Mapping forest change using stacked generalization: An ensemble approach

Healey, Sean P.; Cohen, Warren B.; Yang, Zhiqiang; Brewer, C. Kenneth; Brooks, Evan B.; Gorelick, Noel; Hernandez, Alexander J.; Huang, Chengquan; Hughes, Michael J.; Kennedy, Robert E.; Loveland, Thomas R.; Moisen, Gretchen G.; Schroeder, Todd A.; Stehman, Stephen V.; Vogelmann, James E.; Woodcock, Curtis E.; Yang, Limin; Zhu, Zhe

doi:10.1016/j.rse.2017.09.029

Cited by 214 publications

(124 citation statements)

References 62 publications

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“…However, several automated disturbance-mapping processes based on remote sensing imagery from satellites such as Landsat are beginning to offer high-quality disturbance maps across the country and the globe [62,63]. To the degree that forest disturbance affects water yield-and all evidence here and elsewhere suggests that it does-automated remote disturbance detection offers tremendous opportunity for water management decision support.…”

Section: Usefulness Of Swat Outputs In Forested Watershedsmentioning

confidence: 99%

Improved Prediction of Stream Flow Based on Updating Land Cover Maps with Remotely Sensed Forest Change Detection

Hernandez

Healey

Huang

et al. 2018

Forests

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Abstract:The water balance in a watershed can be disrupted by forest disturbances such as harvests and fires. Techniques to accurately and efficiently map forest cover changes due to disturbance are evolving quickly, and it is of interest to ask how useful maps of different types of disturbances over time can be in the prediction of water yield. We assessed the benefits of using land cover maps produced at annual vs. five-year intervals in the prediction of monthly streamflows across 10 watersheds contained entirely within the US National Forest System. We found that annually updating land cover maps with forest disturbance data significantly improved water yield predictions using the Soil and Water Assessment Tool (SWAT; p < 0.01 improvement for both the Nash-Sutcliffe efficiency measure and the ratio of the root mean square error to the standard deviation of the measured data). Improvement related to using annually updated land cover maps was directly related to the amount of disturbance observed in a watershed. Our results lay a foundation to apply new high-resolution disturbance datasets in the field of hydrologic modeling to monitor ungauged watersheds and to explore potential water yield changes in watersheds if climate conditions or management practices were to change forest disturbance processes.

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Section: Usefulness Of Swat Outputs In Forested Watershedsmentioning

confidence: 99%

Improved Prediction of Stream Flow Based on Updating Land Cover Maps with Remotely Sensed Forest Change Detection

Hernandez

Healey

Huang

et al. 2018

Forests

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“…Single Landsat pixel-size (30 m × 30 m) plots were analyzed throughout the baseline and analysis periods. The response design was created for the US Forest Service Landscape Change Monitoring System (LCMS) [28] and USGS Landscape Change Monitoring Assessment and Projection (LCMAP) [29] projects to provide consistent depictions of land cover, land use, and change process. Rigorous analyst training and calibration was used to overcome the subjective nature of analyzing data in this manner.…”

Section: Accuracy Assessment Methodsmentioning

confidence: 99%

“…The southwestern United States, including the Rio Grande National Forest study area, has been impacted for the last 20 years by prolonged drought conditions, contributing to slow-onset insect infestations including mountain pine beetle and spruce bark beetle [24,28]. This study focused on the change that occurred in 2013 and 2014.…”

Section: Rio Grande National Forestmentioning

confidence: 99%

An Evaluation of Forest Health Insect and Disease Survey Data and Satellite-Based Remote Sensing Forest Change Detection Methods: Case Studies in the United States

