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State-of-the-Art Remote Sensing in North America 2019

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 63786

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Special Issue Editors

Prof. Dar Roberts

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Guest Editor

Department of Geography, University of California Santa Barbara, Santa Barbara, CA 93106, USA
Interests: imaging spectrometry; remote sensing of vegetation; spectroscopy (urban and natural cover); land-use/land-cover change mapping with satellite time series; height mapping with lidar; fire danger assessment; remote sensing of methane
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Prof. Susan Ustin

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Guest Editor

Distinguished Professor of Environmental and Resource Science, University of California Davis, Davis, CA 95616, USA
Interests: remote sensing of environmental properties and landscape analysis; spectroscopy (wetlands, rangeland and forests); radiation interactions in plant canopies; detection of ecophysiological properties; vegetation stress; application to hydrological and ecological problems
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Dr. Cathleen Jones

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Guest Editor

Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
Interests: synthetic aperture radar; GNSS; coastal and delta subsidence; oil spill
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Recent developments in airborne sensors and access to spaceborne data have drastically improved our ability to map the properties of land, water and air and to quantify change. Examples include new passive sensors, such as the Airborne Visible/Infrared Imaging Spectrometer Next Generation (NG), the Hyperspectral Thermal Emission Spectrometer (HYTES), the Portable Remote Imaging Spectrometer (PRISM) and active sensors, such as the Sentinel-1 C-band Synthetic Aperture Radar (SAR), the Land, Vegetation and Ice Sensor (LVIS)-Global Hawk laser altimeter, the UAVSAR L-band SAR, and the AirMOSS P-band SAR. New opportunities for sensor fusion are now possible by combining multiple sensors on a single platform, such as NEON AOP (Imaging spectrometer and LiDAR), the Goddard LiDAR, Hyperspectral and Thermal (G-LiHT) airborne sensor and the HyspIRI Airborne Campaign (AVIRIS/MASTER). Small airborne platforms, such as UAVs, offer the potential for improved near–surface imaging for applications such as precision agriculture and forestry. Finally, improved access to long-term medium-resolution-scale spaceborne data sets, such as those from the Sentinel-1A/B constellation, Landsat suite (TM, ETM+, OLI) and continuity with newer assets such as Sentinel-2, offer new opportunities for monitoring disturbance and seasonal changes in land-cover for forestry, agriculture and urban analysis, and for surface deformation studies that differentiate long term changes from seasonal and episodic events.

For this Special Issue, we encourage the submission of articles that utilize novel remote sensing datasets to address important environmental research questions pertinent to North America. Articles that focus on data fusion from multiple sensors (e.g., HyspIRI AC, NEON-AOP), from multiple platforms (airborne data combined with satellite imagery), newly available airborne datasets (e.g. HYTES, PHyTIR, AVIRIS-NG, Lidar) or the potential for novel time series analyses are particularly encouraged. Studies utilizing time series from SAR instruments like Sentinel-1 and UAVSAR to evaluate the dynamics of surface and ecosystem change are also encouraged.

Prof. Dar Roberts
Prof. Susan Ustin
Dr. Cathleen Jones
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

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Keywords

Regional or Continental analysis (North America)
Imaging spectroscopy
Hyperspectral or multiband thermal
Synthetic aperture radar
Waveform or multiband Lidar
Time series analysis
Change detection
SAR interferometry (InSAR)
Sensor Fusion
Lidar and Imaging spectrometry fusion

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Published Papers (9 papers)

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Research

19 pages, 7933 KiB

Open AccessFeature PaperArticle

Methane Mapping with Future Satellite Imaging Spectrometers

by Alana K. Ayasse, Philip E. Dennison, Markus Foote, Andrew K. Thorpe, Sarang Joshi, Robert O. Green, Riley M. Duren, David R. Thompson and Dar A. Roberts

Remote Sens. 2019, 11(24), 3054; https://doi.org/10.3390/rs11243054 - 17 Dec 2019

