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Feature Papers in the Remote Sensors Section 2022

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

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 42646

Special Issue Editors


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Guest Editor
Institute of Atmospheric Sciences and Climate (ISAC), National Research Council (CNR), Via del Fosso del Cavaliere 100, 00133 Rome, Italy
Interests: landscape evolution; geophysical hazards; archaeology; cultural heritage; remote sensing; earth observation; InSAR; landslides; land subsidence; ground instability
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Dipartimento di Ingegneria Elettrica e Tecnologie dell'Informazione, Università degli Studi di Napoli Federico II, Via Claudio 21, 80125 Napoli, Italy
Interests: electromagnetics; scattering; propagation; synthetic aperture radar; SAR
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We are pleased to announce that the Section Remote Sensors is now compiling a collection of papers submitted by the Editorial Board Members (EBMs) of our Section and outstanding scholars in this research field. We welcome contributions as well as recommendations from the EBMs.

The purpose of this Special Issue is to publish a set of papers that typify the very best insightful and influential original research articles or reviews where our Section’s EBMs discuss key topics in the field. We expect these papers to be widely read and highly influential within the field. All papers in this Special Issue will be collected into a printed edition book after the deadline and will be well promoted. 

We would also like to take this opportunity to call on more scholars to join the Section Remote Sensors so that we can work together to further develop this exciting field of research. Potential topics include but are not limited to the following:

  • Sensors:
    • Altimeters;
    • Cameras;
    • Lidar;
    • Radar;
    • Radiometers;
    • Topographic Sensors;
    • Hyperspectral and Multispectral Sensors;
    • Seismometers and Geophones;
    • Polarimeters.
  • Devices, Platforms, and Systems:
    • Aircrafts;
    • Autonomous Vehicles;
    • Satellites;
    • Autonomous Underwater Vehicles;
    • Unmanned Aerial Vehicles.

Dr. Francesca Cigna
Prof. Dr. Antonio Iodice
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. 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 2600 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 (13 papers)

