Conterminous United States crop field size quantification from multi-temporal Landsat data

Yan, Lin; Roy, David P.

doi:10.1016/j.rse.2015.10.034

Cited by 167 publications

(117 citation statements)

References 70 publications

Supporting

Mentioning

114

Contrasting

Order By: Relevance

“…Based on analyses presented by Yan and Roy [19], the average field size in the CA Central Valley is 0.234 km 2 , or approximately 500 × 500 m (Yan, 2017, personal comm.). Satellite-based ET retrievals at 100-m resolution or finer enable mapping at these critical spatial scales, allowing sub-field sampling and exclusion of border pixels in most fields.…”

mentioning

confidence: 99%

Field-Scale Assessment of Land and Water Use Change over the California Delta Using Remote Sensing

et al. 2018

View full text Add to dashboard Cite

Abstract:The ability to accurately monitor and anticipate changes in consumptive water use associated with changing land use and land management is critical to developing sustainable water management strategies in water-limited climatic regions. In this paper, we present an application of a remote sensing data fusion technique for developing high spatiotemporal resolution maps of evapotranspiration (ET) at scales that can be associated with changes in land use. The fusion approach combines ET map timeseries developed using an multi-scale energy balance algorithm applied to thermal data from Earth observation platforms with high spatial but low temporal resolution (e.g., Landsat) and with moderate resolution but frequent temporal coverage (e.g., MODIS (Moderate Resolution Imaging Spectroradiometer)). The approach is applied over the Sacramento-San Joaquin Delta region in California-an area critical to both agricultural production and drinking water supply within the state that has recently experienced stresses on water resources due to a multi-year (2012-2017) extreme drought. ET "datacubes" with 30-m resolution and daily timesteps were constructed for the 2015-2016 water years and related to detailed maps of land use developed at the same spatial scale. The ET retrievals are evaluated at flux sites over multiple land covers to establish a metric of accuracy in the annual water use estimates, yielding root-mean-square errors of 1.0, 0.8, and 0.3 mm day −1 at daily, monthly, and yearly timesteps, respectively, for all sites combined. Annual ET averaged over the Delta changed only 3 mm year −1 between water years, from 822 to 819 mm year −1 , translating to an area-integrated total change in consumptive water use of seven thousand acre-feet (TAF). Changes were largest in areas with recorded land-use change between water years-most significantly, fallowing of crop land presumably in response to reductions in water availability and allocations due to the drought. Moreover, the time evolution in water use associated with wetland restoration-an effort aimed at reducing subsidence and carbon emissions within the inner Delta-is assessed using a sample wetland chronosequence. Region-specific matrices of consumptive water use associated with land use changes may be an effective tool for policymakers and farmers to understand how land use conversion could impact consumptive use and demand.

show abstract

mentioning

confidence: 99%

Field-Scale Assessment of Land and Water Use Change over the California Delta Using Remote Sensing

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Compositing procedures are applied independently on a per-pixel basis to the gridded WELD time series to reduce cloud and aerosol contamination, fill missing values due to the SLC failure (that removed about 22% of each Landsat ETM+ image [56]), and to reduce data volume. The WELD data have an established land cover mapping provenance and have been used, for example, to make land cover, land cover change, burned area, and field size maps at national scale [24,[57][58][59].…”

Section: Existing Land Cover Maps For 2008 and 2011mentioning

confidence: 99%

Rapid Land Cover Map Updates Using Change Detection and Robust Random Forest Classifiers

