This stunning study of forests reveals good news for the future of the planet's green lungs

This stunning study of forests reveals good news for the future of the planet's green lungs

Global analysis of forest management types: towards sustainable forest restoration and better carbon assessment.

Forests play a crucial role in regulating global climate, acting as important carbon stores and natural filters of air and water. Therefore, effective management of these forest ecosystems is essential not only for preserving biodiversity but also in combating climate change. However, the lack of global forest management maps hampers the implementation of sustainable forest restoration practices and accurate assessment of biomass and carbon stocks. Our study aims to fill this gap by using forest detection and stochastic variation algorithms to generate annual maps of forest management types from 2001 to 2020.

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Innovative techniques for forest management mapping

Traditional approaches to forest mapping have often faced challenges posed by spectral similarity between different types of forest management. To overcome this, we applied advanced machine learning techniques, especially random forest algorithms, combined with change detection methods, using multi-source datasets. This methodology allowed a fine distinction between the six types of forest management identified: natural regenerating forests (managed and unmanaged), planted forests (cycle > 15 years and ≥ 15 years), oil palm plantations and agroforestry.

Spatial and temporal differences in types of forest management

The analysis revealed significant differences in the spatial distribution and temporal trends of forest management types across continents. In particular, a significant increase in areas of planted forests and agroforestry was observed, which partially offset the decrease in naturally regenerating forests. This expansion reflects a trend towards reforestation and afforestation practices, although the reduction of natural forests raises concerns in terms of loss of biodiversity and ecosystem services.

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Carbon stocks and biomass changes

Estimating annual carbon stocks in different types of forest management revealed that despite the loss of naturally renewable forests, the expansion of planted forests, palm oil plantations and agroforestry offset a significant loss in forest area share and carbon stock. This highlights the importance of forest management practices in mitigating climate change, although the quality and type of forest cover are also crucial factors to take into account.

Chart 1
fig. 1. Spatial distribution, differences and transitions of different forest management types from 2001 to 2020. (a) Spatial distribution of different forest management types in 2015. (b) Annual areas of forest management types from 2001 to 2020. (c) Transitions in forest management types from 2001 to 2020. For better visualization, the values ​​in the chord diagram in (C) have been normalized as the proportion of area contributed by other forest management types to the surface area by a given forest management type. Detailed surface values ​​are listed in Table S12. Taking the increase in NRF-NM as an example, values ​​were calculated as the proportion of area of ​​NRF-WM, PFr>15, PFr≥15, oil palm plantations and agroforestry converted to NRF-NM compared to that of the total increase in NRF-WM, respectively.

Implications for forest management and climate change mitigation

The results of our study provide valuable information to policy makers, forest managers and the scientific community, facilitating the implementation of nature-based forest management practices and forest restoration planning. Moreover, they contribute to a better understanding of the impact of different types of forest management on carbon stocks and biodiversity, providing a basis for more informed and targeted climate change mitigation strategies.

Limitations and future perspectives

Although our approach represents a major advance in forest management mapping, finer spatial resolutions and more precise carbon stock assessments are needed to improve the accuracy of forest maps and carbon estimates. Future research should focus on integrating high-resolution data and studying the long-term impacts of different management practices on forest ecosystems and global climate.

This article explores the application of machine learning techniques to generate annual maps of forest management types globally, revealing significant changes in forest cover and management from 2001 to 2020. The study highlights the partial compensation of loss of naturally renewable forests through expansion of planted areas. Forestry and agroforestry, as well as the importance of forest management in mitigating climate change.

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source : Remote Sensing Journal

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