Browsing by Subject "remote sensing"

Sort by: Order: Results:

Now showing items 1-20 of 60
  • Verronen, Pekka (Finnish Meteorological Institute, 2017)
    Raportteja - Rapporter - Reports 4:2017
    The 13th International Workshop on Greenhouse Gas Measurements from Space (IWGGMS) will be held on 6-8 June, 2017, at the University of Helsinki in Helsinki, Finland. The workshop is organised by the Finnish Meteorological Institute with support from the University of Helsinki. The workshop gathers together more than 160 scientists from the EU, USA, Japan, China, Australia, Canada, and Russia. This report is the official abstract book of the workshop. Background. Success in space-based global measurement of greenhouse gases, such as carbon dioxide and methane, is critical for advancing the understanding of carbon cycle. The recent developments in observations and in interpreting the data are very promising. Space-based greenhouse gas measurement, however, poses a wide array of challenges, many of which are complex and thus demand close international cooperation. The goal of the workshop is to review the state of the art in remote sensing of CO 2 , CH 4 , and other greenhouse gases from space including the current satellite missions, missions to be launched in the near future, emission hot spots on regional and global scales, process studies and interactions of carbon cycle and climate, pre-flight and on-orbit instrument calibration techniques, retrieval algorithms and uncertainty quantification, validation methods and instrumentation, related ground-based, shipboard, and airborne measurements, and flux inversion from space based measurements. The workshop is part of the programme for the centenary of Finland's independence in 2017. The workshop is also one of the activities arranged by the Finnish Meteorological Institute to support Finland's chairmanship of the Arctic Council, 2017 - 2019. The workshop is sponsored by the Finnish Meteorological Institute, the University of Helsinki, the European Space Agency, the City of Helsinki, the Federation of Finnish Learned Societies, and ABB Inc.
  • Verronen, Pekka T. (2009)
    Raportteja - Rapporter - Reports 2009:6
  • Poso, Simo; Häme, Tuomas; Paananen, Raito (Suomen metsätieteellinen seura, 1984)
  • Bhattacharjee, Joy; Rabbil, Mehedi; Fazel, Nasim; Darabi, Hamid; Choubin, Bahram; Khan, Md. Motiur Rahman; Marttila, Hannu; Haghighi, Ali Torabi (Elsevier, 2021)
    Science of the Total Environment 797 (2021), 149034
    Lake water level fluctuation is a function of hydro-meteorological components, namely input, and output to the system. The combination of these components from in-situ and remote sensing sources has been used in this study to define multiple scenarios, which are the major explanatory pathways to assess lake water levels. The goal is to analyze each scenario through the application of the water balance equation to simulate lake water levels. The largest lake in Iran, Lake Urmia, has been selected in this study as it needs a great deal of attention in terms of water management issues. We ran a monthly water balance simulation of nineteen scenarios for Lake Urmia from 2003 to 2007 by applying different combinations of data, including observed and remotely sensed water level, flow, evaporation, and rainfall. We used readily available water level data from Hydrosat, Hydroweb, and DAHITI platforms; evapotranspiration from MODIS and rainfall from TRMM. The analysis suggests that the consideration of field data in the algorithm as the initial water level can reproduce the fluctuation of Lake Urmia water level in the best way. The scenario that combines in-situ meteorological components is the closest match to the observed water level of Lake Urmia. Almost all scenarios showed good dynamics with the field water level, but we found that nine out of nineteen scenarios did not vary significantly in terms of dynamics. The results also reveal that, even without any field data, the proposed scenario, which consists entirely of remote sensing components, is capable of estimating water level fluctuation in a lake. The analysis also explains the necessity of using proper data sources to act on water regulations and managerial decisions to understand the temporal phenomenon not only for Lake Urmia but also for other lakes in semi-arid regions.
