Detecting forest disturbance in the Pacific Northwest from MODIS time series using temporal segmentation

Sulla‐Menashe, Damien; Kennedy, Robert E.; Yang, Zhiqiang; Braaten, Justin; Krankina, Olga N.; Friedl, M. A.

doi:10.1016/j.rse.2013.07.042

Cited by 67 publications

(58 citation statements)

References 56 publications

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“…We calculated the proportion of disturbed Landsat pixels, weighted by magnitude, within each 800-m pixel from 2000 to 2012. All pixels with weighted disturbance N30% were masked, following Sulla-Menashe et al (2014). There is no FVI value associated with masked pixels.…”

Section: Filtering Abrupt Disturbancesmentioning

confidence: 99%

A forest vulnerability index based on drought and high temperatures

Mildrexler

Yang

Cohen

et al. 2016

Remote Sensing of Environment

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Increasing forest stress and tree mortality has been directly linked to combinations of drought and high temperatures. The climatic changes expected during the next decades -large increases in mean temperature, increased heat waves, and significant long-term regional drying in the western USA -will likely increase chronic forest stress and mortality. The aim of this research is to develop and apply a new forest vulnerability index (FVI) associated with drought and high temperatures across the Pacific Northwest region (PNW; Oregon and Washington) of the USA during the MODIS Aqua era (since 2003). Our technique incorporates the alterations to canopy water and energy exchange processes caused by drought and high temperatures with spatially continuous MODIS land surface temperature (LST) and evapotranspiration (ET), and with Parameter-elevation Relationships on Independent Slopes Model (PRISM) precipitation (P) data. With P and ET, we calculate a monthly water balance variable for each individual pixel normalized by forest type group (FTG), and then difference the water balance with the corresponding normalized monthly mean LST to calculate a monthly forest stress index (FSI). We then extract the pixel-specific (800-m resolution) statistically significant temporal trends of the FSI from 2003 to 2012 by month (April to October). The FVI is the slope of the monthly FSI across years, such that there is a FVI for each month. Statistically significant positive slopes indicate interannual increases in stress leading to expected forest vulnerability (positive FVI) for a given month. Positive FVI values were concentrated in the months of August and September, with peak vulnerability occurring at different times for different FTGs. Overall, increased vulnerability rates were the highest in drier FTGs such as Ponderosa Pine, Juniper, and Lodgepole Pine. Western Larch and Fir/Spruce/Mountain Hemlock groups occupy moister sites but also had relatively high proportion of positive FVI values. The Douglas-fir group had the second largest total area of increased vulnerability due to its large areal extent in the study area. Based on an analysis using imagery viewed in Google Earth, we confirm that areas with increased vulnerability are associated with greater amounts of stress and mortality. The FVI is a new way to conceptualize and monitor forest vulnerability based on first-order principles and has the potential to be generalized to other geographical areas.

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Section: Filtering Abrupt Disturbancesmentioning

confidence: 99%

A forest vulnerability index based on drought and high temperatures

Mildrexler

Yang

Cohen

et al. 2016

Remote Sensing of Environment

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“…However, equally important has been the coinciding progression in methodologies used to monitor forest and change dynamics. Earlier approaches that largely focused on bi-temporal change analysis [9,10] are being superseded by advanced methods taking a more dense time-series approach [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

“…Methods focussed on seeking deviations have often used annual or biennial time-steps [11,[16][17][18] and are generally suited for the detection of abrupt change events such as clear-cutting, fire damage, flooding, and wind throw. Methods that use trends describe complete time series trajectories [19,20], and when multiple trend segments are used, are capable of capturing both abrupt and gradual change [12,15] including forest degradation, disease and insect attack, climatic adaptation, and regeneration.…”

Section: Introductionmentioning

confidence: 99%

Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from MODIS Time Series: Does Forest Type Matter?

