A review of large area monitoring of land cover change using Landsat data

By Matthew C. Hansen, Thomas R. Loveland. 2012

1,027 citations recorded by the source corpus.

Subfields: Ecology, Environmental Engineering, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/96c9e5bc14a469af8b753fc4f85fcfce6a8d25c4

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In Ecology

In Environmental Engineering

In Global and Planetary Change

Citations in the corpus

Satellite remote sensing for applied ecologists: opportunities and challenges (2014, background): “…British Ecological Society, Journal of Applied Ecology, 51, 839–848 threats to biological diversity and ecosystem services: deforestation (see e.g. Hansen & Loveland 2012), atmospheric pollution (see e.g. Beirle et al. 2011; Hao et al. 2011), land degradation and fragmentation (see e.g. Asner…”
Relationships of groundwater quality and associated health risks with land use/land cover patterns: A case study in a loess area, Northwest China (2019, background): “Currently, with the rapid development of remote sensing technology, LULC information with higher accuracy can be extracted from remote sensing image data (Hansen and Loveland 2012; Zhang and Roy 2017; Zhang et al. 2019).”
A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring (2017, background): “This will benefit a large number of remote sensing applications, particularly land cover and change monitoring applications [5,6,18,38].”
Operational High Resolution Land Cover Map Production at the Country Scale Using Satellite Image Time Series (2017, background): “Experimental and operational classifications are two different approaches for large area land cover mapping and monitoring [15,16].”
Change detection from remotely sensed images: From pixel-based to object-based approaches (2013, background): “One of the immediate applications of COCD is updating maps or GIS layers (Hansen and Loveland, 2012; Holland et al., 2008; 2004; Xian and Homer, 2010; Xian et al., 2009).”
Monitoring Land-Cover Changes: A Machine-Learning Perspective (2016, neutral): “[2] M.”
CHANGE DETECTION IN REMOTE SENSING IMAGES USING CONDITIONAL ADVERSARIAL NETWORKS (2018, methodology): “The framework based on post-classification (Blaschke, 2005) presumes extracting objects and independently classifying them (Im and Jensen, 2005; Hansen and Loveland, 2012).”
Change detection using landsat time series: A review of frequencies, preprocessing, algorithms, and applications (2017, background): “…et al., 2012; Hayes and Sader, 2001; Sader et al., 2003; Sader and Legaard, 2008; Wilson and Sader, 2002) and high (Hansen et al., 2016, 2014, 2013; Hansen and Loveland, 2012; Potapov et al., 2011, 2015, 2012) frequencies of Landsat time series, while only a few studies are based on a medium…”
Developments in Landsat Land Cover Classification Methods: A Review (2017, background): “While many review articles covered topics related to Landsat and land cover classification [9,14,15,19]; there is no review of the development of Landsat land cover classification methods.”
A survival guide to Landsat preprocessing. (2017, background): “Landsat 4–8 have a revisit interval of 16 d (Schowengerdt 2007), which facilitates studies of landscapes through time (for a review, see Hansen and Loveland 2012, Willis 2015).”
Sentinel-2 Data for Land Cover/Use Mapping: A Review (2020, background): “Compared to the swath width of Landsat missions of 185 km [29], the Sentinel-2 mission has a wide swath of 290 km field of view [30].”
Free and open-access satellite data are key to biodiversity conservation (2015, background): “land cover and land use dynamics, climate variables, and sea surface conditions) from a local to global level (Hansen and Loveland, 2012; Townshend et al., 2012; Zhu et al., 2012).”
The global Landsat archive: Status, consolidation, and direction (2016, methodology): “…product (that is, implementation of a suite of processing steps previously requiredof data end-users, includingprecision georegistration, radiometric calibration, and orthorectification using digital topography (for additional detail, see Wulder et al. (2012) and Hansen and Loveland (2012)).”
Nominal 30-m Cropland Extent Map of Continental Africa by Integrating Pixel-Based and Object-Based Algorithms Using Sentinel-2 and Landsat-8 Data on Google Earth Engine (2017, methodology): “As it is difficult to apply a single classifier over large areas (Figure 1) for cropland mapping, an ensemble of machine learning algorithms were investigated [47,76].”