Mapping Global Urban Impervious Surface and Green Space Fractions Using Google Earth Engine

Urban impervious surfaces area (ISA) and green space (GS), two primary components of urban environment, are pivotal in detecting urban environmental quality and addressing global environmental change issues. However, the current global mapping of ISA and GS is not effective enough to accurately delineate in urban areas due to the mosaicked and complex structure. To address the issue, the hierarchical architecture principle and subpixel metric method were applied to map 30 m global urban ISA and GS fractions for the years 2015 and circa 2020. We use random forest algorithms for retrieval of the Normalized Settlement Density Index and Normalized Green Space Index from Landsat images using Google Earth Engine. The correlation coefficients of global urban ISA and GS fractions were all higher than 0.9 for 2015 and circa 2020. Our results show global urban ISA and GS areas in circa 2020 were 31.19 × 104 km2 and 17.16 × 104 km2, respectively. The novel ISA and GS fractions product can show potential applications in assessing the effects of urbanization on climate, ecology, and urban sustainability.

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Vegetation Types Mapping Using Multi-Temporal Landsat Images in the Google Earth Engine Platform

Remote Sensing ◽

10.3390/rs13224683 ◽

2021 ◽

Vol 13 (22) ◽

pp. 4683

Author(s):

Masoumeh Aghababaei ◽

Ataollah Ebrahimi ◽

Ali Asghar Naghipour ◽

Esmaeil Asadi ◽

Jochem Verrelst

Keyword(s):

Vegetation Index ◽

Google Earth ◽

Optimal Time ◽

Quantitative Detection ◽

Landsat 8 ◽

Vegetation Types ◽

Landsat Images ◽

Multi Temporal ◽

Google Earth Engine ◽

Land Covers

Vegetation Types (VTs) are important managerial units, and their identification serves as essential tools for the conservation of land covers. Despite a long history of Earth observation applications to assess and monitor land covers, the quantitative detection of sparse VTs remains problematic, especially in arid and semiarid areas. This research aimed to identify appropriate multi-temporal datasets to improve the accuracy of VTs classification in a heterogeneous landscape in Central Zagros, Iran. To do so, first the Normalized Difference Vegetation Index (NDVI) temporal profile of each VT was identified in the study area for the period of 2018, 2019, and 2020. This data revealed strong seasonal phenological patterns and key periods of VTs separation. It led us to select the optimal time series images to be used in the VTs classification. We then compared single-date and multi-temporal datasets of Landsat 8 images within the Google Earth Engine (GEE) platform as the input to the Random Forest classifier for VTs detection. The single-date classification gave a median Overall Kappa (OK) and Overall Accuracy (OA) of 51% and 64%, respectively. Instead, using multi-temporal images led to an overall kappa accuracy of 74% and an overall accuracy of 81%. Thus, the exploitation of multi-temporal datasets favored accurate VTs classification. In addition, the presented results underline that available open access cloud-computing platforms such as the GEE facilitates identifying optimal periods and multitemporal imagery for VTs classification.

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Mapping coastal wetlands of China using time series Landsat images in 2018 and Google Earth Engine

ISPRS Journal of Photogrammetry and Remote Sensing ◽

10.1016/j.isprsjprs.2020.03.014 ◽

2020 ◽

Vol 163 ◽

pp. 312-326 ◽

Cited By ~ 9

Author(s):

Xinxin Wang ◽

Xiangming Xiao ◽

Zhenhua Zou ◽

Luyao Hou ◽

Yuanwei Qin ◽

...

Keyword(s):

Time Series ◽

Coastal Wetlands ◽

Google Earth ◽

Landsat Images ◽

Google Earth Engine

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Change Detection of Urban Areas in Ankara through Google Earth Engine

2018 41st International Conference on Telecommunications and Signal Processing (TSP) ◽

10.1109/tsp.2018.8441377 ◽

2018 ◽

Author(s):

Naime Celik

Keyword(s):

Change Detection ◽

Urban Areas ◽

Google Earth ◽

Google Earth Engine

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Global Changes in Urban Vegetation Cover

Remote Sensing ◽

10.3390/rs12010023 ◽

2019 ◽

Vol 12 (1) ◽

pp. 23 ◽

Cited By ~ 3

Author(s):

Daniel R. Richards ◽

Richard N. Belcher

Keyword(s):

Vegetation Cover ◽

Urban Areas ◽

Well Being ◽

Google Earth ◽

Eastern North America ◽

Urban Vegetation ◽

Global Changes ◽

Suburban Sprawl ◽

The World ◽

Google Earth Engine

Urban vegetation provides many ecosystem services that make cities more liveable for people. As the world continues to urbanise, the vegetation cover in urban areas is changing rapidly. Here we use Google Earth Engine to map vegetation cover in all urban areas larger than 15 km2 in 2000 and 2015, which covered 390,000 km2 and 490,000 km2 respectively. In 2015, urban vegetation covered a substantial area, equivalent to the size of Belarus. Proportional vegetation cover was highly variable, and declined in most urban areas between 2000 and 2015. Declines in proportional vegetated cover were particularly common in the Global South. Conversely, proportional vegetation cover increased in some urban areas in eastern North America and parts of Europe. Most urban areas that increased in vegetation cover also increased in size, suggesting that the observed net increases were driven by the capture of rural ecosystems through low-density suburban sprawl. Far fewer urban areas achieved increases in vegetation cover while remaining similar in size, although this trend occurred in some regions with shrinking populations or economies. Maintaining and expanding urban vegetation cover alongside future urbanisation will be critical for the well-being of the five billion people expected to live in urban areas by 2030.

