Early Detection of Bark Beetle Attack Using Remote Sensing and Machine Learning: A Review

Bark beetle outbreaks can result in a devastating impact on forest ecosystem processes, biodiversity, forest structure and function, and economies. Accurate and timely detection of bark beetle infestations is crucial to mitigate further damage, develop proactive forest management activities, and minimize economic losses. Incorporating remote sensing (RS) data with machine learning (ML) (or deep learning (DL)) can provide a great alternative to the current approaches that rely on aerial surveys and field surveys, which are impractical over vast geographical regions. This paper provides a comprehensive review of past and current advances in the early detection of bark beetle-induced tree mortality from three key perspectives: bark beetle & host interactions, RS, and ML/DL. We parse recent literature according to bark beetle species & attack phases, host trees, study regions, imagery platforms & sensors, spectral/spatial/temporal resolutions, spectral signatures, spectral vegetation indices (SVIs), ML approaches, learning schemes, task categories, models, algorithms, classes/clusters, features, and DL networks & architectures. This review focuses on challenging early detection, discussing current challenges and potential solutions. Our literature survey suggests that the performance of current ML methods is limited (less than 80%) and depends on various factors, including imagery sensors & resolutions, acquisition dates, and employed features & algorithms/networks. A more promising result from DL networks and then the random forest (RF) algorithm highlighted the potential to detect subtle changes in visible, thermal, and short-wave infrared (SWIR) spectral regions.Comment: Under review, 33 pages, 5 figures, 8 Table

Early detection of forest stress from European spruce bark beetle attack, and a new vegetation index: Normalized distance red & SWIR (NDRS)

The European spruce bark beetle (Ips typographus [L.]) is one of the most damaging pest insects of European spruce forests. A crucial measure in pest control is the removal of infested trees before the beetles leave the bark, which generally happens before the end of June. However, stressed tree crowns do not show any significant color changes in the visible spectrum at this early-stage of infestation, making early detection difficult. In order to detect the related forest stress at an early stage, we investigated the differences in radar and spectral signals of healthy and stressed trees. How the characteristics of stressed trees changed over time was analyzed for the whole vegetation season, which covered the period before attacks (April), early-stage infestation ('green-attacks', May to July), and middle to late-stage infestation (August to October). The results show that spectral differences already existed at the beginning of the vegetation season, before the attacks. The spectral separability between the healthy and infested samples did not change significantly during the 'green-attack' stage. The results indicate that the trees were stressed before the attacks and had spectral signatures that differed from healthy ones. These stress-induced spectral changes could be more efficient indicators of early infestations than the 'green-attack' symptoms.In this study we used Sentinel-1 and 2 images of a test site in southern Sweden from April to October in 2018 and 2019. The red and SWIR bands from Sentinel-2 showed the highest separability of healthy and stressed samples. The backscatter from Sentinel-1 and additional bands from Sentinel-2 contributed only slightly in the Random Forest classification models. We therefore propose the Normalized Distance Red & SWIR (NDRS) index as a new index based on our observations and the linear relationship between the red and SWIR bands. This index identified stressed forest with accuracies from 0.80 to 0.88 before the attacks, from 0.80 to 0.82 in the early-stage infestation, and from 0.81 to 0.91 in middle- and late-stage infestations. These accuracies are higher than those attained by established vegetation indices aimed at 'green-attack' detection, such as the Normalized Difference Water Index, Ratio Drought Index, and Disease Stress Water Index. By using the proposed method, we highlight the potential of using NDRS with Sentinel-2 images to estimate forest vulnerability to European spruce bark beetle attacks early in the vegetation season

Assessing the detectability of European spruce bark beetle green attack in multispectral drone images with high spatial- and temporal resolutions

