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

Deadline for manuscript submissions: closed (25 April 2024) | Viewed by 21147

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Special Issue Editors

Dr. Nicola Genzano

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School of Engineering, University of Basilicata, via dell'Ateneo Lucano 10, 85100 Potenza, Italy
Interests: satellite remote sensing; natural hazards; earthquake risks, volcano monitoring
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Dr. Carolina Filizzola

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Institute of Methodologies for Environmental Analysis, National Research Council, 85050 Potenza, Italy
Interests: monitoring and mitigation of forest fires; remote sensing of natural/anthropogenic risks
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Dr. Francesco Marchese

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Istituto Di Metodologie Per L'analisi Ambientale, Tito Scalo, Italy
Interests: satellite remote sensing of volcanoes; fires; dust outbreaks; natural hazards
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Prof. Dr. Valerio Tramutoli

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Guest Editor

School of Engineering, University of Basilicata, 85100 Potenza, Italy
Interests: satellite remote sensing; robust satellite techniques for natural; environmental and industrial risks forecast and monitoring: floods, forest fires, earthquakes, volcanic eruptions, sand storms, air and water pollution, oil spills and energetic pipelines accidents
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Special Issue Information

Dear Colleagues,

In recent years, cloud computing infrastructures have contributed to the large diffusion of remote sensing data and applications in the scientific community.

Among the different cloud computing platforms, the Google Earth Engine (GEE) platform allows users to analyze both historical and recently acquired satellite imagery (e.g., Landsat 1-8, MODIS, Sentinel 1-5), as well as geospatial data set (e.g., reanalysis data from NCEP/NCAR). On the GEE platform, ready-to-use datasets are handled through JavaScript and Python libraries. Moreover, machine-learning techniques were also enabled by the recently added TensorFlow library.

In this Special Issue, we solicit studies using GEE to investigate and monitor natural hazards. In particular, manuscripts focusing on the following topics are welcome:

innovative methods, techniques, and algorithms for the analysis of Earth observation datasets;
new multi-temporal approaches toward satellite data analysis;
investigations at a planetary scale;
machine learning and artificial intelligence applications to multi-spectral, multi-temporal EO data;
advanced APPs and tools aimed to monitor and map natural and environmental phenomena;
advanced methods integrating GEE processing within more complex platforms;
advanced APPs and GEE processing, supporting education in geosciences for scholars.

Dr. Nicola Genzano
Dr. Carolina Filizzola
Dr. Francesco Marchese
Prof. Dr. Valerio Tramutoli
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

Google Earth Engine
satellite time-series analysis
natural hazards
education in Geosciences
big data processing
artificial intelligence and machine learning applied to Earth observation data

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Published Papers (7 papers)

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Research

28 pages, 13295 KiB

Open AccessArticle

Optimized Parameters for Detecting Multiple Forest Disturbance and Recovery Events and Spatiotemporal Patterns in Fast-Regrowing Southern China

by Yuwei Tu, Kaiping Liao, Yuxuan Chen, Hongbo Jiao and Guangsheng Chen

Remote Sens. 2024, 16(12), 2240; https://doi.org/10.3390/rs16122240 - 20 Jun 2024

Viewed by 1034

Abstract

The timing, location, intensity, and drivers of forest disturbance and recovery are crucial for developing effective management strategies and policies for forest conservation and ecosystem resilience. Although many algorithms and improvement methods have been developed, it is still difficult to guarantee the detection accuracy for forest disturbance and recovery patterns in southern China due to the complex climate and topography, faster forest recovery after disturbance, and the low availability of noise-free Landsat images. Here, we improved the LandTrendr parameters for different provinces to detect forest disturbances and recovery trajectories based on the LandTrendr change detection algorithm and time-series Landsat images on the GEE platform, and then applied the secondary random forest classifier to classify the forest disturbance and recovery patterns in southern China during 1990–2020. The accuracy evaluation indicated that our approach and improved parameters of the LandTrendr algorithm can increase the detection accuracy for both the spatiotemporal patterns and multiple events of forest disturbance and recovery, with an overall accuracy greater than 86% and a Kappa coefficient greater than 0.91 for different provinces. The total forest loss area was 1.54 × 10⁵ km² during 1990–2020 (4931 km²/year); however, most of these disturbed forests were recovered and only 6.39 × 10⁴ km² was a net loss area (converted to other land cover types). The area with two or more times of disturbance events accounted for 11.50% of the total forest loss area. The total forest gain area (including gain after loss and the afforestation area) was 5.44 × 10⁵ km², among which, the forest gain area after loss was 8.94 × 10⁴ km², and the net gain area from afforestation was 4.55 × 10⁵ km². The timing of the implementation of forestry policies significantly affected the interannual variations in forest disturbance and recovery, with large variations among different provinces. The detected forest loss and gain area was further compared against with inventory and other geospatial products, and proved the effectiveness of our method. Our study suggests that parameter optimization in the LandTrendr algorithm could greatly increase the accuracy for detecting the multiple and lower rate disturbance/recovery events in the fast-regrowing forested areas. Our findings also offer a long-term, moderate spatial resolution, and precise forest dynamic data for achieving sustainable forest management and the carbon neutrality goal in southern China. Full article

