Assessment of Machine Learning Algorithms for Modeling the Spatial Distribution of Bark Beetle Infestation

Abstract

Machine learning algorithms (MLAs) are used to solve complex non-linear and high-dimensional problems. The objective of this study was to identify the MLA that generates an accurate spatial distribution model of bark beetle (Ips typographus L.) infestation spots. We first evaluated the performance of 2 linear (logistic regression, linear discriminant analysis), 4 non-linear (quadratic discriminant analysis, k-nearest neighbors classifier, Gaussian naive Bayes, support vector classification), and 4 decision trees-based MLAs (decision tree classifier, random forest classifier, extra trees classifier, gradient boosting classifier) for the study area (the Horní Planá region, Czech Republic) for the period 2003–2012. Each MLA was trained and tested on all subsets of the 8 explanatory variables (distance to forest damage spots from previous year, distance to spruce forest edge, potential global solar radiation, normalized difference vegetation index, spruce forest age, percentage of spruce, volume of spruce wood per hectare, stocking). The mean phi coefficient of the model generated by extra trees classifier (ETC) MLA with five explanatory variables for the period was significantly greater than that of most forest damage models generated by the other MLAs. The mean true positive rate of the best ETC-based model was 80.4%, and the mean true negative rate was 80.0%. The spatio-temporal simulations of bark beetle-infested forests based on MLAs and GIS tools will facilitate the development and testing of novel forest management strategies for preventing forest damage in general and bark beetle outbreaks in particular.

Integrating GRASS GIS and Jupyter Notebooks to facilitate advanced geospatial modeling education

“…In the future, we also hope to expand the curriculum of GIS714 at North Carolina State University to include additional geocomputation domains such as machine‐learning algorithms (MLAs), clustering and classification and the processing and analysis of remote sensing data. For example, the r.learn and v.class tool families both include several machine learning classification algorithms and they have been applied in several studies (Flasse et al, 2021; Koreň et al, 2021; Silver et al, 2019). GRASS GIS can also be integrated with third‐party MLAs like MaxEnt (Andreo et al, 2021) which could be demonstrated using a notebook tutorial.…”

“…GRASS GIS is an open‐source Geographic Information System (GIS) software that provides powerful raster, vector, and geospatial processing tools, accessible through a GUI, Python API, command‐line API, and integration with QGIS and R (Landa et al, 2022; Neteler & Mitasova, 2013). Tools, which are referred to as modules in the GRASS GIS documentation, include advanced terrain and hydrological modeling (Harmon et al, 2019; Jasiewicz & Stepinski, 2013; Mitasova et al, 2004), satellite and aerial imagery processing (Rocchini, Petras, Petrasova, Chemin, et al, 2017; Rocchini, Petras, Petrasova, Horning, et al, 2017), point cloud processing (Petras et al, 2017), visualization of raster and vector data and, clustering and classification algorithms including machine‐learning methods (Flasse et al, 2021; Koreň et al, 2021; Silver et al, 2019). For multi‐temporal raster and vector data, GRASS GIS provides a temporal framework with associated processing and visualization tools (Gebbert & Pebesma, 2017).…”

Hierarchical Integration of UAS and Sentinel-2 Imagery for Spruce Bark Beetle Grey-Attack Detection by Vegetation Index Thresholding Approach

“…ML uses algorithms that analyse data, learn from it, and then employ the knowledge gained to propose intelligent solutions. The need for analysing massive amounts of RS data then led to the development of deep learning (DL), based on a hierarchy of concepts [25]. In contrast to ML, DL algorithms, like convolutional neural networks (CNNs), use the raw input RS data as a training set to perform advanced (deep) self-learning, and require high-performance computer power [26,27].…”

Machine Learning Methods to Estimate Productivity of Harvesters: Mechanized Timber Harvesting in Brazil

“…Associating the knowledge generated from the application of data wrangling in a dataset with machine learning algorithms can promote intelligent decision making. The union of techniques of machine learning can improve the system performance of cut-tolength timber, forest resources, and services management [32][33][34][35].…”