Researchers have harnessed a new statistical technique that allows forest managers to use incomplete satellite imagery for precise forest inventories , bypassing the need for complex and often uncertain data repair processes. The method, known as a " hybrid estimator ," is particularly valuable for leveraging decades of archived data from the aging Landsat 7 satellite, which has been collecting images with systematic gaps since 2003.
The study, published in Forest Ecosystems , addresses a critical challenge in forestry and climate science. Remote sensing is vital for large-scale forest monitoring, but missing data, such as the wedge-shaped gaps in Landsat 7 imagery, compromised the credibility of estimates for forest volume, biomass, and carbon stocks. Traditionally, scientists have used "gap-filling" algorithms to reconstruct this missing data, but these methods propagate their own errors that are not always quantifiable, and thereby misleading decision-makings relying on inventory estimates.
In contrast, instead of trying to create a perfect, wall-to-wall image, the hybrid estimator uses probability-based sampling to work directly with the available, non-wall-to-wall data.
"The core idea is to use a statistically robust sample of the existing good pixels, rather than relying on a guess-filled complete map," the researchers explained. "This allows us to generate reliable population-level estimates for forest attributes without the uncertainty that comes from gap-filling."
The research team put their method to the test in the forests of Inner Mongolia, China. They compared their hybrid estimator, which used the Landsat 7 data, against a conventional model-based method that required pristine, wall-to-wall imagery from the Sentinel-2 satellite.
The results indicated that hybrid estimator achieved a sampling precision of over 90% , meeting China's national standard for forest inventory. Most importantly, its efficiency was comparable to the conventional model-based method using the superior Sentinel-2 data.
"Our findings show that we don't have to discard the archived Landsat 7 data," the authors stated. "By using this hybrid approach, we can extract valuable, reliable information from it directly. This activates a huge historical dataset for long-term trend analysis and provides a cost-effective tool for large-scale forest surveys."
The study also provides practical guidance for forest managers, suggesting that using a larger number of smaller sample clusters further optimizes the precision of the estimates. This new method holds the potential to make forest monitoring more flexible, cost-effective, and reliable, especially in regions where access to the latest satellite imagery is limited.
Forest Ecosystems
Leveraging missing-data remote sensing for forest inventory
31-Oct-2025