scholarly journals National scale mapping of larch plantations for Wales using the Sentinel-2 data archive

2021 ◽  
Vol 501 ◽  
pp. 119679
Author(s):  
Suvarna M. Punalekar ◽  
Carole Planque ◽  
Richard M. Lucas ◽  
Dai Evans ◽  
Vera Correia ◽  
...  
Keyword(s):  
Data Archive ◽  
National Scale ◽  
Sentinel 2

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 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.

Peatland restoration age (Scotland, UK) can be better reproduced by a classification model based on Sentinel-2 than with high resolution aerial imagery

2020 ◽  
Author(s):  
Rebekka Artz ◽  
Jonathan Ball ◽  
Catherine Smart ◽  
Gillian Donaldson-Selby ◽  
Neil Cowie ◽  
...  
Keyword(s):  
Landscape Scale ◽  
Aerial Imagery ◽  
Classification Model ◽  
Vegetation Composition ◽  
National Scale ◽  
Model Uncertainties ◽  
Peatland Restoration ◽  
Natural Climate ◽  
Main Model ◽  
Sentinel 2

&lt;p&gt;Damage to peatland globally causes significant contributions to the current net greenhouse gas emissions and pose a further future risk as such damaged peatlands are vulnerable to future climatic stress. Globally, peatland restoration efforts are rapidly increasing in scale as natural climate solutions, yet relatively little effort has been it into effective monitoring of landscape scale restoration projects. We developed a classification model that uses remote observations (Sentinel-2 or national scale aerial imagery from Getmapping) to detect restoration efficacy by training it against a dataset from a chronosequence of spatially collocated peatland restoration sites that had previously been converted to plantation forestry. The Sentinel-2 based model greatly outperformed the aerial imagery-based model (RGB and IR, 25 and 50 cm, respectively). Adding slope to the classification improved kappa by less than 0.02. Prediction of the starting (forestry) and target (restored) state was very robust, and both recent and the oldest restoration sites were spatially well predicted. The main model uncertainties lie with sites of intermediate age, where on-the-ground restoration trajectories based on vegetation composition also differ the most, and with sites where additional layers of management after the initial restoration management have been applied.&lt;/p&gt;

scholarly journals From local to global: A transfer learning-based approach for mapping poplar plantations at national scale using Sentinel-2

2021 ◽  
Vol 171 ◽  
pp. 76-100
Author(s):  
Yousra Hamrouni ◽  
Eric Paillassa ◽  
Véronique Chéret ◽  
Claude Monteil ◽  
David Sheeren
Keyword(s):  
Transfer Learning ◽  
National Scale ◽  
Poplar Plantations ◽  
Sentinel 2

scholarly journals National Mapping of New Zealand Pasture Productivity Using Temporal Sentinel-2 Data

2021 ◽  
Vol 13 (8) ◽  
pp. 1481
Author(s):  
Alexander C. Amies ◽  
John R. Dymond ◽  
James D. Shepherd ◽  
David Pairman ◽  
Coby Hoogendoorn ◽  
...  
Keyword(s):  
Land Use ◽  
Dry Matter ◽  
Vegetation Index ◽  
Temporal Segmentation ◽  
National Scale ◽  
Normalised Difference Vegetation Index ◽  
Spatio Temporal ◽  
Pasture Yield ◽  
Sentinel 2 ◽  
Pasture Productivity

A national map of pasture productivity, in terms of mass of dry matter yield per unit area and time, enables evaluation of regional and local land-use suitability. Difficulty in measuring this quantity at scale directed this research, which utilises four years of Sentinel-2 satellite imagery and collected pasture yield measurements to develop a model of pasture productivity. The model uses a Normalised Difference Vegetation Index (NDVI), with spatio-temporal segmentation and averaging, to estimate mean annual pasture productivity across all of New Zealand’s grasslands with a standard error of prediction of 2.2 t/ha/y. Regional aggregates of pasture yield demonstrate expected spatial variations. The pasture productivity map may be used to classify grasslands objectively into stratified levels of production on a national scale. Due to its ability to highlight areas of land use intensification suitability, the national map of pasture productivity is of value to landowners, land users, and environmental scientists.

Towards national-scale characterization of grassland use intensity from integrated Sentinel-2 and Landsat time series

2020 ◽  
Vol 238 ◽  
pp. 111124 ◽  
Author(s):  
Patrick Griffiths ◽  
Claas Nendel ◽  
Jürgen Pickert ◽  
Patrick Hostert
Keyword(s):  
Time Series ◽  
National Scale ◽  
Scale Characterization ◽  
Sentinel 2

scholarly journals Accuracy estimation of two global burned area products at national scale

2021 ◽  
Vol 932 (1) ◽  
pp. 012001
Author(s):  
T Katagis ◽  
I Z Gitas
Keyword(s):  
Reference Data ◽  
Initial Assessment ◽  
Burned Area ◽  
National Scale ◽  
Spatial Performance ◽  
Consistent Performance ◽  
In Fire ◽  
Higher Sensitivity ◽  
Sentinel 2 ◽  
Good Agreement

Abstract In this work we perform an initial assessment of the accuracy of two publicly available MODIS burned area products, MCD64A1 C6 and MODIS FireCCI51, at national scale in a Mediterranean region. The research focused on two fire seasons for the years 2016 and 2017 and comparison was performed against a higher resolution Sentinel-2 dataset. The specific objectives were to assess their capabilities in detection of fire events occurring primarily in forest and semi-natural lands and also to investigate their spatial uncertainties. The analysis combined monthly fire observations and accuracy metrics derived from error matrices. Satisfactory performance was achieved by the two products in detection of larger fires (> 100 ha) whereas their spatial performance exhibited good agreement with the reference data. MCD64A1 C6 exhibited a more consistent performance overall and the 250 m FireCCI51 product exhibited relatively higher sensitivity in detection of smaller (<100 ha) fires. Although additional work is required for a more rigorous assessment of the variability of these burned area products, our research has implications for their usability in fire-related applications at finer scales.

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