Climate-Triggered Insect Defoliators and Forest Fires Using Multitemporal Landsat and TerraClimate Data in NE Iran: An Application of GEOBIA TreeNet and Panel Data Analysis.

Omid Abdi

doi:10.3390/s19183965

Abstract

Despite increasing the number of studies for mapping remote sensing insect-induced forest infestations, applying novel approaches for mapping and identifying its triggers are still developing. This study was accomplished to test the performance of Geographic Object-Based Image Analysis (GEOBIA) TreeNet for discerning insect-infested forests induced by defoliators from healthy forests using Landsat 8 OLI and ancillary data in the broadleaved mixed Hyrcanian forests. Moreover, it has studied mutual associations between the intensity of forest defoliation and the severity of forest fires under TerraClimate-derived climate hazards by analyzing panel data models within the TreeNet-derived insect-infested forest objects. The TreeNet optimal performance was obtained after building 333 trees with a sensitivity of 93.7% for detecting insect-infested objects with the contribution of the top 22 influential variables from 95 input object features. Accordingly, top image-derived features were the mean of the second principal component (PC2), the mean of the red channel derived from the gray-level co-occurrence matrix (GLCM), and the mean values of the normalized difference water index (NDWI) and the global environment monitoring index (GEMI). However, tree species type has been considered as the second rank for discriminating forest-infested objects from non-forest-infested objects. The panel data models using random effects indicated that the intensity of maximum temperatures of the current and previous years, the drought and soil-moisture deficiency of the current year, and the severity of forest fires of the previous year could significantly trigger the insect outbreaks. However, maximum temperatures were the only significant triggers of forest fires. This research proposes testing the combination of object features of Landsat 8 OLI with other data for monitoring near-real-time defoliation and pathogens in forests.

Highlights

The western parts were infested by the defoliators of Erannis defoliaria and Operophtera brumata, while the eastern parts were affected by Lymantria dispar (Figure 1)
The highest value recorded for the mean of PC2, and after that the mean of red channel derived from gray-level co-occurrence matrix (GLCM) and the mean values of vegetation indices, including the normalized difference water index (NDWI) and global environment monitoring index (GEMI), respectively
Analyzing one partial dependency revealed that the probability of the presence of defoliation increases along with the increase of average values of the PC2 and the mean of the red channel derived from GLCM, while it decreases by the increase of values of the NDWI and GEMI (Figure 3)

Summary

Introduction

Despite prosperous traditional approaches such as dendrological assessment and field observations for identifying driving forces of insect outbreaks from individual tree to stand scales [1], remotely sensed approaches are extensively progressing either for delineation insect-infested objects or for the mensuration of infestations induced by abiotic and biotic agents throughout forest biomes [2,3,4,5].some novel algorithms for data mining and machine learning such as TreeNet [6] for delineation insect-infested objects from non-insect-infested objects of images, some high-resolution climate data such as TerraClimate [7] for assessing drought and climate hazards dimensions, and some associations such as interactions between insect outbreaks, forest fires, and climate hazards have received less attention in earlier studies.Sensors 2019, 19, 3965; doi:10.3390/s19183965 www.mdpi.com/journal/sensors monitoring the bark beetle infestation and coniferous defoliation are dependent on high-resolution and multi-spectral images [8,9], detecting broadleaved defoliation has been predestined by the spectral–temporal information of images, even by single near-infrared-derived vegetation indices of images with high-temporal resolutions [10]. Some novel algorithms for data mining and machine learning such as TreeNet [6] for delineation insect-infested objects from non-insect-infested objects of images, some high-resolution climate data such as TerraClimate [7] for assessing drought and climate hazards dimensions, and some associations such as interactions between insect outbreaks, forest fires, and climate hazards have received less attention in earlier studies. Landsat images have indicated high accuracy for detecting forest-infested patches using either classification algorithms in a specific date [11] or by applying multitemporal spectral-derived indices [12,13,14]. Several number of studies exerted data mining and machine learning algorithms such as random forest [4,16,17] and decision tree [18] or maximum likelihood classification [19,20] to discern insect-defoliated from non-insect-defoliated forests. Rullán-Silva et al [14]

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Conclusion