  • Susiluoto, Jouni (2019)
    Finnish Meteorological Institute Contributions 154
    Climate change is one of the most important, pressing, and furthest reaching global challenges that humanity faces in the 21st century. Already affecting daily lives of many directly and everyone indirectly, changes in climate are projected to have many catastrophic consequences. For this reason, researching climate and climate change is needed. Studying complex geoscientific phenomena such as climate change consists of a patchwork of challenging mathematical, statistical, and computational problems. To solve these problems, local and global process models and statistical models are combined with both small in situ observation data sets with only few observations, and equally well with enormous global remote sensing data products containing hundreds of millions of data points. This integration of models and data can be done in a Bayesian inverse modeling setting if the algorithms and computational methods used are chosen and implemented carefully. The methods used in the four publications on which this thesis is based range from high-dimensional Bayesian spatial statistical models and Markov chain Monte Carlo methods to time series modeling and point estimation via optimization. The particular geoscientific problems considered are: finding the spatio-temporal distribution of atmospheric carbon dioxide based on sparse remote sensing data, quantifying uncertainties in modeling methane emissions from boreal wetlands, analyzing and quantifying the effect of climate change on growing season in the boreal region, and using statistical methods to calibrate a terrestrial ecosystem model. In addition to analyzing these problems, the research and the results help to understand model performance and how modeling uncertainties in very large computational problems can be approached, also providing algorithm implementations on top of which future efforts may be built.
  • Vuorinne, Ilja (Helsingin yliopisto, 2020)
    Biomass is an important parameter for crop monitoring and management, as well as for assessing carbon cycle. In the field, allometric models can be used for non-destructive biomass assessment, whereas remote sensing is a convenient method for upscaling the biomass estimations over large areas. This study assessed the dry leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre and biofuel production in tropical and subtropical regions. First, an allometric model was developed for predicting the leaf biomass. Then, Sentinel-2 multispectral satellite imagery was used to model the leaf biomass at 8851 ha plantation in South-Eastern Kenya. For the allometric model 38 leaves were sampled and measured. Plant height and leaf maximum diameter were combined into a volume approximation and the relation to biomass was formalised with linear regression. A strong log-log linear relation was found and leave-one-out cross-validation for the model showed good prediction accuracy (R2 = 0.96, RMSE = 7.69g). The model was used to predict biomass for 58 field plots, which constituted a sample for modelling the biomass with Sentinel-2 data. Generalised additive models were then used to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (D2 = 74%, RMSE = 4.96 Mg/ha) was achieved with VIs based on the red-edge (R740 and R783), near-infrared (R865) and green (R560) spectral bands. Highly heterogeneous growing conditions, mainly variation in the understory vegetation seemed to be the main factor limiting the model performance. The best performing VI (R740/R783) was used to predict the biomass at plantation level. The leaf biomass ranged from 0 to 45.1 Mg/ha, with mean at 9.9 Mg/ha. This research resulted a newly established allometric equation that can be used as an accurate tool for predicting the leaf biomass of sisal. Further research is required to account for other parts of the plant, such as the stem and the roots. The biomass-VI modelling results showed that multispectral data is suitable for assessing sisal leaf biomass over large areas, but the heterogeneity of the understory vegetation limits the model performance. Future research should address this by investigating the background effects of understory and by looking into complementary data sources. The carbon stored in the leaf biomass at the plantation corresponds to that in the woody aboveground biomass of natural bushlands in the area. Future research is needed on soil carbon sequestration and soil and plant carbon fluxes, to fully understand the carbon cycle at sisal plantation.
  • Saarinen, Ninni; Vastaranta, Mikko; Nasi, Roope; Rosnell, Tomi; Hakala, Teemu; Honkavaara, Eija; Wulder, Michael A.; Luoma, Ville; Tommaselli, Antonio M. G.; Imai, Nilton N.; Ribeiro, Eduardo A. W.; Guimaraes, Raul B.; Holopainen, Markus; Hyyppa, Juha (2018)
    Forests are the most diverse terrestrial ecosystems and their biological diversity includes trees, but also other plants, animals, and micro-organisms. One-third of the forested land is in boreal zone; therefore, changes in biological diversity in boreal forests can shape biodiversity, even at global scale. Several forest attributes, including size variability, amount of dead wood, and tree species richness, can be applied in assessing biodiversity of a forest ecosystem. Remote sensing offers complimentary tool for traditional field measurements in mapping and monitoring forest biodiversity. Recent development of small unmanned aerial vehicles (UAVs) enable the detailed characterization of forest ecosystems through providing data with high spatial but also temporal resolution at reasonable costs. The objective here is to deepen the knowledge about assessment of plot-level biodiversity indicators in boreal forests with hyperspectral imagery and photogrammetric point clouds from a UAV. We applied individual tree crown approach (ITC) and semi-individual tree crown approach (semi-ITC) in estimating plot-level biodiversity indicators. Structural metrics from the photogrammetric point clouds were used together with either spectral features or vegetation indices derived from hyperspectral imagery. Biodiversity indicators like the amount of dead wood and species richness were mainly underestimated with UAV-based hyperspectral imagery and photogrammetric point clouds. Indicators of structural variability (i.e., standard deviation in diameter-at-breast height and tree height) were the most accurately estimated biodiversity indicators with relative RMSE between 24.4% and 29.3% with semi-ITC. The largest relative errors occurred for predicting deciduous trees (especially aspen and alder), partly due to their small amount within the study area. Thus, especially the structural diversity was reliably predicted by integrating the three-dimensional and spectral datasets of UAV-based point clouds and hyperspectral imaging, and can therefore be further utilized in ecological studies, such as biodiversity monitoring.
