Seeing through Satellites, Streets and Stories: Assessing Nature-Based Solutions in the Santa Rosa-Silang Watershed, Philippines
Bibliographic record
Abstract
The effectiveness of Nature-Based Solutions (NBS) in rapidly urbanizing cities in the Global South remains underexplored (Puskás et al., 2021;Wolff et al., 2022), particularly in emerging cities where unchecked growth, fragmented governance, and limited institutional capacity exacerbate vulnerability to climate risks and environmental degradation (Adelina et al., 2021;Yasmin et al., 2023).NBS can mitigate flooding, water scarcity and heat island effects, while also providing critical ecological benefits like habitat restoration, pollution reduction, and enhanced biodiversity -when implemented with careful attention to local contexts.Yet, in many Global South contexts effective NBS planning is complicated when external climate finance and expertise overshadows local priorities and, as a consequence, may exacerbate inequalities (Sharma et al., 2016;Escobedo et al., 2019;Eakin et al., 2022;Goodwin et al., 2023).Currently, there is limited understanding of how to effectively align NBS goals across scales and this challenge is most acute within informal settlements (Dovey, 2013;Diep et al., 2022) where traditional planning approaches often fail to address rapid, unregulated growth, urban poverty, and low resources for adaptive capacity (Gulati & Scholtz, 2020;Mabon & Shih, 2021).This research leverages the tools of cartography to advance our understanding of how to align NBS goals across scales in order to more effectively address social and ecological needs.It uses a mixed-methods approach that integrates satellite imagery, street-level observations, and community-generated narratives to visualize and assess the spatial distribution and governance of NBS in the Santa Rosa-Silang watershed, Philippines.In general, the focus is on expressing the socio-ecological dynamics that shape NBS interventions in the Santa Rosa-Silang watershed.In a context where official data is often scarce, outdated, or exclusionary, this method enables the co-production of maps that surface socio-spatial inequalities and centre multiple knowledge systems.By blending remote sensing with qualitative methods, this research offers a spatially grounded methodological approach that maps not only ecological conditions and climate risks but also power, knowledge, and exclusion in the design and implementation of urban NBS.Findings show that NBS projects in ecologically sensitive, flood-prone areas do reduce environmental risks, but their benefits are unevenly distributed.Local governments struggle to scale up NBS initiatives due to limited public land, constrained budgets, and lack of technical expertise.Wealthier, less vulnerable neighbourhoods-such as newly built 'eco-city' developments-benefit from privately implemented NBS embedded in exclusive residential and commercial design, while informal settlements, often located in the most climate-sensitive and flood-prone areas, remain excluded from formal NBS projects despite their heightened vulnerability.Community-driven ecological interventions, though promising, frequently lack the institutional support necessary to sustain them, often exacerbating existing inequalities rather than alleviating them.To develop these findings, the study overcomes the challenges of fragmented data and governance by combining satellite-based geospatial analysis with on-the-ground observations and community-driven data collection.These three modes-satellites, streets, and stories-are complementary and integrative, enabling a cartographic synthesis of ecological patterns and lived experiences.Together, they inform a multi-modal cartographic output: an interactive, web-based exploratory map allowing users to engage with geospatial and narrative data layers, and a composite synthesis map for print and policy use, visualizing the ecological and social dimensions of urban NBS implementation across the watershed.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".