A review of major factors influencing the accuracy of mapping green-attack stage of bark beetle infestations using satellite imagery: Prospects to avoid data redundancy

Cong

et al. 2022

Sensors

Plant pests are the primary biological threats to agricultural and forestry production as well as forest ecosystem. Monitoring forest-pest damage via satellite images is crucial for the development of prevention and control strategies. Previous studies utilizing deep learning to monitor pest-infested damage in satellite imagery adopted RGB images, while multispectral imagery and vegetation indices were not used. Multispectral images and vegetation indices contain a wealth of useful information for detecting plant health, which can improve the precision of pest damage detection. The aim of the study is to further improve forest-pest infestation area segmentation by combining multispectral, vegetation indices and RGB information into deep learning. We also propose a new image segmentation method based on UNet++ with attention mechanism module for detecting forest damage induced by bark beetle and aspen leaf miner in Sentinel-2 images. The ResNeSt101 is used as the feature extraction backbone, and the attention mechanism scSE module is introduced in the decoding phase for improving the image segmentation results. We used Sentinel-2 imagery to produce a dataset based on forest health damage data gathered by the Ministry of Forests, Lands, Natural Resource Operations and Rural Development (FLNRORD) in British Columbia (BC), Canada, during aerial overview surveys (AOS) in 2020. The dataset contains the 11 original Sentinel-2 bands and 13 vegetation indices. The experimental results confirmed that the significance of vegetation indices and multispectral data in enhancing the segmentation effect. The results demonstrated that the proposed method exhibits better segmentation quality and more accurate quantitative indices with overall accuracy of 85.11%, in comparison with the state-of-the-art pest area segmentation methods.

Section: Study Area and Datamentioning

confidence: 99%

Detecting Pest-Infested Forest Damage through Multispectral Satellite Imagery and Improved UNet++

Cong

et al. 2022

Sensors

“…This can be explained by the fact that the Red Edge and NIR parts of the electromagnetic spectrum are sensitive to changes in leaf pigment, as well as canopy structure [15]. Red Edge values, found to be useful to estimate chlorophyll concentrations as well as minimizing the effects of background soil reflectance [23]. Furthermore, Red Edge spectral reflectance indices should be used to increase the accuracy of mapping green-attacked trees since they are less correlated with changes in forest structure, needle age, leaf intercellular structure of air-to-cell wall interfaces, forest floor moisture content, and tree physiology over the growing seasons in healthy forests [23].…”

Section: Discussionmentioning

confidence: 99%

“…The Normalised Difference Red Edge Index (NDREI, Eq. 8) can be applied to estimate chlorophyll concentrations minimizing the effects of background soil reflectance with values below 0.5 indicating a condition of stress [23]:…”

Section: Single Bands and Vegetation Indicesmentioning

confidence: 99%

“…In this study [15], NIR bands seemed less appropriate for early bark beetle detection, despite the recent evidence from Abdullah et al [20] pointing to significant differences in NIR leaf-level spectra between healthy and infested trees. Spectral indices using Red-Edge and Short-Wave Infrared might be potentially useful to detect infestation even earlier than indices based solely on VIS/NIR region [15,23]. Approaches based on multi-temporal spectral analysis have proven to be the most effective to detect bark beetle infestations at an early stage with Sentinel-2 data [14][15][16][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

“…Spectral indices using Red-Edge and Short-Wave Infrared might be potentially useful to detect infestation even earlier than indices based solely on VIS/NIR region [15,23]. Approaches based on multi-temporal spectral analysis have proven to be the most effective to detect bark beetle infestations at an early stage with Sentinel-2 data [14][15][16][22][23][24]. Hereby, we propose a new multi-temporal framework for regional scale wind and bark beetle damage mapping from Sentinel-2 imagery and field surveys.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Sentinel-2 based multi-temporal monitoring framework for wind and bark beetle detection and damage mapping

Candotti¹,

Giglio²,

Dubbini³

et al. 2022

Preprint

The occurrence of extreme windstorms and increasing heat and drought events induced by climate change leads to coniferous forests showing severe damage and stress and making trees more vulnerable to spruce bark beetle infestations. The combination of abiotic and biotic disturbances in forests can cause drastic environmental and economic losses. The first step for containing such damage is the establishment of a monitoring framework for early detection of vulnerable plots and distinguishing the cause of forest damage at the scale from management unit to region. For developing and evaluating the functionality of such a monitoring framework, we first selected an area of interest affected by wind throw damages and bark beetles at the border between Italy and Austria in the Friulian Dolomites, Carnic and Julian Alps and the Carinthian Gailtal. Secondly, we implemented a framework for time-series analysis with open access Sentinel-2 data over four years (2017-2020) by quantifying single band sensitivity to disturbances. Additionally, we enhanced the framework by deploying vegetation indices, for monitoring spectral changes and performing supervised image classifications for change detection. A mean overall accuracy of 89% was achieved, thus Sentinel-2 imagery proved to be suitable for distinguishing stressed stands, bark beetle attacked canopies and wind fell patches. The advantages of our methodology are its global, large-scale and “FAIR” principles compliant applicability to monitor forest health, forest cover change and its usability to support the development of forest management strategies for dealing with massive bark beetle outbreaks.

Rev Environ Sci Biotechnol

Understanding bark beetle outbreaks: exploring the impact of changing temperature regimes, droughts, forest structure, and prospects for future forest pest management

Singh,

Naseer,

Mogilicherla

et al. 2024

Self Cite

Climate change has increased the susceptibility of forest ecosystems, resulting in escalated forest decline globally. As one of the largest forest biomasses in the Northern Hemisphere, the Eurasian boreal forests are subjected to frequent drought, windthrow, and high-temperature disturbances. Over the last century, bark beetle outbreaks have emerged as a major biotic threat to these forests, resulting in extensive tree mortality. Despite implementing various management strategies to mitigate the bark beetle populations and reduce tree mortality, none have been effective. Moreover, altered disturbance regimes due to changing climate have facilitated the success of bark beetle attacks with shorter and multivoltine life cycles, consequently inciting more frequent bark beetle-caused tree mortality. This review explores bark beetle population dynamics in the context of climate change, forest stand dynamics, and various forest management strategies. Additionally, it examines recent advancements like remote sensing and canine detection of infested trees and focuses on cutting-edge molecular approaches including RNAi-nanoparticle complexes, RNAi-symbiotic microbes, sterile insect technique, and CRISPR/Cas9-based methods. These diverse novel strategies have the potential to effectively address the challenges associated with managing bark beetles and improving forest health in response to the changing climate.