Remote Sensing of Environment 113 (2009) 1939–1947

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Remote Sensing of Environment

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Monitoring the spatio-temporal dynamics of geometrid moth outbreaks in birchforest using MODIS-NDVI data

J.U. Jepsen a,c,⁎, S.B. Hagen a, K.A. Høgda b, R.A. Ims a, S.R. Karlsen b, H. Tømmervik c, N.G. Yoccoz a

a University of Tromsø, Department of Biology, N-9037 Tromsø, Norwayb Norut, Northern Research Institute Tromsø, P.O. Box 6424, N-9294 Tromsø, Norwayc Norwegian Institute for Nature Research, Polar Environmental Centre, N-9296 Tromsø, Norway

⁎ Corresponding author. Current address: NorwegianPolar Environmental Centre, N-9296 Tromsø, Norway. T

E-mail address: jane.jepsen@nina.no (J.U. Jepsen).

0034-4257/$ – see front matter © 2009 Elsevier Inc. Adoi:10.1016/j.rse.2009.05.006

a b s t r a c t

a r t i c l e i n f o

Article history:Received 24 February 2009Received in revised form 12 May 2009Accepted 12 May 2009

Keywords:MODISNDVIDefoliationGeometridsFennoscandiaBirch forest

Defoliation caused by repeated outbreaks of cyclic geometrid moths is the most prominent naturaldisturbance factor in the northern-boreal birch forest. Evidence suggests that recent changes in outbreakdistribution and duration can be attributed to climate warming. There is hence an immediate need formethods that can be applied to characterize the geographical distribution of outbreaks. Here we assess thereliability of MODIS (Moderate Resolution Imaging Spectroradiometer) 16-day NDVI data for generating timeseries of the distribution of defoliation caused by moths attacking birch forest in Fennoscandia. We do so byfirst establishing the relationship between ground measures of moth larval density and a defoliation scorebased on MODIS-NDVI. We then calibrate and validate a model with the MODIS-NDVI defoliation score as aclassifier to discriminate between areas with and without visible defoliation as identified from orthophotosand provide two examples of application of the model. We found the MODIS defoliation score to be a validproxy for larval density (R2=0.88–0.93) above a certain, low threshold (a defoliation score of ~5%). Areaswith and without visible defoliation could be discriminated based on defoliation score with a substantialstrength of agreement (max kappa=0.736), and the resulting model was able to predict the proportion ofarea with visible defoliation in independent test areas with good reliability across the range of proportions.We conclude that satellite-derived defoliation patterns can be an invaluable tool for generating indirectpopulation dynamical data that permits the development of targeted monitoring on relevant regional scales.

© 2009 Elsevier Inc. All rights reserved.

1. Introduction

Defoliation and tree mortality caused by outbreaks of pest insectsis one of the most prominent disturbance factors in forests around theglobe. The ecological and economic impacts of pest insects, inparticular species that exhibit eruptive population dynamics, aremassive (Dale et al., 2001; Wulder et al., 2006a). A continuing effort isinvested in developing and refining tools for monitoring the effectsand predicting the spatial dynamics of forest pest insects. Oneimportant rationale for this investment is a growing concern thatrecent, rapid changes in distribution ranges mediated by climaticwarming and changing seasonality documented for several importantforest pests (Battisti et al., 2005; Jepsen et al., 2008; Parmesan et al.,1999; Tenow et al., 1999) may lead to increased spatial extent andseverity of outbreaks in forest (Fleming & Candau, 1998; Logan et al.,2007; Volney & Fleming, 2000; Williams & Liebhold, 1995).

To understand how the consequences of climate-mediatedchanges in geographical distribution and outbreak severity can be

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mitigated using appropriate management decisions, there is a needfor developing targeted monitoring programmes (Nichols & Williams,2006; Yoccoz et al., 2001) that can provide information on pestoutbreak distribution and dynamics and permit hypotheses to betested at relevant large spatial scales. Studies of complex spatio-temporal dynamics, such as large-scale synchrony and travellingwaves (Bjørnstad et al., 1999; Johnson et al., 2004; Koenig,1999; Rantaet al., 1998) have significantly improved our predictions of the spatialdynamics of outbreaks (Bjørnstad et al., 2002; Cooke & Roland, 2000;Johnson et al., 2004; Liebhold et al., 2006; Peltonen et al., 2002).However, availability of adequate data to assess the predictions onregional scales has become a major obstacle to continued progress(Bjørnstad et al., 1999).

