GSI Study: ForINT’s Data More Accurate than Open-Source Tools

In the world of forest management, accurate data is critical for making informed decisions. ForINT (Forest Intelligence) provides a cutting-edge solution for monitoring forest landscapes over time, offering superior insights compared to publicly available tools like Global Forest Watch (GFW) and the USDA’s Landscape Change Monitoring System (LCMS).

A recent analysis by Global Surface Intelligence (GSI) reveals that ForINT significantly outperforms these open-source datasets, especially when it comes to detecting forest thinning versus clearcutting activities.

Key for above GIF: 
In the ForINT scene, purple = stand age, with darker purples representing older stands. In GFW and LCMS scenes: Green = Agreement within Tolerance of ForINT (±2 years); Yellow = Moderate Deviation (±3 years to ±10 years); Red = High Deviation (>±3 years); Blue = areas where ForINT made predictions but public dataset did not.

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ForINT vs. Open-Source Forest Maps

• ForINT:
  • Monitors forest change over time using over 35 years of Landsat imagery.
  • Provides detailed insights into forest age and thinning history.
  • Accurately distinguishes between clearcut harvests and thinning, preventing costly misclassifications.
• GFW & USDA LCMS:
  • Primarily track forest loss but often misclassify thinning as clearcutting.
  • Can introduce significant errors when used for valuations, forest management or carbon assessments.

 

The Power of ForINT’s Data

ForINT stands out by leveraging decades of historical data to create a unified dataset that tracks forest loss and gain, distinguishing thinning from clearcutting. These distinctions are critical for accurate assessments of forest health, economic value, and carbon storage.

Misclassifications—common in GFW and LCMS—can lead to:

• Exaggerating carbon project baselines, resulting in flawed forecasts and low-quality credits.
• Underestimating forest economic value.
• Increased uncertainty in forest classifications, with up to one-third of data deemed incorrect in the GSI comparison.

The Limitations of Open-Source Data: Why Quality Matters

Both GFW and LCMS are widely used for forest change analysis despite substantial limitations. A significant portion of their data were found to be incorrect in the GSI comparison, particularly when distinguishing thinning from clearcutting. Users of these datasets often face a challenge in verifying predictions, which result in costly errors in carbon calculations, forest management, and yield planning.

In a limited case study of 110 points distributed across South Carolina, ForINT's forest age matched high-resolution imagery in every time.  In comparison, the forest age derived from GFW and LCMS were 10 years or more in error 19.7% and 23.0% respectively.

Key Findings from GSI's Analysis:

• ForINT was correct in all manually verified sample points, while GFW and LCMS showed significant deviation.
• GFW and LCMS often misclassified thinning as clearcutting, especially in about 23% of pixels (GFW) and 19.7% (LCMS).
• ForINT provides better historical context and forest age determination, offering a more complete analysis of forest landscapes.

How ForINT Improves Baselines and Valuations

• Distinguishes between thinning and clearcutting in separate layers.
• Provides estimated site index.
• Tracks both forest loss and gain in one unified dataset, reducing the need for additional analysis.
• Leverages a longer history of data (over 35 years of Landsat imagery) for more comprehensive insights.

Why Data Quality Matters

The implications of using GFW or LCMS data incorrectly can be significant. Misclassifying thinning as clearcutting can lead to underestimating a forest's economic value, or exaggerating carbon project baselines, which affect both forecasting and perceived carbon credit value. ForINT’s ability to largely avoid these misclassifications offers a more reliable foundation for decision-making, empowering forest professionals with the most complete determinations to make better-informed, more accurate assessments.

ForINT is the Future of Forest Intelligence

Reach out at the link above to request a 1:1 demo of ForINT.

ForINT represents a significant leap forward in forest landscape monitoring. While GFW and LCMS offer valuable insights, ForINT’s more precise classification of thinning, clearer differentiation between forest loss and gain, and detailed age-based analysis make it the ideal choice for those seeking a higher level of confidence in their carbon credits and forest management decisions.

Whether you’re working in forestry, carbon markets, or land-use planning, ForINT provides the tools you need to navigate the complexities of forest change with confidence.

If you're ready to take your forest management to the next level, ForINT is the data solution you’ve been looking for.