remotesensing-logo

Journal Browser

Journal Browser

Advances in Remote Sensing for Environmental Monitoring

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 21178

Special Issue Editors


E-Mail Website
Guest Editor
Istituto Nazionale di Geofisica e Vulcanologia, Osservatorio Etneo, 95125 Catania, CT, Italy
Interests: thermal remote sensing; data fusion; lava flow modelling
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
GFZ German Research Center for Geosciences, Telegrafenberg, D-14473Potsdam, and Leibniz University Hannover, Herrenhäuser Str. 2, D-30419 Hannover, Germany
Interests: soil spectroscopy; organic carbon; remote sensing
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
School of Agriculture and Environment, Massey University Manawatu, Palmerston North 4474, New Zealand
Interests: volcanology; remote sensing; geology; geomorphology; GIS

E-Mail Website
Guest Editor
Czech Geological Survey, 118 21 Prague, Czech Republic
Interests: imaging spectroscopy; mineral spectroscopy; environmental monitoring; optical and thermal remote sensing; raw materials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing measurements, from unmanned aerial vehicle (UAV), aircraft and satellite platforms, have increasingly become available and rapidly developing technologies to study and monitor Earth’s surface. These data are precious resources to perform extensive analysis and modeling, with the ultimate goal of supporting decision making. The spectral, spatial and temporal resolutions of remote sensors have been continuously improving, making environmental remote sensing more accurate and comprehensive than ever before. Such progress enables multiscale aspects of high-risk natural phenomena and development of multiplatform and interdisciplinary surveillance monitoring tools. This Special Issue welcomes contributions focusing on present and future perspectives in environmental remote sensing, from multispectral/hyperspectral optical and thermal sensors, and techniques for multiplatform data fusion. Novel solutions and applications toward the monitoring and characterization of environmental changes are encouraged, including, but not limited to, natural hazards from volcanic and seismic activity, mass movements and flows, swelling clays, and environmental hazards, such as contamination and pollution issues and land and soil degradation.

Dr. Annalisa Cappello
Dr. Sabine Chabrillat
Dr. Gaetana Ganci
Dr. Gabor Kereszturi
Dr. Veronika Kopačková-Strandová
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 2700 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

  • multi- and hyperspectral remote sensing
  • natural hazards
  • environmental assessment and monitoring
  • multiscale and multiplatform remote sensing
  • mineral and soil applications

Published Papers (6 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

20 pages, 9783 KiB  
Article
Inversion of Glacier 3D Displacement from Sentinel-1 and Landsat 8 Images Based on Variance Component Estimation: A Case Study in Shishapangma Peak, Tibet, China
by Chengsheng Yang, Chunrui Wei, Huilan Ding, Yunjie Wei, Sainan Zhu and Zufeng Li
Remote Sens. 2023, 15(1), 4; https://doi.org/10.3390/rs15010004 - 20 Dec 2022
Cited by 4 | Viewed by 1572
Abstract
Offset tracking technology is widely studied to evaluate glacier surface displacements. However, few studies have used a cross-platform to this end. In this study, two heterogeneous data sources, Sentinel-1 and Landsat 8, from January 2019 to January 2021, were used to estimate the [...] Read more.
Offset tracking technology is widely studied to evaluate glacier surface displacements. However, few studies have used a cross-platform to this end. In this study, two heterogeneous data sources, Sentinel-1 and Landsat 8, from January 2019 to January 2021, were used to estimate the offset, and then the optimal estimation of the 3D deformation rate of a Himalayan glacier was obtained based on the joint model of variance component estimation. The results show that the maximum deformation rates of the glacier in the east–west direction, north–south direction, and vertical direction are 85, 126, and 88 mm/day, respectively. The results of the joint model were compared and analyzed with the results of simultaneous optical image pixel offset tracking. The results showed that the accuracy of the joint solution model increased by 41% in the east–west direction and 36% in the south–north direction. The regional flow velocity of the moraine glacier after the joint solution was consistent with the vector boundary of the glacier cataloging data. The time-series results of the glacier displacement were calculated using more images. These results indicate that the joint solution model is feasible for calculating temporal glacier velocity. The model can improve the time resolution of the monitoring results and obtain further information on glacier characteristics. Our results show that the glacier velocity is affected by local terrain slope and temperature. However, there is no absolute positive correlation between glacier velocity and slope. This study provides a reference for the joint acquisition of large-scale three-dimensional displacement of glaciers using multi-source remote sensing data and provides support for the identification and early warning of glacier disasters. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Environmental Monitoring)
Show Figures

