Large-Scale Crop Mapping From Multisource Remote Sensing Images in Google Earth Engine

Abstract Periodic forest monitoring needs to be done to avoid forest degradation. In general, forest monitoring can be conducted manually (field surveys) or using technological innovations such as remote sensing data derived from aerial images (drone results) or cloud computing-based image processing. Currently, remote sensing technology provides large-scale forest monitoring using multispectral sensors and various vegetation index processing algorithms. This study aimed to evaluate the use of the Google Earth Engine (GEE) platform, a geospatial dataset platform, in the Vale Indonesia mining concession area to improve accountable forest monitoring. This platform integrates a set of programming methods with a publicly accessible time-series database of satellite imaging services. The method used is NDVI processing on Landsat multispectral images in time series format, which allows for the description of changes in forest density levels over time. The results of this NDVI study conducted on the GEE platform have the potential to be used as a tool and additional supporting data for monitoring forest conditions and improvement in mining regions.

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Monitoring Coastline Changes of the Malay Islands Based on Google Earth Engine and Dense Time-Series Remote Sensing Images

Remote Sensing ◽

10.3390/rs13193842 ◽

2021 ◽

Vol 13 (19) ◽

pp. 3842

Author(s):

Yaxin Ding ◽

Xiaomei Yang ◽

Hailiang Jin ◽

Zhihua Wang ◽

Yueming Liu ◽

...

Keyword(s):

Remote Sensing ◽

Time Series ◽

Large Scale ◽

Dynamic Change ◽

Google Earth ◽

Tidal Range ◽

Remote Sensing Images ◽

Data Platform ◽

Range Observation ◽

Change Monitoring

The use of remote sensing to monitor coastlines with wide distributions and dynamic changes is significant for coastal environmental monitoring and resource management. However, most current remote sensing information extraction of coastlines is based on the instantaneous waterline, which is obtained by single-period imagery. The lack of a unified standard is not conducive to the dynamic change monitoring of a changeable coastline. The tidal range observation correction method can be used to correct coastline observation to a unified climax line, but it is difficult to apply on a large scale because of the distribution of observation sites. Therefore, we proposed a coastline extraction method based on the remote sensing big data platform Google Earth Engine and dense time-series remote sensing images. Through the instantaneous coastline probability calculation system, the coastline information could be extracted without the tidal range observation data to achieve a unified tide level standard. We took the Malay Islands as the experimental area and analyzed the consistency between the extraction results and the existing high-precision coastline thematic products of the same period to achieve authenticity verification. Our results showed that the coastline data deviated 10 m in proportion to a reach of 40% and deviated 50 m within a reach of 89%. The overall accuracy was kept within 100 m. In addition, we extracted 96 additional islands that have not been included in public data. The obtained multi-phase coastlines showed the spatial distribution of the changing hot regions of the Malay Islands’ coastline, which greatly supported our analysis of the reasons for the expansion and retreat of the coastline in this region. These research results showed that the big data platform and intensive time-series method have considerable potential in large-scale monitoring of coastline dynamic change and island reef change monitoring.

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Large-Scale Crop Mapping Based on Machine Learning and Parallel Computation with Grids

Remote Sensing ◽

10.3390/rs11121500 ◽

2019 ◽

Vol 11 (12) ◽

pp. 1500 ◽

Cited By ~ 15

Author(s):

Ning Yang ◽

Diyou Liu ◽

Quanlong Feng ◽

Quan Xiong ◽

Lin Zhang ◽

...

