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

By Thomas Hilker, M. Wulder, Nicholas C. Coops, J. Linke, Gregory J. McDermid, J. Masek, Feng Gao, Joanne C. White. 2009

692 citations recorded by the source corpus.

Subfields: Ecological Modeling, Ecology, Media Technology

View Paper: https://www.semanticscholar.org/paper/713e98cf762c5a7eff85bd12c2d90bcd96331bdc

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Citations in the corpus

Developments in Landsat Land Cover Classification Methods: A Review (2017, methodology): “Generally, when correct algorithms are applied during image fusion of Landsat images with other remote sensing data, improvements are expected on the results of land cover classification [117,123,124].”
Land cover 2.0 (2018, methodology): “Data fusion approaches, such as those based upon blending of MODIS and Landsat data (Gao et al. 2006; Hilker et al. 2009) may be implemented to produce synthetic gap-free composites for classi ﬁ cation purposes (Senf et al. 2015).”
Forest Types Classification Based on Multi-Source Data Fusion (2017, methodology): “Transformation-based models include wavelet and tasseled cap transformations [38,39].”
Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions (2018, methodology): “from dense time series of coarse images to improve STARFM’s performance when land cover type change exists [27].”
A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion (2015, methodology): “This method is particularly useful for detecting gradual changes over large land areas, such as phenology studies (Gao et al., 2006; Hilker et al., 2009).”
Assessing the accuracy of blending Landsat–MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection (2013, methodology): “…(ii) the Enhanced version of STARFM (ESTARFM, Zhu et al., 2010); (iii) Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH, Hilker et al., 2009a); (iv) a downscaling unmixing algorithm based on a linearmixingmodel to produce Landsat-like images from theMedium Resolution…”
Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications (2017, methodology): “To overcome this limitation, fusing Landsat data with coarse resolution images, such as MODIS data provides a solution (Gao et al., 2006; Hilker et al., 2009; Roy et al., 2008; Zhu et al., 2010).”