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Research

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29 pages, 33049 KiB  
Article
Monitoring Land Degradation Dynamics to Support Landscape Restoration Actions in Remote Areas of the Mediterranean Basin (Murcia Region, Spain)
by Marzia Gabriele and Raffaella Brumana
Sensors 2023, 23(6), 2947; https://doi.org/10.3390/s23062947 - 08 Mar 2023
Cited by 2 | Viewed by 1396
Abstract
This study aims to develop a workflow methodology for collecting substantial amounts of Earth Observation data to investigate the effectiveness of landscape restoration actions and support the implementation of the Above Ground Carbon Capture indicator of the Ecosystem Restoration Camps (ERC) Soil Framework. [...] Read more.
This study aims to develop a workflow methodology for collecting substantial amounts of Earth Observation data to investigate the effectiveness of landscape restoration actions and support the implementation of the Above Ground Carbon Capture indicator of the Ecosystem Restoration Camps (ERC) Soil Framework. To achieve this objective, the study will utilize the Google Earth Engine API within R (rGEE) to monitor the Normalized Difference Vegetation Index (NDVI). The results of this study will provide a common scalable reference for ERC camps globally, with a specific focus on Camp Altiplano, the first European ERC located in Murcia, Southern Spain. The coding workflow has effectively acquired almost 12 TB of data for analyzing MODIS/006/MOD13Q1 NDVI over a 20-year span. Additionally, the average retrieval of image collections has yielded 120 GB of data for the COPERNICUS/S2_SR 2017 vegetation growing season and 350 GB of data for the COPERNICUS/S2_SR 2022 vegetation winter season. Based on these results, it is reasonable to asseverate that cloud computing platforms like GEE will enable the monitoring and documentation of regenerative techniques to achieve unprecedented levels. The findings will be shared on a predictive platform called Restor, which will contribute to the development of a global ecosystem restoration model. Full article
(This article belongs to the Special Issue Feature Papers in the Remote Sensors Section 2022)
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20 pages, 5587 KiB  
Article
The Use of Bi-Potentiostat as a Simple and Accurate Electrochemical Approach for the Determination of Orthophosphate in Seawater
by Mahmoud Fatehy Altahan, Mario Esposito, Boie Bogner and Eric P. Achterberg
Sensors 2023, 23(4), 2123; https://doi.org/10.3390/s23042123 - 13 Feb 2023
Cited by 4 | Viewed by 1589
Abstract
Autonomous on-site monitoring of orthophosphate (PO43−), an important nutrient for primary production in natural waters, is urgently needed. Here, we report on the development and validation of an on-site autonomous electrochemical analyzer for PO43− in seawater. The approach [...] Read more.
Autonomous on-site monitoring of orthophosphate (PO43−), an important nutrient for primary production in natural waters, is urgently needed. Here, we report on the development and validation of an on-site autonomous electrochemical analyzer for PO43− in seawater. The approach is based on the use of flow injection analysis in conjunction with a dual electrochemical cell (i.e., a bi-potentiostat detector (FIA-DECD) that uses two working electrodes sharing the same reference and counter electrode. The two working electrodes are used (molybdate/carbon paste electrode (CPE) and CPE) to correct for matrix effects. Optimization of squarewave voltammetry parameters (including step potential, amplitude, and frequency) was undertaken to enhance analytical sensitivity. Possible interferences from non-ionic surfactants and humic acid were investigated. The limit of quantification in artificial seawater (30 g/L NaCl, pH 0.8) was 0.014 µM for a linear concentration range of 0.02–3 µM. The system used a Python script for operation and data processing. The analyzer was tested for ship-board PO43− determination during a four-day research cruise in the North Sea. The analyzer successfully measured 34 samples and achieved a good correlation (Pearson’ R = 0.91) with discretely collected water samples analyzed using a laboratory-based colorimetric reference analyzer. Full article
(This article belongs to the Special Issue Feature Papers in the Remote Sensors Section 2022)
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18 pages, 19431 KiB  
Article
One-Year Seismic Survey of the Tectonic CO2-Rich Site of Mefite d’Ansanto (Southern Italy): Preliminary Insights in the Seismic Noise Wavefield
by Simona Morabito, Paola Cusano, Danilo Galluzzo, Guido Gaudiosi, Lucia Nardone, Pierdomenico Del Gaudio, Anna Gervasi, Mario La Rocca, Girolamo Milano, Simona Petrosino, Luciano Zuccarello, Roberto Manzo, Ciro Buonocunto and Francesca Di Luccio
Sensors 2023, 23(3), 1630; https://doi.org/10.3390/s23031630 - 02 Feb 2023
Cited by 2 | Viewed by 1340
Abstract
A passive seismic experiment is carried out at the non-volcanic highly degassing site of Mefite d’Ansanto located at the northern tip of the Irpinia region (southern Italy), where the 1980 MS 6.9 destructive earthquake occurred. Between 2020 and 2021, background seismic noise [...] Read more.
A passive seismic experiment is carried out at the non-volcanic highly degassing site of Mefite d’Ansanto located at the northern tip of the Irpinia region (southern Italy), where the 1980 MS 6.9 destructive earthquake occurred. Between 2020 and 2021, background seismic noise was recorded by deploying a broadband seismic station and a seismic array composed of seven 1 Hz three-component sensors. Using two different array configurations, we were allowed to explore in detail the 1–20 Hz frequency band of the seismic noise wavefield as well as Rayleigh wave phase velocities in the 400–800 m/s range. Spectral analyses and array techniques were applied to one year of data showing that the frequency content of the signal is very stable in time. High frequency peaks are likely linked to the emission source, whereas at low frequencies seismic noise is clearly correlated to meteorological parameters. The results of this study show that small aperture seismic arrays probe the subsurface of tectonic CO2-rich emission areas and contribute to the understanding of the link between fluid circulation and seismogenesis in seismically active regions. Full article
(This article belongs to the Special Issue Feature Papers in the Remote Sensors Section 2022)
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19 pages, 10453 KiB  
Article
Impact of Iran’s Forest Nationalization Law on Forest Cover Changes over Six Decades: A Case Study of a Zagros Sparse Coppice Oak Forest
