scholarly journals Terrain Analysis in Google Earth Engine: A Method Adapted for High-Performance Global-Scale Analysis

2020 ◽  
Vol 9 (6) ◽  
pp. 400 ◽  
Author(s):  
José Safanelli ◽  
Raul Poppiel ◽  
Luis Ruiz ◽  
Benito Bonfatti ◽  
Fellipe Mello ◽  
...  
Keyword(s):  
High Performance ◽  
Global Scale ◽  
Google Earth ◽  
Slope Aspect ◽  
Analysis Tool ◽  
Terrain Analysis ◽  
Terrain Attributes ◽  
Visual Correspondence ◽  
Elevation Data ◽  
Google Earth Engine

Terrain analysis is an important tool for modeling environmental systems. Aiming to use the cloud-based computing capabilities of Google Earth Engine (GEE), we customized an algorithm for calculating terrain attributes, such as slope, aspect, and curvatures, for different resolution and geographical extents. The calculation method is based on geometry and elevation values estimated within a 3 × 3 spheroidal window, and it does not rely on projected elevation data. Thus, partial derivatives of terrain are calculated considering the great circle distances of reference nodes of the topographic surface. The algorithm was developed using the JavaScript programming interface of the online code editor of GEE and can be loaded as a custom package. The algorithm also provides an additional feature for making the visualization of terrain maps with a dynamic legend scale, which is useful for mapping different extents: from local to global. We compared the consistency of the proposed method with an available but limited terrain analysis tool of GEE, which resulted in a correlation of 0.89 and 0.96 for aspect and slope over a near-global scale, respectively. In addition to this, we compared the slope, aspect, horizontal, and vertical curvature of a reference site (Mount Ararat) to their equivalent attributes estimated on the System for Automated Geospatial Analysis (SAGA), which achieved a correlation between 0.96 and 0.98. The visual correspondence of TAGEE and SAGA confirms its potential for terrain analysis. The proposed algorithm can be useful for making terrain analysis scalable and adapted to customized needs, benefiting from the high-performance interface of GEE.

scholarly journals FREE GLOBAL DSM ASSESSMENT ON LARGE SCALE AREAS EXPLOITING THE POTENTIALITIES OF THE INNOVATIVE GOOGLE EARTH ENGINE PLATFORM

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 627-633 ◽  
Author(s):  
A. Nascetti ◽  
M. Di Rita ◽  
R. Ravanelli ◽  
M. Amicuzi ◽  
S. Esposito ◽  
...  
Keyword(s):  
High Performance ◽  
Large Scale ◽  
Global Scale ◽  
Google Earth ◽  
Observation Data ◽  
Geometric Accuracy ◽  
Computing Platform ◽  
Aster Gdem ◽  
Google Earth Engine ◽  
Earth Observation Data

The high-performance cloud-computing platform Google Earth Engine has been developed for global-scale analysis based on the Earth observation data. In particular, in this work, the geometric accuracy of the two most used nearly-global free DSMs (SRTM and ASTER) has been evaluated on the territories of four American States (Colorado, Michigan, Nevada, Utah) and one Italian Region (Trentino Alto- Adige, Northern Italy) exploiting the potentiality of this platform. These are large areas characterized by different terrain morphology, land covers and slopes. The assessment has been performed using two different reference DSMs: the USGS National Elevation Dataset (NED) and a LiDAR acquisition. The DSMs accuracy has been evaluated through computation of standard statistic parameters, both at global scale (considering the whole State/Region) and in function of the terrain morphology using several slope classes. The geometric accuracy in terms of Standard deviation and NMAD, for SRTM range from 2-3 meters in the first slope class to about 45 meters in the last one, whereas for ASTER, the values range from 5-6 to 30 meters.<br><br> In general, the performed analysis shows a better accuracy for the SRTM in the flat areas whereas the ASTER GDEM is more reliable in the steep areas, where the slopes increase. These preliminary results highlight the GEE potentialities to perform DSM assessment on a global scale.

scholarly journals RichDEM: High-performance terrain analysis

2018 ◽  
Author(s):  
Richard Barnes
Keyword(s):  
High Performance ◽  
Landscape Evolution ◽  
Curvature Flow ◽  
Terrain Analysis ◽  
Spatial And Temporal Scales ◽  
Flow Accumulation ◽  
Terrain Attributes ◽  
Core Set ◽  
Do So