Cited by 36 | Viewed by 10212

Abstract

This study evaluates a new generation of satellite imaging spectrometers to measure point source methane emissions from anthropogenic sources. We used the Airborne Visible and Infrared Imaging Spectrometer Next Generation(AVIRIS-NG) images with known methane plumes to create two simulated satellite products. One simulation had a 30 m spatial resolution with ~200 Signal-to-Noise Ratio (SNR) in the Shortwave Infrared (SWIR) and the other had a 60 m spatial resolution with ~400 SNR in the SWIR; both products had a 7.5 nm spectral spacing. We applied a linear matched filter with a sparsity prior and an albedo correction to detect and quantify the methane emission in the original AVIRIS-NG images and in both satellite simulations. We also calculated an emission flux for all images. We found that all methane plumes were detectable in all satellite simulations. The flux calculations for the simulated satellite images correlated well with the calculated flux for the original AVIRIS-NG images. We also found that coarsening spatial resolution had the largest impact on the sensitivity of the results. These results suggest that methane detection and quantification of point sources will be possible with the next generation of satellite imaging spectrometers. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing in North America 2019)

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25 pages, 1419 KiB

Open AccessArticle

Monitoring Post-Fire Recovery of Chaparral and Conifer Species Using Field Surveys and Landsat Time Series

by Christopher L. Kibler, Anne-Marie L. Parkinson, Seth H. Peterson, Dar A. Roberts, Carla M. D’Antonio, Susan K. Meerdink and Stuart H. Sweeney

Remote Sens. 2019, 11(24), 2963; https://doi.org/10.3390/rs11242963 - 11 Dec 2019

Cited by 22 | Viewed by 5443

Abstract

Recovery trajectories derived from remote sensing data are widely used to monitor ecosystem recovery after disturbance events, but these trajectories are often retrieved without a precise understanding of the land cover within a scene. As a result, the sources of variability in post-disturbance recovery trajectories are poorly understood. In this study, we monitored the recovery of chaparral and conifer species following the 2007 Zaca Fire, which burned 97,270 ha in Santa Barbara County, California. We combined field survey data with two time series remote sensing products: the relative delta normalized burn ratio (RdNBR) and green vegetation (GV) fractions derived from spectral mixture analysis. Recovery trajectories were retrieved for stands dominated by six different chaparral species. We also retrieved recovery trajectories for stands of mixed conifer forest. We found that the two remote sensing products were equally effective at mapping vegetation cover across the burn scar. The GV fractions (r(78) = 0.552, p < 0.001) and normalized burn ratio (r(78) = 0.555, p < 0.001) had nearly identical correlations with ground reference data of green vegetation cover. Recovery of the chaparral species was substantially affected by the 2011–2017 California drought. GV fractions for the chaparral species generally declined between 2011 and 2016. Physiological responses to fire and drought were important sources of variability between the species. The conifer stands did not exhibit a drought signal that was directly correlated with annual precipitation, but the drought likely delayed the return to pre-fire conditions. As of 2018, 545 of the 756 conifer stands had not recovered to their pre-fire GV fractions. Spatial and temporal variation in species composition were important sources of spectral variability in the chaparral and conifer stands. The chaparral stands in particular had highly heterogeneous species composition. Dominant species accounted for between 30% and 53% of the land cover in the surveyed chaparral patches, so non-dominant land cover types strongly influenced remote sensing signals. Our study reveals that prolonged drought can delay or alter the post-fire recovery of Mediterranean ecosystems. It is also the first study to critically examine how fine-scale variability in land cover affects time series remote sensing analyses. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing in North America 2019)

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26 pages, 6256 KiB

Open AccessArticle

Mapping Water Surface Elevation and Slope in the Mississippi River Delta Using the AirSWOT Ka-Band Interferometric Synthetic Aperture Radar

by Michael Denbina, Marc Simard, Ernesto Rodriguez, Xiaoqing Wu, Albert Chen and Tamlin Pavelsky

Remote Sens. 2019, 11(23), 2739; https://doi.org/10.3390/rs11232739 - 21 Nov 2019