  • Kankare, Ville; Luoma, Ville; Saarinen, Ninni; Peuhkurinen, Jussi; Holopainen, Markus; Vastaranta, Mikko (2019)
    Information on forest trafficability (i.e. carrying capacity of the forest floor) is required before harvesting operations in Southern Boreal forest conditions. It describes the seasons when harvesting operations may take place without causing substantial damage to the forest soil using standard logging machinery. The available trafficability information have been based on subjective observations made during the wood procurement planning. For supporting forest operations, an open access map product has been developed to provide information on trafficability of forests. The forest stands are distributed into classes that characterize different harvesting seasons based on topographic wetness index, amount of vegetation, ground water height and ditch depth. The main goal of this case study was to evaluate the information of the static forest trafficability map in relation to the detected rutting within logging tracks measured in the field. The analysis concentrated on thinning stands since the effect of rutting is significant on the growth of the remaining trees. The results showed that the static trafficability map provided reliable and slightly conservative estimation of the forest trafficability. The majority (91.7%) of the evaluated stands were harvested without causing significant damage if harvesting was timed correctly compared to the trafficability information. However, it should be pointed out that the weather history at small scale, the skills of a driver, and effects of used machinery are not considered in the map product although they can have a considerable impact on the rutting.
  • Vuorinne, Ilja Elias; Heiskanen, Janne; Pellikka, Petri (2021)
    Biomass is a principal variable in crop monitoring and management and in assessing carbon cycling. Remote sensing combined with field measurements can be used to estimate biomass over large areas. This study assessed leaf biomass of Agave sisalana (sisal), a perennial crop whose leaves are grown for fibre production in tropical and subtropical regions. Furthermore, the residue from fibre production can be used to produce bioenergy through anaerobic digestion. First, biomass was estimated for 58 field plots using an allometric approach. Then, Sentinel-2 multispectral satellite imagery was used to model biomass in an 8851-ha plantation in semi-arid south-eastern Kenya. Generalised Additive Models were employed to explore how well biomass was explained by various spectral vegetation indices (VIs). The highest performance (explained deviance = 76%, RMSE = 5.15 Mg ha−1) was achieved with ratio and normalised difference VIs based on the green (R560), red-edge (R740 and R783), and near-infrared (R865) spectral bands. Heterogeneity of ground vegetation and resulting background effects seemed to limit model performance. The best performing VI (R740/R783) was used to predict plantation biomass that ranged from 0 to 46.7 Mg ha−1 (mean biomass 10.6 Mg ha−1). The modelling showed that multispectral data are suitable for assessing sisal leaf biomass at the plantation level and in individual blocks. Although these results demonstrate the value of Sentinel-2 red-edge bands at 20-m resolution, the difference from the best model based on green and near-infrared bands at 10-m resolution was rather small.
