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Novel Methods and Applications in Satellite and Aerial Imagery Time Series Analysis

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

Deadline for manuscript submissions: closed (1 January 2023) | Viewed by 12853

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Dr. Ionut Sandric

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Geoinformation applied in Environmental Studies. Faculty of Geography, University of Bucharest, Bdul. Nicolae Balcescu, No.1, Sect. 1., 010041 Bucharest, Romania
Interests: landslides; geomorphological mapping; engineering geology; geology; statistical analysis; analysis; geological mapping
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Dr. George P. Petropoulos

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Department of Geography, Harokopio University of Athens, Eleftheriou Venizelou Ave., 70, GR-176 76 Athens, Greece
Interests: digital cartography; GPS; simulation process models; mapping of the natural and man-made environment
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Dr. Andrew Pavlides

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Geostatistics, School of Mineral Resources Engineering, Technical University of Crete, 73132 Chania, Greece
Interests: geostatistical analysis

Special Issue Information

Dear Colleagues,

Understanding Earth’s natural processes, especially in the context of global climate change, has been recognized globally as a very urgent and central research direction which needs further exploration. The recent launch of sophisticated satellite platforms with a high revisit time, combined with the increasing abilities for airborne platforms, which allow the collection of on-demand, ultra-high spatial resolution aerial images, has created new opportunities for developing and applying new image-processing algorithms to solve old and new environmental issues.

The purpose of the proposed Special Issue is to gather scientific research related to this topic, aiming to highlight ongoing research and new applications in the field of satellite and aerial time-series imagery. The session’s focus is on presenting studies aimed at the development or exploitation of novel satellite times-series processing algorithms, and applications for different types of Earth Observation data to investigate long-term processes in all branches of Earth (sea, ice, land, atmosphere).

We encourage both applied and theoretical research contributions focusing on novel methods and applications of time series analysis for satellite and aerial imagery from all disciplines of geosciences, and data acquired in all regions of the electromagnetic spectrum.

The submissions may cover the following topics (the list is not exhaustive):

Spatiotemporal environmental patterns modeling;
Natural hazards assessment and modeling using high-resolution imagery;
Landscape dynamics modeling and pattern recognition;
Land–water–atmosphere interactions assessment and modeling;
Gap-filling in optical imagery, from satellite and aerial platforms;
Artificial intelligence applications in satellite and aerial imagery;
SAR applications for the monitoring and modeling of natural hazards in drone applications in environmental studies;
Any other related topic.

Dr. Ionut Sandric
Dr. George P. Petropoulos
Dr. Andrew Pavlides
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.

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20 pages, 24978 KiB

Open AccessArticle

Using UAV Time Series to Estimate Landslides’ Kinematics Uncertainties, Case Study: Chirlești Earthflow, Romania

by Ionuț Șandric, Radu Irimia, Viorel Ilinca, Zenaida Chițu and Ion Gheuca

Remote Sens. 2023, 15(8), 2161; https://doi.org/10.3390/rs15082161 - 19 Apr 2023

Cited by 1 | Viewed by 1198

Abstract

This paper presents a methodology for evaluating the uncertainties caused by the misalignment between two digital elevation models in estimating landslide kinematics. The study focuses on the earthflow near the town of Chirlești, located in the Bend Subcarpathians, Buzău County, Romania, which poses a high risk of blocking the DN10 national road. Four flights were conducted between 2018 and 2022 using a DJI Phantom 4 UAV using the same flight plan. Monte Carlo simulations were used to model uncertainty propagation of the DEM misalignments in the landslide kinematics analysis. The simulations were applied to the accuracy values of the structure from a motion process used to generate the digital elevation models. The degree of uncertainty was assessed using the displaced material’s total amount in conjunction with the spatial correlation of the displaced material between two consecutive flights. The results revealed that the increase in the RMS values did not determine an increase in the displaced earth between two UAV flights. Instead, combining the RMS values and the correlation coefficient clearly indicated the correspondence between the spatial distribution of the displaced earth material and the overall changes reported between the two UAV flights. An RMS value of up to 1 unit associated with a correlation coefficient of 0.95 could be considered the maximum allowable error for estimating landslide kinematics across space and time. The current methodology is reliable when studying slow-movement landslides and when using short intervals between UAV flights. For rapid movements or significant terrain changes, such as translational and rotational landslides, careful analysis of the correlation coefficient in conjunction with the RMS values is recommended. Full article

