Using satellite imagery as ancillary data for increasing the precision of estimates for the Forest Inventory and Analysis program of the USDA Forest Service

McRoberts, Ronald E.; Holden, Geoffrey R.; Nelson, Mark D.; Liknes, Greg C.; Gormanson, Dale D.

doi:10.1139/x05-222

Cited by 60 publications

(58 citation statements)

References 18 publications

(29 reference statements)

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“…Tomppo (1992) compared multinomial logistic regression and discriminant analysis for predicting the site fertility classes of forest stands and found that discriminant analysis performed better than logistic regression. McRoberts et al (2006) constructed a map of the probability of forest cover using a logistic regression model with forest inventory and Landsat data and used the map to construct strata for use with a stratified estimator. A similar map was constructed using the same methods and served as the basis for a model-based approach to estimating forest area (McRoberts, 2006(McRoberts, , 2010a.…”

Section: Parametric Approaches To Pixel-level Classification For Catementioning

confidence: 99%

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

McRoberts

Cohen

Næsset

et al. 2010

Scandinavian Journal of Forest Research

110

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Tremendous advances in the construction and assessment of forest attribute maps and related spatial products have been realized in recent years, partly as a result of the use of remotely sensed data as an information source. This review focuses on the current state of techniques for the construction and assessment of remote sensing-based maps and addresses five topic areas: statistical classification and prediction techniques used to construct maps and related spatial products, accuracy assessment methods, map-based statistical inference, and two emerging topics, change detection and use of lidar data. Multiple general conclusions were drawn from the review: (1) remotely sensed data greatly contribute to the construction of forest attribute maps and related spatial products and to the reduction of inventory costs; (2) parametric prediction techniques, accuracy assessment methods and probability-based (design-based) inferential methods are generally familiar and mature, although inference is surprisingly seldom addressed; (3) non-parametric prediction techniques and modelbased inferential methods lack maturity and merit additional research; (4) change detection methods, with their great potential for adding a spatial component to change estimates, will mature rapidly; and (5) lidar applications, although currently immature, add an entirely new dimension to remote sensing research and will also mature rapidly. Crucial forest sustainability and climate change applications will continue to push all aspects of remote sensing to the forefront of forest research and operations.

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Section: Parametric Approaches To Pixel-level Classification For Catementioning

confidence: 99%

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

McRoberts

Cohen

Næsset

et al. 2010

Scandinavian Journal of Forest Research

110

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“…In the case of the status parameters, namely forest area, ERE values approached 2.2. These relatively greater values were less than but comparable with the EREs for status parameters from McRoberts et al (2006), which had EREs (from other regions of the USA) on the order of 2 or greater. Generally, the finer cluster maps produced larger EREs, which suggests that more partitioning typically allows within-strata variation to be smaller and thus increases the effectiveness of the PS approach.…”

Section: Resultsmentioning

confidence: 56%

“…To accomplish this we quantified the effectiveness by examining ERE (Varðŷ SRS Þ=Varðŷ st ÞÞ for each parameter. We note that the FIA program does not typically use SRS estimators but much of the previous research (e.g., McRoberts et al, 2006) has also used SRS estimators to benchmark increase in precision of proposed PS approaches using ERE. Our results should be interpreted in the context of previous research and not be taken as a direct comparison to current FIA PS approaches.…”

Section: Resultsmentioning

confidence: 99%

“…The Forest Inventory and Analysis (FIA) program of the USDA Forest Service uses satellite data to augment its field measurements (McRoberts, 2010;McRoberts, Holden, Nelson, Liknes, & Gormanson, 2006;McRoberts, Wendt, Nelson and Hansen, 2002). By assigning the field plots to image-derived strata, the FIA program is able to obtain more precise estimates of forest parameters than would have otherwise been possible or financially feasible.…”

Section: Introductionmentioning

confidence: 99%

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Improving the precision of dynamic forest parameter estimates using Landsat

Brooks

Coulston

Wynne

et al. 2016

Remote Sensing of Environment

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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, from 1985 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.

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“…In China [7,8], Ireland [9] and Norway [10], the technique was tested in parts of the country to estimate forest information. Some previous studies revealed that the k-NN technique has the potential to increase the precision of NFI estimates by the post-stratification technique [5,[11][12][13]. Due to its ready availability, the k-NN technique has received considerable attention and merited special discussion in recent years [5].…”

Section: Introductionmentioning

confidence: 99%

Stand Volume Estimation Using the k-NN Technique Combined with Forest Inventory Data, Satellite Image Data and Additional Feature Variables

et al. 2014

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Abstract:The main objective of this study was to evaluate the effectiveness of adding feature variables, such as forest type information and topographic-and climatic-environmental factors to satellite image data, on the accuracy of stand volume estimates made with the k-nearest neighbor (k-NN) technique in southwestern Japan. Data from the Forest Resources Monitoring Survey-a national plot sampling survey in Japan-was used as in situ data in this study. The estimates obtained from three Landsat Enhanced Thematic Mapper Plus (ETM+) datasets acquired in different seasons with various combinations of additional feature variables were compared. The results showed that although the addition of environmental factors to satellite image data did not always help improve estimation OPEN ACCESSRemote Sens. 2015, 7 379 accuracy, the use of summer rainfall (SRF) data had a consistent positive effect on accuracy improvement. Therefore, SRF may be a useful feature variable to consider in stand volume estimation in this study area. Moreover, the use of forest type information is very effective at reducing k-NN estimation errors when using an optimum combination of satellite image data and environmental factors. All of the results indicated that the k-NN technique combined with appropriate feature variables is applicable to nationwide stand volume estimation in Japan.

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Using satellite imagery as ancillary data for increasing the precision of estimates for the Forest Inventory and Analysis program of the USDA Forest Service

Cited by 60 publications

References 18 publications

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

Using remotely sensed data to construct and assess forest attribute maps and related spatial products

Improving the precision of dynamic forest parameter estimates using Landsat

Stand Volume Estimation Using the k-NN Technique Combined with Forest Inventory Data, Satellite Image Data and Additional Feature Variables

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