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Federal Research Center 
"Krasnoyarsk Science Center of the Siberian
Branch of the Russian Academy of Sciences"

 Федеральный исследовательский центр «Красноярский научный центр Сибирского отделения Российской академии наук»

Federal Research Center 
"Krasnoyarsk Science Center of the Siberian
Branch of the Russian Academy of Sciences"

Stability of forests on the planet will be assessed by space monitoring data

28 October 2022 г.

Стабильность лесов на планете оценят по данным космического мониторинга
Scientists have developed a new method for monitoring the state of stable and virgin forests. It does not require machine learning or manual data entry and will allow quickly tracking the state of forests around the world. The results of the study are published in the journal Remote Sensing.

Loss of forests due to human activity has become a global environmental problem. Forests untouched by humans which are of great environmental importance are under particular threat. Such ecosystems have large reserves of carbon, support the biodiversity of plants and animals, including endangered ones, they are more resistant to external effects and have a high natural adaptive capacity. Degradation of these forests can lead to the loss of their ability to absorb carbon and regulate climate, conserve biodiversity, provide a variety of resources, and other consequences.

An international team of scientists from Russia, the USA, Australia and Canada, including researchers from the Krasnoyarsk Science Center of SB RAS, has developed a new method for monitoring and mapping the stability of ecosystems in tropical and boreal forests. The new approach is based on indicators provided by remote sensing of the earth, it does not need machine learning, additional calibration by field measurements, and statistical data to obtain information.

Many existing methods for mapping and classifying forest ecosystems from remote sensing data rely on machine learning calibrated using a large number of field measurements. To avoid this, scientists have developed a new approach to mapping the resilience of forest ecosystems. It is based on MODIS satellite imagery data, in particular, spectral data on the proportion of light absorbed by plants, which is connected with the structure and productivity of vegetation, and the water stress index, which is an indicator of the water content in plant tissues.

The researchers tested the new method in two forest regions at opposite ends of the Earth's climatic and latitudinal gradients: the boreal forests of Siberia and the tropical rainforests of the Amazon Basin. In these areas, there are large areas of undisturbed forests, along with forests intensively used by humans.

Based on remote sensing data, the researchers determined forest stability classes. Similarly, the rate of forest degradation was assessed. Previously, such changes could only be detected using long-term statistical data collection. The results show that the proposed method is accurate, applicable to all forest biomes and allows mapping both large areas and small areas of stable forests at any scale, and therefore can help in the identification and conservation of sustainable forests.

“The conservation of environmentally important old growth forests is the task of many international initiatives. The developed method can be expanded and implemented on a global scale, thus it will become a single source of information about primary forest ecosystems. The resulting data can also be used as an independent source to cross-check other local or global forest assessments. In addition, the new method will make it possible to regularly update the materials as new satellite data become available. Information about sustainable forests is relevant to national and international policies related to biodiversity conservation and climate change mitigation, it can be used to plan environmental activities and prioritize investment in environmental activities,” said Evgeny Shvetsov, Candidate of Technical Sciences, senior researcher at the V.N. Sukachev Institute of Forest SB RAS.




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