Many studies have shown that forest discoloration, includingdefoliation caused by insects, can be successfully identified usingsatellite-derived vegetation reflectance data of varying spatial resolu-tion. Fine resolution satellite imagery, such as Landsat (30 mresolution), has been used to study forest discoloration caused forinstance by gypsy moth, Lymantria dispar, (Hurley et al., 2004;Townsend et al., 2004), jack pine budworm, Choristoneura pinus pinus,(Radeloff et al., 1999), Siberian silk moth, Dendrolinus superanssibiricus (Kharuk et al., 2003; Ranson et al., 2003), mountain pinebeetle, Dentroctonus ponderosa (Skakun et al., 2003) and autumnal

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and winter moth, Epirrita autumnata and Operophtera brumata(Tømmervik et al., 2001). However, Landsat sensors have a limitedtemporal resolution; i.e. rarely more than one or at best a few imagescan be obtained during a growing season. While this represents noproblem in detecting forest discoloration resulting from forest death(Skakun et al., 2003; Wulder et al., 2006b), it severely limits theapplicability of Landsat imagery in detecting the seasonally ephemeraloutbreaks by forest defoliators (de Beurs & Townsend, 2008).Commercial fine resolution sensors are now available with relevanthigh temporal resolutions (such as Formosat and Quickbird). How-ever, the costs of obtaining and individually processing andcalibrating large numbers of fine resolution images has preventedany practical use of fine resolution imagery for monitoring insectoutbreaks on a regional scale. Very coarse resolution imagery, inparticular SPOT_VEGETATION and NOAA AVHRR (>1 km resolution),have been put to use in identifying and mapping forest defoliation onregional scales (Fraser et al., 2005; Fraser & Latifovic, 2005; Kharuket al., 2004). While evaluations of coarse resolution imagery generallyconfirm that forest change patterns can be identified with a goodaccuracy, the coarse resolution limits the applicability of this type ofimagery in detailed monitoring. Coarse resolution imagery hashowever been successfully applied in identifying major forest changeareas which could subsequently be the target of more detailedanalysis in order to establish the cause of the change (Fraser et al.,2005). Very promising results have been obtained usingMODIS whichprovide global moderate resolution (~250–500m) reflectance data ona daily basis, and hence allow mapping both of relative changes inreflectance patterns between years and of short term changesoccurring within a single growing season. 16-day composite MODIS-NDVI and pre- and post-outbreak Landsat data has been used toquantify the extent of an outbreak by Siberian silk moth (Kharuk et al.,2007). In amore comprehensive study de Beurs and Townsend (2008)evaluated a number of vegetation indices derived from both daily, 8-day and 16-day MODIS composite data, in estimating the extent andmagnitude of gypsy moth defoliation.

Satellite-based monitoring of insect damage has yet to find its wayinto operational pest monitoring and management programmes. Yet,time series of satellite-derived defoliation estimates may yieldimportant insight not only into annual defoliation patterns, asdemonstrated in many of the case studies referenced above, but alsointo the spatio-temporal dynamics of forest pest insects such asregional population synchrony and travelling waves (Bjørnstad et al.,2002). Among the most important criteria for success is anappropriate matching of sensor temporal resolution to the temporalmanifestation of the damage as well as verification that the satellite-derived measure of damage is a valid proxy for the variation inpopulation density of the pest insect on the ground.

The goal of the work reported here was to assess the reliability ofMODIS-NDVI data for generating time series of the distribution ofsevere defoliation caused by geometrid moths attacking northern-boreal mountain birch forest in Fennoscandia. Since defoliation ofbirch by geometrids in this region is of a seasonal character (little orno re-foliation in the same growing season), we base our analysis on16-day NDVI composite data obtained during June–August for theyears 2000–2008. We evaluate the validity of the MODIS-NDVIdefoliation score as a proxy for moth larval density using grounddata from 3 separate study regions, differing with respect to localclimate and topography. In addition we calibrate and validate amodel with the MODIS-NDVI defoliation score as a classifier todiscriminate between areas with and without visible defoliation asidentified from orthophotos. Finally we provide two examples ofapplications of themodel. In onewe reconstruct the outbreak historyin an altitudinal gradient at a fairly local scale while in the other wegenerate time series (2000–2008) of the predicted distributionof defoliated areas for the entire birch forest region in northernFennoscandia.

