Landsat Imagery Spectral Trajectories—Important Variables for Spatially Predicting the Risks of Bark Beetle Disturbance

Monitoring and Prediction of Siberian Silk Moth Dendrolimus sibiricus Tschetv. (Lepidoptera: Lasiocampidae) Outbreaks Using Remote Sensing Techniques

The feasibility of risk assessment of a Siberian silk moth (Dendrolimus sibiricus Tschetv.) outbreak was analyzed by means of landscape and weather characteristics and tree condition parameters. Difficulties in detecting forest pest outbreaks (especially in Siberian conditions) are associated with the inability to conduct regular ground surveillance in taiga territories, which generally occupy more than 2 million km2. Our analysis of characteristics of Siberian silk moth outbreak zones under mountainous taiga conditions showed that it is possible to distinguish an altitudinal belt between 400 and 800 m above sea level where an outbreak develops and trees are damaged. It was found that to assess the resistance of forest stands to pest attacks, researchers can employ new parameters: namely, characteristics of a response of remote sensing variables to changes in land surface temperature. Using these parameters, it is possible to identify in advance (2-3 years before an outbreak) forest stands that are not resistant to the pest. Thus, field studies in difficult-to-access taiga forests are not needed to determine these parameters, and hence the task of monitoring outbreaks of forest insects is simplified substantially.

Early Monitoring of Forest Wood-Boring Pests with Remote Sensing

Wood-boring pests (WBPs) pose an enormous threat to global forest ecosystems because their early stage infestations show no visible symptoms and can result in rapid and widespread infestations at later stages, leading to large-scale tree death. Therefore, early-stage WBP detection is crucial for prompt management response. Early detection of WBPs requires advanced and effective methods like remote sensing. This review summarizes the applications of various remote sensing sensors, platforms, and detection methods for monitoring WBP infestations. The current capabilities, gaps in capabilities, and future potential for the accurate and rapid detection of WBPs are highlighted.

Detection of Bark Beetle Disturbance at Tree Level Using UAS Multispectral Imagery and Deep Learning

This study aimed to examine the potential of convolutional neural networks (CNNs) for the detection of individual trees infested by bark beetles in a multispectral high-resolution dataset acquired by an unmanned aerial system (UAS). We compared the performance of three CNN architectures and the random forest (RF) model to classify the trees into four categories: pines, sbbd (longer infested trees when needles turn yellow), sbbg (trees under green attack) and non-infested trees (sh). The best performance was achieved by the Nez4c3b CNN (kappa 0.80) and Safaugu4c3b CNN (kappa 0.76) using only RGB bands. The main misclassifications were between sbbd and sbbg because of the similar spectral responses. Merging sbbd and sbbg into a more general class of infested trees made the selection of model type less important. All tested model types, including RF, were able to detect infested trees with an F-score of the class over 0.90. Nevertheless, the best overall metrics were achieved again by the Safaugu3c3b model (kappa 0.92) and Nez3cb model (kappa 0.87) using only RGB bands. The performance of both models is comparable, but the Nez model has a higher learning rate for this task. Based on our findings, we conclude that the Nez and Safaugu CNN models are superior to the RF models and transfer learning models for the identification of infested trees and for distinguishing between different infestation stages. Therefore, these models can be used not only for basic identification of infested trees but also for monitoring the development of bark beetle disturbance.

Mountain Landscape Dynamics after Large Wind and Bark Beetle Disasters and Subsequent Logging—Case Studies from the Carpathians

High winds and the subsequent infestation of subcortical insect are considered to be the most extensive types of large natural disturbances in the Central European forests. In this paper, we focus on the landscape dynamics of two representative mountain areas of Slovakia, which have been affected by aforementioned natural disturbances during last two decades. For example, on 19 November 2004, the bora caused significant damage to more than 126 km2 of spruce forests in the Tatra National Park (TANAP). Several wind-related events also affected sites in the National Park Low Tatras (NAPALT). Monitoring of related land cover changes during years 2000–2019 was based on CORINE Land Cover data and methodology set up on satellite and aerial images interpretation, on detailed land cover interpretation (1:10,000) for the local case studies, as well as on the results of field research and forestry databases. The dynamics of forest recovery are different in the clear-cuts (usually with subsequent tree planting) and in the naturally developing forest. The area in the vicinity of Tatranská Lonmnica encroaching on the Studená dolina National Nature Reserve in TANAP represents a trend of the gradual return of young forest. The area of Čertovica on the border between NAPALT and its buffer zone are characterized by an increase in clear-cut sites with potentially increasing soil erosion risk, due to repeated wind disasters and widening of bark beetle. Proposed detailed, large-scale approach is being barely used, when considering recent studies dealing with the natural disturbances.

