Disturbances play a substantial role in forest ecosystems, influencing the structure of stands, their regeneration, and the character of the whole forest ecosystem (Dale et al., 2001). As such, forest disturbances are an integral part of these ecosystems including storm impact, droughts, flooding, fires, and insect or disease outbreaks in plants and animals. Here, we used the term disturbance to describe any negative deviation from the long-term enhanced vegetation index (EVI) phenology linked to abiotic or biotic causes. Changes in the severity and frequency of these disturbances, due to an increase in extreme climate events (Seneviratne et al., 2012), raise concerns regarding forest function and provision, thus sparking great interest in large-scale forest assessments (for a list of current remote-sensing-based forest monitoring in Germany refer to Supplementary Material 1). A current increase in disturbance events leads to multiple assessments (Senf and Seidl, 2020). The analysis relies on either remote-sensing products, covering large and continuous scales, as done for mortality (Senf et al., 2018) and land-cover change (Hansen and Loveland, 2012), or on ground assessments such as the ICP Forests Crown Condition Survey (George et al., 2021). In recent years, remote-sensing products have begun to increase the value of ground assessments by being less personnel and resource-demanding, hence producing lower cost and allowing a higher temporal resolution of monitoring cycles (Lausch et al., 2018). Large benefits are the possibility to redo the analysis and repeat it retrospectively, as well as cover continuous areas rather than individual sampling plots in the analysis. Despite the advantages, remote-sensing products and applications alone do not provide all the answers needed (Chávez et al., 2019) in the current forest monitoring and practice seemingly leading to a limited acceptance of foresters. Thus, a workflow for retrieving up-to-date, reliable and precise information on the forest vitality status from satellite data is of great interest for early stage warning and diagnosis, as well as to support in preventative- and countermeasures in today's forest practice (Torresan et al., 2021). However, disturbances in forests lead to a variety of symptoms, from the loss or discoloration of leaves and needles (e.g., insects, fungi, drought) and the disruption of forest structures to the loss of standing wood volume (e.g., storm, fire) (Buma, 2015), which are so far assessed individually (Gao et al., 2020). Providing this information in a single, easy-to-use workflow would improve the usability of forest practice. This is specifically relevant in fast-changing conditions due to regeneration or in forest areas repeatedly affected by biotic or abiotic disturbances (Iglhaut et al., 2019). Therefore, foresters and decision-makers need a spatially explicit and timely assessment of the disturbance, with information on the disturbance history to assess the vulnerability of existing stands and the cause of the disturbance.

During the last decade, several extreme weather events contributed to considerable loss and degradation of forest ecosystems throughout central Europe and Germany (Oeser et al., 2017). With various extreme events likely to increase in the future (Seneviratne et al., 2012), the risk of single and multiple disturbance events increases further. However, the different causes, roughly classified as biotic and abiotic, lead to different disturbance patterns and require distinct silvicultural measures. While water stress and drought are associated as the triggering factors for early defoliation, vitality loss, and forest pathogens (Ghelardini et al., 2017; Bußkamp et al., 2020), water stress is linked to increased vulnerability due to fungal infestation (Blodgett et al., 1997; Fabre et al., 2011). While water-related disturbances vary between site conditions and species strategies, other abiotic disturbances such as fire and storm act at a larger level, which is less tightly linked to these disturbances. However, even here, the detection of the disturbance is increased by the knowledge of forest cover. Windstorms are widely referred to as abiotic and temporally discrete events and are due to their often spatially explicit occurrence that is operationally detectable (Giannetti et al., 2021). The destructive potential of a storm event depends on the (peak) wind speeds, the duration, and the frequency with which forest stands are affected. The severity of storm-related impact on a given forest stand depends on the stocked species, species admixture, and age (Valinger and Fridman, 2011). In combination with the precipitation, the severity and extent of storm-induced destruction as well as mortality rates increase in the forest stand level (Usbeck et al., 2010). Forest fires are commonly classified as temporally discrete, abiotic disturbance events that result in abrupt changes in the form of burned vegetation and soil (Gnilke and Sanders, 2021). In Germany, forest fires usually occur in the summer period from April to August. The spread of forest fire is influenced by meteorological conditions and topography as well as forest and site characteristics (White et al., 1996). Forest areas affected by fire usually consist of a single contiguous burn area or a few compactly shaped burned areas that are locally concentrated. Within the burned areas, a zonal gradation of fire severity can be observed, which typically decreases toward the outer edges. Insects and fungi on the other hand are less pronounced during events that can occur from single trees to larger stands (Kautz et al., 2018). Here, this differentiation between abrupt and gradual changes allows the first differentiation between abiotic and biotic changes, which remained difficult as described in the study of Senf and Seidl (2021) and Francini et al. (2022). So far, a combination of remote-sensing data and models is used to map abiotic disturbances by fire and storm. The examples presented in this study showed that the non-parametric method of the analysis of Moderate Resolution Imaging Spectroradiometer (MODIS)-derived phenological series and the self-calibrating change detection allows the classification of multiple disturbances without relying on pre-assumptions or the need of user-defined thresholds for curve fitting or trend modeling. Abrupt changes can be further analyzed regarding their size, distribution, amplitude of change, and recovery. This allows a determination of disturbances caused by fire or storm limited only by the resolution of the used remote-sensing product.