To answer geomorphological questions at unprecedented spatial and temporal scales, we need to (a) parse terabyte-scale datasets (DEMs), (b) perform millions of model realizations to pinpoint the parameters which govern landscape evolution, and (c) do so with statistical rigor, which may require thousands of additional realizations. A core set of operations underpin many geomorphic models. These include determination of terrain attributes such as slope and curvature; flow routing; depression flooding and breaching; flat resolution; and flow accumulation. Here, I present RichDEM, a high-performance C++ library and set of wrappers for performing these operations. The library incorporates a number of options for performing each operation and makes full use of modern high-performance capabilities. The library can scale to process DEMs of over one trillion cells and operates effectively on laptops or supercomputers.

scholarly journals Investigation of the Accuracy of Google Earth Elevation Data

2016 ◽  
Vol 51 (3) ◽  
pp. 89-97 ◽  
Author(s):  
Khalid L.A. El-Ashmawy
Keyword(s):  
City Planning ◽  
Reference Data ◽  
Google Earth ◽  
Reference Points ◽  
Slope Aspect ◽  
Small Scale ◽  
Maximum Absolute Error ◽  
Engineering Applications ◽  
Elevation Data

Abstract Digital Elevation Models (DEMs) comprise valuable source of elevation data required for many engineering applications. Contour lines, slope - aspect maps are part of their many uses. Moreover, DEMs are used often in geographic information systems (GIS), and are the most common basis for digitally-produced relief maps. This paper proposes a method of generating DEM by using Google Earth elevation data which is easier and free. The case study consisted of three different small regions in the northern beach in Egypt. The accuracy of the Google earth derived elevation data are reported using root mean square error (RMSE), mean error (ME) and maximum absolute error (MAE). All these accuracy statistics were computed using the ground coordinates of 200 reference points for each region of the case study. The reference data was collected with total station survey. The results showed that the accuracies for the prepared DEMs are suitable for some certain engineering applications but inadequate to meet the standard required for fine/small scale DEM for very precise engineering study. The obtained accuracies for terrain with small height difference can be used for preparing large area cadastral, city planning, or land classification maps. In general, Google Earth elevation data can be used only for investigation and preliminary studies with low cost. It is strongly concluded that the users of Google Earth have to test the accuracy of elevation data by comparing with reference data before using it.

scholarly journals Towards Global-Scale Seagrass Mapping and Monitoring Using Sentinel-2 on Google Earth Engine: The Case Study of the Aegean and Ionian Seas

2018 ◽  
Vol 10 (8) ◽  
pp. 1227 ◽  
Author(s):  
Dimosthenis Traganos ◽  
Bharat Aggarwal ◽  
Dimitris Poursanidis ◽  
Konstantinos Topouzelis ◽  
Nektarios Chrysoulakis ◽  
...  
Keyword(s):  
Large Scale ◽  
Anthropogenic Impacts ◽  
Global Scale ◽  
Google Earth ◽  
Support Vector ◽  
Learning Approaches ◽  
Data Set ◽  
Independent Field ◽  
Google Earth Engine ◽  
Sentinel 2

Seagrasses are traversing the epoch of intense anthropogenic impacts that significantly decrease their coverage and invaluable ecosystem services, necessitating accurate and adaptable, global-scale mapping and monitoring solutions. Here, we combine the cloud computing power of Google Earth Engine with the freely available Copernicus Sentinel-2 multispectral image archive, image composition, and machine learning approaches to develop a methodological workflow for large-scale, high spatiotemporal mapping and monitoring of seagrass habitats. The present workflow can be easily tuned to space, time and data input; here, we show its potential, mapping 2510.1 km2 of P. oceanica seagrasses in an area of 40,951 km2 between 0 and 40 m of depth in the Aegean and Ionian Seas (Greek territorial waters) after applying support vector machines to a composite of 1045 Sentinel-2 tiles at 10-m resolution. The overall accuracy of P. oceanica seagrass habitats features an overall accuracy of 72% following validation by an independent field data set to reduce bias. We envision that the introduced flexible, time- and cost-efficient cloud-based chain will provide the crucial seasonal to interannual baseline mapping and monitoring of seagrass ecosystems in global scale, resolving gain and loss trends and assisting coastal conservation, management planning, and ultimately climate change mitigation.

scholarly journals A Google Earth Engine Tool to Investigate, Map and Monitor Volcanic Thermal Anomalies at Global Scale by Means of Mid-High Spatial Resolution Satellite Data

2020 ◽  
Vol 12 (19) ◽  
pp. 3232
Author(s):  
Nicola Genzano ◽  
Nicola Pergola ◽  
Francesco Marchese
Keyword(s):  
Spatial Resolution ◽  
Satellite Data ◽  
Hot Spot ◽  
Global Scale ◽  
Google Earth ◽  
High Temporal Resolution ◽  
Landsat 8 ◽  
Thermal Anomalies ◽  
Google Earth Engine ◽  
Sentinel 2

