Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States

Roy, David P.; Ju, Junchang; Kline, Kristi; Scaramuzza, Pat; Kovalskyy, V.; Hansen, Matthew C.; Loveland, Thomas R.; Vermote, Éric; Zhang, Chunsun

doi:10.1016/j.rse.2009.08.011

Cited by 471 publications

(274 citation statements)

References 59 publications

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“…, in the study that developed the harmonic modeling approach we adapted to detect microrefugia, demonstrated that dense time series of all available Landsat data enabled detection of subtle forest thinning. Other studies, more commonly using time series of annual images (including composite images; eg, Roy et al, 2010), emphasize the power of LTS to detect gradual processes including forest decline due to diffuse disturbances such as insect outbreaks or drought (Ahmed et al, 2017;Cohen et al, 2016;Deel et al, 2012;Kennedy et al, 2010), forest succession and woodland densification (Vogelmann et al, 2012), and variation in ecosystem recovery following disturbance (Kennedy et al, 2007(Kennedy et al, , 2010Lawrence and Ripple, 1999;Storey et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

Detecting microrefugia in semi-arid landscapes from remotely sensed vegetation dynamics

Andrew

Warrener

2017

Remote Sensing of Environment

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(in press). Detecting microrefugia in semi-arid landscapes from remotely sensed vegetation dynamics. Remote Sensing of Environment. Accepted 4 August 2017. DOI:https: //doi.org/10.1016/j.rse.2017.08.005 Disclaimer:The PDF document is a copy of the final version of this manuscript that was subsequently accepted by the journal for publication. The paper has been through peer review, but it has not been subject to any additional copy-editing or journal specific formatting (so will look different from the final version of record, which may be accessed following the DOI above depending on your access situation). 2 AbstractMicrorefugia are sites with stable, high quality habitat within landscapes characterized by dynamic environmental conditions driven by climate variability or ecological disturbances. There is considerable interest in the potential of microrefugia to provide climate change resilience to landscapes and to biodiversity conservation. Although attractive conceptually, there is yet little guidance on how to identify climate change microrefugia in order to study and protect them, and the data required to do so are often lacking. This study demonstrates how time series remote sensing, using all available Landsat images of a study area, can be used to directly detect microrefugia maintained by water subsidies in a semi-arid landscape in southwest Western Australia.Microrefugia were identified as pixels with abundant vegetation and consistent vegetation dynamics between wet and dry years. At every pixel, a harmonic model was fit to the intra-annual time series of vegetation index values compiled from the wettest years in the Landsat-5 Thematic Mapper (TM) archive. This model was then used to predict the phenological cycle of the driest years at that pixel. Candidate microrefugia were defined to be those pixels with (1) high vegetation activity in dry years and (2) highly predictable phenologies that are consistent regardless of the weather conditions experienced in a given year. Spatial relationships between candidate microrefugia and landscape features associated with elevated moisture availability (thought to drive climate microrefugia in these semi-arid landscapes) were assessed. The candidate microrefugia show great promise. Evaluations against high-resolution imagery reveal that candidate microrefugia most likely buffer against drought, although refugia from other disturbances, especially fire, were also detected. In contrast, spatial proxies of the physical features expected to maintain microrefugia failed to adequately represent the distribution of microrefugia across the landscape, likely due to data quality and the heterogeneity of microrefugia. Direct detection of microrefugia with Earth observation data is a promising solution in data limited regions. Landsat time series analyses are well suited to this application as they can characterize both the habitat quality and stability aspects of microrefugia.

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Section: Discussionmentioning

confidence: 99%

Detecting microrefugia in semi-arid landscapes from remotely sensed vegetation dynamics

Andrew

Warrener

2017

Remote Sensing of Environment

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“…This diagnosis was verified as follows. Because of the limited availability of cloud-free Landsat observations at a generic pixel location per year, the Julian day of the year of the observation selected at a given location (pixel) of the annual WELD image composite changes through years (Roy et al, 2010). This is illustrated in Figure 7 where, at any fixed location across a target “ground-truth” area of deciduous forest in a pair of monthly August-November WELD composites, the SIAM spectral labels change significantly, but consistently with the phenological season.…”

