DOI: 10.5281/zenodo.15673191
title: Data and code:A Novel Framework to Assess Long-Term Ecosystem Service Degradation in the Semi-Arid Rift Valley Landscape

description: This repository contains three datasets developed as supplemental materials for the research study titled "A novel study on Ecosystem Service Degradation in the Semi-Arid Rift Valley Landscape: The Ziway-Shalla Sub-basin, Ethiopia" These datasets support the analysis of land use and land cover (LULC) changes, ecosystem service valuation sensitivity analysis (ESV), and stakeholder-driven identification of LULC change drivers in the Ziway-Shalla Sub-Basin, Ethiopia, to inform targeted wetland restoration strategies. The datasets are:

1. Google Earth Engine Script (ZiwayShalla_LandCover_Classification_1995-2025.js):

A JavaScript script for Google Earth Engine (GEE) that processes Landsat imagery for four different years (October –January/1995-2025) to generate a land cover classification map for the Ziway-Shalla Sub-Basin. The script performs cloud filtering, calculates spectral indices (e.g., NDVI, SAVI, MNDWI), incorporates topographic data (SRTM DEM), and applies a random forest classifier to map eight land cover classes (waterbody, wetland, shrubland, farmland, settlement, forestland, grassland, barrenland). Outputs include classified maps, spectral indices, accuracy metrics (e.g., overall accuracy, kappa coefficient), and variable importance data, exported as GeoTIFFs and CSVs to Google Drive.

2. R Script for ESV and Sensitivity Analysis (ZiwayShalla_ESV_Sensitivity_Analysis.R):

An R script that calculates Ecosystem Service Values (ESV) and conducts sensitivity analysis for LULC types in the Ziway-Shalla Sub-Basin across six years (1995, 2005, 2015, 2025, 2035, 2045). The script uses LULC area data (hectares) and value coefficients (USD/ha/year) to compute ESV, assesses percentage changes in ESV between periods, and evaluates sensitivity by adjusting value coefficients by ±50%. Outputs include ESV values, percentage changes, sensitivity coefficients, and a multi-sheet Excel workbook (LULC_Sensitivity_Results.xlsx) with detailed results.

3. Stakeholder Questionnaire (ZiwayShalla_LULC_Driver_Survey.docx):

A Microsoft Word-formatted questionnaire was designed to collect stakeholder insights on the drivers of LULC change in the Ziway-Shalla Sub-Basin. The survey includes two sections for rating direct (e.g., agricultural expansion, overgrazing) and indirect (e.g., population growth, land tenure insecurity) drivers on a 1–5 importance scale, plus open-ended questions for observations and recommendations. The questionnaire supports quantitative driver analysis and informs restoration planning. It is ready for distribution to stakeholders (e.g., farmers, policymakers) and includes ethical considerations (e.g., confidentiality, informed consent).

Purpose: These datasets enable the reproduction of the study’s analyses, including LULC classification, ESV sensitivity estimation, and driver assessment, to support wetland ecosystem restoration in the Ziway-Shalla Sub-Basin. They are provided to ensure transparency, facilitate reuse by researchers, and comply with open science standards.

Files:



ZiwayShalla_LandCover_Classification_1995.js: GEE script for land cover classification.

ZiwayShalla_LandCover_Classification_2005.js: GEE script for land cover classification.

ZiwayShalla_LandCover_Classification_2015.js: GEE script for land cover classification.

ZiwayShalla_LandCover_Classification_2025.js: GEE script for land cover classification.

ZiwayShalla_ESV_Sensitivity_Analysis.R: R script for ESV and sensitivity analysis.

ZiwayShalla_LULC_Driver_Survey.docx: Word questionnaire for LULC driver assessment.

README.md: Documentation file with usage instructions for each dataset.


Usage:



GEE Script: Run in the GEE Code Editor with user-defined ROI and training data to generate land cover maps and metrics.

R Script: Execute in R/RStudio with updated LULC area data to produce ESV and sensitivity results.

Questionnaire: Distribute to stakeholders, collect responses, and analyze data (SPSS v20 software). Detailed instructions are provided in the README.md.