by Hadi Beygi Heidarlou, Abbas Banj Shafiei, Vahid Nasiri, Mihai Daniel Niţă, Stelian Alexandru Borz and David Lopez-Carr
Sensors 2023, 23(2), 871; https://doi.org/10.3390/s23020871 - 12 Jan 2023
Cited by 6 | Viewed by 1677
Abstract
Forest nationalization policies in developing countries have often led to a reduction in local forest ownership rights and short- or long-term exploitative behaviors of stakeholders. The purpose of this research is to quantify the effect of Iran’s Forest Nationalization Law (FNL) in a [...] Read more.
Forest nationalization policies in developing countries have often led to a reduction in local forest ownership rights and short- or long-term exploitative behaviors of stakeholders. The purpose of this research is to quantify the effect of Iran’s Forest Nationalization Law (FNL) in a part of Zagros Forest over a 68-year time period (1955–2022) using 1955 historical aerial photos, 1968 Corona spy satellite photography, and classification of multi-temporal Landsat satellite images. A past classification change detection technique was used to identify the extent and the pattern of land use changes in time. For this purpose, six periods were defined, to cover the time before and after the implementation of FNL. A 0.27% deforestation trend was identified over the period after the FNL. Dense and open forested area has decreased from 7175.62 ha and 68,927.46 ha in 1955 to 5664.26 ha and 59,223.38 ha in 2022. The FNL brought decisive changes in the legal and forest management systems at the state level, mainly by giving their ownership to the state. Accordingly, the FNL and the related conservation plans have not fully succeeded in protecting, rehabilitating, recovering, and developing the sparse Zagros Forest ecosystems, as their most important goals. Full article
(This article belongs to the Special Issue Feature Papers in the Remote Sensors Section 2022)
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25 pages, 17016 KiB  
Article
Initial Performance Analysis of the At-Altitude Radiance Ratio Method for Reflectance Conversion of Hyperspectral Remote Sensing Data
by Luke J. R. DeCoffe, David N. Conran, Timothy D. Bauch, Micah G. Ross, Daniel S. Kaputa and Carl Salvaggio
Sensors 2023, 23(1), 320; https://doi.org/10.3390/s23010320 - 28 Dec 2022
Cited by 3 | Viewed by 1356
Abstract
In remote sensing, the conversion of at-sensor radiance to surface reflectance for each pixel in a scene is an essential component of many analysis tasks. The empirical line method (ELM) is the most used technique among remote sensing practitioners due to its reliability [...] Read more.
In remote sensing, the conversion of at-sensor radiance to surface reflectance for each pixel in a scene is an essential component of many analysis tasks. The empirical line method (ELM) is the most used technique among remote sensing practitioners due to its reliability and production of accurate reflectance measurements. However, the at-altitude radiance ratio (AARR), a more recently proposed methodology, is attractive as it allows reflectance conversion to be carried out in real time throughout data collection, does not require calibrated samples of pre-measured reflectance to be placed in scene, and can account for changes in illumination conditions. The benefits of AARR can substantially reduce the level of effort required for collection setup and subsequent data analysis, and provide a means for large-scale automation of remote sensing data collection, even in atypical flight conditions. In this study, an onboard, downwelling irradiance spectrometer integrated onto a small unmanned aircraft system (sUAS) is utilized to characterize the performance of AARR-generated reflectance from hyperspectral radiance data under a variety of challenging illumination conditions. The observed error introduced by AARR is often on par with ELM and acceptable depending on the application requirements and natural variation in the reflectance of the targets of interest. Additionally, a number of radiometric and atmospheric corrections are proposed that could increase the accuracy of the method in future trials, warranting further research. Full article
(This article belongs to the Special Issue Feature Papers in the Remote Sensors Section 2022)
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19 pages, 2357 KiB  
Article
An Overview of Sensors for Long Range Missile Defense
by Simone Fontana and Federica Di Lauro
Sensors 2022, 22(24), 9871; https://doi.org/10.3390/s22249871 - 15 Dec 2022
Cited by 4 | Viewed by 7599
Abstract
Given the increasing tensions between world powers, missile defense is a topic that is more relevant than ever. However, information on the subject is often fragmented, confusing and untrustworthy. On the other hand, we believe that an informed overview of the current status [...] Read more.
Given the increasing tensions between world powers, missile defense is a topic that is more relevant than ever. However, information on the subject is often fragmented, confusing and untrustworthy. On the other hand, we believe that an informed overview of the current status is important for decision makers and citizens alike. A missile is essentially a guided rocket and therefore the term can be used to describe a very wide range of weapon systems. In this paper, we focus on long-range and intercontinental threats, which we believe are more important and problematic to defend against. We provide an overview of the two most common types of sensors, space-based infrared sensors and radars, and highlight their peculiarities and, most importantly, their drawbacks that severely limit their effectiveness. Full article
(This article belongs to the Special Issue Feature Papers in the Remote Sensors Section 2022)
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23 pages, 583 KiB  
Article
Feasibility of a Real-Time Embedded Hyperspectral Compressive Sensing Imaging System
by Olivier Lim, Stéphane Mancini and Mauro Dalla Mura
Sensors 2022, 22(24), 9793; https://doi.org/10.3390/s22249793 - 13 Dec 2022
Cited by 1 | Viewed by 1343
Abstract
Hyperspectral imaging has been attracting considerable interest as it provides spectrally rich acquisitions useful in several applications, such as remote sensing, agriculture, astronomy, geology and medicine. Hyperspectral devices based on compressive acquisitions have appeared recently as an alternative to conventional hyperspectral imaging systems [...] Read more.
