ESSI2.10 | Enhancing Geospatial Analysis with Google Earth Engine: Innovations, Applications, and Extensions
EDI
Enhancing Geospatial Analysis with Google Earth Engine: Innovations, Applications, and Extensions
Convener: Mathieu GraveyECSECS | Co-conveners: Emma Izquierdo-Verdiguier, TC ChakrabortyECSECS, Alvaro Moreno, Liza GoldbergECSECS
Orals
| Wed, 17 Apr, 08:30–10:15 (CEST)
 
Room 0.94/95
Posters on site
| Attendance Wed, 17 Apr, 16:15–18:00 (CEST) | Display Wed, 17 Apr, 14:00–18:00
 
Hall X4
Posters virtual
| Attendance Wed, 17 Apr, 14:00–15:45 (CEST) | Display Wed, 17 Apr, 08:30–18:00
 
vHall X4
Orals |
Wed, 08:30
Wed, 16:15
Wed, 14:00
As the urgency of addressing complex global challenges such as climate change, biodiversity loss, and sustainable resource management increases, the role of exploiting and producing high-quality, high-resolution geospatial information in these efforts is becoming increasingly crucial. Google Earth Engine has emerged as a powerful and well-established tool for harnessing the potential of these data products, reducing reliance on desktop and in-house computational platforms and providing researchers and developers with a compelling cloud-based alternative for planetary-scale geospatial analysis.

This session invites contributions from developers, researchers, and users providing cloud based solutions to key problems which push the boundaries of what is possible with Google Earth Engine. We welcome submissions focusing on, but not limited to:

Novel applications and case studies demonstrating the use of Earth Engine in addressing real-world problems
Generation and visualization of new databases and remote sensing products customized to specific applications
Development of new tools, extensions, or apps that enhance the functionality of Earth Engine
Methodological innovations in Earth Engine, including the implementation of advanced geospatial algorithms
Efforts to integrate Earth Engine with other data sources or analytical tools
Discussions on challenges, lessons learned, and future directions in the use of Earth Engine for geospatial analysis.

Whether you are using Earth Engine to map deforestation, predict flood risks, track disease spread, or develop new analytical tools, this session is your opportunity to share your work, learn from others, and explore the future of geospatial analysis with Google Earth Engine.

Orals: Wed, 17 Apr | Room 0.94/95

Chairpersons: Mathieu Gravey, Emma Izquierdo-Verdiguier, Alvaro Moreno
08:30–08:35
08:35–08:45
|
EGU24-11307
|
Virtual presentation
Karen Anderson, Magdalena Mleczko, Robert Brewin, Kevin Gaston, Markus Mueller, Shutler Jamie, Xiaoyu Yan, and Ruby Wilkinson

Numbers of Earth Observation (EO) satellites have increased exponentially over the past decade, fuelled by a shift towards constellation models that promise to deliver data at finer spatial, temporal and spectral resolutions compared to the past. The result is the now >1000 EO satellites in orbit, a population that is rapidly increasing because of a booming private-sector interest in space imaging. Flowing from this, EO data volumes have mushroomed in recent years, and data processing has migrated to the cloud, with scientists leveraging tools such as Google Earth Engine for information retrieval. Whilst considerable attention has been given to the launch and in-orbit environmental impacts of satellites (e.g. rocket emissions and space-junk risks), specific environmental impacts from EO missions (data infrastructures and cloud computation); have so far escaped critical scrutiny. It is urgent that the environmental science community address this gap, so that the environmental good of EO can withstand scrutiny.

Data centres consume high quantities of water and energy and they may be situated in sensitive geographical situations far away from both users and launchpads (i.e. a transboundary environmental concern). There are also hidden impacts in the carbon-intensive processes of computer component manufacture, impacting places and communities far from the site of EO information retrieval. We scope the broad suite of transboundary environmental impacts that EO generates. Related to the data aspect of the EO life-cycle, we quantify the current volume of global EO data holdings (> 800 PB currently, increasing by 100 PB / year). Mapping the distribution of datasets across different data centre providers, our work shows high redundancy of datasets, with collections from NASA and ESA replicated across many data centres globally. Storage of this data volume generates annual CO2 equivalent emissions summing to >4000 tonnes/year. We quantify the environmental cost of performing EO functions on the cloud compared to desktop machines, using Google Earth Engine as an exemplar, scaling emissions using the ‘Earth Engine Compute Unit’. We show how large-scale analyses executed within GEE rapidly scale to produce the equivalent emissions of a single ticket on an economy flight ticket from London-Paris. Executing these processes on the cloud takes seconds, and these estimates do not account for emissions from microprocessor manufacture, nor do they account for users running processes multiple times (e.g. during code development).  A major blind-spot is that the geography of GEE data centres is hidden from users, with no choice given to users about where GEE processes are executed. It is important that EO providers become more transparent about the location-specific impacts of EO work, and provide tools for measuring the environmental cost of cloud computation. Furthermore, the EO community as one which is concerned with the fate of Earth’s environment must now urgently and critically consider the broad suite of EO data life-cycle impacts that lie (a) beyond the launchpad, and (b) on Earth rather than in space; taking action to minimise and mitigate them. This is important particularly because EO data will long outlive the satellites that provided them.

