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Early-stage detection of bark beetle infested spruce forest stands using Sentinel-2 data and vegetation indices

Piltz, Karl LU (2022) In Student thesis series INES NGEM01 20221
Dept of Physical Geography and Ecosystem Science
Abstract
The European spruce bark beetle is an insect that is often referred to as a pest. Responsible for the destruction of over 150 million m3 of Norwegian spruce forest in Europe over the last 50 years makes this insect one of the major disturbances to the forest industry. With global warming at large, changes of the distribution of the bark beetle have emerged with large outbreaks now regularly occurring in northern Europe due to warmer and prolonged summer seasons. Sweden has since 2018 been affected by mass outbreaks that have destroyed over 27 million m3 of spruce forest. In order to mitigate the disturbance of forest and limit the spread of further attacks, it is important to detect and cut down infested trees at an early stage. In recent... (More)
The European spruce bark beetle is an insect that is often referred to as a pest. Responsible for the destruction of over 150 million m3 of Norwegian spruce forest in Europe over the last 50 years makes this insect one of the major disturbances to the forest industry. With global warming at large, changes of the distribution of the bark beetle have emerged with large outbreaks now regularly occurring in northern Europe due to warmer and prolonged summer seasons. Sweden has since 2018 been affected by mass outbreaks that have destroyed over 27 million m3 of spruce forest. In order to mitigate the disturbance of forest and limit the spread of further attacks, it is important to detect and cut down infested trees at an early stage. In recent time, many studies have focused on early-stage bark beetle detection using remote sensing methods. Efforts to detect early-stage infestation, i.e., “green attacks” using vegetation indices (VI) on a pixel level, have found varying levels of success but have shown the potential of using VIs sensitivity of changes to biochemical leaf properties to detect early-stage infestation. Hence, the aim of this thesis was to study if the variability between pixels for a vegetation index on a forest stand scale change during a spruce bark beetle outbreak and to test if the variability can be used as an early indicator for bark beetle infestation. This was done by calculating the coefficient of variation between 2017 and the outbreak year 2018 from Sentinel-2 data for four different VIs (NDVI, NDWI, DRS, RDI) in 17 spruce forest stands where 10 stands were infested and 7 were healthy. The coefficient of variation was used to classify the stands into healthy and infested by computing the cumulative sum of each VI in each stand. The classification performance for each VI was evaluated using receiver operating characteristics graphs which were used to find the optimal threshold for each classification. The classification was done using the cumulative sum for two different timeframes, early stage (1st of May-1st of July 2018) and the whole bark beetle season (30th of April-30th of September) The results of the thesis, indicated that the variability within a forest stand does change during a bark beetle outbreak with increased variability over time within stands that have been attacked. It also showed that changes in variability have the potential to be used as an early indicator for bark beetle infestation, and the variability can be used to detect and classify individual forest stands that were infested at an early stage, i.e., green attack stage. It was found that NDWI was the most suitable index during the period May-July to detect infested forest stands. However, for the whole season NDVI and RDI also displayed potential as both were able to detect high rates of infested forest stands while limiting the misclassification of healthy ones. An infested forest stand could be detected as early as ca. the 29th of May. Meaning that the infestation is still in the green attack stage, in which mitigation is still possible to eliminate the spread of further bark beetle attacks. (Less)
Popular Abstract (Swedish)
Den europeiska granbarkborren är en insekt som för många räknas till skadedjur. Den har varit ansvarig för förstörelse av över 150 miljoner m3 granskog i Europa under de senaste 50 åren. Global uppvärmning har orsakat förändringar av barkborrens utbredning då problem uppstått med stora utbrott som nu regelbundet inträffar i norra Europa på grund av varmare och utdragna sommarsäsonger. Sverige har sedan 2018 drabbats av massutbrott som har förstört över 27 miljoner m3 granskog. För att motverka förödelsen av skog och begränsa spridningen av ytterligare attacker är det viktigt att tidigt upptäcka och avverka angripna träd. På senare tid har många studier fokuserat på upptäckt av barkborre i ett tidigt skede med hjälp av fjärranalysmetoder.... (More)
Den europeiska granbarkborren är en insekt som för många räknas till skadedjur. Den har varit ansvarig för förstörelse av över 150 miljoner m3 granskog i Europa under de senaste 50 åren. Global uppvärmning har orsakat förändringar av barkborrens utbredning då problem uppstått med stora utbrott som nu regelbundet inträffar i norra Europa på grund av varmare och utdragna sommarsäsonger. Sverige har sedan 2018 drabbats av massutbrott som har förstört över 27 miljoner m3 granskog. För att motverka förödelsen av skog och begränsa spridningen av ytterligare attacker är det viktigt att tidigt upptäcka och avverka angripna träd. På senare tid har många studier fokuserat på upptäckt av barkborre i ett tidigt skede med hjälp av fjärranalysmetoder. Användning av satellitdata för att upptäcka angrepp i ett tidigt skede, har resulterad i varierande framgång men har visat potentialen i att använda produkter av satellitbilder som vegetationsindex vilka är känsliga för