Google Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities. Scientists, researchers, and developers use Earth Engine to detect changes, map trends, and quantify differences on the Earth's surface. Earth Engine is now available for commercial use, and remains free for academic and research use.

This example is about how to use Google Earth Engine API on Jupyter Notebooks. We show the example of how to get Landsat Net Primary Production (NPP) CONUS DataSet from Google Earth Engine Data Catalog.

This resource contain the training materials from a workshop held at the 2nd Annual Developers Conference at the University of Utah. It delves into the integration of ground-based observations with remote sensing datasets. The workshop facilitated hands-on experience in employing cloud-based technologies such as Google Earth Engine, Compute Engine, and Cloud Storage for data dissemination. Participants learned to create automated systems for data upload, processing, and dissemination, featuring the Stevens River Ice Monitoring System. This approach enhances collaboration and efficiency in environmental studies by streamlining data handling workflows.

A newer version of this dataset with data for 2015-2021 can be found in JAXA/ALOS/PALSAR/YEARLY/SAR_EPOCH The global 25m PALSAR/PALSAR-2 mosaic is a seamless global SAR image created by mosaicking strips of SAR imagery from PALSAR/PALSAR-2. For each year and location, the strip data were selected through visual inspection of the browse mosaics available over the period, with those showing minimum response to surface moisture preferentially used. In cases where the availability was limited (e.g., because of the requirement for observations during specific emergencies), data were necessarily selected from the year before or after, including from 2006. Shimada et al. 2014 There is no data for 2011-2014 due to the gap between ALOS and ALOS-2 temporal coverage. The SAR imagery was ortho-rectificatied and slope corrected using the 90m SRTM Digital Elevation Model. A destriping process (Shimada & Isoguchi, 2002, 2010) was applied to equalize the intensity differences between neighboring strips, occurring largely due to seasonal and daily differences in surface moisture conditions. Polarization data are stored as 16-bit digital numbers (DN). The DN values can be converted to gamma naught values in decibel unit (dB) using the following equation: γ₀ = 10log₁₀(DN²) - 83.0 dB Attention: Backscatter values may vary significantly from path to path over high latitude forest areas. This is due to the change of backscattering intensity caused by freezing trees in winter. More information is available in the provider's Dataset Description.

An accurate global impervious surface map at a resolution of 30-m for 2015 by combining Landsat-8 OLI optical images, Sentinel-1 SAR images and VIIRS NTL images based on the Google Earth Engine (GEE) platform.

The aridity index also known as the dryness index is the ratio of potential evapotranspiration to precipitation. The aridity index indicates water deficiency. The aridity index is used to classify locations as humid or dry. The evaporation ratio (evaporation index) on the other hand indicates the availability of water in watersheds. The evaporation index is inversely proportional to water availability. For long periods renewable water resources availability is residual precipitation after evaporation loss is deducted. These two ratios provide very useful information about water availability. Understating the powerful potential of the aridity index and evaporation ratio, this app is developed on the Google Earth Engine using NLDAS-2 and MODIS products to map temporal variability of the Aridity Index and Evaporation ratio over CONUS. The app can be found at https://cartoscience.users.earthengine.app/view/aridity-index.

Recently, the satellite images have been used in remote sensing allowing observations with high temporal and spatial distribution. The use of water indices has proved to be an effective methodology in the monitoring of surface water resources. However, precise or automatic methodologies using satellite imagery to determine reservoir volumes are lacking. To fulfil that gap, this methodology proposes 3 stages: use Google Earth Engine (GEE) to select images; automatically calculate flooded surface areas applying water indices; determine the volume stored in reservoirs over those years based on the relation between the flooded area and the stored volume. The method was applied in four reservoirs and contemplate Landsat 4 and 5 ETM and Landsat 8 OLI. For the calculation of the flooded area the NDWI Indexes (McFeeters, 1996; Gao, 1996), and the MNDWI index (Xu, 2006) were applied and tested. The estimation of stored volume of water was made based on the area indices and a cross-check between real stored volume and calculated volume was made. Finally, an analysis on the selection of the best fit water indices was made. The results of every case studies herein displayed showed a quantifiable proficiency and reliability for quite a varied natural conditions. As a conclusion, this methodology could be seen as a tool for water resources management in developing countries, and not only, to measure automatically trends of stored volumes and its relation with the precipitation, and could eventually be extended to other types of surface water bodies, as lakes and coastal lagoons.

We sampled Google Earth aerial images to get a representative and globally distributed dataset of treeline locations. Google Earth images are available to everyone, but may not be automatically downloaded and processed according to Google's license terms. Since we only wanted to detect tree individuals, we evaluated the aerial images manually by hand.