Over the years, a variety of remote-sensing platforms are developed with specific or overlapping sensors, with the most commonly used ones being optical imaging systems (for more information, refer to Lechner et al., 2020). To obtain information on forest structure, vitality, or loss, to name just a few, Landsat, MODIS, and sentinel data are used (McDowell et al., 2015). While Landsat provides the longest time series and a high spatial resolution offering information on a small scale or on gradually occurring disturbances (DeVries et al., 2015); however, it still relies on potent computing power and algorithms, e.g., machine-learning classifiers, and lacks in prepossessing and validation (Banskota et al., 2014), as well as has low temporal resolution. MODIS detection accuracy lies in agreement with Landsat (Jin and Sader, 2005) and provides 20 years of prepossessed data in high temporal resolution, and only two scenes cover the entire part of Germany. Sentinel data provide higher spatial-spectral and radiometric resolution, which are ready-to-use products; however, the time series is still short but will provide ample information in the future. Nevertheless, several studies use Sentinel data to detect current changes (Puletti et al., 2019; Senf and Seidl, 2021). All three provide passive optical/NIR and mid-infrared information on the surface reflectance, which allows the application of conventional and innovative vegetation indices and land cover applications. From all these, the mission's spectral vegetation indices (VI), such as the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI), can be deducted and are widely used to assess vegetation conditions and monitor ecological phenomena and their dynamics across various spatial and temporal scales (Bannari et al., 1995; Myneni et al., 1995; Hunt et al., 2011; Decuyper et al., 2020; Schuldt et al., 2020). From these indices, phenological series can be derived, providing the start of the vegetation period by greening and the end by the loss of greenness. While the start and the end of the vegetation period vary slightly between the years, the general curve matches and can therefore be used to detect changes. With a good agreement of the phenological series between MODIS and Sentinel 2 (Thapa et al., 2021), the use of both is possible, and also, the longer time series of MODIS provides a reliable reference curve, whereas Landsat and Sentinel 2 lack the temporal resolution and face occlusions due to cloud coverages in more humid latitudes. Currently, however, even the combination of multiple sources of remote sensing is possible and could increase the information content further (Lu et al., 2016; Wan et al., 2021). Limits, however, do occur in the processing capacity, the data handling, and the interpretation (Arvor et al., 2019).

This need for methodological fine-tuning is met by a huge variety of disturbances. Disturbance events can cause abrupt or gradual changes, showing distinct or non-distinct patterns in space and time (Sturtevant and Fortin, 2021). These patterns are further modified by an overlay of different disturbances (Cannon et al., 2017) resulting in additive effects, influencing the expected patterns, and making the recognition and differentiation more difficult. Small scale and patchy disturbances can retain younger and remnant live trees aiding recovery (Seidl et al., 2014) but also mask the signal received limiting interpretation. The scale of the disturbance will not only influence the recovery potential but also influence the ability to detect it. Storms generally leave younger and remnant trees, whereas fires, especially with high temperatures and in monocultures, can leave nothing behind, making them easy to detect. Another important information is that the severity of the disturbance is linked to the spatial and temporal extent, the severity, and the frequency (Hart and Kleinman, 2018). All these processes will influence the likeliness of detection and therefore need to be considered. The analysis of time series adds the temporal dimension and is therefore an important source of information for the extraction of semantic content from satellite scenes (Zhang et al., 2006; Atzberger et al., 2014; Recuero et al., 2019). Hence, time series analysis methods are useful to efficiently spot forest vitality decline at an early stage, which is often essential to effectively perform damage control and mitigate long-term effects in forests by adaptive forest management (Bolte et al., 2009).

