Species distribution modelling to support forest management. A literature review

M Pecchi, M Marchi, V Burton, F Giannetti… - Ecological …, 2019 - Elsevier
Abstract Species Distribution Modelling (SDM) techniques were originally developed in the
mid-1980s. In this century they are gaining increasing attention in the literature and in …

A quantitative synthesis of the importance of variables used in MaxEnt species distribution models

J Bradie, B Leung - Journal of Biogeography, 2017 - Wiley Online Library
Aim To synthesize the species distribution modelling (SDM) literature to inform which
variables have been used in MaxEnt models for different taxa and to quantify how frequently …

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 …

New vegetation type map of India prepared using satellite remote sensing: Comparison with global vegetation maps and utilities

PS Roy, MD Behera, MSR Murthy, A Roy… - International Journal of …, 2015 - Elsevier
A seamless vegetation type map of India (scale 1: 50,000) prepared using medium-
resolution IRS LISS-III images is presented. The map was created using an on-screen visual …

Modelling invasive alien plant distribution: A literature review of concepts and bibliometric analysis

LD Silva, RB Elias, L Silva - Environmental Modelling & Software, 2021 - Elsevier
In the last decades, the number of publications dedicated to the application of species
distribution models (SDMs) to invasive alien plants (IAPs) has constantly increased …

Predicting the Habitat Suitability of Melaleuca cajuputi Based on the MaxEnt Species Distribution Model

NZ Ab Lah, Z Yusop, M Hashim, J Mohd Salim… - Forests, 2021 - mdpi.com
Gelam tree or Melaleuca cajuputi (M. cajuputi) is an important species for the local economy
as well as coastal ecosystem protection in Terengganu, Malaysia. This study aimed at …

Landslide susceptibility assessment and map** using state-of-the art machine learning techniques

HR Pourghasemi, N Sadhasivam, M Amiri, S Eskandari… - Natural Hazards, 2021 - Springer
Landslides pose a serious risk to human life and the natural environment. Here, we compare
machine learning algorithms including the generalized linear model (GLM), mixture …

Beyond a climate-centric view of plant distribution: edaphic variables add value to distribution models

F Beauregard, S de Blois - PloS one, 2014 - journals.plos.org
Both climatic and edaphic conditions determine plant distribution, however many species
distribution models do not include edaphic variables especially over large geographical …

Identification of the most suitable afforestation sites by Juniperus excels specie using machine learning models: Firuzkuh semi-arid region, Iran

S Yousefi, M Avand, P Yariyan, HJ Goujani… - Ecological …, 2021 - Elsevier
Choosing Selecting suitable sites for afforestation is a complex process that is influenced by
various factors that require the use of new models and methods in order to create better …

A strict maximum likelihood explanation of MaxEnt, and some implications for distribution modelling

R Halvorsen - Sommerfeltia, 2013 - sciendo.com
SOMMERFELTIA 36 (2013) Halvorsen: A strict maximum likelihood explanation of MaxEnt,...
2 illustrate important issues relating to MaxEnt methodology. Arguments for development of …