Proceedings of the eighth annual forest inventory and analysis symposium

McRoberts, Ronald E.; Reams, Gregory A.; Deusen, Paul C. Van; McWilliams, William H.

doi:10.2737/wo-gtr-79

Cited by 6 publications

(2 citation statements)

References 47 publications

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“…2). Fuel loads of fine woody debris (FWD) in 2008 were estimated for woody fuels between 0 and 0.64 cm in diameter (1-hour fuels), 0.64 and 2.54 cm in diameter (10-hour fuels), and 2.54 and 7.6 cm in diameter (100-hour fuels) within the central 2 m section of each 15 m transect and were converted to fuel loads (Mg ha −1 ) per Woodall and Monleon (2006) for ponderosa pine. In 2016, the photoload fuel sampling technique (Keane and Dickinson 2007) was used to estimate CWD and each FWD woody fuel size class (biomass in Mg ha −1 ) to speed field sampling.…”

Section: Field Methodsmentioning

confidence: 99%

Short- and long-term effects of ponderosa pine fuel treatments intersected by the Egley Fire Complex, Oregon, USA

et al. 2019

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Background: Fuel treatments are widely used to alter fuels in forested ecosystems to mitigate wildfire behavior and effects. However, few studies have examined long-term ecological effects of interacting fuel treatments (commercial harvests, pre-commercial thinnings, pile and burning, and prescribed fire) and wildfire. Using annually fitted Landsat satellite-derived Normalized Burn Ratio (NBR) curves and paired pre-fire treated and untreated field sites, we tested changes in the differenced NBR (dNBR) and years since treatment as predictors of biophysical attributes one and nine years after the 2007 Egley Fire Complex in Oregon, USA. We also assessed short-and longterm fuel treatment impacts on field-measured attributes one and nine years post fire. Results: One-year post-fire burn severity (dNBR) was lower in treated than in untreated sites across the Egley Fire Complex. Annual NBR trends showed that treated sites nearly recovered to pre-fire values four years post fire, while untreated sites had a slower recovery rate. Time since treatment and dNBR significantly predicted tree canopy and understory green vegetation cover in 2008, suggesting that tree canopy and understory vegetation cover increased in areas that were treated recently pre fire. Live tree density was more affected by severity than by pre-fire treatment in either year, as was dead tree density one year post fire. In 2008, neither treatment nor severity affected percent cover of functional groups (shrub, graminoid, forb, invasive, and moss-lichen-fungi); however, by 2016, shrub, graminoid, forb, and invasive cover were higher in high-severity burn sites than in low-severity burn sites. Total fuel loads nine years post fire were higher in untreated, high-severity burn sites than any other sites. Tree canopy cover and density of trees, saplings, and seedlings were lower nine years post fire than one year post fire across treatments and severity, whereas live and dead tree basal area, understory surface cover, and fuel loads increased. Conclusions: Pre-fire fuel treatments effectively lowered the occurrence of high-severity wildfire, likely due to successful pre-fire tree and sapling density and surface fuels reduction. This study also quantified the changes in vegetation and fuels from one to nine years post fire. We suggest that low-severity wildfire can meet prescribed fire management objectives of lowering surface fuel accumulations while not increasing overstory tree mortality.

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Section: Field Methodsmentioning

confidence: 99%

Short- and long-term effects of ponderosa pine fuel treatments intersected by the Egley Fire Complex, Oregon, USA

et al. 2019

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“…Since its first release of a full state wide data product in 1997, the Cropland Data Layer (CDL) product of the U.S. Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) has been widely used by growers, agricultural industry, governments, educators and students, and researchers world-wide for crop production, agricultural production planning and management, government policy formulation and decision making, teaching, and various research activities (Liknes et al, 2009;Thompson, Prokopy;Hao et al, 2015;Lark et al, 2015;Di et al, 2017). Currently, the CDL data covers the entire conterminous United States (CONUS) at 30-meter spatial resolution with a high accuracy up to 95% for classifying major crop types (i.e., Corn, Soybean, and Wheat).…”

Section: Introductionmentioning

confidence: 99%

Refinement of Cropland Data Layer Using Machine Learning

Zhang

Yang

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. As the most widely used crop-specific land use data, the Cropland Data Layer (CDL) product covers the entire Contiguous United States (CONUS) at 30-meter spatial resolution with very high accuracy up to 95% for major crop types (i.e., Corn, Soybean) in major crop area. However, the quality of early-year CDL products were not as good as the recent ones. There are many erroneous pixels in the early-year CDL product due to the cloud cover of the original Landsat images, which affect many follow-on researches and applications. To address this issue, we explore the feasibility of using machine learning technology to refine and correct misclassified pixels in the historical CDLs in this study. An end-to-end deep learning-based framework for restoration of misclassified pixels in CDL image is developed and tested. By feeding the CDL time series into the artificial neural network, a crop sequence model is trained and the misclassified pixels in an original CDL map can be restored. In the experiment with the 2005 CDL data of the State of Illinois, the misclassified pixels over Agricultural Statistics Districts (ASD) #1760 were corrected with a reasonable accuracy (> 85%). The findings suggest that the proposed method provides a low-cost and reliable way to refine the historical CDL data, which can be potentially scaled up to the entire CONUS.

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