Influence of lidar, Landsat imagery, disturbance history, plot location accuracy, and plot size on accuracy of imputation maps of forest composition and structure

Zald, Harold S. J.; Ohmann, Janet L.; Roberts, Heather M.; Gregory, Matthew J.; Henderson, Emilie B.; McGaughey, Robert J.; Braaten, Justin

doi:10.1016/j.rse.2013.12.013

Cited by 65 publications

(54 citation statements)

References 69 publications

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“…, Zald et al. ), which is why we relied on the more accurately predicted age and pre‐fire biomass variables as proxies. Surface and ladder fuels are the most important contributors to fire behavior in general (Agee and Skinner ), and surface fuels have been found to be positively correlated to fire severity in plantations within the geographic vicinity of the Douglas Complex (Weatherspoon and Skinner ).…”

Section: Limitationsmentioning

confidence: 99%

“…Accurate and spatially complete quantitative information of forest surface and canopy fuels were not available for the Douglas Complex. More broadly, there are significant limitations to spatial predictions of forest structure and fuels using GNN and other methods that rely on passive optical imagery such as Landsat (Keane et al 2001, Pierce et al 2009, Zald et al 2014, which is why we relied on the more accurately predicted age and pre-fire biomass variables as proxies. Surface and ladder fuels are the most important contributors to fire behavior in general (Agee and Skinner 2005), and surface fuels have been found to be positively correlated to fire severity in plantations within the geographic vicinity of the Douglas Complex (Weatherspoon and Skinner 1995).…”

Section: Limitationsmentioning

confidence: 99%

See 1 more Smart Citation

Severe fire weather and intensive forest management increase fire severity in a multi‐ownership landscape

Zald

Dunn

2018

Ecological Applications

136

120

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Many studies have examined how fuels, topography, climate, and fire weather influence fire severity. Less is known about how different forest management practices influence fire severity in multi-owner landscapes, despite costly and controversial suppression of wildfires that do not acknowledge ownership boundaries. In 2013, the Douglas Complex burned over 19,000 ha of Oregon & California Railroad (O&C) lands in Southwestern Oregon, USA. O&C lands are composed of a checkerboard of private industrial and federal forestland (Bureau of Land Management, BLM) with contrasting management objectives, providing a unique experimental landscape to understand how different management practices influence wildfire severity. Leveraging Landsat based estimates of fire severity (Relative differenced Normalized Burn Ratio, RdNBR) and geospatial data on fire progression, weather, topography, pre-fire forest conditions, and land ownership, we asked (1) what is the relative importance of different variables driving fire severity, and (2) is intensive plantation forestry associated with higher fire severity? Using Random Forest ensemble machine learning, we found daily fire weather was the most important predictor of fire severity, followed by stand age and ownership, followed by topographic features. Estimates of pre-fire forest biomass were not an important predictor of fire severity. Adjusting for all other predictor variables in a general least squares model incorporating spatial autocorrelation, mean predicted RdNBR was higher on private industrial forests (RdNBR 521.85 ± 18.67 [mean ± SE]) vs. BLM forests (398.87 ± 18.23) with a much greater proportion of older forests. Our findings suggest intensive plantation forestry characterized by young forests and spatially homogenized fuels, rather than pre-fire biomass, were significant drivers of wildfire severity. This has implications for perceptions of wildfire risk, shared fire management responsibilities, and developing fire resilience for multiple objectives in multi-owner landscapes.

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Section: Limitationsmentioning

confidence: 99%

Section: Limitationsmentioning

confidence: 99%

Severe fire weather and intensive forest management increase fire severity in a multi‐ownership landscape

