Topic Editors

Department of Agricultural, Food, Environmental and Forestry Sciences and Technologies, University of Florence, 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
25 October 2023
Manuscript submission deadline
25 December 2023
Viewed by
1297

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
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
3.408 3.1 2011 18.6 Days 2000 CHF Submit
Earth
earth
- - 2020 16.2 Days 1000 CHF Submit
Forests
forests
3.282 4.0 2010 18.3 Days 2000 CHF Submit
Land
land
3.905 3.2 2012 12.7 Days 2200 CHF Submit
Remote Sensing
remotesensing
5.349 7.4 2009 19.7 Days 2500 CHF Submit

Preprints is a platform dedicated to making early versions of research outputs permanently available and citable. MDPI journals allow posting on preprint servers such as Preprints.org prior to publication. For more details about reprints, please visit https://www.preprints.org.

Published Papers (1 paper)

Order results
Result details
Journals
Select all
Export citation of selected articles as:
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 1 | Viewed by 627
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
Show Figures

Figure 1

Back to TopTop