Cited by 17 | Viewed by 4376

Abstract

AirSWOT is an airborne Ka-band synthetic aperture radar, capable of mapping water surface elevation (WSE) and water surface slope (WSS) using single-pass interferometry. AirSWOT was designed as a calibration and validation instrument for the forthcoming Surface Water and Ocean Topography (SWOT) mission, an international spaceborne synthetic aperture radar mission planned for launch in 2022 which will enable global mapping of WSE and WSS. As an airborne instrument, capable of quickly repeating overflights, AirSWOT enables measurement of high frequency and fine scale hydrological processes encountered in coastal regions. In this paper, we use data collected by AirSWOT in the Mississippi River Delta and surrounding wetlands of coastal Louisiana, USA, to investigate the capabilities of Ka-band interferometry for mapping WSE and WSS in coastal marsh environments. We introduce a data-driven method to estimate the time-varying interferometric phase drift resulting from radar hardware response to environmental conditions. A system of linear equations based on AirSWOT measurements is solved for elevation bias and time-varying phase calibration parameters using weighted least squares. We observed AirSWOT WSE uncertainty of 12 cm RMS compared to in situ water level measurements when averaged over an area of 0.5 km

^{2}

at incidence angles below

15^{\circ}

. At higher incidence angles, the observed AirSWOT elevation bias is possibly due to residual phase calibration errors or radar backscatter from vegetation. Elevation profiles along the Wax Lake Outlet river channel indicate AirSWOT can measure WSS over a 24 km distance with uncertainty below 0.3 cm/km, 8% of the true water surface slope as measured by in situ data. The data analysis and results presented in this paper demonstrate the potential of AirSWOT to measure water surface elevation and slope within highly dynamic and spatially complex coastal environments. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing in North America 2019)

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16 pages, 1590 KiB

Open AccessEditor’s ChoiceArticle

Regional Scale Dryland Vegetation Classification with an Integrated Lidar-Hyperspectral Approach

by Hamid Dashti, Andrew Poley, Nancy F. Glenn, Nayani Ilangakoon, Lucas Spaete, Dar Roberts, Josh Enterkine, Alejandro N. Flores, Susan L. Ustin and Jessica J. Mitchell

Remote Sens. 2019, 11(18), 2141; https://doi.org/10.3390/rs11182141 - 14 Sep 2019

Cited by 15 | Viewed by 5283

Abstract

The sparse canopy cover and large contribution of bright background soil, along with the heterogeneous vegetation types in close proximity, are common challenges for mapping dryland vegetation with remote sensing. Consequently, the results of a single classification algorithm or one type of sensor to characterize dryland vegetation typically show low accuracy and lack robustness. In our study, we improved classification accuracy in a semi-arid ecosystem based on the use of vegetation optical (hyperspectral) and structural (lidar) information combined with the environmental characteristics of the landscape. To accomplish this goal, we used both spectral angle mapper (SAM) and multiple endmember spectral mixture analysis (MESMA) for optical vegetation classification. Lidar-derived maximum vegetation height and delineated riparian zones were then used to modify the optical classification. Incorporating the lidar information into the classification scheme increased the overall accuracy from 60% to 89%. Canopy structure can have a strong influence on spectral variability and the lidar provided complementary information for SAM’s sensitivity to shape but not magnitude of the spectra. Similar approaches to map large regions of drylands with low uncertainty may be readily implemented with unmixing algorithms applied to upcoming space-based imaging spectroscopy and lidar. This study advances our understanding of the nuances associated with mapping xeric and mesic regions, and highlights the importance of incorporating complementary algorithms and sensors to accurately characterize the heterogeneity of dryland ecosystems. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing in North America 2019)

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27 pages, 3656 KiB

Open AccessArticle

New ECOSTRESS and MODIS Land Surface Temperature Data Reveal Fine-Scale Heat Vulnerability in Cities: A Case Study for Los Angeles County, California

by Glynn Hulley, Sarah Shivers, Erin Wetherley and Robert Cudd

Remote Sens. 2019, 11(18), 2136; https://doi.org/10.3390/rs11182136 - 13 Sep 2019

