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Special Issue "Innovative Belgian Earth Observation Research for the Environment"

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Earth Observation Data".

Deadline for manuscript submissions: closed (15 July 2022) | Viewed by 22577

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Special Issue Editors

Prof. Dr. Bas van Wesemael

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Guest Editor

Georges Lemaître Centre for Earth and Climate Research, Université Catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium
Interests: soil organic carbon; VisNIR spectroscopy; hyperspectral remote sensing; multivariate calibration; digital soil mapping
Special Issues, Collections and Topics in MDPI journals

Dr. Trissevgeni Stavrakou

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Guest Editor

Royal Belgian Institute for Space Aeronomy, Avenue Circulaire 3, 1180 Uccle, Belgium
Interests: emissions; tropospheric chemistry; oxgenated volatile organic compounds; inverse modelling

Dr. Jean-Christophe Schyns
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Guest Editor

Belgian Federal Science Policy Office, Space Research and Applications Division, 1000 Brussels, Belgium
Interests: earth observation; remote sensing

Dr. Joost Vandenabeele
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Guest Editor

Belgian Federal Science Policy Office, Space Research and Applications Division, 1000 Brussels, Belgium
Interests: earth observation; remote sensing

Dr. Hans Lievens

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Guest Editor

Division of Soil and Water Management, Katholiek Universiteit Leuven, 3000 Leuven, Belgium
Interests: SAR; remote sensing; hydrology; soil moisture

Dr. Dimitry van der Zande

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Guest Editor

Royal Belgian Institute of Natural Sciences, 1000 Brussels, Belgium
Interests: remote sensing; marine ecosystems; ocean colour; water quality monitoring

Dr. Kim Calders

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Computational & Applied Vegetation Ecology, Ghent University, Coupure Links 653, 9000 Ghent, Belgium
Interests: terrestrial and UAV based LiDAR; measurement and modelling of full 3D vegetation structure and how this is related to airborne or spaceborne remote sensing signals; radiative transfer modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

For over 35 years now, Belgian scientists have been turning remote sensing data from the poles to the tropics into actionable information supported by the research program STEREO. This continuous support has enabled the development of a rich scientific community and research infrastructure in Belgium. High spatial, spectral and temporal resolution data is increasingly available from satellites, airplanes, unmanned aerial vehicles (UAVs) and terrestrial platforms. In this special issue we aim at collecting papers that (i) illustrate the application and potential of Earth observation tools in the sustainable management of a densely populated country in environments such as agriculture, forests and nature reserves, coasts and cities, (ii) demonstrate the capabilities offered by new optical, radar, hyperspectral and Lidar data to study the Earth system, and (iii) showcase the benefits from a strong remote sensing community for the calibration/validation of sensors and data analysis tools. Any author affiliated to Belgian institutions is invited to contribute and researchers working in the framework of the STEREO program are particularly targeted.

Prof. Dr. Bas van Wesemael
Dr. Trissevgeni Stavrakou
Dr. Jean-Christophe Schyns
Dr. Joost Vandenabeele
Dr. Hans Lievens
Dr. Dimitry van der Zande
Dr. Kim Calders
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Earth observation
multi-missions
Belgium
sustainable management
data analysis tools

Published Papers (14 papers)

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Open AccessArticle

Evaluating Data Inter-Operability of Multiple UAV–LiDAR Systems for Measuring the 3D Structure of Savanna Woodland

Sruthi M. Krishna Moorthy

Shaun R. Levick

Renée Bartolo

and

Hans Verbeeck

Remote Sens. 2022, 14(23), 5992; https://doi.org/10.3390/rs14235992 - 26 Nov 2022