Figure 1

19 pages, 5829 KiB  
Article
Spatial Dynamics and Predictive Analysis of Vegetation Cover in the Ouémé River Delta in Benin (West Africa)
by Abdel Aziz Osseni, Hubert Olivier Dossou-Yovo, Gbodja Houéhanou François Gbesso, Toussaint Olou Lougbegnon and Brice Sinsin
Remote Sens. 2022, 14(23), 6165; https://doi.org/10.3390/rs14236165 - 5 Dec 2022
Cited by 2 | Viewed by 2023
Abstract
The vegetation cover of the Ouémé Delta constitutes a biodiversity hotspot for the wetlands in southern Benin. However, the overexploitation of natural resources in addition to the intensification of agricultural practices led to the degradation of the natural ecosystems in this region. The [...] Read more.
The vegetation cover of the Ouémé Delta constitutes a biodiversity hotspot for the wetlands in southern Benin. However, the overexploitation of natural resources in addition to the intensification of agricultural practices led to the degradation of the natural ecosystems in this region. The present work aims to reconstruct, using remote sensing, the spatial dynamics of land use in the Ouémé Delta in order to assess the recent changes and predict the trends in its vegetation cover. The methodology was based on remote sensing and GIS techniques. Altogether, this process helped us carry out the classification of Landsat images for a period of 30 years (stating year 1990, 2005, and 2020) via the Envi software. The spatial statistics resulting from this processing were combined using ArcGIS software to establish the transition matrices in order to monitor the conversion rates of the land cover classes obtained. Then, the prediction of the plant landscape by the year 2035 was performed using the “Land Change Modeler” extension available under IDRISI. The results showed seven (07) classes of occupation and land use. There were agglomerations, mosaics of fields and fallow land, water bodies, dense forests, gallery forests, swamp forests, and shrubby wooded savannahs. The observation of the vegetation cover over the period of 15 years from 1990 to 2005 showed a decrease from 71.55% to 63.42% in the surface area of the Ouémé Delta. A similar trend was noticed from 2005 to 2020 when it reached 55.19%, entailing a loss of 16.37% of the surface area of natural habitats in 30 years. The two drivers of such changes are the fertility of alluvial soils for agriculture along and urbanization. The predictive modeling developed for 2035 reveals a slight increase in the area of dense forests and shrubby wooded savannas, contrary to the lack of significant decrease in the area of gallery forests and swamp forests. This is key information that is expected to be useful to both policy and decision makers involved in the sustainable management and conservation of natural resources in the study area. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Environmental Monitoring)
Show Figures

Graphical abstract

18 pages, 3206 KiB  
Article
Monitoring Asbestos Mine Remediation Using Airborne Hyperspectral Imaging System: A Case Study of Jefferson Lake Mine, US
by Yongsik Jeong, Jaehyung Yu, Lei Wang, Huy Hoa Huynh and Hyun-Cheol Kim
Remote Sens. 2022, 14(21), 5572; https://doi.org/10.3390/rs14215572 - 4 Nov 2022
Cited by 2 | Viewed by 1787
Abstract
This study investigated an asbestos mine restoration project using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data. The distribution of an abandoned asbestos mine (AAM) and treatment area were analyzed before and after the remediation based on the spectral indices for detecting naturally occurring [...] Read more.
This study investigated an asbestos mine restoration project using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) hyperspectral data. The distribution of an abandoned asbestos mine (AAM) and treatment area were analyzed before and after the remediation based on the spectral indices for detecting naturally occurring asbestos (NOA) indicators and encapsulation. The spectral indices were developed for NOA, host rock, and encapsulation by logistic regression models using spectral bands extracted from the random forest algorithm. The detection models mostly used VNIR spectra rather than SWIR and were statistically significant. The overall accuracy of the detection models was approximately 84%. Notably, the detection accuracy of non-treated and treated areas was increased to about 96%, excluding the host rock index. The NOA index detected asbestos in the mine area as well as those in outcrops outside of the mine. It has been confirmed that the NOA index can be efficiently applied to all cases of asbestos occurrence. The remote sensing data revealed that the mine area was increased by ~5% by the remediation, and the treatment activity reduced asbestos exposure by ~32%. Moreover, the integrative visualization between the detection results and 3D high-resolution images provided an intuitive and realistic understanding of the reclamation project. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Environmental Monitoring)
Show Figures