Analysis of Forest Stand Resistance to Insect Attack According to Remote Sensing Data

Methods for analyzing the resistance of large woodlands (such as Siberian taiga forests) to insect attacks based on remote sensing data are proposed. As an indicator of woodland’s resistance, we suggest a function of normalized difference vegetative index (NDVI) susceptibility to changes in the land surface temperature (LST). Both NDVI and LST are obtained via the TERRA/AQUA satellite system. This indicator function was calculated as the spectral transfer function of the response in the integral equation connecting the changes in NDVI and LST. The analysis was carried out for two test sites, both of which are fir stands of the Krasnoyarsk region taiga zone. In the first case, the fir stands have suffered damage inflicted by Siberian silk moth caterpillars, Dendrolimus sibiricus Tschetv. since 2015. Adjacent intact fir forest areas were also analyzed. In the second case, the object of the study was a fir tree site damaged by Black Fir Sawyer Monochamus urussovii Fischer in 2013. It is demonstrated that the above-mentioned indicator function changed significantly 2–3 years prior to the pest population outbreaks, and therefore this indicator function can be used to assess the risk of pest population outbreak. Thereby, the proposed indicator compares favorably with vegetation cover estimates using NDVI, which register significant defoliation of tree stands and cannot be used for forecasting.

Evaluation of Early Bark Beetle Infestation Localization by Drone-Based Monoterpene Detection

The project PROTECTFOREST deals with improvements in early bark beetle (e.g., Ips typographus and Pityogenes chalcographus) detection to allow for fast and effective response to initial infestation. The removal of trees in the early infestation stage can prohibit bark beetle population gradation and successive timber price decrease. A semiconductor gas sensor array was tested in the lab and attached to a drone under artificial and real-life field conditions. The sensor array was able to differentiate between α-pinene amounts and between different temperatures under lab conditions. In the field, the sensor responded to a strong artificial α-pinene source. The real-life field trial above a spruce forest showed preliminary results, as technical and environmental conditions compromised a proof of principle. Further research will evaluate the detection rate of infested trees for the new proposed sensor concept.

Automatic Tree Crown Extraction from UAS Multispectral Imagery for the Detection of Bark Beetle Disturbance in Mixed Forests

Multispectral imaging using unmanned aerial systems (UAS) enables rapid and accurate detection of pest insect infestations, which are an increasing threat to midlatitude natural forests. Pest detection at the level of an individual tree is of particular importance in mixed forests, where it enables a sensible forest management approach. In this study, we propose a method for individual tree crown delineation (ITCD) followed by feature extraction to detect a bark beetle disturbance in a mixed urban forest using a photogrammetric point cloud (PPC) and a multispectral orthomosaic. An excess green index (ExG) threshold mask was applied before the ITCD to separate targeted coniferous trees from deciduous trees and backgrounds. The individual crowns of conifer trees were automatically delineated as (i) a full tree crown using marker-controlled watershed segmentation (MCWS), Dalponte2016 (DAL), and Li 2012 (LI) region growing algorithms or (ii) a buffer (BUFFER) around a treetop from the masked PPC. We statistically compared selected spectral and elevation features extracted from automatically delineated crowns (ADCs) of each method to reference tree crowns (RTC) to distinguish between the forest disturbance classes and two tree species. Moreover, the effect of PPC density on the ITCD accuracy and feature extraction was investigated. The ExG threshold mask application resulted in the excellent separability of targeted conifer trees and the increasing shape similarity of ADCs compared to RTC. The results revealed a strong effect of PPC density on treetop detection and ITCD. If the PPC density is sufficient (>10 points/m2), the ADCs produced by DAL, MCWS, and LI methods are comparable, and the extracted feature statistics of ADCs insignificantly differ from RTC. The BUFFER method is less suitable for detecting a bark beetle disturbance in the mixed forest because of the simplicity of crown delineation. It caused significant differences in extracted feature statistics compared to RTC. Therefore, the point density was found to be more significant than the algorithm used. We conclude that automatic ITCD methods may constitute a substitute for the time-consuming manual tree crown delineation in tree-based bark beetle disturbance detection and sanitation of individual infested trees using the suggested methodology and high-density (>20 points/m2, 10 points/m2 minimum) PPC.