Several satellite-based systems have been developed over the years to study and monitor thermal volcanic activity. Most of them use high temporal resolution satellite data, provided by sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) that if on the one hand guarantee a continuous monitoring of active volcanic areas on the other hand are less suited to map thermal anomalies, and to provide accurate information about their features. The Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively, onboard the Sentinel-2 and Landsat-8 satellites, providing Short-Wave Infrared (SWIR) data at 20 m (MSI) and 30 m (OLI) spatial resolution, may make an important contribution in this area. In this work, we present the first Google Earth Engine (GEE) App to investigate, map and monitor volcanic thermal anomalies at global scale, integrating Landsat-8 OLI and Sentinel-2 MSI observations. This open tool, which implements the Normalized Hot spot Indices (NHI) algorithm, enables the analysis of more than 1400 active volcanoes, with very low processing times, thanks to the high GEE computational resources. Performance and limitations of the tool, such as its next upgrades, aiming at increasing the user-friendly experience and extending the temporal range of data analyses, are analyzed and discussed.

scholarly journals Detecting Harvest Events in Plantation Forest Using Sentinel-1 and -2 Data via Google Earth Engine

Forests ◽  
2020 ◽  
Vol 11 (12) ◽  
pp. 1283
Author(s):  
Sifiso Xulu ◽  
Nkanyiso Mbatha ◽  
Kabir Peerbhay ◽  
Michael Gebreslasie
Keyword(s):  
South Africa ◽  
High Performance ◽  
Forest Biomass ◽  
Cost Effective ◽  
Google Earth ◽  
Forest Monitoring ◽  
Plantation Forest ◽  
Clear Cut ◽  
Clear Cutting ◽  
Google Earth Engine

South Africa is reported to experience timber shortages as a result of growing timber demands and pulp production, coupled with the government’s reluctance to grant new forestry permits. Rampant timber theft in the country makes these circumstances worse. The emergence of cloud-based platforms, such as Google Earth Engine (GEE), has greatly improved the accessibility and usability of high spatial and temporal Sentinel-1 and -2 data, especially in data-poor countries that lack high-performance computing systems for forest monitoring. Here, we demonstrate the potential of these resources for forest harvest detection. The results showed that Sentinel-1 data are efficient in detecting clear-cut events; both VH and VV backscatter signals decline sharply in accordance with clear-cutting and increase again when forest biomass increases. When correlated with highly responsive NDII, the VH and VV signals reached the best accuracies of 0.79 and 0.83, whereas the SWIR1 achieved –0.91. A Random Forest (RF) algorithm based on Sentinel-2 data also achieved over 90% accuracies for classifying harvested and forested areas. Overall, our study presents a cost-effective method for mapping clear-cut events in an economically important forestry area of South Africa while using GEE resources.

scholarly journals Mangrove Vegetation Mapping Using Sentinel-2A Imagery Based on Google Earth Engine Cloud Computing Platform

2021 ◽  
Vol 6 (1) ◽  
pp. 249-255
Author(s):  
Luhur Moekti Prayogo
Keyword(s):  
Image Processing ◽  
Cloud Computing ◽  
High Performance ◽  
Google Earth ◽  
Mangrove Forests ◽  
Application Development ◽  
Computing Platform ◽  
Mapping Technology ◽  
Google Earth Engine ◽  
Sentinel 2A

Mangroves are trees whose habitat is affected by tides, and their presence has decreased from year to year. Today, mapping technology has undergone many developments, including the availability of images of various resolutions and cloud-based image processing. One of the popular platforms today is the Google Earth Engine. Google Earth Engine is a cloud-based platform that makes it easy to access high-performance computing resources for extensive processing. The advantage of using Google Earth Engine is that users do not have to be IT experts without experts in application development, WEB programming, and HTML. This study aims to conduct a study on mangrove mapping in Gili Genting District with Sentinel-2A imagery using a Google Earth Engine. This location was chosen since there are still many mangroves, especially on the Gili Raja and Gili Genting Islands. From this research, it can be concluded that cloud computing-based Sentinel-2A image processing shows that the vegetation value of NDVI results ranges from -0.923208 to 0.75579. The classification results show that mangrove forests' overall presence on Gili Genting Island is more expansive than Gili Raja Island with 16.74 ha and 14.75 ha. The use of the Google Earth Engine platform simplifies the analysis process because image processing can be done once with various scripts so that analysis becomes faster.

scholarly journals RichDEM: High-performance terrain analysis

2018 ◽  
Author(s):  
Richard Barnes
Keyword(s):  
High Performance ◽  
Landscape Evolution ◽  
Curvature Flow ◽  
Terrain Analysis ◽  
Spatial And Temporal Scales ◽  
Flow Accumulation ◽  
Terrain Attributes ◽  
Core Set ◽  
Do So