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Time Series ◽

Parallel Computing ◽

Random Forest ◽

Large Scale ◽

Remote Sensing Data ◽

Remote Sensing Images ◽

Medium Resolution ◽

Crop Mapping

Large-scale crop mapping provides important information in agricultural applications. However, it is a challenging task due to the inconsistent availability of remote sensing data caused by the irregular time series and limited coverage of the images, together with the low spatial resolution of the classification results. In this study, we proposed a new efficient method based on grids to address the inconsistent availability of the high-medium resolution images for large-scale crop classification. First, we proposed a method to block the remote sensing data into grids to solve the problem of temporal inconsistency. Then, a parallel computing technique was introduced to improve the calculation efficiency on the grid scale. Experiments were designed to evaluate the applicability of this method for different high-medium spatial resolution remote sensing images and different machine learning algorithms and to compare the results with the widely used nonparallel method. The computational experiments showed that the proposed method was successful at identifying large-scale crop distribution using common high-medium resolution remote sensing images (GF-1 WFV images and Sentinel-2) and common machine learning classifiers (the random forest algorithm and support vector machine). Finally, we mapped the croplands in Heilongjiang Province in 2015, 2016, 2017, which used a random forest classifier with the time series GF-1 WFV images spectral features, the enhanced vegetation index (EVI) and normalized difference water index (NDWI). Ultimately, the accuracy was assessed using a confusion matrix. The results showed that the classification accuracy reached 88%, 82%, and 85% in 2015, 2016, and 2017, respectively. In addition, with the help of parallel computing, the calculation speed was significantly improved by at least seven-fold. This indicates that using the grid framework to block the data for classification is feasible for crop mapping in large areas and has great application potential in the future.

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Rapid Mapping of Large-Scale Greenhouse Based on Integrated Learning Algorithm and Google Earth Engine

Remote Sensing ◽

10.3390/rs13071245 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1245

Author(s):

Jinhuang Lin ◽

Xiaobin Jin ◽

Jie Ren ◽

Jingping Liu ◽

Xinyuan Liang ◽

...

Keyword(s):

Remote Sensing ◽

Agricultural Production ◽

Large Scale ◽

Texture Features ◽

Google Earth ◽

Distribution Data ◽

Classification Algorithms ◽

Google Earth Engine ◽

Remote Sensing Mapping ◽

Terrain Features

A greenhouse is an important land-use type, which can effectively improve agricultural production conditions and increase crop yields. It is of great significance to obtain the spatial distribution data of greenhouses quickly and accurately for regional agricultural production and food security. Based on the Google Earth Engine cloud platform and Landsat 8 images, this study selected a total of 18 indicators from three aspects of spectral features, texture features and terrain features to construct greenhouse identification features. From a variety of classification algorithms for remote-sensing recognition of greenhouses, this study selected three classifiers with higher accuracy (classification and regression trees (CART), random forest model (randomForest) and maximum entropy model (gmoMaxEnt)) to construct an integrated classification algorithm, and then extracted the spatial distribution data of greenhouses in Jiangsu Province. The results show that: (1) Google Earth Engine with its own massive data and cloud computing capabilities, combined with integrated classification algorithms, can achieve rapid remote-sensing mapping of large-scale greenhouses under complex terrain, and the classification accuracy is higher than that of a single classification algorithm. (2) The combination of different spectral, texture and terrain features has a greater impact on the extraction of regional greenhouses, the combination of all three aspects of features has the highest accuracy. Spectral features are the key factors for greenhouse remote-sensing mapping, but terrain and texture features can also enhance classification accuracy. (3) The greenhouse in Jiangsu Province has significant spatial differentiation and spatial agglomeration characteristics. The most widely distributed greenhouses are mainly concentrated in the agriculturally developed areas such as Dongtai City, Hai’an County, Rudong County and Pizhou City.

