Investigating crop performance on urban green roofs using hyperspectral data
Bibliographic record
Abstract
This study explores the dynamic relationship between crop plant performance and variety of treatment effects (water levels, diversity, and sedum) on extensive urban green roofs. Green roofs have gained recognition as a sustainable urban design strategy, offering benefits such as stormwater management, energy efficient, and improved aesthetic enhancements. However, environmental stressors can challenge plant growth and performance on these emerging extensive green roofs. To help cope with these stressors, certain planting techniques have been implemented on green roofs such as plants grown with sedum and increasing diversity types. However, monitoring the effects of these treatments in a rapid, timely manner are still in its infancy. To monitor plant health (specifically bush beans (Phaseolus vulgaris)) on green roofs, this study utilizes hyperspectral remote sensing technology using two crucial optical parameters: red edge point (REP) and red well point (RWP) to offer new valuable insights into monitoring plant health on green roofs. The results found that both REP and RWP information can detect significant differences between the treatment effects of water treatment (high and low) (p = 0.006, p = 0.001) and plants with or without existence of sedum (Crassulaceae) (p = 0.01, p = 0.07) but could not find significant difference in diversity effects (single crop or multiple crops) (p = 0.20, p = 0.45). The results of REP and RWP was also able to find that plants with sedum under low watering treatments showed worse plant performance than plants without sedum under low watering treatments (p = 0.006, p = 0.006). This provides insights that remote sensing technology can detect that there may have been a competition of resources under low watering treatments and that that REP and RWP has the potential for a future sophisticated green roof monitoring platform.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".