Despite the successful application at local to regional scales, results often show huge variances between the ground estimates and the remote-sensing assessments, leading to a lack of acceptance in foresters and politicians. To overcome these issues, a substantiated validation of the remote-sensing results with reliable ground data is the key factor (Frolking et al., 2009). While we are convinced that remote sensing is an important addition to ground-based monitoring systems, we believe that methods need to be robust to bridge data gaps that occur, e.g., cloud cover, independent of artificial thresholds for trend modeling or curve fitting and to be ready to use across various forest cover types and growth conditions. In addition, they need to be useable by a non-specialist and without high computer performance while still providing reliable information. Here, we used a self-calibrating non-parametric approach for time series analysis of MODIS EVI data for detecting and assessing various disturbed areas in Germany observed over the last 20 years to improve the understanding of disturbance regimes in forest ecosystem to (a) detect patterns of disturbance, (b) capture distinct features determining abrupt and gradual change, and (c) assess their magnitudes of severity, as well as (d) disturbance interactions.

Disturbance-specific features seen as phenological metrics, such as duration, slope, and amplitude, were used to deduce the timing and quantify the abruptness and the severity of the deviation relative to the long-term normal (estimated EVI phenology vs. expected EVI) (Jönsson and Eklundh, 2004). These prototypes are used to create a multistage, rule-based decision tree for disturbance-specific pixel classification (Figures 1, 2). While the duration describes the temporal factor over which the disturbance response evolves, starting from the point in time when the deviation of observed EVI values from the long-term EVI phenology first occurs, where the amplitude defines the drop of the EVI anomaly, and the slope is a combination of these two factors. The utilization of additional remote sensing products, e.g. multi-temporal differenced normalized burn ratio (dNBR) (Veraverbeke et al., 2011) could further improve the distinction between different disturbance types and their potential causes. However, there is still a limitation regarding how to quantify the magnitude of severity, which currently is not comparable between different sites easily but does rather provide a grading within each investigated site of this study. In the next step, these disturbance patterns could be exploited as disturbance markers to detect similar patterns within the EVI chronologies of the last 20 years potentially across multiple pixels. However, the problem of interpreting spectral time series signatures of irreversible damage changing forest cover type, e.g., from coniferous forest to mixed or deciduous broad-leaf forest remains. However, the kernel density estimation (KDE)-based phenological reconstruction algorithm is based on data inherent thresholds (non-parametric), and thus, it follows a robust statistical approach not relying on any artificial or given thresholds. It can be applied at any multispectral pixel and across various vegetation cover types or biomes allowing high flexibility in application. The pixel-by-pixel approach is further robust to missing data, e.g., data gaps at any given pixel position within the analyzed sequence. Therefore, no gap filling in the sense of spatial or temporal interpolation is needed to reduce uncertainty in not masking highly dynamic or small-scale phenomena (Figure 2).

The method provides an easy-to-use workflow for the distinction between abrupt and gradual disturbance types from the phenological series at a pixel level. Using the automatically extracted parameters of duration, amplitude, and slope, the derived patterns allow a further separation into some main causes: fire, storm, insect, and fungi (Figure 1) by pattern analysis.