Cited by 89 | Viewed by 14041

Abstract

Rapid 21st century urbanization combined with anthropogenic climate warming are significantly increasing heat-related health threats in cities worldwide. In Los Angeles (LA), increasing trends in extreme heat are expected to intensify and exacerbate the urban heat island effect, leading to greater health risks for vulnerable populations. Partnerships between city policymakers and scientists are becoming more important as the need to provide data-driven recommendations for sustainability and mitigation efforts becomes critical. Here we present a model to produce heat vulnerability index (HVI) maps driven by surface temperature data from National Aeronautics and Space Administration’s (NASA) new Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) thermal infrared sensor. ECOSTRESS was launched in June 2018 with the capability to image fine-scale urban temperatures at a 70 m resolution throughout different times of the day and night. The HVI model further includes information on socio-demographic data, green vegetation abundance, and historical heatwave temperatures from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Aqua spacecraft since 2002. During a period of high heat in July 2018, we identified the five most vulnerable communities at a sub-city block scale in the LA region. The persistence of high HVI throughout the day and night in these areas indicates a clear and urgent need for implementing cooling technologies and green infrastructure to curb future warming. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing in North America 2019)

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20 pages, 3120 KiB

Open AccessArticle

The Influence of Signal to Noise Ratio of Legacy Airborne and Satellite Sensors for Simulating Next-Generation Coastal and Inland Water Products

by Raphael M. Kudela, Stanford B. Hooker, Henry F. Houskeeper and Meredith McPherson

Remote Sens. 2019, 11(18), 2071; https://doi.org/10.3390/rs11182071 - 4 Sep 2019

Cited by 15 | Viewed by 5417

Abstract

Presently, operational ocean color satellite sensors are designed with a legacy perspective for sampling the open ocean primarily in the visible domain, while high spatial resolution sensors such as Sentinel-2, Sentinel-3, and Landsat8 are increasingly used for observations of coastal and inland water quality. Next-generation satellites such as the NASA Plankton, Aerosol, Cloud and ocean Ecosystem (PACE) and Surface Biology and Geology (SBG) sensors are anticipated to increase spatial and/or spectral resolution. An important consideration is determining the minimum signal-to-noise ratio (SNR) needed to retrieve typical biogeochemical products, such as biomass, in aquatic systems, and whether legacy sensors can be used for algorithm development. Here, we evaluate SNR and remote-sensing reflectance (R_rs) uncertainty for representative bright and dim targets in coastal California, USA. The majority of existing sensors fail to meet proposed criteria. Despite these limitations, uncertainties in retrieved biomass as chlorophyll or normalized difference vegetation index (NDVI) remain well below a proposed threshold of 17.5%, suggesting that existing sensors can be used in coastal systems. Existing commercially available in-water and airborne instrument suites can exceed all proposed thresholds for SNR and R_rs uncertainty, providing a path forward for collection of calibration and validation data for future satellite missions. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing in North America 2019)

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24 pages, 6009 KiB

Open AccessArticle

Pushing the Limits of Seagrass Remote Sensing in the Turbid Waters of Elkhorn Slough, California

by Heidi M. Dierssen, Kelley J. Bostrom, Adam Chlus, Kamille Hammerstrom, David R. Thompson and Zhongping Lee

Remote Sens. 2019, 11(14), 1664; https://doi.org/10.3390/rs11141664 - 12 Jul 2019

Cited by 18 | Viewed by 6478

Abstract

Remote sensing imagery has been successfully used to map seagrass in clear waters, but here we evaluate the advantages and limitations of different remote sensing techniques to detect eelgrass in the tidal embayment of Elkhorn Slough, CA. Pseudo true-color imagery from Google Earth and broadband satellite imagery from Sentinel-2 allowed for detection of the various beds, but retrievals particularly in the deeper Vierra bed proved unreliable over time due to variable image quality and environmental conditions. Calibrated water-leaving reflectance spectrum from airborne hyperspectral imagery at 1-m resolution from the Portable Remote Imaging SpectroMeter (PRISM) revealed the extent of both shallow and deep eelgrass beds using the HOPE semi-analytical inversion model. The model was able to reveal subtle differences in spectral shape, even when remote sensing reflectance over the Vierra bed was not visibly distinguishable. Empirical methods exploiting the red edge of reflectance to differentiate submerged vegetation only retrieved the extent of shallow alongshore beds. The HOPE model also accurately retrieved the water column absorption properties, chlorophyll-a, and bathymetry but underestimated the particulate backscattering and suspended matter when benthic reflectance was represented as a horizontal eelgrass leaf. More accurate water column backscattering could be achieved by the use of a darker bottom spectrum representing an eelgrass canopy. These results illustrate how high quality atmospherically-corrected hyperspectral imagery can be used to map eelgrass beds, even in regions prone to sediment resuspension, and to quantify bathymetry and water quality. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing in North America 2019)