Viewed by 1168

Abstract

For vegetation monitoring, it is crucial to understand which changes are caused by the measurement setup and which changes are true representations of vegetation dynamics. UAV–LiDAR offers great possibilities to measure vegetation structural parameters; however, UAV–LiDAR sensors are undergoing rapid developments, and the characteristics are expected to keep changing over the years, which will introduce data inter-operability issues. Therefore, it is important to determine whether datasets acquired by different UAV–LiDAR sensors can be interchanged and if changes through time can accurately be derived from UAV–LiDAR time series. With this study, we present insights into the magnitude of differences in derived forest metrics in savanna woodland when three different UAV–LiDAR systems are being used for data acquisition. Our findings show that all three systems can be used to derive plot characteristics such as canopy height, canopy cover, and gap fractions. However, there are clear differences between the metrics derived with different sensors, which are most apparent in the lower parts of the canopy. On an individual tree level, all UAV–LiDAR systems are able to accurately capture the tree height in a savanna woodland system, but significant differences occur when crown parameters are measured with different systems. Less precise systems result in underestimations of crown areas and crown volumes. When comparing UAV–LiDAR data of forest areas through time, it is important to be aware of these differences and ensure that data inter-operability issues do not influence the change analysis. In this paper, we want to stress that it is of utmost importance to realise this and take it into consideration when combining datasets obtained with different sensors. Full article

(This article belongs to the Special Issue Innovative Belgian Earth Observation Research for the Environment)

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Open AccessArticle

Assimilation of Backscatter Observations into a Hydrological Model: A Case Study in Belgium Using ASCAT Data

and

Remote Sens. 2022, 14(22), 5740; https://doi.org/10.3390/rs14225740 - 13 Nov 2022

Viewed by 829

Abstract

We investigated the possibilities of improving hydrological simulations by assimilating radar backscatter observations from the advanced scatterometer (ASCAT) in the hydrological model SCHEME using a calibrated water cloud model (WCM) as an observation operator. The WCM simulates backscatter based on soil moisture and vegetation data and can therefore be used to generate observation predictions for data assimilation. The study was conducted over two Belgian catchments with different hydrological regimes: the Demer and the Ourthe catchment. The main differences between the two catchments can be summarized in precipitation and streamflow levels, which are higher in the Ourthe. The data assimilation method adopted here was the ensemble Kalman filter (EnKF), whereby the uncertainty of the state estimate was described via the ensemble statistics. The focus was on the optimization of the EnKF, and possible solutions to address biases introduced by ensemble perturbations were investigated. The latter issue contributes to the fact that backscatter data assimilation only marginally improves the overall scores of the discharge simulations over the deterministic reference run, and only for the Ourthe catchment. These performances, however, considerably depend on the period considered within the 5 years of analysis. Future lines of research on bias correction, the data assimilation of soil moisture and backscatter data are also outlined. Full article

(This article belongs to the Special Issue Innovative Belgian Earth Observation Research for the Environment)

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Open AccessArticle

Using Hyperspectral Remote Sensing to Monitor Water Quality in Drinking Water Reservoirs

and

Remote Sens. 2022, 14(21), 5607; https://doi.org/10.3390/rs14215607 - 07 Nov 2022

Viewed by 1416

Abstract

At the Blankaart Water Production Center, a reservoir containing 3 million m

^{3}

At the Blankaart Water Production Center, a reservoir containing 3 million m

^{3}

of raw surface water acts as a first biologic treatment step before further processing to drinking water. Over the past decade, severe algal blooms have occurred in the reservoir, hampering the water production. Therefore, strategies (e.g., the injection of algaecide) have been looked at to prevent these from happening or try to control them. In this context, the HYperspectral Pointable System for Terrestrial and Aquatic Radiometry (HYPSTAR), installed since early 2021, helps in monitoring the effectiveness of these strategies. Indeed, the HYPSTAR provides, at a very high temporal resolution, bio-optical parameters related to the water quality, i.e., Chlorophyll-a (

C h l_{a}

) concentrations and suspended particulate matter (

S P M

). The present paper shows how the raw in situ hyperspectral data (a total of 8116 spectra recorded between 2021-02-03 and 2022-08-03, of which 2988 spectra passed the quality check) are processed to find the water-leaving reflectance and how

S P M

and

C h l_{a}

are derived from it. Based on a limited number of validation data, we also discuss the potential of retrieving phycocyanin (an accessory pigment unique to freshwater cyanobacteria). The results show the benefits of the high temporal resolution of the HYPSTAR to provide near real-time water quality indicators. The study confirms that, in conjunction with a few water sampling data used for validation, the HYPSTAR can be used as a quick and cost-effective method to detect and monitor phytoplankton blooms. Full article

(This article belongs to the Special Issue Innovative Belgian Earth Observation Research for the Environment)

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Open AccessArticle

Can Mangrove Silviculture Be Carbon Neutral?