förändringar av biokemiska förlopp inom löv och barr. Syftet med denna avhandling var att studera om variationen mellan pixlar i satellitbilder för vegetationsindex på en skogsbeståndsskala förändras under ett granbarkborreutbrott och att testa om variabiliteten kan användas som en tidig indikator för barkborreangrepp, en metod som tidigare inte testats. Detta gjordes genom att beräkna variationskoefficienten mellan 2017 och utbrottsåret 2018 från satellitdata (Sentinel-2 data) för fyra olika vegetationsindex (NDVI, NDWI, DRS, RDI) i 17 granskogsbestånd där 10 bestånd var angripna och 7 var friska. Variationskoefficienten användes för att klassificera bestånden i friska och angripna genom att beräkna den kumulativa summan av varje index i varje bestånd. Klassificeringsprestandan för varje index utvärderades med hjälp av ROC grafer som användes för att hitta det optimala tröskelvärdet för varje klassificering. Klassificeringen gjordes med den kumulativa summan för två olika tidsperioder, tidigt stadie (1 maj 2018 - 1 juli 2018) och hela barkborresäsongen (30 april 2018 - 30 september 2018). Resultaten av studien indikerade att variationen inom ett skogsbestånd förändras under ett barkborresutbrott med ökad variabilitet över tid inom bestånd som har attackerats. Den visade också att förändringar i variabilitet har potential att användas som en tidig indikator för barkborreangrepp, och variabiliteten kan användas för att upptäcka och klassificera enskilda skogsbestånd som angripits i ett tidigt skede. NDWI var det lämpligaste indexet under perioden maj-juli för att upptäcka angripna skogsbestånd. Men under hela säsongen visade NDVI och RDI också potential eftersom båda kunde upptäcka höga andelar angripna skogsbestånd samtidigt som de begränsade felklassificeringen av friska bestånd. Ett angripet skogsbestånd kunde upptäckas så tidigt som ca. den 29 maj. Detta innebär att angreppet fortfarande befinner sig i ett tidigt stadie, då det fortfarande är möjligt att lindra spridningen av ytterligare barkborresattacker. (Less)
Please use this url to cite or link to this publication:
author
Piltz, Karl LU
supervisor
organization
course
NGEM01 20221
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Geomatics, Remote sensing, Bark beetle, Early-stage, Vegetation index, Sentinel-2, Spruce, Forestry
publication/series
Student thesis series INES
report number
573
language
English
id
9092698
date added to LUP
2022-06-23 11:03:40
date last changed
2023-06-23 03:41:25
@misc{9092698,
  abstract     = {{The European spruce bark beetle is an insect that is often referred to as a pest. Responsible for the destruction of over 150 million m3 of Norwegian spruce forest in Europe over the last 50 years makes this insect one of the major disturbances to the forest industry. With global warming at large, changes of the distribution of the bark beetle have emerged with large outbreaks now regularly occurring in northern Europe due to warmer and prolonged summer seasons. Sweden has since 2018 been affected by mass outbreaks that have destroyed over 27 million m3 of spruce forest. In order to mitigate the disturbance of forest and limit the spread of further attacks, it is important to detect and cut down infested trees at an early stage. In recent time, many studies have focused on early-stage bark beetle detection using remote sensing methods. Efforts to detect early-stage infestation, i.e., “green attacks” using vegetation indices (VI) on a pixel level, have found varying levels of success but have shown the potential of using VIs sensitivity of changes to biochemical leaf properties to detect early-stage infestation. Hence, the aim of this thesis was to study if the variability between pixels for a vegetation index on a forest stand scale change during a spruce bark beetle outbreak and to test if the variability can be used as an early indicator for bark beetle infestation. This was done by calculating the coefficient of variation between 2017 and the outbreak year 2018 from Sentinel-2 data for four different VIs (NDVI, NDWI, DRS, RDI) in 17 spruce forest stands where 10 stands were infested and 7 were healthy. The coefficient of variation was used to classify the stands into healthy and infested by computing the cumulative sum of each VI in each stand. The classification performance for each VI was evaluated using receiver operating characteristics graphs which were used to find the optimal threshold for each classification. The classification was done using the cumulative sum for two different timeframes, early stage (1st of May-1st of July 2018) and the whole bark beetle season (30th of April-30th of September) The results of the thesis, indicated that the variability within a forest stand does change during a bark beetle outbreak with increased variability over time within stands that have been attacked. It also showed that changes in variability have the potential to be used as an early indicator for bark beetle infestation, and the variability can be used to detect and classify individual forest stands that were infested at an early stage, i.e., green attack stage. It was found that NDWI was the most suitable index during the period May-July to detect infested forest stands. However, for the whole season NDVI and RDI also displayed potential as both were able to detect high rates of infested forest stands while limiting the misclassification of healthy ones. An infested forest stand could be detected as early as ca. the 29th of May. Meaning that the infestation is still in the green attack stage, in which mitigation is still possible to eliminate the spread of further bark beetle attacks.}},
  author       = {{Piltz, Karl}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Student thesis series INES}},
  title        = {{Early-stage detection of bark beetle infested spruce forest stands using Sentinel-2 data and vegetation indices}},
  year         = {{2022}},
}