Satellite change detection algorithms enable automated processing of large amounts of data, based on batch scripts, and thus can be used to establish monitoring routines that can serve as an early warning system. Change detection algorithms use deviations observed in VI signals of a certain observation period in relation to the “normal” long-term VI to detect the so-called phenological anomalies indicating a change in forest condition (Pasquarella et al., 2017). The schematic development of greenness during the vegetation period increases, without disturbance, to a maximum after which it is decreasing toward the end of the vegetation period. This development is interrupted by a disturbance event, causing a (significant) decrease in greenness prior to the phenological maximum indicating early defoliation (De Beurs and Townsend, 2008) or a more sudden decline after the maximum was reached. The accuracy of satellite-based forest change detection considerably depends on (a) the spectral, spatial, and temporal resolution of the time series data (Stenberg et al., 2008; Ghamisi et al., 2019), (b) the precise co-registration, standardization, and normalization of the multi-temporal images (Nguyen et al., 2020), and (c) the phenological estimation and change detection methods used (Atkinson and Urwin, 2012; Kandasamy et al., 2012; Petitjean et al., 2012). The common goal for disturbance detection is to use one method for any evaluation at a, at least, national scale. However, this leads to the multiple previously reported limitations which can roughly be classed in the lack of detection or overassessment, partly linked to the missing knowledge on individual species distribution, tempering the meaning within the analysis.

Thus, filling this gap regarding the current lack of operable methods mentioned by Atzberger et al., the method presented here can be used for all multispectral remote-sensing products providing a high intra-annual resolution and is expected to provide improved results as soon as possible, e.g., COPERNICUS mission provides sufficient years. Using the higher resolution of Sentinel or Landsat will benefit the spatial explicitly. Nevertheless, there are shortfalls. Atmospheric distortion variations in the background signal of observed pixels and clouds heavily affect obtained VI signatures (Pragnère et al., 1999), and thus, cloud masking is a crucial component in the pre-processing of optical satellite data for time series analysis.

While NDVI saturates over large water bodies and is prone to canopy background interactions in the presence of bare soil, red litter, or understory, EVI is expected to show a better performance where NDVI tends to mask the vegetation signal of the canopy due to the implemented background adjustment term (Huete et al., 1997). Regarding seasonality, the EVI values exhibited a smoother, more symmetrical seasonal profile with a narrower, well-defined peak greenness period. Furthermore, the EVI is very sensitive to needleleaf/broadleaf canopy structures with EVI values over needleleaf forests approximately one half of those over broadleaf forests resulting in sharper contrasts between the two forest types and a more pronounced broadleaf dry-down phase. Within the dynamic range, the EVI showed a smaller variation than that of the NDVI and a strong contrast in EVI values between broadleaf and needleleaf forests.

Using Sentinel 2A/B for establishing the phenological time series will, in 2022, still be less reliable due to the few available years. Due to the very high number and severity of disturbances linked to droughts, insects, fungi, fires, and storms, which occurred in the years 2017–2019, this will likely lead to a poorer detection rate of disturbances in consecutive years. Landsat does provide the longest time series at all, but it comes with the need for a high computing power and a lower temporal resolution, which might hamper the phenological series due to insufficient intra-annual scenes. Still, using MODIS data, the disturbed site needs to be above the pixel size, depending on the resolution of the remote-sensing product used, to be detectable. Ambiguous or indifferent phenomena occurring at the subpixel level caused by the individual decline of individual trees provide a mixed signal of the pixel. This problem is well known and linked to pixel resolution and the issue of mixed pixels (Schwalbe et al., 2006). The proposed pixel-by-pixel approach, therefore, remains at the level of the pixel resolution but does not consider their neighborhood relationships yet. This could, however, provide an important next step to describe the spatial extent and distribution of detected changes in the canopy of forests in terms of their uniformity and diversity, e.g., by means of the Shannon index (Spellerberg and Fedor, 2003), and thus further determine possible causes of disturbance in the respective study area.

Due to the lack of continuous long-term information on annual variation in the onset of the vegetation period (referring to the tree species-specific phenological phases as described in the ICP Forests documentation), additional expert knowledge and contextual information on the growth and weather conditions need to be implemented for adequate evaluation of the actual forest condition (Glenn et al., 2008). As concerned by Hansen et al. (2014), a miscomprehension of remote-sensing product validation is related to forest cover change in central Europe. Hence, detected forest loss can result from forest management in support of forest transition (forestry operation), due to insect infestation or water shortage defoliation (disturbance) or extensive removal due to bark beetle infestation (clearing operations). Whereas at the same time, potential loss of greenness in the most upper canopy layer might be masked by understory or shifts in the tree species composition after a storm, resulting in above-average greenness values due to seasonality effects introduced by mixed forests superimposing the long-term phenology signatures, which had been defined by needleleaf dominance in the pre-storm period.