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A COMPARISON OF TREE-BASED ALGORITHMS FOR COMPLEX WETLAND CLASSIFICATION USING THE GOOGLE EARTH ENGINE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlvi-4-w5-2021-313-2021 ◽

2021 ◽

Vol XLVI-4/W5-2021 ◽

pp. 313-319

Author(s):

A. Jamali ◽

M. Mahdianpari ◽

İ. R. Karaş

Keyword(s):

Remote Sensing ◽

Large Scale ◽

Well Being ◽

Google Earth ◽

The Other ◽

Gradient Boosting ◽

Wetland Classification ◽

Observation Methods ◽

Extreme Gradient Boosting ◽

Google Earth Engine

Abstract. Wetlands are endangered ecosystems that are required to be systematically monitored. Wetlands have significant contributions to the well-being of human-being, fauna, and fungi. They provide vital services, including water storage, carbon sequestration, food security, and protecting the shorelines from floods. Remote sensing is preferred over the other conventional earth observation methods such as field surveying. It provides the necessary tools for the systematic and standardized method of large-scale wetland mapping. On the other hand, new cloud computing technologies for the storage and processing of large-scale remote sensing big data such as the Google Earth Engine (GEE) have emerged. As such, for the complex wetland classification in the pilot site of the Avalon, Newfoundland, Canada, we compare the results of three tree-based classifiers of the Decision Tree (DT), Random Forest (RF), and Extreme Gradient Boosting (XGB) available in the GEE code editor using Sentinel-2 images. Based on the results, the XGB classifier with an overall accuracy of 82.58% outperformed the RF (82.52%) and DT (77.62%) classifiers.

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EXTRACTION OF RIVERS AND LAKES ON TIBETAN PLATEAU BASED ON GOOGLE EARTH ENGINE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w13-1797-2019 ◽

2019 ◽

Vol XLII-2/W13 ◽

pp. 1797-1801

Author(s):

R. G. Xu ◽

G. Qiao ◽

Y. J. Wu ◽

Y. J. Cao

Keyword(s):

Remote Sensing ◽

Image Processing ◽

Remote Sensing Image ◽

Google Earth ◽

Remote Sensing Images ◽

Yarlung Zangbo River ◽

Rivers And Lakes ◽

Google Earth Engine ◽

Remote Sensing Image Processing ◽

Hydrological Station

Abstract. Tibetan Plateau (TP) is the most abundant area of water resources and water energy resources in China. It is also the birthplace of the main rivers in Southeast Asia and plays an important strategic role. However, due to its remote location and complex topography, the observation of surface hydrometeorological elements is extremely scarce, which seriously restricts the understanding of the water cycle in this area. Using remote sensing images to extract rivers and lakes on TP can obtain a lot of valuable water resources information. However, the downloading and processing of remote sensing images is very time-consuming, especially the processing of remote sensing images with large-scale and long time series often involves hundreds of gigabytes of data, which requires a high level of personal computers and is inefficient. As a cloud platform dedicated to data processing and analysis of geoscience, Google Earth Engine(GEE) integrates many excellent remote sensing image processing algorithms. It does not need to download images and supports online remote sensing image processing, which greatly improves the output efficiency. Based on GEE, the monthly data of Yarlung Zangbo River at Nuxia Hydrological Station and the annual data of typical lakes were extracted and vectorized from the pre-processed Landsat series images. It was found that the area of Yarlung Zangbo River at Nuxia Hydrological Station varies periodically. The changing trend of typical lakes is also revealed.

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The Google Earth Engine Digitisation Tool (GEEDiT) and the Margin change Quantification Tool (MaQiT) – simple tools for the rapid mapping and quantification of changing Earth surface margins

Earth Surface Dynamics ◽

10.5194/esurf-6-551-2018 ◽

2018 ◽

Vol 6 (3) ◽

pp. 551-561 ◽

Cited By ~ 17

Author(s):

James M. Lea

Keyword(s):