Giovanna Wolswijk

Africa Barrios Trullols

and

Remote Sens. 2022, 14(12), 2920; https://doi.org/10.3390/rs14122920 - 18 Jun 2022

Cited by 1 | Viewed by 1756

Abstract

Matang Mangrove Forest Reserve (MMFR) in peninsular Malaysia has been managed for pole and charcoal production from Rhizophora stands with a 30-year rotation cycle since 1902. The aim of this study is to estimate the carbon budget of the MMFR by considering the carbon stock of the forest, evaluated from remote sensing data (Landsat TM and ETM+, JERS-1 SAR, ALOS PALSAR, ALOS-2 PALSAR-2, SRTM, TANDEM-X, and WorldView-2) for aboveground carbon and field data for belowground carbon. This was investigated in combination with the emissions from the silvicultural activities in the production chain, plus the distribution and consumer-related activities covering the supply chain, estimated with appropriate emission factors. The aboveground biomass carbon stock of the productive forest was of 1.4 TgC, while for the protective forest (not used for silviculture) it was at least equal to 1.2 TgC. The total soil carbon of ca. 32 TgC shows the potential of the MMFR as a carbon sink. However, the commercial exploitation of mangroves also generates greenhouse gasses with an estimate of nearly 152.80 Mg C ha⁻¹ during charcoal production and up to 0.53 Mg C ha⁻¹ during pole production, for a total emission of 1.8 TgC. Consequently, if the productive forest alone is considered, then the carbon budget is negative, and the ongoing silvicultural management seems to be an unsustainable practice that needs a reduction in the exploited area of at least 20% to achieve carbon neutrality. However, even with the current management, and considering the protective forest together with the productive zones, the MMFR carbon budget is slightly positive, thus showing the importance of mangrove conservation as part of the management for the preservation of the carbon stock. Full article

(This article belongs to the Special Issue Innovative Belgian Earth Observation Research for the Environment)

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Open AccessArticle

UAV Remote Sensing for Detecting within-Field Spatial Variation of Winter Wheat Growth and Links to Soil Properties and Historical Management Practices. A Case Study on Belgian Loamy Soil

and

Remote Sens. 2022, 14(12), 2806; https://doi.org/10.3390/rs14122806 - 11 Jun 2022

Cited by 3 | Viewed by 1438

Abstract

Intra-field heterogeneity of soil properties, such as soil organic carbon (SOC), nitrogen (N), phosphorous (P), exchangeable cations, pH, or soil texture, is a function of complex interactions between biological factors, physical factors, and historic agricultural management. Mapping the crop growth and final yield heterogeneity and quantifying their link with soil properties can contribute to an optimization of amendment/fertilizer application and crop yield in a management variable zones (MVZ) approach. To this end, we studied a field of 17 ha consisting of four former fields that were merged in early 2017 and cropped with winter wheat in 2018. Historical management practices data were collected. The topsoil characteristics were analyzed by grid-based sampling and kriged to create maps. We tested the capacity of a multispectral MicaSense^® RedEdge-M^TM camera sensor embedded on an unmanned aerial vehicle (UAV) to map in-season growth of winter wheat. Relating several vegetation indices (VIs) to the plant area index (PAI) measured in the field highlighted the red-edge NDVI (RENDVI) as the most suitable to follow the crop growth throughout the growing season. The georeferenced final grain yield of the winter wheat was measured by a combine harvester. The spatial patterns in RENDVI at three phenological stages were mapped and analyzed together with the yield map. For each of these images a conditional inference forest (CI-forest) algorithm was used to identify the soil properties significantly influencing these spatial patterns. Historical management practices of the four former fields have induced significant heterogeneity in soil properties and crop growth. The spatial patterns of RENDVI are rather constant over time and their Spearman rank correlation with yield is similar along the growing season (r ≃ 0.7). Soil properties explain between 87% (mid-March) to 78% (mid-May) of the variance in RENDVI throughout the growing season, as well as 66% of the variance in yield. The pH and exchangeable K are the most significant factors explaining from 15 to 26% of the variance in crop growth. The methodology proposed in this paper to quantify the importance of soil parameters based on the CI-forest algorithm can contribute to a better management of amendment/fertilizer inputs by stressing the most important parameters to take into consideration for site-specific management. We also showed that heterogeneity induced by the soil properties can be described by a crop map early in the season and that this crop map can be used to optimize soil sampling and thus amendment/fertilizer management. Full article

