New Tool Uses Algorithms For Precise Deforestation Detection

Monday - May 22 2023, 10:51 UTC - 1 year ago

Scientists have unveiled an innovative and comprehensive strategy to effectively detect and track large-scale forest disturbances, using algorithms to detect deforestation and create more accurate land cover mapping. The tool is created the NLCD 1986-2019 forest disturbance product, using two and time-series change detection methods, and the ultimate goal is to automatically produce forest disturbance maps with high accuracy in near real-time.

Scientists have unveiled an innovative and comprehensive strategy to effectively detect and track large-scale forest disturbances, according to a new study published in the Journal of Remote Sensing.

Approximately 27 football fields' worth of forests are lost every minute around the globe, resulting in a massive annual loss of 15 billion trees, according to the WWF. Given this concerning context, the new forest monitoring approach could be a valuable tool for effectively monitoring and managing forests as they undergo changes over time.

"Our strategy leads to more accurate land cover mapping and updating," said Suming Jin, a physical scientist with the EROS Center, in a statement.

New tool uses algorithms for precise deforestation detection .

Scientists use the National Land Cover Database to comprehensively view landscape changes. It transforms satellite images (Landsat) into detailed maps of different features.

From 2001 to 2016, almost half of the land cover change in the contiguous United States (CONUS)— the 48 adjoining states located within North America, excluding Alaska and Hawaii— occurred in forests, as revealed by the database.

Jin emphasized that to ensure the quality of the database's products, accurately detecting the location and timing of forest disturbance is critical. To do this, Jin and her team combined 2-date and time-series change detection methods, which improved mapping efficiency, flexibility, and accuracy.

To explain simply, 2-date algorithms are more flexible and use richer spectral information to detect forest disturbances accurately by analyzing changes in image bands, indices, classifications, and combinations.

However, they only work for one time period. They may need extra information to differentiate forest changes from other land cover changes.

Time-series algorithms consider spectral and long-term temporal information, simultaneously providing changes for multiple dates. However, adding a new date requires processing the entire algorithm, which can be challenging for continuous monitoring and may introduce inconsistencies.

Previous studies suggested ensemble approaches, like stacking, to enhance forest change mapping accuracy by combining different methods. Although effective, stacking requires significant computational resources and reference data for training.

In this latest study, the researcher's combined approach created the NLCD 1986-2019 forest disturbance product. It displays the most recent forest disturbance date within two to three-year intervals between 1986 and 2019.

"The TSUN index detects multi-date forest land cover changes and was shown to be easily extended to a new date even when new images were processed in a different way than previous date images," Jin stated.

The research team intends to enhance the tool by increasing the frequency of time measurements and generating an annual forest disturbance product covering the period from 1986 to the present.

"Our ultimate goal is to automatically produce forest disturbance maps with high accuracy with the capability of continually monitoring forest disturbance, hopefully in near real-time," said Jin.