2. Methods

2.1. Study system

Northern Fennoscandia lies in the arctic/alpine–boreal transitionzone and includes the northern parts of Norway, Sweden and Finland.The Scandinavian mountain chain (the Scandes) divides the area intoa humid oceanic region along the western coast (Tromsø, 69°38′N,18°57′E: mean July: 11.8 °C, mean January: −3.8 °C, annualprecipitation: 1000 mm), and a dry continental region to thesoutheast (Karasjok, 69°28′N, 25°30′E: mean July: 13.1 °C, meanJanuary: −17.1 °C, annual precipitation: 366 mm). Birch (Betulapubescens, Ehrh.) forests dominate the lowland, except in thesoutheast where dominance gradually shifts to conifers. In thewestern region, wind-protected areas are characterized by tall birchforest types where herbs and ferns dominate the ground cover. Incontrast, the continental east is characterized by low-growing andopen, multi-stemmed birch forest types where lichen and crowberrydominate the ground cover. Throughout the whole region, inter-mediate bilberry birch forest types cover large areas (Hämet-Ahti,1963; Johansen & Karlsen, 2005; Väre, 2001). The growing season lastsfrom late May–early June until early September (Karlsen et al., 2008).

In the northern-boreal birch forests of Fennoscandia, twogeometrid species, winter moth and autumnal moth (the namesreflect a difference in the timing of emergence of adults in autumn),are the most important cause of disturbance. Both species exhibitcyclic population outbreaks at approx. 10-year intervals in this region(Bylund, 1999; Hogstad, 1997; Neuvonen et al., 1999; Tenow, 1972).The cyclicity of the outbreaks is documented in qualitative historicalrecords as far back as the 1860s (Nilssen et al., 2007; Tenow, 1972).Individual outbreaks vary greatly in amplitude, duration, spatialextent as well as the degree of spatial synchronicity (Klemola et al.,2006; Ruohomäki et al., 2000; Tenow, 1972; Tenow et al., 2007).Where the two species occur sympatrically within their outbreakrange, there is a partial interspecific synchrony in the timing of theoutbreaks, but with winter moth often lagging 1–2 years behindautumnal moth (Klemola et al., 2008; Tenow et al., 2007). Theoutbreaks can be massive and usually have dramatic effects, withsevere defoliation over vast areas and occasionally death of the forest(Lehtonen & Heikkinen, 1995; Tenow, 1972; Tenow & Bylund, 2000).Defoliation of birch by geometrids in this region is of a seasonalcharacter. The larvae of both species hatch in approximate synchronywith budburst (late May–early June) and the feeding periods last for4–8 weeks depending on temperature and forage quality (Ruohomäkiet al., 2000). At high larval densities a large proportion of birch leavesare consumed at the bud stage and never unfold. Although theoccurrence of some re-foliation in heavily defoliated birch has beenreported from Fennoscandia (Kaitaniemi et al., 1997), the degree towhich this occurs is in our experience very limited. The short growingseason and the rapidly decreasing temperatures during August andearly September do not allow a crown layer of any substance todevelop. The total extent and distribution of forest damage caused bygeometrids in the Fennoscandian birch forest is not known, as nosystematic monitoring is in place.

In the present study we considered Fennoscandia north of 68°N(Fig.1). We delineated the study region to the east by the approximatebirch/mixed birch–coniferous forest limit, a total area of about106,700 km2 of which roughly 30% are forested.

2.2. Satellite imagery and NDVI change analysis

The Normalized Difference Vegetation Index (NDVI) is a commonlyused vegetation index defined as NDVI=(Rnir−Rred)/(Rnir+Rred), where Rnir and Rred are the reflectance measured in thenear infrared and red channel, respectively. For this study we used theMODIS TERRA NDVI data product with a 16-day temporal resolution

Fig. 1. A map showing the extent of the mapped region and the location of the areas used for ground truthing. Small quadrats (full line) show the 3 field plots where larval densitieswere measured (Hana, Skogsfjord and Storelva). Large rectangles (hatched line) show the 3 areas inwhich defoliationwas mapped using orthophotos (the training area Polmak, andthe 2 test areas Varanger and Valjok). Grey shaded areas show the distribution of birch-dominated forest in the region.

Fig. 2. A typical MODIS-NDVI time series extracted for a 1×1 km cell within the Valjokarea (25°56′E, 69°45′N, Fig.1). This areawas affected by severe defoliation in 2005. Boldhatched line: pre-defoliation (2003), bold full line: during defoliation (2005), thinhatched line: reference value. The time window used during the current analysis isindicated by the two vertical lines (days 177–241).