(This article belongs to the Special Issue Natural Hazard Mapping with Google Earth Engine)

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22 pages, 25403 KiB

Open AccessArticle

A Comprehensive Assessment of Climate Change and Anthropogenic Effects on Surface Water Resources in the Lake Urmia Basin, Iran

by Mohammad Kazemi Garajeh, Rojin Akbari, Sepide Aghaei Chaleshtori, Mohammad Shenavaei Abbasi, Valerio Tramutoli, Samsung Lim and Amin Sadeqi

Remote Sens. 2024, 16(11), 1960; https://doi.org/10.3390/rs16111960 - 29 May 2024

Cited by 3 | Viewed by 1489

Abstract

In recent decades, the depletion of surface water resources within the Lake Urmia Basin (LUB), Iran, has emerged as a significant environmental concern. Both anthropogenic activities and climate change have influenced the availability and distribution of surface water resources in this area. This research endeavors to provide a comprehensive evaluation of the impacts of climate change and anthropogenic activities on surface water resources across the LUB. Various critical climatic and anthropogenic factors affecting surface water bodies, such as air temperature (AT), cropland (CL), potential evapotranspiration (PET), snow cover, precipitation, built-up areas, and groundwater salinity, were analyzed from 2000 to 2021 using the Google Earth Engine (GEE) cloud platform. The JRC-Global surface water mapping layers V1.4, with a spatial resolution of 30 m, were employed to monitor surface water patterns. Additionally, the Mann–Kendall (MK) non-parametric trend test was utilized to identify statistically significant trends in the time series data. The results reveal negative correlations of −0.56, −0.89, −0.09, −0.99, and −0.79 between AT, CL, snow cover, built-up areas, and groundwater salinity with surface water resources, respectively. Conversely, positive correlations of 0.07 and 0.12 were observed between precipitation and PET and surface water resources, respectively. Notably, the findings indicate that approximately 40% of the surface water bodies in the LUB have remained permanent over the past four decades. However, there has been a loss of around 30% of permanent water resources, transitioning into seasonal water bodies, which now account for nearly 13% of the total. The results of our research also indicate that December and January are the months with the most water presence over the LUB from 1984 to 2021. This is because these months align with winter in the LUB, during which there is no water consumption for the agriculture sector. The driest months in the study area are August, September, and October, with the presence of water almost at zero percent. These months coincide with the summer and autumn seasons in the study area. In summary, the results underscore the significant impact of human activities on surface water resources compared to climatic variables. Full article

(This article belongs to the Special Issue Natural Hazard Mapping with Google Earth Engine)

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29 pages, 9386 KiB

Open AccessArticle

Detecting Coseismic Landslides in GEE Using Machine Learning Algorithms on Combined Optical and Radar Imagery

by Stefan Peters, Jixue Liu, Gunnar Keppel, Anna Wendleder and Peiliang Xu

Remote Sens. 2024, 16(10), 1722; https://doi.org/10.3390/rs16101722 - 13 May 2024

