Divergence of apparent and intrinsic snow albedo over a season at a sub-alpine site with implications for remote sensing

Intrinsic albedo is the bihemispherical reflectance of a substance with a smooth surface. Conversely, the apparent albedo is the bihemispherical reflectance of the same substance with a rough surface. For snow, the surface is often rough, and these two optical quantities have different uses: intrinsic albedo is used in scattering equations whereas apparent albedo should be used in energy balance models. Complementing numerous studies devoted to surface roughness and its effect on snow reflectance, this work analyzes a timeseries of intrinsic and apparent snow albedos over a season at a sub-alpine site using an automated terrestrial laser scanner to map the snow surface topography. An updated albedo model accounts for shade, and in situ albedo measurements from a field spectrometer are compared to those from a spaceborne multispectral sensor. A spectral unmixing approach using a shade endmember (to address the common problem of unknown surface topography) produces grain size and impurity solutions; the modeled shade fraction is compared to the intrinsic and apparent albedo difference. As expected and consistent with other studies, the results show that intrinsic albedo is consistently greater than apparent albedo. Both albedos decrease rapidly as ablation hollows form during melt, combining effects of impurities on the surface and increasing roughness. Intrinsic broadband albedos average 7 % greater than apparent albedos, with the difference being about 6 % in the near-infrared or 3–4 % if the average (planar) topography is known and corrected. Field measurements of spectral surface reflectance confirm that multispectral sensors see the apparent albedo but lack the spectral resolution to distinguish between darkening from ablation hollows versus low concentrations of impurities. In contrast, measurements from the field spectrometer have sufficient resolution to discern darkening from the two sources. Based on these results, conclusions are: 1) impurity estimates from multispectral sensors are only reliable for relatively dirty snow with high snow fraction; 2) a shade endmember must be used in spectral mixture models, even for in situ spectroscopic measurements; and 3) snow albedo models should produce apparent albedos by accounting for the shade fraction. The conclusion re-iterates that albedo is the most practical snow reflectance quantity for remote sensing.

Learning a Transform Base for the Multi- to Hyperspectral Sensor Network with K-SVD

A promising low-cost solution for monitoring spectral information, e.g., on agricultural fields, is that of wireless sensor networks. In contrast to remote sensing, these can achieve more continuous monitoring due to their long-term deployment and are less impacted by the atmosphere, making them a promising solution for the calibration of satellite data. In this paper, we explore an alternative approach for processing data from such a network. Hyperspectral sensors were found to be too complex for such a network. While previous work considered fusing the data from different multispectral sensors in order to derive hyperspectral data, we shift the assessment of the hyperspectral modeling in a separate preprocessing step based on machine learning. We then use the learned data as additional input while using identical multispectral sensors, further reducing the complexity of the sensors. Despite requiring careful parametrization, the approach delivers hyperspectral data of similar and in some cases even better quality.

Use of multispectral remote sensing data to map magnetite bodies in the Bushveld Complex, South Africa: a case study of Roossenekal, Limpopo.