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(the MOD13Q1 product, version 5; Huete et al., 2002). Defoliation bymoth in this region is seasonal with no or very little re-foliation in thesame growing season. This, along with the fact that frequent cloudcover in the region severely limits the number of daily images that canbe obtained, means that the benefit of producing a shorter composit-ing period would be very limited. This has also been the experience inother studies using MODIS-NDVI data in northern Fennoscandia(Karlsen et al., 2008). In a study of gypsy moth defoliation, de Beursand Townsend (2008) reported two versions of the NormalizedDifference Infrared Index (NDII6 and NDII7), using MODIS channels 6and 7 respectively, to be more consistent than MODIS-NDVI inmapping the ephemeral defoliation caused by gypsy moth. However,applying these MODIS channels instead of the NDVI data would limitthe spatial resolution to approx. 500m in our region (the resolution ofboth channels 6 and 7). This would prevent any mapping ofdefoliation in the topographically heterogeneous regions in thewestern part of our region.

The MODIS-NDVI dataset is delivered from NASA georeferencedwith a spatial resolution of 236 m. The NDVI product is produced fromsurface reflectance data corrected for molecular scattering, ozoneabsorption and aerosols (Vermote et al., 2002). Before the composit-ing of the NDVI data, a filter selects cloud-free near-nadir observa-tions. The two highest NDVI values after this filtering are considered,and the observation closest to nadir view is selected to represent the16-day composite cycle. The NDVI dataset hence contains 23 NDVIobservations, each representing a 16-day period, per pixel per year. Inaddition, the NDVI dataset is accompanied by quality assurance data(QA). This information includes parameters such as the degree ofcloud contamination, likelihood of snow cover, and an overallindication of the data quality of each pixel scaled from 15 to 0, with0 indicating ‘perfect quality’. For further details on the NASAprocessing and the algorithm theoretical basis see http://tbrs.arizona.edu/project/MODIS/UserGuide_doc.php and Huete et al.(1999).

Our aim was to establish which years each pixel was affected bydefoliation during the time period 2000–2008. Our approach rests ontwo basic assumptions: 1) a pixel will show a lower NDVI duringsummer in years where it is affected by defoliation, than in yearswhere it is not (Fig. 2), 2) since a moth outbreaks typically lasts 1–3 years, no single pixel is affected by defoliation during all 9 years.Consequently therewill always be one or more unaffected years in thetime series that can be used as a reference NDVI.We obtained summerNDVI data covering the four 16-day periods starting from day 177 (e.g.late June–late August). The maximum number of NDVI values thatcould be obtained per pixel for the 9 years considered was hence 36 (4periods in each of 9 years). Since defoliation, snow and cloud cover aswell as atmospheric noise act to lower NDVI, we initially consideredthe maximum NDVI value obtained as the reference NDVI. However,

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manual inspection of the reference NDVI values showed that anumber of pixels had positive outliers (i.e. unrealistically highmaximum NDVI values) which would result in unrealistically highdefoliation scores. As a conservative measure we therefore chose touse the 2nd highest NDVI value obtained during the whole period2000–2008 as the reference NDVI value.

To detect forest pixels affected by defoliation, we used thefollowing step-wise approach:

1. Select forest pixels with high data quality:Only pixels located in forest (judged from an independent map offorest distribution in Northern Fennoscandia, Johansen et al., 2006)andwith a reference NDVI value>0.1 were included in the analysis.The overall data quality, as indicated in QA data, was restricted tothe range 0–4 (highest quality) for all periods.

2. Identify pixels with forest damage:Forest pixels affected by moth defoliation in a given year can beassumed to show summer NDVI values during that year that areconsistently lower than the reference NDVI. In other words, if anobserved decrease in NDVI is caused by moth defoliation thedecrease should be evident throughout the summer. The basicmodel was therefore that pixels that had a lower NDVI value thanthe reference value in at least 3 consecutive 16-day periods out ofthe 4 periods investigated, were considered affected by defoliationin that particular year. Consequently a decrease in NDVI evidentvery early in the summer (the 1st 16-day period), should beevident at least during the 2nd and 3rd periods.While a decrease inNDVI evident a little later in the summer (2nd period) should lastalso during the 3rd and 4th periods. This rather conservativeapproach minimizes the interference from cloud cover and noise,while still maintains some flexibility with respect to the onset andduration of the growing period. Finally, pixels selected as beingaffected by moth defoliation were assigned a defoliation scoreequal to the median of the percentage decrease observed in theperiods considered.