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Doing so, we scaled Google Earth’s GUI interface to a buffer size of approximately 6000 m from a perspective of 100 m (+/- 20 m) above Earth’s surface. Within this buffer zone, we took coordinates and elevation of the highest realized treeline locations. In some remote areas of Russia and Canada, individual trees were not identifiable due to insufficient image resolution. If this was the case, no treeline was sampled, unless we detected another visible treeline within the 6,000 m buffer and took this next highest treeline. We did not apply an automated image processing approach. We calculated mass elevation effect as the distance to t...

Here's a collection of resources related to the TEAM application (https://jamesmcoll.users.earthengine.app/view/team) Raw Code: https://code.earthengine.google.com/f55a05fbf6e2468e01744d87ca178461

Machine learning algorithms have been widely adopted in the monitoring ecosystem. British Columbia suffers from grassland degradation but the province does not have an accurate spatial database for effective grassland management. Moreover, computational power and storage space remain two of the limiting factors in developing the database. In this study, we leverage supervised machine learning algorithms using the Google Earth Engine to better annual grassland inventory through an automated process. The pilot study was conducted over the Rocky Mountain district. We compared two different classification algorithms: the Random forest, and the Support vector machine. Training data was sampled through stratified and grided sampling. 19 predictor variables were chosen from Sentinel-1 and Sentinel-2 imageries and relevant topological derivatives, spectral indices, and textural indices using a wrapper-based feature selection method. The resultant map was post-processed to remove land features that were confounded with grasslands. Random forest was chosen as the prototype because the algorithm predicted features relevant to the project’s scope at relatively higher accuracy (67% - 86%) than its counterparts (50% - 76%). The prototype was good at delineating the boundaries between treed and non-treed areas and ferreting out opened patches among closed forests. These opened patches are usually disregarded by the VRI but they are deemed essential to grassland stewardship and wildlife ecologists. The prototype demonstrated the feasibility of automating grassland delineation by a Random forest classifier using the Google Earth Engine. Furthermore, grassland stewards can use the product to identify monitoring and restoration areas strategically in the future.

My final project in my Spring 2023 Remote Sensing class. This project is an exploration into land classification using google earth engine via python and hydroshare's jupyter notebook. This project identifies a region in the South Metro Denver, CO, pulls in NLCD and landsat8 images from multiple years to identify changes to land cover classification over time.

Maps of cropland conversion classes, year of conversion, and pre- and post-conversion land cover associated with Lark et al. (2020). This repository also includes maps of 'local' and 'national' yield differentials for corn, soybeans, and wheat that are associated with the same publication. Code used to generate these data can be found here.

• Lark, T.J., S.A. Spawn, M.F. Bougie, H.K. Gibbs. Cropland expansion in the United States produces marginal yields with disproportionate costs to wildlife. Nature Communications (In review)

Cropland conversion maps are included in a zipped ESRI Geodatabase titled "US_land_conversion_2008-16.gdb". Each feature layer encompasses all of the conterminous United States at a 30m spatial resolution. Feature layers include:

• mtr = "Multi-temporal results"; Classifies land as being one of five broad land use change classes during the 2008-16 study period:
  1. "stable non-cropland" -- areas of consistent non-cropland throughout the duration of the study period.
  2. "stable cropland" -- areas of consistent cropland throughout the duration of the study period.
  3. "cropland expansion" -- areas converted to crop production between 2008 and 2016.
  4. "cropland abandonment" -- areas converted away from crop production between 2008 and 2016.
  5. "intermittent cropland/confusion" -- areas that were cropped for at least two years but show no clear trend towards or away from cropland. These could include areas under a crop-pasture rotation, fallow rotations, or simply areas with repeated classifier confusion.
• ytc = "year to cropland"; Indicates the year in which pixels with an mtr classification of "3" (i.e. "cropland expansion") were converted from non-cropland to cropland. e.g., a value of 2009 represents land that was converted between the 2008 growing season and the 2009 growing season.
• yfc = "year from cropland"; Indicates the year in which pixels with an mtr classification of "4" (i.e. "cropland abandonment") were converted from cropland to non-cropland. e.g., a value of 2009 represents land that was still cropped in 2008 and no longer cropped during the 2009 growing season.
• bfc = "before first crop"; Indicates the last land cover class before a non-crop pixel was converted to cropland. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table here.
• fc = "first crop"; Indicates the class of the first crop planted after a non-crop pixel was converted to cropland. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table here.
• bfnc = "before first non-crop"; Indicates the last cropland class of a pixel before it was abandoned to non-crop land cover. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table here.
• fnc = "first non-crop"; Indicates the first non-crop class of a pixel after it was abandoned to non-crop land cover. Pixel values correspond to the classification schema of the USDA Cropland Data Layer (CDL) as described in the lookup table here.