Hyperspectral imaging has been attracting considerable interest as it provides spectrally rich acquisitions useful in several applications, such as remote sensing, agriculture, astronomy, geology and medicine. Hyperspectral devices based on compressive acquisitions have appeared recently as an alternative to conventional hyperspectral imaging systems and allow for data-sampling with fewer acquisitions than classical imaging techniques, even under the Nyquist rate. However, compressive hyperspectral imaging requires a reconstruction algorithm in order to recover all the data from the raw compressed acquisition. The reconstruction process is one of the limiting factors for the spread of these devices, as it is generally time-consuming and comes with a high computational burden. Algorithmic and material acceleration with embedded and parallel architectures (e.g., GPUs and FPGAs) can considerably speed up image reconstruction, making hyperspectral compressive systems suitable for real-time applications. This paper provides an in-depth analysis of the required performance in terms of computing power, data memory and bandwidth considering a compressive hyperspectral imaging system and a state-of-the-art reconstruction algorithm as an example. The results of the analysis show that real-time application is possible by combining several approaches, namely, exploitation of system matrix sparsity and bandwidth reduction by appropriately tuning data value encoding. Full article
(This article belongs to the Special Issue Feature Papers in the Remote Sensors Section 2022)
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25 pages, 9358 KiB  
Article
An Advanced Data Fusion Method to Improve Wetland Classification Using Multi-Source Remotely Sensed Data
by Aaron Judah and Baoxin Hu
Sensors 2022, 22(22), 8942; https://doi.org/10.3390/s22228942 - 18 Nov 2022
Cited by 7 | Viewed by 1679
Abstract
The goal of this research was to improve wetland classification by fully exploiting multi-source remotely sensed data. Three distinct classifiers were designed to distinguish individual or compound wetland categories using random forest (RF) classification. They were determined, in part, to best use the [...] Read more.
The goal of this research was to improve wetland classification by fully exploiting multi-source remotely sensed data. Three distinct classifiers were designed to distinguish individual or compound wetland categories using random forest (RF) classification. They were determined, in part, to best use the available remotely sensed features in order to maximize that information and to maximize classification accuracy. The results from these classifiers were integrated according to Dempster–Shafer theory (D–S theory). The developed method was tested on data collected from a study area in Northern Alberta, Canada. The data utilized were Landsat-8 and Sentinel-2 (multi-spectral), Sentinel-1 (synthetic aperture radar—SAR), and digital elevation model (DEM). Classification of fen, bog, marsh, swamps, and upland resulted in an overall accuracy of 0.93 using the proposed methodology, an improvement of 5% when compared to a traditional classification method based on the aggregated features from these data sources. It was noted that, with the traditional method, some pixels were misclassified with a high level of confidence (>85%). Such misclassification was significantly reduced (by ~10%) by the proposed method. Results also showed that some features important in separating compound wetland classes were not considered important using the traditional method based on the RF feature selection mechanism. When used in the proposed method, these features increased the classification accuracy, which demonstrated that the proposed method provided an effective means to fully employ available data to improve wetland classification. Full article
(This article belongs to the Special Issue Feature Papers in the Remote Sensors Section 2022)
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14 pages, 3612 KiB  
Article
Performance and Accuracy Comparisons of Classification Methods and Perspective Solutions for UAV-Based Near-Real-Time “Out of the Lab” Data Processing
by Zsófia Varga, Fanni Vörös, Márton Pál, Béla Kovács, András Jung and István Elek
Sensors 2022, 22(22), 8629; https://doi.org/10.3390/s22228629 - 09 Nov 2022
Cited by 2 | Viewed by 1188
Abstract
Today, integration into automated systems has become a priority in the development of remote sensing sensors carried on drones. For this purpose, the primary task is to achieve real-time data processing. Increasing sensor resolution, fast data capture and the simultaneous use of multiple [...] Read more.
Today, integration into automated systems has become a priority in the development of remote sensing sensors carried on drones. For this purpose, the primary task is to achieve real-time data processing. Increasing sensor resolution, fast data capture and the simultaneous use of multiple sensors is one direction of development. However, this poses challenges on the data processing side due to the increasing amount of data. Our study intends to investigate how the running time and accuracy of commonly used image classification algorithms evolve using Altum Micasense multispectral and thermal acquisition data with GSD = 2 cm spatial resolution. The running times were examined for two PC configurations, with a 4 GB and 8 GB DRAM capacity, respectively, as these parameters are closer to the memory of NRT microcomputers and laptops, which can be applied “out of the lab”. During the accuracy assessment, we compared the accuracy %, the Kappa index value and the area ratio of correct pixels. According to our results, in the case of plant cover, the Spectral Angles Mapper (SAM) method achieved the best accuracy among the validated classification solutions. In contrast, the Minimum Distance (MD) method achieved the best accuracy on water surface. In terms of temporality, the best results were obtained with the individually constructed decision tree classification. Thus, it is worth developing these two directions into real-time data processing solutions. Full article
(This article belongs to the Special Issue Feature Papers in the Remote Sensors Section 2022)
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15 pages, 6208 KiB  
Article
Optimization and Evaluation of Sensor Angles for Precise Assessment of Architectural Traits in Peach Trees
by Mugilan Govindasamy Raman, Eduardo Fermino Carlos and Sindhuja Sankaran
Sensors 2022, 22(12), 4619; https://doi.org/10.3390/s22124619 - 18 Jun 2022
Cited by 7 | Viewed by 1650
Abstract
Fruit industries play a significant role in many aspects of global food security. They provide recognized vitamins, antioxidants, and other nutritional supplements packed in fresh fruits and other processed commodities such as juices, jams, pies, and other products. However, many fruit crops including [...] Read more.