One exception from the classification protocol described above hadto be adopted. The years 2001 and 2008were affected by noise in partsof the investigated area. In these areas it was necessary to use a QAdata quality range of 0–2 instead of 0–4. Although data in the QArange 0–4 are usually of excellent quality, QA data supplied with theMODIS products may be unreliable and should be subject to a manualevaluation. This has also been observed in other studies (Karlsen et al.,2008). Setting a higher data quality requirement for 2001 and 2008resulted obviously in a decrease in the total area that could be suc-cessfully mapped in those particular years. Still, the approach waspreferred over one which would either map a larger area but with lessconsistent reliability (i.e. by using QA 0–4 in all years) or one whichwould drastically decrease the area mapped in all years (i.e. by usingQA 0–2 in all years).

As a last step a time series of raster maps showing the defoliationscore for each pixel were produced for the years 2000–2008.

2.3. Defoliation score as a proxy for larval density

Larval density was estimated at three field locations (Fig. 1), twothat experienced severe moth outbreaks (Skogsfjord, 69°55′N,19°18′Eand Hana, 70°14′N, 28°27′E), and one control with barely detectablelarvae densities (Storelva, 69°42′N, 18°47′E). The sampling wasdesigned to capture both temporal (between years) and spatial(between sites) variations in larval density. At the control site(Storelva) larval density was estimated at each of 44 sites distributedalong 4 altitudes (50, 100, 170 and 240 m above sea level where 240 mequals the approximate tree line). Sampling was done annually for3 years (2006–2008). The western outbreak location (Skogsfjord)experienced a moth outbreak with massive larval densities at the treeline and very low densities at the lowest altitudes. A grid with 40 sites

along 4 altitudes (50,100,170 and 240m.a.s.l.) was used to sample theentire gradient during 1 year (2008). The eastern outbreak location(Hana) was sampled annually at 1 location (~230 m.a.s.l.) for 9 years(2000–2008) covering an entire outbreak cycle (Klemola et al., 2008).The plot size used was in all cases smaller than a MODIS pixel (10–20 m radius). For details on larval density estimation see Hagen et al.(2006) and Mjaaseth et al. (2005) (Storelva/Skogsfjord) and Klemolaet al. (2008) (Hana). The MODIS defoliation score for each of thesample sites was extracted by overlaying the sample coordinates onthe annual maps of defoliation score for the relevant years.

2.4. Model calibration and validation procedures

We assessed the extent to which areas mapped as either visiblydefoliated or not visibly defoliated based on orthophotos could becorrectly classified using the defoliation score as a classifier. We used atraining dataset to determine the defoliation score which resulted inthe best discrimination between defoliated and non-defoliated areasand independent test data to assess the reliability of the resultingmodel.

2.4.1. Identification of defoliated areas from orthophotosSevere defoliation by geometrid moth in birch forest gives a clear

brownish contrast to areas withmoderate or no damage and are easilyrecognized from the air (Hagen et al., 2007). To produce a datasetcomparable to the commonly used aerial sketch maps, we mappeddefoliated areas visible from orthophotos obtained for 3 geographicalareas all affected by a severe defoliation in the same year (2005). The 3areas were Varanger (70°15′N, 29°0′E) and Polmak (70°15′N, 28°0′E)in the north-eastern part of the mapped region and Valjok (69°40′N,26°0′E) in the most continental part (Fig. 1). Detailed orthophotos(true-color RGB, 0.5 m pixel size, 1:40,000, The Norwegian MappingAuthority) acquired on July 4th and 6th 2005 covering a total area ofapprox. 1300 km2 were used for the analysis. We identified theapproximate outlines of defoliated areas by sub-sampling to asystematic grid of 200×200 m cells, separated by a centre-to-centredistance of 500 m. Grid cells, in which orthophoto quality was sub-standard due to image blur, haze or cloud cover, were omitted fromthe analysis. The same was true for grid cells containing less thanapprox. 10% forest. The remaining grid cells were classified as eitherdefoliated (>~5% of forest in cell visibly defoliated) or not defoliated(<~5% of forest in cell visibly defoliated). All cells were classifiedindependently by two observers and any cases of disagreement werere-examined by both observers. The MODIS defoliation score for eachof the grid cells was extracted by overlaying the grid cell coordinateson the map of defoliation score for 2005.