Cited by 3 | Viewed by 1956

Abstract

Landslides, resulting from disturbances in slope equilibrium, pose a significant threat to landscapes, infrastructure, and human life. Triggered by factors such as intense precipitation, seismic activities, or volcanic eruptions, these events can cause extensive damage and endanger nearby communities. A comprehensive understanding of landslide characteristics, including spatio-temporal patterns, dimensions, and morphology, is vital for effective landslide disaster management. Existing remote sensing approaches mostly use either optical or synthetic aperture radar sensors. Integrating information from both these types of sensors promises greater accuracy for identifying and locating landslides. This study proposes a novel approach, the ML-LaDeCORsat (Machine Learning-based coseismic Landslide Detection using Combined Optical and Radar Satellite Imagery), that integrates freely available Sentinel-1, Palsar-2, and Sentinel-2 imagery data in Google Earth Engine (GEE). The approach also integrates relevant spectral indices and suitable bands used in a machine learning-based classification of coseismic landslides. The approach includes a robust and reproducible training and validation strategy and allows one to choose between five classifiers (CART, Random Forest, GTB, SVM, and Naive Bayes). Using landslides from four different earthquake case studies, we demonstrate the superiority of our approach over existing solutions in coseismic landslide identification and localization, providing a GTB-based detection accuracy of 87–92%. ML-LaDeCORsat can be adapted to other landslide events (GEE script is provided). Transfer learning experiments proved that our model can be applied to other coseismic landslide events without the need for additional training data. Our novel approach therefore facilitates quick and reliable identification of coseismic landslides, highlighting its potential to contribute towards more effective disaster management. Full article

(This article belongs to the Special Issue Natural Hazard Mapping with Google Earth Engine)

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19 pages, 8454 KiB

Open AccessArticle

Detection of Large-Scale Floods Using Google Earth Engine and Google Colab

by Rosa Johary, Christophe Révillion, Thibault Catry, Cyprien Alexandre, Pascal Mouquet, Solofoarisoa Rakotoniaina, Gwenaelle Pennober and Solofo Rakotondraompiana

Remote Sens. 2023, 15(22), 5368; https://doi.org/10.3390/rs15225368 - 15 Nov 2023

Cited by 4 | Viewed by 4082

Abstract

This paper presents an operational approach for detecting floods and establishing flood extent using Sentinel-1 radar imagery with Google Earth Engine. The methodology relies on change detection, comparing pre-event and post-event images. The change-detection method is based on the normalised difference ratio. Additionally, the HAND model is employed to delineate zones for processing only in flood-prone areas. The approach was tested and calibrated at a small scale to optimise parameters. In these calibration tests, an accuracy of 85% is achieved. The approach was then applied to the whole of the island of Madagascar after Cyclone Batsirai in 2022. The proposed method is enabled by the computing power and data availability of Google Earth Engine and Google Colab. The results show satisfactory accuracy in delineating flooded areas. The advantages of this approach are its rapidity, online availability and ability to detect floods over a wide area. The approach relying on Google Tools thus offers an effective solution for generating a large-scale synoptic picture to inform hazard management decision making. However, one of the method’s drawbacks is that it depends to a large extent on frequent radar imagery being available at the time of flood events and on free access to the platform. These drawbacks will need to be taken into account in an operational scenario. Full article

(This article belongs to the Special Issue Natural Hazard Mapping with Google Earth Engine)

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24 pages, 46157 KiB

Open AccessArticle

Automatic Mapping of Burned Areas Using Landsat 8 Time-Series Images in Google Earth Engine: A Case Study from Iran

by Houri Gholamrezaie, Mahdi Hasanlou, Meisam Amani and S. Mohammad Mirmazloumi

Remote Sens. 2022, 14(24), 6376; https://doi.org/10.3390/rs14246376 - 16 Dec 2022

Cited by 10 | Viewed by 4350

Abstract

Due to the natural conditions and inappropriate management responses, large part of plains and forests in Iran have been burned in recent years. Given the increasing availability of open-access satellite images and open-source software packages, we developed a fast and cost-effective remote sensing methodology for characterizing burned areas for the entire country of Iran. We mapped the fire-affected areas using a post-classification supervised method and Landsat 8 time-series images. To this end, the Google Earth Engine (GEE) and Google Colab computing services were used to facilitate the downloading and processing of images as well as allowing for effective implementation of the algorithms. In total, 13 spectral indices were calculated using Landsat 8 images and were added to the nine original bands of Landsat 8. The training polygons of the burned and unburned areas were accurately distinguished based on the information acquired from the Iranian Space Agency (ISA), Sentinel-2 images, and Fire Information for Resource Management System (FIRMS) products. A combination of Genetic Algorithm (GA) and Neural Network (NN) approaches was then implemented to specify 19 optimal features out of the 22 bands. The 19 optimal bands were subsequently applied to two classifiers of NN and Random Forest (RF) in the timespans of 1 January 2019 to 30 December 2020 and of 1 January 2021 to 30 September 2021. The overall classification accuracies of 94% and 96% were obtained for these two classifiers, respectively. The omission and commission errors of both classifiers were also less than 10%, indicating the promising capability of the proposed methodology in detecting the burned areas. To detect the burned areas caused by the wildfire in 2021, the image differencing method was used as well. The resultant models were finally compared to the MODIS fire products over 10 sampled polygons of the burned areas. Overall, the models had a high accuracy in detecting the burned areas in terms of shape and perimeter, which can be further implicated for potential prevention strategies of endangered biodiversity. Full article