(This article belongs to the Special Issue Innovative Belgian Earth Observation Research for the Environment)

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Open AccessEditor’s ChoiceArticle

Green Area Index and Soil Moisture Retrieval in Maize Fields Using Multi-Polarized C- and L-Band SAR Data and the Water Cloud Model

and

Remote Sens. 2022, 14(10), 2496; https://doi.org/10.3390/rs14102496 - 23 May 2022

Cited by 1 | Viewed by 1375

Abstract

The green area index (GAI) and the soil moisture under the canopy are two key variables for agricultural monitoring. The current most accurate GAI estimation methods exploit optical data and are rendered ineffective in the case of frequent cloud cover. Synthetic aperture radar (SAR) measurements could allow the remote estimation of both variables at the parcel level, on a large scale and regardless of clouds. In this study, several methods were implemented and tested for the simultaneous estimation of both variables using the water cloud model (WCM) and dual-polarized radar backscatter measurements. The methods were tested on the BELSAR-Campaign data set consisting of in-situ measurements of bio-geophysical variables of vegetation and soil in maize fields combined with multi-polarized C- and L-band SAR data from Sentinel-1 and BELSAR. Accurate GAI estimates were obtained using a random forest regressor for the inversion of a pair of WCMs calibrated using cross and vertical co-polarized SAR data in L- and C-band, with correlation coefficients of 0.79 and 0.65 and RMSEs of 0.77 m² m⁻² and 0.98 m² m⁻², respectively, between estimates and in-situ measurements. The WCM, however, proved inadequate for soil moisture monitoring in the conditions of the campaign. These promising results indicate that GAI retrieval in maize crops using only dual-polarized radar data could successfully substitute for estimates derived from optical data. Full article

(This article belongs to the Special Issue Innovative Belgian Earth Observation Research for the Environment)

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Open AccessArticle

Impact of Drought on Isoprene Fluxes Assessed Using Field Data, Satellite-Based GLEAM Soil Moisture and HCHO Observations from OMI

Beata Opacka

Jean-François Müller

Trissevgeni Stavrakou

and

Remote Sens. 2022, 14(9), 2021; https://doi.org/10.3390/rs14092021 - 22 Apr 2022

Cited by 4 | Viewed by 1498

Abstract

Biogenic volatile organic compounds (BVOCs), primarily emitted by terrestrial vegetation, are highly reactive and have large effects on the oxidizing potential of the troposphere, air quality and climate. In terms of global emissions, isoprene is the most important BVOC. Droughts bring about changes in the surface emission of biogenic hydrocarbons mainly because plants suffer water stress. Past studies report that the current parameterization in the state-of-the-art Model of Emissions of Gases and Aerosols from Nature (MEGAN) v2.1, which is a function of the soil water content and the permanent wilting point, fails at representing the strong reduction in isoprene emissions observed in field measurements conducted during a severe drought. Since the current algorithm was originally developed based on potted plants, in this study, we update the parameterization in the light of recent ecosystem-scale measurements of isoprene conducted during natural droughts in the central U.S. at the Missouri Ozarks AmeriFlux (MOFLUX) site. The updated parameterization results in stronger reductions in isoprene emissions. Evaluation using satellite formaldehyde (HCHO), a proxy for BVOC emissions, and a chemical-transport model, shows that the adjusted parameterization provides a better agreement between the modelled and observed HCHO temporal variability at local and regional scales in 2011–2012, even if it worsens the model agreement in a global, long-term evaluation. We discuss the limitations of the current parameterization, a function of highly uncertain soil properties such as porosity. Full article

(This article belongs to the Special Issue Innovative Belgian Earth Observation Research for the Environment)

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Open AccessArticle

Nitrous Oxide Profiling from Infrared Radiances (NOPIR): Algorithm Description, Application to 10 Years of IASI Observations and Quality Assessment