Zald

Dunn

2018

Ecological Applications

136

120

View full text Add to dashboard Cite

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“…Landsat surface reflectance bands and derived indices have long been used to produce spatio-temporal biomass maps and to differentiate forest structure and composition over landscapes [8,9]. Although the passive Landsat optical data are less sensitive to complex forest conditions [10], their continuous data collection for large scenes since 1972, moderate spatial resolution (30-m × 30-m) and synoptic coverage characteristics can improve cost-efficiency in forest inventories across large spatial and temporal domains [11,12]. Furthermore, improved accuracy of AGB prediction can be attained when Landsat data are combined with LiDAR-derived fine resolution metrics, because Landsat spectral data can more accurately predict species composition [13,14].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

et al. 2017

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Large-area assessment of aboveground tree biomass (AGB) to inform regional or national forest monitoring programs can be efficiently carried out by combining remotely sensed data and field sample measurements through a generic statistical model, in contrast to site-specific models. We integrated forest inventory plot data with spatial predictors from Landsat time-series imagery and LiDAR strip samples at four sites across the eastern USA-Minnesota (MN), Maine (ME), Pennsylvania-New Jersey (PANJ) and South Carolina (SC)-in statistical modeling frameworks to analyze the performance of generic (all sites combined) versus site-specific models. The major objective was to evaluate the prediction accuracy of generic and site-specific models when applied to particular sites. Pixel-level polynomial model fitting was applied to the time-series of near-anniversary date Landsat variables to obtain projected metrics in the target year 2014 for which LiDAR strip samples were available. Two forms of models based on ordinary least-squares multiple linear regressions (MLR) and the random forest (RF) machine learning approach were developed for each site and for the pooled (i.e., generic) reference data frame. The models were evaluated using national forest inventory (NFI) data for the USA. We observed stronger fit statistics with the MLR than with RF for both the site-specific and the generic models. The proportions of variances explained (adjusted R 2 ) with the site-specific models were 0.86, 0.78, 0.82 and 0.92 for ME, MN, PANJ and SC, respectively while the generic model had adjusted R 2 = 0.85. A test of statistical equivalence of observed and predicted AGB for the NFI locations did not reveal equivalence with any of the models, possibly due to the different resolutions of the observed and predicted data. In contrast, predictions by the generic and site-specific models were equivalent. We conclude that a generic model provides accuracies comparable to the site-specific models for large-area AGB assessment across our study sites in the eastern USA.

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“…The accuracy of field data has been previously reported as negatively influencing predictions of forest attributes (McRoberts 2010). However, Zald et al (2014) reported that improved GPS plot locations had little influence on the accuracy of predictive maps that link remote sensing and field data in their study. These authors affirmed that factors other than accuracy of field data in relation to the spatial resolution of explanatory data are more relevant in determining the overall accuracy, and that standard plot locations are sufficient for large-landscape mapping.…”

Section: Detection Of Selective Logging Impacts With Optical Satellitmentioning

confidence: 90%

Estimates of selective logging impacts in tropical forest canopy cover using RapidEye imagery and field data

Pinagé¹,

Matricardi²,

Leal³

et al. 2016

iForest

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Selective logging is one of the leading causes of forest degradation in the Brazilian Amazon region. The Brazilian Federal government has adopted a forest concession policy as a strategy to mitigate impacts of selective logging and regulate operations of the tropical timber industry in Brazil. This study used fractional forest coverage derived from satellite imagery and field data to assess forest degradation in two selectively logged study sites within the Jamari National Forest, a protected area located in the western Brazilian state of Rondônia. Initially, we estimated the fractional coverage from vegetation indices using RapidEye imagery and compared to gap fraction data derived from hemispherical photos acquired in the field. Subsequently, we estimated the impacts of different types of selective logging activities (log decks, primary and secondary roads, tree fall gaps, and skid trails) on forest cover using the fractional coverage dataset. The NDVI showed the highest R 2 (0.56), indicating that 56% of the sample variation in fractional coverage derived from ground measurements can be explained by fractional coverage derived from the NDVI model. Our results also showed that the intensity of canopy impacts may vary according to the selective logging activity, ranging from skid trails to log decks which had the lightest and the heaviest canopy impacts, respectively.

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Influence of lidar, Landsat imagery, disturbance history, plot location accuracy, and plot size on accuracy of imputation maps of forest composition and structure

Cited by 65 publications

References 69 publications

Severe fire weather and intensive forest management increase fire severity in a multi‐ownership landscape

Severe fire weather and intensive forest management increase fire severity in a multi‐ownership landscape

Evaluating Site-Specific and Generic Spatial Models of Aboveground Forest Biomass Based on Landsat Time-Series and LiDAR Strip Samples in the Eastern USA

Estimates of selective logging impacts in tropical forest canopy cover using RapidEye imagery and field data

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