scholarly journals Automatic High-Resolution Land Cover Production in Madagascar Using Sentinel-2 Time Series, Tile-Based Image Classification and Google Earth Engine

2020 ◽  
Vol 12 (21) ◽  
pp. 3663
Author(s):  
Meinan Zhang ◽  
Huabing Huang ◽  
Zhichao Li ◽  
Kwame Oppong Hackman ◽  
Chong Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Google Earth ◽  
Visual Interpretation ◽  
Entire Study ◽  
Heterogeneous Landscapes ◽  
Land Cover Map ◽  
Google Earth Engine ◽  
The Tropics ◽  
Sentinel 2

Madagascar, one of Earth’s biodiversity hotpots, is characterized by heterogeneous landscapes and huge land cover change. To date, fine, reliable and timely land cover information is scarce in Madagascar. However, mapping high-resolution land cover map in the tropics has been challenging due to limitations associated with heterogeneous landscapes, the volume of satellite data used, and the design of methodology. In this study, we proposed an automatic approach in which the tile-based model was used on each tile (defining an extent of 1° × 1° as a tile) for mapping land cover in Madagascar. We combined spectral-temporal, textural and topographical features derived from all available Sentinel-2 observations (i.e., 11,083 images) on Google Earth Engine (GEE). We generated a 10-m land cover map for Madagascar, with an overall accuracy of 89.2% based on independent validation samples obtained from a field survey and visual interpretation of very high-resolution (0.5–5 m) images. Compared with the conventional approach (i.e., the overall model used in the entire study area), our method enables reduce the misclassifications between several land cover types, including impervious land, grassland and wetland. The proposed approach demonstrates a great potential for mapping land cover in other tropical or subtropical regions.

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>

scholarly journals Accurate extraction of surface water in complex environment based on Google Earth Engine and Sentinel-2

PLoS ONE ◽  
2021 ◽  
Vol 16 (6) ◽  
pp. e0253209
Author(s):  
Jianfeng Li ◽  
Biao Peng ◽  
Yulu Wei ◽  
Huping Ye
Keyword(s):  
Surface Water ◽  
Google Earth ◽  
Water Extraction ◽  
Practical Significance ◽  
Commission Error ◽  
Complex Environment ◽  
Visual Interpretation ◽  
Water Index ◽  
Google Earth Engine ◽  
Sentinel 2

To realize the accurate extraction of surface water in complex environment, this study takes Sri Lanka as the study area owing to the complex geography and various types of water bodies. Based on Google Earth engine and Sentinel-2 images, an automatic water extraction model in complex environment(AWECE) was developed. The accuracy of water extraction by AWECE, NDWI, MNDWI and the revised version of multi-spectral water index (MuWI-R) models was evaluated from visual interpretation and quantitative analysis. The results show that the AWECE model could significantly improve the accuracy of water extraction in complex environment, with an overall accuracy of 97.16%, and an extremely low omission error (0.74%) and commission error (2.35%). The AEWCE model could effectively avoid the influence of cloud shadow, mountain shadow and paddy soil on water extraction accuracy. The model can be widely applied in cloudy, mountainous and other areas with complex environments, which has important practical significance for water resources investigation, monitoring and protection.

Mapping land use on Irish raised bogs using Sentinel-2 imagery and Google Earth Engine

2021 ◽  
Author(s):  
Wahaj Habib ◽  
John Connolly ◽  
Kevin McGuiness
Keyword(s):  
Land Use ◽  
High Resolution ◽  
Land Use Change ◽  
Optical Sensors ◽  
Ghg Emissions ◽  
Google Earth ◽  
Good Prospect ◽  
Raised Bogs ◽  
Google Earth Engine ◽  
Sentinel 2

