Improved Mapping of Long-Term Forest Disturbance and Recovery Dynamics in the Subtropical China Using All Available Landsat Time-Series Imagery on Google Earth Engine Platform

Impervious surfaces are commonly acknowledged as major components of human settlements. The expansion of impervious surfaces could lead to a series of human−dominated environmental and ecological issues. Tracing impervious surface dynamics at a finer temporal−spatial scale is a critical way to better understand the increasingly human-dominated system of Earth. In this study, we put forward a new scheme to conduct long-term monitoring of impervious−relevant land disturbances using high frequency Landsat archives and the Google Earth Engine (GEE). First, the developed region was identified using a classification-based approach. Then, the GEE-version LandTrendr (Landsat-based detection of Trends in Disturbance and Recovery) was used to detect land disturbances, characterizing the conversion from vegetation to impervious surfaces. Finally, the actual disturbance areas within the developed regions were derived and quantitatively evaluated. A case study was conducted to detect impervious surface dynamics in Nanjing, China, from 1988 to 2018. Results show that our scheme can efficiently monitor impervious surface dynamics at yearly intervals with good accuracy. The overall accuracy (OA) of the classification results for 1988 and 2018 are 95.86% and 94.14%. Based on temporal−spatial accuracy assessments of the final detection result, the temporal accuracy is 90.75%, and the average detection time deviation is −1.28 a. The OA, precision, and recall of the sampling inspection, respectively, are 84.34%, 85.43%, and 96.37%. This scheme provides new insights into capturing the expansion of impervious−relevant land disturbances with high frequency Landsat archives in an efficient way.

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Tracking Rates of Forest Disturbance and Associated Carbon Loss in Areas of Illegal Amber Mining in Ukraine Using Landsat Time Series

Remote Sensing ◽

10.3390/rs12142235 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2235

Author(s):

Viktor Myroniuk ◽

Andrii Bilous ◽

Yevhenii Khan ◽

Andrii Terentiev ◽

Pavlo Kravets ◽

...

Keyword(s):

Time Series ◽

Forest Management ◽

Sustainable Forest Management ◽

Quality Management System ◽

Forest Disturbance ◽

Google Earth ◽

Forest Stewardship Council ◽

Carbon Loss ◽

Disturbed Area ◽

Disturbance And Recovery

Mapping forest disturbance is crucial for many applications related to decision-making for sustainable forest management. This study identified the effect of illegal amber mining on forest change and accumulated carbon stock across a study area of 8125.5 ha in northern Ukraine. Our method relies on the Google Earth Engine (GEE) implementation of the Landsat-based Detection of Trends in Disturbance and Recovery (LandTrendr) temporal segmentation algorithm of Landsat time-series (LTS) to derive yearly maps of forest disturbance and recovery in areas affected by amber extraction operations. We used virtual reality (VR) 360 interactive panoramic images taken from the sites to attribute four levels of forest disturbance associated with the delta normalized burn ratio (dNBR) and then calculated the carbon loss. We revealed that illegal amber extraction in Ukraine has been occurring since the middle of the 1990s, yielding 3260 ha of total disturbed area up to 2019. This study indicated that the area of forest disturbance increased dramatically during 2013–2014, and illegal amber operations persist. As a result, regrowth processes were mapped on only 375 ha of total disturbed area. The results were integrated into the Forest Stewardship Council® (FSC®) quality management system in the region to categorize Forest Management Units (FMUs) conforming to different disturbance rates and taking actions related to their certification status. Moreover, carbon loss evaluation allows the responsible forest management systems to be streamlined and to endorse ecosystem service assessment.

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Predicting post-fire vegetation recovery patterns in three different forest types

10.5194/egusphere-egu21-12800 ◽

2021 ◽

Author(s):

Spatola Maria Floriana ◽

Borghetti Marco ◽

Rita Angelo ◽

Nolè Angelo

Keyword(s):