Yield differential maps are included in the "yieldDifferentials.zip" folder as GeoTIFF rasters with a ~10km spatial resolution. Raster values represent relative (%) differences between the representative yields of new croplands (mtr = 3) and those of stable croplands (mtr = 1) planted to that crop within either (i) the larger 10km x 10km gridcell in which those fields are situated ("local" differentials) or (ii) the entire nation ("national" differentials).

• corn_relDiff_local.tif = local yield differential (%) of corn grain.
• corn_relDiff_national.tif = national yield differential (%) of corn grain.
• soy_relDiff_local.tif = local yield differential (%) of soybeans.
• soy_relDiff_national.tif = national yield differential (%) of soybeans.
• wheat_relDiff_local.tif = local yield differential (%) of wheat.
• wheat_relDiff_national.tif = national yield differential (%) of wheat.

This JavaScript code has been developed to retrieve NDSI_Snow_Cover from MODIS version 6 for SNOTEL sites using the Google Earth Engine platform. To successfully run the code, you should have a Google Earth Engine account. An input file, called NWM_grid_Western_US_polygons_SNOTEL_ID.zip, is required to run the code. This input file includes 1 km grid cells of the NWM containing SNOTEL sites. You need to upload this input file to the Assets tap in the Google Earth Engine code editor. You also need to import the MOD10A1.006 Terra Snow Cover Daily Global 500m collection to the Google Earth Engine code editor. You may do this by searching for the product name in the search bar of the code editor.

The JavaScript works for s specified time range. We found that the best period is a month, which is the maximum allowable time range to do the computation for all SNOTEL sites on Google Earth Engine. The script consists of two main loops. The first loop retrieves data for the first day of a month up to day 28 through five periods. The second loop retrieves data from day 28 to the beginning of the next month. The results will be shown as graphs on the right-hand side of the Google Earth Engine code editor under the Console tap. To save results as CSV files, open each time-series by clicking on the button located at each graph's top right corner. From the new web page, you can click on the Download CSV button on top.

Here is the link to the script path: https://code.earthengine.google.com/?scriptPath=users%2Figarousi%2Fppr2-modis%3AMODIS-monthly

Then, run the Jupyter Notebook (merge_downloaded_csv_files.ipynb) to merge the downloaded CSV files that are stored for example in a folder called output/from_GEE into one single CSV file which is merged.csv. The Jupyter Notebook then applies some preprocessing steps and the final output is NDSI_FSCA_MODIS_C6.csv.

Are you looking to identify B2B leads to promote your business, product, or service? Outscraper Google Maps Scraper might just be the tool you've been searching for. This powerful software enables you to extract business data directly from Google's extensive database, which spans millions of businesses across countless industries worldwide.

Outscraper Google Maps Scraper is a tool built with advanced technology that lets you scrape a myriad of valuable information about businesses from Google's database. This information includes but is not limited to, business names, addresses, contact information, website URLs, reviews, ratings, and operational hours.

Whether you are a small business trying to make a mark or a large enterprise exploring new territories, the data obtained from the Outscraper Google Maps Scraper can be a treasure trove. This tool provides a cost-effective, efficient, and accurate method to generate leads and gather market insights.

By using Outscraper, you'll gain a significant competitive edge as it allows you to analyze your market and find potential B2B leads with precision. You can use this data to understand your competitors' landscape, discover new markets, or enhance your customer database. The tool offers the flexibility to extract data based on specific parameters like business category or geographic location, helping you to target the most relevant leads for your business.

In a world that's growing increasingly data-driven, utilizing a tool like Outscraper Google Maps Scraper could be instrumental to your business' success. If you're looking to get ahead in your market and find B2B leads in a more efficient and precise manner, Outscraper is worth considering. It streamlines the data collection process, allowing you to focus on what truly matters – using the data to grow your business.

https://outscraper.com/google-maps-scraper/

As a result of the Google Maps scraping, your data file will contain the following details:

Query Name Site Type Subtypes Category Phone Full Address Borough Street City Postal Code State Us State Country Country Code Latitude Longitude Time Zone Plus Code Rating Reviews Reviews Link Reviews Per Scores Photos Count Photo Street View Working Hours Working Hours Old Format Popular Times Business Status About Range Posts Verified Owner ID Owner Title Owner Link Reservation Links Booking Appointment Link Menu Link Order Links Location Link Place ID Google ID Reviews ID

If you want to enrich your datasets with social media accounts and many more details you could combine Google Maps Scraper with Domain Contact Scraper.

