Application of Google Earth Engine for land Cover Classification in Yasuni National Park, Ecuador

Velastegui-Montoya, Andrés; Rivera-Torres, Hugo; Herrera-Matamoros, Viviana; Sadeck, Luis Waldyr Rodrigues; Quevedo, Renata Pacheco

doi:10.1109/igarss46834.2022.9884886

Cited by 6 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These drier regions have a semi-arid climate, with the annual aridity index reaching a value of 0.2, and are distributed across the northeastern agreste and sertão [14]. These areas are still associated with low mean annual rainfall (~600 mm), and higher altitudes when compared to the rest of the East Atlantic Basin, thus receiving a higher incidence of solar energy [18].…”

Section: Discussionmentioning

confidence: 99%

“…The use of cloud GEE revealed benefits when compared with GIS software, especially in terms of speed and processing capacity [18,19,40]. Further, GEE also stands out for the high-quality digital databases already available in its public catalog, streamlining.…”

Section: Environmentally Sensitive Areas (Esas)mentioning

confidence: 99%

“…However, limited computer power has been a major limitation to studies analyzing the many variables involved in desertification; therefore, studies have generally focused on small areas to decrease computational requirements [15]. The high computer power necessary to analyze large amounts of data can be addressed by cloud computing platforms, such as Google Earth Engine, which allow convenient and effective data processing, analysis, and storage [16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Assessment of Land Desertification in the Brazilian East Atlantic Region Using the Medalus Model and Google Earth Engine

Macêdo,

Tagliaferre,

Lopes da Silva

et al. 2023

Preprint

View full text Add to dashboard Cite

Many factors drive land desertification, especially in arid and semi-arid regions. However, the numerous driving factors of desertification make analyses computer expensive. Cloud computing comes along to address this problem, especially in developing countries. The objective of this work was to assess the sensitivity of the East Atlantic Basin, Brazil, to desertification using the Mediterranean Desertification and Land Use (MEDALUS) model and Google Earth Engine (GEE). The model is composed of four environmental Quality Indices (QIs) associated with soil (SQI), vegetation (VQI), climate (CQI) and management (MQI), and contains factors influencing the desertification process. Digital databases of the factors were pre-processed and uploaded to GEE for analysis. We report Environmentally Sensitive Areas (ESAs) and Environmentally Critical Factors (ECF) maps of the East Atlantic Basin, which showed that most of the basin is in either critical (49.4%) or fragile (35.7%) state of sensitivity. In contrast, only a smaller portion of the area is unaffected (5%) or potentially affected (10.1%). Sensitivity to desertification was inversely correlated with the presence of vigorous vegetation. A joint analysis of ESAs and ECF shed light on the importance of each factor in the sensitivity to desertification. The East Atlantic Basin shows a high degree of sensitivity to desertification, thereby demanding more attention and establishment of measures to mitigate the negative impacts of the desertification process.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Environmentally Sensitive Areas (Esas)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Assessment of Land Desertification in the Brazilian East Atlantic Region Using the Medalus Model and Google Earth Engine

Macêdo,

Tagliaferre,

Lopes da Silva

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Research methodologies are constantly changing and innovated to construct knowledge [13]. In the area of geoscience and remote sensing, GEE has become a powerful tool for remote sensing, given its multiple applications in fields such as agricultural productivity [14], vegetation monitoring [15], grassland monitoring [16], mangrove mapping [17], land use and cover [18,19], risk and disaster management [20], islands of heat [21], surface temperature [22], forest fires [23], bathymetry [24], surface water [25], built-up area [26], mining [27], among others. Its multiple applications show the GEE platform's potential to manage large data sets and contribute to the development of scientific research [28].…”

Section: Introductionmentioning

confidence: 99%

Google Earth Engine: A Global Analysis and Future Trends

et al. 2023

Self Cite

View full text Add to dashboard Cite

The continuous increase in the volume of geospatial data has led to the creation of storage tools and the cloud to process data. Google Earth Engine (GEE) is a cloud-based platform that facilitates geoprocessing, making it a tool of great interest to the academic and research world. This article proposes a bibliometric analysis of the GEE platform to analyze its scientific production. The methodology consists of four phases. The first phase corresponds to selecting “search” criteria, followed by the second phase focused on collecting data during the 2011 and 2022 periods using Elsevier’s Scopus database. Software and bibliometrics allowed to review the published articles during the third phase. Finally, the results were analyzed and interpreted in the last phase. The research found 2800 documents that received contributions from 125 countries, with China and the USA leading as the countries with higher contributions supporting an increment in the use of GEE for the visualization and processing of geospatial data. The intellectual structure study and knowledge mapping showed that topics of interest included satellites, sensors, remote sensing, machine learning, land use and land cover. The co-citations analysis revealed the connection between the researchers who used the GEE platform in their research papers. GEE has proven to be an emergent web platform with the potential to manage big satellite data easily. Furthermore, GEE is considered a multidisciplinary tool with multiple applications in various areas of knowledge. This research adds to the current knowledge about the Google Earth Engine platform, analyzing its cognitive structure related to the research in the Scopus database. In addition, this study presents inferences and suggestions to develop future works with this methodology.

show abstract

“…However, limited computer power has been a major limitation to studies analyzing the many variables involved in desertification and larger regions; therefore, studies have generally focused on small areas to reduce computational requirements [28]. The high computer power necessary to analyze large amounts of data can be addressed by cloud computing platforms, such as Google Earth Engine, which allow convenient and effective data processing, analysis, and storage [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Assessment of Land Desertification in the Brazilian East Atlantic Region Using the Medalus Model and Google Earth Engine

Macêdo,

Tagliaferre,

da Silva

et al. 2023

Land

View full text Add to dashboard Cite

Many factors drive land desertification, especially in arid and semi-arid regions. However, the sheer number of these driving factors of desertification makes analyses computer-intensive. Cloud computing offers a solution to address this problem, especially in developing countries. The objective of this work was to assess the sensitivity of the East Atlantic Basin, Brazil, to desertification using the Mediterranean Desertification and Land Use (MEDALUS) model and Google Earth Engine (GEE). The model is composed of four environmental Quality Indices (QIs) associated with soil (SQI), vegetation (VQI), climate (CQI), and management (MQI), each encompassing factors that influence the desertification process. Digital databases corresponding to these factors were pre-processed and uploaded to GEE for analysis. We report Environmentally Sensitive Areas (ESAs) and Environmentally Critical Factors (ECF) maps of the East Atlantic Basin, which show that most of the basin is in either a critical (49.4%) or fragile (35.7%) state of sensitivity. In contrast, only a smaller portion of the area is unaffected (5%) or potentially affected (10.1%). The analysis also revealed an inverse correlation between desertification sensitivity and the presence of vigorous vegetation. A joint evaluation of ESAs and ECF shed light on the importance of each factor in the sensitivity to desertification. The East Atlantic Basin shows a high degree of sensitivity to desertification, thereby demanding more attention and the establishment of measures to mitigate the negative impacts of the desertification process.

show abstract

Application of Google Earth Engine for land Cover Classification in Yasuni National Park, Ecuador

Cited by 6 publications

References 15 publications

Assessment of Land Desertification in the Brazilian East Atlantic Region Using the Medalus Model and Google Earth Engine

Assessment of Land Desertification in the Brazilian East Atlantic Region Using the Medalus Model and Google Earth Engine

Google Earth Engine: A Global Analysis and Future Trends

Assessment of Land Desertification in the Brazilian East Atlantic Region Using the Medalus Model and Google Earth Engine

Contact Info

Product

Resources

About