Monitoring ecosystem restoration of multiple surface coal mine sites in China via Landsat images on Google Earth Engine

The restoration of surface mining is a key to meet the global ecosystem restoration target. With increased data accessibility and computing tool capabilities, it becomes possible to expand mine restoration monitoring from single mine sites to multiple mine sites on a large scale. This study constructed a new index, Mine Landscape Restoration Index (MLRI), by coupling Land Surface Temperature (LST) and Enhanced Vegetation Index (EVI) to simultaneously monitor the restoration of regional multiple mine sites. We analyze historical and future trends of restoration using Mann-Kendall test, Sen’ slope, and Hurst exponent for MLRI time series. The restoration effects of 46 surface coal mine sites located in the northwestern ecologically fragile region of China from 2000 to 2019 were assessed, based on 3675 Landsat images on Google Earth Engine. The results showed that MLRI was effective in identifying restoration areas and processes in surface mine sites, which was validated by high-resolution images and field investigation of mine samples. The restoration area overall percentage was significantly higher in mines started mining before 2000 than after 2000. According to the restoration effects, we clustered the 46 sites into high, medium, and low restoration area percentage clusters with 13, 11, and 22 mine sites, respectively. Individual clusters have aggregation characteristics within each mine region, but are distributed irregularly across the different six mine regions. This study provides a new approach to monitoring the restoration of surface coal mine sites and inform government managers in developing mine restoration programs and sustainable mining development plans.

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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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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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High-resolution multi-temporal mapping of global urban land using Landsat images based on the Google Earth Engine Platform

Remote Sensing of Environment ◽

10.1016/j.rse.2018.02.055 ◽

2018 ◽

Vol 209 ◽

pp. 227-239 ◽

Cited By ~ 161

Author(s):

Xiaoping Liu ◽

Guohua Hu ◽

Yimin Chen ◽

Xia Li ◽

Xiaocong Xu ◽

...

Keyword(s):

High Resolution ◽

Urban Land ◽

Google Earth ◽

Landsat Images ◽

Multi Temporal ◽

Google Earth Engine ◽

Land Using

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Detecting industrial oil palm plantations on Landsat images with Google Earth Engine

Remote Sensing Applications Society and Environment ◽

10.1016/j.rsase.2016.11.003 ◽

2016 ◽

Vol 4 ◽

pp. 219-224 ◽

Cited By ~ 30

Author(s):

Janice Ser Huay Lee ◽

Serge Wich ◽

Atiek Widayati ◽

Lian Pin Koh

Keyword(s):

Oil Palm ◽

Google Earth ◽

Landsat Images ◽

Industrial Oil ◽

Google Earth Engine

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Monitoring spatial and temporal patterns of rubber plantation dynamics using time-series Landsat images and Google Earth Engine

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2021.3110763 ◽

2021 ◽

pp. 1-1

Author(s):

Yuchen Li ◽

Chenli Liu ◽

Jun Zhang ◽

Ping Zhang ◽

Yufei Xue

Keyword(s):

Time Series ◽

Temporal Patterns ◽

Google Earth ◽

Rubber Plantation ◽

Spatial And Temporal Patterns ◽

Landsat Images ◽

Google Earth Engine

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Contextualizing the 2019–2020 Kangaroo Island Bushfires: Quantifying Landscape-Level Influences on Past Severity and Recovery with Landsat and Google Earth Engine

Remote Sensing ◽

10.3390/rs12233942 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3942

Author(s):

Mitchell T. Bonney ◽

Yuhong He ◽

Soe W. Myint

Keyword(s):

Time Series ◽

Vegetation Change ◽

Fire History ◽

South Australia ◽

Fire Frequency ◽

Google Earth ◽

Bivariate Analysis ◽

Landsat Images ◽

Google Earth Engine ◽

Landscape Level

The 2019–2020 Kangaroo Island bushfires in South Australia burned almost half of the island. To understand how to avoid future severe ‘mega-fires’ and how vegetation may recover from 2019–2020, we can utilize information from the bulk of historical fires in an area. Landsat time-series of vegetation change provide this opportunity, but there has been little analysis of large numbers of fires to build a landscape-level understanding and quantify drivers in an Australian context. In this study, we built a yearly cloud-free surface reflectance normalized burn ratio (NBR) time-series (1988–2020) using all available summer Landsat images over Kangaroo Island. Data were collected in Google Earth Engine and fitted with LandTrendr. Burn severity and post-fire recovery were quantified for 47 fires, with a new recovery metric facilitating comparison where fire frequency is high. Variables representing the current burn, fire history, vegetation structure, and topography were related to severity and yearly recovery with random forest and bivariate analysis. Results show that the 2019–2020 bushfires were the most widespread and severe, followed by 2007–2008. Vegetation recovers quickly, with NBR stabilizing ten years post-fire on average. Severity is most influenced by fire frequency, vegetation capacity and land use with more severe burns in nature conservation areas with dense vegetation and a history of frequent fires. Influence on recovery varied with time since fire, with initial (year 1–3) faster recovery observed in areas with less surviving vegetation. Later (year 6–10) recovery was most influenced by a variable representing burn year and further investigation indicates that precipitation increases in later post-fire years likely facilitated faster recovery. The relative abundance of eucalypt woodlands also has a positive influence on recovery in middle and later years. These results provide valuable information to land managers on Kangaroo Island and in similar environments, who should consider adjusting practices to limit future mega-fire risk and potential ecosystem shifts if severe fires become more frequent with climate change.

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