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    An estimate of persistent green cover per season. This is intended to estimate the portion of vegetation that does not completely senesce within a year, which primarily consists of woody vegetation (trees and shrubs), although there are exceptions where non-woody cover remains green all year round. It is derived by fitting a multi-iteration minimum weighted smoothing spline through the green fraction of the seasonal fractional cover (dp1) time series. A single band image is produced: persistent green vegetation cover (in percent). The no data value is 255.

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    An estimate of persistent green cover per season. This is intended to estimate the portion of vegetation that does not completely senesce within a year, which primarily consists of woody vegetation (trees and shrubs), although there are exceptions where non-woody cover remains green all year round. It is derived by fitting a multi-iteration minimum weighted smoothing spline through the green fraction of the seasonal fractional cover (dim) time series.

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    The linear seasonal persistent green trend is derived from analysis of the seasonal persistent green product over time. The current version is based on the 1987-2014 period. <br> Seasonal persistent green cover is derived from seasonal fractional cover using a weighted smooth spline fitting routine. This weights a smooth line to the minimum values of the seasonal green cover. This smooth minimum is designed to represent the slower changing green component, ideally consisting of perennial vegetation including over-storey, mid-storey and persistent ground cover. The seasonal persistent green is then summarized using simple linear regression, and the slope of the fitted line is captured in this product. The original units are percentage points per year. Values are later truncated and scaled.

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    The dataset consists of composited seasonal surface reflectance images (4 seasons per year) created from the full time series of Landsat TM/ETM+/OLI imagery. The imagery has been composited over a season to produce imagery which is representative of that period, using techniques which will reduce contamination by cloud and other problems. This creates a regular time series of reflectance values which captures the variability at seasonal time scales. The benefits are a regular time series with minimal missing data or contamination from various sources of noise as well as data reduction. Each season has exactly one value (per band) for each pixel (or is null, i.e., missing), and the value for that season is assumed to be the representative of the whole season. The algorithm is based on the medoid (in reflectance space) over the time period (the medoid is a multi-dimensional analogue of the median), which is robust against extreme values.

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    <p>This dataset shows the crops grown in Queensland's main cropping areas, for the winter and summer growing-seasons, from 1988 to the current year. The winter growing-season is defined as June to October, and the summer growing-season is November to May. The basis of the maps is imagery from the (when available) Landsat-5 TM, Landsat-7 ETM+, Landsat-(8,9) OLI, and Sentinel-2(A,B) satellites; MODIS MOD13Q1 imagery was used as a backup in the case of large, temporal data gaps. Clusters of temporally similar pixels, termed 'segments', were identified in the imagery for each growing season, and served as an approximation of field boundaries. Per-segment phenological information, derived from the satellite imagery, was then combined with a tiered, tree-based statistical classifier, using >10000 field observations as training data, and >4000 independent observations for validation. The dataset supersedes a former crop-mapping effort <a href ="https://doi.org/10.3390/rs8040312">(Schmidt et al., 2016)</a>.</p>

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    Long term temporal statistic products derived from the seasonal ground cover product for each fraction. Statistics include: 5th percentile minimum, mean, median, 95th percentile maximum, standard deviation and observation count. There is one raster image for each season and each bare and green fraction for the full time series of imagery available. Min/max (5th and 95th percentile) products are also made for each fraction using all seasonal ground cover images available.

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    The seasonal fractional ground cover product is a spatially explicit raster product that shows the proportion of bare ground, green and non-green ground cover at medium resolution (30 m per-pixel) for each 3-month calendar season. It is derived directly from the seasonal fractional cover product, also produced by Queensland's Remote Sensing Centre. A 3 band (byte) image is produced: band 1 – bare ground fraction (in percent), band 2 - green vegetation fraction (in percent), band 3 – non-green vegetation fraction (in percent). The no data value is 255.</br> The seasonal fractional cover product predicts vegetation cover, but does not distinguish tree and mid-level woody foliage and branch cover from green and dry ground cover. As a result, in areas with even minimal tree cover (>15%), estimates of ground cover become uncertain.</br> With the development of the fractional cover time-series, it has become possible to derive an estimate of ‘persistent green’ based on time-series analysis. The persistent green vegetation product provides an estimate of the vertically-projected green-vegetation fraction where vegetation is deemed to persist over time. These areas are nominally woody vegetation. This separation of the 'persistent green' from the fractional cover product, allows for the adjustment of the underlying spectral signature of the fractional cover image and the creation of a resulting 'true' ground cover estimate for each season. The estimates of cover are restricted to areas of <60% woody vegetation.

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    The seasonal fractional ground cover product shows the proportion of bare ground, green and non-green ground cover and is derived directly from the seasonal fractional cover product, also produced by Queensland's Remote Sensing Centre. The seasonal fractional cover product is a spatially explicit raster product, which predicts vegetation cover at medium resolution (30 m per-pixel) for each 3-month calendar season. However, the seasonal fractional cover product does not distinguish tree and mid-level woody foliage and branch cover from green and dry ground cover. As a result, in areas with even minimal tree cover (>15%), estimates of ground cover become uncertain. With the development of the fractional cover time-series, it has become possible to derive an estimate of ‘persistent green’ based on time-series analysis. The persistent green vegetation product provides an estimate of the vertically-projected green-vegetation fraction where vegetation is deemed to persist over time. These areas are nominally woody vegetation. This separation of the 'persistent green' from the fractional cover product, allows for the adjustment of the underlying spectral signature of the fractional cover image and the creation of a resulting 'true' ground cover estimate for each season. The estimates of cover are restricted to areas of <60% woody vegetation. Currently, this is an experimental product which has not been fully validated.

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    The seasonal fractional cover product shows representative values for the proportion of bare, green and non-green cover across a season. It is a spatially explicit raster product, which predicts vegetation cover at medium resolution (30 m per-pixel) for each 3-month calendar season. The green and non-green fractions may include a mix of woody and non-woody vegetation.

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    The seasonal fractional cover product shows representative values for the proportion of bare, green and non-green cover across a season. It is a spatially explicit raster product, which predicts vegetation cover at medium resolution (30 m per-pixel) for each 3-month calendar season. The green and non-green fractions may include a mix of woody and non-woody vegetation. A 3 band (byte) image is produced: band 1 – bare ground fraction (in percent), band 2 - green vegetation fraction (in percent), band 3 – non-green vegetation fraction (in percent). The no data value is 255.