How to cite: Anderson, K., Mleczko, M., Brewin, R., Gaston, K., Mueller, M., Jamie, S., Yan, X., and Wilkinson, R.: Quantifying the transboundary environmental impacts of Earth observation in the 'big data' and constellation era, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11307, https://doi.org/10.5194/egusphere-egu24-11307, 2024.

08:45–08:55
|
EGU24-5123
|
ECS
|
On-site presentation
Viola Heinrich, Stephen Sitch, Thais Rosan, Celso Silva Junior, and Luiz Aragão

Secondary forests, forest naturally regrowing on areas of previously deforested, now abandoned lands are crucial to help maintain and increase the carbon sink on land, helping to tackle the climate and ecological emergencies. There is a growing research field on improving our understanding of the carbon dynamics of secondary forests, often using novel remote sensing techniques to map the temporal and spatial patterns of change. However, the full datasets from the research are often not fully accessible to all users, either because the whole dataset is not published, or because it is presented in a specialist format. Given both the scientific and policy interest in forest-carbon dynamics, it is critical to ensure datasets are accessible to a wide range of audiences. 

Here we present “RE:Growth” – a user-friendly toolkit designed in Google Earth Engine, enabling users to view and download the aboveground carbon dynamics spatially and temporally in secondary forests in one of the largest tropical forest regions; the Brazilian Amazon. Designed to be easily updated as more temporal and spatial data are made available, ‘RE:Growth’ provides spatial and aggregated information on carbon flux dynamics in secondary forests through time based on Earth Observation. Uniquely, the user can draw their own region or select a jurisdictional boundary in the Brazilian Amazon to focus the analysis to their region of interest. Such information can be used within the measurement, reporting and verification framework, which is critical for results-based payments at all scales. For example, the toolkit can be used to provide spatial and quantitative data to inform the spatial prioritization of secondary forest conservation and expansion in line with Brazil’s Nationally Determined Contributions (NDC) to the Paris Agreement.

How to cite: Heinrich, V., Sitch, S., Rosan, T., Silva Junior, C., and Aragão, L.: RE:Growth—A Google Earth Engine toolkit analyzing secondary forest carbon dynamics in the Brazilian Amazon, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-5123, https://doi.org/10.5194/egusphere-egu24-5123, 2024.

08:55–09:05
|
EGU24-20371
|
On-site presentation
Mariapia Faruolo, Nicola Pergola, Nicola Genzano, and Francesco Marchese

Two innovative and powerful Google Earth Engine (GEE) Apps have recently been developed to identify and map volcanic thermal features and investigate gas flaring sources at global scale in daylight conditions. Both the GEE Apps are based on the Normalized Hotspot Indices (NHI; Marchese et al., 2019), which analyze the Near Infrared (NIR) and Short-Wave Infrared (SWIR) radiances from the Multispectral Instrument (MSI) and the Operational Land Imager (OLI/OLI-2) sensors, respectively aboard the Sentinel-2 and Landsat 8/9 satellites, to detect high-temperature features. The NHI tool enables the analysis of volcanic thermal anomalies through plots of hotspot pixel number, total SWIR radiance and total hotspot area. In addition, an automated module of the tool notifies the active volcanoes over the past 48 hours (https://sites.google.com/view/nhi-tool/home-page). DAFI (Daytime Approach for gas Flaring Investigation) by performing a multitemporal analysis of the NHI identifies the gas flares on annual basis both onshore and offshore, providing information about the gas flaring sources in terms of persistence of thermal activity and through the computation of the radiative power (https://sites.google.com/view/flaringsitesinventory). These systems demonstrate the relevance of the GEE platform in supporting the analysis, monitoring and characterization of hot targets (both natural and industrial ones) thanks to the massive computational resources and the availability of extended datasets of multisource satellite observations.

How to cite: Faruolo, M., Pergola, N., Genzano, N., and Marchese, F.: Two powerful Google Earth Engine (GEE) Apps for the worldwide high-temperature features monitoring and investigation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20371, https://doi.org/10.5194/egusphere-egu24-20371, 2024.

09:05–09:15
|
EGU24-21280
|
On-site presentation
|
William Ouellette and Jinal Surti

This session delves into the evolving landscape of climate regulations and voluntary commitments facing supply chains, with a specific focus on land-based supply chains such as agriculture and forestry. Recognizing the pivotal role of the first mile in contributing over 50% of greenhouse gas emissions and environmental externalities, the discussion centers on addressing the challenges posed by scope 3 emissions and the distributed responsibility inherent in the supply chain.

