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Scientists have introduced a new technique to measure forest heights using advanced satellite technology, reducing the reliance on traditional, labor-intensive fieldwork. A recent study, published on July 29 in the 'Journal of Remote Sensing', involved an international team of researchers who used high-resolution satellite imagery from China's GF-7 satellite and historical images from Google Ear by Simon Mansfield
Sydney, Australia (SPX) Aug 27, 2024
Scientists have introduced a new technique to measure forest heights using advanced satellite technology, reducing the reliance on traditional, labor-intensive fieldwork. A recent study, published on July 29 in the 'Journal of Remote Sensing', involved an international team of researchers who used high-resolution satellite imagery from China's GF-7 satellite and historical images from Google Earth to achieve precise forest height measurements.
The GF-7 satellite, known for its very high-resolution (VHR) stereoscopic images, captures detailed views of the Earth's surface, including forests. Historically, measuring forest heights with such images required ground control points (GCPs) obtained through extensive and costly fieldwork, posing challenges in remote or mountainous regions.
To overcome these challenges, researchers developed a method called Multi-temporal Averaging of Google Earth (MAGE). This technique uses Google Earth's historical imagery to gather GCPs without needing extensive field surveys. By averaging coordinates from multiple historical images, MAGE enhances accuracy and simplifies the process.
The study applied this method in Switzerland, northeast, and south China. Using GF-7 stereoscopic images and MAGE-derived GCPs, researchers created a digital surface model (DSM) from the GF-7 images and subtracted ground surface elevations to determine forest heights.
The results were significant: The average errors in coordinates using the MAGE method were less than 2.0 meters. Initial forest height measurements had an average error of 12.3 meters, but this improved to just 1.5 meters with the optimized MAGE method. The accuracy of the height prediction model also saw a substantial increase, with the correlation between predicted and actual heights rising from 0.72 to 0.95.
These findings underscore the effectiveness of the MAGE method in refining the geometric processing of VHR imagery, leading to more accurate forest height estimations.
"This advancement holds significant potential for various environmental and forestry applications. It enables more efficient monitoring and management of forest resources, which is crucial for understanding ecological health, carbon storage, and biodiversity conservation," said Ni Wenjian from the Aerospace Information Research Institute (AIR) of the Chinese Academy of Sciences.
Additionally, the reduction in the need for fieldwork makes it feasible to conduct large-scale forest height assessments even in hard-to-reach areas, promoting better forest management practices globally, he adds.
The study marks an important step forward in remote sensing technology, illustrating how innovative use of existing data can address traditional challenges in environmental monitoring.
Research Report:Forest Height Extraction Using GF-7 Very High-Resolution Stereoscopic Imagery and Google Earth Multi-Temporal Historical Imagery
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