scholarly journals Understanding the Spatially Explicit Distribution of Regional Tree Species Using Multi-Seasonal Sentinel-1&2 Imagery within Google Earth Engine

2021 ◽  
Author(s):  
Bo Xie ◽  
Chunxiang Cao ◽  
Min Xu ◽  
Barjeece Bashir ◽  
Yiyu Chen ◽  
...  
Keyword(s):  
High Resolution ◽  
Tree Species ◽  
Learning Algorithm ◽  
Google Earth ◽  
Accurate Information ◽  
Natural Forests ◽  
Source Information ◽  
Forestry Management ◽  
Google Earth Engine ◽  
Total Forest Area

Abstract Background Accurate information on tree species is much in demand for forestry management and further investigations on biodiversity and forest ecosystem services. Over regional or large areas, discriminating tree species at high resolution is deemed challenging by lack of representative features and computational power. Methods A novel methodology to delineate the explicit spatial distribution of dominated six tree species (Pinus, Quercus, Betula, Populus, Larch, and Apricot) and one residual class using the analysis-ready large volume multi-sensor imagery within Google Earth Engine (GEE) platform is demonstrated and used to map a 10 m classification with detail analysis of spatial pattern for an area covering over 90,000 km 2 between 41° N and 45° N. Random Forest (RF) algorithm built into GEE was used for tree species mapping, together with the multi-temporal features extracted from Sentinel-1/2 and topographic imagery data. The composition of tree species in natural forests and plantations in city and county-level were performed in detail afterwards. Results The proposed model achieved a reliable overall agreement (77.5%, 0.71 kappa), and the detailed analysis on the spatial distributing of targeted species indicated that the plantations (Pinus, Populus, Larch, and Apricot) outnumber natural forests (Quercus and Betula) by 6%, and they were mainly grown in the northern and southern regions, respectively. Moreover, Arhorchin had the largest total forest area of over 4,500 km 2 , while Hexingten and Aohan ranked first in natural forest and plantation area, and the class proportion of the number of tree species in Karqin and Ningcheng was more balanced. Conclusions It is our belief that combined multi-source information of the machine learning algorithm within cloud platforms is beneficial to map a reliable spatial tree species over large areas on a fine scale. High-resolution tree species information based on online tools could be more easily considered for practical forestry management and further studies on forest ecosystems.

scholarly journals Analysis of Regional Distribution of Tree Species Using Multi-Seasonal Sentinel-1&2 Imagery within Google Earth Engine

Forests ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 565
Author(s):  
Bo Xie ◽  
Chunxiang Cao ◽  
Min Xu ◽  
Robert Shea Duerler ◽  
Xinwei Yang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Tree Species ◽  
Regional Distribution ◽  
Google Earth ◽  
Pinus Tabulaeformis ◽  
Accurate Information ◽  
Natural Forests ◽  
Distribution Models ◽  
Google Earth Engine ◽  
Populus Spp

Accurate information on tree species is in high demand for forestry management and further investigations on biodiversity and environmental monitoring. Over regional or large areas, distinguishing tree species at high resolutions faces the challenges of a lack of representative features and computational power. A novel methodology was proposed to delineate the explicit spatial distribution of six dominant tree species (Pinus tabulaeformis, Quercus mongolia, Betula spp., Populus spp., Larix spp., and Armeniaca sibirica) and one residual class at 10 m resolution. Their spatial patterns were analyzed over an area covering over 90,000 km2 using the analysis-ready large volume of multisensor imagery within the Google Earth engine (GEE) platform afterwards. Random forest algorithm built into GEE was used together with the 20th and 80th percentiles of multitemporal features extracted from Sentinel-1/2, and topographic features. The composition of tree species in natural forests and plantations at the city and county-level were performed in detail afterwards. The classification achieved a reliable accuracy (77.5% overall accuracy, 0.71 kappa), and the spatial distribution revealed that plantations (Pinus tabulaeformis, Populus spp., Larix spp., and Armeniaca sibirica) outnumber natural forests (Quercus mongolia and Betula spp.) by 6% and were mainly concentrated in the northern and southern regions. Arhorchin had the largest forest area of over 4500 km2, while Hexingten and Aohan ranked first in natural forest and plantation area. Additionally, the class proportion of the number of tree species in Karqin and Ningcheng was more balanced. We suggest focusing more on the suitable areas modeling for tree species using species’ distribution models and environmental factors based on the classification results rather than field survey plots in further studies.

