Spelling suggestions: "subject:"multitemporal image classification"" "subject:"multitemporale image classification""
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Mapping eastern redcedar (Juniperus Virginiana L.) and quantifying its biomass in Riley County, KansasBurchfield, David Richard January 1900 (has links)
Master of Arts / Department of Geography / Kevin P. Price / Due primarily to changes in land management practices, eastern redcedar (Juniperus virginiana L.), a native Kansas conifer, is rapidly invading onto valuable rangelands. The suppression of fire and increase of intensive grazing, combined with the rapid growth rate, high reproductive output, and dispersal ability of the species have allowed it to dramatically expand beyond its original range. There is a growing interest in harvesting this species for use as a biofuel. For economic planning purposes, density and biomass quantities for the trees are needed. Three methods are explored for mapping eastern redcedar and quantifying its biomass in Riley County, Kansas. First, a land cover classification of redcedar cover is performed using a method that utilizes a support vector machine classifier applied to a multi-temporal stack of Landsat TM satellite images. Second, a Small Unmanned Aircraft System (sUAS) is used to measure individual redcedar trees in an area where they are encroaching into a pasture. Finally, a hybrid approach is used to estimate redcedar biomass using high resolution multispectral and light detection and ranging (LiDAR) imagery. These methods showed promise in the forestry, range management, and bioenergy industries for better understanding of an invasive species that shows great potential for use as a biofuel resource.
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A Segment-based Approach To Classify Agricultural Lands Using Multi-temporal Kompsat-2 And Envisat Asar DataOzdarici Ok, Asli 01 February 2012 (has links) (PDF)
Agriculture has an important role in Turkey / hence automated approaches are crucial to
maintain sustainability of agricultural activities. The objective of this research is to
classify eight crop types cultivated in Karacabey Plain located in the north-west of
Turkey using multi-temporal Kompsat-2 and Envisat ASAR satellite data. To fulfill this
objective, first, the fused Kompsat-2 images were segmented separately to define
homogenous agricultural patches. The segmentation results were evaluated using multiple
goodness measures to find the optimum segments. Next, multispectral single-date
Kompsat-2 images with the Envisat ASAR data were classified by MLC and SVMs
algorithms. To combine the thematic information of the multi-temporal data set,
probability maps were generated for each classification result and the accuracies of the
thematic maps were then evaluated using segment-based manner. The results indicated
that the segment-based approach based on the SVMs method using the multispectral
Kompsat-2 and Envisat ASAR data provided the best classification accuracies. The
combined thematic maps of June-August and June-July-August provided the highest
overall accuracy and kappa value around 92% and 0.90, respectively, which was 4%
better than the highest result computed with the MLC method. The produced thematic
maps were also evaluated based on field-based manner and the analysis revealed that the
classification performances are directly proportional to the size of the agricultural fields.
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