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Forests

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An alternative approach for estimating above ground biomass using Resourcesat-2 satellite data and artificial neural network in Bundelkhand region of India.

Environmental monitoring and assessment
Determination of above ground biomass (AGB) of any forest is a longstanding scientific endeavor, which helps to estimate net primary productivity, carbon stock and other biophysical parameters of that forest. With advancement of geospatial technology...

Physical evolution of the Three Gorges Reservoir using advanced SVM on Landsat images and SRTM DEM data.

Environmental science and pollution research international
The Three Gorges Reservoir (TGR) is one of the largest hydropower reservoirs in the world. However, changes of the important physical characteristics of the reservoir covering pre-, during-, and post- dam have not been well studied. This study analyz...

Artificial neural networks: Modeling tree survival and mortality in the Atlantic Forest biome in Brazil.

The Science of the total environment
Models to predict tree survival and mortality can help to understand vegetation dynamics and to predict effects of climate change on native forests. The objective of the present study was to use Artificial Neural Networks, based on the competition in...

Predictive analysis across spatial scales links zoonotic malaria to deforestation.

Proceedings. Biological sciences
The complex transmission ecologies of vector-borne and zoonotic diseases pose challenges to their control, especially in changing landscapes. Human incidence of zoonotic malaria ( Plasmodium knowlesi) is associated with deforestation although mechani...

A Machine Learning Ensemble Approach Based on Random Forest and Radial Basis Function Neural Network for Risk Evaluation of Regional Flood Disaster: A Case Study of the Yangtze River Delta, China.

International journal of environmental research and public health
The Yangtze River Delta (YRD) is one of the most developed regions in China. This is also a flood-prone area where flood disasters are frequently experienced; the situations between the people-land nexus and the people-water nexus are very complicate...

Identifying the vegetation type in Google Earth images using a convolutional neural network: a case study for Japanese bamboo forests.

BMC ecology
BACKGROUND: Classifying and mapping vegetation are crucial tasks in environmental science and natural resource management. However, these tasks are difficult because conventional methods such as field surveys are highly labor-intensive. Identificatio...

Characterizing and forecasting the responses of tropical forest leaf phenology to El Nino by machine learning algorithms.

PloS one
Climate change and global warming have serious adverse impacts on tropical forests. In particular, climate change may induce changes in leaf phenology. However, in tropical dry forests where tree diversity is high, species responses to climate change...

Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm.

PloS one
Effective soil spectral band selection and modeling methods can improve modeling accuracy. To establish a hyperspectral prediction model of soil organic matter (SOM) content, this study investigated a forested Eucalyptus plantation in Huangmian Fores...

Predicting direct and indirect non-target impacts of biocontrol agents using machine-learning approaches.

PloS one
Biological pest control (i.e. 'biocontrol') agents can have direct and indirect non-target impacts, and predicting these effects (especially indirect impacts) remains a central challenge in biocontrol risk assessment. The analysis of ecological netwo...

Prediction of soil-bearing capacity on forest roads by statistical approaches.

Environmental monitoring and assessment
The soil-bearing capacity is one of the important criteria in dimensioning the superstructure. In Turkey, predictability of California Bearing Ratio values, which may be used in the planning and dimensioning of forest roads, of which about 26% lacks ...