Remote Sens. 2022, 14(8), 1810; https://doi.org/10.3390/rs14081810 - 08 Apr 2022

Viewed by 2312

Abstract

Nitrous oxide (N₂O) is the third most abundant anthropogenous greenhouse gas (after carbon dioxide and methane), with a long atmospheric lifetime and a continuously increasing concentration due to human activities, making it an important gas to monitor. In this work, we present a new method to retrieve N₂O concentration profiles (with up to two degrees of freedom) from each cloud-free satellite observation by the Infrared Atmospheric Sounding Interferometer (IASI), using spectral micro-windows in the N₂O

ν

₃ band, the Radiative Transfer for TOVS (RTTOV) tools and the Tikhonov regularization scheme. A time series of ten years (2011–2020) of IASI N₂O profiles and integrated partial columns has been produced and validated with collocated ground-based Network for the Detection of Atmospheric Composition Change (NDACC) and Total Carbon Column Observing Network (TCCON) data. The importance of consistency in the ancillary data used for the retrieval for generating consistent time series has been demonstrated. The Nitrous Oxide Profiling from Infrared Radiances (NOPIR) N₂O partial columns are of very good quality, with a positive bias of 1.8 to 4% with respect to the ground-based data, which is less than the sum of uncertainties of the compared values. At high latitudes, the comparisons are a bit worse, due to either a known bias in the ground-based data, or to a higher uncertainty in both ground-based and satellite retrievals. Full article

(This article belongs to the Special Issue Innovative Belgian Earth Observation Research for the Environment)

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Open AccessArticle

Soil Moisture Retrieval Using Multistatic L-Band SAR and Effective Roughness Modeling

and

Remote Sens. 2022, 14(7), 1650; https://doi.org/10.3390/rs14071650 - 30 Mar 2022

Cited by 5 | Viewed by 1491

Abstract

The interest in bistatic SAR systems for soil moisture monitoring has grown over recent years, since theoretical studies suggest that the impact of surface roughness on the retrieval of soil moisture decreases when multistatic, i.e., simultaneous mono- and bistatic, radar measurements are used. This paper presents a semi-empirical method to retrieve soil moisture over bare agricultural fields, based on effective roughness modeling, and applies it to a series of L-band fully-polarized SAR backscatter and bistatic scattering observations. The main advantage of using effective roughness parameters is that surface roughness no longer needs to be measured in the field, what is known to be the main source of error in soil moisture retrieval applications. By means of cross-validation, it is shown that the proposed method results in accurate soil moisture retrieval with an RMSE well below 0.05 m³/m³, with the best performance observed for the cross-polarized backscatter signal. In addition, different experimental SAR monostatic and bistatic configurations are evaluated in this study using the proposed retrieval technique. Results illustrate that the soil moisture retrieval performance increases by using backscatter data in multiple polarizations simultaneously, compared to the case where backscatter observations in only one polarization mode are used. Furthermore, the retrieval performance of a multistatic system has been evaluated and compared to that of a traditional monostatic system. The recent BELSAR campaign (in 2018) provides time-series of experimental airborne SAR measurements in two bistatic geometries, i.e., the across-track (XTI) and along-track (ATI) flight configuration. For both configurations, bistatic observations are available in the backward region. The results show that the simultaneous use of backscatter and bistatic scattering data does not result in a profound increase in retrieval performance for the bistatic configuration flown during BELSAR 2018. As theoretical studies demonstrate a strong improvement in retrieval performance when using backscatter and bistatic scattering coefficients in the forward region simultaneously, the introduction of additional bistatic airborne campaigns with more promising multistatic SAR configurations is highly recommended. Full article

(This article belongs to the Special Issue Innovative Belgian Earth Observation Research for the Environment)

