The Trajectories, Trends, and Opportunities for Assessing Urban Ecosystem Services: A Systematic Review of Geospatial Methods
Bibliographic record
Abstract
Urban ecosystem services (UES) are indispensable for life. Stakeholders are improvising strategies for a more sustainable provisioning of UES. For this purpose and for identifying orientations towards geospatial data in UES studies, the “bibliometric analysis” technique was deployed. The inclinations facilitate assessments pertaining to spatio-temporal oscillations in the supply–demand equilibrium. The propensities are gaining recognition due to time and cost effectiveness. Besides this, Remote Sensing (RS) in conjunction with Geographic Information System (GIS), enables the conduct of synoptic and robust periodic evaluations. The study analyzes inclinations towards RS in contemporary research (2010–2020) focusing, particularly, on urban ecosystem services. It specifically focuses on methodological frameworks and major sources of remotely sensed data. Therefore, a total of 261 records of research articles were identified and retrieved. Subsequently, 79 articles were selected for further processing and content analysis. It transpired that approximately 30% of the selected publications deployed remotely sensed data for assessment purposes. The majority (96%) of such studies were conducted in economically developed and industrialized countries. However, the researchers from both developed and developing countries prefer open software and free data sources. Besides this, they prefer satellite-based optical sensors over image sensors such as TIR, SAR, or light sensors for acquiring data. The findings formulate that Land Use Land Cover (LULC)-based methodologies and inclinations for assessing regulating services are more frequently pursued. The findings revealed that enhanced research collaborations, access to data, and assessment gadgets are obligatory for capacity building in developing regions. Knowledge sharing and cost-effective access to RS and GIS based platforms are incumbent for ensuring urban environmental sustainability in developing economies.
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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 | 0.013 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.050 | 0.048 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".