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Machine learning in landscape ecological analysis: a review of recent approaches

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Landscape Ecology Aims and scope Submit manuscript

Abstract

Context

Artificial Intelligence (AI) has rapidly developed over the past several decades. Several related AI approaches, such as Machine Learning (ML), have been applied to research on landscape patterns and ecological processes.

Objectives

Our goal was to review the methods of AI, particularly ML, used in studies related to landscape ecology and the main topics addressed. We aimed to assess the trend in the number of ML papers and the methods used therein, and provide a synopsis and prospectus of current use and future applications of ML in landscape ecology.

Methods

We conducted a systematic literature search and selected 125 papers for review. These were examined and scored according to multiple criteria regarding methods and topic. We applied quantitative statistical methods, including cluster analysis based on titles, abstracts, and keywords and a non-metric multidimensional scaling based on attributes assigned during the review. We used Random Forests machine learning to describe the differences between identified clusters in terms of the topics and methods they included.

Results

The most frequent method found was Random Forests, but it is noteworthy to mention the increasing popularity of tools related to Deep Learning. The topics cover both ecologically oriented issues and the landscape-human interface. There has been a rapid increase in ML and AI methods in landscape ecology research, with Deep Learning and complex multi-step pipeline AI methods emerging in the last several years.

Conclusions

The rapid increase in the number of ML papers in landscape ecology research, and the range of methods employed in them, suggest explosive growth in application of these methods in landscape ecology. The increase of Deep Learning approaches in the most recent years suggest a major change in analytical paradigms and methodologies that we feel may transform the field and enable analyses of more complex pattern process relationships across vaster data sets than has been possible previously.

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adapted from Willcock et al. 2018, Fayyad et al. 1996. The machine learning approaches are adapted from Géron 2019, Hapke and Nelson 2020, and Zhou 2018

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Data availability

Data included as supplementary material.

Code availability

Not applicable.

Abbreviations

AI:

Artificial intelligence

BRT:

Boosted regression trees

CNN:

Convolutional neural networks

DT:

Decision trees

ES:

Expert systems

GAM:

Generalized additive models

LoR:

Logistic regression

ML:

Machine learning

MaxEnt:

Maximum entropy

MIR:

Model improvement ratio

NMDS:

Non-metric multidimensional scaling

NN:

Neural networks

RF:

Random forests

RNN:

Recurrent neural networks

SML:

Supervised learning

SVM:

Support vector machines

UsML:

Unsupervised learning

WoS:

Web of Science

XGBoo:

XGBoost—gradient boosting machine

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Authors and Affiliations

  1. Faculty of Mathematics and Computer Science, University of Bucharest, Academiei, 14, 010014, Bucharest, Romania

    Mihai-Sorin Stupariu

  2. Institute of Research of University of Bucharest, ICUB, Transdisciplinary Research Centre Landscape-Territory-Information Systems, CeLTIS, Splaiul Independentei nr. 91-95, 050095, Bucharest, Romania

    Mihai-Sorin Stupariu, Alin-Ionuţ Pleşoianu & Ileana Pătru-Stupariu

  3. USDA, Rocky Mountain Research Station, 2500 S. Pine Knoll Dr, Flagstaff, AZ, 86001, USA

    Samuel A. Cushman

  4. Department of Regional Geography and Environment, Faculty of Geography, University of Bucharest, Bd. N. Bălcescu, 1, 010041, Bucharest, Romania

    Ileana Pătru-Stupariu

  5. Institute for Geoscience and Geography, Chair for Sustainable Landscape Development, Martin Luther University Halle, Halle, Germany

    Christine Fürst

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  1. Mihai-Sorin Stupariu
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Idea for the article: M-SS, CF. Conceptualization: M-SS, SAC. Literature search: M-SS, IP-S. Data analysis: SAC, M-SS, A-IP, IP-S. The first draft of the manuscript was written by M-SS and critically revised by SAC. All authors read and approved the final manuscript.

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Stupariu, MS., Cushman, S.A., Pleşoianu, AI. et al. Machine learning in landscape ecological analysis: a review of recent approaches. Landsc Ecol 37, 1227–1250 (2022). https://doi.org/10.1007/s10980-021-01366-9

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