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Open AccessArticle

Characterizing Garden Greenspace in a Medieval European City: Added Values of Spatial Resolution and Multi-Temporal Stereo Imagery

and

Remote Sens. 2022, 14(5), 1169; https://doi.org/10.3390/rs14051169 - 26 Feb 2022

Viewed by 1267

Abstract

Domestic gardens provide residents with immediate access to landscape amenities and numerous ecological provisions. These ecological provisions have been proven to be largely determined by greenspace composition and landscape, but the fragmentation and heterogeneity of garden environments present challenges to greenspace mapping. Here, we first developed a recognition method to create a garden parcel data set in the medieval Leuven city of Belgium, based on the land use layers and agricultural land parcels. Then, we applied multi-sourced satellite imagery to evaluate the added value of spatial resolution, plant phenology and 3D structure in identifying four vegetation types. Finally, we characterized the greenspace landscapes in garden parcels. Compared with single ALOS-2 imagery, SPOT-7 imagery and Pleiades-1A imagery increased the overall accuracy by 4% and 8%, respectively. The accuracy improvement (21%) produced from multi-temporal stereo Pleiades-1A imagery strongly verified the significance of plant phenology and 3D structure in garden mapping. The average greenspace cover in garden parcels was 71% but varied from 56% in urban gardens to 82% in rural gardens. The garden greenspace landscape is fragmented by the artificial structures in urban areas but has a more aggregated size and less complex shapes in rural areas. This study calls for greater attention to be paid to gardens, and for multi-disciplinary studies conducted in collaboration with urban ecologists and landscape designers to maximize the benefits to residents of both immediate landscape amenities and ecological provisions, in the face of global environmental changes and public health risks. Full article

(This article belongs to the Special Issue Innovative Belgian Earth Observation Research for the Environment)

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Open AccessArticle

Harmonization of Multi-Mission High-Resolution Time Series: Application to BELAIR

and

Remote Sens. 2022, 14(5), 1163; https://doi.org/10.3390/rs14051163 - 26 Feb 2022

Viewed by 1452

Abstract

High-resolution data are increasingly used for various applications, yet the revisit time is still low for some applications, particularly in frequently cloud-covered areas. Therefore, sensors are often combined, which raises issues on data consistency. In this study, we start from L1 to L3 data, and investigate the impact of harmonization measures, correcting for difference in radiometric gain and spectral response function (SRF), and the use of a common processing chain with the same atmospheric correction for Sentinel-2A/B, Landsat-8, DEIMOS-1, and Proba-V center cameras. These harmonization measures are evaluated step-wise in two applications: (1) agricultural monitoring, and (2) hydrological modelling in an urban context, using biophysical parameters and NDVI. The evaluation includes validation with in situ data, relative consistency analysis between different sensors, and the evaluation of the time series noise. A higher accuracy was not obtained when validating against in situ data. Yet, the relative analysis and the time series noise analysis clearly demonstrated that the largest improvement in consistency between sensors was obtained when applying the same atmospheric correction to all sensors. The gain correction obtained and its impact on the results was small, indicating that the sensors were already well calibrated. We could not demonstrate an improved consistency after SRF correction. It is likely that other factors, such as anisotropy effects, play a larger role, requiring further research. Full article

(This article belongs to the Special Issue Innovative Belgian Earth Observation Research for the Environment)

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Open AccessArticle

Assessing Storm Response of Multiple Intertidal Bars Using an Open-Source Automatic Processing Toolbox

and

Remote Sens. 2022, 14(4), 1005; https://doi.org/10.3390/rs14041005 - 18 Feb 2022

Cited by 1 | Viewed by 1044

Abstract

Intertidal bars are common features of sandy beaches in meso- and macro-tidal environments, yet their behaviour under storm impact and subsequent recovery remain poorly documented. Intensive surveys provide valuable information; however, it takes time to process the vast amount of data. This study presents the morphological response of a multibarred macro-tidal beach along the Belgian coast after a severe storm that happened on 8–12 February 2020, and to develop and apply an oPen-source Raster prOcessing Toolbox for invEstigation Coast intertidal bar displacemenT (PROTECT) in Python for automated bar extraction. This toolbox was applied to the digital surface models of pre- and post-storm airborne LiDAR surveys of a multibarred intertidal beach. The PROTECT toolbox is capable of detecting the position and elevation of intertidal bars accurately. The uncertainty in the elevation characteristics of the bars induces an error in the elevation dimension of 0.10 m. Using the toolbox, the results showed that the intertidal bars changed in term of variations in bar number, dimensions and shape across the storm event. Overall, the storm significantly eroded the dune and the upper-beach zone with a sand loss equivalent elevation decrease of −0.14 m. This was followed by a continuous and full recovery after 9 months under fair weather conditions. In contrast, the sand budget in the intertidal zone did not change over the entire monitoring period although the bars showed significant morphological change. Applying the PROTECT toolbox on high-resolution 3D topographic datasets allows to increase the temporal mapping resolution of intertidal bars from long-term (years) to short (storm events) time scales. Similar assessments at locations worldwide would allow the improvement of our knowledge on the morphodynamical role of multibarred beaches and to forecast their evolution, thus contributing to manage future storm response and the progressively accelerating sea level rise. Full article

