Toward near real-time monitoring of forest disturbance by fusion of MODIS and Landsat data

Xin, Qinchuan; Olofsson, Pontus; Zhu, Zhe; Tan, Bin; Woodcock, Curtis E.

doi:10.1016/j.rse.2013.04.002

Cited by 109 publications

(70 citation statements)

References 38 publications

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“…The pixel-level differences observed in the Western U.S. were mainly due to the smaller size of land patches (which is comparable to a MODIS pixel), as well as the distortion of MODIS pixels at middle to high latitude. Hence, it is not surprising that smaller change patches suffer more omission and/or commission errors than relatively larger patches [68], a conclusion consistent with previous land cover change studies using MODIS data [69]. Pixel-level errors are mostly distributed on the edge of land parcels, especially when the edge is between two or more disturbed patches where the disturbances occurred in different years (see Figure 9 for an illustration).…”

Section: Qualitative Assessmentsupporting

confidence: 84%

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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Section: Qualitative Assessmentsupporting

confidence: 84%

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

et al. 2014

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“…To account for inherent noise in MODIS data due to cloud cover, atmospheric interference, and uncertainty in the ground area measured by the MODIS sensor (Xin et al, 2013), we used median NBAR values taken over 3 × 3 windows of 500 m pixels centered on PhenoCam locations. The resulting time series were then smoothed using the median of a three-point moving window to remove spikes due to snowfall and other sources of noise that were not captured using the MCD43A2 product.…”

Section: Remote Sensing Datamentioning

confidence: 99%

“…Consequently, the MCD12Q2 data are more susceptible to gridding artifacts of remote sensing measurements and other sources of noise (see Fig. 1 in Xin et al, 2013). Spatial averaging, which accounts for the values in neighboring pixels, appears to improve the remote sensing representation of deciduous canopy phenology in comparison to nearsurface measurements: the simple sigmoid method applied to MODIS NBAR data here yielded results with generally lower RMSD and bias with respect to ground measurements, relative to the MCD12Q2 product (Tables 4 and 7).…”

Section: Remote Sensing Phenology Productsmentioning

confidence: 99%

Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery

Klosterman

Hufkens²,

Gray³

et al. 2014

Biogeosciences

282

234

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Abstract. Plant phenology regulates ecosystem services at local and global scales and is a sensitive indicator of global change. Estimates of phenophase transition dates, such as the start of spring or end of fall, can be derived from sensorbased time series, but must be interpreted in terms of biologically relevant events. We use the PhenoCam archive of digital repeat photography to implement a consistent protocol for visual assessment of canopy phenology at 13 temperate deciduous forest sites throughout eastern North America, and to perform digital image analysis for time-series-based estimation of phenophase transition dates. We then compare these results to remote sensing metrics of phenophase transition dates derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) sensors. We present a new type of curve fit that uses a generalized sigmoid function to estimate phenology dates, and we quantify the statistical uncertainty of phenophase transition dates estimated using this method. Results show that the generalized sigmoid provides estimates of dates with less statistical uncertainty than other curve-fitting methods. Additionally, we find that dates derived from analysis of high-frequency PhenoCam imagery have smaller uncertainties than satellite remote sensing metrics of phenology, and that dates derived from the remotely sensed enhanced vegetation index (EVI) have smaller uncertainty than those derived from the normalized difference vegetation index (NDVI). Near-surface time-series estimates for the start of spring are found to closely match estimates derived from visual assessment of leaf-out, as well as satellite remote-sensing-derived estimates of the start of spring. However late spring and fall phenology metrics exhibit larger differences between near-surface and remote scales. Differences in late spring phenology between near-surface and remote scales are found to correlate with a landscape metric of deciduous forest cover. These results quantify the effect of landscape heterogeneity when aggregating to the coarser spatial scales of remote sensing, and demonstrate the importance of accurate curve fitting and vegetation index selection when analyzing and interpreting phenology time series.

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“…Our algorithm does not account for this scale of sub-pixel variation, which means that results will be most reliable for areas with large field sizes. The effects of spatial heterogeneity is a common challenge for MODIS-based studies of land use and land cover change because most land use and land cover conversions occur below the spatial resolution of MODIS [68][69][70].…”

Section: Factors That Influence the Mapping Accuracymentioning

confidence: 99%

Mapping Crop Cycles in China Using MODIS-EVI Time Series

et al. 2014

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Abstract:As the Earth's population continues to grow and demand for food increases, the need for improved and timely information related to the properties and dynamics of global agricultural systems is becoming increasingly important. Global land cover maps derived from satellite data provide indispensable information regarding the geographic distribution and areal extent of global croplands. However, land use information, such as cropping intensity (defined here as the number of cropping cycles per year), is not routinely available over large areas because mapping this information from remote sensing is challenging. In this study, we present a simple but efficient algorithm for automated mapping of cropping intensity based on data from NASA's (NASA: The National Aeronautics and Space Administration) MODerate Resolution Imaging Spectroradiometer (MODIS). The proposed algorithm first applies an adaptive Savitzky-Golay filter to smooth Enhanced Vegetation Index (EVI) time series derived from MODIS surface reflectance data. It then uses an iterative moving-window methodology to identify cropping cycles from the smoothed EVI time series. Comparison of results from our algorithm with OPEN ACCESSRemote Sens. 2014, 6 2474 national survey data at both the provincial and prefectural level in China show that the algorithm provides estimates of gross sown area that agree well with inventory data. Accuracy assessment comparing visually interpreted time series with algorithm results for a random sample of agricultural areas in China indicates an overall accuracy of 91.0% for three classes defined based on the number of cycles observed in EVI time series. The algorithm therefore appears to provide a straightforward and efficient method for mapping cropping intensity from MODIS time series data.

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Toward near real-time monitoring of forest disturbance by fusion of MODIS and Landsat data

Cited by 109 publications

References 38 publications

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

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

Evaluating remote sensing of deciduous forest phenology at multiple spatial scales using PhenoCam imagery

Mapping Crop Cycles in China Using MODIS-EVI Time Series

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