2.4.2. Model development and evaluationThe threshold defoliation score that yielded the best discrimina-

tion between areas with and without visible defoliation mapped fromorthophotos was estimated using kappa statistics (Congalton, 1991;Guisan et al., 1998) on all grid cells from the Polmak area (n=897).The defoliation score that maximized the kappa coefficient was usedto produce a map of the predicted distribution of defoliated areas inthe relevant year (2005). The reliability of themodel was evaluated bycomparing predicted defoliation with observed defoliation in theValjok and Varanger areas (n=1505). We followed the approach ofCox (1958) and Pearce and Ferrier (2000): i) the map of predicteddistribution of defoliated areas was overlaid by a 2×2 km grid and thepredicted proportion of defoliation (Dpre) was calculated for eachaggregate grid cell, ii) Dpre was binned in 10 equi-interval bins (0–0.1,0.1–0.2 etc) and the median Dpre for each bin was plotted against theobserved proportion of 200 m grid cells mapped as defoliated withineach bin. In a reliable model the observed and predicted proportionsshould lie along a 45° line. Systematic departures from this line wouldindicate the existence of calibration bias and/or spread error (Pearce

Fig. 3. Themean defoliation score in response to spatial and temporal variations in larval density. a) Storelva. Non-outbreak location sampled over 3 years at 4 altitudes (2006=filledcircles, 2007=open circles, 2008=squares), b) Hana. Outbreak location, sampled over 8 years at 1 altitude, c) Skogsfjord. Outbreak location, sampled in 1 year at 4 altitudes. Notedifferent scale on a).

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& Ferrier, 2000). Wemodelled the observed values as a function of thelogit of the predicted values using a quasibinomial distribution. If theobserved and predicted values are in good agreement the slope andintercept of this regression line should not be different from 1 and 0respectively.

3. Results

3.1. Validation and calibration

3.1.1. Defoliation score as a proxy for larval densityThere was a consistent and significant relationship between larval

densitymeasured in the field and the defoliation score calculated fromMODIS-NDVI, once larval densities exceeded a certain threshold(Fig. 3). At the control site (Fig. 3a), no relationship between larvaldensity and defoliation score was evident and variation in meandefoliation score was very low. In Hana (Fig. 3b) two successive peaksin larval density were seen (2002–03 and 2005–06). Both these peakswere accompanied by a drastic change in mean defoliation score. InSkogsfjord (Fig. 3c) the altitudinal gradient in larval density observedin the field was almost perfectly reflected in the mean defoliationscore.

3.1.2. Optimal threshold for discriminating between defoliated and non-defoliated areas

Kappa statistics indicate the presence of a clear optimal thresholddefoliation score for discriminating between areas with and withoutvisible defoliation mapped from orthophotos. The maximum kappa

Fig. 4. Kappa statistics for the Polmak region used to determine the optimal thresholdfor discriminating between areas with and without visible defoliation. Maximum kappa(Kmax=0.736) is obtained using a defoliation score of 14%.

coefficient (Kmax=0.736) was obtained at a threshold defoliationscore of 14%, corresponding to a 14% decrease in pixel NDVI comparedto the reference value (Fig. 4). According to the original notation byLandis and Koch (1977) the strength of agreement based on kappastatistics can be interpreted as: ‘poor’ K<0, ‘slight ‘0<K<0.2, ‘fair’0.2<K<0.4, ‘moderate’ 0.4<K<0.6, ‘substantial’ 0.6<K<0.8 and‘almost perfect’ 0.8<K<1. The optimal threshold resulted in anomission error rate of 14.4%, a commission error rate of 11.2% and86.8% correctly classified cells.

3.1.3. Model reliabilityPlots of the predicted defoliation, based on the optimal threshold

defoliation score, against observed defoliation in the Valjok andVaranger areas (Fig. 5) show that the observed proportion of cellsmapped as defoliated can be predicted based on defoliation score withgood reliability. The points are distributed approximately along the45° line with predicted values covering the full range of proportions.Modelling the observed data as a function of the logit of predictedvalues showed that intercept was not different from zero (intercept=−0.151, S.E.=0.124, t=−1.22, p=0.22), hence the model can beconsidered unbiased. The slope of the regression deviates somewhatfrom 1 (slope=0.80, S.E.=0.07, t=11.1, p<0.001) indicating a slightspread error in the model. This is caused mainly by observedproportions in the highest bin (0.9–1) being lower than predicted.

Fig. 5. The relationship between the observed and predicted proportion of 200 m cellscontaining visible defoliation (n=728) for the test areas Varanger and Valjok. The totalproportion of cells mapped as containing visible defoliation is plotted against themedian of predicted values in each of 10 equi-interval bin. In a perfectly calibratedmodel the points would lie along the hatched line. The logistic regression line shows theactual relationship between the observed and predicted proportions. Numbers indicateobserved sample size in each bin.

