Qualitative analysis of wetland conversion

The PLOS ONE study (2 July 2026) reports 3, 355 ha (≈48.8%) of three riverine wetlands in Bure and Womberma were converted to farmland between 1985 and 2021, driven mainly by land allocation to youth and landless households and institutional priorities rather than only subsistence need.

• The core datasets were Landsat imagery (1985–2021), a household survey (n=396), an experts/head survey (n=75), and 12 key informant interviews, analyzed with supervised classification, descriptive statistics, ordered probit regressions, and thematic coding.
• Classification accuracy for 2021 was reported as overall accuracy 97.27% and Kappa 87.19%, supporting the LULC change findings.
• Triangated mixed-methods workflows that link annotated change maps to interview narratives and segment-level analyses (age, landholding, office affiliation) produce policy-ready decision briefs quickly.

Fast take: A mixed-methods study published 2 July 2026 finds 3, 355 ha (≈48.8%) of three riverine wetlands in Bure and Womberma were converted to farmland between 1985 and 2021, driven largely by land allocation to young and landless households and by institutional priorities rather than simple subsistence need.

• Full study: PLOS ONE (Getnet Eneyew & Worie Assefa, PLOS ONE, July 2, 2026).
• Key datasets: Landsat imagery (1985–2021), household survey n=396, experts n=75, KIIs n=12; ordered probit regressions and thematic analysis.

The study combined remote sensing, a structured household survey, an experts/head survey, and key informant interviews to measure LULC change and local perceptions.

• Remote sensing: Landsat TM/ETM+/OLI median composites with <10% cloud cover, supervised maximum-likelihood classification, validation via confusion matrices.
• Qualitative: audio-recorded KIIs, transcription, and thematic coding to capture local narratives on land allocation and institutional drivers.
• Quantitative: Likert items on ecosystem perceptions; ordered probit to model ordinal attitudes (1–5 scale).

This 7-step workflow shows inputs and outputs you can hand to policy teams in two weeks for a pilot.

• 1) Ingest datasets: Landsat LULC tables, survey CSVs, interview audio and transcripts into an Evidano project.
• 2) Auto-transcribe and translate audio (Amharic to English) using a custom dictionary for local terms.
• 3) Predefine segments (age bands, landholding, office) and attach to each record.
• 4) Run automated thematic extraction on transcripts; review and lock a codebook with a human-in-the-loop.
• 5) Produce cross-segment frequency tables and co-occurrence maps that link themes to LULC change epochs.
• 6) Export stakeholder-ready artifacts: a one-page decision brief, annotated maps, quote bank, and reproducible tables for regression checks.
• 7) Iterate: launch targeted AI avatar interviews for follow-ups on policy options (for example, eco-friendly livelihoods).

Wrapping up: Use a mixed-methods workflow that preserves quotes, quantifies theme prevalence, and links findings to maps to convert multi-source evidence into policy-ready insight for wetlands or any landscape study.

• The PLOS study (PLOS ONE) is a clear example: 48.8% conversion over 36 years driven by institutional allocation and youth land demands.
• Ready to try it? Try Evidano for free to ingest your transcripts and surveys, run thematic and cross-segment analyses, and generate stakeholder-ready briefs in days.