What is going on within google earth engine? A systematic review and meta-analysis

Pérez‐Cutillas, Pedro; Pérez-Navarro, Alberto; Conesa-García, Carmelo; Zema, Demetrio Antonio; Amado-Álvarez, Jesús P.

doi:10.1016/j.rsase.2022.100907

Cited by 31 publications

(15 citation statements)

References 130 publications

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“…Results showed that such detection and identification is possible. From the five classification algorithms (RF, CART, GTB, MMD and SVM) used to train the classification model, RF had the highest accuracy values and this is confirmed by Pérez-Cutillas et al (2023) who found that among the non-parametric classification methods the most frequently used algorithm was Random Forest, with 31% of the cases used. This study employs five different machine-learning algorithms because the existing literature is contradictory in terms of the classification accuracy of various classifiers for vegetation species.…”

Section: Discussionmentioning

confidence: 69%

Detecting and distinguishing between apicultural plants using UAV multispectral imaging

Papachristoforou

Prodromou

Hadjimitsis

et al. 2023

PeerJ

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Detecting and distinguishing apicultural plants are important elements of the evaluation and quantification of potential honey production worldwide. Today, remote sensing can provide accurate plant distribution maps using rapid and efficient techniques. In the present study, a five-band multispectral unmanned aerial vehicle (UAV) was used in an established beekeeping area on Lemnos Island, Greece, for the collection of high-resolution images from three areas where Thymus capitatus and Sarcopoterium spinosum are present. Orthophotos of UAV bands for each area were used in combination with vegetation indices in the Google Earth Engine (GEE) platform, to classify the area occupied by the two plant species. From the five classifiers (Random Forest, RF; Gradient Tree Boost, GTB; Classification and Regression Trees, CART; Mahalanobis Minimum Distance, MMD; Support Vector Machine, SVM) in GEE, the RF gave the highest overall accuracy with a Kappa coefficient reaching 93.6%, 98.3%, 94.7%, and coefficient of 0.90, 0.97, 0.92 respectively for each case study. The training method used in the present study detected and distinguish the two plants with great accuracy and results were confirmed using 70% of the total score to train the GEE and 30% to assess the method’s accuracy. Based on this study, identification and mapping of Thymus capitatus areas is possible and could help in the promotion and protection of this valuable species which, on many Greek Islands, is the sole foraging plant of honeybees.

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Section: Discussionmentioning

confidence: 69%

Detecting and distinguishing between apicultural plants using UAV multispectral imaging

Papachristoforou

Prodromou

Hadjimitsis

et al. 2023

PeerJ

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“…Google Earth Engine (GEE), a cloud‐based computing platform for planetary‐scale geospatial analyses, answers this need, offering access to petabytes worth of remotely sensed Earth observation data (Gorelick et al, 2017), enabling bio‐geomorphological analyses over large spatial extents and at fine temporal resolutions (Boothroyd, Nones, & Guerrero, 2021; Vos et al, 2019). Despite some limitations, GEE has become the most widely spread cloud processing tool nowadays, playing a crucial role in analysing spatial datasets (Gorelick et al, 2017; Pérez‐Cutillas et al, 2023; Tamiminia et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Remote sensing assessment of anthropogenic and climate variation effects on river channel morphology and vegetation: Impact of dry periods on a European piedmont river

Nones,

Guerrero,

Schippa

et al. 2024

Earth Surf Processes Landf

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Rivers are subject to increasing human pressure, resulting in a loss of their natural characteristics, further enhanced by climate change. The present study focuses on a piedmont reach of the Italian Po River and combines Landsat satellite information with hydrological records to investigate changes in sandbars exposure and riparian vegetation coverage, considering the summer period of 1984–2022. Satellite data were handled via Google Earth Engine, looking at common indexes such as the Modified Normalized Difference Water Index, which was used to identify temporal variations in wetted channel area, and the Normalized Difference Vegetation Index, considered as a proxy of vegetation coverage variations. Changes in the river hydrology were analysed by looking at the annual minimum water levels, the discharge duration curve, the stage–discharge relationship and the yearly annual water volume. Results suggest that a process of oversimplification is affecting this reach of the Po River, showing how climate change can affect piedmont European watercourses. Indeed, after an initial period where bars tended to be barer and somehow stable over time, in the most recent decade a different trend appeared, with vegetation able to colonize the exposed sandbars more. Using the Po River as an exemplary case study, this work suggests that prolonged dry periods, which are more common in recent decades, might impact large and medium rivers located in temperate climates, favouring the development of vegetation on exposed sandbars, eventually resulting in a less dynamic active channel.

