Monitoring Canada’s forests. Part 1: Completion of the EOSD land cover project

Wulder, Michael A.; White, Joanne C.; Cranny, Morgan; Hall, Ronald J.; Luther, Joan E.; Beaudoin, A.; Goodenough, D.G.; Dechka, J.A.

doi:10.5589/m08-066

Cited by 211 publications

(153 citation statements)

References 32 publications

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“…To characterize land cover at the study sites, 30 m resolution National Land Cover Database (NLCD) data were used for sites in the US, and Earth Observation for Sustainable Development of Forests (EOSD) data were used for sites in Canada (Table 1) (Vogelmann et al, 2001;Wulder et al, 2008). The MODIS Collection 5 Land Cover Type product classification at 500 m resolution was also used to characterize land cover .…”

Section: Study Sitesmentioning

confidence: 99%

Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery

Klosterman

Hufkens²,

Gray³

et al. 2014

Biogeosciences

275

233

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Abstract. Plant phenology regulates ecosystem services at local and global scales and is a sensitive indicator of global change. Estimates of phenophase transition dates, such as the start of spring or end of fall, can be derived from sensorbased time series, but must be interpreted in terms of biologically relevant events. We use the PhenoCam archive of digital repeat photography to implement a consistent protocol for visual assessment of canopy phenology at 13 temperate deciduous forest sites throughout eastern North America, and to perform digital image analysis for time-series-based estimation of phenophase transition dates. We then compare these results to remote sensing metrics of phenophase transition dates derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors. We present a new type of curve fit that uses a generalized sigmoid function to estimate phenology dates, and we quantify the statistical uncertainty of phenophase transition dates estimated using this method. Results show that the generalized sigmoid provides estimates of dates with less statistical uncertainty than other curve-fitting methods. Additionally, we find that dates derived from analysis of high-frequency PhenoCam imagery have smaller uncertainties than satellite remote sensing metrics of phenology, and that dates derived from the remotely sensed enhanced vegetation index (EVI) have smaller uncertainty than those derived from the normalized difference vegetation index (NDVI). Near-surface time-series estimates for the start of spring are found to closely match estimates derived from visual assessment of leaf-out, as well as satellite remote-sensing-derived estimates of the start of spring. However late spring and fall phenology metrics exhibit larger differences between near-surface and remote scales. Differences in late spring phenology between near-surface and remote scales are found to correlate with a landscape metric of deciduous forest cover. These results quantify the effect of landscape heterogeneity when aggregating to the coarser spatial scales of remote sensing, and demonstrate the importance of accurate curve fitting and vegetation index selection when analyzing and interpreting phenology time series.

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Section: Study Sitesmentioning

confidence: 99%

Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery

Klosterman

Hufkens²,

Gray³

et al. 2014

Biogeosciences

275

233

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“…Approximately 65% of Canada is occupied by forest-dominated ecosystems [1], of which 350 million hectares are represented by trees and other wooded land [2], with the remainder dominated by lakes and wetlands. Knowledge on the extent, condition, and management of forest resources is critical to meet both national [3,4] and international [5] expectations of sustainable forest management and to support reporting requirements.…”

Section: Introductionmentioning

confidence: 99%

Differentiation of Alternate Harvesting Practices Using Annual Time Series of Landsat Data

Jarron

Hermosilla

Coops

et al. 2016

Forests

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Sustainable forest management practices allow for a range of harvest prescriptions, including clearcut, clearcut with residual, and partial or selective cutting, which are largely distinguished by the amount of canopy cover removed. The different prescriptions are aimed to emulate natural disturbance, encourage regeneration (seed trees), or offer other ecosystem services, such as the maintenance of local biodiversity or habitat features. Using remotely sensed data, stand-replacing disturbance associated with clearcutting is commonly accurately detected. Novel time series-based change detection products offer an opportunity to determine the capacity to detect and label a wider range of harvest practices. In this research, we demonstrate the capacity of time series imagery, spectral metrics, and related attributed change products, to distinguish between different harvesting practices over a study area in central British Columbia, Canada. Producer's accuracy of harvest attribution was 79%, with 93% of harvest blocks >5 ha accurately identified. In relation to the amount of canopy cover removed, clearcut harvesting was the most accurately classified (84%), followed by clearcut with residual (79%), and partial cut (64%). Applying detailed spectral metrics derived from Landsat data revealed clearcut and partial cuts to be spectrally distinct. The annual nature of the Landsat time series also offers spatial harvest information within typical, often decadal, forest inventory update cycles. The statistically significant (p < 0.05) relationship between harvest practices and Landsat spectral information indicates a capacity to add increased attribution richness to remote sensing depictions of forest harvest.