Figure 1

22 pages, 3846 KiB  
Article
Using PRISMA Hyperspectral Satellite Imagery and GIS Approaches for Soil Fertility Mapping (FertiMap) in Northern Morocco
by Anis Gasmi, Cécile Gomez, Abdelghani Chehbouni, Driss Dhiba and Mohamed El Gharous
Remote Sens. 2022, 14(16), 4080; https://doi.org/10.3390/rs14164080 - 20 Aug 2022
Cited by 19 | Viewed by 5367
Abstract
Quickly and correctly mapping soil nutrients significantly impact accurate fertilization, food security, soil productivity, and sustainable agricultural development. We evaluated the potential of the new PRISMA hyperspectral sensor for mapping soil organic matter (SOM), available soil phosphorus (P2O5), and [...] Read more.
Quickly and correctly mapping soil nutrients significantly impact accurate fertilization, food security, soil productivity, and sustainable agricultural development. We evaluated the potential of the new PRISMA hyperspectral sensor for mapping soil organic matter (SOM), available soil phosphorus (P2O5), and potassium (K2O) content over a cultivated area in Khouribga, northern Morocco. These soil nutrients were estimated using (i) the random forest (RF) algorithm based on feature selection methods, including feature subset evaluation and feature ranking methods belonging to three categories (i.e., filter, wrapper, and embedded techniques), and (ii) 107 soil samples taken from the study area. The results show that the RF-embedded method produced better predictive accuracy compared with the filter and wrapper methods. The model for SOM showed moderate accuracy (Rval2 = 0.5, RMSEP = 0.43%, and RPIQ = 2.02), whereas that for soil P2O5 and K2O exhibited low efficiency (Rval2 = 0.26 and 0.36, RMSEP = 51.07 and 182.31 ppm, RPIQ = 0.65 and 1.16, respectively). The interpolation of RF-residuals by ordinary kriging (OK) methods reached the highest predictive results for SOM (Rval2 = 0.69, RMSEP = 0.34%, and RPIQ = 2.56), soil P2O5 (Rval2 = 0.44, RMSEP = 44.10 ppm, and RPIQ = 0.75), and soil K2O (Rval2 = 0.51, RMSEP = 159.29 ppm, and RPIQ = 1.34), representing the best fitting ability between the hyperspectral data and soil nutrients. The result maps provide a spatially continuous surface mapping of the soil landscape, conforming to the pedological substratum. Finally, the hyperspectral remote sensing imagery can provide a new way for modeling and mapping soil fertility, as well as the ability to diagnose nutrient deficiencies. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Environmental Monitoring)
Show Figures