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“…The availability of datasets, particularly from Landsat and Sentinel archives, has enhanced the understanding of wetland ecosystems, facilitating effective management and conservation [ 25 ]. Google Earth Engine (GEE) exemplifies this innovative technology by providing access to vast amounts of satellite data and a web-based workbench environment for geospatial data analysis, incorporating image processing and machine learning (ML) algorithms [ 6 , 26 ]. Its parallel processing architecture enables the storage and analysis of a massive volume of geospatial data that speed up the mapping and monitoring tasks [ 27 ], supporting diverse applications [ 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Wetland Mapping: Integrating Sentinel-1/2, GEDI Data, and Google Earth Engine

Jafarzadeh,

Mahdianpari,

Gill

et al. 2024

Sensors

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Wetlands are amongst Earth’s most dynamic and complex ecological resources, serving productive and biodiverse ecosystems. Enhancing the quality of wetland mapping through Earth observation (EO) data is essential for improving effective management and conservation practices. However, the achievement of reliable and accurate wetland mapping faces challenges due to the heterogeneous and fragmented landscape of wetlands, along with spectral similarities among different wetland classes. The present study aims to produce advanced 10 m spatial resolution wetland classification maps for four pilot sites on the Island of Newfoundland in Canada. Employing a comprehensive and multidisciplinary approach, this research leverages the synergistic use of optical, synthetic aperture radar (SAR), and light detection and ranging (LiDAR) data. It focuses on ecological and hydrological interpretation using multi-source and multi-sensor EO data to evaluate their effectiveness in identifying wetland classes. The diverse data sources include Sentinel-1 and -2 satellite imagery, Global Ecosystem Dynamics Investigation (GEDI) LiDAR footprints, the Multi-Error-Removed Improved-Terrain (MERIT) Hydro dataset, and the European ReAnalysis (ERA5) dataset. Elevation data and topographical derivatives, such as slope and aspect, were also included in the analysis. The study evaluates the added value of incorporating these new data sources into wetland mapping. Using the Google Earth Engine (GEE) platform and the Random Forest (RF) model, two main objectives are pursued: (1) integrating the GEDI LiDAR footprint heights with multi-source datasets to generate a 10 m vegetation canopy height (VCH) map and (2) seeking to enhance wetland mapping by utilizing the VCH map as an input predictor. Results highlight the significant role of the VCH variable derived from GEDI samples in enhancing wetland classification accuracy, as it provides a vertical profile of vegetation. Accordingly, VCH reached the highest accuracy with a coefficient of determination (R2) of 0.69, a root-mean-square error (RMSE) of 1.51 m, and a mean absolute error (MAE) of 1.26 m. Leveraging VCH in the classification procedure improved the accuracy, with a maximum overall accuracy of 93.45%, a kappa coefficient of 0.92, and an F1 score of 0.88. This study underscores the importance of multi-source and multi-sensor approaches incorporating diverse EO data to address various factors for effective wetland mapping. The results are expected to benefit future wetland mapping studies.

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What is going on within google earth engine? A systematic review and meta-analysis

Cited by 31 publications

References 130 publications

Detecting and distinguishing between apicultural plants using UAV multispectral imaging

Detecting and distinguishing between apicultural plants using UAV multispectral imaging

Remote sensing assessment of anthropogenic and climate variation effects on river channel morphology and vegetation: Impact of dry periods on a European piedmont river

Enhancing Wetland Mapping: Integrating Sentinel-1/2, GEDI Data, and Google Earth Engine

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