Section: Validation Sessionmentioning

confidence: 99%

“…The open-access U.S. Geological Survey (USGS) 30 m resolution annual Web Enabled Landsat Data (WELD) image composite of the conterminous U.S. (CONUS) for the years 2006 to 2009, radiometrically calibrated into TOARF values (Homer, Huang, Yang, Wylie, & Coan, 2004; Roy et al, 2010; WELD, 2016), was identified as a viable input dataset. The 30 m resolution 16-class U.S. National Land Cover Data (NLCD) 2006 map, delivered in 2011 by the USGS Earth Resources Observation Systems (EROS) Data Center (EDC) (Environmental Protection Agency (EPA), 2007; Vogelmann et al, 2001; Vogelmann, Sohl, Campbell, & Shaw, 1998; Wickham, Stehman, Fry, Smith, & Homer, 2010; Wickham et al, 2013; Xian & Homer, 2010), was selected as the reference thematic map at the CONUS spatial extent.…”

Section: Introductionmentioning

confidence: 99%

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation – Part 2: Validation

et al. 2018

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ESA defines as Earth Observation (EO) Level 2 information product a multi-spectral (MS) image corrected for atmospheric, adjacency, and topographic effects, stacked with its data-derived scene classification map (SCM), whose legend includes quality layers cloud and cloud-shadow. No ESA EO Level 2 product has ever been systematically generated at the ground segment. To fill the information gap from EO big data to ESA EO Level 2 product in compliance with the GEO-CEOS stage 4 validation (Val) guidelines, an off-the-shelf Satellite Image Automatic Mapper (SIAM) lightweight computer program was selected to be validated by independent means on an annual 30 m resolution Web-Enabled Landsat Data (WELD) image composite time-series of the conterminous U.S. (CONUS) for the years 2006 to 2009. The SIAM core is a prior knowledge-based decision tree for MS reflectance space hyperpolyhedralization into static (non-adaptive to data) color names. For the sake of readability, this paper was split into two. The present Part 2—Validation—accomplishes a GEO-CEOS stage 4 Val of the test SIAM-WELD annual map time-series in comparison with a reference 30 m resolution 16-class USGS National Land Cover Data (NLCD) 2006 map. These test and reference map pairs feature the same spatial resolution and spatial extent, but their legends differ and must be harmonized, in agreement with the previous Part 1 - Theory. Conclusions are that SIAM systematically delivers an ESA EO Level 2 SCM product instantiation whose legend complies with the standard 2-level 4-class FAO Land Cover Classification System (LCCS) Dichotomous Phase (DP) taxonomy.

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“…Automated cloud classification methods based on a single Landsat image [41][42][43][44][45][46][47][48] achieved high accuracies in detecting clouds and their shadows. Recent cloud classification efforts based on multi-temporal images [49][50][51][52][53][54][55][56] have been proposed to better detect clouds and cloud shadows.…”

Section: Etm+ Ndvi Composingmentioning

confidence: 99%

Building a Better Urban Picture: Combining Day and Night Remote Sensing Imagery

Zhang

Thau

et al. 2015

Remote Sensing

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Urban areas play a very important role in global climate change. There is increasing need to understand global urban areas with sufficient spatial details for global climate change mitigation. Remote sensing imagery, such as medium resolution Landsat daytime multispectral imagery and coarse resolution Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light imagery, has provided a powerful tool for characterizing and mapping cities, with advantages and disadvantages. Here we propose a framework to merge cloud and cloud shadow-free Landsat Normalized Difference Vegetation Index (NDVI) composite and DMSP/OLS Night Time Light (NTL) to characterize global urban areas at a 30 m resolution, through a Normalized Difference Urban Index (NDUI) to make full use of them while minimizing their limitations. We modify the maximum NDVI value multi-date image compositing method to generate the cloud and cloud shadow-free Landsat NDVI composite, which is critical for generating a global NDUI. Evaluation results show the NDUI can effectively increase the separability between urban areas and bare lands as well as farmland, capturing large scale urban extents and, at the same time, providing sufficient spatial details inside urban areas. With advanced

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Web-enabled Landsat Data (WELD): Landsat ETM+ composited mosaics of the conterminous United States

Cited by 471 publications

References 59 publications

Detecting microrefugia in semi-arid landscapes from remotely sensed vegetation dynamics

Detecting microrefugia in semi-arid landscapes from remotely sensed vegetation dynamics

GEO-CEOS stage 4 validation of the Satellite Image Automatic Mapper lightweight computer program for ESA Earth observation level 2 product generation – Part 2: Validation

Building a Better Urban Picture: Combining Day and Night Remote Sensing Imagery

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