Fig. 6. The defoliation history of the Skogsfjord study area from 2000 to 2008. No field records are available to tell when the present moth outbreak started. The defoliation historyinferred from MODIS-NDVI indicates that the first year with severe defoliation was 2006.

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3.2. Applications

3.2.1. Reconstructing outbreak history in an altitudinal gradientGiven the strong relationship between large changes in larval

density and defoliation score, time series of defoliation score for agiven area may provide an indication of the outbreak history in yearswhere no field records are available. This is demonstrated using datafrom the Skogsfjord area (Fig. 6). No ground data exist for this locationprior to 2008, but the defoliation history derived from MODIS-NDVIindicates that an altitudinal defoliation gradient was present as earlyas 2006.

Table 1The total area (km2) mapped, the total area (km2) predicted to be severely defoliated using adefoliated.

2000 2001 2002 2003 2004

Mapped area 17,108 4110 30,536 34,479 30,20Defoliated area 218.3 88.6 1156.0 977.3 329Defoliated % 1.3 2.2 3.9 2.8 1

Results for ‘all years’ show the total area predicted to be severely defoliated at least once durithis is not equal to the sum of the individual years. The difference in the total areamapped betin particular).

3.2.2. Extent and distribution of defoliated areas on a regional scaleThe peak of the defoliation occurred in 2004 and 2005 where an

estimated 10–15% of the birch-dominated forest in northern Fennos-candia was defoliated annually (Table 1). Approximately one third(10,600 km2) of the forest is estimated to have been affected by severedefoliation in one or more years during this outbreak (Table 1). Thetime series of the predicted annual distribution of defoliated areas(Fig. 7) show that large geometrid outbreaks have started more or lesssimultaneously in the northern and southern parts of the region.Defoliation initiated in the southern part appears to have beentransient (1–2 year's duration at any one location) and has spread

threshold defoliation score of 14%, and the % of mapped forest predicted to be severely

2005 2006 2007 2008 All years

7 27,715 34,167 30,277 11,8883.2 4271.0 1668.4 2333.6 975.9 10,600 km2

0.9 15.4 4.9 7.7 8.2

ng the entire outbreak 2000–2008. Note that, due to repeated defoliation in some areas,ween years is due to poor quality of theMODIS-NDVI data in some years (2001 and 2008

Fig. 7. Annual maps (2000–2008) showing the distribution of areas with a defoliation score exceeding the cut-off of 14%. These are the areas predicted by the model to be affected bysevere defoliation. The distribution of birch-dominated forest in the region is shown in green.

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north-eastwards along themountain chain. In the northern part of theregion defoliation has been retained in the Polmak–Varanger region(compare to Fig. 1), but has been long-lasting with two consecutivedefoliation peaks (2003 and 2006).

4. Discussion

Defoliation is a commonly used proxy for larval density infolivorous insects, although surprisingly few studies actually docu-ment this relationship (but see for instance Baltensweiler & Rubli,1999; Bjørnstad et al., 2002; Williams et al., 1991). In order forsatellite-derived time series of defoliation to be used in analysis ofspatio-temporal population dynamics of forest pest insects, aproportional relationship between insect density and the observeddefoliation measure must be documented. We show here thatdefoliation scores based on MODIS 16-day NDVI can be considered avalid proxy for moth larval outbreak density in our system. Defoliationscores convincingly captured the observed variation in larval densitiesboth between sites in the same year (Skogsfjord) and between yearson the same site (Hana). The relationship appears unaffected by thefact that the actual defoliation score at high larval densities is much

smaller in Skogsfjord that in Hana. The difference in defoliation scorebetween the two sites is most likely caused by the forest in the coastalaltitudinal gradient in Skogsfjord being both fragmented (by mires,fens, mountain/coastal heaths and some infrastructure) and havingaltitudinal differences in forest structure. Most MODIS pixels hencecontain a mixture of several forested and non-forested land covertypes. The typical defoliation pattern in such altitudinal gradients inour area is that defoliation is visible as a narrow belt at and just belowthe tree line (Hagen et al., 2007). By comparison the Hana areaconsists of reasonably continuous and homogenous forest with lessaltitudinal variation.

Our data show that defoliation scores in the approximate range 0–5% do not reflect variations in larval densities (e.g. can be considerednoise). To obtain defoliation scores >5% requires log larval densities inthe range 4–5 or higher. In the present study this corresponds to 5–15larvae per arm-length branch (observed range of larval density was 0–170 larvae per branch). A similar log larval density threshold for whendefoliation is visible in mountain birch forest was estimated based onempirical observations (Klemola et al., 2008). These authors give avalue of 4.6, corresponding to approx. 10 larvae per branch in thepresent study (Klemola et al., 2008, Fig. 1a).

