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Special Issue "Remote Sensing and Its Applications in the Bio-Geosciences"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors, Control, and Telemetry".

Deadline for manuscript submissions: closed (20 December 2018).

Special Issue Editors

Assoc. Prof. Frank Veroustraete
E-Mail Website
Guest Editor
University of Antwerp, Department of Bioscience Engineering, Campus Groenenborger, Groenenborgerlaan 171, 2020 Antwerpen, Belgium
Interests: remote sensing of bio-geophysical variables; environmental impact assessment
Dr. Manuela Balzarolo
E-Mail Website
Guest Editor
CREAF, Center for Ecological and Forestry Applications, 08193 Cerdanyola del Vallès, Catalonia, Spain
Interests: remote sensing; ecosystem ecology; phenology; climate change; environmental analysis
Dr. Willem W. Verstraeten
E-Mail Website
Guest Editor
Royal Meteorological Institute of Belgium, Ringlaan 3, B-1180, Brussels, Belgium
Interests: terrestrial and atmospheric environment; ecology; hydrology; infectious diseases; climate change
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue of Sensors aims at the publication of both review and original research papers related to the following keyword-indicated research topics:

  • Remote Sensing, Remote Sensing of Bio-geophysical variables;
  • Remote Sensing of Climate Change;
  • Remote Sensing of Ecology;
  • Remote Sensing of Phenology;
  • Remote Sensing of Hydrology;
  • Remote Sensing of Environmental Impact Assessment;
  • Remote Sensing and Environmental analysis;
  • Remote Sensing of Terrestrial and Atmospheric Environments;
  • Remote Sensing of Infectious Diseases;
  • Remote Sensing based on satellite, airborne and UAV observations,
  • Remote Sensing of Agriculture, and hence Food Security;
  • Remote Sensing of Nature Conservation and hence Biodiversity:
  • Remote Sensing of epidemiology of plant-related diseases;
  • Remote Sensing of Air Pollution including stratospheric and tropospheric ozone and its precursors such as, anthropogenic and natural VOC’s, NO2.
  • It goes without saying that we also welcome papers focusing on the assimilation of remote sensing and in-situ measurements in bio-geophysical and atmospheric models, as well as RS data extraction techniques (from raw to high added value products) and the ICT environments to generate these products.

The Special Issue is open to contributions ranging from review papers and focus papers presenting strategies, methodologies or approaches leading to the assimilation of remote sensing products from different electromagnetic wavelength regions, whether reflected in the optical range or emitted as fluorescence, far-infrared or microwave radiation, as well as techniques based on different observation and solar angular constellations. Data and in situ measuring methods for product validation purposes are also welcomed.

Assoc. Prof. Frank Veroustraete
Dr. Manuela Balzarolo
Dr. Willem W. Verstraeten
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 papers will be 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. Sensors 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 1800 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.

Published Papers (5 papers)