(This article belongs to the Special Issue Novel Methods and Applications in Satellite and Aerial Imagery Time Series Analysis)

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18 pages, 3630 KiB

Open AccessArticle

Using GEOBIA and Vegetation Indices to Assess Small Urban Green Areas in Two Climatic Regions

by Ana Maria Popa, Diana Andreea Onose, Ionut Cosmin Sandric, Evangelos A. Dosiadis, George P. Petropoulos, Athanasios Alexandru Gavrilidis and Antigoni Faka

Remote Sens. 2022, 14(19), 4888; https://doi.org/10.3390/rs14194888 - 30 Sep 2022

Cited by 5 | Viewed by 1953

Abstract

The importance of small urban green areas has increased in the context of rapid urbanization and the densification of the urban tissue. The analysis of these areas through remote sensing has been limited due to the low spatial resolution of freely available satellite images. We propose a timeseries analysis on 3 m resolution Planet images, using GEOBIA and vegetation indices, with the aim of extracting and assessing the quality of small urban green areas in two different climatic and biogeographical regions: temperate (Bucharest, Romania) and mediterranean (Athens, Greece). Our results have shown high accuracy (over 91%) regarding the extraction of small urban green areas in both cities across all the analyzed images. The timeseries analysis showed consistency with respect to location for around 55% of the identified surfaces throughout the entire period. The vegetation indices registered higher values in the temperate region due to the vegetation characteristics and city plan of the two cities. For the same reasons, the increase in the vegetation density and quality, as a result of the distance from the city center, and the decrease in the density of built-up areas, is more obvious in Athens. The proposed method provides valuable insights into the distribution and quality of small urban green areas at the city level and can represent the basis for many analyses, which is currently limited by poor spatial resolution. Full article

(This article belongs to the Special Issue Novel Methods and Applications in Satellite and Aerial Imagery Time Series Analysis)

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20 pages, 10470 KiB

Open AccessArticle

Investigation of the Sensitivity of Microwave Land Surface Emissivity to Soil Texture in MLEM

by Ying Wu, Jinwang Bao, Zhiyan Liu, Yansong Bao and George P. Petropoulos

Remote Sens. 2022, 14(13), 3045; https://doi.org/10.3390/rs14133045 - 24 Jun 2022

Viewed by 1428

Abstract

This study analyzes the spectral characteristics of desert surface emissivity according to soil classification and the influence of mineral materials and soil texture information using simulation results from the microwave land emissivity model (MLEM). It also aims at exploring the feasibility of reducing the simulation error in MLEM by refining the soil classification characteristic parameters (such as soil composition content, distribution of particle size, etc.). The surface emissivity of the Taklimakan Desert is derived, to our knowledge for the first time, from FY-3B/MWRI (FengYun-3B Microwave Radiation Imager), and then the spectral characteristics of the study area for different soil types are further analyzed according to soil classification. In addition, emissivity spectra of the four most widely mineral materials in the desert area are reproduced using an MLEM under different conditions. Results showed that microwave land emissivity is highly correlated with the soil type and changes are markedly affected by the soil water content, soil texture, mineral composition, and soil particle size. For the desert soil, the emissivity of horizontal/vertical polarization is affected by the frequency in those soils dominated by large-size particles. However, for those dominated by smaller particles, the surface emissivity is almost constant or appears to be somehow dependent on the frequency. Moreover, the season effect on emissivity characteristics is clear, especially for soils composed of small-size particles. The emissivity of horizontal polarization shows stronger seasonal variation than that of vertical polarization. The study findings also showed that refining soil texture information (soil component content, distribution of particle size) improves the simulation accuracy in desert areas. For example, for the soil dominated by clay and clay loam, the simulation error is reduced from 6–9% to less than 6%. The latter is evident, especially for soil types containing a large number of small particles, such as clay and clay loam, for which the simulation error is reduced. All in all, our study contributes to a better understanding of the influencing factors of soil texture and stratification of the near-surface soil, helping to improve microwave land surface emissivity prediction by the studied here model. As MLEM consists of an important part of the global meteorological data assimilation and weather forecast system, results can also help towards increasing the use of satellite data in desert areas and in improving the accuracy of numerical weather forecast models. Full article

