A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS

Hilker, Thomas; Wulder, Michael A.; Coops, Nicholas C.; Linke, J.; McDermid, Gregory J.; Masek, Jeffrey G.; Gao, Feng; White, Joanne C.

doi:10.1016/j.rse.2009.03.007

Cited by 586 publications

(406 citation statements)

References 57 publications

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“…Predicted Landsat images are degraded in quality for cases featuring fine-grained spatial heterogeneity and complex mixtures of cover types. Hilker et al (2009) demonstrated the value of STARFM for monitoring forest disturbance in Canada, where clouds often obscure Landsat images for several sequential 16-day acquisitions, by adapting the algorithm using 8-day MODIS composites to detect the 8-day period within which individual, Landsat-resolution disturbances occurred.…”

Section: Integrated Change Detection Methodsmentioning

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: Integrated Change Detection Methodsmentioning

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

View full text Add to dashboard Cite

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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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“…To alleviate this, we used a combination of historical aerial interpreted photographs with disturbance maps from land management agencies. Change or disturbance-based classifications require time-series reference data to define change areas through auto segmentation, or they are manually digitized by experienced image analysts [25,29,60]. We collected aerial photography from the National Historical Air Photo Archive (NHAP) for the early 1980s and the National Agriculture Imagery Program (NAIP) for the early 1990s and 2000s to evaluate the forest harvest and insect disturbances that we detected with our classification.…”

Section: Independent Classification Evaluation Datamentioning

confidence: 99%

An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data

et al. 2014

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Forests contain a majority of the aboveground carbon (C) found in ecosystems, and understanding biomass lost from disturbance is essential to improve our C-cycle knowledge. Our study region in the Wisconsin and Minnesota Laurentian Forest had a strong decline in Normalized Difference Vegetation Index (NDVI) from 1982 to 2007, observed with the National Ocean and Atmospheric Administration's (NOAA) series of Advanced Very High Resolution Radiometer (AVHRR). To understand the potential role of disturbances in the terrestrial C-cycle, we developed an algorithm to map forest disturbances from either harvest or insect outbreak for Landsat time-series stacks. We merged two image analysis approaches into one algorithm to monitor forest change that included: (1) multiple disturbance index thresholds to capture clear-cut harvest; and (2) a OPEN ACCESS Remote Sens. 2014, 6 2783 spectral trajectory-based image analysis with multiple confidence interval thresholds to map insect outbreak. We produced 20 maps and evaluated classification accuracy with air-photos and insect air-survey data to understand the performance of our algorithm. We achieved overall accuracies ranging from 65% to 75%, with an average accuracy of 72%. The producer's and user's accuracy ranged from a maximum of 32% to 70% for insect disturbance, 60% to 76% for insect mortality and 82% to 88% for harvested forest, which was the dominant disturbance agent. Forest disturbances accounted for 22% of total forested area (7349 km 2 ). Our algorithm provides a basic approach to map disturbance history where large impacts to forest stands have occurred and highlights the limited spectral sensitivity of Landsat time-series to outbreaks of defoliating insects. We found that only harvest and insect mortality events can be mapped with adequate accuracy with a non-annual Landsat time-series. This limited our land cover understanding of NDVI decline drivers. We demonstrate that to capture more subtle disturbances with spectral trajectories, future observations must be temporally dense to distinguish between type and frequency in heterogeneous landscapes.

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A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS

Cited by 586 publications

References 57 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

Mapping Crop Cycles in China Using MODIS-EVI Time Series

An Automated Approach to Map the History of Forest Disturbance from Insect Mortality and Harvest with Landsat Time-Series Data

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