Epoch introduces the SCo2-API, a comprehensive web application leveraging Google Earth Engine and Google Cloud. This platform facilitates the management of geospatial assets within the supply chain, encompassing plot data, agricultural practices, supply shed, and payment details. The SCo2-API further provides API endpoints to derive on-demand, spatio-temporally relevant sustainability metrics.

The presented metrics include deforestation monitoring for compliance with European Union Deforestation Regulation (EUDR), Land Use Change (LUC), and Land Management (non-LUC) emissions estimates. These metrics serve to identify intervention hotspots and monitor environmental co-benefits such as carbon removals, water use, and biodiversity resulting from landscape restoration interventions.

Additionally, the SCo2-API incorporates advanced capabilities such as automated sampling design and minimum sampling density requirements for field data collection. These features are crucial for validating and enhancing confidence in the sustainability metrics generated. The framework also integrates payment capabilities to support Payments for Ecosystem Services (PES) schemes based on validated sustainability metrics.

Algorithmically, the SCo2-API ensures near-real-time results through a suite of scientific workflows. These include time series change detection using the Continuous Change Detection and Classification (CCDC) algorithm (Zhu et al., 2014), machine learning models predicting above-ground biomass and canopy heights using GEDI and ICESat data, canopy height heterogeneity as a proxy for landscape diversity (Rocchini et al., 2018), and evapotranspiration modeling as a proxy for water use (Melton et al., 2022). All scientific workflows are based on open datasets and satellite collections, aligning with open-source principles to ensure reproducibility and auditability of generated figures.

This session aims to explore the scientific and technological dimensions of the SCo2-API framework, providing insights into its applications for advancing supply chain sustainability and meeting regulatory and voluntary commitments.

How to cite: Ouellette, W. and Surti, J.: Geospatial Analytics for Enhanced Supply Chain Sustainability: Introducing the SCo2-API Framework, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-21280, https://doi.org/10.5194/egusphere-egu24-21280, 2024.

09:15–09:25
|
EGU24-11472
|
ECS
|
On-site presentation
Moctar Dembélé, Mansoor Leh, Darshana Wickramasinghe, Naga Velpuri, Karamoko Sanogo, Desalegne Tegegne, Mariangel Garcia Andarcia, and Petra Schmitter

Enabling the resilience of local food systems is crucial to ensure a steady supply of nutritious food to people living in fragile and conflict-affected locations. While the majority of interventions often focus on staple crops, there is an increasing tendency by humanitarian organizations to include vegetable production solutions in their programs. However, information on land suitability for vegetable production is usually lacking or available at a coarse spatial resolution, thereby limiting targeted interventions for smallholder farmers.

This study proposes a comprehensive geospatial data-driven framework for mapping suitable areas for vegetable production in Africa using a machine learning algorithm (ML) implemented in Google Earth Engine (GEE) and a Multi-Criteria Decision Analysis (MCA) approach. Mali (West Africa) and Ethiopia (East Africa) are selected as case studies given the current fragility of both countries, and support provided by the USAID's Bureau for Humanitarian Assistance (BHA). Field data of vegetable production locations was collected to train and validate the ML and MCA models. Several publicly available geospatial datasets, including FAO’s WaPOR database, were reviewed to select the predictor variables, which include information on climate, soil, topography, surface water, groundwater, socioeconomics and disaster risks. A suitability map was produced for all vegetables, and separate suitability maps were generated for the top five most cultivated vegetables in Mali and Ethiopia.

Comparison of the ML approach to the MCA approach revealed a lower performance of the former due to the limited availability of field data, thereby highlighting the benefit of expert knowledge in addition to the data-driven approach. The results show that the most suitable areas are found in the region of Segou in Mali (up to 88%), while the region of Oromia has the most suitable areas in Ethiopia (up to 85%). The resulting maps of land suitability for vegetable production serve to develop an irrigation investment targeting tool, which can be used to assist humanitarian organizations in implementing suitable irrigation solutions for vegetables.

How to cite: Dembélé, M., Leh, M., Wickramasinghe, D., Velpuri, N., Sanogo, K., Tegegne, D., Andarcia, M. G., and Schmitter, P.: Geospatial suitability mapping for targeted vegetable production in fragile African regions, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11472, https://doi.org/10.5194/egusphere-egu24-11472, 2024.