Detecting disease- or insect-infested forests as early as possible is a classic application of remote sensing. Under conditions of climate change and global warming, outbreaks of the European spruce bark beetle (Ips typographus, L.) are threatening spruce forests and the related timber industry across Europe, and early detection of infestations is important for damage control. Infested trees without visible discoloration (green attack) have been identified using multispectral images, but how early green attacks can be detected is still unknown. This study aimed to determine when infested trees start to show an abnormal spectral response compared with healthy trees, and to quantify the detectability of infested trees during the infestation process. Pheromone bags were used to attract bark beetles in a controlled experiment, and subsequent infestations were assessed in the field on a weekly basis. In total, 977 trees were monitored, including 208 attacked trees. Multispectral drone images were obtained before and during the insect attacks, representing different periods of infestation. Individual tree crowns (ITC) were delineated by marker-controlled watershed segmentation, and the average reflectance of ITCs was analyzed based on the duration of infestation. The detectability of green attacks and driving factors were examined. We propose new Multiple Ratio Disease-Water Stress Indices (MR-DSWIs) as vegetation indices (VI) for detecting infestations. We defined a VI range of 5-95% as a healthy tree, and a VI value outside that range as an infested tree. Detection rates using multispectral images were always higher than discoloration rates observed in the field, and the newly proposed MR-DSWIs detected more infested trees than the established VIs. Infestations were detectable at 5 and 10 weeks after an attack at a rate of 15% and 90%, respectively, from the multispectral drone images. Weeks 5-10 of infestation therefore represent a suitable period for using the proposed methodology to map infestation at an early stage

Mapping a European spruce bark beetle outbreak using sentinel-2 remote sensing data

Insect outbreaks affect forests, causing the deaths of trees and high economic loss. In this study, we explored the detection of European spruce bark beetle (Ips typographus, L.) outbreaks at the individual tree crown level using multispectral satellite images. Moreover, we explored the possibility of tracking the progression of the outbreak over time using multitemporal data. Sentinel-2 data acquired during the summer of 2020 over a bark beetle–infested area in the Italian Alps were used for the mapping and tracking over time, while airborne lidar data were used to automatically detect the individual tree crowns and to classify tree species. Mapping and tracking of the outbreak were carried out using a support vector machine classifier with input vegetation indices extracted from the multispectral data. The results showed that it was possible to detect two stages of the outbreak (i.e., early, and late) with an overall accuracy of 83.4%. Moreover, we showed how it is technically possible to track the evolution of the outbreak in an almost bi-weekly period at the level of the individual tree crowns. The outcomes of this paper are useful from both a management and ecological perspective: it allows forest managers to map a bark beetle outbreak at different stages with a high spatial accuracy, and the maps describing the evolution of the outbreak could be used in further studies related to the behavior of bark beetle

Potential of mapping forest damage from remotely sensed data

Remote sensing is an efficient tool for mapping, monitoring, and assessing forest damage and the risk of damage. This report presents ongoing research on those topics with preliminary results as well as research planned by the Department of Forest Resource Management, SLU in Umeå, in the near future. The damage types include spruce bark beetle attacks, storm damage, and forest fire. The report also outlines proposed continued research in the area and possible collaborations within and outside SLU

A First Assessment of Canopy Cover Loss in Germany’s Forests after the 2018–2020 Drought Years

Central Europe was hit by several unusually strong periods of drought and heat between 2018 and 2020. These droughts affected forest ecosystems. Cascading effects with bark beetle infestations in spruce stands were fatal to vast forest areas in Germany. We present the first assessment of canopy cover loss in Germany for the period of January 2018–April 2021. Our approach makes use of dense Sentinel-2 and Landsat-8 time-series data. We computed the disturbance index (DI) from the tasseled cap components brightness, greenness, and wetness. Using quantiles, we generated the monthly DI composites and calculated anomalies in a reference period (2017). After applying a threshold, we were able to determine the date of canopy cover loss for all pixels where anomalies were recorded until the end of the observation period. From the resulting map, we calculated the canopy cover loss statistics for administrative entities. Our results show a canopy cover loss of 501,000 ha for Germany, with large regional differences. The losses were largest in central Germany and reached up to two-thirds of coniferous forest loss in some districts. Our map has high spatial (10 m) and temporal (monthly) resolution and can be updated at any time

Wykorzystanie danych termalnych pozyskanych z pułapu lotniczego do określania stanu zdrowotnego wybranych gatunków drzew

