AIMC Topic: Forests

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Assessing the performance of machine learning algorithms for analyzing land use changes in the Hyrcanian forests of Iran.

Environmental science and pollution research international Nov 30, 2024
Land use changes are of critical importance in understanding and managing environmental sustainability and resource utilization. Machine learning algorithms (MLAs) have emerged as powerful tools for analyzing and predicting land use changes, offering...
Algorithms Environmental Monitoring Support Vector Machine Machine Learning Conservation of Natural Resources Forests Iran
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Understanding ecosystem services of detailed forest and wetland types using remote sensing and deep learning techniques in Northern China.

Journal of environmental management Nov 20, 2024
Spanning both temperate and sub-frigid zones, Northeast China boasts typical boreal forests and abundant wetland resources. Because of these attributes, the region is critically significant for global climate regulation, carbon sequestration, and bio...
Neural Networks, Computer Soil Biodiversity China Forests Ecosystem Conservation of Natural Resources Wetlands Remote Sensing Technology Deep Learning
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Forest Aboveground Biomass Estimation Based on Unmanned Aerial Vehicle-Light Detection and Ranging and Machine Learning.

Sensors (Basel, Switzerland) Nov 2, 2024
is a widely planted species in plantation forests because of its outstanding characteristics, such as fast growth rate and high adaptability. Accurate and rapid prediction of biomass is important for plantation forest management and the prediction ...
Biomass Support Vector Machine Remote Sensing Technology Algorithms Ecosystem Forests Machine Learning Trees Eucalyptus Unmanned Aerial Devices Light
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Deep learning models map rapid plant species changes from citizen science and remote sensing data.

Proceedings of the National Academy of Sciences of the United States of America Sep 5, 2024
Anthropogenic habitat destruction and climate change are reshaping the geographic distribution of plants worldwide. However, we are still unable to map species shifts at high spatial, temporal, and taxonomic resolution. Here, we develop a deep learni...
Citizen Science Forests Wildfires Conservation of Natural Resources California Climate Change Plants Biodiversity Deep Learning Remote Sensing Technology Humans Ecosystem
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Soil organic carbon estimation using remote sensing data-driven machine learning.

PeerJ Aug 1, 2024
Soil organic carbon (SOC) is a crucial component of the global carbon cycle, playing a significant role in ecosystem health and carbon balance. In this study, we focused on assessing the surface SOC content in Shandong Province based on land use type...
Environmental Monitoring Ecosystem Remote Sensing Technology Machine Learning Support Vector Machine China Forests Carbon Soil
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European beech spring phenological phase prediction with UAV-derived multispectral indices and machine learning regression.

Scientific reports Jul 9, 2024
Acquiring phenological event data is crucial for studying the impacts of climate change on forest dynamics and assessing the risks associated with the early onset of young leaves. Large-scale mapping of forest phenological timing using Earth observat...
Climate Change Unmanned Aerial Devices Plant Leaves Fagus Forests Machine Learning Seasons
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The consistent fuzzy suitability assessment of forest land resources with multi-source heterogeneous data.

Environmental monitoring and assessment Jun 4, 2024
In view of the suitability assessment of forest land resources, a consistent fuzzy assessment method with heterogeneous information is proposed. Firstly, some formulas for transforming large-scale real data and interval data into fuzzy numbers are pr...
Fuzzy Logic Forests Environmental Monitoring Conservation of Natural Resources
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Predicting the pulse of the Amazon: Machine learning insights into deforestation dynamics.

Journal of environmental management Jun 4, 2024
This study aims to analyze deforestation in the Brazilian Amazon from 1999 to 2020 using machine learning techniques to assess 16 critical factors. Our approach leverages the capabilities of machine learning, particularly Random Forest, which proved ...
Forests Conservation of Natural Resources Machine Learning Brazil
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Multi-year soundscape recordings and automated call detection reveals varied impact of moonlight on calling activity of neotropical forest katydids.

Philosophical transactions of the Royal Society of London. Series B, Biological sciences May 6, 2024
Night-time light can have profound ecological effects, even when the source is natural moonlight. The impacts of light can, however, vary substantially by taxon, habitat and geographical region. We used a custom machine learning model built with the ...
Forests Vocalization, Animal Machine Learning Animals Light
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Forest fire susceptibility assessment under small sample scenario: A semi-supervised learning approach using transductive support vector machine.

Journal of environmental management Apr 26, 2024
Forest fires threaten global ecosystems, socio-economic structures, and public safety. Accurately assessing forest fire susceptibility is critical for effective environmental management. Supervised learning methods dominate this assessment, relying o...
Support Vector Machine Ecosystem Supervised Machine Learning Forests China Fires Wildfires
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