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DOI10.1016/j.foreco.2020.118370
Change in forest condition: Characterizing non-stand replacing disturbances using time series satellite imagery
Coops N.C.; Shang C.; Wulder M.A.; White J.C.; Hermosilla T.
发表日期2020
ISSN0378-1127
卷号474
英文摘要Objective information is required to monitor and characterize disturbances and disturbance regimes as related to changing climate and anthropogenic pressures. Forest disturbances occur over a range of spatial and temporal scales, with varying extent, severity, and persistence. To date, most of our understanding of detecting forest disturbances using remotely sensed imagery has been based on detecting abrupt and rapid, stand replacing disturbances, such as those related to wildfire and forest harvesting. Conversely, more continuous, subtle, and gradual non-stand replacing (NSR) disturbances, such as those related to drought stress or insect infestation have been subject to less focus. This can be attributed to the variability of NSR in space and time, as well as detection difficulties due to their often-subtle alterations to forest canopies and structure. Time series remote sensing techniques offer new opportunities for the capture and characterization of NSR disturbances. In this research, we investigate NSR disturbances as detected using a Landsat time series-based disturbance detection algorithm, Composite2Change (C2C) applied to the 650 Mha forested ecosystems of Canada. We first examined three key characteristics of the spectral trajectory of NSR disturbances: pre-disturbance conditions, disturbance severity and disturbance persistence. From these characteristics, we defined seven typical NSR patterns (exemplars) for Canada's forested ecosystems. Using annual information on predicted stand-level cover and biomass, we found that all NSR exemplars resulted in a loss of canopy cover, ranging from an average loss of between 5 and 31%. For instance, the most common exemplar was linked to a 15% reduction in canopy cover and a 13 Mg/ha reduction in biomass. While stands lost biomass, over the duration of the NSR, most stands increased slightly in stand height likely due to growth of the remaining trees or release from competition from the remaining stems. Comparing exemplars across Canada with independent data on NSR disturbance events confirmed that NSR exemplars that had larger severities, and shorter disturbance persistence times, were more commonly associated with insect infestations (e.g. mountain pine beetle (Dendroctonus ponderosae) and spruce budworm (Choristoneura fumiferana)), whereas exemplars with a lower severity, and longer persistence were more commonly associated with drought events. Developing a nomenclature around remote sensing based characteristics of NSR disturbances provides an approach to both map NSR disturbances and better understand their impact on forest attributes, as well as provide tools to assess if these disturbanes are changing over space and time. © 2020
关键词BiomassDroughtEcosystemsRemote sensingSatellite imagerySpace opticsTime seriesAnthropogenic pressuresChoristoneura fumiferanaDendroctonus ponderosaeDisturbance detectionObjective informationRemote sensing techniquesRemotely sensed imagerySpatial and temporal scaleForestryalgorithmdisturbancedrought stressforest canopyforest ecosysteminsectLandsatremote sensingsatellite imageryspatiotemporal analysistime seriesBiomassCharacteristicsCoveringsDroughtEcosystemsForestryRemote SensingReplacementCanadaChoristoneura fumiferanaDendroctonus ponderosaeHexapoda
语种英语
来源机构Forest Ecology and Management
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/132641
推荐引用方式
GB/T 7714
Coops N.C.,Shang C.,Wulder M.A.,et al. Change in forest condition: Characterizing non-stand replacing disturbances using time series satellite imagery[J]. Forest Ecology and Management,2020,474.
APA Coops N.C.,Shang C.,Wulder M.A.,White J.C.,&Hermosilla T..(2020).Change in forest condition: Characterizing non-stand replacing disturbances using time series satellite imagery.,474.
MLA Coops N.C.,et al."Change in forest condition: Characterizing non-stand replacing disturbances using time series satellite imagery".474(2020).
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