Fruit industries play a significant role in many aspects of global food security. They provide recognized vitamins, antioxidants, and other nutritional supplements packed in fresh fruits and other processed commodities such as juices, jams, pies, and other products. However, many fruit crops including peaches (Prunus persica (L.) Batsch) are perennial trees requiring dedicated orchard management. The architectural and morphological traits of peach trees, notably tree height, canopy area, and canopy crown volume, help to determine yield potential and precise orchard management. Thus, the use of unmanned aerial vehicles (UAVs) coupled with RGB sensors can play an important role in the high-throughput acquisition of data for evaluating architectural traits. One of the main factors that define data quality are sensor imaging angles, which are important for extracting architectural characteristics from the trees. In this study, the goal was to optimize the sensor imaging angles to extract the precise architectural trait information by evaluating the integration of nadir and oblique images. A UAV integrated with an RGB imaging sensor at three different angles (90°, 65°, and 45°) and a 3D light detection and ranging (LiDAR) system was used to acquire images of peach trees located at the Washington State University’s Tukey Horticultural Orchard, Pullman, WA, USA. A total of four approaches, comprising the use of 2D data (from UAV) and 3D point cloud (from UAV and LiDAR), were utilized to segment and measure the individual tree height and canopy crown volume. Overall, the features extracted from the images acquired at 45° and integrated nadir and oblique images showed a strong correlation with the ground reference tree height data, while the latter was highly correlated with canopy crown volume. Thus, selection of the sensor angle during UAV flight is critical for improving the accuracy of extracting architectural traits and may be useful for further precision orchard management. Full article
(This article belongs to the Special Issue Feature Papers in the Remote Sensors Section 2022)
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15 pages, 1427 KiB  
Article
A Novel Hyperspectral Method to Detect Moldy Core in Apple Fruits
by Andrea Genangeli, Giorgio Allasia, Marco Bindi, Claudio Cantini, Alice Cavaliere, Lorenzo Genesio, Giovanni Giannotta, Franco Miglietta and Beniamino Gioli
Sensors 2022, 22(12), 4479; https://doi.org/10.3390/s22124479 - 14 Jun 2022
Cited by 10 | Viewed by 2212
Abstract
An innovative low-cost device based on hyperspectral spectroscopy in the near infrared (NIR) spectral region is proposed for the non-invasive detection of moldy core (MC) in apples. The system, based on light collection by an integrating sphere, was tested on 70 apples cultivar [...] Read more.
An innovative low-cost device based on hyperspectral spectroscopy in the near infrared (NIR) spectral region is proposed for the non-invasive detection of moldy core (MC) in apples. The system, based on light collection by an integrating sphere, was tested on 70 apples cultivar (cv) Golden Delicious infected by Alternaria alternata, one of the main pathogens responsible for MC disease. Apples were sampled in vertical and horizontal positions during five measurement rounds in 13 days’ time, and 700 spectral signatures were collected. Spectral correlation together with transmittance temporal patterns and ANOVA showed that the spectral region from 863.38 to 877.69 nm was most linked to MC presence. Then, two binary classification models based on Artificial Neural Network Pattern Recognition (ANN-AP) and Bagging Classifier (BC) with decision trees were developed, revealing a better detection capability by ANN-AP, especially in the early stage of infection, where the predictive accuracy was 100% at round 1 and 97.15% at round 2. In subsequent rounds, the classification results were similar in ANN-AP and BC models. The system proposed surpassed previous MC detection methods, needing only one measurement per fruit, while further research is needed to extend it to different cultivars or fruits. Full article
(This article belongs to the Special Issue Feature Papers in the Remote Sensors Section 2022)
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15 pages, 5032 KiB  
Article
Integrating GEDI and Landsat: Spaceborne Lidar and Four Decades of Optical Imagery for the Analysis of Forest Disturbances and Biomass Changes in Italy
by Saverio Francini, Giovanni D’Amico, Elia Vangi, Costanza Borghi and Gherardo Chirici
Sensors 2022, 22(5), 2015; https://doi.org/10.3390/s22052015 - 04 Mar 2022
Cited by 38 | Viewed by 7473
Abstract
Forests play a prominent role in the battle against climate change, as they absorb a relevant part of human carbon emissions. However, precisely because of climate change, forest disturbances are expected to increase and alter forests’ capacity to absorb carbon. In this context, [...] Read more.
Forests play a prominent role in the battle against climate change, as they absorb a relevant part of human carbon emissions. However, precisely because of climate change, forest disturbances are expected to increase and alter forests’ capacity to absorb carbon. In this context, forest monitoring using all available sources of information is crucial. We combined optical (Landsat) and photonic (GEDI) data to monitor four decades (1985–2019) of disturbances in Italian forests (11 Mha). Landsat data were confirmed as a relevant source of information for forest disturbance mapping, as forest harvestings in Tuscany were predicted with omission errors estimated between 29% (in 2012) and 65% (in 2001). GEDI was assessed using Airborne Laser Scanning (ALS) data available for about 6 Mha of Italian forests. A good correlation (r2 = 0.75) between Above Ground Biomass Density GEDI estimates (AGBD) and canopy height ALS estimates was reported. GEDI data provided complementary information to Landsat. The Landsat mission is capable of mapping disturbances, but not retrieving the three-dimensional structure of forests, while our results indicate that GEDI is capable of capturing forest biomass changes due to disturbances. GEDI acquires useful information not only for biomass trend quantification in disturbance regimes but also for forest disturbance discrimination and characterization, which is crucial to further understanding the effect of climate change on forest ecosystems. Full article
(This article belongs to the Special Issue Feature Papers in the Remote Sensors Section 2022)
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Review