(This article belongs to the Special Issue Novel Methods and Applications in Satellite and Aerial Imagery Time Series Analysis)

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41 pages, 10856 KiB

Open AccessArticle

Modelling of Greek Lakes Water Quality Using Earth Observation in the Framework of the Water Framework Directive (WFD)

by Vassiliki Markogianni, Dionissios Kalivas, George P. Petropoulos and Elias Dimitriou

Remote Sens. 2022, 14(3), 739; https://doi.org/10.3390/rs14030739 - 04 Feb 2022

Cited by 13 | Viewed by 2511

Abstract

Given the great importance of lakes in Earth’s environment and human life, continuous water quality (WQ) monitoring within the frame of the Water Framework Directive (WFD) is the most crucial aspect for lake management. In this study, Earth Observation (EO) data from Landsat 7 Enhanced Thematic Mapper Plus (ETM⁺) and Landsat 8 Operational Land Imager (OLI) sensors have been combined with co-orbital in situ measurements from 50 lakes located in Greece with the main objective of delivering robust WQ assessment models. Correlation analysis among in situ co-orbital WQ data (Chlorophylla, Secchi depths, Total phosphorus-TP-) contributed to distinguishing their inter-relationships and improving the WQ models’ accuracy. Subsequently, stepwise multiple regression analysis (MLR) of the available TP and Secchi depth datasets was implemented to explore the potential to establish optimal quantitative models regardless of lake characteristics. Then, further MLR analysis concerning whether the lakes are natural or artificial was conducted with the basic aim of generating different remote sensing derived models for different types of lakes, while their combination was further utilized to assess their trophic status. Correlation matrix results showed a high and positive relationship between TP and Chlorophyll-a (0.85), whereas high negative relationships were found between Secchi depth with TP (−0.84) and Chlorophyll-a (−0.83). MLRs among Landsat data and Secchi depths resulted in 3 optimal models concerning the assessment of Secchi depth of all lakes (Secchi_general; R = 0.78; RMSE = 0.24 m), natural (Secchi_natural; R = 0.95; RMSE = 0.14 m) and artificial (Secchi_artificial; R = 0.62; RMSE = 0.1 m), with reliable accuracy. Study findings showed that TP-related MLR analyses failed to deliver a statistically acceptable model for the reservoirs; nevertheless, they delivered a robust TP_general (R = 0.71; RMSE = 1.41 mg/L) and TP_natural model (R = 0.93; RMSE = 1.43 mg/L). Subsequently, trophic status classification was conducted herein, calculating Carlson’s Trophic State Index (TSI) initially throughout all lakes and then oriented toward natural-only and artificial-only lakes. Those three types of TSI (general, natural, artificial) were calculated based on previously published satellite-derived Chlorophyll-a (Chl-a) assessment models and the hereby specially designed WQ models (Secchi depth, TP). The higher deviation of satellite-derived TSI values in relation to in situ ones was detected in reservoirs and shallower lakes (mean depth < 5 m), indicating noticeable divergences among natural and artificial lakes. All in all, the study findings provide important support toward the perpetual WQ monitoring and trophic status prediction of Greek lakes and, by extension, their sustainable management, particularly in cases when ground truth data is limited. Full article

(This article belongs to the Special Issue Novel Methods and Applications in Satellite and Aerial Imagery Time Series Analysis)