Remote Sensing ◽

High School Students ◽

Large Scale ◽

Research Question ◽

Google Earth ◽

Earth Surface ◽

School Students ◽

Glacier Terminus ◽

Wide Range ◽

Google Earth Engine

Abstract. Changes in margins derived from satellite imagery are quantitative indicators of the environmental processes and drivers acting on the Earth's surface, for example retreating ice margins or coastal changes with rising sea level. However, the large-scale rapid visualisation and analysis of the satellite record is often impractical due to factors such as computer processing power, software availability, internet connection speed and/or user expertise in remote sensing. Here are presented three new, freely accessible tools that together can be used to process, visualise and review data from the full Landsat 4–8 and Sentinel 1–2 satellite records in seconds, enabling efficient mapping (through manual digitisation) and automated quantification of margin changes. These tools are highly accessible for users from a range of remote-sensing expertise (from academics to high school students), with minimal computational, licensing and knowledge-based barriers to access. The Google Earth Engine Digitisation Tool (GEEDiT) allows users to define a point anywhere on the planet and filter data from each satellite for user-defined time frames, maximum acceptable cloud cover extent, and options of predefined or custom image band combinations via a simple graphical user interface (GUI). GEEDiT allows georeferenced vectors to be easily and rapidly mapped from each image, with image metadata and user notes automatically appended to each vector, which can then be exported for subsequent analysis. The GEEDiT Reviewer tool allows users to quality control their own/others' data and also filter existing datasets based on the spatial/temporal requirements for their particular research question. The Margin change Quantification Tool (MaQiT) is complementary to GEEDiT and GEEDiT Reviewer, allowing the rapid quantification of these margin changes by utilising two well-established methods that have previously been used to measure glacier margin change and two new methods via a similarly simple GUI. A case study of the lake-terminating glacier Breiðamerkurjökull, Iceland, is used to demonstrate the complementary functionality of GEEDiT, GEEDiT Reviewer and MaQiT, though it should be noted that MaQiT is also suitable for the (re-)analysis of existing datasets not generated by GEEDiT. MaQiT has been developed with the original aim of quantifying tidewater glacier terminus change, though the methods included within the tool have potential for wide applications in multiple areas of Earth surface science (e.g. coastal and vegetation extent change). It is hoped that these tools will allow a wide range of researchers and students across the geosciences to efficiently map, analyse and access volumes of data that would have previously proven prohibitive.

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Continent-wide bimonthly mapping of Antarctic surface meltwater using Google Earth Engine

10.5194/egusphere-egu21-7431 ◽

2021 ◽

Author(s):

Peter Tuckett ◽

Jeremy Ely ◽

Andrew Sole ◽

Stephen Livingstone ◽

James Lea

Keyword(s):

Remote Sensing ◽

Spatial Analysis ◽

Mass Balance ◽

Large Scale ◽

Ice Sheet ◽

Google Earth ◽

Landsat 8 ◽

Supraglacial Lakes ◽

Google Earth Engine ◽

Temporal And Spatial

Surface meltwater is widespread around the margin of the Antarctic Ice Sheet during the austral summer. This meltwater, typically transported via surface streams and rivers and stored in supraglacial lakes, has the potential to influence ice-sheet mass balance through ice-dynamic and albedo feedbacks. To predict the impact that surface melt will have on mass balance over coming decades, it is important to understand spatial and temporal variability in surface meltwater extent. A variety of methods have been used to detect supraglacial lakes in Antarctica, yet a multi-annual, continent-wide study of Antarctic supraglacial meltwater has yet to be conducted. Cloud-based computational platforms, such as Google Earth Engine (GEE), enable large-scale temporal and spatial analysis of remote sensing datasets at minimal time expense. Here, we implement an automated method for meltwater detection in GEE to generate continent-wide, bimonthly repeat assessments of supraglacial lake extent between 2013 and 2020. We use a band-threshold based approach to delineate surface water from Landsat-8 imagery. Furthermore, our method incorporates a novel technique for quantifying meltwater extent that accounts for variability in optical image coverage and cloud cover, enabling an upper uncertainty bound to be attached to minimum mapped lake areas. We present results from continent-wide mapping, and highlight initial findings that indicate evolution of lakes in Antarctica over the past seven years. This work demonstrates how platforms such as GEE have revolutionized our ability to undertake large-scale projects from remote sensing datasets, allowing for greater temporal and spatial analysis of cryospheric processes than previously possible.

