Introduction
To truly understand the value of GEOEVO, it helps to see the platform in action. This case study walks through a realistic, end-to-end scenario showing how an environmental NGO could use GEOEVO to combat deforestation and monitor reforestation in a tropical region.
The example is hypothetical, but the workflows, tools, and insights closely mirror real-world environmental monitoring projects. The goal is to demonstrate how GEOEVO combines satellite imagery, geospatial AI, contextual data, and its AI Chat Assistant into a single, coherent solution.
Background: The Environmental Challenge
The focus region lies in Southeast Asia, covering tropical rainforest, agricultural frontiers, and several protected areas. Over the past 15 years, the region has experienced significant forest loss driven mainly by:
- expansion of palm oil plantations
- selective and illegal logging
- new road and infrastructure development
In response, a community-based reforestation initiative began in 2018, aiming to restore degraded land through tree planting and sustainable land management.
The NGO wants to answer:
- Where and how fast is deforestation occurring?
- What are the dominant drivers in different locations?
- Are protected areas effectively limiting forest loss?
- Is reforestation succeeding, and where?
- How can these findings be clearly communicated to policymakers and communities?
Step 1: Defining Analysis Areas
Using GEOEVO, the analyst defines multiple areas of interest:
- the full regional boundary
- individual protected areas
- reforestation project sites
Areas can be drawn manually or created via the AI Chat Assistant (for example: "Create an analysis area for Gunung Leuser National Park"). Structuring analysis this way enables clear comparisons across land-use types.
Step 2: Establishing a Satellite Baseline
The NGO retrieves cloud-filtered satellite composites for benchmark years (e.g. 2010 and 2020). GEOEVO automatically selects appropriate imagery sources such as Landsat for historical periods and Sentinel-2 for recent years.
Side-by-side visualization immediately reveals forest fragmentation and loss, validating the baseline before deeper analysis.
Step 3: AI-Powered Change Detection
Using GEOEVO’s change detection workflow, the NGO generates:
- a spatial forest-loss map
- quantitative statistics for total area changed
Results indicate that approximately 10% of forest cover was lost between 2010 and 2020, amounting to several thousand square kilometers.
Step 4: Identifying Deforestation Drivers
GEOEVO’s semantic change analysis classifies detected loss into likely causes using spatial patterns, indices, and contextual data.
Findings show:
- ~70% conversion to agriculture (primarily palm oil)
- ~10% linked to infrastructure and settlements
- ~20% from mixed or uncertain causes (e.g. selective logging)
Deforestation hotspots align strongly with newly developed road corridors.
Step 5: Assessing Protected Area Effectiveness
Overlaying forest loss with protected-area boundaries reveals:
- minimal deforestation inside protected areas
- significantly higher loss outside boundaries
One smaller reserve shows edge encroachment, highlighting a need for improved enforcement or buffer-zone protection.
Step 6: Monitoring Reforestation Progress
Reforestation sites established in 2018 are analyzed using time-series vegetation indices and phenology metrics.
GEOEVO reveals:
- steadily increasing NDVI values
- longer growing seasons compared to pre-restoration conditions
- clear canopy recovery trends
Most sites show ~30% vegetation recovery by 2025, indicating strong tree survival. One underperforming site suggests drought stress, prompting adaptive management.
Step 7: Adding Socioeconomic and Climate Context
To explain observed patterns, the NGO integrates contextual data through GEOEVO:
- road expansion from OpenStreetMap
- population growth indicators
- drought and rainfall anomalies
High deforestation districts correlate with rapid population growth and infrastructure development. A severe drought year coincides with increased fire-driven forest loss.
Step 8: AI Chat Assistant – From Analysis to Insight
The NGO asks:
"Summarize the key findings of our analysis."
The GEOEVO AI Chat Assistant generates a structured summary covering:
- deforestation trends and drivers
- protected area performance
- reforestation outcomes
- climate and fire risks
- socioeconomic pressures
This summary can be reused directly in reports, presentations, or policy briefings.
Step 9: Driving Real-World Impact
With clear, data-backed insights, the NGO:
- briefs policymakers using credible evidence
- communicates transparently with local communities
- monitors restoration success over time
What once required months of fragmented GIS work is completed in days, with far greater clarity and confidence.
Conclusion
This case study shows how GEOEVO turns complex environmental monitoring into a unified, AI-driven workflow. By combining satellite imagery, change detection, phenology analysis, contextual datasets, and conversational AI, GEOEVO enables organizations to move from observation to action faster than ever.
From identifying deforestation hotspots to validating reforestation success and communicating insights clearly, GEOEVO demonstrates how geospatial AI can directly support environmental protection, restoration, and policy decisions.
