Monitoring the Spatiotemporal Dynamics of Aeolian Desertification Using Google Earth Engine

Northern China has been long threatened by aeolian desertification. In recent years, all levels of the Chinese government have performed a series of ecological protection and sand control projects. To grasp the implementation effects of these projects and adjust policies in time, it is necessary to understand the process of aeolian desertification quickly and accurately. Remote sensing technologies play an irreplaceable role in aeolian desertification monitoring. In this study, the Zhenglan Banner, which is in the hinterland of the Hunshandake Sandy Land, was considered as the research area. Based on unmanned aerial vehicle (UAV) images, ground survey data, and Landsat images called in Google Earth Engine (GEE), the aeolian desertified land (ADL) in 2000, 2004, 2010, 2015, and 2019 was extracted using spectral mixture analysis. A desertification index (DI) was constructed to evaluate the spatial and temporal dynamics of the ADL in the Zhenglan Banner. Finally, a residual analysis explored the driving forces of aeolian desertification. The results showed that (1) the ADL area in the Zhenglan Banner has been trending downwards over the past 20 years but rebounded from 2004 to 2010; (2) over the past 20 years, the area of slightly, moderately, and severely desertified land has decreased at annual rates of 0.4%, 2.7%, and 3.4%, respectively; (3) human activities had significantly positive and negative impacts on the aeolian desertification trend for 20.0% and 21.0% of the study area, respectively, but not for the rest. This paper explored new techniques for rapid aeolian desertification monitoring and is of great significance for controlling and managing aeolian desertification in this region.

Download Full-text

Spatial-temporal changes and driving forces of aeolian desertification of grassland in the Sanjiangyuan region from 1975 to 2015 based on the analysis of Landsat images

Environmental Monitoring and Assessment ◽

10.1007/s10661-020-08763-8 ◽

2020 ◽

Vol 193 (1) ◽

Author(s):

Xiaohui Zhai ◽

Changzhen Yan ◽

Xuegang Xing ◽

Haowei Jia ◽

Xiaoxu Wei ◽

...

Keyword(s):

Driving Forces ◽

Temporal Changes ◽

Landsat Images ◽

Aeolian Desertification

Download Full-text

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.

Download Full-text

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

Download Full-text

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.

Download Full-text

Examining Urban Expansion in the Greater Toronto Area Using Landsat Imagery from 1974–2014

GEOMATICA ◽

10.5623/cig2015-203 ◽

2015 ◽

Vol 69 (2) ◽

pp. 161-172 ◽

Cited By ~ 4

Author(s):

Lanying Wang ◽

Wei Li ◽

Shiqian Wang ◽

Jonathan Li

Keyword(s):

Temporal Dynamics ◽

Landsat Imagery ◽

Urban Expansion ◽

Population Data ◽

Landsat Images ◽

Statistical Population ◽

The Past ◽

Greater Toronto Area ◽

Spatial And Temporal Dynamics ◽

Urban Extent

The Greater Toronto Area is the most vital economic centre in Canada and has experienced rapid urban expansion in the past 40 years. This research uses Landsat images to detect the spatial and temporal dynamics of urban expansion in the Greater Toronto Area from 1974 to 2014. We quantitatively analyzed the extent of urban expansion and spatial patterns of growth from classified Landsat images. We then integrated our expansion findings with population data to observe the relationships between urban growth and population. We found that the Greater Toronto Area had significant growth of 1115 km2, expanding mainly in radiated and ribbon expansion modes. There was substantial correlation between urban extent and population in the period of study. These results demonstrate the efficacy of combining statistical population data with remote sensing imagery for the analysis of urban expansion.

Download Full-text

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

Download Full-text

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.

Download Full-text

Dynamics and Drivers of the Alpine Timberline on Gongga Mountain of Tibetan Plateau-Adopted from the Otsu Method on Google Earth Engine

Remote Sensing ◽

10.3390/rs12162651 ◽

2020 ◽

Vol 12 (16) ◽

pp. 2651

Author(s):

Wen He ◽

Chongchong Ye ◽

Jian Sun ◽

Junnan Xiong ◽

Jinniu Wang ◽

...

Keyword(s):

Tibetan Plateau ◽

Vegetation Index ◽

Temporal Dynamics ◽

Google Earth ◽

Vegetation Coverage ◽

The Tibetan Plateau ◽

Gongga Mountain ◽

Alpine Timberline ◽

Otsu Method ◽

Google Earth Engine

The alpine timberline, an ecosystem ecotone, indicates climatic change and is tending to shift toward higher altitudes because of an increase in global warming. However, spatiotemporal variations of the alpine timberline are not consistent on a global scale. The abundant and highest alpine timberline, located on the Tibetan Plateau, is less subject to human activity and disturbance. Although many studies have investigated the alpine timberline on the Tibetan Plateau, large-scale monitoring of spatial-temporal dynamics and driving mechanisms of the alpine timberline remain uncertain and inaccurate. Hence, the Gongga Mountain on the southeastern Tibetan Plateau was chosen as the study area because of the most complete natural altitudinal zonation. We used the Otsu method on Google Earth Engine to extract the alpine timberline from 1987–2019 based on the normalized difference vegetation index (NDVI). Then, the alpine timberline spatiotemporal patterns and the effect of topography on alpine timberline distribution were explored. Four hillsides on the western Gongga Mountain were selected to examine the hillside differences and drivers of the alpine timberline based on principal component analysis (PCA) and multiple linear regression (MLR). The results indicated that the elevation range of alpine timberline was 3203–4889 m, and the vegetation coverage increased significantly (p < 0.01) near the alpine timberline ecotone on Gongga Mountain. Moreover, there was spatial heterogeneity in dynamics of alpine timberline, and some regions showed no regular trend in variations. The spatial pattern of the alpine timberline was generally high in the west, low in the east, and primarily distributed on 15–55° slopes. Besides, the drivers of the alpine timberline have the hillside differences, and the sunny and shady slopes possessed different driving factors. Thus, our results highlight the effects of topography and climate on the alpine timberline on different hillsides. These findings could provide a better approach to study the dynamics and formation of alpine timberlines.

Download Full-text