Figure 1

22 pages, 68227 KiB  
Article
Satellite Multi-Sensor Data Fusion for Soil Clay Mapping Based on the Spectral Index and Spectral Bands Approaches
by Anis Gasmi, Cécile Gomez, Abdelghani Chehbouni, Driss Dhiba and Hamza Elfil
Remote Sens. 2022, 14(5), 1103; https://doi.org/10.3390/rs14051103 - 24 Feb 2022
Cited by 20 | Viewed by 4992
Abstract
Integrating satellite data at different resolutions (i.e., spatial, spectral, and temporal) can be a helpful technique for acquiring soil information from a synoptic point of view. This study aimed to evaluate the advantage of using satellite mono- and multi-sensor image fusion based on [...] Read more.
Integrating satellite data at different resolutions (i.e., spatial, spectral, and temporal) can be a helpful technique for acquiring soil information from a synoptic point of view. This study aimed to evaluate the advantage of using satellite mono- and multi-sensor image fusion based on either spectral indices or entire spectra to predict the topsoil clay content. To this end, multispectral satellite images acquired by various sensors (i.e., Landsat-5 Thematic Mapper (TM), Landsat-8 Operational Land Imager (OLI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and Sentinel2-MultiSpectral Instrument (S2-MSI)) have been used to assess their potential in identifying bare soil pixels over an area in northeastern Tunisia, the Lebna and Chiba catchments. A spectral index image and a spectral bands image are generated for each satellite sensor (i.e., TM, OLI, ASTER, and S2-MSI). Then, two multi-sensor satellite image fusions are generated, one from the spectral index images and the other from spectral bands. The resulting spectral index and spectral band images based on mono-and multi-sensor satellites are compared through their spectral patterns and ability to predict the topsoil clay content using the Multilayer Perceptron with backpropagation learning algorithm (MLP-BP) method. The results suggest that for clay content prediction: (i) the spectral bands’ images outperformed the spectral index images regardless of the used satellite sensor; (ii) the fused images derived from the spectral index or bands provided the best performances, with a 10% increase in the prediction accuracy; and (iii) the bare soil images obtained by the fusion of many multispectral sensor satellite images can be more beneficial than using mono-sensor images. Soil maps elaborated via satellite multi-sensor data fusion might become a valuable tool for soil survey, land planning, management, and precision agriculture. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Environmental Monitoring)
Show Figures

Graphical abstract

20 pages, 9142 KiB  
Article
UAV Thermal Images for Water Presence Detection in a Mediterranean Headwater Catchment
by Massimo Micieli, Gianluca Botter, Giuseppe Mendicino and Alfonso Senatore
Remote Sens. 2022, 14(1), 108; https://doi.org/10.3390/rs14010108 - 27 Dec 2021
Cited by 8 | Viewed by 3468
Abstract
As Mediterranean streams are highly dynamic, reconstructing space–time water presence in such systems is particularly important for understanding the expansion and contraction phases of the flowing network and the related hydro–ecological processes. Unmanned aerial vehicles (UAVs) can support such monitoring when wide or [...] Read more.
As Mediterranean streams are highly dynamic, reconstructing space–time water presence in such systems is particularly important for understanding the expansion and contraction phases of the flowing network and the related hydro–ecological processes. Unmanned aerial vehicles (UAVs) can support such monitoring when wide or inaccessible areas are investigated. In this study, an innovative method for water presence detection in the river network based on UAV thermal infrared remote sensing (TIR) images supported by RGB images is evaluated using data gathered in a representative catchment located in Southern Italy. Fourteen flights were performed at different times of the day in three periods, namely, October 2019, February 2020, and July 2020, at two different heights leading to ground sample distances (GSD) of 2 cm and 5 cm. A simple methodology that relies on the analysis of raw data without any calibration is proposed. The method is based on the identification of the thermal signature of water and other land surface elements targeted by the TIR sensor using specific control matrices in the image. Regardless of the GSD, the proposed methodology allows active stream identification under weather conditions that favor sufficient drying and heating of the surrounding bare soil and vegetation. In the surveys performed, ideal conditions for unambiguous water detection in the river network were found with air–water thermal differences higher than 5 °C and accumulated reference evapotranspiration before the survey time of at least 2.4 mm. Such conditions were not found during cold season surveys, which provided many false water pixel detections, even though allowing the extraction of useful information. The results achieved led to the definition of tailored strategies for flight scheduling with different levels of complexity, the simplest of them based on choosing early afternoon as the survey time. Overall, the method proved to be effective, at the same time allowing simplified monitoring with only TIR and RGB images, avoiding any photogrammetric processes, and minimizing postprocessing efforts. Full article
(This article belongs to the Special Issue Advances in Remote Sensing for Environmental Monitoring)
Show Figures

Figure 1

Back to TopTop