Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes

Stehman, Stephen V.

doi:10.1080/01431161.2014.930207

Cited by 180 publications

(123 citation statements)

References 12 publications

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“…The binary tree cover map achieved an overall accuracy (calculated as the total proportion of correctly classified pixels as a percentage of all pixels [67]) of 88% (˘0.51% standard error), when compared to the 6648 visually attributed reference pixels (Table 1). Prior to editing and sub-tile thresholding the overall accuracy was 87%.…”

Section: Visual Assessmentmentioning

confidence: 96%

“…As each lidar tile created a spatially continuous map, the size of each contiguous region, and the distance of each pixel to the nearest pixel of the opposite class was also calculated. As the lidar data were a stratified sample of the map area, the proportion of each stratum was used when calculating the error matrix statistics [67]. The proportions were calculated from the final classified map, for the 34 strata (tree and non-tree pixels from each of the 17 vegetation formations).…”

Section: Airborne Lidar Datamentioning

confidence: 99%

“…Each point was visually assessed against the high resolution imagery and classified as either tree or not-tree. The proportion of each stratum, determined from the Landsat map, was used when calculating the error matrix statistics, including the standard errors [67].…”

Section: Comparison To Landsatmentioning

confidence: 99%

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Large-Area, High-Resolution Tree Cover Mapping with Multi-Temporal SPOT5 Imagery, New South Wales, Australia

Fisher

Day²,

Gill

et al. 2016

Remote Sensing

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Tree cover maps are used for many purposes, such as vegetation mapping, habitat connectivity and fragmentation studies. Small remnant patches of native vegetation are recognised as ecologically important, yet they are underestimated in remote sensing products derived from Landsat. High spatial resolution sensors are capable of mapping small patches of trees, but their use in large-area mapping has been limited. In this study, multi-temporal Satellite pour l'Observation de la Terre 5 (SPOT5) High Resolution Geometrical data was pan-sharpened to 5 m resolution and used to map tree cover for the Australian state of New South Wales (NSW), an area of over 800,000 km 2 . Complete coverages of SPOT5 panchromatic and multispectral data over NSW were acquired during four consecutive summers (2008-2011) for a total of 1256 images. After pre-processing, the imagery was used to model foliage projective cover (FPC), a measure of tree canopy density commonly used in Australia. The multi-temporal imagery, FPC models and 26,579 training pixels were used in a binomial logistic regression model to estimate the probability of each pixel containing trees. The probability images were classified into a binary map of tree cover using local thresholds, and then visually edited to reduce errors. The final tree map was then attributed with the mean FPC value from the multi-temporal imagery. Validation of the binary map based on visually assessed high resolution reference imagery revealed an overall accuracy of 88% (˘0.51% standard error), while comparison against airborne lidar derived data also resulted in an overall accuracy of 88%. A preliminary assessment of the FPC map by comparing against 76 field measurements showed a very good agreement (r 2 = 0.90) with a root mean square error of 8.57%, although this may not be representative due to the opportunistic sampling design. The map represents a regionally consistent and locally relevant record of tree cover for NSW, and is already widely used for natural resource management in the state.

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Section: Visual Assessmentmentioning

confidence: 96%

Section: Airborne Lidar Datamentioning

confidence: 99%

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Large-Area, High-Resolution Tree Cover Mapping with Multi-Temporal SPOT5 Imagery, New South Wales, Australia

Fisher

Day²,

Gill

et al. 2016

Remote Sensing

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“…The detailed information of measurements retrieved from error matrix, including overall accuracy (OA), user's accuracy (UA), and producer's accuracy (PA), is available in [56]. …”

Section: Accuracy Estimationmentioning

confidence: 99%

Validation of Land Cover Maps in China Using a Sampling-Based Labeling Approach

et al. 2015

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This paper presents a rigorous validation of five widely used global land cover products, i.e., GLCC (Global Land Cover Characterization), UMd (University of Maryland land cover product), GLC2000 (Global Land Cover 2000 project data), MODIS LC (Moderate Resolution Imaging Spectro-radiometer Land Cover product) and GlobCover (GLOBCOVER land cover product), and a national land cover map GLCD-2005 (Geodata Land Cover Dataset for year 2005) against an independent reference data set over China. The land cover reference data sets in three epochs (1990, 2000, and 2005) were collected on a web-based prototype system using a sampling-based labeling approach. Results show that, in China, the highest overall accuracy is observed in GLCD-2005 (72.3%), followed by MODIS LC (68.9%), GLC2000 (65.2%), GlobCover (57.7%) and GLCC (57.2%), while UMd has the lowest accuracy (48.6%); all of the products performed best in representing "Trees" and "Others", well with "Grassland" and "Cropland", OPEN ACCESSRemote Sens. 2015, 7 10590 but problematic with "Water" and "Urban" across China in general. Moreover, in respect of GLCD-2005, there are significant accuracy differences across seven geographical locations of China, ranging from 46.3% in the Southwest, 77.5% in the South, 79.2% in the Northwest, 80.8% in the North, 81.8% in the Northeast, 82.6% in the Central, to 89.0% in the East. This study indicates that a regionally focused land cover map would in fact be more accurate than extracting the same region from a globally produced map.

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“…Since some reference datasets are not based on probability sampling, design-based statistical inference cannot be used. Moreover, design-based statistical inference using multiple reference datasets with different statistical sampling designs requires known inclusion probabilities (Stehman 2014).…”

Section: On the Use Of Available Reference Datasets For Integrationmentioning

confidence: 99%

Global land cover map validation, comparison and integration for different user communities

Tsendbazar¹

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Estimating area and map accuracy for stratified random sampling when the strata are different from the map classes

Cited by 180 publications

References 12 publications

Large-Area, High-Resolution Tree Cover Mapping with Multi-Temporal SPOT5 Imagery, New South Wales, Australia

Large-Area, High-Resolution Tree Cover Mapping with Multi-Temporal SPOT5 Imagery, New South Wales, Australia

Validation of Land Cover Maps in China Using a Sampling-Based Labeling Approach

Global land cover map validation, comparison and integration for different user communities

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