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26 pages, 1592 KiB  
Review
A Review of Mobile Mapping Systems: From Sensors to Applications
by Mostafa Elhashash, Hessah Albanwan and Rongjun Qin
Sensors 2022, 22(11), 4262; https://doi.org/10.3390/s22114262 - 02 Jun 2022
Cited by 40 | Viewed by 10641
Abstract
The evolution of mobile mapping systems (MMSs) has gained more attention in the past few decades. MMSs have been widely used to provide valuable assets in different applications. This has been facilitated by the wide availability of low-cost sensors, advances in computational resources, [...] Read more.
The evolution of mobile mapping systems (MMSs) has gained more attention in the past few decades. MMSs have been widely used to provide valuable assets in different applications. This has been facilitated by the wide availability of low-cost sensors, advances in computational resources, the maturity of mapping algorithms, and the need for accurate and on-demand geographic information system (GIS) data and digital maps. Many MMSs combine hybrid sensors to provide a more informative, robust, and stable solution by complementing each other. In this paper, we presented a comprehensive review of the modern MMSs by focusing on: (1) the types of sensors and platforms, discussing their capabilities and limitations and providing a comprehensive overview of recent MMS technologies available in the market; (2) highlighting the general workflow to process MMS data; (3) identifying different use cases of mobile mapping technology by reviewing some of the common applications; and (4) presenting a discussion on the benefits and challenges and sharing our views on potential research directions. Full article
(This article belongs to the Special Issue Feature Papers in the Remote Sensors Section 2022)
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