Domain Contact Scraper can scrape these details:

Email Facebook Github Instagram Linkedin Phone Twitter Youtube

Contains the annotations (WDPA polygons).. Visit https://dataone.org/datasets/sha256%3A5f2db5b57ae438910a1f32601d0dac34209304d5044969710343ed21a6a06a2a for complete metadata about this dataset.

A first trial for merging the Google Earth Engine API into HydroShare.

Google EarthEngine API http://href="https://developers.google.com/earth-engine/api_docs

The code here only provides connection to Earth Engine API yet. Does not interact with HS data or functions yet.

It uses git code published by Erik Tyler href="https://github.com/tylere/eeus2017-python" https://github.com/tylere/eeus2017-python%3C/a" rel="nofollow">rel="nofollow">https://github.com/tylere/eeus2017-python> to demonstrate adequate installation and setup

This dataset accompanies a manuscript submitted for review to the Journal of Remote Sensing. Lakes in direct contact with glaciers (ice-marginal lakes) are found across alpine and polar landscapes. As dynamic features that experience short-term (i.e., day to year) variations in area and volume, they form an important yet understudied element of the complete hydrologic system of glaciers with which they are in contact. To accelerate the study of ice-marginal lakes over large temporal and spatial extents, we automate the mapping of ice-marginal lakes by implementing a trained minimum-distance classifier of monthly Landsat 8 data products in Google Earth Engine. We produce maps of ice-marginal lakes in south Alaska for the summer months March through August for each year from 2013 through 2019. These maps are manually reviewed for accuracy. By spatially joining all maps, we can identify lakes throughout time, even if they are changing rapidly or dramatically. This dataset includes the spatial join of all lakes and shapefiles of each individual lake identified, grouped by lake. Within these lake shapefiles is illustrated an individual history of lake change; each feature is a delineation of the lake at a specific point in time.

These data were compiled to calibrate suspended sediment concentration (SSC) remote sensing algorithms. They were collected originally by several agencies at each of 430 suspended sediment sampling locations operated in the United States (USGS) [USGS, 2018], Canada (HYDAT) [HYDAT, 2018], South America (ANA) [ANA, 2018], and Taiwan (WRA) [WRA, 2018] between 1984–present. Landsat data are from Landsat missions 5 and 7. The Landsat LT1 Spectral Reflectance product data in this compilation were retrieved automatically using Google Earth Engine.

DATA SOURCES:

SSC ANA (2018), Agência Nacional de Águas, edited, doi:July, 2017. HYDAT (2018), The Water Survey of Canada, edited, doi:August, 2018. USGS (2018), U.S. Geological Survey, edited, doi:August, 2018. WRA (2018), Taiwan Water Resource Agency, edited, doi:August, 2018.

IMAGERY USGS Landsat LT1 Surface Reflectance, retrieved via Google Earth Engine

Google is not only popular in its home country, but is also the dominant internet search provider in many major online markets, frequently generating between ** and ** percent of desktop search traffic. The search engine giant has a market share of over ** percent in India and accounted for the majority of the global search engine market, way ahead of other competitors such as Yahoo, Bing, Yandex, and Baidu. Google’s online dominance All roads lead to Rome, or if you are browsing the internet, all roads lead to Google. It is hard to imagine an online experience without the online behemoth, as the company offers a wide range of online products and services that all seamlessly integrate with each other. Google search and advertising are the core products of the company, accounting for the vast majority of the company revenues. When adding this up with the Chrome browser, Gmail, Google Maps, YouTube, Google’s ownership of the Android mobile operating system, and various other consumer and enterprise services, Google is basically a one-stop shop for online needs. Google anti-trust rulings However, Google’s dominance of the search market is not always welcome and is keenly watched by authorities and industry watchdogs – since 2017, the EU commission has fined Google over ***** billion euros in antitrust fines for abusing its monopoly in online advertising. In March 2019, European Commission found that Google violated antitrust regulations by imposing contractual restrictions on third-party websites in order to make them less competitive and fined the company *** billion euros.

This dataset is a China terrace map at 30 m resolution in 2018. It was developed through supervised pixel-based classification using multisource and multi-temporal data based on the Google Earth Engine platform. The overall accuracy and kappa coefficient achieved 94% and 0.72, respectively. The first 30 m China terrace map will be valuable for studies on soil erosion, food security, biogeochemical cycle, biodiversity, and ecosystem service assessments. Detailed dataset description can be found at: https://essd.copernicus.org/articles/13/2437/2021/