Characterizing spatial patterns of pine bark beetle outbreaks during the dry and rainy season’s in Honduras with the aid of geographic information systems and remote sensing data

Valdez, Miguel; Chen, Chi Farn; Lin, Yi; Kuo, Yao Cheng; Chen, Yan You; Medina, Daryl; Diaz, Karla

doi:10.1016/j.foreco.2020.118162

Cited by 7 publications

(2 citation statements)

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“…According to the researches on the pest influence on large areas [14,21,22,27], the topographic variables, vegetation condition variables, the distribution of forest type, and human imprint variables were derived as driving factors to model the relationship with pest occurrence. In this research, we collected the topographic data, the forest type information, human imprint data, and vegetation indices which reflect the vegetation conditions to derive predictors.…”

Section: Methodsmentioning

confidence: 99%

“…The methods used in each step can be replaced by different methods if more suitable ones are available, and we provide a recommendation based on our evaluation result and selection criteria according to our experience to look for an appropriate method. Although there are a number of works that have already used machine learning techniques and remote sensing data to model the spatial distribution of an outbreak [14,[21][22][23], the existing processes may not be generalized as they provide no recommendations on how to select the methods. This paper aims to address the following questions: (1) how can machine learning algorithms perform in identifying the occurrence and mapping the distribution of PWN disease and which is the best model for predicting the probability of presence and the risk levels of PWN?…”

Section: Introductionmentioning

confidence: 99%

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Characterizing Spatial Patterns of Pine Wood Nematode Outbreaks in Subtropical Zone in China

et al. 2021

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Pine wood nematode (PWN), Bursaphelenchus xyophilus, originating from North America, has caused great ecological and economic hazards to pine trees worldwide, especially affecting the coniferous forests and mixed forests of masson pine in subtropical regions of China. In order to prevent PWN disease expansion, the risk level and susceptivity of PWN outbreaks need to be predicted in advance. For this purpose, we established a prediction model to estimate the susceptibility and risk level of PWN with vegetation condition variables, anthropogenic activity variables, and topographic feature variables across a large-scale district. The study was conducted in Dangyang City, Hubei Province in China, which was located in a subtropical zone. Based on the location of PWN points derived from airborne imagery and ground survey in 2018, the predictor variables were conducted with remote sensing and geographical information system (GIS) data, which contained vegetation indices including normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), normalized burn ratio (NBR), and normalized red edge index (NDRE) from Sentinel-2 imagery in the previous year (2107), the distance to different level roads which indicated anthropogenic activity, topographic variables in including elevation, slope, and aspect. We compared the fitting effects of different machine learning algorithms such as random forest (RF), K-neighborhood (KNN), support vector machines (SVM), and artificial neural networks (ANN) and predicted the probability of the presence of PWN disease in the region. In addition, we classified PWN points to different risk levels based on the density distribution of PWN sites and built a PWN risk level model to predict the risk levels of PWN outbreaks in the region. The results showed that: (1) the best model for the predictive probability of PWN presence is the RF classification algorithm. For the presence prediction of the dead trees caused by PWN, the detection rate (DR) was 96.42%, the false alarm rate (FAR) was 27.65%, the false detection rate (FDR) was 4.16%, and the area under the receiver operating characteristic curve (AUC) was equal to 0.96; (2) anthropogenic activity variables had the greatest effect on PWN occurrence, while the effects of slope and aspect were relatively weak, and the maximum, minimum, and median values of remote sensing indices were more correlated with PWN occurrence; (3) modeling analysis of different risk levels of PWN outbreak indicated that high-risk level areas were the easiest to monitor and identify, while lower incidence areas were identified with relatively low accuracy. The overall accuracy of the risk level of the PWN outbreak was identified with an AUC value of 0.94. From the research findings, remote sensing data combined with GIS data can accurately predict the probability distribution of the occurrence of PWN disease. The accuracy of identification of high-risk areas is higher than other risk levels, and the results of the study may improve control of PWN disease spread.

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Section: Methodsmentioning

confidence: 99%