Celem pracy było sprawdzenie, czy dane termalne z zakresu średniej podczerwieni (3,6–4,9 μm) pozyskane z pułapu lotniczego mogą być wykorzystane do badań kondycji zdrowotnej drzew. W tym celu przeprowadzono trzy analizy na niezależnych zbiorach danych w różnych środowiskach. Badania wykonane na danych termalnych pozyskanych w ciągu dnia wykazały, że temperatura korony jest cechą specyficzną dla gatunku i zależy od położenia drzewa w terenie. Drzewa znajdujące się wewnątrz lasu miały niższą temperaturę koron do 0,70oC niż te rosnące poza lasem. Gatunkiem o najwyższej temperaturze, niezależnie od godziny pozyskania danych lotniczych, był Pinus sylvestris. Niskimi temperaturami charakteryzowały się Alnus glutinosa, Quercus rubra i Quercus petraea. Badania nad identyfikacją miejsc żerowania kornika drukarza wykazały, że fuzja danych termalnych i skanowania laserowego umożliwiły wyznaczenie temperatury koron pojedynczych drzew Picea abies i sklasyfikowanie ich do trzech klas zdrowotnych (drzewa 'zdrowe' o średniej temperaturze 27,70oC; 'o osłabionej kondycji' 28,57oC i 'martwe' 30,17oC). Opracowany został schemat postępowania wykorzystujący automatyczną segmentację i uczenie maszynowe do identyfikacji drzew 'o osłabionej kondycji' i 'martwych'. Badania przeprowadzone w środowisku miejskim wykazały statystycznie istotne różnice między klasami kondycji zdrowotnej drzew zarówno na danych pozyskanych w dzień jak i w nocy. Korony drzew zdrowych były chłodniejsze w porównaniu do koron drzew zamierających. Średnia wartość różnicy wynosiła 3,28oC w ciągu dnia oraz 1,06oC w nocy. Podsumowując, lotnicze dane termalne z zakresu średniej podczerwieni mogą być wykorzystane do badań kondycji zdrowotnej wybranych gatunków drzew. Zmienność temperatur koron jest cechą zależną od gatunku i może być wskaźnikiem stanu zdrowotnego w środowisku naturalnym i miejskim."InterDOC-STARt – Interdyscyplinarne Studia Doktoranckie na Wydziale BiOŚ UŁ” – Program Operacyjny Wiedza Edukacja Rozwój 2014-2020, Oś priorytetowa III. Szkolnictwo wyższe dla gospodarki i rozwoju, Działanie 3.2 Studia doktoranckie. Nr projektu: POWR.03.02.00-IP.08-00-DOK/16. Realizowany w latach 2018-2022

Drone-based spectral and 3D remote sensing applications for forestry and agriculture

