Using Intra-Annual Landsat Time Series for Attributing Forest Disturbance Agents in Central Europe

Oeser, Julian; Pflugmacher, Dirk; Senf, Cornelius; Heurich, Marco; Hostert, Patrick

doi:10.3390/f8070251

Cited by 44 publications

(36 citation statements)

References 100 publications

(188 reference statements)

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“…For example, areas with high snow cover tend to be located at higher elevations where higher levels of natural forest disturbance are found (Oeser et al. ), and disturbed forest stands are important habitat features for lynx, red deer and roe deer (Heurich et al. ; Filla et al.…”

Section: Discussionmentioning

confidence: 99%

Habitat metrics based on multi‐temporal Landsat imagery for mapping large mammal habitat

Oeser

Heurich

Senf

et al. 2019

Remote Sens Ecol Conserv

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Up‐to‐date and fine‐scale habitat information is essential for managing and conserving wildlife. Studies assessing wildlife habitat commonly rely on categorical land‐cover maps as predictors in habitat models. However, broad land‐cover categories often do not adequately capture key habitat features and generating robust land‐cover maps is challenging and laborious. Continuous variables derived directly from satellite imagery provide an alternative for capturing land‐cover characteristics in habitat models. Improved data availability and processing capacities now allow integrating all available images from medium‐resolution sensors in compositing approaches that derive spectral‐temporal metrics at the pixel level, summarizing spectral responses over time. In this study, we assessed the usefulness of such metrics derived from Landsat imagery for mapping wildlife habitat. We categorize spectral‐temporal metrics into habitat metrics characterizing different aspects of wildlife habitat. Comparing the performance of these metrics against categorical land‐cover maps in habitat models for lynx, red deer and roe deer, we found that models using habitat metrics consistently outperformed models based on categorical land‐cover maps, with average improvements of 13.7% in model AUC and 9.7% in the Continuous Boyce Index. Performance increases were larger for seasonal habitat models, indicating that the habitat metrics capture intra‐annual variability in habitat conditions better than land‐cover maps. Comparing suitability maps to ancillary data further revealed that our habitat metrics were sensitive to fine‐scale heterogeneity in habitat associated with forest structure. Overall, our study highlights the considerable potential of Landsat‐based spectral temporal metrics for assessing wildlife habitat. Given these metrics can be derived directly and in an automatized fashion from globally and freely available Landsat imagery, they open up new possibilities for monitoring habitat dynamics in space and time.

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Section: Discussionmentioning

confidence: 99%

Habitat metrics based on multi‐temporal Landsat imagery for mapping large mammal habitat

Oeser

Heurich

Senf

et al. 2019

Remote Sens Ecol Conserv

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“…In the rare instances where an object was comprised entirely of cloudy pixels, a mean value of the year before and after was assigned to the object. Based on previous land cover change studies (e.g., [33,[49][50][51]), the following spectral indices were calculated: normalized difference vegetation index (NDVI), red-green index (RGI), band5 (B5), the Band 5/Band 4 ratio (B54R), normalized difference moisture index (NDMI), normalized burn ratio (NBR), tasseled cap brightness (BRI), tasseled cap greenness (GRE) and tasseled cap wetness (WET) ( Table 1). ρ red = red reflectance, ρ green = green reflectance, ρ NIR = near infrared reflectance, ρ SWIR = short − wave infrared reflectance, ρ blue = blue reflectance.…”

Section: Object-level Spectral Indices and Temporal Trajectoriesmentioning

confidence: 99%

Object-Based Classification of Forest Disturbance Types in the Conterminous United States

