0000000000338255

AUTHOR

Lammert Kooistra

0000-0001-5549-5993

showing 3 related works from this author

Combining hyperspectral UAV and multispectral Formosat-2 imagery for precision agriculture applications

2014

Remote sensing is a key tool for precision agriculture applications as it is capable of capturing spatial and temporal variations in crop status. However, satellites often have an inadequate spatial resolution for precision agriculture applications. High-resolution Unmanned Aerial Vehicles (UAV) imagery can be obtained at flexible dates, but operational costs may limit the collection frequency. The current study utilizes data fusion to create a dataset which benefits from the temporal resolution of Formosat-2 imagery and the spatial resolution of UAV imagery with the purpose of monitoring crop growth in a potato field. The correlation of the Weighted Difference Vegetation Index (WDVI) from …

precision agricultureComputer sciencebusiness.industryUAVMultispectral imageHyperspectral imagingcomputer.software_genreSensor fusionPE&RCField (geography)Laboratory of Geo-information Science and Remote SensingWDVIunmixing-based data fusionTemporal resolutionComputer visionLaboratorium voor Geo-informatiekunde en Remote SensingArtificial intelligencePrecision agricultureSTARFMbusinesscomputerImage resolutionData integrationRemote sensing
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Mapping Vegetation Density in a Heterogeneous River Floodplain Ecosystem Using Pointable CHRIS/PROBA Data

2012

River floodplains in the Netherlands serve as water storage areas, while they also have the function of nature rehabilitation areas. Floodplain vegetation is therefore subject to natural processes of vegetation succession. At the same time, vegetation encroachment obstructs the water flow into the floodplains and increases the flood risk for the hinterland. Spaceborne pointable imaging spectroscopy has the potential to quantify vegetation density on the basis of leaf area index (LAI) from a desired view zenith angle. In this respect, hyperspectral pointable CHRIS data were linked to the ray tracing canopy reflectance model FLIGHT to retrieve vegetation density estimates over a heterogeneous…

010504 meteorology & atmospheric sciencesFloodplainWater flowpointable sensors; CHRIS/PROBA; leaf area index (LAI); inversion; radiative transfer (RT) model; FLIGHT; river floodplain ecosystem; vegetation density; hydraulic roughnessleaf area index (LAI)0211 other engineering and technologiesClimate change02 engineering and technologyCHRIS/PROBA01 natural sciencesforestinversionLaboratory of Geo-information Science and Remote SensingLaboratorium voor Geo-informatiekunde en Remote SensingLeaf area indexcoverlcsh:ScienceZenithriver floodplain ecosystem021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensinggeographychris-proba datahyperspectral brdf datageography.geographical_feature_categoryFLIGHTFlood mythrhine basinradiative-transfer modelHyperspectral imagingEnhanced vegetation index15. Life on landpointable sensorsPE&RCradiative transfer (RT) modelsugar-beetclimate-changeGeneral Earth and Planetary SciencesEnvironmental sciencehydraulic roughnesslcsh:Qflow resistanceleaf-area indexvegetation densityRemote Sensing
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Mapping a-priori defined plant associations using remotely sensed vegetation characteristics

2014

Abstract Incorporation of a priori defined plant associations into remote sensing products is a major challenge that has only recently been confronted by the remote sensing community. We present an approach to map the spatial distribution of such associations by using plant indicator values (IVs) for salinity, moisture and nutrients as an intermediate between spectral reflectance and association occurrences. For a 12 km 2 study site in the Netherlands, the relations between observed IVs at local vegetation plots and visible and near-infrared (VNIR) and short-wave infrared (SWIR) airborne reflectance data were modelled using Gaussian Process Regression (GPR) (R 2 0.73, 0.64 and 0.76 for sali…

endmember selectionCalibration (statistics)Vegetation classificationcontinuous floristic gradientsSoil Scienceimaging spectroscopy/dk/atira/pure/sustainabledevelopmentgoals/clean_water_and_sanitationLaboratory of Geo-information Science and Remote SensingKrigingmoistureLaboratorium voor Geo-informatiekunde en Remote SensingComputers in Earth SciencesRemote sensingtropical forestsHyperspectral imagingGeologyVegetationPE&RCRegressionVNIRhyperspectral imageryclassificationaviris dataellenberg indicator valuesEnvironmental scienceregressionIndicator valueSDG 6 - Clean Water and SanitationRemote Sensing of Environment
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