The south area showed lower variance compared to other areas in karst regions. (2) Only 0.03% of the pixels showed high resistance, whereas a wide part of the study area showed low resilience, with 72.95 % of the pixels had low recovery rate and 39.27% of the pixels being able to be restored to their original state after the disturbance. Our results revealed following: (1) of the entire karst area, 87.97% of the pixels were disturbed in the past 31 years, the majority of the maximum disturbance events occurred during 2004 in the northeast and mid-west of Guangxi. Three components of ecosystem stability, namely resistance, resilience, and variability, were derived by applying a time decomposition algorithm, and then the correlations of each component were analyzed to explore the dimensionality of ecosystem stability.
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The objective of this study was to quantify ecosystem stability in multiple dimensions in a karst peak cluster depression region in southwest China, using dense Landsat time series from 1988 to 2018. However, historically this field has been limited to spatiotemporal scales based on the use of discrete ground-based measurements. Large-scale quantification of ecosystem stability in multiple dimensions is crucial to understanding underlying ecological processes and informing ecological management decision-making. The promotion of novel approaches can, on the other hand, prove beneficial for various downstream applications, although typically only theoretical uncertainties are provided. To sustain comprehensive and standardized validation efforts, the provision of spatiotemporally dense validation data in order to estimate actual differences between measurement and the true state has to be maintained. The hereby corresponding validation efforts show only minor methodological diversification in the past two decades. Trends indicate an increase in remote sensing-based studies that feature long-term datasets of land surface variables. The reviewed studies are mainly based on optical sensors and orientated towards global coverage, with vegetation-related variables as the focus. Categorization according to utilized validation data revealed that product intercomparisons and comparison to reference data are the conventional validation methods. This review presents an overview of validation approaches concerning temporally dense time-series of land surface geo-information products that cover the continental to global scale. Since various error sources in the acquisition chain of datasets exist, only properly validated results can be of value for research and downstream decision processes. Large-area remote sensing time-series offer unique features for the extensive investigation of our environment. It was generally observed that the number of GEE publications has significantly increased during the past few years, and it is expected that GEE will be utilized by more users from different fields to resolve their big data processing challenges. GEE has also been employed in a broad range of applications, such as Land Cover/land Use (LCLU) classification, hydrology, urban planning, natural disaster, climate analyses, and image processing.
Moreover, supervised machine learning algorithms, such as Random Forest (RF), were more widely applied to image classification tasks. It was observed that Landsat and Sentinel datasets were extensively utilized by GEE users. For this purpose, 450 journal articles published in 150 journals between January 2010 and May 2020 were studied.
Therefore, this study aims to comprehensively explore different aspects of the GEE platform, including its datasets, functions, advantages/limitations, and various applications. Although this platform was launched in 2010 and has proved its high potential for different applications, it has not been fully investigated and utilized for RS applications until recent years. In particular, this platform facilitates processing big geo data over large areas and monitoring the environment for long periods of time. In this regard, Google has developed a cloud computing platform, called Google Earth Engine (GEE), to effectively address the challenges of big data analysis.
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Remote Sensing (RS) systems have been collecting massive volumes of datasets for decades, managing and analyzing of which are not practical using common software packages and desktop computing resources.
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