scholarly journals Temporal Information Extraction for Afforestation in the Middle Section of the Yarlung Zangbo River Using Time-Series Landsat Images Based on Google Earth Engine

2021 ◽  
Vol 13 (23) ◽  
pp. 4785
Author(s):  
Hao Fu ◽  
Wei Zhao ◽  
Qiqi Zhan ◽  
Mengjiao Yang ◽  
Donghong Xiong ◽  
...  
Keyword(s):  
Time Series ◽  
Google Earth ◽  
Thematic Mapper ◽  
Temporal Information ◽  
Middle Section ◽  
Landsat Images ◽  
Planting Time ◽  
Yarlung Zangbo River ◽  
Ndvi Time Series ◽  
Google Earth Engine

Afforestation is one of the most efficient ways to control land desertification in the middle section of the Yarlung Zangbo River (YZR) valley. However, the lack of a quantitative way to record the planting time of artificial forest (AF) constrains further management for these forests. The long-term archived Landsat images (including the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI)) provide a good opportunity to capture the temporal change information about AF plantations. Under the condition that there would be an abrupt increasing trend in the normalized difference vegetation index (NDVI) time-series curve after afforestation, and this characteristic can be thought of as the indicator of the AF planting time. To extract the indicator, an algorithm based on the Google Earth Engine (GEE) for detecting this trend change point (TCP) on the maximum NDVI time series within the growing season (May to September) was proposed. In this algorithm, the time-series NDVI was initially smoothed and segmented into two subspaces. Then, a trend change indicator Sdiff was calculated with the difference between the fitting slopes of the subspaces before and after each target point. A self-adaptive method was applied to the NDVI series to find the right year with the maximum TCP, which is recorded as the AF planting time. Based on the proposed method, the AF planting time of the middle section of the YZR valley from 1988 to 2020 was derived. The detected afforestation temporal information was validated by 222 samples collected from the field survey, with a Pearson correlation coefficient of 0.93 and a root mean squared error (RMSE) of 2.95 years. Meanwhile, the area distribution of the AF planted each year has good temporal consistency with the implementation of the eco-reconstruction project. Overall, the study provides a good way to map AF planting times that is not only helpful for sustainable management of AF areas but also provides a basis for further research on the impact of afforestation on desertification control.

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

2020 ◽  
Vol 163 ◽  
pp. 312-326 ◽  
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

scholarly journals Automatic Land-Cover Mapping using Landsat Time-Series Data based on Google Earth Engine

2019 ◽  
Vol 11 (24) ◽  
pp. 3023 ◽  
Author(s):  
Shuai Xie ◽  
Liangyun Liu ◽  
Xiao Zhang ◽  
Jiangning Yang ◽  
Xidong Chen ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Vegetation Index ◽  
Time Series Data ◽  
Land Cover Classification ◽  
Google Earth ◽  
Thematic Mapper ◽  
Series Data ◽  
Land Cover Mapping ◽  
Google Earth Engine

The Google Earth Engine (GEE) has emerged as an essential cloud-based platform for land-cover classification as it provides massive amounts of multi-source satellite data and high-performance computation service. This paper proposed an automatic land-cover classification method using time-series Landsat data on the GEE cloud-based platform. The Moderate Resolution Imaging Spectroradiometer (MODIS) land-cover products (MCD12Q1.006) with the International Geosphere–Biosphere Program (IGBP) classification scheme were used to provide accurate training samples using the rules of pixel filtering and spectral filtering, which resulted in an overall accuracy (OA) of 99.2%. Two types of spectral–temporal features (percentile composited features and median composited monthly features) generated from all available Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) data from the year 2010 ± 1 were used as input features to a Random Forest (RF) classifier for land-cover classification. The results showed that the monthly features outperformed the percentile features, giving an average OA of 80% against 77%. In addition, the monthly features composited using the median outperformed those composited using the maximum Normalized Difference Vegetation Index (NDVI) with an average OA of 80% against 78%. Therefore, the proposed method is able to generate accurate land-cover mapping automatically based on the GEE cloud-based platform, which is promising for regional and global land-cover mapping.