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24 pages, 6024 KiB

Open AccessEditor’s ChoiceArticle

Improving the Transferability of Suspended Solid Estimation in Wetland and Deltaic Waters with an Empirical Hyperspectral Approach

by Daniel Jensen, Marc Simard, Kyle Cavanaugh, Yongwei Sheng, Cédric G. Fichot, Tamlin Pavelsky and Robert Twilley

Remote Sens. 2019, 11(13), 1629; https://doi.org/10.3390/rs11131629 - 9 Jul 2019

Cited by 32 | Viewed by 5678

Abstract

The deposition of suspended sediment is an important process that helps wetlands accrete surface material and maintain elevation in the face of sea level rise. Optical remote sensing is often employed to map total suspended solids (TSS), though algorithms typically have limited transferability in space and time due to variability in water constituent compositions, mixtures, and inherent optical properties. This study used in situ spectral reflectances and their first derivatives to compare empirical algorithms for estimating TSS using hyperspectral and multispectral data. These algorithms were applied to imagery collected by NASA’s Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) over coastal Louisiana, USA, and validated with a multiyear in situ dataset. The best performing models were then applied to independent spectroscopic data collected in the Peace–Athabasca Delta, Canada, and the San Francisco Bay–Delta Estuary, USA, to assess their robustness and transferability. A derivative-based partial least squares regression (PLSR) model applied to simulated AVIRIS-NG data showed the most accurate TSS retrievals (R² = 0.83) in these contrasting deltaic environments. These results highlight the potential for a more broadly applicable generalized algorithm employing imaging spectroscopy for estimating suspended solids. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing in North America 2019)

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29 pages, 7097 KiB

Open AccessArticle

Discrimination of Canopy Structural Types in the Sierra Nevada Mountains in Central California

by Margarita Huesca, Keely L. Roth, Mariano García and Susan L. Ustin

Remote Sens. 2019, 11(9), 1100; https://doi.org/10.3390/rs11091100 - 8 May 2019

Cited by 10 | Viewed by 5336

Abstract

Accurate information about ecosystem structure and biogeochemical properties is essential to providing better estimates ecosystem functioning. Airborne LiDAR (light detection and ranging) is the most accurate way to retrieve canopy structure. However, accurately obtaining both biogeochemical traits and structure parameters requires concurrent measurements from imaging spectrometers and LiDARs. Our main objective was to evaluate the use of imaging spectroscopy (IS) to provide vegetation structural information. We developed models to estimate structural variables (i.e., biomass, height, vegetation heterogeneity and clumping) using IS data with a random forests model from three forest ecosystems (i.e., an oak-pine low elevation savanna, a mixed conifer/broadleaf mid-elevation forest, and a high-elevation montane conifer forest) in the Sierra Nevada Mountains, California. We developed and tested general models to estimate the four structural variables with accuracies greater than 75%, for the structurally and ecologically different forest sites, demonstrating their applicability to a diverse range of forest ecosystems. The model R² for each structural variable was least in the conifer/broadleaf forest than either the low elevation savanna or the montane conifer forest. We then used the structural variables we derived to discriminate site-specific, ecologically meaningful descriptions of canopy structural types (CST). Our CST results demonstrate how IS data can be used to create comprehensive and easily interpretable maps of forest structural types that capture their major structural features and trends across different vegetation types in the Sierra Nevada Mountains. The mixed conifer/broadleaf forest and montane conifer forest had the most complex structures, containing six and five CSTs respectively. The identification of CSTs within a site allowed us to better identify the main drivers of structural variability in each ecosystem. CSTs in open savanna were driven mainly by differences in vegetation cover; in the mid-elevation mixed forest, by the combination of biomass and canopy height; and in the montane conifer forest, by vegetation heterogeneity and clumping. Full article

(This article belongs to the Special Issue State-of-the-Art Remote Sensing in North America 2019)

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