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Research

Open AccessArticle
Retrieving the Diurnal FPAR of a Maize Canopy from the Jointing Stage to the Tasseling Stage with Vegetation Indices under Different Water Stresses and Light Conditions
Sensors 2018, 18(11), 3965; https://doi.org/10.3390/s18113965 - 15 Nov 2018
Abstract
The fraction of absorbed photosynthetically active radiation (FPAR) is a key variable in the model of vegetation productivity. Vegetation indices (VIs) that were derived from instantaneous remote-sensing data have been successfully used to estimate the FPAR of a day or a longer period. [...] Read more.
The fraction of absorbed photosynthetically active radiation (FPAR) is a key variable in the model of vegetation productivity. Vegetation indices (VIs) that were derived from instantaneous remote-sensing data have been successfully used to estimate the FPAR of a day or a longer period. However, it has not yet been verified whether continuous VIs can be used to accurately estimate the diurnal dynamics of a vegetation canopy FPAR, which may fluctuate dramatically within a day. In this study, we measured the high temporal resolution spectral data (480 to 850 nm) and FPAR data of a maize canopy from the jointing stage to the tasseling stage under different irrigation and illumination conditions using two automatic observation systems. To estimate the FPAR, we developed regression models based on a quadratic function using 13 kinds of VIs. The results show the following: (1) Under nondrought conditions, although the illumination condition (sunny or cloudy) influenced the trend of the canopy diurnal FPAR, it had only a slight effect on the model accuracies of the FPAR-VIs. The maximum coefficients of determination (R2) of the FPAR-VIs models generated for the sunny nondrought data, the cloudy nondrought data, and all of the nondrought data were 0.895, 0.88, and 0.828, respectively. The VIs—including normalized difference vegetation index (NDVI), green NDVI (GNDVI), red-edge simple ratio (SR705), modified simple ratio 2 (mSR2), red-edge normalized difference vegetation index (NDVI705), and enhanced vegetation index (EVI)—that were related to the canopy structure had higher estimation accuracies (R2 > 0.8) than the other VIs that were related to the soil adjustment, chlorophyll, and physiology. The estimation accuracies of the GNDVI and some red-edge VIs (including NDVI705, SR705, and mSR2) were higher than the estimation accuracy of the NDVI. (2) Under drought stress, the FPAR decreased significantly because of leaf wilting and the effective leaf area index decrease around noon. When we included drought data in the model, accuracies were reduced dramatically and the R2 value of the best model was only 0.59. When we built the regression models based only on drought data, the EVI, which can weaken the influence of soil, had the best estimate accuracy (R2 = 0.68). Full article
(This article belongs to the Special Issue Remote Sensing and Its Applications in the Bio-Geosciences)
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Open AccessArticle
Calibrating the Severity of Forest Defoliation by Pine Processionary Moth with Landsat and UAV Imagery
Sensors 2018, 18(10), 3278; https://doi.org/10.3390/s18103278 - 29 Sep 2018
Cited by 5
Abstract
The pine processionary moth (Thaumetopoea pityocampa Dennis and Schiff.), one of the major defoliating insects in Mediterranean forests, has become an increasing threat to the forest health of the region over the past two decades. After a recent outbreak of T. pityocampa [...] Read more.
The pine processionary moth (Thaumetopoea pityocampa Dennis and Schiff.), one of the major defoliating insects in Mediterranean forests, has become an increasing threat to the forest health of the region over the past two decades. After a recent outbreak of T. pityocampa in Catalonia, Spain, we attempted to estimate the damage severity by capturing the maximum defoliation period over winter between pre-outbreak and post-outbreak images. The difference in vegetation index (dVI) derived from Landsat 8 was used as the change detection indicator and was further calibrated with Unmanned Aerial Vehicle (UAV) imagery. Regression models between predicted dVIs and observed defoliation degrees by UAV were compared among five selected dVIs for the coefficient of determination. Our results found the highest R-squared value (0.815) using Moisture Stress Index (MSI), with an overall accuracy of 72%, as a promising approach for estimating the severity of defoliation in affected areas where ground-truth data is limited. We concluded with the high potential of using UAVs as an alternative method to obtain ground-truth data for cost-effectively monitoring forest health. In future studies, combining UAV images with satellite data may be considered to validate model predictions of the forest condition for developing ecosystem service tools. Full article
(This article belongs to the Special Issue Remote Sensing and Its Applications in the Bio-Geosciences)
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Open AccessArticle
Water Level Reconstruction and Prediction Based on Space-Borne Sensors: A Case Study in the Mekong and Yangtze River Basins
Sensors 2018, 18(9), 3076; https://doi.org/10.3390/s18093076 - 13 Sep 2018
Cited by 3
Abstract
Water level (WL) measurements denote surface conditions that are useful for monitoring hydrological extremes, such as droughts and floods, which both affect agricultural productivity and regional development. Due to spatially sparse in situ hydrological stations, remote sensing measurements that capture localized instantaneous responses [...] Read more.