09:25–09:35
|
EGU24-6454
|
ECS
|
On-site presentation
Oliver Miguel Lopez Valencia, Ting Li, Bruno Jose Luis Aragon Solorio, and Matthew Francis McCabe

Monitoring agricultural water use is essential to ensure water security, especially in regions facing water scarcity. Satellite-acquired multi-spectral images of the Earth’s surface provide crucial data to enable frequent estimations of crop water use. In large data-scarce regions, these estimations represent a key source of information for water management. The emergence of cloud-based platforms, such as Google Earth Engine (GEE), has made it feasible, accessible, and cost-effective to automate crop water use monitoring pipelines. Here, we demonstrate the potential benefits of a cloud-based crop water use estimation and monitoring framework by estimating a decade's worth of agricultural water use over Saudi Arabia. In Saudi Arabia, large-scale agricultural activities account for the majority  (>80%) of water use, water which is sourced primarily from non-renewable groundwater resources from the Arabian Shelf. Saudi Arabia’s large land area (> 2 million  km2) and the long study period (+10 years) forms the basis of a case-study for our cloud-based model. Previous mapping efforts provided annual maps of individual field boundary delineations at, identifying more than 30,000 fields covering a total of more than 10,000 km2 of croplands that are distributed across several large-scale agricultural clusters within the Kingdom. As a preprocessing step, we developed an approach to generate large-scale delineations of irrigated agricultural regions over arid areas. This approach helped reduce processing efforts for field delineations, and at the same time reducing the water use estimation computations. Our GEE cloud-based model implements a two-source energy balance model (TSEB) and automatically incorporates all available Landsat collection 2 surface reflectance and surface temperature products from Landsat 7, 8, and 9, along with climate reanalysis data from the ECMWF ERA5-Land hourly product. The model can be readily applied elsewhere by defining just the date range and study geometry, while allowing the flexibility for more advanced users to control parameters within the TSEB model. Total crop water use (evapotranspiration term only; not accounting for irrigation efficiency) was estimated at between 7 and 12 BCM per year of study, with the highest use in 2016 and the lowest in 2020. Both Riyadh and al Jawf administrative regions collectively shared more than half of the total study cropland area, and a similar contribution of water use. This study represents the convergence of a number of efforts towards developing operational crop water use monitoring, while motivating further applications in other regions and providing a rich dataset for further food and water security related studies.

How to cite: Lopez Valencia, O. M., Li, T., Aragon Solorio, B. J. L., and McCabe, M. F.: Cloud-based agricultural crop water use monitoring across Saudi Arabia, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6454, https://doi.org/10.5194/egusphere-egu24-6454, 2024.

09:35–09:45
|
EGU24-19440
|
ECS
|
On-site presentation
Neha Joshi, Daniel Simms, and Paul Burgess

The Google Earth Engine platform has transformed access to long term scientific datasets as more than thirty years of image data are readily available for analysis on the cloud. Despite this rapid access to long term data and cloud computing resources of GEE, there is still the requirement to remove noise (e.g., from cloud and haze) and correct for the effects of calibration between image types to process raw data into information about changes in the Earth’s surface. Using the Python Earth Engine API, we developed an algorithm to combine timeseries datasets from high spatial resolution sensors (Landsat-8 and Sentinel-2) and filter noise whilst still retaining high temporal resolution. A second algorithm was then developed to automate the decomposition of pre-processed timeseries data into individual agricultural seasons for the extraction phenological stages for sugarcane fields across India. Our approach was developed and validated on over 800 sugar cane field parcels and overcomes the challenge of previous machine-learning methods for sugarcane monitoring using remote sensing that rely on information on planting and harvesting. Fully automated monitoring of sugarcane is possible over wide areas without the need to download image datasets or process time series data locally. This approach can significantly improve the sustainability of sugarcane production by optimising the harvest to maintain efficiency in the supply of sugarcane to mills during the crushing season and reduce waste by avoiding the harvest of immature cane. The use of GEE means that this approach can be easily modified for use with other crops and in other geographical areas to improve satellite-based monitoring of crops. These tools are essential for realising the goals of sustainable development, and time-series analysis can be used to help producers demonstrate commodities have not come from recently deforested land (for example, the EU regulation on deforestation-free products).

How to cite: Joshi, N., Simms, D., and Burgess, P.: The application of Google Earth Engine to monitor sugarcane development in India., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19440, https://doi.org/10.5194/egusphere-egu24-19440, 2024.

