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Multi-temporal Green Roof Vegetation Assessment Using Sentinel-2: A Pilot Study

Year: 2026

Type: article

Abstract: . The City of Toronto has reported having over 1,000 green roofs (GRs) since implementing its green roof bylaw, but their conditions are not systematically monitored and require a remote, cost-effective method. Satellite remote sensing has proven useful in studies of urban green spaces to evaluate vegetation health and conditions, but it has not yet been explored for long-term GR vegetation monitoring. The study aims to assess open-source Sentinel-2 images from Google Earth Engine to monitor vegetation density and water-stress conditions in four selected GR modules. Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), and Normalized Difference Moisture Index (NDMI) are used to measure vegetation health and water stress from the summer months of 2018 to 2025 (April - September). These multi-temporal indices highlight the seasonal and intra-seasonal dynamics of vegetation, showing potential loss and gain over the period. Vegetation gain was observed in the GRs until 2021 with a gradual increase in NDVI and EVI, followed by a steady trend afterward. Occasional lower NDVI and EVI values have been observed due to potential moisture content deficit, as indicated by low NDMI.

Source: The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences

DOI: 10.5194/isprs-archives-xlix-b3-2026-865-2026

Authors: Afsana Alam Akhie, Darko Joksimovic

Institution: Toronto Metropolitan University

Language: English

Topic: Remote Sensing in Agriculture

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

SDGs: Sustainable cities and communities, Climate action

Open Access status: diamond

OpenAlex ID: W7171857146

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