<p>Peatlands are one of the most space-efficient terrestrial carbon stores. They cover approximately 3 % of the terrestrial land surface and account for about one-third of the total soil organic carbon stock. Peatlands have been under severe strain for centuries all over the world due to management related activities. In Ireland, peatlands span over approximately 14600 km<sup>2</sup>, and 85 % of that has already been degraded to some extent. To achieve temperature goals agreed in the Paris agreement and fulfil the EU’s commitment to quantifying the Carbon/Green House Gases (C/GHG) emissions from land use, land use change forestry, accurate mapping and identification of management related activities (land use) on peatlands is important.</p><p>High-resolution multispectral satellite imagery by European Space Agency (ESA) i.e., Sentinel-2 provides a good prospect for mapping peatland land use in Ireland. However, due to persistent cloud cover over Ireland, and the inability of optical sensors to penetrate the clouds makes the acquisition of clear sky imagery a challenge and hence hampers the analysis of the landscape. Google Earth Engine (a cloud-based planetary-scale satellite image platform) was used to create a cloud-free image mosaic from sentinel-2 data was created for raised bogs in Ireland (images collected for the time period between 2017-2020). A preliminary analysis was conducted to identify peatland land use classes, i.e., grassland/pasture, crop/tillage, built-up, cutover, cutaway and coniferous, broadleaf forests using this mosaicked image. The land-use classification results may be used as a baseline dataset since currently, no high-resolution peatland land use dataset exists for Ireland. It can also be used for quantification of land-use change on peatlands. Moreover, since Ireland will now be voluntarily accounting the GHG emissions from managed wetlands (including bogs), this data could also be useful for such type of assessment.</p>

scholarly journals Advancing the Mapping of Mangrove Forests at National-Scale Using Sentinel-1 and Sentinel-2 Time-Series Data with Google Earth Engine: A Case Study in China

2020 ◽  
Vol 12 (19) ◽  
pp. 3120
Author(s):  
Luojia Hu ◽  
Nan Xu ◽  
Jian Liang ◽  
Zhichao Li ◽  
Luzhen Chen ◽  
...  
Keyword(s):  
Time Series ◽  
High Resolution ◽  
Mangrove Forest ◽  
Time Series Data ◽  
Landsat Imagery ◽  
Google Earth ◽  
Series Data ◽  
National Scale ◽  
Google Earth Engine ◽  
Sentinel 2

A high resolution mangrove map (e.g., 10-m), including mangrove patches with small size, is urgently needed for mangrove protection and ecosystem function estimation, because more small mangrove patches have disappeared with influence of human disturbance and sea-level rise. However, recent national-scale mangrove forest maps are mainly derived from 30-m Landsat imagery, and their spatial resolution is relatively coarse to accurately characterize the extent of mangroves, especially those with small size. Now, Sentinel imagery with 10-m resolution provides an opportunity for generating high-resolution mangrove maps containing these small mangrove patches. Here, we used spectral/backscatter-temporal variability metrics (quantiles) derived from Sentinel-1 SAR (Synthetic Aperture Radar) and/or Sentinel-2 MSI (Multispectral Instrument) time-series imagery as input features of random forest to classify mangroves in China. We found that Sentinel-2 (F1-Score of 0.895) is more effective than Sentinel-1 (F1-score of 0.88) in mangrove extraction, and a combination of SAR and MSI imagery can get the best accuracy (F1-score of 0.94). The 10-m mangrove map was derived by combining SAR and MSI data, which identified 20003 ha mangroves in China, and the area of small mangrove patches (<1 ha) is 1741 ha, occupying 8.7% of the whole mangrove area. At the province level, Guangdong has the largest area (819 ha) of small mangrove patches, and in Fujian, the percentage of small mangrove patches 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 map is expected to provide critical information to conservation managers, scientists, and other stakeholders in monitoring the dynamics of the mangrove forest.

scholarly journals High-Resolution Urban Land Mapping in China from Sentinel 1A/2 Imagery Based on Google Earth Engine

2019 ◽  
Vol 11 (7) ◽  
pp. 752 ◽  
Author(s):  
Zhongchang Sun ◽  
Ru Xu ◽  
Wenjie Du ◽  
Lei Wang ◽  
Dengsheng Lu
Keyword(s):  
High Resolution ◽  
Vegetation Index ◽  
Urban Land ◽  
Google Earth ◽  
Economic Research ◽  
Optical Data ◽  
Large Area ◽  
Google Earth Engine ◽  
Sar Data ◽  
Sentinel 2