(This article belongs to the Special Issue Innovative Belgian Earth Observation Research for the Environment)

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Open AccessArticle

A Semi-Empirical Anisotropy Correction Model for UAS-Based Multispectral Images of Bare Soil

and

Remote Sens. 2022, 14(3), 537; https://doi.org/10.3390/rs14030537 - 23 Jan 2022

Viewed by 2348

Abstract

The recent developments in the performance and miniaturization of uncrewed aircraft systems (UAS) and multispectral imaging sensors provide new tools for the assessment of the spatial and temporal variability of soil properties at sub-meter resolution and at relatively low costs, in comparison to traditional chemical analysis. The accuracy of multispectral data is nevertheless influenced by the anisotropic behaviour of natural surfaces, framed in the general theory of the bidirectional reflectance distribution function (BRDF). Accounting for BRDF effects in multispectral data is paramount before formulating any scientific interpretation. This study presents a semi-empirical spectral normalization methodology for UAS-based multispectral imaging datasets of bare soils to account for the effects of the BRDF, based on the application of an anisotropy factor (ANIF). A dataset of images from 15 flights over bare soil fields in the Belgian loam belt was used to calibrate a model relating the ANIF to a wide range of illumination geometry conditions by using only two angles: relative sensor-pixel-sun zenith and relative sensor-pixel-sun azimuth. The employment of ANIF-corrected images for multispectral orthomosaic generation with photogrammetric software provided spectral maps free of anisotropic-related artefacts in most cases, as assessed by several ad hoc indexes, and was also tested on an independent validation set. Most notably, the standard deviation in the measured reflectance of the same georeferenced point by different pictures decreased from 0.032 to 0.023 (p < 0.05) in the calibration dataset and from 0.037 to 0.030 in the validation dataset. The validation dataset, however, showed the presence of some systematic errors, the causes of which require further investigation. Full article

(This article belongs to the Special Issue Innovative Belgian Earth Observation Research for the Environment)

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Open AccessTechnical Note

The Multi-Satellite Environmental and Socioeconomic Predictors of Vector-Borne Diseases in African Cities: Malaria as an Example

Nicole P. M. van Lipzig

Eléonore Wolff

Sébastien Dujardin

and

Catherine Linard

Remote Sens. 2022, 14(21), 5381; https://doi.org/10.3390/rs14215381 - 27 Oct 2022

Viewed by 1252

Abstract

Remote sensing has been used for decades to produce vector-borne disease risk maps aiming at better targeting control interventions. However, the coarse and climatic-driven nature of these maps largely hampered their use in the fight against malaria in highly heterogeneous African cities. Remote sensing now offers a large panel of data with the potential to greatly improve and refine malaria risk maps at the intra-urban scale. This research aims at testing the ability of different geospatial datasets exclusively derived from satellite sensors to predict malaria risk in two sub-Saharan African cities: Kampala (Uganda) and Dar es Salaam (Tanzania). Using random forest models, we predicted intra-urban malaria risk based on environmental and socioeconomic predictors using climatic, land cover and land use variables among others. The combination of these factors derived from different remote sensors showed the highest predictive power, particularly models including climatic, land cover and land use predictors. However, the predictive power remained quite low, which is suspected to be due to urban malaria complexity and malaria data limitations. While huge improvements have been made over the last decades in terms of remote sensing data acquisition and processing, the quantity and quality of epidemiological data are not yet sufficient to take full advantage of these improvements. Full article

(This article belongs to the Special Issue Innovative Belgian Earth Observation Research for the Environment)

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