scholarly journals Incorporating the Plant Phenological Trajectory into Mangrove Species Mapping with Dense Time Series Sentinel-2 Imagery and the Google Earth Engine Platform

2019 ◽  
Vol 11 (21) ◽  
pp. 2479 ◽  
Author(s):  
Huiying Li ◽  
Mingming Jia ◽  
Rong Zhang ◽  
Yongxing Ren ◽  
Xin Wen
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Google Earth ◽  
Late Winter ◽  
Mangrove Forests ◽  
Mangrove Species ◽  
Ndvi Time Series ◽  
Google Earth Engine ◽  
Species Mapping ◽  
Sentinel 2

Information on mangrove species composition and distribution is key to studying functions of mangrove ecosystems and securing sustainable mangrove conservation. Even though remote sensing technology is developing rapidly currently, mapping mangrove forests at the species level based on freely accessible images is still a great challenge. This study built a Sentinel-2 normalized difference vegetation index (NDVI) time series (from 2017-01-01 to 2018-12-31) to represent phenological trajectories of mangrove species and then demonstrated the feasibility of phenology-based mangrove species classification using the random forest algorithm in the Google Earth Engine platform. It was found that (i) in Zhangjiang estuary, the phenological trajectories (NDVI time series) of different mangrove species have great differences; (ii) the overall accuracy and Kappa confidence of the classification map is 84% and 0.84, respectively; and (iii) Months in late winter and early spring play critical roles in mangrove species mapping. This is the first study to use phonological signatures in discriminating mangrove species. The methodology presented can be used as a practical guideline for the mapping of mangrove or other vegetation species in other regions. However, future work should pay attention to various phenological trajectories of mangrove species in different locations.

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

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.

scholarly journals 30 m Resolution Global Annual Burned Area Mapping Based on Landsat Images and Google Earth Engine

2019 ◽  
Vol 11 (5) ◽  
pp. 489 ◽  
Author(s):  
Tengfei Long ◽  
Zhaoming Zhang ◽  
Guojin He ◽  
Weili Jiao ◽  
Chao Tang ◽  
...  
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Google Earth ◽  
Burned Area ◽  
Landsat 8 ◽  
Landsat Images ◽  
Burned Areas ◽  
Random Decision Forests ◽  
Burned Area Mapping ◽  
Google Earth Engine

Heretofore, global Burned Area (BA) products have only been available at coarse spatial resolution, since most of the current global BA products are produced with the help of active fire detection or dense time-series change analysis, which requires very high temporal resolution. In this study, however, we focus on an automated global burned area mapping approach based on Landsat images. By utilizing the huge catalog of satellite imagery, as well as the high-performance computing capacity of Google Earth Engine, we propose an automated pipeline for generating 30-m resolution global-scale annual burned area maps from time-series of Landsat images, and a novel 30-m resolution Global annual Burned Area Map of 2015 (GABAM 2015) was released. All the available Landsat-8 images during 2014–2015 and various spectral indices were utilized to calculate the burned probability of each pixel using random decision forests, which were globally trained with stratified (considering both fire frequency and type of land cover) samples, and a seed-growing approach was conducted to shape the final burned areas after several carefully-designed logical filters (NDVI filter, Normalized Burned Ratio (NBR) filter, and temporal filter). GABAM 2015 consists of spatial extent of fires that occurred during 2015 and not of fires that occurred in previous years. Cross-comparison with the recent Fire_cci Version 5.0 BA product found a similar spatial distribution and a strong correlation ( R 2 = 0.74) between the burned areas from the two products, although differences were found in specific land cover categories (particularly in agriculture land). Preliminary global validation showed the commission and omission errors of GABAM 2015 to be 13.17% and 30.13%, respectively.

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

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

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

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

<p>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.</p>

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