35 research outputs found

    Improving the precision of dynamic forest parameter estimates using Landsat

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    The use of satellite-derived classification maps to improve post-stratified forest parameter estimates is well established.When reducing the variance of post-stratification estimates for forest change parameters such as forest growth, it is logical to use a change-related strata map. At the stand level, a time series of Landsat images is ideally suited for producing such a map. In this study, we generate strata maps based on trajectories of Landsat Thematic Mapper-based normalized difference vegetation index values, with a focus on post-disturbance recovery and recent measurements. These trajectories, from1985 to 2010, are converted to harmonic regression coefficient estimates and classified according to a hierarchical clustering algorithm from a training sample. The resulting strata maps are then used in conjunction with measured plots to estimate forest status and change parameters in an Alabama, USA study area. These estimates and the variance of the estimates are then used to calculate the estimated relative efficiencies of the post-stratified estimates. Estimated relative efficiencies around or above 1.2 were observed for total growth, total mortality, and total removals, with different strata maps being more effective for each. Possible avenues for improvement of the approach include the following: (1) enlarging the study area and (2) using the Landsat images closest to the time of measurement for each plot. Multitemporal satellite-derived strata maps show promise for improving the precision of change parameter estimates

    Improving the precision of dynamic forest parameter estimates using Landsat

    Get PDF
    The use of satellite-derived classification maps to improve post-stratified forest parameter estimates is well established.When reducing the variance of post-stratification estimates for forest change parameters such as forest growth, it is logical to use a change-related strata map. At the stand level, a time series of Landsat images is ideally suited for producing such a map. In this study, we generate strata maps based on trajectories of Landsat Thematic Mapper-based normalized difference vegetation index values, with a focus on post-disturbance recovery and recent measurements. These trajectories, from1985 to 2010, are converted to harmonic regression coefficient estimates and classified according to a hierarchical clustering algorithm from a training sample. The resulting strata maps are then used in conjunction with measured plots to estimate forest status and change parameters in an Alabama, USA study area. These estimates and the variance of the estimates are then used to calculate the estimated relative efficiencies of the post-stratified estimates. Estimated relative efficiencies around or above 1.2 were observed for total growth, total mortality, and total removals, with different strata maps being more effective for each. Possible avenues for improvement of the approach include the following: (1) enlarging the study area and (2) using the Landsat images closest to the time of measurement for each plot. Multitemporal satellite-derived strata maps show promise for improving the precision of change parameter estimates

    Erratum: Corrigendum: Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution

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    International Chicken Genome Sequencing Consortium. The Original Article was published on 09 December 2004. Nature432, 695–716 (2004). In Table 5 of this Article, the last four values listed in the ‘Copy number’ column were incorrect. These should be: LTR elements, 30,000; DNA transposons, 20,000; simple repeats, 140,000; and satellites, 4,000. These errors do not affect any of the conclusions in our paper. Additional information. The online version of the original article can be found at 10.1038/nature0315

    Using Window Regression to Gap-Fill Landsat ETM+ Post SLC-Off Data

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    The continued development of algorithms using multitemporal Landsat data creates opportunities to develop and adapt imputation algorithms to improve the quality of that data as part of preprocessing. One example is de-striping Enhanced Thematic Mapper Plus (ETM+, Landsat 7) images acquired after the Scan Line Corrector failure in 2003. In this study, we apply window regression, an algorithm that was originally designed to impute low-quality Moderate Resolution Imaging Spectroradiometer (MODIS) data, to Landsat Analysis Ready Data from 2014–2016. We mask Operational Land Imager (OLI; Landsat 8) image stacks from five study areas with corresponding ETM+ missing data layers, using these modified OLI stacks as inputs. We explored the algorithm’s parameter space, particularly window size in the spatial and temporal dimensions. Window regression yielded the best accuracy (and moderately long computation time) with a large spatial radius (a 7 × 7 pixel window) and a moderate temporal radius (here, five layers). In this case, root mean square error for deviations from the observed reflectance ranged from 3.7–7.6% over all study areas, depending on the band. Second-order response surface analysis suggested that a 15 × 15 pixel window, in conjunction with a 9-layer temporal window, may produce the best accuracy. Compared to the neighborhood similar pixel interpolator gap-filling algorithm, window regression yielded slightly better accuracy on average. Because it relies on no ancillary data, window regression may be used to conveniently preprocess stacks for other data-intensive algorithms

    Edyn: Dynamic Signaling of Changes to Forests Using Exponentially Weighted Moving Average Charts