  • Aalto, Iris (Helsingin yliopisto, 2020)
    Global warming is expected to have detrimental consequences on fragile ecosystems in the tropics and to threaten both the global biodiversity as well as food security of millions of people. Forests have the potential to buffer the temperature changes, and the microclimatic conditions below tree canopies usually differ substantially from the ambient macroclimate. Trees cool down their surroundings through several biophysical mechanisms, and the cooling benefits occur also with trees outside forest. Remote sensing technologies offer new possibilities to study how tree cover affects temperatures both in local and regional scales. The aim of this study was to examine canopy cover’s effect on microclimate and land surface temperature (LST) in Taita Hills, Kenya. Temperatures recorded by 19 microclimate sensors under different canopy covers in the study area and LST estimated by Landsat 8 thermal infrared sensor (TIRS) were studied. The main interest was in daytime mean and maximum temperatures measured with the microclimate sensors in June-July 2019. The Landsat 8 imagery was obtained in July 4, 2019 and LST was retrieved using the single-channel method. The temperature records were combined with high-resolution airborne laser scanning (ALS) data of the area from years 2014 and 2015 to address how topographical factors and canopy cover affect temperatures in the area. Four multiple regression models were developed to study the joint impacts of topography and canopy cover on LST. The results showed a negative linear relationship between daytime mean and maximum temperatures and canopy cover percentage (R2 = 0.6–0.74). Any increase in canopy cover contributed to reducing temperatures at all microclimate measuring heights, the magnitude being the highest at soil surface level. The difference in mean temperatures between 0% and 100% canopy cover sites was 4.6–5.9 ˚C and in maximum temperatures 8.9–12.1 ˚C. LST was also affected negatively by canopy cover with a slope of 5.0 ˚C. It was found that canopy cover’s impact on LST depends on altitude and that a considerable dividing line existed at 1000 m a.s.l. as canopy cover’s effect in the highlands decreased to half compared to the lowlands. Based on the results it was concluded that trees have substantial effect on both microclimate and LST, but the effect is highly dependent on altitude. This indicates trees’ increasing significance in hot environments and highlights the importance of maintaining tree cover particularly in the lowland areas. Trees outside forests can increase climate change resilience in the area and the remaining forest fragments should be conserved to control the regional temperatures.
  • Huttunen, Jani (Finnish Meteorological Institute, 2017)
    Finnish Meteorological Institute Contributions 135
    Aerosols affect the climate both directly and indirectly. The direct effect comes from their influence on the radiation balance by scattering and absorption of solar radiation, while the indirect effect is based on the ways in which aerosols interact via clouds. Currently the total anthropogenic aerosol forcing includes one of the main uncertainties in the assessment of human induced climate change. The aerosol direct radiative effect (ADRE) can be simulated with either the radiative transfer modelling or estimated with solar radiation and aerosol amount measurements. Both approaches include significant uncertainties and this thesis focuses on the uncertainties on the measurement based estimation of ADRE and the uncertainties therein. The main scientific objectives of this thesis are to seek answers to the following four questions: 1) are the machine learning algorithms better than the a traditional lookup table (LUT) approach in estimating aerosol load (aerosol optical depth, AOD)?; 2) what is the role of water vapor (WVC) variability in the measurementbased regression method used to estimate the surface ADRE?; 3) how well do the radiative transfer codes, typically used in global aerosol models, agree?; 4) what is the impact of typically neglected diurnal aerosol variability in ADRE estimation? The results show that: 1) the machine learning algorithms are able to provide AOD more accurately than the LUT approach for conditions of varying aerosol optical properties, since in the LUT approach the aerosol model (e.g. single scattering albedo, asymmetry factor) needs to be fixed in advance. 2) It was found that covariability of AOD and WVC can have an influence in ADRE estimates, when using groundbased measurements of surface solar radiation and AOD. This has not been taken into account previously, but needs to be considered when these methods are applied. 3) The model intercomparison study, in which the models estimated the radiative fluxes for the same atmospheric states, revealed that there is relatively large diversity between models regarding the results from their radiative transfer modelling. 4) The main conclusion from the study focusing on the impact of systematic diurnal AOD cycles in aerosol direct radiative effect, was that even a notable diurnal change in AOD does not typically affect the 24h-average ADRE significantly.