et al. 2016

View full text Add to dashboard Cite

Abstract:The paper evaluated the Landsat Automated Land Cover Update Mapping (LALCUM) system designed to rapidly update a land cover map to a desired nominal year using a pre-existing reference land cover map. The system uses the Iteratively Reweighted Multivariate Alteration Detection (IRMAD) to identify areas of change and no change. The system then automatically generates large amounts of training samples (n > 1 million) in the no-change areas as input to an optimized Random Forest classifier. Experiments were conducted in the KwaZulu-Natal Province of South Africa using a reference land cover map from 2008, a change mask between 2008 and 2011 and Landsat ETM+ data for 2011. The entire system took 9.5 h to process. We expected that the use of the change mask would improve classification accuracy by reducing the number of mislabeled training data caused by land cover change between 2008 and 2011. However, this was not the case due to exceptional robustness of Random Forest classifier to mislabeled training samples. The system achieved an overall accuracy of 65%-67% using 22 detailed classes and 72%-74% using 12 aggregated national classes. "Water", "Plantations", "Plantations-clearfelled", "Orchards-trees", "Sugarcane", "Built-up/dense settlement", "Cultivation-Irrigated" and "Forest (indigenous)" had user's accuracies above 70%. Other detailed classes (e.g., "Low density settlements", "Mines and Quarries", and "Cultivation, subsistence, drylands") which are required for operational, provincial-scale land use planning and are usually mapped using manual image interpretation, could not be mapped using Landsat spectral data alone. However, the system was able to map the 12 national classes, at a sufficiently high level of accuracy for national scale land cover monitoring. This update approach and the highly automated, scalable LALCUM system can improve the efficiency and update rate of regional land cover mapping.

show abstract

“…The segmentation parameters appear to not separate the strips at the highest level of detail, e.g., if the strip cropping is very narrow (i.e., 1-3 pixels wide). Within-field variation can cause the algorithm to split one field or pivot circle if the heterogeneity is high [33]. This over splitting issue does not pose a problem for the overall approach as the spatially aggregated values are classified as Crop or No-Crop only.…”

Section: Image Segmentationmentioning

confidence: 99%

“…The over-splitting, however does not pose a problem to the classification as these polygons are attributed and classified separately. Under-splitting may occur, if the field sizes due to strip-cropping become small (e.g., a few pixels in width) or, e.g., if field boundaries are diffuse [33]. A more detailed segmentation could help to separate these strips further.…”

Section: Synthetic Image Generation and Segmentationmentioning

confidence: 99%

See 1 more Smart Citation

A Framework for Large-Area Mapping of Past and Present Cropping Activity Using Seasonal Landsat Images and Time Series Metrics

et al. 2016

View full text Add to dashboard Cite

Abstract:Crop extent and frequency maps are an important input to inform the debate around land value and competitive land uses, in particular between cropping and mining in the case of Queensland, Australia. Such spatial datasets are useful for supporting decisions on natural resource management, planning and policy. For the major broadacre cropping regions of Queensland, Australia, the complete Landsat Time Series (LTS) archive from 1987 to 2015 was used in a multi-temporal mapping approach, where spatial, spectral and temporal information were combined in multiple crop-modelling steps, supported by training data sampled across space and time for the classes Crop and No-Crop. Temporal information within summer and winter growing seasons were summarised for each year, and combined with various vegetation indices and band ratios computed from a pixel-based mid-season spectral synthetic image. All available temporal information was spatially aggregated to the scale of image segments in the mid-season synthetic image for each growing season and used to train a number of different predictive models for a Crop and No-Crop classification. Validation revealed that the predictive accuracy varied by growing season and region and a random forest classifier performed best, with κ = 0.88 to 0.91 for the summer growing season and κ = 0.91 to 0.97 for the winter growing season, and are thus suitable for mapping current and historic cropping activity.

show abstract

Conterminous United States crop field size quantification from multi-temporal Landsat data

Cited by 167 publications

References 70 publications

Field-Scale Assessment of Land and Water Use Change over the California Delta Using Remote Sensing

Field-Scale Assessment of Land and Water Use Change over the California Delta Using Remote Sensing

Rapid Land Cover Map Updates Using Change Detection and Robust Random Forest Classifiers

A Framework for Large-Area Mapping of Past and Present Cropping Activity Using Seasonal Landsat Images and Time Series Metrics

Contact Info

Product

Resources

About