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Landsat Images Classification Algorithm (LICA) to Automatically Extract Land Cover Information in Google Earth Engine Environment

Remote Sensing ◽

10.3390/rs12071201 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1201 ◽

Cited By ~ 3

Author(s):

Alessandra Capolupo ◽

Cristina Monterisi ◽

Eufemia Tarantino

Keyword(s):

Big Data ◽

Land Cover ◽

Open Data ◽

Google Earth ◽

Classification Algorithm ◽

Specific Class ◽

Landsat Images ◽

Sparse Vegetation ◽

Main Technique ◽

Google Earth Engine

Remote sensing has been recognized as the main technique to extract land cover/land use (LC/LU) data, required to address many environmental issues. Therefore, over the years, many approaches have been introduced and explored to optimize the resultant classification maps. Particularly, index-based methods have highlighted its efficiency and effectiveness in detecting LC/LU in a multitemporal and multisensors analysis perspective. Nevertheless, the developed indices are suitable to extract a specific class but not to completely classify the whole area. In this study, a new Landsat Images Classification Algorithm (LICA) is proposed to automatically detect land cover (LC) information using satellite open data provided by different Landsat missions in order to perform a multitemporal and multisensors analysis. All the steps of the proposed method were implemented within Google Earth Engine (GEE) to automatize the procedure, manage geospatial big data, and quickly extract land cover information. The algorithm was tested on the experimental site of Siponto, a historic municipality located in Apulia Region (Southern Italy) using 12 radiometrically and atmospherically corrected satellite images collected from Landsat archive (four images, one for each season, were selected from Landsat 5, 7, and 8, respectively). Those images were initially used to assess the performance of 82 traditional spectral indices. Since their classification accuracy and the number of identified LC categories were not satisfying, an analysis of the different spectral signatures existing in the study area was also performed, generating a new algorithm based on the sequential application of two new indices (SwirTirRed (STRed) index and SwiRed index). The former was based on the integration of shortwave infrared (SWIR), thermal infrared (TIR), and red bands, whereas the latter featured a combination of SWIR and red bands. The performance of LICA was preferable to those of conventional indices both in terms of accuracy and extracted classes number (water, dense and sparse vegetation, mining areas, built-up areas versus water, and dense and sparse vegetation). GEE platform allowed us to go beyond desktop system limitations, reducing acquisition and processing times for geospatial big data.

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Mapping major land cover dynamics in Beijing using all Landsat images in Google Earth Engine

Remote Sensing of Environment ◽

10.1016/j.rse.2017.02.021 ◽

2017 ◽

Vol 202 ◽

pp. 166-176 ◽

Cited By ~ 116

Author(s):

Huabing Huang ◽

Yanlei Chen ◽

Nicholas Clinton ◽

Jie Wang ◽

Xiaoyi Wang ◽

...

Keyword(s):

Land Cover ◽

Google Earth ◽

Landsat Images ◽

Land Cover Dynamics ◽

Google Earth Engine

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Mapping Annual Land Disturbance and Reclamation in a Surface Coal Mining Region Using Google Earth Engine and the LandTrendr Algorithm: A Case Study of the Shengli Coalfield in Inner Mongolia, China

Remote Sensing ◽

10.3390/rs12101612 ◽

2020 ◽

Vol 12 (10) ◽

pp. 1612 ◽

Cited By ~ 2

Author(s):

Wu Xiao ◽

Xinyu Deng ◽

Tingting He ◽

Wenqi Chen

Keyword(s):

Inner Mongolia ◽

Land Reclamation ◽

Underground Mining ◽

Surface Mining ◽

Mining Area ◽

Google Earth ◽

Open Pit ◽

Open Pit Mining ◽

Landsat Images ◽

Google Earth Engine

The development and utilization of mining resources are basic requirements for social and economic development. Both open-pit mining and underground mining have impacts on land, ecology, and the environment. Of these, open-pit mining is considered to have the greatest impact due to the drastic changes wrought on the original landform and the disturbance to vegetation. As awareness of environmental protection has grown, land reclamation has been included in the mining process. In this study, we used the Shengli Coalfield in the eastern steppe region of Inner Mongolia to demonstrate a mining and reclamation monitoring process. We combined the Google Earth Engine platform with time series Landsat images and the LandTrendr algorithm to identify and monitor mining disturbances to grassland and land reclamation in open-pit mining areas of the coalfield between 2003 and 2019. Pixel-based trajectories were used to reconstruct the temporal evolution of vegetation, and sequential Landsat archive data were used to achieve accurate measures of disturbances to vegetation. The results show that: (1) the proposed method can be used to determine the years in which vegetation disturbance and recovery occurred with accuracies of 86.53% and 78.57%, respectively; (2) mining in the Shengli mining area resulted in the conversion of 89.98 km2 of land from grassland, water, etc., to barren earth, and only 23.54 km2 was reclaimed, for a reclamation rate of 26.16%; and (3) the method proposed in this paper can achieve fast, efficient identification of surface mining land disturbances and reclamation, and has the potential to be applied to other similar areas.

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