Water level (WL) measurements denote surface conditions that are useful for monitoring hydrological extremes, such as droughts and floods, which both affect agricultural productivity and regional development. Due to spatially sparse in situ hydrological stations, remote sensing measurements that capture localized instantaneous responses have recently been demonstrated to be a viable alternative to WL monitoring. Despite a relatively good correlation with WL, a traditional passive remote sensing derived WL is reconstructed from nearby remotely sensed surface conditions that do not consider the remotely sensed hydrological variables of a whole river basin. This method’s accuracy is also limited. Therefore, a method based on basin-averaged, remotely sensed precipitation from the Tropical Rainfall Measuring Mission (TRMM) and gravimetrically derived terrestrial water storage (TWS) from the Gravity Recovery and Climate Experiment (GRACE) is proposed for WL reconstruction in the Yangtze and Mekong River basins in this study. This study examines the WL reconstruction performance from these two remotely sensed hydrological variables and their corresponding drought indices (i.e., TRMM Standardized Precipitation Index (TRMM-SPI) and GRACE Drought Severity Index (GRACE-DSI)) on a monthly temporal scale. A weighting procedure is also developed to explore a further potential improvement in the WL reconstruction. We found that the reconstructed WL derived from the hydrological variables compares well to the observed WL. The derived drought indices perform even better than those of their corresponding hydrological variables. The indices’ performance rate is owed to their ability to bypass the influence of El Niño Southern Oscillation (ENSO) events in a standardized form and their basin-wide integrated information. In general, all performance indicators (i.e., the Pearson Correlation Coefficient (PCC), Root-mean-squares error (RMSE), and Nash–Sutcliffe model efficiency coefficient (NSE)) reveal that the remotely sensed hydrological variables (and their corresponding drought indices) are better alternatives compared with traditional remote sensing indices (e.g., Normalized Difference Vegetation Index (NDVI)), despite different geographical regions. In addition, almost all results are substantially improved by the weighted averaging procedure. The most accurate WL reconstruction is derived from a weighted TRMM-SPI for the Mekong (and Yangtze River basins) and displays a PCC of 0.98 (and 0.95), a RMSE of 0.19 m (and 0.85 m), and a NSE of 0.95 (and 0.89); by comparison, the remote sensing variables showed less accurate results (PCC of 0.88 (and 0.82), RMSE of 0.41 m (and 1.48 m), and NSE of 0.78 (and 0.67)) for its inferred WL. Additionally, regardless of weighting, GRACE-DSI displays a comparable performance. An external assessment also shows similar results. This finding indicates that the combined usage of remotely sensed hydrological variables in a standardized form and the weighted averaging procedure could lead to an improvement in WL reconstructions for river basins affected by ENSO events and hydrological extremes. Full article
(This article belongs to the Special Issue Remote Sensing and Its Applications in the Bio-Geosciences)
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Open AccessArticle
Top-Down NOX Emissions of European Cities Based on the Downwind Plume of Modelled and Space-Borne Tropospheric NO2 Columns
Sensors 2018, 18(9), 2893; https://doi.org/10.3390/s18092893 - 31 Aug 2018
Cited by 3
Abstract
Top-down estimates of surface NOX emissions were derived for 23 European cities based on the downwind plume decay of tropospheric nitrogen dioxide (NO2) columns from the LOTOS-EUROS (Long Term Ozone Simulation-European Ozone Simulation) chemistry transport model (CTM) and from Ozone [...] Read more.
Top-down estimates of surface NOX emissions were derived for 23 European cities based on the downwind plume decay of tropospheric nitrogen dioxide (NO2) columns from the LOTOS-EUROS (Long Term Ozone Simulation-European Ozone Simulation) chemistry transport model (CTM) and from Ozone Monitoring Instrument (OMI) satellite retrievals, averaged for the summertime period (April–September) during 2013. Here we show that the top-down NOX emissions derived from LOTOS-EUROS for European urban areas agree well with the bottom-up NOX emissions from the MACC-III inventory data (R2 = 0.88) driving the CTM demonstrating the potential of this method. OMI top-down NOX emissions over the 23 European cities are generally lower compared with the MACC-III emissions and their correlation is slightly lower (R2 = 0.79). The uncertainty on the derived NO2 lifetimes and NOX emissions are on average ~55% for OMI and ~63% for LOTOS-EUROS data. The downwind NO2 plume method applied on both LOTOS-EUROS and OMI tropospheric NO2 columns allows to estimate NOX emissions from urban areas, demonstrating that this is a useful method for real-time updates of urban NOX emissions with reasonable accuracy. Full article
(This article belongs to the Special Issue Remote Sensing and Its Applications in the Bio-Geosciences)
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Open AccessArticle
A Coarse-to-Fine Geometric Scale-Invariant Feature Transform for Large Size High Resolution Satellite Image Registration
Sensors 2018, 18(5), 1360; https://doi.org/10.3390/s18051360 - 27 Apr 2018
Cited by 5
Abstract
Large size high resolution (HR) satellite image matching is a challenging task due to local distortion, repetitive structures, intensity changes and low efficiency. In this paper, a novel matching approach is proposed for the large size HR satellite image registration, which is based [...] Read more.
Large size high resolution (HR) satellite image matching is a challenging task due to local distortion, repetitive structures, intensity changes and low efficiency. In this paper, a novel matching approach is proposed for the large size HR satellite image registration, which is based on coarse-to-fine strategy and geometric scale-invariant feature transform (SIFT). In the coarse matching step, a robust matching method scale restrict (SR) SIFT is implemented at low resolution level. The matching results provide geometric constraints which are then used to guide block division and geometric SIFT in the fine matching step. The block matching method can overcome the memory problem. In geometric SIFT, with area constraints, it is beneficial for validating the candidate matches and decreasing searching complexity. To further improve the matching efficiency, the proposed matching method is parallelized using OpenMP. Finally, the sensing image is rectified to the coordinate of reference image via Triangulated Irregular Network (TIN) transformation. Experiments are designed to test the performance of the proposed matching method. The experimental results show that the proposed method can decrease the matching time and increase the number of matching points while maintaining high registration accuracy. Full article
(This article belongs to the Special Issue Remote Sensing and Its Applications in the Bio-Geosciences)
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