Sumatra Deforestation Data Research
Budget: $1,500 – $3,000 USD
I need a solid, verifiable picture of how fast Sumatra’s forests are disappearing. The sole focus is research and data collection, with deforestation rates as the primary metric. I already have scattered figures from government briefs and NGO reports; what’s missing is a consistent, island-wide dataset I can trust and cite.
Your task is to source, process, and present that data in a way that lets me track annual forest-cover loss, compare provinces, and spot the hottest deforestation fronts. Satellite imagery (e.g., Landsat, Sentinel) is the obvious toolset, but if you can tap reputable field surveys or other remote-sensing archives, I’m open to your approach. Extra context on wildlife impacts or carbon emissions would be welcome bonuses, yet they remain optional; the headline deliverable is the deforestation-rate timeline itself.
Please include:
• A cleaned, well-documented spreadsheet or geodatabase showing yearly forest-cover loss for Sumatra (ideally 2000-present).
• High-resolution maps and/or interactive layers that clearly visualise the loss hotspots.
• A short methods memo explaining data sources, processing steps, and any assumptions or error margins.
Accuracy and transparency matter more than flashy graphics, so cite every source and keep the workflow reproducible (Python, QGIS, Google Earth Engine—whatever stack you prefer, as long as it’s clear). If you have previous examples of similar land-use change analyses, feel free to reference them when you respond.
Your task is to source, process, and present that data in a way that lets me track annual forest-cover loss, compare provinces, and spot the hottest deforestation fronts. Satellite imagery (e.g., Landsat, Sentinel) is the obvious toolset, but if you can tap reputable field surveys or other remote-sensing archives, I’m open to your approach. Extra context on wildlife impacts or carbon emissions would be welcome bonuses, yet they remain optional; the headline deliverable is the deforestation-rate timeline itself.
Please include:
• A cleaned, well-documented spreadsheet or geodatabase showing yearly forest-cover loss for Sumatra (ideally 2000-present).
• High-resolution maps and/or interactive layers that clearly visualise the loss hotspots.
• A short methods memo explaining data sources, processing steps, and any assumptions or error margins.
Accuracy and transparency matter more than flashy graphics, so cite every source and keep the workflow reproducible (Python, QGIS, Google Earth Engine—whatever stack you prefer, as long as it’s clear). If you have previous examples of similar land-use change analyses, feel free to reference them when you respond.
Related categories:
Excel
Statistics
Remote Sensing
Geospatial
Statistical Analysis
SPSS Statistics
Data Analysis
Data Collection