Hyperspectral Remote Sensing with Emphasis on Land Cover Mapping: From Ground to Satellite Observations

Petropoulos, George P.; Manevski, Kiril; Carlson, Toby N.

doi:10.1002/9781118801628.ch15

Cited by 7 publications

(3 citation statements)

References 92 publications

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“…For example, the upper left and upper right and bottom right patches in January 2014 ( Figure 6) were white flowering plants in February 2013 ( Figure 5). Since we used canopy-level hyperspectral data, the shape and geometry of flowering tree species could have affected the spectral responses of flowering classes [9,58] and hence the mapping accuracy of these classes. We applied a pixel-based classification approach which often leads to a "salt-and-pepper" pattern in the mapping result [59][60][61].…”

Section: Discussionmentioning

confidence: 99%

The Utility of AISA Eagle Hyperspectral Data and Random Forest Classifier for Flower Mapping

et al. 2015

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Knowledge of the floral cycle and the spatial distribution and abundance of flowering plants is important for bee health studies to understand the relationship between landscape and bee hive productivity and honey flow. The key objective of this study was to show how AISA Eagle hyperspectral data and random forest (RF) can be optimally utilized to produce flowering and spatially explicit land use/land cover (LULC) maps for a study site in Kenya. AISA Eagle imagery was captured at the early flowering period (January 2014) and at the peak flowering season (February 2013). Data on white and yellow flowering trees as well as LULC classes in the study area were collected and used as ground-truth points. We utilized all 64 AISA Eagle bands and also used variable importance in RF to identify the most important bands in both AISA Eagle data sets. The results showed that flowering was most accurately mapped using the AISA Eagle data from the peak flowering period (85.71%-88.15% overall accuracy for the peak flowering OPEN ACCESSRemote Sens. 2015, 7 13299 season imagery versus 80.82%-83.67% for the early flowering season). The variable optimization (i.e., variable selection) analysis showed that less than half of the AISA bands (n = 26 for the February 2013 data and n = 21 for the January 2014 data) were important to attain relatively reliable classification accuracies. Our study is an important first step towards the development of operational flower mapping routines and for understanding the relationship between flowering and bees' foraging behavior.

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Section: Discussionmentioning

confidence: 99%

The Utility of AISA Eagle Hyperspectral Data and Random Forest Classifier for Flower Mapping

et al. 2015

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“…Spectral confusion is compounded in semi-arid biomes where bi-directional scattering between sub-pixel elements can be very high (Petropoulos, Manevski, & Carlson, 2014).…”

Section: Accuracy Assessmentmentioning

confidence: 99%

Application of hyperspectral remote sensing for flower mapping in African savannas

Landmann

Piiroinen

Makori

et al. 2015

Remote Sensing of Environment

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“…It was only in 2017-2018 that National Aeronautics and Space Administration (NASA) acquired Airborne Visible Infrared Imaging Spectrometer Next Generation (AVIRIS NG) data over Alaska's boreal forests [19]. The HySpex airborne data set, such as presented in this pilot study, complements the NASA AVIRIS NG dataset and has the advantage that it can be collected locally at targeted sites with a greater and controlled frequency, and at lower costs than the NASA AVIRIS NG dataset [20]. The campaign also has other general advantages and disadvantages of airborne remote sensing for wetland mapping [21].…”

Section: Introductionmentioning

confidence: 99%

Airborne Hyperspectral Data Acquisition and Processing in the Arctic: A Pilot Study Using the Hyspex Imaging Spectrometer for Wetland Mapping

et al. 2021

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A pilot study for mapping the Arctic wetlands was conducted in the Yukon Flats National Wildlife Refuge (Refuge), Alaska. It included commissioning the HySpex VNIR-1800 and the HySpex SWIR-384 imaging spectrometers in a single-engine Found Bush Hawk aircraft, planning the flight times, direction, and speed to minimize the strong bidirectional reflectance distribution function (BRDF) effects present at high latitudes and establishing improved data processing workflows for the high-latitude environments. Hyperspectral images were acquired on two clear-sky days in early September, 2018, over three pilot study areas that together represented a wide variety of vegetation and wetland environments. Steps to further minimize BRDF effects and achieve a higher geometric accuracy were added to adapt and improve the Hyspex data processing workflow, developed by the German Aerospace Center (DLR), for high-latitude environments. One-meter spatial resolution hyperspectral images, that included a subset of only 120 selected spectral bands, were used for wetland mapping. A six-category legend was established based on previous U.S. Geological Survey (USGS) and U.S. Fish and Wildlife Service (USFWS) information and maps, and three different classification methods—hybrid classification, spectral angle mapper, and maximum likelihood—were used at two selected sites. The best classification performance occurred when using the maximum likelihood classifier with an averaged Kappa index of 0.95; followed by the spectral angle mapper (SAM) classifier with a Kappa index of 0.62; and, lastly, by the hybrid classifier showing lower performance with a Kappa index of 0.51. Recommendations for improvements of future work include the concurrent acquisition of LiDAR or RGB photo-derived digital surface models as well as detailed spectra collection for Alaska wetland cover to improve classification efforts.

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Hyperspectral Remote Sensing with Emphasis on Land Cover Mapping: From Ground to Satellite Observations

Cited by 7 publications

References 92 publications

The Utility of AISA Eagle Hyperspectral Data and Random Forest Classifier for Flower Mapping

The Utility of AISA Eagle Hyperspectral Data and Random Forest Classifier for Flower Mapping

Application of hyperspectral remote sensing for flower mapping in African savannas

Airborne Hyperspectral Data Acquisition and Processing in the Arctic: A Pilot Study Using the Hyspex Imaging Spectrometer for Wetland Mapping

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