A comparative analysis of three different MODIS NDVI datasets for Alaska and adjacent Canada

Ji, Lei; Wylie, Bruce K.; Ramachandran, Bhaskar; Jenkerson, Calli B.

doi:10.5589/m10-015

Cited by 18 publications

(15 citation statements)

References 21 publications

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Section: Objectivesmentioning

confidence: 99%

“…Prior studies have compared and evaluated various MODIS products for Alaska including eMODIS and standard MODIS [12,13]. The study by Ji et al (2010) showed that 250 m-resolution eMODIS Alaska NDVI data had significantly improved geometric features over standard MODIS data, while retaining the original MODIS radiometric characteristics. More cloudy data were identified in eMODIS 7-day composites as compared to standard MODIS 16-day (MOD/MYD13A2) products [13] and probably were related to a lower probability of finding a cloud-free observation over a shorter time span.…”

Section: Objectivesmentioning

confidence: 99%

“…However, the prior study demonstrated the geometric fidelity of the eMODIS system for data produced over Alaska [12]. The algorithms for agreement measures, including the Geometric Mean (GM) regression, the Agreement Coefficient (AC), Root Mean Square Deviation (RMDS), Mean Square Difference (MSD), systematic and unsystematic (or random) Mean Product-Difference (MPDs and MPDu) statistics are all found in Ji et al [12] and were calculated for this study from MODIS zonal data pairs (Standard Aqua, Standard Terra, eMODIS Aqua and eMODIS Terra). Once all masks were applied, zonal NDVI statistics (e.g., mean, standard deviation, etc.)…”

Section: Remote Sens 2015 7 10mentioning

confidence: 99%

“…The zones were calculated using a fishnet tool in ESRI ArcGIS resulting in 160 zones completely within the four standard MODIS tiles of the study. An earlier study by Ji et al [12] highlighted the challenges of comparing data that have differing geometric characteristics from source projections requiring subsequent reprojection and resampling steps in order to conduct pixel-to-pixel comparison. Errors introduced by resampling can be significant.…”

Section: Remote Sens 2015 7 10mentioning

confidence: 99%

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Application-Ready Expedited MODIS Data for Operational Land Surface Monitoring of Vegetation Condition

et al. 2015

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Monitoring systems benefit from high temporal frequency image data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) system. Because of near-daily global coverage, MODIS data are beneficial to applications that require timely information about vegetation condition related to drought, flooding, or fire danger. Rapid satellite data streams in operational applications have clear benefits for monitoring vegetation, especially when information can be delivered as fast as changing surface conditions. An "expedited" processing system called "eMODIS" operated by the U.S. Geological Survey provides rapid MODIS surface reflectance data to operational applications in less than 24 h offering tailored, consistently-processed information products that complement standard MODIS products. We assessed eMODIS quality and consistency by comparing to standard MODIS data. Only land data with known high quality were analyzed in a central U.S. study area. When compared to standard MODIS (MOD/MYD09Q1), the eMODIS Normalized Difference Vegetation Index (NDVI) maintained a strong, significant relationship to standard MODIS NDVI, whether from morning (Terra) or afternoon (Aqua) orbits. The Aqua eMODIS data were more prone to noise than the Terra data, likely due to differences in the internal cloud mask used in MOD/MYD09Q1 or compositing rules. Post-processing temporal smoothing decreased noise in eMODIS data.

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Section: Objectivesmentioning

confidence: 99%

Section: Objectivesmentioning

confidence: 99%

Section: Remote Sens 2015 7 10mentioning

confidence: 99%

Section: Remote Sens 2015 7 10mentioning

confidence: 99%

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Application-Ready Expedited MODIS Data for Operational Land Surface Monitoring of Vegetation Condition

et al. 2015

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“…The 250 m expedited MODIS (eMODIS) [72] 7-day NDVI composites were rescaled from the NDVI's −1 to 1 range to 0 to 100, and all negative values (clouds and water) were set to 0. Soil and litter effects were minimized by subtracting 0.09 from NDVI values [73] and to improve GSN sensitivity to vegetation [69].…”

Section: Actual Performancementioning

confidence: 99%

Effects of Disturbance and Climate Change on Ecosystem Performance in the Yukon River Basin Boreal Forest

Wylie

Rigge²,

Brisco

et al. 2014

Remote Sensing

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A warming climate influences boreal forest productivity, dynamics, and disturbance regimes. We used ecosystem models and 250 m satellite Normalized Difference Vegetation Index (NDVI) data averaged over the growing season (GSN) to model current, and estimate future, ecosystem performance. We modeled Expected Ecosystem Performance (EEP), or anticipated productivity, in undisturbed stands over the 2000-2008 period from a variety of abiotic data sources, using a rule-based piecewise regression tree. The EEP model was applied to a future climate ensemble A1B projection to quantify expected changes to mature boreal forest performance. Ecosystem Performance Anomalies (EPA), were identified as the residuals of the EEP and GSN relationship and represent performance departures from expected performance conditions. These performance data were used to monitor successional events following fire. Results suggested that maximum EPA occurs 30-40 years following fire, and deciduous stands generally have higher EPA than coniferous stands. Mean undisturbed EEP is projected to increase 5.6% by 2040 and 8.7% by 2070, suggesting an increased deciduous component OPEN ACCESSRemote Sens. 2014, 6 9146 in boreal forests. Our results contribute to the understanding of boreal forest successional dynamics and its response to climate change. This information enables informed decisions to prepare for, and adapt to, climate change in the Yukon River Basin forest.

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Upscaling terrestrial carbon dioxide fluxes in Alaska with satellite remote sensing and support vector regression

et al. 2013

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[1] Carbon dioxide (CO 2 ) fluxes from a network of 21 eddy covariance towers were upscaled to estimate the Alaskan CO 2 budget from 2000 to 2011 by combining satellite remote sensing data, disturbance information, and a support vector regression model. Data were compared with the CO 2 budget from an inverse model (CarbonTracker). Observed gross primary productivity (GPP), ecosystem respiration (RE), and net ecosystem exchange (NEE) were each well reproduced by the model on the site scale; root-mean-square errors (RMSEs) for GPP, RE, and NEE were 0.52, 0.23, and 0.48 g C m À2 d À1 , respectively. Landcover classification was the most important input for predicting GPP, whereas visible reflectance index of green ratio was the most important input for predicting RE. During the period of 2000-2011, predicted GPP and RE were 369 ± 22 and 362 ± 12 Tg C yr À1 (mean ± interannual variability) for Alaska, respectively, indicating an approximately neutral CO 2 budget for the decade. CarbonTracker also showed an approximately neutral CO 2 budget during 2000-2011 (growing season RMSE = 14 g C m À2 season À1 ; annual RMSE = 13 g C m À2 yr À1 ). Interannual CO 2 flux variability was positively correlated with air temperature anomalies from June to August, with Alaska acting as a greater CO 2 sink in warmer years. CO 2 flux trends for the decade were clear in disturbed ecosystems; positive trends in GPP and CO 2 sink were observed in areas where vegetation recovered for about 20 years after fire.

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A comparative analysis of three different MODIS NDVI datasets for Alaska and adjacent Canada

Cited by 18 publications

References 21 publications

Application-Ready Expedited MODIS Data for Operational Land Surface Monitoring of Vegetation Condition

Application-Ready Expedited MODIS Data for Operational Land Surface Monitoring of Vegetation Condition

Effects of Disturbance and Climate Change on Ecosystem Performance in the Yukon River Basin Boreal Forest

Upscaling terrestrial carbon dioxide fluxes in Alaska with satellite remote sensing and support vector regression

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