To answer geomorphological questions at unprecedented spatial and temporal scales, we need to (a) parse terabyte-scale datasets (DEMs), (b) perform millions of model realizations to pinpoint the parameters which govern landscape evolution, and (c) do so with statistical rigor, which may require thousands of additional realizations. A core set of operations underpin many geomorphic models. These include determination of terrain attributes such as slope and curvature; flow routing; depression flooding and breaching; flat resolution; and flow accumulation. Here, I present RichDEM, a high-performance C++ library and set of wrappers for performing these operations. The library incorporates a number of options for performing each operation and makes full use of modern high-performance capabilities. The library can scale to process DEMs of over one trillion cells and operates effectively on laptops or supercomputers.

scholarly journals Harmonization of Landsat and Sentinel 2 for Crop Monitoring in Drought Prone Areas: Case Studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon)

2020 ◽  
Vol 12 (2) ◽  
pp. 281 ◽  
Author(s):  
Minh Nguyen ◽  
Oscar Baez-Villanueva ◽  
Duong Bui ◽  
Phong Nguyen ◽  
Lars Ribbe
Keyword(s):  
Spatial Resolution ◽  
Global Scale ◽  
Google Earth ◽  
Distribution Functions ◽  
Landsat 8 ◽  
Data Set ◽  
Crop Monitoring ◽  
Landsat 7 ◽  
Google Earth Engine ◽  
Sentinel 2

Proper satellite-based crop monitoring applications at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions Sentinel 2 (ESA) and Landsat 7/8 (NASA) provides this unprecedented opportunity at a global scale; however, this is rarely implemented because these procedures are data demanding and computationally intensive. This study developed a robust stream processing for the harmonization of Landsat 7, Landsat 8 and Sentinel 2 in the Google Earth Engine cloud platform, connecting the benefit of coherent data structure, built-in functions and computational power in the Google Cloud. The harmonized surface reflectance images were generated for two agricultural schemes in Bekaa (Lebanon) and Ninh Thuan (Vietnam) during 2018–2019. We evaluated the performance of several pre-processing steps needed for the harmonization including the image co-registration, Bidirectional Reflectance Distribution Functions correction, topographic correction, and band adjustment. We found that the misregistration between Landsat 8 and Sentinel 2 images varied from 10 m in Ninh Thuan (Vietnam) to 32 m in Bekaa (Lebanon), and posed a great impact on the quality of the final harmonized data set if not treated. Analysis of a pair of overlapped L8-S2 images over the Bekaa region showed that, after the harmonization, all band-to-band spatial correlations were greatly improved. Finally, we demonstrated an application of the dense harmonized data set for crop mapping and monitoring. An harmonic (Fourier) analysis was applied to fit the detected unimodal, bimodal and trimodal shapes in the temporal NDVI patterns during one crop year in Ninh Thuan province. The derived phase and amplitude values of the crop cycles were combined with max-NDVI as an R-G-B false composite image. The final image was able to highlight croplands in bright colors (high phase and amplitude), while the non-crop areas were shown with grey/dark (low phase and amplitude). The harmonized data sets (with 30 m spatial resolution) along with the Google Earth Engine scripts used are provided for public use.

scholarly journals BRAZILIAN MIDWEST NATIVE VEGETATION MAPPING BASED ON GOOGLE EARTH ENGINE

2020 ◽  
Vol XLII-3/W12-2020 ◽  
pp. 303-308
Author(s):  
N. V. Estrabis ◽  
L. Osco ◽  
A. P. Ramos ◽  
W. N. Gonçalves ◽  
V. Liesenberg ◽  
...  
Keyword(s):  
High Performance ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Vegetation Mapping ◽  
Google Earth ◽  
Native Vegetation ◽  
Data Input ◽  
Kappa Index ◽  
Spectral Bands ◽  
Google Earth Engine

Abstract. Google Earth Engine (GEE) platform is an online tool, which generates fast solutions in terms of image classification and does not require high performance computers locally. We investigate several data input scenarios for mapping native-vegetation and non-native-vegetation in the Atlantic Forest region encompassed in a Landsat scene (224/076) acquired on November 28, 2019. The data input scenarios were: I- spectral bands (blue to shortwave infrared); II- NDVI (Normalized Difference Vegetation Index); III- mNDWI (modified Normalized Difference Water Index); IV- scenarios I and II; and V- scenarios I to III. Our results showed that the use of spectral bands added NDVI and mNDWI (scenario V) provided the best performance for the native-vegetation mapping, with accuracy of 96.64% and kappa index of 0.91.

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