Special Issue "Random Forests for Forest Ecology"

A special issue of Forests (ISSN 1999-4907). This special issue belongs to the section "Forest Inventory, Modeling and Remote Sensing".

Deadline for manuscript submissions: closed (15 May 2021) | Viewed by 2810

Special Issue Editor

Prof. Richard Cutler
E-Mail Website
Guest Editor
Department of Mathematics and Statistics, Utah State University, Logan, UT, USA
Interests: applications of machine learning; environmental and ecological statistics; linear and additive modeling

Special Issue Information

Dear Colleagues,

Random Forests was first proposed by Breiman (2001) and was subsequently developed by Breiman and Adele Cutler. Their code formed the basis of the Salford Systems (now Minitab) and R (Liaw and Wiener 2002) implementations of Random Forests. Due to its highly predictive accuracy for both classification and regression, without any tuning, its novel model selection algorithm, and its ability to perform both imputation and unsupervised learning, Random Forests has rapidly become one of the most popular and widely used machine/statistical learning methodologies. Random Forests is now implemented in most statistical packages and programming languages, including SAS and Python. Development and analysis of the algorithms that make up Random Forests continue with notable contributions, including Survival Random Forests (Ishwaran et al. 2008) and conditional variable importance for Random Forests (Strobl et al. 2008).

Random Forests has been applied to data in almost every area of research in which regression, survival analysis, and classification are used. One of the earliest published papers (Prasad et al. 2006) was an application of Random Forests to forest data. For this Special Issue, we are seeking papers in all areas of application of Random Forests to forest and remote sensing data, including comparison with other methods. We are particularly interested in novel uses of prediction, model selection, and visualization of relationships between response and predictor variables.

Prof. Richard Cutler
Guest Editor

References

  1. Breiman, L. (2001). Random Forests. Machine Learning 45 5—32.
  2. Iswaran, H., U. B. Kogalur, E. H. Blackstone, and M. S. Lauer (2008). Survival Random Forests. Annals of Applied Statistics 4(3):841—860.
  3. Liaw, A. and M. Wiener (2002). Classification and Regression by randomForest. R News 2(3):18—22.
  4. Prasad, A. M., L. R. Iverson, and A. Liaw. (2006). Newer Classification and Regression Techniques: Bagging and Random Forests for Ecological Prediction. Ecosystems 9:181—199.
  5. Strobl, C., A-L. Boulesteix, T. Kneib, T. Augustin, and A. Zeileis. (2008). Conditional Variable Importance for Random Forests. BMC Informatics 9(307).

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forests is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Random Forests
  • Machine/statistical learning
  • Prediction
  • Model selection
  • Partial dependence

Published Papers (2 papers)

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Research

Article
Accumulated Heating and Chilling Are Important Drivers of Forest Phenology and Productivity in the Algonquin-to-Adirondacks Conservation Corridor of Eastern North America
Forests 2021, 12(3), 282; https://doi.org/10.3390/f12030282 - 02 Mar 2021
Viewed by 1089
Abstract
Research Highlights: Forest phenology and productivity were responsive to seasonal heating and chilling accumulation, but responses differed across the temperature range. Background and Objectives: Temperate forests have responded to recent climate change worldwide, but the pattern and magnitude of response have [...] Read more.
Research Highlights: Forest phenology and productivity were responsive to seasonal heating and chilling accumulation, but responses differed across the temperature range. Background and Objectives: Temperate forests have responded to recent climate change worldwide, but the pattern and magnitude of response have varied, necessitating additional studies at higher spatial and temporal resolutions. We investigated climatic drivers of inter-annual variation in forest phenology and productivity across the Algonquin-to-Adirondacks (A2A) conservation corridor of eastern North America. Methods: We used remotely sensed indices from the AVHRR sensor series and a suite of gridded climate data from the Daymet database spanning from 1989–2014. We used random forest regression to characterize forest–climate relationships between forest growth indices and climatological variables. Results: A large portion of the annual variation in phenology and productivity was explained by climate (pR2 > 80%), with variation largely driven by accumulated heating and chilling degree days. Only very minor relationships with precipitation-related variables were evident. Conclusions: Our results indicate that anthropogenic climate change in the A2A has not yet reached the point of triggering widespread changes in forest phenology and productivity, but the sensitivity of forest growth to inter-annual variation in seasonal temperature accumulation suggests that more temperate forest area will be affected by climate change as warming continues. Full article
(This article belongs to the Special Issue Random Forests for Forest Ecology)
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Article
Assessment of Sentinel-2 Satellite Images and Random Forest Classifier for Rainforest Mapping in Gabon
Forests 2020, 11(9), 941; https://doi.org/10.3390/f11090941 - 28 Aug 2020
Cited by 11 | Viewed by 1327
Abstract
This study is focused on the assessment of the potential of Sentinel-2 satellite images and the Random Forest classifier for mapping forest cover and forest types in northwest Gabon. The main goal was to investigate the impact of various spectral bands collected by [...] Read more.
This study is focused on the assessment of the potential of Sentinel-2 satellite images and the Random Forest classifier for mapping forest cover and forest types in northwest Gabon. The main goal was to investigate the impact of various spectral bands collected by the Sentinel-2 satellite, normalized difference vegetation index (NDVI) and digital elevation model (DEM), and their combination on the accuracy of the classification of forest cover and forest type. Within the study area, five classes of forest type were delineated: semi-evergreen moist forest, lowland forest, freshwater swamp forest, mangroves, and disturbed natural forest. The classification was performed using the Random Forest (RF) classifier. The overall accuracy for the forest cover ranged between 92.6% and 98.5%, whereas for forest type, the accuracy was 83.4 to 97.4%. The highest accuracy for forest cover and forest type classifications were obtained using a combination of spectral bands at spatial resolutions of 10 m and 20 m and DEM. In both cases, the use of the NDVI did not increase the classification accuracy. The DEM was shown to be the most important variable in distinguishing the forest type. Among the Sentinel-2 spectral bands, the red-edge followed by the SWIR contributed the most to the accuracy of the forest type classification. Additionally, the Random Forest model for forest cover classification was successfully transferred from one master image to other images. In contrast, the transferability of the forest type model was more complex, because of the heterogeneity of the forest type and environmental conditions across the study area. Full article
(This article belongs to the Special Issue Random Forests for Forest Ecology)
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