<p>The use of remote sensing in mineral detection and lithological mapping has become a generally accepted augmentative tool in exploration. With the advent of multispectral sensors (e.g. ASTER, Landsat, Sentinel and PlanetScope) having suitable wavelength coverage and bands in the Shortwave Infrared (SWIR) and Thermal Infrared (TIR) regions, multispectral sensors have become increasingly efficient at routine lithological discrimination and mineral potential mapping. It is with this paradigm in mind that this project sought to evaluate and discuss the detection and mapping of vanadium bearing magnetite, found in discordant bodies and magnetite layers, on the Eastern Limb of the Bushveld Complex. The Bushveld Complex hosts the world’s largest resource of high-grade primary vanadium in magnetitite layers, so the wide distribution of magnetite, its economic importance, and its potential as an indicator of many important geological processes warranted the delineation of magnetite.</p><p> </p><p>The detection and mapping of the vanadium bearing magnetite was evaluated using specialized traditional, and advanced machine learning algorithms. Prior to this study, few studies had looked at the detection and exploration of magnetite using remote sensing, despite remote sensing tools having been regularly applied to diverse aspects of geosciences. Maximum Likelihood, Minimum Distance to Means, Artificial Neural Networks, Support Vector Machine classification algorithms were assessed for their respective ability to detect and map magnetite using the PlanetScope data in ENVI, QGIS, and Python. For each classification algorithm, a thematic landcover map was attained and the accuracy assessed using an error matrix, depicting the user's and producer's accuracies, as well as kappa statistics.</p><p> </p><p>The Maximum Likelihood Classifier significantly outperformed the other techniques, achieving an overall classification accuracy of 84.58% and an overall kappa value of 0.79. Magnetite was accurately discriminated from the other thematic landcover classes with a user’s accuracy of 76.41% and a producer’s accuracy of 88.66%. The erroneous classification of some mining activity pixels as magnetite in the Maximum Likelihood was inherent to all classification algorithms. The overall results of this study illustrated that remote sensing techniques are effective instruments for geological mapping and mineral investigation, especially in iron oxide mineralization in the Eastern Limb of Bushveld Complex. </p><p> </p>

Monitoring the Efficacy of Crested Floatingheart (Nymphoides cristata) Management with Object-Based Image Analysis of UAS Imagery

This study investigates the use of unmanned aerial systems (UAS) mapping for monitoring the efficacy of invasive aquatic vegetation (AV) management on a floating-leaved AV species, Nymphoides cristata (CFH). The study site consists of 48 treatment plots (TPs). Based on six unique flights over two days at three different flight altitudes while using both a multispectral and RGB sensor, accuracy assessment of the final object-based image analysis (OBIA)-derived classified images yielded overall accuracies ranging from 89.6% to 95.4%. The multispectral sensor was significantly more accurate than the RGB sensor at measuring CFH areal coverage within each TP only with the highest multispectral, spatial resolution (2.7 cm/pix at 40 m altitude). When measuring response in the AV community area between the day of treatment and two weeks after treatment, there was no significant difference between the temporal area change from the reference datasets and the area changes derived from either the RGB or multispectral sensor. Thus, water resource managers need to weigh small gains in accuracy from using multispectral sensors against other operational considerations such as the additional processing time due to increased file sizes, higher financial costs for equipment procurements, and longer flight durations in the field when operating multispectral sensors.

DEPTH RETRIEVAL FROM A RESERVOIR USING A CONDITIONAL-BASED MODEL

Water depth is an important measure for nautical charts. Accurate methods to provide water depth information are expensive and time costing. For this reason, since late 70’s, it started to be estimate by multispectral sensors with empirical models. In the literature there is no investigation using empirical models partitioned in depth intervals, for this reason, we evaluated the accuracy of partitioned and single bathymetric models. The results have shown that to retrieve depth in from 0 to 15 m the single model provided an RMSE of 3.57 m, with a bias of about −0.83 m; while the RMSE for the partitioned model was 2.29 m with a bias of 0.41 m. For updating nautical charts using multispectral sensors it was concluded that the partitioned model can provide a better result than using a single model.

CHANGE PATTERN EXPLORATION WITH HIERARCHICAL BI-CLUSTERING ON SENTINEL-1 SAR AND NIGHTTIME LIGHT DATA

In the last few decades, urbanization activities have promoted the emergence of megacities, megalopolis, urban clusters or large urban aggregations, but only a few studies have analyzed them using remote sensing data in both the spatial and the temporal domains. In this paper, combining SAR and multispectral sensors with different resolutions, a novel approach, improved by means of a hierarchical clustering technique, is proposed. Urban changes are mapped in the form of multiple spatio-temporal patterns, visualized by change vectors exploiting the combination of SAR and nighttime light data.