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“…Although suitable for detection of large--area disturbances such as fire and large--scale clearcuts, coarse--resolution imagery is less capable of detecting forest management activities than finer--grained observations from satellites such as Landsat (Skole and Tucker, 1993; Tucker and 5 Townshend, 2000;Bucha & Stibig, 2008;Wulder et al, 2008;Potapov et al, 2009). Studies that use imagery of finer spatial grain have either mapped at too coarse a temporal grain to detect short--term changes in forest disturbance rate Hansen et al, 2010) The study reported here was derived from the first two phases of NAFD, which employed a geographic sample of Landsat observations at a relatively high temporal frequency (approximately annual time step) over a 20--year period to characterize the dynamics of recent US forest disturbance history.…”

mentioning

confidence: 99%

United States Forest Disturbance Trends Observed Using Landsat Time Series

et al. 2013

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Disturbance events strongly affect the composition, structure, and function of forest ecosystems; however, existing U.S. land management inventories were not designed to monitor disturbance. To begin addressing this gap, the North American Forest Dynamics (NAFD) project has examined a geographic sample of 50 Landsat satellite image time series to assess trends in forest disturbance across the conterminous United States for 1985--2005. The geographic sample design used a probability--based scheme to encompass major forest types and maximize geographic dispersion. For each sample location disturbance was identified in the Landsat series using the Vegetation Change Tracker (VCT) algorithm.The NAFD analysis indicates that, on average, 2.77 Mha/yr of forests were disturbed annually, representing 1.09%/yr of US forestland. These satellite--based national disturbance rates estimates tend to be lower than those derived from land management inventories, reflecting both methodological and definitional differences. In particular the VCT approach used with a biennial time step has limited sensitivity to low--intensity disturbances. Unlike prior satellite studies, our biennial forest disturbance rates vary by nearly a factor of two between high and low years. High western US disturbance rates were associated with active fire years and insect activity, while variability in the east is more strongly related to harvest rates in managed forests. We note that generating a geographic sample based on representing forest type and variability may be problematic since the spatial pattern of disturbance does not necessarily correlate with forest type. We also find that the prevalence of diffuse, non--stand clearing disturbance in US forests makes the application of a biennial geographic sample problematic. Future satellite--based studies of disturbance at regional and national scales should focus on wall--to--wall analyses with annual time step for improved accuracy. IntroductionChange is ubiquitous in forest ecosystems. Forests experience both seasonality as well as long--term growth cycles that can vary in duration between 50 years and 500 or more years (Waring & Running, 2007). These long--term changes are punctuated by mostly short--term disturbances from fire, insects, disease, and harvest which strongly alter the state and functioning of the forest (He & Mlandenoff, 1999). Both climate change and the increasing global demand for wood and fiber products are likely to drive increases in forest disturbance rates Nepstad et al., 2008). These changes in disturbance will alter the water and carbon cycles of forest stands as well as impact the habitat and biodiversity of these ecosystems (Lindenmayer et al., 2006;Gardner et al., 2009). With respect to the carbon cycle, forest disturbance is now recognized as a major driver of non--fossil--fuels--related terrestrial fluxes to the atmosphere (Running, 2008; Amiro et al., 2010).To effectively understand how forest disturbance impacts forest state and functioning, disturbance rates...

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Monitoring Canada’s forests. Part 1: Completion of the EOSD land cover project

Cited by 211 publications

References 32 publications

Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery

Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery

Differentiation of Alternate Harvesting Practices Using Annual Time Series of Landsat Data

United States Forest Disturbance Trends Observed Using Landsat Time Series

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