(This article belongs to the Special Issue Natural Hazard Mapping with Google Earth Engine)

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17 pages, 6221 KiB

Open AccessFeature PaperArticle

A Tailored Approach for the Global Gas Flaring Investigation by Means of Daytime Satellite Imagery

by Mariapia Faruolo, Nicola Genzano, Francesco Marchese and Nicola Pergola

Remote Sens. 2022, 14(24), 6319; https://doi.org/10.3390/rs14246319 - 13 Dec 2022

Cited by 10 | Viewed by 2840

Abstract

The Daytime Approach for gas Flaring Investigation (DAFI), running in Google Earth Engine (GEE) environment, exploits a Normalized Hotspot Index (NHI), analyzing near-infrared and short-wave infrared radiances, to detect worldwide high-temperature gas flaring sites (GFs). Daytime Landsat 8—Operational Land Imager (OLI) observations, of 2013–2021, represents the employed dataset. A temporal persistence criterion is applied to a gas flaring customized NHI product to select the GFs. It assures the 99% detection accuracy of more intense and stable GFs, with a very low false positive rate. As a result, the first daytime database and map of GF sites, operating during the last 9 years at global scale, has been generated. For each site, geographical metadata, frequency of occurrence and time persistence levels, at both monthly and annual scale, may be examined, through the specific developed GEE App. The present database will complement/integrate existing gas flaring maps. The joint use of global scale daytime and nighttime GFs inventories, in fact, will allow for tracking gas flaring dynamics in a timely manner. Moreover, it enables a better evaluation of GF emissions into the atmosphere. Finally, the next DAFI implementation on Landsat 9 and Sentinel 2 data will further improve our capabilities in identifying, mapping, monitoring and characterizing the GFs. Full article

(This article belongs to the Special Issue Natural Hazard Mapping with Google Earth Engine)

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26 pages, 9163 KiB

Open AccessArticle

Impacts of the Urmia Lake Drought on Soil Salinity and Degradation Risk: An Integrated Geoinformatics Analysis and Monitoring Approach

by Bakhtiar Feizizadeh, Davoud Omarzadeh, Keyvan Mohammadzadeh Alajujeh, Thomas Blaschke and Mohsen Makki

Remote Sens. 2022, 14(14), 3407; https://doi.org/10.3390/rs14143407 - 15 Jul 2022

Cited by 6 | Viewed by 3258

Abstract

Recent improvements in earth observation technologies and Geographical Information System (GIS) based spatial analysis methods require us to examine the efficiency of the different data-driven methods and decision rules for soil salinity monitoring and degradation mapping. The main objective of this study was to analyze the environmental impacts of the Lake Urmia drought on soil salinity and degradation risk in the plains surrounding the hyper-saline lake. We monitored the impacts of the lake drought on soil salinity by applying spatiotemporal indices to time-series satellite images (1990–2020) in Google Earth Engine environment. We also computed the soil salinity ratio to validate the results and determine the most efficient soil salinity monitoring techniques. We then mapped the soil degradation risk based on GIS spatial decision-making methods. Our results indicated that the Urmia Lake drought is leading to the formation of extensive salt lands, which impact the fertility of the farmlands. The land affected by soil salinity has increased from 2.86% in 1990 to 16.68% in 2020. The combined spectral response index, with a performance of 0.95, was the most efficient image processing method to assess soil salinity. The soil degradation risk map showed that 38.45% of the study area has a high or very high risk of degradation, which is a significant threat to food production. This study presents an integrated geoinformation approach for time-series soil salinity monitoring and degradation risk mapping that supports future studies by comparing the efficiency of different methods as state of the art. From a practical perspective, the results also provide key information for decision-makers, authorities, and local stakeholders in their efforts to mitigate the environmental impacts of lake drought and sustain the food production to sustain the 7.3 million residents. Full article

(This article belongs to the Special Issue Natural Hazard Mapping with Google Earth Engine)

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