09:45–09:55
|
EGU24-13730
|
ECS
|
On-site presentation
Guofeng Tao and Kun Jia

The Qinghai-Tibetan Plateau (QTP) is one of the most sensitive and vulnerable regions under global climate change. Vegetation is the key component of the QTP ecosystem and is closely related to the ecological vulnerability. Fractional vegetation cover (FVC) is an important parameter to characterize vegetation conditions in the horizontal direction. Therefore, dense time-series of spatially continuous FVC at high spatial resolution is essential for understanding the detailed spatiotemporal dynamic changes in vegetation across QTP. Landsat is an ideal remote sensing data source for high spatial resolution FVC monitoring. However, frequent cloud cover during the growing season on QTP makes it challenging to observe FVC constantly only using Landsat. Spatiotemporal fusion methods integrating the advantages of Landsat and MODIS have been widely developed. Currently, most spatiotemporal fusion methods assume that the relationship between Landsat and MODIS is fixed over the prediction period. For regions with strong heterogeneity and large temporal variations, the relationship between Landsat and MODIS is variable along time. In addition, most methods fuse the reflectance bands separately without considering the interrelationship between bands. Therefore, a method blending Landsat and MODIS reflectance to generate FVC with 30m spatial resolution and 8-day interval based on Google Earth Engine (GEE) is proposed in this study. This method considers the dynamic relationship between MODIS and Landsat by analyzing the time-series data collected from multiple years. And a novel two-band simultaneous smoothing strategy is developed in this method, which can generate smoothed and consistent time-series of red and near-infrared bands simultaneously. Compared with three previous typical methods in two challenging QTP regions with rapid vegetation change, it can be found that the synthesized 30m reflectance data generated by the proposed method can get more accurate FVC. The validation results compared with the field-measured FVC further confirm the validity of the proposed method. The generated FVC products across QTP exhibit spatial continuity and reasonable time-series profiles. The proposed method is thus expected to provide high-quality FVC time-series with high spatiotemporal resolution over multiple years for QTP and other regions with frequent data missing based on GEE.

How to cite: Tao, G. and Jia, K.: Generating dense time-series of spatially continuous 30m fractional vegetation cover for the Qinghai-Tibetan Plateau based on Google Earth Engine, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13730, https://doi.org/10.5194/egusphere-egu24-13730, 2024.

09:55–10:05
|
EGU24-18012
|
ECS
|
Virtual presentation
Héctor Izquierdo Sanz, Enrique Moltó garcía, and Sergio Morell Monzó

Effective agricultural management policies rely heavily on accurate crop identification across various scales. This is particularly challenging in highly fragmented agricultural landscapes, such as those found in many European regions, particularly for fruit tree orchard identification. While solutions exist, the confidence in individual orchard classification is often overlooked, despite its importance in enhancing classification accuracy (precision, recall and specificity).

Several confidence metrics at pixel level have been proposed by estimating the probabilities of a pixel to belong to each of the possible classes. The higher the probability of class membership for a given class, the greater the confidence associated with that class. In this sense, a measure of confidence can be based in the differences of probability between the first two highest values (sometimes called the distance to the second cluster).

This study introduces an innovative methodological approach to build a classification confidence metric at object (orchard) level. Once segmentation is completed, all pixels whose confidence is not above a certain threshold are masked out. Then, each orchard is initially assigned to a class by computing the mode of the unmasked pixels inside its perimeter. In a subsequent step, a confidence metric at orchard level is estimated, based on the number of mode class pixels, the total number of pixels completely inside the orchard, and the proportion of the mode pixels and unmasked pixels within the orchard. This confidence metric allows for a balance between increased precision and a reduction in the number of classified orchards (those with insufficient confidence in their classification).

The proposed method, fully implemented in Google Earth Engine, was tested in a highly fragmented area in Valencia (Spain). The system’s performance was assessed by using a Random Forest classification algorithm on Fourier coefficients of spectral indexes time-series at pixel-level plus a specific spatial cross-validation procedure. By setting a 70% orchard classification confidence level, the mean overall accuracy increased from 88.74 ± 3.03% to 93.58 ± 2.85%, and the Kappa index from 0.78 ± 0.06% to 0.87 ± 0.05%, albeit at the cost of leaving 12.60 ± 7.18 % of orchards unclassified.

How to cite: Izquierdo Sanz, H., Moltó garcía, E., and Morell Monzó, S.: Enhancing Orchard Classification Accuracy through Object-Level Confidence Metrics: A Case Study in Automatic Orchard Identification in Valencia, Spain, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-18012, https://doi.org/10.5194/egusphere-egu24-18012, 2024.

10:05–10:15
|
EGU24-19017
|
Virtual presentation
Vinay Shivamurthy, Satyam Pawale, Yash Chandrashekar Shetty, and Sudarshan T Bhat
This research offers a new hybrid strategy for Land Use and Land Cover (LULC) classification that overcomes the constraints of k-Nearest Neighbors (KNN) through the use of Support Vector Machines (SVM). Our study, that makes use of the Google Earth Engine (GEE) API in conjunction with Colab, focuses on custom preprocessing that enhances data richness and context. We perform custom preprocessing, including feature scaling and data fusion of spatial, temporal, spectral, and radiometric dimensions, in order to enhance the data obtained from satellite imagery, incorporating spatial and temporal materials made of composites spectral fusion, and radiometric calibration. The approach we employ uses Rasterio for satellite images and Shapely for vector data. Geopandas encourages the smooth management of data related to geography implementing the GeoJSON format, strengthening compatibility with the Google Earth Engine, whereas Digital Elevation Models (DEMs) and Landgren software enrich LULC analysis.
The hybrid approach eliminates k-Nearest Neighbours (KNN) inefficiencies through the incorporation of Support Vector Machines (SVMs). The drawbacks of KNN, including computational intensity, sensitivity to irrelevant features, susceptibility to noise, and the need for optimal hyperparameter selection, are mitigated by leveraging SVM's strengths. SVM, which has been appreciated for its information technology efficiency, ability to withstand noise and outliers, and relevance-driven decision boundary learning, is a successful complement to KNN. The combination approach encompasses pre-processing with SVM in order to enhance data quality, learning the decision boundary with SVM, and selectively applying KNN in localized regions of interest. The perpetual enhancement of the hybrid model via validation enables a balanced use of SVM's robustness and KNN's flexibility. The proposed hybrid technique is an intriguing option that could enhance the efficiency and performance of LULC classification tasks, catering to the specific characteristics of the dataset and analysis goals.
Libraries for Python (Folium, Matplotlib, Seaborn) enable integration, allowing users to produce distinctive visualizations adapted to the specifications of remote sensing products designed for specific applications. Folium is used for producing interactive geographical maps, Matplotlib delivers configurable static plots, and Seaborn focuses on statistical data visualization. This combination facilitates complete investigation of complicated satellite picture collections using a variety of viewing approaches.
Overall, this hybrid methods, aided by improved preprocessing, data fusion, and visualization tools, presents a promising strategy for improving the efficiency and effectiveness of LULC classification while adapting to particular characteristics of the dataset and able to analyze objectives.
 
