Google Earth Engine Applications for Monitoring Natural Ecosystems and Land Use

Topic Information

Dear Colleagues,

Global ecosystems play a major role in mitigating global warming, but climate change is increasing the number and the magnitude of stressors, making ecosystem monitoring more important than ever. In this context, remote sensing data and the Google Earth Engine cloud computing platform represent crucial tools for comprehensively and exhaustively monitoring ecosystems globally. Google Earth Engine provides access to the vast majority of freely available, public, multi-temporal remote sensing data and offers free cloud-based computational power to apply complex algorithms over large areas.

The Topic “Google Earth Engine Applications for Monitoring Natural Ecosystems and Land Use” welcomes high-quality studies that focus on applications exploiting GEE for monitoring natural ecosystems and land use. Relevant themes include, but are not limited to: (a) ecosystem disturbance near real-time prediction and monitoring, (b) carbon storage prediction, (c) forest species classification, (d) forest harvestings, wind damages, and fires prediction, (e) climate change impact on global ecosystems, (f) drought monitoring, (g) innovative time series analysis and machine learning approaches for ecosystem monitoring, (h) development and validation of ecosystem disturbance monitoring methods, (i) forest degradation monitoring, (j) natural disaster monitoring, (k) precision and accuracy estimation and modeling of forest structure and function parameters, (l) agroforestry ecosystem visualization and management, (m) land cover and land-use change monitoring, and (n) hydrological and eco-hydrological processes monitoring.

Prof. Dr. Gherardo Chirici
Dr. Saverio Francini
Dr. Noel Gorelick
Prof. Dr. Nicholas Coops
Topic Editors

Keywords

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Agriculture agriculture	3.6	3.6	2011	17.7 Days	CHF 2600
Earth earth	-	1.6	2020	17.6 Days	CHF 1200
Forests forests	2.9	4.5	2010	16.9 Days	CHF 2600
Land land	3.9	3.7	2012	14.8 Days	CHF 2600
Remote Sensing remotesensing	5.0	7.9	2009	23 Days	CHF 2700

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Published Papers (5 papers)

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15 pages, 6600 KiB  

Open AccessArticle

Developing a Wilderness Quality Index for Continental Europe

by Iurii Strus and Stephen Carver

Land 2024, 13(4), 428; https://doi.org/10.3390/land13040428 - 27 Mar 2024

Viewed by 1037

Abstract

This paper presents an updated wilderness quality map, WQI 2.0, for Europe, which extends the existing map (WQI 1.0) to include non-EU states in Eastern Europe. The analysis utilizes the Google Earth Engine (GEE) cloud platform and incorporates contemporary datasets to assess wilderness [...] Read more.

This paper presents an updated wilderness quality map, WQI 2.0, for Europe, which extends the existing map (WQI 1.0) to include non-EU states in Eastern Europe. The analysis utilizes the Google Earth Engine (GEE) cloud platform and incorporates contemporary datasets to assess wilderness quality across the continent. WQI 2.0 is compared to the previous version from the EU Wilderness register and global data from the WCS Human Influence Index (HII). Results indicate a high level of consistency between the versions, validating the robustness of the approach and the value of up-to-date datasets. WQI 2.0 serves as a valuable tool for developing a coordinated European policy on wilderness protection, encompassing both EU and non-EU states. By identifying areas outside current protected boundaries, the map helps to identify regions at risk of degradation and loss, due to resource exploitation. While small changes are seen between WQI 1.0 and WQI 2.0, expanding the coverage over the whole of continental Europe provides a foundation for the longer-term monitoring and evaluation of conservation targets. The findings contribute to meeting international commitments, such as the COP15 Kunming–Montreal Agreement and CBD targets, by highlighting the importance of preserving intact wilderness areas and increasing protected areas through restoration and rewilding efforts. Future iterations, such as WQI 3.0+, can track trends and potential threats to wilderness areas, while also identifying opportunities for ecosystem recovery through restoration and rewilding. To ensure comprehensive coverage, there is a need to update the existing Wilderness Register 1.0 and expand its scope to include non-EU states. This can be facilitated through collaboration with national WQI mapping programs, building on the experiences of countries such as Scotland, France, Iceland, and Germany, which have well-established national mapping initiatives. Overall, WQI 2.0 and the proposed updates provide valuable tools for informed decision-making in wilderness conservation and restoration efforts across Europe. Full article

(This article belongs to the Topic Google Earth Engine Applications for Monitoring Natural Ecosystems and Land Use)

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20 pages, 11514 KiB  

Open AccessArticle

Finding Navigable Paths through Tidal Flats with Synthetic Aperture Radar

by Ruaridh A. Clark, Ciara N. McGrath, Astrid A. Werkmeister, Christopher J. Lowe, Gwilym Gibbons and Malcolm Macdonald

Remote Sens. 2024, 16(6), 1057; https://doi.org/10.3390/rs16061057 - 16 Mar 2024

Viewed by 717

Abstract

Tidal flats are some of the most dynamic coastal environments in the world, where traditional coastal mapping and monitoring provide insufficient temporal resolution to reliably map channels and sand flats. Satellite-based Synthetic Aperture Radar (SAR) enables regular cloud-penetrating detection of water flowing through [...] Read more.