et al. 2016

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Tropical environments present a unique challenge for optical time series analysis, primarily owing to fragmented data availability, persistent cloud cover and atmospheric aerosols. Additionally, little is known of whether the performance of time series change detection is affected by diverse forest types found in tropical dry regions. In this paper, we develop a methodology for mapping forest clearing in Southeast Asia using a study region characterised by heterogeneous forest types. Moderate Resolution Imaging Spectroradiometer (MODIS) time series are decomposed using Breaks For Additive Season and Trend (BFAST) and breakpoints, trend, and seasonal components are combined in a binomial probability model to distinguish between cleared and stable forest. We found that the addition of seasonality and trend information improves the change model performance compared to using breakpoints alone. We also demonstrate the value of considering forest type in disturbance mapping in comparison to the more common approach that combines all forest types into a single generalised forest class. By taking a generalised forest approach, there is less control over the error distribution in each forest type. Dry-deciduous and evergreen forests are especially sensitive to error imbalances using a generalised forest model i.e., clearances were underestimated in evergreen forest, and overestimated in dry-deciduous forest. This suggests that forest type needs to be considered in time series change mapping, especially in heterogeneous forest regions. Our approach builds towards improving large-area monitoring of forest-diverse regions such as Southeast Asia. The findings of this study should also be transferable across optical sensors and are therefore relevant for the future availability of dense time series for the tropics at higher spatial resolutions.

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“…In total, we collected 106 training samples, among which, 53 were sites with MPB mortality, 37 were clearcuts and 16 were healthy. We selected the relatively small number of training samples, because many studies suggested that trajectory-based methods perform well when trained with a relatively small sample size [23,26,41]. Statistical classifiers require more training data to fully represent each class in the feature space [42].…”

Section: Reference Sample Selectionmentioning

confidence: 99%

“…Details can be found in Kennedy et al [26], and the final effect of temporal segmentation, whether it was under-segmentation (split up into too few parts) or over-segmentation (subdivided into too many parts), was determined by the parameters listed in Table 3. Optimal LandTrendr parameter settings have been tested in the forests of the Northwest Pacific Region at both Landsat and MODIS scales [26,41]. However, because we are not aware of reported parameter results in the literature specific to the Southern Rocky Mountain ecoregion, we tested a range of candidate parameter values (Table 3).…”

Section: Temporal Segmentationmentioning

confidence: 99%

Mapping Mountain Pine Beetle Mortality through Growth Trend Analysis of Time-Series Landsat Data

Chen

Hawbaker

et al. 2014

Remote Sensing

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Disturbances are key processes in the carbon cycle of forests and other ecosystems. In recent decades, mountain pine beetle (MPB; Dendroctonus ponderosae) outbreaks have become more frequent and extensive in western North America. Remote sensing has the ability to fill the data gaps of long-term infestation monitoring, but the elimination of observational noise and attributing changes quantitatively are two main challenges in its effective application. Here, we present a forest growth trend analysis method that integrates Landsat temporal trajectories and decision tree techniques to derive annual forest disturbance maps over an 11-year period. The temporal trajectory component successfully captures the disturbance events as represented by spectral segments, whereas decision tree modeling efficiently recognizes and attributes events based upon the characteristics of the segments. Validated against a point set sampled across a gradient of MPB mortality, 86.74% to 94.00% overall accuracy was achieved with small variability in accuracy among years. In contrast, the overall accuracies of single-date classifications ranged from 37.20% to 75.20% and only become comparable with our approach when the training sample size was increased at least four-fold. This demonstrates that the advantages of this time series work flow exist in its small training sample size requirement. The easily OPEN ACCESSRemote Sens. 2014, 6 5697 understandable, interpretable and modifiable characteristics of our approach suggest that it could be applicable to other ecoregions.

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Detecting forest disturbance in the Pacific Northwest from MODIS time series using temporal segmentation

Cited by 67 publications

References 56 publications

A forest vulnerability index based on drought and high temperatures

A forest vulnerability index based on drought and high temperatures

Mapping Clearances in Tropical Dry Forests Using Breakpoints, Trend, and Seasonal Components from MODIS Time Series: Does Forest Type Matter?

Mapping Mountain Pine Beetle Mortality through Growth Trend Analysis of Time-Series Landsat Data

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