Keywords: LULC, Hybrid classification, SVM, KNN, Data fusion, Geospatial analysis, visualization

How to cite: Shivamurthy, V., Pawale, S., Chandrashekar Shetty, Y., and Bhat, S. T.: Comprehensive Hybrid Approach for LULC classification using GEE-API and ML, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19017, https://doi.org/10.5194/egusphere-egu24-19017, 2024.

Posters on site: Wed, 17 Apr, 16:15–18:00 | Hall X4

Display time: Wed, 17 Apr 14:00–Wed, 17 Apr 18:00
Chairpersons: TC Chakraborty, Alvaro Moreno, Emma Izquierdo-Verdiguier
X4.160
|
EGU24-11283
|
ECS
Gisela Domej and Kacper Pluta

Google Earth Engine offers a multitude of options for retrieving and analyzing satellite imagery from various satellite missions, which optimizes GIS studies, especially in the aspect of download and storage of extensive data volumes.

However, the compatibility with traditional desktop or web-based GIS software remains a gap that was attempted to overcome with the JavaScript code CataEx – a multi-functional tool that exemplifies several essential types of computations and finally exports imagery and its additionally created layers as GeoTIFFs. We presented several basic functionalities of CataEx: identifying images out of collections (e.g., the least cloudy image in a time frame), the definition of a location of interest (as coordinates and/or as a polygon), cloud masking for different collections, evaluation of satellite band properties and their projections, layer creation in Google Earth Engine, index computation, pixel-based statistics and histogram plotting, layer visualization and export to Google Drive.

CataEx is available in six versions for Landsat 4/5/7, Landsat 8/9, and Sentinel-2, each separated for top-of-atmosphere reflectance and surface reflectance collection. The code is deliberately kept simple to allow for easy brick-like recombination, adaption, and customization of code sections, and, hence, can be used as an example toolkit for students or beginners writing their first JavaScript routines for Google Earth Engine.

This work is funded by the Polish National Science Center (no. 2021/42/E/ST10/00186); the code is available on Zenodo (https://doi.org/10.5281/zenodo.8407939). 

How to cite: Domej, G. and Pluta, K.: CataEx: How to get started with JavaScript in Google Earth Engine?, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11283, https://doi.org/10.5194/egusphere-egu24-11283, 2024.

X4.161
|
EGU24-15855
|
ECS
Mathieu Gravey

The integration of Python scripting within the Google Earth Engine (GEE) code editor, enabled by the Open Earth Engine Extension (OEEex), introduces a practical and accessible approach to geospatial analysis. This development, powered by Pyodide —a Python runtime built for browser environments— allows users to write and execute Python code directly within the GEE interface without the need for local Python installations.

This feature caters especially to those more familiar with Python than JavaScript, providing a user-friendly platform for conducting Earth observation and analysis. It also facilitates the use of popular Python libraries such as Matplotlib for data visualization and scikit-learn for machine learning, directly within the GEE ecosystem.

The presentation will highlight some of the technical implementation, emphasizing how Python scripts can be executed in a browser environment without additional setup requirements. We will discuss the integration process, the challenges faced, and the solutions developed to ensure seamless functionality.

Demonstrations will showcase the capabilities of this integration, highlighting how Python can be utilized for various geospatial processing tasks in GEE. We aim to provide a realistic overview of the extension's capabilities, its impact on enhancing the flexibility of GEE, and its potential applications in remote sensing and Earth system science research.

How to cite: Gravey, M.:  Python Scripting in Google Earth Engine Code Editor with the Open Earth Engine extension, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15855, https://doi.org/10.5194/egusphere-egu24-15855, 2024.

