Faster Insights: qualitative analysis of wetland conversion

The PLOS ONE study (published 2 July 2026) documents that 48.8% of three study wetlands were converted to farmland between 1985 and 2021, and that institutional drivers rather than only subsistence motives explain much of the change.

Reproducing the study requires combining Landsat-derived LULC maps with household surveys, expert surveys, and transcribed key informant interviews in a single, queryable workspace.

• The study reports a loss of 3, 355 hectares (48.8%) across three wetlands from 1985 to 2021, using Landsat supervised classification and accuracy assessment.
• Household surveys (n=396), expert surveys (n=75) and 12 KIIs supported thematic validation and ordered probit analysis of attitudes.
• Institutional events such as 1997 land redistribution and allocations to landless youth were identified as primary drivers, shifting policy responses toward employment diversification and agro-ecological appraisal.

The PLOS ONE study (published 2 July 2026) documents that 48.8% of three study wetlands were converted to farmland between 1985 and 2021; drivers include land redistribution events (1997), allocation to landless youth and institutional priorities favoring agriculture.

• Why this matters: mixed remote-sensing and qualitative evidence expose policy and institutional drivers, not just poverty-driven subsistence.
• Quick payoff for researchers: reproduce the authors' land-use-to-attitudes linkage by combining transcripts, survey sheets and Landsat metadata into a single AI-assisted analysis pipeline with reproducible codebooks.

Source: PLOS ONE.

The authors combined multi-date Landsat classifications with household and expert Likert surveys and 12 oral key informant interviews to link mapped change with stakeholder narratives.

• Mixed inputs included raster LULC maps, structured survey spreadsheets, and audio-recorded KIIs (Amharic translated to English).
• Key methods were supervised maximum-likelihood classification in ArcGIS, accuracy assessment via confusion matrices, thematic analysis of KIIs, descriptive statistics in SPSS for perceptions, and ordered probit regression in Stata for attitudes' drivers.

Follow these seven steps to recreate a mixed-methods analysis of wetland conversion and scale the workflow across sites.

• 1) Collect and centralize inputs: Landsat epochs, survey CSVs, audio KIIs with geotags and consent metadata.
• 2) Transcribe and translate with a custom dictionary of local wetland terms, and redact PII.
• 3) Auto-generate themes and frequency counts from KIIs and open-ended survey items.
• 4) Link LULC polygons to household and respondent IDs to enable parcel-level segment analysis.
• 5) Run cross-segment contrasts (age, landholding, livelihood) and export an annotated codebook.
• 6) Validate with selected quotes and Kappa or accuracy checks, and iterate the codebook where disagreement exceeds 10%.
• 7) Produce a one-page stakeholder brief combining map snapshots, top themes, and three policy recommendations.

If you analyze land-use change with interviews and surveys, treat spatial and textual evidence as a single dataset to make findings actionable.

The PLOS ONE study (2 July 2026) is a clear example where institutional drivers dominated conversion and mixed qualitative methods explained why allocations happened despite acknowledged ecosystem value, see PLOS ONE.

• Start small by piloting the seven-step checklist on one wetland polygon and 20 transcripts.
• Ready to try an integrated pipeline? Try Evidano for free to convert mixed evidence into stakeholder-ready insights.