[PDF][PDF] Adoption of machine learning techniques in ecology and earth science

A Thessen - One Ecosystem, 2016 - oneecosystem.pensoft.net
This is largely due to 1) a lack of communication and collaboration between the machine
learning research community and natural scientists, 2) a lack of communication about …

Application of machine-learning methods in forest ecology: recent progress and future challenges

Z Liu, C Peng, T Work, JN Candau… - Environmental …, 2018 - cdnsciencepub.com
Machine learning, an important branch of artificial intelligence, is increasingly being applied
in sciences such as forest ecology. Here, we review and discuss three commonly used …

Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector …

E Adam, O Mutanga, J Odindi… - International Journal of …, 2014 - Taylor & Francis
Map** of patterns and spatial distribution of land-use/cover (LULC) has long been based
on remotely sensed data. In the recent past, efforts to improve the reliability of LULC maps …

Sampling and modelling rare species: Conceptual guidelines for the neglected majority

A Jeliazkov, Y Gavish, CJ Marsh… - Global change …, 2022 - Wiley Online Library
Biodiversity conservation faces a methodological conundrum: Biodiversity measurement
often relies on species, most of which are rare at various scales, especially prone to …

A machine learning framework for multi-hazards modeling and map** in a mountainous area

S Yousefi, HR Pourghasemi, SN Emami, S Pouyan… - Scientific Reports, 2020 - nature.com
This study sought to produce an accurate multi-hazard risk map for a mountainous region of
Iran. The study area is in southwestern Iran. The region has experienced numerous extreme …

Using deep learning to predict plant growth and yield in greenhouse environments

B Alhnaity, S Pearson, G Leontidis… - … Symposium on Advanced …, 2019 - actahort.org
Effective plant growth and yield prediction is an essential task for greenhouse growers and
for agriculture in general. Develo** models which can effectively model growth and yield …

[HTML][HTML] Classification and map** of paddy rice by combining Landsat and SAR time series data

S Park, J Im, S Park, C Yoo, H Han, J Rhee - Remote Sensing, 2018 - mdpi.com
Rice is an important food resource, and the demand for rice has increased as population
has expanded. Therefore, accurate paddy rice classification and monitoring are necessary …

Map** and Monitoring of the Invasive Species Dichrostachys cinerea (Marabú) in Central Cuba Using Landsat Imagery and Machine Learning (1994–2022)

A Valero-Jorge, R González-De Zayas, F Matos-Pupo… - Remote Sensing, 2024 - mdpi.com
Invasive plants are a serious problem in island ecosystems and are the main cause of the
extinction of endemic species. Cuba is located within one of the hotspots of global …

High-resolution topographic variables accurately predict the distribution of rare plant species for conservation area selection in a narrow-endemism hotspot in New …

G Lannuzel, J Balmot, N Dubos, M Thibault… - Biodiversity and …, 2021 - Springer
Species distribution models (SDMs) represent a widely acknowledged tool to identify priority
areas on the basis of occurrence data and environmental factors. However, high levels of …

Presence-only species distribution models are sensitive to sample prevalence: Evaluating models using spatial prediction stability and accuracy metrics

L Grimmett, R Whitsed, A Horta - Ecological Modelling, 2020 - Elsevier
Species distribution modelling (SDM) is an important tool for ecologists, but different
algorithms and different sampling strategies produce different results. Using virtual species …