X4.162
|
EGU24-8086
|
ECS
Chiara Bottaro and Giovanna Sona

Urban areas are being recognized as ecosystems, where biodiversity is essential for sustaining their functionality. Healthy ecosystems offer numerous services that contribute to the well-being of human populations. The EU Biodiversity Strategy and the Nature Restoration law call for no-net loss of urban green spaces and a minimum 10% tree canopy cover in European cities. This commitment is driving a surge in city tree planting projects, with expectations for further increase in the coming years.

In this context, we present an investigation focused on the multiscale mapping and monitoring of urban green biodiversity, with a primary emphasis on trees. Urban trees, besides providing various ecosystem services, play a crucial role in mitigating the urban heat load during summers, thereby alleviating adverse effects on human health, and reducing energy consumption. Unfortunately, the challenging conditions within urban environments, including increased temperatures and water scarcity due to impervious surfaces, can impact the phenology and physiology of trees, often compromising their health and functionality.

In order to better explore these aspects, we introduce an application based on Google Earth Engine with the aim to extract geospatial data related to tree cover and temperatures across various spatial scales in urban environments. Utilizing machine learning algorithms, the application downscales thermal infrared satellite imagery and classifies vegetation features. Users have the flexibility to investigate the relationship between temperatures and vegetation by selecting specific time windows and areas of interest, along with access to significant spectral indices and correlation coefficients. In a pilot case study on the city of Milan, we use the application to perform a detailed analysis at the tree species level, involving the assessment of individual tree canopy temperature response in different areas of the city.

This application aims to provide researchers, urban planners, green managers, and other professionals with a valuable tool to comprehend the spatial dynamics of vegetation in urban environments, assess the impacts of stressors on their fitness, and in the long run to evaluate the effectiveness of mitigation efforts, such as urban reforestations and tree planting.

How to cite: Bottaro, C. and Sona, G.: Multiscale mapping and monitoring of green biodiversity in urban areas , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-8086, https://doi.org/10.5194/egusphere-egu24-8086, 2024.

X4.163
|
EGU24-19202
Roman Shults and Ashraf Farahat

Floods are considered the most dangerous and destructive geohazards, leading to human victims and severe economic outcomes. Yearly, a large number of regions around the world suffer from devasting floods. The estimation of flood aftermaths is one of the high priorities for the global community. One such flood took place in northern Libya in September 2023. The presented study is aimed at the Libyan flood aftermath evaluation using Google Earth Engine opportunities. The primary task is obtaining and analyzing data that provide high accuracy and detail for the study region. Among those data that are of importance for flood and its aftermath assessment are remote sensing images. The last decades have seen an increased interest in remote sensing data thanks to the data accessibility and variety of free, open-source computational platforms. The data from Landsat, Sentinel, and similar missions are ubiquitous and well-studied. On the other hand, such software as Google Earth Engine or QGIS has a powerful toolbox for different solutions. The goal stated in the paper is related to image classification and change detection problems. The mentioned software provides various solutions based on machine-learning approaches for image classification and change detection. Miscellaneous data have been used to reach the paper’s goal. The first stage of the study was the development of a workflow for data analysis. This workflow includes three parallel processes of data analysis. The study comprised Sentinel 2 data for image classification using multispectral bands. Different supervised classification methods were examined, including random forest, support vector machines, naïve-Bayes, and CART. The different sets of hyperparameters for classification were considered. GEOEYE-1 and WorldView-2 images of four cities, Dernah, Susah, Al-Bayda, and Brega, were investigated for change detection algorithms. In addition, different NDVIs were calculated to facilitate the recognition of damaged regions. At the final stage, the analysis results were fused using the QGIS platform to obtain the damage estimation for the studied regions. As the main output, the area changes for the primary classes, and the maps that portray these changes were obtained. The recommendations for data usage and further processing in Google Earth Engine were developed.

How to cite: Shults, R. and Farahat, A.: Multi-temporal Remote Sensing Data Analysis for Devastating Flood Study in Northern Libya, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19202, https://doi.org/10.5194/egusphere-egu24-19202, 2024.

X4.164
|
EGU24-11492
|
ECS
Luca Salerno and Carlo Camporeale

This study investigates the eco-morphodynamic activity of major tropical rivers in the Tropics, aiming to quantify the carbon flux originating from riparian vegetation into inland waters. Employing a comprehensive multi-temporal analysis of satellite data, developed on the Google Earth Engine cloud computing platform, spanning from 2000 to 2019, at a spatial resolution of 30 meters, we focused on all tropical rivers with a width exceeding 200 meters. Our research reveals the existence of a highly efficient Carbon Pump mechanism, where river morphodynamics play a pivotal role in driving carbon export from the riparian zone. This, in turn, stimulates net primary production through a synergistic process involving floodplain rejuvenation and colonization.