Machine Learning ◽

Time Series ◽

Random Forest ◽

Google Earth ◽

Vegetation Recovery ◽

Oak Forests ◽

Data Mining Algorithm ◽

Recovery Rates ◽

Google Earth Engine

Wildfire disturbances severely modifies the ecosystem structure and natural regeneration processes. Predicting mid- to long-term post-fire vegetation recovery patterns, is pivotal to improve post-fire management and restoration of burned areas forest ecosystems management. Currently, many research efforts have been conducted, in order to monitor and predict wildfires, using Machine Learning and Remote sensing techniques. Instead, the method proposed in this study combines Satellite images and Data Mining algorithm to process data collected by time series and regional forest dataset to predict post-fire vegetation recovery patterns. For this reason, we analysed Normalized Burn Ratio (NBR) patterns from Landsat Time series (LTS), to assess post-fire vegetation recovery for several wildfires that occurred in three different forest Corine Land Cover classes (311, 312, 313) in the Basilicata region during the period 2005-2012. Random Forest model, was used to classify the observed recovery patterns and investigate the influence of burn severity, topographic variables, climate and spectral vegetation indices on post-fire recovery. Image acquisition and Random Forest classifier was undertaken in Google Earth Engine (GEE). Results of bootstrapping classification, across forest type, show high percentage for high recovered (HR) classes and moderate recovered (MR) classes and moderate-low percentage for low (LR) and unrecovered (UR) classes. Specifically, in the holm- and cork-oak and oak forests show medium to high recovery rates, while Mediterranean pine and conifer-oak forests show the slowest recovery rates. Different post-fire recovery patterns are related to fire severity, vegetation type and post-fire environmental conditions. Our methodology shows that post-fire recovery classification, using RF classifier provides a robust method for both local and broad scale monitoring for mid- to long-term recovery response.Keywords: Wildfires, Post-fire recovery, Landsat Time Series (LTS), Google Earth Engine, Wildfires, Machine Learning, Random Forest.

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Vegetation scenario of Indian part of Ganga Delta: a change analysis using Sentinel-1 time series data on Google earth engine platform

Safety in Extreme Environments ◽

10.1007/s42797-021-00041-1 ◽

2021 ◽

Author(s):

Bijay Halder ◽

Jatisankar Bandyopadhyay

Keyword(s):

Time Series ◽

Time Series Data ◽

Google Earth ◽

Series Data ◽

Change Analysis ◽

Google Earth Engine

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Time-series snowmelt detection over the Antarctic using Sentinel-1 SAR images on Google Earth Engine

Remote Sensing of Environment ◽

10.1016/j.rse.2021.112318 ◽

2021 ◽

Vol 256 ◽

pp. 112318

Author(s):

Dong Liang ◽

Huadong Guo ◽

Lu Zhang ◽

Yun Cheng ◽

Qi Zhu ◽

...

Keyword(s):

Time Series ◽

Google Earth ◽

Sar Images ◽

Google Earth Engine ◽

The Antarctic

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An updated national-scale mangrove forest map in China Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine

10.5194/egusphere-egu21-5310 ◽

2021 ◽

Author(s):

Luojia Hu ◽

Wei Yao ◽

Zhitong Yu ◽

Yan Huang

Keyword(s):

Time Series ◽

High Resolution ◽

Mangrove Forest ◽

Google Earth ◽

Series Data ◽

National Scale ◽

Mangrove Area ◽

Google Earth Engine ◽

Forest Maps ◽

Sentinel 2

A high resolution mangrove map (e.g., 10-m), which can identify mangrove patches with small size (< 1 ha), is a central component to quantify ecosystem functions and help government take effective steps to protect mangroves, because the increasing small mangrove patches, due to artificial destruction and plantation of new mangrove trees, are vulnerable to climate change and sea level rise, and important for estimating mangrove habitat connectivity with adjacent coastal ecosystems as well as reducing the uncertainty of carbon storage estimation. However, latest national scale mangrove forest maps mainly derived from Landsat imagery with 30-m resolution are relatively coarse to accurately characterize the distribution of mangrove forests, especially those of small size (area < 1 ha). Sentinel imagery with 10-m resolution provide the opportunity for identifying these small mangrove patches and generating high-resolution mangrove forest maps. Here, we used spectral/backscatter-temporal variability metrics (quantiles) derived from Sentinel-1 SAR (Synthetic Aperture Radar) and sentinel-2 MSI (Multispectral Instrument) time-series imagery as input features for random forest to classify mangroves in China. We found that Sentinel-2 imagery is more effective than Sentinel-1 in mangrove extraction, and a combination of SAR and MSI imagery can get a better accuracy (F1-score of 0.94) than using them separately (F1-score of 0.88 using Sentinel-1 only and 0.895 using Sentinel-2 only). The 10-m mangrove map derived by combining SAR and MSI data identified 20,003 ha mangroves in China and the areas of small mangrove patches (< 1 ha) was 1741 ha, occupying 8.7% of the whole mangrove area. The largest area (819 ha) of small mangrove patches is located in Guangdong Province, and in Fujian the percentage of small mangrove patches in total mangrove area is the highest (11.4%). A comparison with existing 30-m mangrove products showed noticeable disagreement, indicating the necessity for generating mangrove extent product with 10-m resolution. This study demonstrates the significant potential of using Sentinel-1 and Sentinel-2 images to produce an accurate and high-resolution mangrove forest map with Google Earth Engine (GEE). The mangrove forest maps are expected to provide critical information to conservation managers, scientists, and other stakeholders in monitoring the dynamics of mangrove forest.

