Accuracy assessment of ASTER, SRTM, ALOS, and TDX DEMs for Hispaniola and implications for mapping vulnerability to coastal flooding

The implications of change on local processes have attracted significant research interest in recent times. In urban settings, green spaces and forests have attracted much attention. Here, we present an assessment of change within the predominantly desert Middle Eastern city of Riyadh, an understudied setting. We utilized high-resolution SPOT 5 data and two classification techniques—maximum likelihood classification and object-oriented classification—to study the changes in Riyadh between 2004 and 2014. Imagery classification was completed with training data obtained from the SPOT 5 dataset, and an accuracy assessment was completed through a combination of field surveys and an application developed in ESRI Survey 123 tool. The Survey 123 tool allowed residents of Riyadh to present their views on land cover for the 2004 and 2014 imagery. Our analysis showed that soil or ‘desert’ areas were converted to roads and buildings to accommodate for Riyadh’s rapidly growing population. The object-oriented classifier provided higher overall accuracy than the maximum likelihood classifier (74.71% and 73.79% vs. 92.36% and 90.77% for 2004 and 2014). Our work provides insights into the changes within a desert environment and establishes a foundation for understanding change in this understudied setting.

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The impact of the microphysical properties of aerosol on the atmospheric correction of hyperspectral data in coastal waters

Atmospheric Measurement Techniques ◽

10.5194/amt-8-1593-2015 ◽

2015 ◽

Vol 8 (3) ◽

pp. 1593-1604 ◽

Cited By ~ 12

Author(s):

C. Bassani ◽

C. Manzo ◽

F. Braga ◽

M. Bresciani ◽

C. Giardino ◽

...

Keyword(s):

Accuracy Assessment ◽

Atmospheric Correction ◽

Solar Spectrum ◽

Hyperspectral Data ◽

High Spectral Resolution ◽

Quality Analysis ◽

Microphysical Properties ◽

Quantitative Remote Sensing ◽

The Impact ◽

Aerosol Types

Abstract. Hyperspectral imaging provides quantitative remote sensing of ocean colour by the high spectral resolution of the water features. The HICO™ (Hyperspectral Imager for the Coastal Ocean) is suitable for coastal studies and monitoring. The accurate retrieval of hyperspectral water-leaving reflectance from HICO™ data is still a challenge. The aim of this work is to retrieve the water-leaving reflectance from HICO™ data with a physically based algorithm, using the local microphysical properties of the aerosol in order to overcome the limitations of the standard aerosol types commonly used in atmospheric correction processing. The water-leaving reflectance was obtained using the HICO@CRI (HICO ATmospherically Corrected Reflectance Imagery) atmospheric correction algorithm by adapting the vector version of the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) radiative transfer code. The HICO@CRI algorithm was applied on to six HICO™ images acquired in the northern Mediterranean basin, using the microphysical properties measured by the Acqua Alta Oceanographic Tower (AAOT) AERONET site. The HICO@CRI results obtained with AERONET products were validated with in situ measurements showing an accuracy expressed by r2 = 0.98. Additional runs of HICO@CRI on the six images were performed using maritime, continental and urban standard aerosol types to perform the accuracy assessment when standard aerosol types implemented in 6SV are used. The results highlight that the microphysical properties of the aerosol improve the accuracy of the atmospheric correction compared to standard aerosol types. The normalized root mean square (NRMSE) and the similar spectral value (SSV) of the water-leaving reflectance show reduced accuracy in atmospheric correction results when there is an increase in aerosol loading. This is mainly when the standard aerosol type used is characterized with different optical properties compared to the local aerosol. The results suggest that if a water quality analysis is needed the microphysical properties of the aerosol need to be taken into consideration in the atmospheric correction of hyperspectral data over coastal environments, because aerosols influence the accuracy of the retrieved water-leaving reflectance.

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Accuracy assessment and scale effect investigation of UAV thermography for underground coal fire surface temperature monitoring

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2021.102426 ◽

2021 ◽

Vol 102 ◽

pp. 102426

Author(s):

Gang Yuan ◽

Yunjia Wang ◽

Feng Zhao ◽

Teng Wang ◽

Leixin Zhang ◽

...

Keyword(s):

Surface Temperature ◽

Scale Effect ◽

Accuracy Assessment ◽

Temperature Monitoring ◽

Coal Fire

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The Accuracy Assessment of Precise Orbits Computed from Doppler Tracking Data

The Journal of the Astronautical Sciences ◽

10.1007/bf03546402 ◽

1997 ◽

Vol 45 (4) ◽

pp. 451-469

Author(s):

George W. Davis ◽

John C. Ries ◽

Byron D. Tapley

Keyword(s):

Accuracy Assessment ◽

Tracking Data ◽

Doppler Tracking

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Semi-Automatic Fractional Snow Cover Monitoring from Near-Surface Remote Sensing in Grassland

Remote Sensing ◽

10.3390/rs13112045 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2045

Author(s):

Anaí Caparó Bellido ◽

Bradley C. Rundquist

Keyword(s):

Snow Cover ◽

Near Infrared ◽

Accuracy Assessment ◽

Important Variable ◽

Field Station ◽

Near Surface ◽

Fractional Snow Cover ◽

Rgb Images ◽

Phenological Metrics

Snow cover is an important variable in both climatological and hydrological studies because of its relationship to environmental energy and mass flux. However, variability in snow cover can confound satellite-based efforts to monitor vegetation phenology. This research explores the utility of the PhenoCam Network cameras to estimate Fractional Snow Cover (FSC) in grassland. The goal is to operationalize FSC estimates from PhenoCams to inform and improve the satellite-based determination of phenological metrics. The study site is the Oakville Prairie Biological Field Station, located near Grand Forks, North Dakota. We developed a semi-automated process to estimate FSC from PhenoCam images through Python coding. Compared with previous research employing RGB images only, our use of the monochrome RGB + NIR (near-infrared) reduced pixel misclassification and increased accuracy. The results had an average RMSE of less than 8% FSC compared to visual estimates. Our pixel-based accuracy assessment showed that the overall accuracy of the images selected for validation was 92%. This is a promising outcome, although not every PhenoCam Network system has NIR capability.

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