The quantification of this unique pumping mechanism underscores its substantial contribution, alone accounting for an annual mobilization of 12 ± 0.96 million tons of carbon in these tropical rivers. We identified distinct signatures of fluvial eco-morphological activity that serve as proxies for assessing the carbon mobilization capability linked to river dynamics. The study also delves into the interplay between river migration and carbon mobilization, shedding light on the potential impacts on the carbon intensity of planned hydroelectric dams in the Neotropics.

We highlight the significance of the river-carbon nexus, emphasizing the necessity for a comprehensive approach in formulating effective water policies that consider the intricate relationships among river dynamics, carbon flux, and environmental phenomena.

 

How to cite: Salerno, L. and Camporeale, C.: Morphodynamics of the world’s large tropical rivers drive a carbon pumping mechanism  , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11492, https://doi.org/10.5194/egusphere-egu24-11492, 2024.

Posters virtual: Wed, 17 Apr, 14:00–15:45 | vHall X4

Display time: Wed, 17 Apr 08:30–Wed, 17 Apr 18:00
Chairpersons: Liza Goldberg, TC Chakraborty, Mathieu Gravey
vX4.29
|
EGU24-7620
|
ECS
Zolo Kiala, Karthikeyan Matheswaran, Andarcia, Mariangel Garcia, and Chris Dickens

Most of the global available freshwater for food production is utilized for irrigation. Irrigation expansion is crucial for agriculture production as it can increase crop yields and be a dependable adaptation measure against climate change. Accurate information on the spatial extent of irrigated areas and their dynamic shifts is therefore essential for efficiently managing already pressured water resources. The multiplicity of remotely sensed data sources and state-of-the-art machine techniques offer new avenues for producing more accurate irrigation maps. This study presents the results from a monthly monitoring framework for fine-scale mapping of irrigated areas in the Limpopo River Basin. The proposed framework uses high to moderate-resolution earth observation data, the extra-tree classifier, and a series of land cover masks in differentiating rain-fed and irrigated areas. We found that the area of irrigated land during the dry season in 2021 varied from 356589 ha to 612738 ha between and September. The overall accuracy of classified maps varied from 98 to 100%.  The proposed framework offers an automatic and replicable cost-effective means of mapping irrigated areas using Google Earth engine, multisource data, and machine learning algorithms. 

How to cite: Kiala, Z., Matheswaran, K., Garcia, A. M., and Dickens, C.: Machine learning implementation for mapping irrigated areas at fine temporal and spatial resolutions in the Limpopo River Basin , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7620, https://doi.org/10.5194/egusphere-egu24-7620, 2024.

vX4.30
|
EGU24-17960
|
ECS
Niraimathi Janardhanam and Subbarayan Saravanan

The Cauvery Delta Zone (CDZ) in southern India, supported by the Cauvery River, is an essential agricultural region renowned for its high production and rich ecological variety. The CDZ, characterized by its dynamic interaction between land and water and its wide range of soil series, supports a highly productive cropping system referred to as the rice bowl of Tamilnadu. The vegetation in CDZ plays a vital role in maintaining ecological equilibrium. Gaining knowledge about the timing of its life cycle and tracking changes are crucial for evaluating the effects of climate change and human actions. The utilization of satellite technology, specifically Sentinel-2, presents unparalleled prospects for ecological research, offering comprehensive worldwide coverage and imagery with exceptional levels of detail.   The study combines geographical analysis with satellite-derived vegetation indices (VIs) to get insights into the agricultural dynamics of the region, specifically focusing on rice agriculture, pulse crops, and a variety of perennial crops. This study investigates the Vegetation Indices (VIs) derived from satellite data, specifically the Normalised Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Biomass Index (BI), Chlorophyll Index (CI), and Differential Vegetation Index (DVI). The analysis focuses on using Sentinel-2 data to examine the spatial and temporal patterns in the CDZ. The research highlights the significance of NDVI in doing qualitative vegetation analysis. Furthermore, the contributions of EVI, BI, and DVI in comprehending vegetation health and land cover changes are investigated on a monthly basis from June 2022 to May 2023. The Google Earth Engine platform is utilized for the procedure involves the acquisition of Sentinel-2 data, the elimination of clouds, pre-processing of the data, computation of various vegetative indices (VIs), analysis of the results, and exporting them. The outcome demonstrates the fluctuations of satellite-derived vegetation indices, with the peak values observed in September and the lowest values in November. The values of NDVI and DVI exhibit a strong positive association, whereas EVI and BI also have a strong positive correlation. Substantial fluctuations in the results are observed on a monthly basis. The findings enhance scientific progress and facilitate informed decision-making for sustainable development, by effectively balancing human activities and environmental conservation.

How to cite: Janardhanam, N. and Saravanan, S.: Monitoring Vegetation Dynamics in the Cauvery Delta Zone (CDZ) Using Satellite-Derived Vegetation Indices with Google Earth Engine, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17960, https://doi.org/10.5194/egusphere-egu24-17960, 2024.