1946 J.U. Jepsen et al. / Remote Sensing of Environment 113 (2009) 1939–1947

Defoliated areas mapped by visual inspection of orthophotosprovide a dataset very similar to aerial sketch maps of defoliatedpolygons commonly used to calibrate and validate satellite-basedmaps of forest defoliation on a regional scale (de Beurs & Townsend,2008; Fraser & Latifovic, 2005; Goodwin et al., 2008; Hurley et al.,2004). Our results show that areas with and without visibledefoliation judged from orthophotos can be discriminated with verygood accuracy using defoliation score as a classifier. We found a veryclear optimal threshold defoliation score of 14% using the training datafrom the Polmak area. This result was only to a very limited degreeaffected by the choice of training vs test datasets (Kmax is obtained ateither 14 or 15% using data from any of the areas for training theclassifier). This is particularly assuring since the three areas (Varanger,Valjok and Polmak) differ with respect to topography, forestcomposition and local climate. The forest in all three areas isreasonably continuous though. This is in contrast to the westerncoastal regions where natural forest fragmentation is high, resulting ina high proportion of mixed pixels and lower defoliation scores. Theconsequence is that defoliation predicted based on a thresholdobtained in continuous forest almost certainly will underestimatethe area with visible defoliation in the western coastal region. Forproducing regional estimates of the area affected by defoliation inFennoscandia this is of little concern, as the fragmented strips ofcoastal forest contribute very little to the total forest area. But in orderto produce accurate local estimates of defoliated areas in the westerncoastal region, a recalibration of the model using local data would beneeded. Judging from the data in Skogsfjord (Fig. 3c), where visibledefoliation is present at approx. 200–240 m.a.s.l., a defoliationthreshold of 9–10% may provide more realistic local estimates ofvisibly defoliation in this region. It is important to note however, thatlow defoliation scores caused by mixed pixels do not appear toseverely affect our ability to detect outbreaks based on time series ofdefoliation score (as exemplified in Fig. 6).

Based on a recent analysis of historical outbreak records (Tenowet al., 2007), it has been concluded that last outbreak (1990–1999) ofgeometrid moth in Northern Fennoscandia moved in a wave-likefashion from NE towards SW, and it has been speculated whether thisis a consistent spatial pattern of outbreaks in the region. Evenwithouta formal analysis of the spatial dynamics of defoliation, our dataindicate that this is not so. Future analysis of the spatial–temporaldynamics of defoliation patterns will substantiate this.

5. Implications for research and management

Satellite-derived estimates of forest discoloration have repeatedlybeen shown to be suitable for mapping and monitoring of insectoutbreaks on both local and regional scales. We argue that thisapproach can be refined to generate indirect population dynamicaldata on relevant regional scales. We envisage four principal applica-tions of satellite-derived time series of outbreak dynamics: one is as abasic large-scale and cost-effective monitoring tool. This is in urgentneed as, climate warming is expected to cause range expansions orshifts in many insect pest species, including birch forest moths(Battisti et al., 2005; Jepsen et al., 2008; Tenow et al., 1999; Virtanenet al., 1998). The second application is to provide a basis for targetedstudies of the effects of severe defoliation events on NDVI during theyears following defoliation as well as on the regeneration process ofdifferent types of birch forest. This would provide feedback that woulddirectly improve MODIS-based defoliation mapping. The thirdapplication is in outbreak risk analysis, where a main objective is toidentify and characterize areas that have been especially prone tooutbreaks in the past, in an attempt to increase our understanding ofthe local environmental factors that permit outbreaks, and eventuallyoutbreaks with varying duration and severity. Finally, a verypromising application is in modelling of the spatio-temporaldynamics of outbreaks. Here regionally consistent time series of

outbreak distributionwill open for better analysis aimed at evaluatinghypothesis about the mechanisms underlying spatio-temporal out-break dynamics. In Fennoscandia, progress in all four fields iswarranted in order to implement a future targeted monitoring(Nichols & Williams, 2006; Yoccoz et al., 2001) of birch forest mothdamage.

Acknowledgements

This work was funded by the Department of Biology, University ofTromsø and the Research Council of Norway. We thank K. Ruohomäkifor providing us access to larval density data from Hana. BerntJohansen, Norut AS, kindly permitted us to use unpublished informa-tion on forest type distribution in the study region.

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