2018

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Abstract:The Operational Remote Sensing (ORS) program leverages Landsat and MODIS data to detect forest disturbances across the conterminous United States (CONUS). The ORS program was initiated in 2014 as a collaboration between the US Department of Agriculture Forest Service Geospatial Technology and Applications Center (GTAC) and the Forest Health Assessment and Applied Sciences Team (FHAAST). The goal of the ORS program is to supplement the Insect and Disease Survey (IDS) and MODIS Real-Time Forest Disturbance (RTFD) programs with imagery-derived forest disturbance data that can be used to augment traditional IDS data. We developed three algorithms and produced ORS forest change products using both Landsat and MODIS data. These were assessed over Southern New England and the Rio Grande National Forest. Reference data were acquired using TimeSync to conduct an independent accuracy assessment of IDS, RTFD, and ORS products. Overall accuracy for all products ranged from 71.63% to 92.55% in the Southern New England study area and 63.48% to 79.13% in the Rio Grande National Forest study area. While the accuracies attained from the assessed products are somewhat low, these results are similar to comparable studies. Although many ORS products met or exceeded the overall accuracy of IDS and RTFD products, the differences were largely statistically insignificant at the 95% confidence interval. This demonstrates the current implementation of ORS is sufficient to provide data to augment IDS data.

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“…The six scenes (given by numeric WRS-2 Path/Row) included one each in: northern Maine ('ME': 12/28, excluding the Canadian portion); eastern Pennsylvania and central New Jersey ('PA/NJ': 14/32); coastal South Carolina ('SC': 16/37); northern Minnesota ('MN': 27/27); northwestern Colorado ('CO': 35/32); and western Oregon ('OR': 45/30) ( figure 3). The selected areas represented a wide range of forest ecosystems and disturbance processes, as described by Cohen et al (2017) and Healey et al (2018). Small-footprint lidar was collected at each site in the pattern displayed in figure 3, and a waveform simulator developed by the GEDI Science Definition Team was used to simulate GEDI waveforms from the airborne data.…”

Section: Supporting Datasetsmentioning

confidence: 99%

Statistical properties of hybrid estimators proposed for GEDI—NASA’s global ecosystem dynamics investigation

Patterson

Healey

Ståhl

et al. 2019

Environ. Res. Lett.

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NASA's Global Ecosystem Dynamics Investigation (GEDI) mission will collect waveform lidar data at a dense sample of ∼25 m footprints along ground tracks paralleling the orbit of the International Space Station (ISS). GEDI's primary science deliverable will be a 1 km grid of estimated mean aboveground biomass density (Mg ha −1 ), covering the latitudes overflown by ISS (51.6°S to 51.6°N). One option for using the sample of waveforms contained within an individual grid cell to produce an estimate for that cell is hybrid inference, which explicitly incorporates both sampling design and model parameter covariance into estimates of variance around the population mean. We explored statistical properties of hybrid estimators applied in the context of GEDI, using simulations calibrated with lidar and field data from six diverse sites across the United States. We found hybrid estimators of mean biomass to be unbiased and the corresponding estimators of variance appeared to be asymptotically unbiased, with under-estimation of variance by approximately 20% when data from only two clusters (footprint tracks) were available. In our study areas, sampling error contributed more to overall estimates of variance than variability due to the model, and it was the design-based component of the variance that was the source of the variance estimator bias at small sample sizes. These results highlight the importance of maximizing GEDI's sample size in making precise biomass estimates. Given a set of assumptions discussed here, hybrid inference provides a viable framework for estimating biomass at the scale of a 1 km grid cell while formally accounting for both variability due to the model and sampling error.

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Mapping forest change using stacked generalization: An ensemble approach

Cited by 214 publications

References 62 publications

Improved Prediction of Stream Flow Based on Updating Land Cover Maps with Remotely Sensed Forest Change Detection

Improved Prediction of Stream Flow Based on Updating Land Cover Maps with Remotely Sensed Forest Change Detection

An Evaluation of Forest Health Insect and Disease Survey Data and Satellite-Based Remote Sensing Forest Change Detection Methods: Case Studies in the United States

Statistical properties of hybrid estimators proposed for GEDI—NASA’s global ecosystem dynamics investigation

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