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Sentinel-1 Time-Series Analysis for Fires Monitoring using Google Earth Engine Tools

10.1109/rtsi50628.2021.9597373 ◽

2021 ◽

Author(s):

Massimiliano Gargiulo ◽

Antonio Iodice ◽

Daniele Riccio ◽

Giuseppe Ruello

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Google Earth ◽

Series Analysis ◽

Google Earth Engine

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Socio-environmental land cover time series analysis of mining landscapes using Google Earth Engine and web-based mapping

Remote Sensing Applications Society and Environment ◽

10.1016/j.rsase.2020.100458 ◽

2020 ◽

pp. 100458

Author(s):

Michelle Li Ern Ang ◽

Dirk Arts ◽

Danielle Crawford ◽

Bonifacio V. Labatos ◽

Khanh Duc Ngo ◽

...

Keyword(s):

Time Series ◽

Land Cover ◽

Time Series Analysis ◽

Google Earth ◽

Cover Time ◽

Web Based ◽

Series Analysis ◽

Google Earth Engine

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Mapping SAR geometric distortions and their stability along time: a new tool in Google Earth Engine based on Sentinel-1 image time series

International Journal of Remote Sensing ◽

10.1080/01431161.2021.1992035 ◽

2021 ◽

Vol 42 (23) ◽

pp. 9126-9145

Author(s):

De Petris Samuele ◽

Sarvia Filippo ◽

Tommaso Orusa ◽

Borgogno-Mondino Enrico

Keyword(s):

Time Series ◽

Google Earth ◽

Geometric Distortions ◽

Google Earth Engine

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Monitoring of water table dynamics in peatlands with OPTRAM: Towards globally applicable algorithms in Google Earth Engine using Landsat and Sentinel-2

10.5194/egusphere-egu21-4698 ◽

2021 ◽

Author(s):

Iuliia Burdun ◽

Michel Bechtold ◽

Viacheslav Komisarenko ◽

Annalea Lohila ◽

Elyn Humphreys ◽

...

Keyword(s):

Time Series ◽

Water Table ◽

Input Data ◽

Short Wave ◽

Google Earth ◽

Google Earth Engine ◽

Short Wave Infrared ◽

Northern Peatlands ◽

Sentinel 2

Fluctuations of water table depth (WTD) affect many processes in peatlands, such as vegetation development and emissions of greenhouse gases. Here, we present the OPtical TRApezoid Model (OPTRAM) as a new method for satellite-based monitoring of the temporal variation of WTD in peatlands. OPTRAM is based on the response of short-wave infrared reflectance to the vegetation water status. For five northern peatlands with long-term in-situ WTD records, and with diverse vegetation cover and hydrological regimes, we generate a suite of OPTRAM index time series using (a) different procedures to parametrise OPTRAM (peatland-specific manual vs. globally applicable automatic parametrisation in Google Earth Engine), and (b) different satellite input data (Landsat vs. Sentinel-2). The results based on the manual parametrisation of OPTRAM indicate a high correlation with in-situ WTD time-series for pixels with most suitable vegetation for OPTRAM application (mean Pearson correlation of 0.7 across sites), and we will present the performance differences when moving from a manual to an automatic procedure. Furthermore, for the overlap period of Landsat and Sentinel-2, which have different ranges and widths of short-wave infrared bands used for OPTRAM calculation, the impact of the satellite input data to OPTRAM will be analysed. Eventually, the challenge of merging different satellite missions in the derivation of OPTRAM time series will be explored as an important step towards a global application of OPTRAM for the monitoring of WTD dynamics in northern peatlands.

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