AIMC Topic: Trees

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Climate and traits drive bark decomposition patterns at global scale.

Nature communications
Tree bark represents a large global carbon stock, comprising 2-20 % of woody biomass, and plays a distinct role in carbon and nutrient cycling. It is poorly understood how different abiotic and biotic drivers contribute to bark decomposition globally...

Flash droughts threaten global managed forests.

Nature communications
Flash droughts, characterized by rapid onset and increasing frequency, pose significant threats to ecosystem stability and function. However, there remains no global consensus regarding forest responses to flash droughts. Here, using a reconstructed ...

A new approach improving koala habitat prediction using hyperspectral airborne imagery.

The Science of the total environment
Koala populations are declining primarily due to habitat loss, making large-scale habitat quality prediction essential for conservation. A first approach to defining koala habitat quality involves identifying the number of different 'koala' trees spe...

Automated forest land division using deep learning and drone imagery.

PloS one
This paper proposes an automated solution for tree enumeration in areas designated for forest land division using drone image processing. Traditional tree counting methods are time-consuming and error-prone. Our approach leverages drone imagery and a...

Deep learning model BiFPN-YOLOv8m for tree counting in mango orchards using satellite remote sensing data​.

Scientific reports
Mango is a fruit of great economic importance in India. India is the top mango-producing nation in the world, accounting for over half of global mango output. In order to determine the production capability of the insured orchards, a complete invento...

Mapping spatiotemporal distribution of forest carbon density in Xizang, China.

PloS one
Climate warming is a major global challenge, and forests, essential carbon sinks, are critical in mitigating its effects. Forest carbon density is a key parameter in assessing the carbon sinks. Traditional estimating methods of forest carbon density ...

Multi-branch and multi-label tree species classification using deep learning for UAV aerial photography and Sentinel remote sensing images.

Scientific reports
The classification and identification of forest tree species is of great value in the study of species diversity and forest monitoring. With the development of emerging technologies, the combination of remote sensing images and deep learning methods ...

A meta-analysis of predictive accuracies and errors of biomass estimation models in Sub-Saharan Africa.

The Science of the total environment
Accurate biomass estimation is essential for forest monitoring, energy planning and carbon accounting in Sub-Saharan Africa (SSA), where destructive sampling is often impractical. Biomass estimation models (BEMs) offer scalable alternatives, but thei...

Aboveground biomass estimation using multimodal remote sensing observations and machine learning in mixed temperate forest.

Scientific reports
Plants sequester carbon in their aboveground components, making aboveground tree biomass a key metric for assessing forest carbon storage. Traditional methods of aboveground biomass (AGB) estimation via Forest Inventory and Analysis (FIA) plots lack ...

Inversion and validation of soil water-holding capacity in a wild fruit forest, using hyperspectral technology combined with machine learning.

Scientific reports
Soil water retention is a critical aspect of water conservation. To quantitatively assess the Soil Water-Holding Capacity (SWHC), this study focused on a typical wild fruit forest in Xinjiang, China. The spectral characteristics of the forest canopy ...