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Remote Sensing Based Quantification of Forest Cover Change in Somalia for the Period 2000 to 2019

10.5772/intechopen.99365 ◽

2021 ◽

Author(s):

Sylus Kipngeno Musei ◽

Justine Muhoro Nyaga ◽

Abdi Zeila Dubow

Keyword(s):

Remote Sensing ◽

Land Degradation ◽

Large Scale ◽

Forest Cover ◽

Computing System ◽

Google Earth ◽

Forest Trees ◽

Forest Cover Change ◽

Charcoal Production ◽

Google Earth Engine

Deforestation is a driver of land degradation and a major environmental problem in Somalia, and has been linked to frequent incidences of drought over the years. Monitoring of changes in forest cover is therefore critical for the country’s environment. The problem of land degradation has been worsened by the large scale charcoal production that is witnessed in the country. This study aimed at estimating forest cover change between 2000 and 2019 in Somalia using Landsat-based forest cover datasets. Google Earth Engine (GEE), a cloud based computing system was used to provide a platform for this analysis. Based on the 30% threshold recommended by International Geosphere Biosphere Program for differentiating forest from non-forest trees, approximately 23% forest cover loss was found, from 87, 294 hectares in 2000 to 67, 199 hectares in 2019. Most of the country’s forest is within the southern and central parts of the country, and significant forest cover losses occurred mainly around Mogadishu and Kismayo port throughout the study period. There is therefore a need for the Federal Ministry of Environment and environment ministries in the federal member states to design mechanisms and strategies for restoration of the degraded forests and to curb deforestation.

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Google Earth Engine Digitisation Tool (GEEDiT), and Margin change Quantification Tool (MaQiT) – simple tools for the rapid mapping and quantification of changing Earth surface margins

10.5194/esurf-2018-24 ◽

2018 ◽

Author(s):

James M. Lea

Keyword(s):

Remote Sensing ◽

Surface Science ◽

Large Scale ◽

Google Earth ◽

Earth Surface ◽

Boundary Change ◽

Glacier Terminus ◽

Processing Power ◽

Wide Range ◽

Google Earth Engine

Abstract. The visualisation and exploration of satellite imagery archives coupled with the quantification of margin/boundary changes are frequently used within earth surface sciences as key indicators of the environmental processes and drivers acting within a system. However, the large scale rapid visualisation and analysis of this imagery is often impractical due to factors such as computer processing power, software availability, internet connection speed, and user expertise in remote sensing. Here are described two separate tools that together can be used to process and visualise the full Landsat 4–8 and Sentinel 1–2 satellite records in seconds, enabling efficient mapping (through manual digitisation) and automated quantification of margin changes. These tools are highly accessible for users from a range of remote sensing expertise, with minimal computational, licensing and knowledge-based barriers to access. The Google Earth Engine Digitisation Tool (GEEDiT) allows users to define a point anywhere on the planet and access all Landsat 4–8/Sentinel 1–2 imagery at that location, filtered for user defined time frames, maximum acceptable cloud cover extent, and options of predefined or custom image band combinations via a simple Graphical User Interface (GUI). GEEDiT also allows georeferenced vectors to be easily and rapidly mapped from each image with image metadata and user notes automatically appended to each vector. This data can then be exported to a user's Google Drive for subsequent analysis. The Margin change Quantification Tool (MaQiT) is complimentary to GEEDiT, allowing the rapid quantification of these margin changes utilising two well-established methods that have previously been used to measure glacier margin change and two new methods via a similarly simple GUI. MaQiT is also suitable for the (re-)analysis of existing datasets not generated by GEEDiT. Although MaQiT has been developed with the aim of quantifying tidewater glacier terminus change, the tool can be applied to other margin changes within earth surface science where margin/boundary change through time is of interest (e.g. coastal and vegetation extent change). It is hoped that these tools will allow a wide range of researchers and students across the geosciences to have access to, efficiently map and analyse volumes of data that may have previously proven prohibitive.

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