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Our results showed that very dense time series allowed for very high accuracy classifications (>90%) of small scale vegetation types.
In particular, we focused on dense time series of land surface temperature as predictors for urban and rural air temperature.
Hurni, K. et al. Mapping the expansion of boom crops in Mainland Southeast Asia using dense time stacks of Landsat data.
Change detection with spectral trend analysis uses a dense time series of images, such as pixel-based composites, to quantify the year, amount, and magnitude of landscape changes.
The NAFD project is evaluating forest disturbance patterns and rates of disturbance by integrating U.S. Department of Agriculture USDA Forest Service Inventoryry and Analysis (FIA) field observations with temporally dense time series Landsat imagery.
With dense time series of Landsat data, it appears possible to quantify land use intensity also in heterogeneous landscapes where fine-scale processes cannot be monitored with previously available datasets.
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The filters are simple to construct, and offer smooth scaling functions and wavelets, as well as dense time-scale grid.
The opening up of the Landsat archive at no-cost by the USGS revolutionized how we can monitor the Earth's surface by allowing dense time-series analysis.
At a finer scale, a dense time-series of SPOT-4 (20 m) images was used to map different forest cover percentages for intact and degraded forest in Tanzania [37].
Canopy cover change can be monitored using a dense time-series (comprising optical or SAR observations or a combination of both), while spectral fractions, unmixing or classification can be used to separate degraded and intact forest.
By taking advantage of its relatively high spatial resolution and frequent repeat cycle, some researchers demonstrated its potential in constructing dense time-series data by data fusion with MODIS [11, 15].
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