Journal Article

Performances of machine learning algorithms for mapping fractional cover of an invasive plant species in a dryland ecosystem

Shiferaw, Hailu, Bewket, Woldeamlak, Eckert, Sandra

2019

Overview

In recent years, an increasing number of distribution maps of invasive alien plant species (IAPS) have been published using different machine learning algorithms (MLAs). However, for designing spatially explicit management strategies, distribution maps should include information on the local cover/abundance of the IAPS. This study compares the performances of five MLAs: gradient boosting machine in two different implementations, random forest, support vector machine and deep learning neural network, one ensemble model and a generalized linear model; thereby identifying the best‐performing ones in mapping the fractional cover/abundance and distribution of IPAS, in this case called Prosopis juliflora (SW. DC.). Field level Prosopis cover and spatial datasets of seventeen biophysical and anthropogenic variables were collected, processed, and used to train and validate the algorithms so as to generate fractional cover maps of Prosopis in the dryland ecosystem of the Afar Region, Ethiopia. Out of the seven tested algorithms, random forest performed the best with an accuracy of 92% and sensitivity and specificity >0.89. The next best‐performing algorithms were the ensemble model and gradient boosting machine with an accuracy of 89% and 88%, respectively. The other tested algorithms achieved comparably low performances. The strong explanatory variables for Prosopis distributions in all models were NDVI, elevation, distance to villages and distance to rivers; rainfall, temperature, near‐infrared and red reflectance, whereas topographic variables, except for elevation, did not contribute much to the current distribution of Prosopis. According to the random forest model, a total of 1.173 million ha (12.33% of the study region) was found to be invaded by Prosopis to varying degrees of cover. Our findings demonstrate that MLAs can be successfully used to develop fractional cover maps of plant species, particularly IAPS so as to design targeted and spatially explicit management strategies. After comparing machine learning algorithms (MLAs) for species distribution mapping, we found that the random forest algorithm is the best for species distribution fractional cover mapping. Our findings demonstrate that MLAs can be successfully used to develop fractional cover maps of plant species in view of a targeted, spatially explicit management.

• Publisher: John Wiley & Sons, Inc,John Wiley and Sons Inc
• Subject: Abundance / Afar Region / Algorithms / Anthropogenic factors / Arid zones / Artificial intelligence / Current distribution / Deep learning / dryland ecosystems / Ecosystems / Elevation / Ethiopia / Flowers & plants / fractional cover mapping / Generalized linear models / invasive alien plant species / Invasive plants / Learning algorithms / Machine learning / machine learning algorithms / Mapping / Neural networks / Original Research / Plant species / Prosopis / Prosopis juliflora / Rainfall / Reflectance / Rivers / Species / Statistical models / Support vector machines