Sentinel-2 Data in an Evaluation of the Impact of the Disturbances on Forest Vegetation

In this article, we investigated the detection of forest vegetation changes during the period of 2017 to 2019 in the Low Tatras National Park (Slovakia) and the Sumava National Park (Czechia) using Sentinel-2 data. The evaluation was based on a time-series analysis using selected vegetation indices. The case studies represented five different areas according to the type of the forest vegetation degradation (one with bark beetle calamity, two areas with forest recovery mode after a bark beetle calamity, and two areas without significant disturbances). The values of the trajectories of the vegetation indices (normalized difference vegetation index (NDVI) and normalized difference moisture index (NDMI)) and the orthogonal indices (tasseled cap greenness (TCG) and tasseled cap wetness (TCW)) were analyzed and validated by in situ data and aerial photographs. The results confirm the abilities of the NDVI, the NDMI and the TCW to distinguish disturbed and undisturbed areas. The NDMI vegetation index was particularly useful for the detection of the disturbed forest and forest recovery after bark beetle outbreaks and provided relevant information regarding the health of the forest (the individual stages of the disturbances and recovery mode). On the contrary, the TCG index demonstrated only limited abilities. The TCG could distinguish healthy forest and the gray-attack disturbance phase; however, it was difficult to use this index for detecting different recovery phases and to distinguish recovery phases from healthy forest. The areas affected by the disturbances had lower values of NDVI and NDMI indices (NDVI quartile range Q2–Q3: 0.63–0.71; NDMI Q2–Q3: 0.10–0.19) and the TCW index had negative values (Q2–Q3: −0.06–−0.05)). The analysis was performed with a cloud-based tool—Sentinel Hub. Cloud-based technologies have brought a new dimension in the processing and analysis of satellite data and allowed satellite data to be brought to end-users in the forestry sector. The Copernicus program and its data from Sentinel missions have evoked new opportunities in the application of satellite data. The usage of Sentinel-2 data in the research of long-term forest vegetation changes has a high relevance and perspective due to the free availability, distribution, and well-designed spectral, temporal, and spatial resolution of the Sentinel-2 data for monitoring forest ecosystems.

Evaluating the Potential of WorldView-3 Data to Classify Different Shoot Damage Ratios of Pinus yunnanensis

Tomicus yunnanensis Kirkendall and Faccoli and Tomicus minor Hartig have caused serious shoot damage in Yunnan pine (Pinus yunnanensis Faranch) forests in the Yunnan province of China. However, very few remote sensing studies have been conducted to detect the different shoot damage ratios of individual trees. The aim of the study was to evaluate the suitability of eight-band WorldView-3 satellite image for detecting different shoot damage ratios (e.g., “healthy”, “slightly”, “moderately”, and “severely”). An object-based supervised classification method was used in this study. The tree crowns were delineated on a 0.3 m pan-sharpened worldview-3 image as reference data. Besides the original eight bands, normalized two-band indices were derived as spectral variables. For classifying individual trees, three classifiers—multinomial logistic regression (MLR), a stepwise linear discriminant analysis (SDA), and random forest (RF)—were evaluated and compared in this study. Results showed that SDA classifier based on all spectral variables had the highest classification accuracy (78.33%, Kappa = 0.712). Compared to original eight bands of Worldview-3, normalized two-band indices could improve the overall accuracy. Furthermore, the shoot damage ratio was a good indicator for detecting different levels of individual damaged trees. We concluded that the Worldview-3 satellite data were suitable to classify different levels of damaged trees; therefore, the best mapping results of damaged trees was predicted based on the best classification model which is very useful for forest managers to take the appropriate measures to decrease shoot beetle damage in Yunnan pine forests.

A Comparison of WorldView-2 and Landsat 8 Images for the Classification of Forests Affected by Bark Beetle Outbreaks Using a Support Vector Machine and a Neural Network: A Case Study in the Sumava Mountains

The objective of this paper is to assess WorldView-2 (WV2) and Landsat OLI (L8) images in the detection of bark beetle outbreaks in the Sumava National Park. WV2 and L8 images were used for the classification of forests infected by bark beetle outbreaks using a Support Vector Machine (SVM) and a Neural Network (NN). After evaluating all the available results, the SVM can be considered the best method used in this study. This classifier achieved the highest overall accuracy and Kappa index for both classified images. In the cases of WV2 and L8, total overall accuracies of 86% and 71% and Kappa indices of 0.84 and 0.66 were achieved with SVM, respectively. The NN algorithm using WV2 also produced very promising results, with over 80% overall accuracy and a Kappa index of 0.79. The methods used in this study may be inspirational for testing other types of satellite data (e.g., Sentinel-2) or other classification algorithms such as the Random Forest Classifier.

