Modeling a Historical Mountain Pine Beetle Outbreak Using Landsat MSS and Multiple Lines of Evidence

Assal, Timothy J.; Sibold, Jason S.; Reich, Robin M.

doi:10.1016/j.rse.2014.09.002

Cited by 37 publications

(39 citation statements)

References 72 publications

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“…Studies have reported a decrease [106,107], no change [108][109][110][111][112], an increase [113,114], and a mixed response [115,116] in fire severity following bark beetle-caused tree mortality. The extent and severity of bark-beetle caused tree mortality can vary greatly at the landscape scale, e.g., [117][118][119]; how this variability in severity of mortality influences fire severity is unclear [51], although time since attack is an important factor. The decreases in ACF and CBD and the increase in CBH that we found six years after a bark beetle outbreak in our study area suggest a corresponding decrease in potential crown fire, although additional research on the relationship between beetle-caused tree mortality and fire is still needed, given the complex temporal and spatial variation in these two processes.…”

Section: Discussionmentioning

confidence: 99%

Prediction of Forest Canopy and Surface Fuels from Lidar and Satellite Time Series Data in a Bark Beetle-Affected Forest

Bright

Hudak

Meddens

et al. 2017

Forests

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Wildfire behavior depends on the type, quantity, and condition of fuels, and the effect that bark beetle outbreaks have on fuels is a topic of current research and debate. Remote sensing can provide estimates of fuels across landscapes, although few studies have estimated surface fuels from remote sensing data. Here we predicted and mapped field-measured canopy and surface fuels from light detection and ranging (lidar) and Landsat time series explanatory variables via random forest (RF) modeling across a coniferous montane forest in Colorado, USA, which was affected by mountain pine beetles (Dendroctonus ponderosae Hopkins) approximately six years prior. We examined relationships between mapped fuels and the severity of tree mortality with correlation tests. RF models explained 59%, 48%, 35%, and 70% of the variation in available canopy fuel, canopy bulk density, canopy base height, and canopy height, respectively (percent root-mean-square error (%RMSE) = 12-54%). Surface fuels were predicted less accurately, with models explaining 24%, 28%, 32%, and 30% of the variation in litter and duff, 1 to 100-h, 1000-h, and total surface fuels, respectively (%RMSE = 37-98%). Fuel metrics were negatively correlated with the severity of tree mortality, except canopy base height, which increased with greater tree mortality. Our results showed how bark beetle-caused tree mortality significantly reduced canopy fuels in our study area. We demonstrated that lidar and Landsat time series data contain substantial information about canopy and surface fuels and can be used for large-scale efforts to monitor and map fuel loads for fire behavior modeling at a landscape scale.

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Section: Discussionmentioning

confidence: 99%

Prediction of Forest Canopy and Surface Fuels from Lidar and Satellite Time Series Data in a Bark Beetle-Affected Forest

Bright

Hudak

Meddens

et al. 2017

Forests

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“…Based on canopy color, the damage stages caused by MPB are classified into green, yellow, red, and gray stages [22]. Therefore, MPB outbreak time could be reported by aerial detection surveys or field surveys from local agencies, such as the USDA (United States Department of Agriculture) Forest Service [23][24][25]. Due to their clear …”

Section: Introductionmentioning

confidence: 99%

Detecting Shoot Beetle Damage on Yunnan Pine Using Landsat Time-Series Data

Huang

Zong

et al. 2018

Forests

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Tomicus yunnanensis and Tomicus minor have caused serious shoot damage in Yunnan pine forests in the Yunnan Province of China. However, very few remote sensing studies have estimated the shoot damage ratio (SDR). Thus, we used multi-date Landsat satellite imagery to quantify SDRs and assess the possibility of using spectral indices to determine the beetle outbreak time and spread direction. A new threshold-based classification method was proposed to identify damage levels (i.e., healthy, slightly to moderately infested, and severely infested forests) using time series of moisture stress index (MSI). Permanent plots and temporal field inspection data were both used as references for training and evaluation. Results show that a single threshold value can produce a total classification accuracy of 86.38% (Kappa = 0.80). Furthermore, time series maps detailing damage level were reconstructed from 2004 to 2016. The shoot beetle outbreak year was estimated to be 2013. Another interesting finding is the movement path of the geometric center of severe damage, which is highly consistent with the wind direction. We conclude that the time series of shoot damage level maps can be produced by using continuous MSI images. This method is very useful to foresters for determining the outbreak time and spread direction.

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“…The user and producer accuracies were also calculated through a confusion matrix [19]. A kappa coefficient of more than 0.8 indicates a satisfactory accuracy of classified images, i.e., classified images are analogous to the reference data [50][51][52][53][54].…”

Section: Land-use and Landcover Classification And Accuracy Assessmentmentioning

confidence: 99%

A Remote Sensing Approach to Environmental Monitoring in a Reclaimed Mine Area

Padmanaban

Bhowmik

Cabral

2017

IJGI

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Abstract:Mining for resources extraction may lead to geological and associated environmental changes due to ground movements, collision with mining cavities, and deformation of aquifers. Geological changes may continue in a reclaimed mine area, and the deformed aquifers may entail a breakdown of substrates and an increase in ground water tables, which may cause surface area inundation. Consequently, a reclaimed mine area may experience surface area collapse, i.e., subsidence, and degradation of vegetation productivity. Thus, monitoring short-term landscape dynamics in a reclaimed mine area may provide important information on the long-term geological and environmental impacts of mining activities. We studied landscape dynamics in Kirchheller Heide, Germany, which experienced extensive soil movement due to longwall mining without stowing, using Landsat imageries between 2013 and 2016. A Random Forest image classification technique was applied to analyze land-use and landcover dynamics, and the growth of wetland areas was assessed using a Spectral Mixture Analysis (SMA). We also analyzed the changes in vegetation productivity using a Normalized Difference Vegetation Index (NDVI). We observed a 19.9% growth of wetland area within four years, with 87.2% growth in the coverage of two major waterbodies in the reclaimed mine area. NDVI values indicate that the productivity of 66.5% of vegetation of the Kirchheller Heide was degraded due to changes in ground water tables and surface flooding. Our results inform environmental management and mining reclamation authorities about the subsidence spots and priority mitigation areas from land surface and vegetation degradation in Kirchheller Heide.

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Modeling a Historical Mountain Pine Beetle Outbreak Using Landsat MSS and Multiple Lines of Evidence

Cited by 37 publications

References 72 publications

Prediction of Forest Canopy and Surface Fuels from Lidar and Satellite Time Series Data in a Bark Beetle-Affected Forest

Prediction of Forest Canopy and Surface Fuels from Lidar and Satellite Time Series Data in a Bark Beetle-Affected Forest

Detecting Shoot Beetle Damage on Yunnan Pine Using Landsat Time-Series Data

A Remote Sensing Approach to Environmental Monitoring in a Reclaimed Mine Area

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