Accuracy assessment of the vegetation continuous field tree cover product using 3954 ground plots in the south‐western USA

White, Michael A.; Shaw, John D.; Ramsey, R. Douglas

doi:10.1080/01431160500080626

Cited by 47 publications

(44 citation statements)

References 6 publications

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“…Hansen et al (2002) provided the first de facto Á although not independent Á estimates of MODISC VCF accuracy by comparing an experimental version of the dataset to the Landsat data used to train the generating model. Later, White et al (2005) compared the MODIS VCF Version 1 to independently gathered field data across the arid southwestern United States, and Montesano et al (2009) validated the Version 4 MODIS VCF against independent reference data derived from photo-interpreted high-resolution images across the boreal-taiga ecotone. Also, Heiskanen (2008) and Song et al (2011) compared the MODIS VCF to other remotely sensed global datasets.…”

Section: Introductionmentioning

confidence: 99%

Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error

Sexton

Song

Feng

et al. 2013

International Journal of Digital Earth

601

446

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, but showed improved accuracy in agricultural areas and increased discrimination of small forest patches. Against lidar measurements, the Landsat-based estimates exhibited accuracy slightly less than that of the MODIS VCF (RMSE 016.8% for MODIS-based vs. 17.4% for Landsat-based estimates), but RMSE of Landsat estimates was 3.3 percentage points lower than that of the MODIS data in an agricultural region. The Landsat data retained the saturation artifact of the MODIS VCF at greater than or equal to 80% tree cover but showed greater potential for removal of errors through calibration to lidar, with post-calibration RMSE of 9.4% compared to 13.5% in MODIS estimates. Provided for free download at the Global Land Cover Facility (GLCF) website (www.landcover. org), the 30-m resolution GLCF tree cover dataset is the highest-resolution multitemporal depiction of Earth's tree cover available to the Earth science community.

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Section: Introductionmentioning

confidence: 99%

Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error

Sexton

Song

Feng

et al. 2013

International Journal of Digital Earth

601

446

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show abstract

“…Additionally, many studies have cross-compared the MODIS VCF to other remotely-sensed global land cover datasets [53][54][55][56]. These independent assessments found that saturation of the optical signal, phenological noise and confusion with dense herbaceous vegetation led to errors in the earlier MODIS VCF, varying between 10%-31% in terms of root-mean-square error (RMSE) and 7%-21% in the latest version [48,51,52]. The latest Collection 5 MODIS VCF product is used in this study.…”

Section: Datamentioning

confidence: 99%

“…The Collection 1 global VCF product was validated against independent field data across the arid Southwestern United States [51], and later, the Collection 4 product was evaluated using reference data derived from high-resolution images across the boreal-taiga ecotone [52]. Recently, the error of the Collection 5 VCF was estimated against measurements of tree cover from small-footprint light detection and ranging (LiDAR) data in four sites across three different forest biomes [48].…”

Section: Datamentioning

confidence: 99%

Annual Detection of Forest Cover Loss Using Time Series Satellite Measurements of Percent Tree Cover

et al. 2014

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Abstract:We introduce and test a new method to detect annual forest cover loss from time series estimates of percent tree cover. Our approach is founded on two realistic assumptions: (1) land cover disturbances are rare events over large geographic areas that occur within a short time frame; and (2) spatially discrete land cover disturbances are continuous processes over time. Applying statistically rigorous algorithms, we first detect disturbance pixels as outliers of an underlying chi-square distribution. Then, we fit nonlinear, logistic curves for each identified change pixel to simultaneously characterize the magnitude and timing of the disturbance. Our method is applied using the yearly Vegetation Continuous Fields (VCF) tree cover product from Moderate Resolution Imaging Spectroradiometer (MODIS), and the resulting disturbance-year estimates are evaluated using a large sample of Landsat-based forest disturbance data. Temporal accuracy is ~65% at 250-m, annual resolution and increases to >85% when temporal resolution is relaxed to ±1 yr. The r 2 of MODIS VCF-based disturbance rates against Landsat ranges from 0.7 to 0.9 at 5-km spatial resolution. The general approach developed in this study can be potentially applied at a global scale and to other land cover types characterized as continuous variables from satellite data.

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“…Rigorous validation is crucial in producing useful land cover products [11,[51][52][53][54]. However, the challenges, including a very large commitment of resources and time, have generally discouraged thorough validation in large scale, high resolution maps [55].…”

Section: Validationmentioning

confidence: 99%

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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Accuracy assessment of the vegetation continuous field tree cover product using 3954 ground plots in the south‐western USA

Cited by 47 publications

References 6 publications

Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error

Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error

Annual Detection of Forest Cover Loss Using Time Series Satellite Measurements of Percent Tree Cover

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

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