  • Laurila, Heikki Arvid; Karjalainen, Mika; Kleemola, Jouko; Hyyppa, Juha (2010)
  • von Lerber, Annakaisa (Finnish Meteorological Institute, 2018)
    Finnish Meteorological Institute Contributions 143
    Globally, snow influences Earth and its ecosystems in several ways by having a significant impact on, e.g., climate and weather, Earth radiation balance, hydrology, and societal infrastructures. In mountainous regions and at high latitudes snowfall is vital in providing freshwater resources by accumulating water within the snowpack and releasing the water during the warm summer season. Snowfall also has an impact on transportation services, both in aviation and road maintenance. Remote sensing instrumentation, such as radars and radiometers, provide the needed temporal and spatial coverage for monitoring precipitation globally and on regional scales. In microwave remote sensing, the quantitative precipitation estimation is based on the assumed relations between the electromagnetic and physical properties of hydrometeors. To determine these relations for solid winter precipitation is challenging. Snow particles have an irregular structure, and their properties evolve continuously due to microphysical processes that take place aloft. Hence also the scattering properties, which are dependent on the size, shape, and dielectric permittivity of the hydrometeors, are changing. In this thesis, the microphysical properties of snowfall are studied with ground-based measurements, and the changes in prevailing snow particle characteristics are linked to remote sensing observations. Detailed ground observations from heavily rimed snow particles to openstructured low-density snowflakes are shown to be connected to collocated triple-frequency signatures. As a part of this work, two methods are implemented to retrieve mass estimates for an ensemble of snow particles combining observations of a video-disdrometer and a precipitation gauge. The changes in the retrieved mass-dimensional relations are shown to correspond to microphysical growth processes. The dependence of the C-band weather radar observations on the microphysical properties of snow is investigated and parametrized. The results apply to improve the accuracy of the radar-based snowfall estimation, and the developed methodology also provides uncertainties of the estimates. Furthermore, the created data set is utilized to validate space-borne snowfall measurements. This work demonstrates that the C-band weather radar signal propagating through a low melting layer can significantly be attenuated by the melting snow particles. The expected modeled attenuation is parametrized according to microphysical properties of snow at the top of the melting layer.
  • Varjo, Jari (The Finnish Society of Forest Science and The Finnish Forest Research Institute, 1997)
    A method was developed for relative radiometric calibration of single multitemporal Landsat TM image, several multitemporal images covering each others, and several multitemporal images covering different geographic locations. The radiometricly calibrated difference images were used for detecting rapid changes on forest stands. The nonparametric Kernel method was applied for change detection. The accuracy of the change detection was estimated by inspecting the image analysis results in field. The change classification was applied for controlling the quality of the continuously updated forest stand information. The aim was to ensure that all the manmade changes and any forest damages were correctly updated including the attribute and stand delineation information. The image analysis results were compared with the registered treatments and the stand information base. The stands with discrepancies between these two information sources were recommended to be field inspected.
  • Bohlmann, Stephanie (Ilmatieteen laitos - Finnish Meteorological Institute, 2021)
    Finnish Meteorological Institute Contributions 175
    Atmospheric pollen is a well-known health threat causing allergy-related diseases. As a biogenic aerosol, pollen also affects the climate by directly absorbing and scattering solar radiation and by acting as cloud condensation or ice nuclei. A good understanding of pollen distribution and transport mechanisms is needed to evaluate the environmental and health impacts of pollen. However, pollen observations are usually performed close to ground and vertical information, which could be used to evaluate and improve pollen transport models, is widely missing. In this thesis, the applicability of lidar measurements to detect pollen in the atmosphere is investigated. For this purpose, measurements of the multiwavelength Raman polarization lidar PollyXT at the rural forest site in Vehmasmäki (Kuopio), Eastern Finland have been utilized. The depolarization ratio was identified to be the most valuable optical property for the detection of atmospheric pollen, as nonspherical pollen like pine and spruce pollen causes high depolarization ratios. However, detected depolarization ratios coincide with typical values for dusty mixtures and additional information such as backward trajectories need to be considered to ensure the absence of other depolarizing aerosols like dust. To separate pollen from background aerosol, a method to estimate the optical properties of pure pollen using lidar measurements was developed. Under the assumption that the Ångström exponent of pure pollen is zero, the depolarization ratio of pure pollen can be estimated. Depolarization ratios for birch and pine pollen at 355 and 532 nm were determined and suggested a wavelength dependence of the depolarization ratio. To further investigate this wavelength dependence, the possibility to use depolarization measurements of Halo Doppler lidars (1565 nm) was explored. In the lower troposphere, Halo Doppler lidars can provide reasonable depolarization values with comparable quality to PollyXT measurements. Finally, measurements of PollyXT and a Halo StreamLine Doppler lidar were used to determine the depolarization ratio at three wavelengths. A wavelength dependence of the particle depolarization ratio with maximum depolarization at 532 nm was found. This could be a characteristic feature of non-spherical pollen and the key to distinguish pollen from other depolarizing aerosol types.
