kosmosu)

NDVI - Normalized Difference Vegetation Index

Effective for quantifying green vegetation. Positively correlated with vegetation greenness.

NDVI = (NIR – Red) / (NIR + R)

NDVI range value is -1 to 1

NDVI value on 12.09.2018 (Source: Publication Warszawa z kosmosu)

• NDII (Normalized Difference Infrared Index)
• MSI (Moisture Stress Index)
• NDWI (Normalized Difference Water Index)
• SAVI (Soil Adjusted Vegetation Index)
• EVI (Enhanced Vegetation Index)

Index database

NDII value on 31.08.2017 (Source: Publication Warszawa z kosmosu)

Applications:

• Identification and classification of different types of green areas
• Estimation of green urban areas
• Proximity of green urban areas
• Change detection
• Vegetation and water indices

Limitations:

• Lack of attributes for the single tree (taxon, height, ...)
• Impossible single tree health assessment

Comparison of images with different spatial resolution
- 10 m (left: Sentinel-2) and 1 m (right: orthophoto)

Actions undertaken by city authorities:

• Declaration of Tree Rights
• Urban Greening Guidelines
• Tree Crown Map
• Tree Trunks Location Map
• Tree Risk Management

Methods of developing the Tree Crown Map (TCM) included:

• It was developed with the use of aerial remote sensing
• The analyses included three types of remote sensing data and data obtained from tree inventory
• Machine learning was used in the analyses

Trees included:

• 23606 trees under inventory
• 8 mln of trees investigated (out of 9 mln)

Criteria:

• Canopy is visible from aerial perspective
• Canopy area > 7m²
• Tree height > 3m

Information gained from the data:

• Number of trees
• Canopy size
• Total canopy area

Potential use:

• Urban greenery per 1 inhabitant/ha of land
• Most precious trees
• Supplementary plantings
• New investment planning
• Recognition of age/size structure of trees
• Adaptation to climate change

Example of analysis:

• Selection of areas without tree canopies
• Including them as a priority for reclamation
• Planting trees and/or changing surfaces for high albedo surfaces

Example of analysis:

• Selection of buildings where energy consumption is affected by trees
• Improving the optimal performance of new tree plantings

Example of analysis:

• Identification of areas with impervious surfaces and lack of trees
• Including them as a priority for reclamation
• Planting trees and/or changing surfaces for pervious surfaces

Information gained from the data:

• Taxonomic differentiation
• Spatial distribution of taxa

Potential use:

• Analysis of taxonomic differentiation of a specific area
• Planning new planting
• Recognition of the age/size structure of trees of a given species
• Adaptation to climate change

Information gained from the data:

• Differentiation of canopy condition
• Diversity in the health of selected tree species

Potential use:

• Identification of dead and dying trees
• Identification of disease/victimites sites
• Help in identifying the sources of poor tree condition
• Adaptation to climate change