2024-03-17 00:27:10
Global analysis of forest management types: Towards sustainable forest restoration and better carbon assessment.
Forests play a crucial role in regulating the global climate, acting as important carbon stores and natural filters for air and water. The effective management of these forest ecosystems is therefore essential not only for the conservation of biodiversity but also in the fight once morest climate change. However, the lack of global forest management maps hampers the implementation of sustainable forest restoration practices and the accurate assessment of biomass and carbon stocks. Our study aims to fill this gap by using random forest and change detection algorithms to generate annual maps of forest management types from 2001 to 2020.
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Innovative techniques for forest management mapping
Conventional approaches to forest mapping have often struggled with the challenges posed by the spectral similarity of different types of forest management. To overcome this, we applied advanced machine learning techniques, particularly random forest algorithms, in conjunction with change detection methods, using multi-source datasets. This methodology allowed the fine distinction of the six types of forest management identified: regenerated natural forests (managed and unmanaged), planted forests (rotation > 15 years and ≤ 15 years), oil palm plantations and agroforestry.
Spatial and temporal variations in forest management types
The analysis revealed significant variations in the spatial distribution and temporal trends of forest management types across continents. In particular, a notable increase in planted forest areas and agroforestry was observed, partly offsetting the reduction in naturally regenerated forests. This expansion reflects a trend toward reforestation and afforestation practices, although the reduction of natural forests raises concerns in terms of loss of biodiversity and ecosystem services.
Carbon stocks and biomass changes
Estimation of annual carbon stocks in different types of forest management revealed that, despite the loss of naturally regenerated forests, the expansion of planted forests, palm oil plantations and agroforestry offset a significant share loss of forest area 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 critical factors to consider.
Fig. 1. Spatial distribution, variations and transitions of different types of forest management from 2001 to 2020. (A) Spatial distribution of different types of forest management in 2015. (B) Annual areas of types of forest management from 2001 to 2020. (C ) Transitions in forest management types from 2001 to 2020. For better visualization, the values in the chord plot in (C) have been normalized as the proportion of the area contributed by other forest management types in the surface area increased by a certain type of forest management. Detailed surface values are listed in Table S12. Taking the increase in NRF-NM as an example, the 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 for policymakers, forest managers and the scientific community, facilitating the implementation of nature-based forest management practices and forest restoration planning. Furthermore, 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 significant 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 higher resolution data and examining the long-term impacts of different management practices on forest ecosystems and global climate.
This paper 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 for the loss of naturally regenerated forests through the expansion of planted forests and agroforestry, as well as the importance of forest management in mitigating climate change.
Source : Journal of remote sensing
1710644946
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