  • Kaasalainen, Sanna; Holopainen, Markus; Karjalainen, Mika; Vastaranta, Mikko; Kankare, Ville; Karila, Kirsi; Osmanoglu, Batuhan (2015)
  • Joro, Sauli (Helsingfors universitet, 2004)
  • Forsius, Martin; Kujala, Heini; Minunno, Francesco; Holmberg, Maria; Leikola, Niko; Mikkonen, Ninni; Autio, Iida; Paunu, Ville-Veikko; Tanhuanpää, Topi; Hurskainen, Pekka; Mäyrä, Janne; Kivinen, Sonja; Keski-Saari, Sarita; Kosenius, Anna-Kaisa; Kuusela, Saija; Virkkala, Raimo; Viinikka, Arto; Vihervaara, Petteri; Akujarvi, Anu; Bäck, Jaana; Karvosenoja, Niko; Kumpula, Timo; Kuzmin, Anton; Mäkelä, Annikki; Moilanen, Atte; Ollikainen, Markku; Pekkonen, Minna; Peltoniemi, Mikko; Poikolainen, Laura; Rankinen, Katri; Rasilo, Terhi; Tuominen, Sakari; Valkama, Jari; Vanhala, Pekka; Heikkinen, Risto K (2021)
    The challenges posed by climate change and biodiversity loss are deeply interconnected. Successful co-managing of these tangled drivers requires innovative methods that can prioritize and target management actions against multiple criteria, while also enabling cost-effective land use planning and impact scenario assessment. This paper synthesises the development and application of an integrated multidisciplinary modelling and evaluation framework for carbon and biodiversity in forest systems. By analysing and spatio-temporally modelling carbon processes and biodiversity elements, we determine an optimal solution for their co-management in the study landscape. We also describe how advanced Earth Observation measurements can be used to enhance mapping and monitoring of biodiversity and ecosystem processes. The scenarios used for the dynamic models were based on official Finnish policy goals for forest management and climate change mitigation. The development and testing of the system were executed in a large region in southern Finland (Kokemäenjoki basin, 27 024 km2) containing highly instrumented LTER (Long-Term Ecosystem Research) stations; these LTER data sources were complemented by fieldwork, remote sensing and national data bases. In the study area, estimated total net emissions were currently 4.2 TgCO2eq a-1, but modelling of forestry measures and anthropogenic emission reductions demonstrated that it would be possible to achieve the stated policy goal of carbon neutrality by low forest harvest intensity. We show how this policy-relevant information can be further utilised for optimal allocation of set-aside forest areas for nature conservation, which would significantly contribute to preserving both biodiversity and carbon values in the region. Biodiversity gain in the area could be increased without a loss of carbon-related benefits.
  • Olsson, Per-Ola; Kantola, Tuula; Lyytikäinen-Saarenmaa, Päivi; Jönsson, Anna Maria; Eklundh, Lars (2016)
    We investigated if coarse-resolution satellite data from the MODIS sensor can be used for regional monitoring of insect disturbances in Fennoscandia. A damage detection method based on z-scores of seasonal maximums of the 2-band Enhanced Vegetation Index (EVI2) was developed. Time-series smoothing was applied and Receiver Operating Characteristics graphs were used for optimisation. The method was developed in fragmented and heavily managed forests in eastern Finland dominated by Scots pine (Pinus sylvestris L.) (pinaceae) and with defoliation of European pine sawfly (Neodiprion sertifer Geoffr.) (Hymenoptera: Diprionidae) and common pine sawfly (Diprion pini L.) (Hymenoptera: Diprionidae). The method was also applied to subalpine mountain birch (Betula pubescens ssp. Czerepanovii N. I. Orlova) forests in northern Sweden, infested by autumnal moth (Epirrita autumnata Borkhausen) and winter moth (Operophtera brumata L.). In Finland, detection accuracies were fairly low with 50% of the damaged stands detected, and a misclassification of healthy stands of 22%. In areas with long outbreak histories the method resulted in extensive misclassification. In northern Sweden accuracies were higher, with 75% of the damage detected and a misclassification of healthy samples of 19%. Our results indicate that MODIS data may fail to detect damage in fragmented forests, particularly when the damage history is long. Therefore, regional studies based on these data may underestimate defoliation. However, the method yielded accurate results in homogeneous forest ecosystems and when long-enough periods without damage could be identified. Furthermore, the method is likely to be useful for insect disturbance detection using future medium-resolution data, e. g. from Sentinel-2.