The Use of UAV Mounted Sensors for Precise Detection of Bark Beetle Infestation

The bark beetle (Ips typographus) disturbance represents serious environmental and economic issue and presents a major challenge for forest management. A timely detection of bark beetle infestation is therefore necessary to reduce losses. Besides wood production, a bark beetle outbreak affects the forest ecosystem in many other ways including the water cycle, nutrient cycle, or carbon fixation. On that account, (not just) European temperate coniferous forests may become endangered ecosystems. Our study was performed in the unmanaged zone of the Krkonoše Mountains National Park in the northern part of the Czech Republic where the natural spreading of bark beetle is slow and, therefore, allow us to continuously monitor the infested trees that are, in contrast to managed forests, not being removed. The aim of this work is to evaluate possibilities of unmanned aerial vehicle (UAV)-mounted low-cost RGB and modified near-infrared sensors for detection of different stages of infested trees at the individual level, using a retrospective time series for recognition of still green but already infested trees (so-called green attack). A mosaic was created from the UAV imagery, radiometrically calibrated for surface reflectance, and five vegetation indices were calculated; the reference data about the stage of bark beetle infestation was obtained through a combination of field survey and visual interpretation of an orthomosaic. The differences of vegetation indices between infested and healthy trees over four time points were statistically evaluated and classified using the Maximum Likelihood classifier. Achieved results confirm our assumptions that it is possible to use a low-cost UAV-based sensor for detection of various stages of bark beetle infestation across seasons; with increasing time after infection, distinguishing infested trees from healthy ones grows easier. The best performance was achieved by the Greenness Index with overall accuracy of 78%–96% across the time periods. The performance of the indices based on near-infrared band was lower.

Evaluation of the Influence of Disturbances on Forest Vegetation Using the Time Series of Landsat Data: A Comparison Study of the Low Tatras and Sumava National Parks

This study focused on the evaluation of forest vegetation changes from 1992 to 2015 in the Low Tatras National Park (NAPANT) in Slovakia and the Sumava National Park in Czechia using a time series (TS) of Landsat images. The study area was damaged by wind and bark beetle calamities, which strongly influenced the health state of the forest vegetation at the end of the 20th and beginning of the 21st century. The analysis of the time series was based on the ten selected vegetation indices in different types of localities selected according to the type of forest disturbances. The Landsat data CDR (Climate Data Record/Level 2) was normalized using the PIF (Pseudo-Invariant Features) method and the results of the Time Series were validated by in-situ data. The results confirmed the high relevance of the vegetation indices based on the SWIR bands (e.g., NDMI) for the purpose of evaluating the individual stages of the disturbance (especially the bark beetle calamity). Usage of the normalized Landsat data Climate Data Record (CDR/Level 2) in the research of long-term forest vegetation changes has a high relevance and perspective due to the free availability of the corrected data.

Remote sensing of forest insect disturbances: Current state and future directions

Insect disturbance are important agents of change in forest ecosystems around the globe, yet their spatial and temporal distribution and dynamics are not well understood. Remote sensing has gained much attention in mapping and understanding insect outbreak dynamics. Consequently, we here review the current literature on the remote sensing of insect disturbances. We suggest to group studies into three insect types: bark beetles, broadleaved defoliators, and coniferous defoliators. By so doing, we systematically compare the sensors and methods used for mapping insect disturbances within and across insect types. Results suggest that there are substantial differences between methods used for mapping bark beetles and defoliators, and between methods used for mapping broadleaved and coniferous defoliators. Following from this, we highlight approaches that are particularly suited for each insect type. Finally, we conclude by highlighting future research directions for remote sensing of insect disturbances. In particular, we suggest to: 1) Separate insect disturbances from other agents; 2) Extend the spatial and temporal domain of analysis; 3) Make use of dense time series; 4) Operationalize near-real time monitoring of insect disturbances; 5) Identify insect disturbances in the context of coupled human-natural systems; and 6) Improve reference data for assessing insect disturbances. Since the remote sensing of insect disturbances has gained much interest beyond the remote sensing community recently, the future developments identified here will help integrating remote sensing products into operational forest management. Furthermore, an improved spatiotemporal quantification of insect disturbances will support an inclusion of these processes into regional to global ecosystem models.