Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges

By John R. Townshend, J. Masek, Chengquan Huang, Eric F. Vermote, Feng Gao, Saurabh Channan, Joseph O. Sexton, Min Feng, R. Narasimhan, Do-Hyung Kim and 8 more. International Journal of Digital Earth, 2012

313 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/318d7ba70fc4df864aee671aa536e1dd0c3dd15a

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Citations in the corpus

A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research (2016, unknown): “…et al., 2012), forest monitoring (Asner et al., 2006; Gong et al., 2013; Hansen et al., 2008, 2013; Myneni et al., 2007; Potapov et al., 2015; Townshend et al., 2012), medical science and epidemiology studies (Evans et al., 2013; Gilbert et al., 2008; Liu & Weng, 2012; Lobitz et al., 2000)…”
A global, high-resolution (30-m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm (2016, background): “Landsat has enabled high-resolution mapping of small water bodies at the national and global extents (Townshend and Justice 1988; Wulder et al. 2003; Homer et al. 2004; Townshend et al. 2012; Liao et al. 2014), and a recent global inventory focused on lakes was based on Landsat images (Verpoorter et”
Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery (2015, background): “As pointed out by [19], continental- or even global-scale analysis using remote sensing imagery, such as Landsat’s, was generally regarded as not feasible because of the absence of well-registered multi-temporal datasets, skills and processing power.”
A Pixel-Based Landsat Compositing Algorithm for Large Area Land Cover Mapping (2013, background): “The ability to produce PBCs from the Landsat archive for large areas with surface reflectance at 30 m spatial resolution is a great asset for many applications [48].”
The biodiversity of species and their rates of extinction, distribution, and protection (2014, background): “efforts to collect and calibrate global repositories of Landsat images going back to the early 1970s (132, 133).”
Global, 30-m resolution continuous fields of tree cover: Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error (2013, background): “Much progress has been made in overcoming many challenges e.g. automation and corrections for atmospheric, terrain, and phenological noise (Kim et al. 2011; Townshend et al. 2012; Sexton et al. 2013b).”
Land cover 2.0 (2018, background): “Land cover informs the functional relationship between terrain, climate and soils, providing biophysical insights into the environment and drivers of change (DeFries et al. 2002; Townshend et al. 2012; Franklin and Wulder 2002; Andrew, Wulder, and Nelson 2014).”