2020
DOI: 10.3390/rs12071086
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Mapping Kenyan Grassland Heights Across Large Spatial Scales with Combined Optical and Radar Satellite Imagery

Abstract: Grassland monitoring can be challenging because it is time-consuming and expensive to measure grass condition at large spatial scales. Remote sensing offers a time- and cost-effective method for mapping and monitoring grassland condition at both large spatial extents and fine temporal resolutions. Combinations of remotely sensed optical and radar imagery are particularly promising because together they can measure differences in moisture, structure, and reflectance among land cover types. We combined multi-dat… Show more

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Cited by 13 publications
(20 citation statements)
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“…Consequently, we conclude that S2 outperformed the other two sensors because of its spectral configuration encompassing three bands in the red edge and two bands in the SWIR part of the electromagnetic radiation. The higher spatial resolution of WV2 does not equalize the disadvantage of the lower spectral resolution of its sensor in homogeneous grasslands such as the Eastern Mongolian Steppe (Spagnuolo et al, 2020).…”
Section: Discussionmentioning
confidence: 99%
“…Consequently, we conclude that S2 outperformed the other two sensors because of its spectral configuration encompassing three bands in the red edge and two bands in the SWIR part of the electromagnetic radiation. The higher spatial resolution of WV2 does not equalize the disadvantage of the lower spectral resolution of its sensor in homogeneous grasslands such as the Eastern Mongolian Steppe (Spagnuolo et al, 2020).…”
Section: Discussionmentioning
confidence: 99%
“…The Masai Mara National Reserve (MMNR; 1,530 km 2 ) in southwestern Kenya (1°40'S, 35°50'E) is a rolling grassland habitat that constitutes the northernmost portion of the Mara-Serengeti ecosystem (38)(39)(40)(41)(42). The Reserve has two dry seasons (late December-March, and late June-mid November) and two rainy seasons (late November-early December, and April-early June) (28,43).…”
Section: Sample and Metadata Collectionmentioning
confidence: 99%
“…The general procedure for creating such maps, outlined in [15], involves using training data, typically generated by image interpreters using high resolution aerial or satellite images, and validation data, gathered in the field via a stratified random sampling approach, to classify an image stack using the Random Forests classifier [49]. This method has been utilized for grasslands, boreal peatlands, and tropical alpine peatlands as well [50][51][52] and tends to produce maps with overall accuracy above 85%. Utilization of RADARSAT-2 data, which has approximately 6 cm wavelength as opposed to the 24 cm wavelength of L-band SAR, limits the feasibility of mapping higher biomass woody wetland types because the shorter wavelength of C-band SAR does not have the same ability to penetrate canopy as L-band, though careful selection of incidence angle and image capture date can produce adequate accuracies (see Section 4.2.3).…”
Section: Multi-sensor Multi-temporal Approachmentioning
confidence: 99%