An alternative approximation of the annual phenological baseline we have built on in this study is to use the observed frequency values and define the expected distribution directly from observed data without reference to a theoretical model (Chávez et al., 2019). The advantage of this approach is its flexibility to adapt to the particular conditions of every site and also to account for natural variability in annual phenology over time, which is smoothed over by the parametric functions. This approach is based on probabilistic estimations (Morio et al., 2015) of the annual phenology, from which disturbances measured as anomalies from the expected phenology can be assessed in terms of the frequency distribution of historical records, providing both a map showing the likelihood of the change detection and the change detection result itself. Current methods based on the parametric functions lack such a likelihood measure (Berger and Wolpert, 1984).

Other inaccuracies such as the spatial uncertainties of remote-sensing products remain relatively low in both daily and composited products (Huete et al., 2002) when using MODIS VI products for this study the geolocation accuracy (co-registration) allowing a pixel-to-pixel comparison through time was especially important which is given by MODIS data to subpixel accuracy, approaching the operational MODIS geolocation goal of 50 m (1r) at nadir (Wolfe et al., 2002).

Therefore, the advantages are that (a) 2-dimensional KDE (bi-variate Gaussian kernel) can capture any functional form, (b) the same number of points per cycle is not mandatory, and (c) the magnitude of the anomaly can be leveled at a probabilistic base.

Atmospheric distortions and variations in the background signal of observed pixels heavily affect obtained VI signatures (Pragnère et al., 1999), and thus, cloud masking is a crucial component in the pre-processing of optical satellite data for time series analysis. The pixel-by-pixel time series approach provides a flexible method, which is transferable to various vegetation cover types and conditions and is reliable across phenology regimes and disturbances. The non-parametric phenology estimation and kernel density-based change detection algorithms are robust to missing data and noise, which are especially related to the distinction of anomalies from noise (e.g., due to clouds, geometric errors) and time series reconstruction is independent of curve-fitting models for smoothing and generalization and change detection does not rely on artificially set thresholds. Therefore, the change detection result does not rely on user defined threshold (=artificially introduced thresholds are a source of uncertainty) (a) 2-dimensional KDE (Gaussian kernel) can capture any functional form, (b) the same number of points per cycle is not mandatory, and (c) the magnitude of the anomaly can be judged at a probabilistic base.

Forest ecosystems move through different phases, from seedlings to mature trees to the death of individuals or stands. Therefore, there was always a previous disturbance (Jentsch and White, 2019), and there will always be one following (Holling, 1973). Disturbances alter the structure and composition, and they influence their neighbors and shape large-scale patterns (Hart and Kleinman, 2018). However, in recent times, with large-scale forest disturbances and decline, the need for a fast and reliable assessment arises to extract disturbance patterns and causes (Vaglio Laurin et al., 2021), thus enabling aid recovery processes. Therefore, remote sensing provides a valuable addition to understanding forest disturbances but still needs ground-truthing, reliable monitoring data, and expert knowledge. To do so, the use of the EVI phenological series proved valuable. Specifically, the pixel-by-pixel approach is independent of a prior species determination, which is still problematic to do reliably with remote sensing, thus reducing the risk of false alarms due to the timing of the assessment within the phenological series, as well as the assuming the wrong species. A step-wise approach classifying first abrupt vs. gradual change to later gain more details but adding further information, whether by ground-truthing or using different bands, further reduces the overestimation of disturbances.

In this study, we used a combination of ground assessments and spatially explicit EVI phenological time series to develop an identification scheme for four different forest disturbances. The patterns identified and validated by the ground assessments reliably showed enough further disturbance events. This method can distinguish abrupt and gradual changes in forests using robust remote-sensing products while a combination of ground assessments and remote-sensing time series is still needed for accuracy assessment and a precise determination of the type of insect or fungi.

Remote-sensing techniques are widely used for forest monitoring applications. Due to the complexity of the cause-and-effect relationships of forest ecosystems and the variety of factors involved, the stress-response of forests and trees has not been fully decoded yet. In the context of climate change, an in-depth understanding of this relationship is crucial for forest transformation and adaptation (Keenan, 2015; White et al., 2016). Thus, the differentiation and quantification of factors and their contribution to forest degradation and disturbance require further research. While we believe that remote sensing is an important addition to ground-based monitoring systems such as ICP Forests, we believe that methods need to be harmonized and validated in a similar way to provide valid information.