Accurate and timely urban land mapping is fundamental to supporting large area environmental and socio-economic research. Most of the available large-area urban land products are limited to a spatial resolution of 30 m. The fusion of optical and synthetic aperture radar (SAR) data for large-area high-resolution urban land mapping has not yet been widely explored. In this study, we propose a fast and effective urban land extraction method using ascending/descending orbits of Sentinel-1A SAR data and Sentinel-2 MSI (MultiSpectral Instrument, Level 1C) optical data acquired from 1 January 2015 to 30 June 2016. Potential urban land (PUL) was identified first through logical operations on yearly mean and standard deviation composites from a time series of ascending/descending orbits of SAR data. A Yearly Normalized Difference Vegetation Index (NDVI) maximum and modified Normalized Difference Water Index (MNDWI) mean composite were generated from Sentinel-2 imagery. The slope image derived from SRTM DEM data was used to mask mountain pixels and reduce the false positives in SAR data over these regions. We applied a region-specific threshold on PUL to extract the target urban land (TUL) and a global threshold on the MNDWI mean, and slope image to extract water bodies and high-slope regions. A majority filter with a three by three window was applied on previously extracted results and the main processing was carried out on the Google Earth Engine (GEE) platform. China was chosen as the testing region to validate the accuracy and robustness of our proposed method through 224,000 validation points randomly selected from high-resolution Google Earth imagery. Additionally, a total of 735 blocks with a size of 900 × 900 m were randomly selected and used to compare our product’s accuracy with the global human settlement layer (GHSL, 2014), GlobeLand30 (2010), and Liu (2015) products. Our method demonstrated the effectiveness of using a fusion of optical and SAR data for large area urban land extraction especially in areas where optical data fail to distinguish urban land from spectrally similar objects. Results show that the average overall, producer’s and user’s accuracies are 88.03%, 94.50% and 82.22%, respectively.

The Potential of Sentinel-2 Satellite Images for Land-Cover/Land-Use and Forest Biomass Estimation: A Review

2021 ◽  
Author(s):  
Crismeire Isbaex ◽  
Ana Margarida Coelho
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Satellite Images ◽  
Forest Biomass ◽  
Local Scale ◽  
Google Earth ◽  
Biomass Estimation ◽  
Promising Tool ◽  
Google Earth Engine ◽  
Sentinel 2

Mapping land-cover/land-use (LCLU) and estimating forest biomass using satellite images is a challenge given the diversity of sensors available and the heterogeneity of forests. Copernicus program served by the Sentinel satellites family and the Google Earth Engine (GEE) platform, both with free and open services accessible to its users, present a good approach for mapping vegetation and estimate forest biomass on a global, regional, or local scale, periodically and in a repeated way. The Sentinel-2 (S2) systematically acquires optical imagery and provides global monitoring data with high spatial resolution (10–60 m) images. Given the novelty of information on the use of S2 data, this chapter presents a review on LCLU maps and forest above-ground biomass (AGB) estimates, in addition to exploring the efficiency of using the GEE platform. The Sentinel data have great potential for studies on LCLU classification and forest biomass estimates. The GEE platform is a promising tool for executing complex workflows of satellite data processing.

scholarly journals Understanding the Spatial Distribution of Urban Forests in China Using Sentinel-2 Images with Google Earth Engine

Forests ◽  
2019 ◽  
Vol 10 (9) ◽  
pp. 729 ◽  
Author(s):  
Qianwen Duan ◽  
Minghong Tan ◽  
Yuxuan Guo ◽  
Xue Wang ◽  
Liangjie Xin
Keyword(s):  
High Resolution ◽  
Forest Cover ◽  
Urban Forest ◽  
Urban Forests ◽  
Well Being ◽  
Google Earth ◽  
Machine Learning Algorithms ◽  
Sustainable Urban Development ◽  
Google Earth Engine ◽  
Sentinel 2

Urban forests are vitally important for sustainable urban development and the well-being of urban residents. However, there is, as yet, no country-level urban forest spatial dataset of sufficient quality for the scientific management of, and correlative studies on, urban forests in China. At present, China attaches great importance to the construction of urban forests, and it is necessary to map a high-resolution and high-accuracy dataset of urban forests in China. The open-access Sentinel images and the Google Earth Engine platform provide a significant opportunity for the realization of this work. This study used eight bands (B2–B8, B11) and three indices of Sentinel-2 in 2016 to map the urban forests of China using the Random Forest machine learning algorithms at the pixel scale with the support of Google Earth Engine (GEE). The 7317 sample points for training and testing were collected from field visits and very high resolution images from Google Earth. The overall accuracy, producer’s accuracy of urban forest, and user’s accuracy of urban forest assessed by independent validation samples in this study were 92.30%, 92.27%, and 92.18%, respectively. In 2016, the percentage of urban forest cover was 19.2%. Nearly half of the cities had an urban forest cover between 10% and 20%, and the average percentage of large cities whose urban populations were over 5 million was 24.8%. Cities with less than half of the average were mainly distributed in northern and western parts of China, which should be focused on in urban greening planning.

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