Practising sustainable agriculture and forestry requires information on the state of forests and crops to support management. In precision agriculture, crops are observed in order to treat them precisely in the right place and at the right time, saving both production costs and the environment. Similarly, in forests, information on the composition and state of forest health are crucial to enable their sustainable management. In particular, climate-change-driven insect pests have increased, but economic and ecological losses can be reduced by the right actions if up-to-date and precise information on the health of forests is available. In recent years, drones with cameras have evolved into a flexible way to collect remote sensing data locally. Spectral cameras provide accurate information about the reflection properties of objects, and photogrammetric methods also provide a cost-effective way to collect three-dimensional (3D) data from an object. The objective of this work was to develop and assess drone-based 3D and spectral remote sensing techniques to classify the health status of individual trees and to estimate crop biomass, various biochemical parameters such as nitrogen content, and grass-feeding quality. The work developed a processing chain in which spectral and 3D features were extracted from remote sensing data. Then, combining the features with observations and reference measurements collected from plants, machine learning models were developed for tree health classification and estimation of crop-related parameters. The effects of different factors related to data collection and processing on classification and estimation accuracies were studied in order to generate knowledge on optimal sensors and methods. In general, radiometric corrections, spectral resolution, and the combined use of spectral and 3D features improved classification and estimation accuracies. However, the optimal sensors as well as the data collection and processing methods depend on the different applications and their accuracy requirements. This work was the first to demonstrate the ability of drone hyperspectral data to map the health status of a forest by classifying individual trees infested by bark beetles. The results of the work also showed that drone-based mapping offers a great tool to estimate agricultural crop parameters which can be applied to the optimization of various precision agriculture tasks.Kestävän maa- ja metsätalouden harjoittaminen vaatii tietoa metsien ja viljelykasvien tilasta päätöksenteon tueksi. Täsmämaataloudessa viljelykasveja havainnoidaan, jotta viljelytoimenpiteet voidaan kohdistaa oikeaan paikkaan ja oikea-aikaisesti säästäen sekä tuotantokustannuksia että ympäristöä. Metsissä tieto metsien terveydentilasta on tärkeää, jotta voidaan hillitä metsätuhojen leviämistä. Erityisesti hyönteistuhot ovat lisääntyneet voimakkaasti ilmastonmuutoksen vauhdittamana, mutta taloudellisia ja ekologisia tappiota voidaan vähentää oikeilla toimenpiteillä, jos on olemassa ajantasaisesta tietoa metsien terveydentilasta. Dronet ja niihin asennettavat kamerat ovat kehittyneet viime vuosina joustavaksi tavaksi kerätä kaukokartoitusaineistoa paikallisesti. Spektrikameroilla saadaan tarkkaa tietoa kohteen heijastusominaisuuksista, ja fotogrammetriset menetelmät mahdollistavat myös kustannustehokkaan tavan kerätä kohteesta kolmiulotteista (3D) tietoa. Tämän työn tavoitteena oli kehittää näihin aineistoihin nojautuen kaukokartoitusmenetelmiä yksittäisten puiden terveydentilan luokitteluun sekä viljelykasvien biomassan, erilaisten biokemiallisten parametrien, kuten typpipitoisuuden sekä nurmen ruokintalaadun, kuten D-arvon estimointiin. Työssä kehitettiin prosessointiketju, jossa kaukokartoitusaineistoista irrotettiin spektri- ja 3D-piirteitä, yhdistettiin ne kasveista kerättyihin havaintoihin ja mittauksiin sekä muodostettiin koneoppimismalleja puiden luokittelua ja viljelykasveihin liittyvien parametrien estimointia varten. Työssä verrattiin useiden aineistonkeräykseen ja -prosessointiin liittyvien tekijöiden vaikutuksia luokittelu- ja estimointitulosten tarkkuuteen optimaalisten menetelmien löytämiseksi. Esimerkiksi spektri- ja 3D-piirteiden hyödyntäminen yhdessä sekä radiometriset korjaukset paransivat yleisesti luokittelu- ja estimointitarkkuuksia. Optimaaliset sensorit sekä aineistonkeräys- ja käsittelytavat riippuvat kuitenkin eri sovelluksista ja niiden tarkkuusvaatimuksista. Työssä osoitettiin ensimmäistä kertaa dronesta kerätyn hyperspektrisen aineiston kyvykkyys metsän terveydentilan havainnoinnissa luokittelemalla kuuset kolmeen luokkaan kirjanpainajan aiheuttaman tuhon perusteella. Työn tulokset myös osoittivat drone-pohjaisen kartoituksen kyvyn estimoida erilaisia viljelykasvien parametreja, joita voidaan edelleen soveltaa suunniteltaessa esimerkiksi lisälannoitusta tai säilörehun optimaalista korjuuaikaa

Sensitivity of Landsat-8 OLI and TIRS Data to Foliar Properties of Early Stage Bark Beetle (Ips typographus, L.) Infestation

In this study, the early stage of European spruce bark beetle (Ips typographus, L.) infestation (so-called green attack) is investigated using Landsat-8 optical and thermal data. We conducted an extensive field survey in June and the beginning of July 2016, to collect field data measurements from a number of infested and healthy trees in the Bavarian Forest National Park (BFNP), Germany. In total, 157 trees were selected, and leaf traits (i.e. stomatal conductance, chlorophyll fluorescence, and water content) were measured. Three Landsat-8 images from May, July, and August 2016 were studied, representing early stage, advanced stage, and post-infestation, respectively. Spectral vegetation indices (SVIs) sensitive to the measured traits were calculated from the optical domain (VIS, NIR and SWIR), and canopy surface temperature (CST) was calculated from the thermal infrared band using the Mono-window algorithm. The leaf traits were used to examine the impact of bark beetle infestation on the infested trees and to explore the link between these traits and remote sensing data (CST and SVIs). The differences between healthy and infested samples regarding measured leaf traits were assessed using the Student’s t-test. The relative importance of the CST and SVIs for estimating measured leaf traits was evaluated based on the variable importance of the projection (VIP) obtained from the partial least square regression (PLSR) analysis. A temporal comparison was then made for SVIs with a VIP > 1, including CST, using boxplot. Finally, the clustering method using a principal components analysis (PCA) was used to visually examine how well the two groups of sample plots (healthy and infested) are separated in 2-D space based on principal component scores. The results revealed that all measured leaf traits were significantly different (p 1, improving the results of clustering when used with other SVIs. The new insight offered by this study is that the stress induced by the early stage of bark beetle infestation is more pronounced by Landsat-8 thermal bands than the SVIs calculated from its optical bands. The potential of CST in detecting the green attack stage would have positive implications for the forest practice