Tidal flats are some of the most dynamic coastal environments in the world, where traditional coastal mapping and monitoring provide insufficient temporal resolution to reliably map channels and sand flats. Satellite-based Synthetic Aperture Radar (SAR) enables regular cloud-penetrating detection of water flowing through channels within the tidal flats, referred to as tidal channels. This paper presents a method for detecting a path through tidal channels, using satellite imagery, that supports our understanding and safe exploitation of this valuable coastal environment. This approach is the first proposed to identify navigable paths in all conditions, with SAR images susceptible to variation due to weather and tidal conditions. Tidal channels are known to vary in SAR presentation, and we find that tidal flat presentation is also influenced by conditions. The most influential factor is the wind, with high winds causing an inversion in how both tidal flats and tidal channels present in SAR images. The presented method for the automatic detection of tidal channels accounts for this variability by using previous channel paths as a reference to reliably correct imagery and detect the latest path. The final algorithm produces paths with minor errors in 17.6% of images; the error rate increases to 71.7%, with an almost tenfold increase in errors, when the SAR image and paths are not adjusted to account for conditions. This capability has been used to support the Nith Inshore Rescue in attending call-outs from their base in Glencaple, UK, while the insights from monitoring tidal channels for a year demonstrate how periods of high river flow preceded major changes in the channel path. Full article

(This article belongs to the Topic Google Earth Engine Applications for Monitoring Natural Ecosystems and Land Use)

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22 pages, 1502 KiB  

Open AccessArticle

Landsat-7 ETM+, Landsat-8 OLI, and Sentinel-2 MSI Surface Reflectance Cross-Comparison and Harmonization over the Mediterranean Basin Area

by Martina Perez and Marcello Vitale

Remote Sens. 2023, 15(16), 4008; https://doi.org/10.3390/rs15164008 - 12 Aug 2023

Cited by 1 | Viewed by 1999

Abstract

In the Mediterranean area, vegetation dynamics and phenology analysed over a long time can have an important role in highlighting changes in land use and cover as well as the effect of climate change. Over the last 30 years, remote sensing has played [...] Read more.

In the Mediterranean area, vegetation dynamics and phenology analysed over a long time can have an important role in highlighting changes in land use and cover as well as the effect of climate change. Over the last 30 years, remote sensing has played an essential role in bringing about these changes thanks to many types of observations and techniques. Satellite images are to be considered an important tool to grasp these dynamics and evaluate them in an inexpensive and multidisciplinary way thanks to Landsat and Sentinel satellite constellations. The integration of these tools holds a dual potential: on the one hand, allowing us to obtain a longer historical series of reflectance data, while on the other hand making data available with a higher frequency even within a specific timeframe. The study aims to conduct a comprehensive cross-comparison analysis of long-time-series pixel values in the Mediterranean regions. For this scope comparisons between Landsat-7 (ETM+), Landsat-8 (OLI), and Sentinel-2 (MSI) satellite sensors were conducted based on surface reflectance products. We evaluated these differences using Ordinary Least Squares (OLS) and Major Axis linear regression (RMA) analysis on points extracted from over 15,000 images across the Mediterranean basin area from 2017 to 2020. Minor but consistent differences were noted, necessitating the formulation of suitable adjustment equations to better align Sentinel-2 reflectance values with those of Landsat-7 or Landsat-8. The results of the analysis are compared with the most-used harmonization coefficients proposed in the literature, revealing significant differences. The root-mean-square deviation, the mean difference and the orthogonal distance regression (ODR) slope show an improvement of the parameters for both models used (OLS and RMA) in this study. The discrepancies in reflectance values leads to corresponding variations in the estimation of biophysical parameters, such as NDVI, showing an increase in the ODR slope of 0.3. Despite differences in spatial, spectral, and temporal characteristics, we demonstrate that integration of these datasets is feasible through the application of band-wise regression corrections for a sensitive and heterogeneous area like those of the Mediterranean basin area. Full article

