Continuous fields of land cover for the conterminous United States using Landsat data: first results from the Web-Enabled Landsat Data (WELD) project

Hansen, Matthew C.; Egorov, Alexey; Roy, David P.; Potapov, Peter; Ju, Junchang; Turubanova, Svetlana; Kommareddy, Indrani; Loveland, Thomas R.

doi:10.1080/01431161.2010.519002

Cited by 116 publications

(75 citation statements)

References 23 publications

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“…For the generation of the Landsat canopy density product [46], for instance, USGS Level 1 terrain-corrected (L1T) Landsat ETM+ data had been used, for which radiometric calibration and geometric corrections were applied using a DEM. For the pre-processing of the ALOS PALSAR data, a new approach for compensating for the dependence of the pixel scattering area contributing to the measured backscatter on topography was applied that has been shown to allow significant improvements in the calibration of radar data over mountainous terrain compared to previously developed approaches when having a DEM with a spatial resolution comparable to that of the radar imagery [56,93].…”

Section: Discussionmentioning

confidence: 99%

“…30 m). Hansen et al (2011) produced canopy density maps for the United States and Mexico that were generated from multi-annual sets of Landsat observations [46]; a first global Landsat map was published recently [61]. For the mapping of canopy density, Landsat Level-1 terrain corrected (L1T) imagery was used to generate radiometrically consistent monthly, seasonal and annual mosaics of reflectances for bands 3 to 5 and 7, the thermal band (6) version of the map (Series IV) was generated by means of manual interpretation of SPOT data [47].…”

Section: Earth Observation Datamentioning

confidence: 99%

“…The approach used in this study relies on L-band SAR data from ALOS PALSAR, Landsat based estimates of canopy density [46], land use maps [47], and geophysical gradient data from SRTM. ALOS PALSAR acquired wall-to-wall coverage of Mexico in the high-resolution Fine-Beam Dual-Polarization (FBD) mode (HH and HV polarizations) between the fall of 2006 (operational mission start) and the spring of 2011 (mission end).…”

Section: Mapping Forest Aboveground Carbon Density Across Mexicomentioning

confidence: 99%

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A National, Detailed Map of Forest Aboveground Carbon Stocks in Mexico

et al. 2014

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Section: Discussionmentioning

confidence: 99%

Section: Earth Observation Datamentioning

confidence: 99%

Section: Mapping Forest Aboveground Carbon Density Across Mexicomentioning

confidence: 99%

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A National, Detailed Map of Forest Aboveground Carbon Stocks in Mexico

et al. 2014

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“…For instance, Hansen et al [30] performed 30-m annual land cover estimates of trees, other vegetation (including grasses, shrubs and forbs), bare ground cover and water presence over the continental USA.…”

Section: Previous Workmentioning

confidence: 99%

Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery

et al. 2015

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Crop area extent estimates and crop type maps provide crucial information for agricultural monitoring and management. Remote sensing imagery in general and, more specifically, high temporal and high spatial resolution data as the ones which will be available with upcoming systems, such as Sentinel-2, constitute a major asset for this kind of application. The goal of this paper is to assess to what extent state-of-the-art supervised classification methods can be applied to high resolution multi-temporal optical imagery to produce accurate crop type maps at the global scale. Five concurrent strategies for automatic crop type map production have been selected and benchmarked using SPOT4 (Take5) and Landsat 8 data over 12 test sites spread all over the globe (four in Europe, four in Africa, two in America and two in Asia). This variety of tests sites allows one to draw conclusions applicable to a wide variety of landscapes and crop systems. The results show that a random forest classifier operating on linearly temporally gap-filled images can achieve Remote Sens. 2015, 7 12357 overall accuracies above 80% for most sites. Only two sites showed low performances: Madagascar due to the presence of fields smaller than the pixel size and Burkina Faso due to a mix of trees and crops in the fields. The approach is based on supervised machine learning techniques, which need in situ data collection for the training step, but the map production is fully automatic.

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“…Several recent studies have used visual interpretation of high resolution imagery in the GE desktop application to provide low-cost and reasonably accurate reference data for both producing land-cover maps and testing their accuracy [9][10][11][12]. The imagery has a spatial accuracy of <40 m, which is adequate for comparison to coarser-resolution satellite imagery [9,13].…”

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confidence: 99%

Virtual Interpretation of Earth Web-Interface Tool (VIEW-IT) for Collecting Land-Use/Land-Cover Reference Data

Clark

Aide

2011

Remote Sensing

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Abstract:Web-based applications that integrate geospatial information, or the geoweb, offer exciting opportunities for remote sensing science. One such application is a Web-based system for automating the collection of reference data for producing and verifying the accuracy of land-use/land-cover (LULC) maps derived from satellite imagery. Here we describe the capabilities and technical components of the Virtual Interpretation of Earth Web-Interface Tool (VIEW-IT), a collaborative browser-based tool for -crowdsourcing‖ interpretation of reference data from high resolution imagery. The principal component of VIEW-IT is the Google Earth plug-in, which allows users to visually estimate percent cover of seven basic LULC classes within a sample grid. The current system provides a 250 m square sample to match the resolution of MODIS satellite data, although other scales could be easily accommodated. Using VIEW-IT, a team of 23 student and 7 expert interpreters collected over 46,000 reference samples across Latin America and the Caribbean. Samples covered all biomes, avoided spatial autocorrelation, and spanned years 2000 to 2010. By embedding Google Earth within a Web-based application with an intuitive user interface, basic interpretation criteria, distributed Internet access, server-side storage, and automated error-checking, VIEW-IT provides a time and cost efficient means of collecting a large dataset of samples across space and time. When matched with predictor variables from satellite imagery, these data can provide robust mapping algorithm calibration and accuracy assessment. This development is particularly important for regional to global scale LULC mapping efforts, which have traditionally OPEN ACCESSRemote Sens. 2011, 3 602 relied on sparse sampling of medium resolution imagery and products for reference data. Our ultimate goal is to make VIEW-IT available to all users to promote rigorous, global land-change monitoring.

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Continuous fields of land cover for the conterminous United States using Landsat data: first results from the Web-Enabled Landsat Data (WELD) project

Cited by 116 publications

References 23 publications

A National, Detailed Map of Forest Aboveground Carbon Stocks in Mexico

A National, Detailed Map of Forest Aboveground Carbon Stocks in Mexico

Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery

Virtual Interpretation of Earth Web-Interface Tool (VIEW-IT) for Collecting Land-Use/Land-Cover Reference Data

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