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: Kyrill windthrow data by WaldInfo.NRW | OpenGeodata 2021, under dl-de/by-2-0: https://www.opengeodata.nrw.de/produkte/umwelt_klima/wald_forst/wald/windwurfschadflaechen-kyrill_EPSG25832_Shape.zip; Copernicus Emergency Management Service © 2019 European Union, [EMSR266] Bad Karlshafen: Delineation Map, v1: https://emergency.Copernicus.eu/mapping/list-of-components/EMSR266; Copernicus Emergency Management Service © 2018 European Union, [EMSR307] Jüterbog: Delineation Map, Monitoring 1: https://emergency.Copernicus.eu/mapping/list-of-components/EMSR307; Copernicus Emergency Management Service © 2019 European Union, [EMSR363] Luckenwalde: Delineation Map, Monitoring 3: https://emergency.Copernicus.eu/mapping/list-of-components/EMSR363; Forstliche Versuchsflächen des Landesbetriebes Forst Brandenburg: (Silvicultural monitoring area for nun moth (Lymantria monacha) defoliation and Metadata: Forstliche Versuchsflächen des Landes Brandenburg Forstliche Umweltkontrolle Geodatensatz https://www.metaver.de/trefferanzeige?cmd=doShowDocument&docuuid=CAEA58C3-EA6D-4BCC-B995-A48CDDD3A0FC&plugid=/ingrid-group:ige-iplug-bb (accessed: May 09, 2022); Resource Reviere/Forstämter Sachsenanhalt https://www.waldgeoportal.de/layers/lzw_geonode_data:geonode:Waldflaeche_FGK (accessed: May 09, 2022); disturbance meta data “Pilotstudie zu den lokalen Auswirkungen des Klimawandels auf die Forstwirtschaft in ausgewählten Regionen Sachsen-Anhalts”; LZW Betr.-FA Letzlingen, Rev. Wannefeld: https://www.nw-fva.de/fileadmin/user_upload/Verwaltung/Publikationen/2015/NWFVA_Bd13_2015_Klimawandel.pdf; Insektenfraß-Schäden / Monitoring BB: https://forst.brandenburg.de/sixcms/media.php/9/bwin_verlust.pdf; https://forst.brandenburg.de/sixcms/media.php/9/efs62.pdf; https://www.researchgate.net/publication/236960911_Untersuchung_zur_Quantifizierung_von_Befallsflachen_phytophager_Insekten_in_Kiefern-Bestanden_mit_Hilfe_von_Fernerkundungs-Methoden_am_Beispiel_des_Nonnenraupenfrasses_im_Jahre_2003_in_der_Schorfheide; Treuenbritzen Waldbrand – Detail _> “Die Auswirkungen des Dürre-jahres 2018 auf den Wald in Brandenburg”: https://forst.brandenburg.de/sixcms/media.php/9/efs67.pdf Waldzustandsbericht Brandenburg 2018; https://mluk.brandenburg.de/cms/media.php/lbm1.a.3310.de/Waldzustandsbericht_BB_2018.pdf p. 22.

AG and TS jointly developed the idea and developed the methods. AG contributed to data acquisition, preparation, and processing, as well as preparation of the manuscript. TS initiated the study, writing, literature research, and final version of the manuscript. Both authors contributed to the article and approved the submitted version.

AG was supported by funding from the FNR in Germany within the project ErWiN—Grant Number: 2219WK54A.

All data used in this study are free and publicly available. The programs used are open-source applications and can be run on a standard computer, independent of the operating system and across platforms. The individual data processing routines are designed to be resource-efficient and make no special demands on memory or computer performance. Processing results can be stored locally and exported in various standard formats (e.g. GeoTiff, csv) and are compatible with common GIS applications. The proposed batch script functionality allows existing time series to be automatically scheduled or manually updated at any time with newly available satellite data, making it a useful satellite-based monitoring tool and early warning system to complement terrestrial surveys and 660 forest health surveys in operational forestry (see Figure 12).

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.