Huo¹,

Boschetti²,

Sparks³

2019

Remote Sensing

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Forest ecosystems provide critical ecosystem goods and services, and any disturbance-induced changes can have cascading impacts on natural processes and human socioeconomic systems. Forest disturbance frequency, intensity, and spatial and temporal scale can be altered by changes in climate and human activity, but without baseline forest disturbance data, it is impossible to quantify the magnitude and extent of these changes. Methodologies for quantifying forest cover change have been developed at the regional-to-global scale via several approaches that utilize data from high (e.g., IKONOS, Quickbird), moderate (e.g., Landsat) and coarse (e.g., Moderate Resolution Imaging Spectroradiometer (MODIS)) spatial resolution satellite imagery. While detection and quantification of forest cover change is an important first step, attribution of disturbance type is critical missing information for establishing baseline data and effective land management policy. The objective here was to prototype and test a semi-automated methodology for characterizing high-magnitude (>50% forest cover loss) forest disturbance agents (stress, fire, stem removal) across the conterminous United States (CONUS) from 2003–2011 using the existing University of Maryland Landsat-based Global Forest Change Product and Web-Enabled Landsat Data (WELD). The Forest Cover Change maps were segmented into objects based on temporal and spatial adjacency, and object-level spectral metrics were calculated based on WELD reflectance time series. A training set of objects with known disturbance type was developed via high-resolution imagery and expert interpretation, ingested into a Random Forest classifier, which was then used to attribute disturbance type to all 15,179,430 forest loss objects across CONUS. Accuracy assessments of the resulting classification was conducted with an independent dataset consisting of 4156 forest loss objects. Overall accuracy was 88.1%, with the highest omission and commission errors observed for fire (32.8%) and stress (31.9%) disturbances, respectively. Of the total 172,686 km2 of forest loss, 83.75% was attributed to stem removal, 10.92% to fire and 5.33% to stress. The semi-automated approach described in this paper provides a promising framework for the systematic characterization and monitoring of forest disturbance regimes.

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“…The disturbance year was also estimated from the Landsat time series, with 80% of the distur- specific agents was conducted, but contextual knowledge of the sites from local field studies suggests that the vast majority of disturbances resulted from either wind or Ips typographus infestation. As these two disturbance agents are often difficult to separate at the pixel level using Landsat data (Oeser, Pflugmacher, Senf, Heurich, & Hostert, 2017), we decided to conduct our analyses of spatiotemporal patterns at the level of the disturbance regime, that is jointly addressing wind and bark beetle disturbance. For details on detecting disturbances in European temperate forests from Landsat see .…”

Section: Study Sites and Disturbance Datamentioning

confidence: 99%

Natural disturbances are spatially diverse but temporally synchronized across temperate forest landscapes in Europe

Senf

Seidl

2017

Global Change Biology

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Natural disturbance regimes are changing substantially in forests around the globe. However, large-scale disturbance change is modulated by a considerable spatiotemporal variation within biomes. This variation remains incompletely understood particularly in the temperate forests of Europe, for which consistent large-scale disturbance information is lacking. Here, our aim was to quantify the spatiotemporal patterns of forest disturbances across temperate forest landscapes in Europe using remote sensing data and determine their underlying drivers. Specifically, we tested two hypotheses: (1) Topography determines the spatial patterns of disturbance, and (2) climatic extremes synchronize natural disturbances across the biome. We used novel Landsat-based maps of forest disturbances 1986-2016 in combination with landscape analysis to compare spatial disturbance patterns across five unmanaged forest landscapes with varying topographic complexity. Furthermore, we analyzed annual estimates of disturbances for synchronies and tested the influence of climatic extremes on temporal disturbance patterns. Spatial variation in disturbance patterns was substantial across temperate forest landscapes. With increasing topographic complexity, natural disturbance patches were smaller, more complex in shape, more dispersed, and affected a smaller portion of the landscape. Temporal disturbance patterns, however, were strongly synchronized across all landscapes, with three distinct waves of high disturbance activity between 1986 and 2016. All three waves followed years of pronounced drought and high peak wind speeds. Natural disturbances in temperate forest landscapes of Europe are thus spatially diverse but temporally synchronized. We conclude that the ecological effect of natural disturbances (i.e., whether they are homogenizing a landscape or increasing its heterogeneity) is strongly determined by the topographic template. Furthermore, as the strong biome-wide synchronization of disturbances was closely linked to climatic extremes, large-scale disturbance episodes are likely in Europe's temperate forests under climate changes.

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Using Intra-Annual Landsat Time Series for Attributing Forest Disturbance Agents in Central Europe

Cited by 44 publications

References 100 publications

Habitat metrics based on multi‐temporal Landsat imagery for mapping large mammal habitat

Habitat metrics based on multi‐temporal Landsat imagery for mapping large mammal habitat

Object-Based Classification of Forest Disturbance Types in the Conterminous United States

Natural disturbances are spatially diverse but temporally synchronized across temperate forest landscapes in Europe

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