ArticleScientific data2025
Biennial sub-meter tree coverage dataset of Orlando (2013-2021).
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
0 citing papers in PubMed.
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Authors and funding
3 authors.
Funding
Abstract
Rapid urban development often comes at the cost of significant vegetation loss, and the loss of urban trees is a particularly concerning issue. Many cities have recognized the crucial role of tree canopy cover in addressing a range of pressing environmental challenges, and have set goals in their development plans. However, the lack of resources for monitoring its spatiotemporal changes directly undermines efforts of cities to balance urban growth with environmental sustainability. We developed high-resolution biennial tree canopy maps (2013-2021) for the Orlando metropolitan area using National Agricultural Imagery Program (NAIP) orthoimagery. Our approach addresses challenges such as sensor inconsistencies, shadows, variable observation times, and environmental factors. Extensive ground-truth validation demonstrates 78%-89% accuracy in tree detection, outperforming existing products. This dataset provides the crucial link between planning and action, and supports urban forest management and has broader applications in urban planning, environmental and public health monitoring, and education programs.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.