Topic Editors

GeoLAB—Laboratorio di Geomatica Forestale, Dipartimento di Scienze e Tecnologie Agrarie, Alimentari, Ambientali e Forestali, Università degli Studi di Firenze, Via San Bonaventura 13, 50145 Firenze, Italy
1. Department of Agriculture, Food, Environment and Forestry, University of Florence, Florence, Italy
2. Fondazione per il futuro delle città, Firenze, Italy
Google Switzerland, Brandschenkestrasse 110, 8002 Zurich, Switzerland
Integrated Remote Sensing Studio, Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada

Google Earth Engine Applications for Monitoring Natural Ecosystems and Land Use

Abstract submission deadline
closed (25 October 2023)
Manuscript submission deadline
closed (25 December 2023)
Viewed by
8096

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

  • forests
  • ecosystems
  • land-cover and land-use change
  • Google Earth Engine (GEE)
  • remote sensing
  • hydrology
  • artificial intelligence
  • big data
  • decision making
  • carbon storage estimation
  • sustainability
  • biodiversity

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 (3 papers)

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22 pages, 1502 KiB  
Article
Landsat-7 ETM+, Landsat-8 OLI, and Sentinel-2 MSI Surface Reflectance Cross-Comparison and Harmonization over the Mediterranean Basin Area
Remote Sens. 2023, 15(16), 4008; https://doi.org/10.3390/rs15164008 - 12 Aug 2023
Viewed by 1682
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
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18 pages, 3443 KiB  
Article
Spruce Bark Beetle Outbreak Prediction through Automatic Classification of Sentinel-2 Imagery
Forests 2023, 14(6), 1116; https://doi.org/10.3390/f14061116 - 28 May 2023
Cited by 3 | Viewed by 1825
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
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26 pages, 3908 KiB  
Article
Estimating Afforestation Area Using Landsat Time Series and Photointerpreted Datasets
Remote Sens. 2023, 15(4), 923; https://doi.org/10.3390/rs15040923 - 07 Feb 2023
Cited by 8 | Viewed by 2355
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
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