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    Remote detection of forest disturbance remains a key area of interest for scientists and land managers. Subtle disturbances such as drought, disease, insect activity, and thinning harvests have a significant impact on carbon budgeting and forest productivity, but current change detection algorithms struggle to accurately identify them, especially over decadal timeframes. We introduce an algorithm called Edyn, which inputs a time series of residuals from harmonic regression into a control chart to signal low-magnitude, consistent deviations from the curve as disturbances. After signaling, Edyn retrains a new baseline curve. We compared Edyn with its parent algorithm (EWMACD—Exponentially Weighted Moving Average Change Detection) on over 3500 visually interpreted Landsat pixels from across the contiguous USA, with reference data for timing and type of disturbance. For disturbed forested pixels, Edyn had a mean per-pixel commission error of 31.1% and omission error of 70.0%, while commission and omission errors for EWMACD were 39.9% and 65.2%, respectively. Edyn had significantly less overall error than EWMACD (F1 = 0.19 versus F1 = 0.13). These patterns generally held for all of the reference data, including a direct comparison to other contemporary change detection algorithms, wherein Edyn and EWMACD were found to have lower omission error rates for a category of subtle changes over long periods

    Cloud-Sourcing: Using an Online Labor Force to Detect Clouds and Cloud Shadows in Landsat Images

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    We recruit an online labor force through Amazon.com’s Mechanical Turk platform to identify clouds and cloud shadows in Landsat satellite images. We find that a large group of workers can be mobilized quickly and relatively inexpensively. Our results indicate that workers’ accuracy is insensitive to wage, but deteriorates with the complexity of images and with time-on-task. In most instances, human interpretation of cloud impacted area using a majority rule was more accurate than an automated algorithm (Fmask) commonly used to identify clouds and cloud shadows. However, cirrus-impacted pixels were better identified by Fmask than by human interpreters. Crowd-sourced interpretation of cloud impacted pixels appears to be a promising means by which to augment or potentially validate fully automated algorithms

    Comparison of Results of Detection of Rhinovirus by PCR and Viral Culture in Human Nasal Wash Specimens from Subjects with and without Clinical Symptoms of Respiratory Illnessâ–¿

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    Human rhinoviruses (HRV) cause acute upper respiratory illness. The frequency of HRV-associated illnesses appears greater when PCR assays are used to detect rhinoviruses. The present study performed PCR-based detection of HRV upon entry of subjects into respiratory syncytial virus and parainfluenza type 3 vaccine trials when subjects were symptom-free and upon subsequent development of clinical symptoms of respiratory illness during the trial. The background of HRV PCR positivity in symptom-free individuals (30/139 [22%]) was only slightly lower than in those with respiratory illness (28/77 [36%]). For subjects with multiple samples, it was estimated that HRV was detectable by PCR for approximately 100 days before, during, and after clinical symptoms were documented. PCR is a remarkably more sensitive method of detecting HRV than is tissue culture. The presence of HRV RNA may not always reflect an association with infectious virus production. The limited association of HRV RNA with illness suggests caution in assigning causality of HRV PCR positivity to clinical symptoms of respiratory illness

    Predictors of Type 2 Diabetes and Diabetes-Related Hospitalisation in an Australian Aboriginal Cohort

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    Predictors of diabetes and diabetes-related hospitalisations were examined in 15-88-year-old Aboriginal Australians (256 women, 258 men), surveyed in 1988-1989. Linkage to death records and hospitalisations to 2002 allowed proportional hazards or negative binomial modelling. Forty-five men (18%) and 59 women (24%) developed diabetes. Risk of diabetes was predicted positively by waist girth (hazard ratio (HR) 1.08, 95% CI 1.04, 1.13), smoking (HR 2.05, 95% CI 1.23, 3.39) and eating processed meats > 4 times/month (HR 1.58, 95% CI 1.05, 2.40) and negatively by lower alcohol intake (HR 0.69, 95%CI 0.49, 0.99), preferring wine (HR 0.13,95% CI 0.02,0.97) and eating bush meats > 4 times/month (HR 0.34,95% CI 0.13,0.90). Hospitalisation was predicted positively by smoking (Incidence rate ratio (IRR) 3.72, 95% CI 1.70, 8.18) and eating processed meats (IRR 1.03, 95% CI 1.01, 1.06), and negatively by exercise > once/week (IRR 0.23, 95% CI 0.08, 0.65), eating bush meats (IRR 0.95, 95% CI 0.91, 0.99) and trimming fat from meats (IRR 0.53, 95% CI 0.30, 0.94). Length of hospital stay was predicted positively by eating processed meats (HR 1.76, 95% CI 1.23, 2.53) and added salt (HR 1.52, 95% CI 1.02, 2.26) and negatively by lower alcohol intake (HR 0.90, 95% CI 0.40, 0.92) and exercise (HR 0.66, 95% CI 0.46, 0.95). Central obesity and adverse lifestyle increase risk for diabetes or related hospitalisation among Aboriginal Australians. (c) 2007 Elsevier Ireland Ltd. All rights reserved

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