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26 pages, 22685 KiB

Open AccessArticle

Detection of Change Points in Pseudo-Invariant Calibration Sites Time Series Using Multi-Sensor Satellite Imagery

by Neha Khadka, Cibele Teixeira Pinto and Larry Leigh

Remote Sens. 2021, 13(11), 2079; https://doi.org/10.3390/rs13112079 - 25 May 2021

Cited by 6 | Viewed by 2161

Abstract

The remote sensing community has extensively used Pseudo-Invariant Calibration Sites (PICS) to monitor the long-term in-flight radiometric calibration of Earth-observing satellites. The use of the PICS has an underlying assumption that these sites are invariant over time. However, the site’s temporal stability has not been assured in the past. This work evaluates the temporal stability of PICS by not only detecting the trend but also locating significant shifts (change points) lying behind the time series. A single time series was formed using the virtual constellation approach in which multiple sensors data were combined for each site to achieve denser temporal coverage and overcome the limitation of dependence related to a specific sensor. The sensors used for this work were selected based on radiometric calibration uncertainty and availability of the data: operational land imager (Landsat-8), enhanced thematic mapper (Landsat-7), moderate resolution imaging spectroradiometer (Terra and Aqua), and multispectral instrument (Sentinel-2A). An inverse variance weighting method was applied to the Top-of-Atmosphere (TOA) reflectance time series to reveal the underlying trend. The sequential Mann–Kendall test was employed upon the weighted TOA reflectance time-series recorded over 20 years to detect abrupt changes for six reflective bands. Statistically significant trends and abrupt changes have been detected for all sites, but the magnitude of the trends (maximum of 0.215% change in TOA reflectance per year) suggest that these sites are not changing substantially over time. Hence, it can be stated that despite minor changes in all evaluated PICS, they can be used for radiometric calibration of optical remote sensing sensors. The new approach provides useful results by revealing underlying trends and providing a better understanding of PICS’ stability. Full article

(This article belongs to the Special Issue Novel Methods and Applications in Satellite and Aerial Imagery Time Series Analysis)

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11 pages, 2058 KiB

Open AccessTechnical Note

Radiant Power Patterns Inferred from Remote Sensing Using a Cloud Computing Platform, during the 2021 Fagradalsfjall Eruption, Iceland

by Muhammad Aufaristama, Armann Hoskuldsson, Mark van der Meijde, Harald van der Werff, William Michael Moreland and Ingibjorg Jonsdottir

Remote Sens. 2022, 14(18), 4528; https://doi.org/10.3390/rs14184528 - 10 Sep 2022

Viewed by 2049

Abstract

The effusive eruption at Mt. Fagradalsfjall began on 19 March 2021 and it ended a period of about 800 years of volcano dormancy on the Reykjanes Peninsula. To monitor and evaluate power output of the eruption, we compiled in total 254 freely available satellite images from Terra MODIS and Landsat 8 OLI-TIRS via the Google Earth Engine platform over a six-month period. This cloud computing platform offers unique opportunities for remote sensing data collection, processing, analysis, and visualizations at a regional scale with direct access to a multi-petabyte analysis-ready data catalogue. The average radiant power from the lava during this time was 437 MW, with a maximum flux of 3253 MW. The intensity thermal power output of the 2021 Fagradalsfjall (3253 MW) is in marked contrast to radiant power observed at the 2014–2015 Holuhraun Iceland (11956 MW) where, while both eruptions also hosted active lava pools and channel, Holuhraun exhibited a much greater variability in radiant power over the same period of time. We performed Spearman correlation coefficient (SCC). Our results show a positive correlation (0.64) with radiative power from the MODVOLC system, which suggests that both results follow the same general trend. The results provide a unique temporal data set of heat flux, hosted, and processed by a cloud computing platform. This enabled the rapid assessment of eruption evolution via a cloud computing platform which can collect and process time series data within minutes. Full article

(This article belongs to the Special Issue Novel Methods and Applications in Satellite and Aerial Imagery Time Series Analysis)

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