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ZENODO
Dataset . 2025
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2025
License: CC BY
Data sources: Datacite
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Data from: Leveraging the full potential of Landsat to investigate inequalities in post-development residential canopy cover (1972-2020)

Authors: Bonney, Mitchell;

Data from: Leveraging the full potential of Landsat to investigate inequalities in post-development residential canopy cover (1972-2020)

Abstract

Paper Abstract: Residential tree canopy cover (CC), which benefits local inhabitants and is managed by municipalities, changes as neighbourhoods develop and is connected to demographics. We investigate residential CC and related inequalities across suburbanizing municipalities since 1972 using Landsat’s temporal depth. In the Region of Peel, Ontario, Canada, we built a 49-year (1972-2020) 30 m residential CC time-series by identifying when current neighbourhoods transitioned from pre-development. We observe residential CC at the population-weighted municipal and dissemination area level and compare with census data (collected 9 times between 1971-2016) to quantify changing CC inequalities along built-form, economic, and racial gradients. Municipal residential CC has remained relatively stable since suburbanization, with new developments offset by CC growth in aging neighbourhoods. Post-development, municipalities have gained residential CC at similar rates but from different starting positions based on pre-development forest cover. Different populations have lacked equal access to residential CC, with higher values related to lower population density and visible minority percentages. Inequalities, especially along racial and population density gradients, have strengthened and shifted along with demographic change. We are the first to utilize long-term Landsat time-series for this purpose, with important implications for urban forest management due to the unmatched spatial-temporal coverage of this archive. Dataset details: pred_yearly_residential_cc.zip: Yearly 30 m tree CC predictions - useful for residental areas only (1972-2020) Transition Year.zip: Raster of transition year to developed state for all residential pixels TCA Biggest Loss.zip: Raster to LandTrendr TCA biggest loss end year for all residential pixel See paper: Full article: Leveraging the Full Potential of Landsat to Investigate Inequalities in Post-Development Residential Canopy Cover (1972–2020) See code on GitHub: ZZMitch/PredictTreeCC_Landsat_1972to2020: Code from the portion of my PhD about using Landsat time-series to predict tree canopy cover from 1972 - 2020. If you use these data, please reference: Bonney, M.T., He, Y., 2026. Leveraging the Full Potential of Landsat to Investigate Inequalities in Post-Development Residential Canopy Cover (1972–2020). Can. J. Remote Sens. 52. https://doi.org/10.1080/07038992.2026.2622730 

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Keywords

time-series, canopy cover, suburban development, Landsat, random forest

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
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