(This article belongs to the Topic Google Earth Engine Applications for Monitoring Natural Ecosystems and Land Use)

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18 pages, 3443 KiB  

Open AccessArticle

Spruce Bark Beetle Outbreak Prediction through Automatic Classification of Sentinel-2 Imagery

by Aurora Bozzini, Saverio Francini, Gherardo Chirici, Andrea Battisti and Massimo Faccoli

Forests 2023, 14(6), 1116; https://doi.org/10.3390/f14061116 - 28 May 2023

Cited by 7 | Viewed by 2038

Abstract

Extreme weather events are increasing in frequency and intensity, posing a threat to forest ecosystems and eliciting forest-pest outbreaks. In the southern Italian Alps, a dramatic windthrow called Vaia occurred in October 2018, shifting populations of the European spruce bark beetle (Ips [...] Read more.

Extreme weather events are increasing in frequency and intensity, posing a threat to forest ecosystems and eliciting forest-pest outbreaks. In the southern Italian Alps, a dramatic windthrow called Vaia occurred in October 2018, shifting populations of the European spruce bark beetle (Ips typographus) from an endemic to an epidemic phase. Remote-sensing methods are often employed to detect areas affected by disturbances, such as forest-pest outbreaks, over large regions. In this study, a random forest model on the Sentinel-2 images acquired over the south-eastern Alps in 2021 and 2022 was used to detect the outbreak spots. The automatic classification model was tested and validated by exploiting ground data collected through a survey conducted in 2021 and 2022 in both healthy and infested spots, characterized by variable sizes and degrees of infestation. The model correctly identified the forest conditions (healthy or infested) with an overall accuracy of 72% for 2022 and 58% for 2021. These results highlight the possibility of locating I. typographus outbreaks, even in small spots (between 5 and 50 trees) or spots intermixed with healthy trees. The prompt detection of areas with a higher frequency of outbreaks could be a useful tool to integrate field surveys and select forest areas in which to concentrate management operations. Full article

(This article belongs to the Topic Google Earth Engine Applications for Monitoring Natural Ecosystems and Land Use)

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26 pages, 3908 KiB  

Open AccessArticle

Estimating Afforestation Area Using Landsat Time Series and Photointerpreted Datasets

by Alice Cavalli, Saverio Francini, Ronald E. McRoberts, Valentina Falanga, Luca Congedo, Paolo De Fioravante, Mauro Maesano, Michele Munafò, Gherardo Chirici and Giuseppe Scarascia Mugnozza

Remote Sens. 2023, 15(4), 923; https://doi.org/10.3390/rs15040923 - 07 Feb 2023

Cited by 9 | Viewed by 2957

Abstract

Afforestation processes, natural and anthropogenic, involve the conversion of other land uses to forest, and they represent one of the most important land use transformations, influencing numerous ecosystem services. Although remotely sensed data are commonly used to monitor forest disturbance, only a few [...] Read more.

Afforestation processes, natural and anthropogenic, involve the conversion of other land uses to forest, and they represent one of the most important land use transformations, influencing numerous ecosystem services. Although remotely sensed data are commonly used to monitor forest disturbance, only a few reported studies have used these data to monitor afforestation. The objectives of this study were two fold: (1) to develop and illustrate a method that exploits the 1985–2019 Landsat time series for predicting afforestation areas at 30 m resolution at the national scale, and (2) to estimate afforestation areas statistically rigorously within Italian administrative regions and land elevation classes. We used a Landsat best-available-pixel time series (1985–2019) to calculate a set of temporal predictors that, together with the random forests prediction technique, facilitated construction of a map of afforested areas in Italy. Then, the map was used to guide selection of an estimation sample dataset which, after a complex photointerpretation phase, was used to estimate afforestation areas and associated confidence intervals. The classification approach achieved an accuracy of 87%. At the national level, the afforestation area between 1985 and 2019 covered 2.8 ± 0.2 million ha, corresponding to a potential C-sequestration of 200 million t. The administrative region with the largest afforested area was Sardinia, with 260,670 ± 58,522 ha, while the smallest area of 28,644 ± 12,114 ha was in Valle d’Aosta. Considering elevation classes of 200 m, the greatest afforestation area was between 400 and 600 m above sea level, where it was 549,497 ± 84,979 ha. Our results help to understand the afforestation process in Italy between 1985 and 2019 in relation to geographical location and altitude, and they could be the basis of further studies on the species composition of afforestation areas and land management conditions. Full article

(This article belongs to the Topic Google Earth Engine Applications for Monitoring Natural Ecosystems and Land Use)

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