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 Forest ecological monitoring of the Shiyang River basin based on Google Earth Engine

2021 ◽  
Vol 932 (1) ◽  
pp. 012011
Author(s):  
Y Wang
Keyword(s):  
River Basin ◽  
Terrestrial Ecosystems ◽  
Google Earth ◽  
Ecological Environment ◽  
Ecological Quality ◽  
Natural Forests ◽  
Shiyang River Basin ◽  
Natural Factors ◽  
Google Earth Engine

Abstract The Shiyang River basin is a typical inland arid region and one of the most fragile and sensitive areas of terrestrial ecosystems in China, and it is important to understand its ecological changes in a timely and accurate manner. This article selects the Shiyang River basin forest as the research area and uses Google Earth Engine (GEE) to evaluate and monitor the ecological environment quality of the Shiyang River basin from 1990 to 2020. The geographical detector model (GDM) was also used to analyse the sensitivity of the forest ecological environment to three natural factors: elevation, temperature and altitude. The results showed that the ecological quality of the natural forest is significantly better than that of the man-made forest area, and the ecological quality grade is higher. The forest change area RSEI has a large annual variation in ecological quality and is vulnerable to external factors. Among the influencing natural factors, the sensitive factors of precipitation and altitude are both greater than 84%. The temperature sensitivity of natural forests is stronger than that of man-made forests, ranging from 66% to 92% overall.

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 Invasive buffelgrass detection using high‐resolution satellite and UAV imagery on Google Earth Engine

2019 ◽  
Vol 5 (4) ◽  
pp. 318-331 ◽  
Author(s):  
Kaitlyn Elkind ◽  
Temuulen T. Sankey ◽  
Seth M. Munson ◽  
Clare E. Aslan
Keyword(s):  
High Resolution ◽  
Google Earth ◽  
Google Earth Engine

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.

scholarly journals A weekly, near real-time dataset of the probability of large wildfire across western US forests and woodlands

2018 ◽  
Author(s):  
Miranda E. Gray ◽  
Luke J. Zachmann ◽  
Brett G. Dickson
Keyword(s):  
Real Time ◽  
Learning Algorithm ◽  
Google Earth ◽  
Large Fire ◽  
Digital Repository ◽  
Wildfire Occurrence ◽  
Western Us ◽  
Predicted Probability ◽  
Google Earth Engine

Abstract. There is broad consensus that wildfire activity is likely to increase in western US forests and woodlands over the next century. Therefore, spatial predictions of the potential for large wildfires have immediate and growing relevance to near- and long-term research, planning, and management objectives. Fuels, climate, weather, and the landscape all exert controls on wildfire occurrence and spread, but the dynamics of these controls vary from daily to decadal timescales. Accurate spatial predictions of large wildfires should therefore strive to integrate across these variables and timescales. Here, we describe a high spatial resolution dataset (250-m pixel) of the probability of large wildfire (> 405 ha) across all western US forests and woodlands, from 2005 to the present. The dataset is automatically updated on a weekly basis and in near real-time (i.e., up to the present week) using Google Earth Engine and a "Continuous Integration" pipeline. Each image in the dataset is the output of a machine-learning algorithm, trained on 10 independent, random samples of historic small and large wildfires, and represents the predicted probability of an individual pixel burning in a large fire. This novel workflow is able to integrate the short-term dynamics of fuels and weather into weekly predictions, while also integrating longer-term dynamics of fuels, climate, and the landscape. As a near real-time product, the dataset can provide operational fire managers with immediate, on-the-ground information to closely monitor changing potential for large wildfire occurrence and spread. It can also serve as a foundational dataset for longer-term planning and research, such as strategic targeting of fuels management, fire-smart development at the wildland urban interface, and analysis of trends in wildfire potential over time. Weekly large fire probability GeoTiff products from 2005 through 2017 are archived on Figshare online digital repository with the DOI 10.6084/m9.figshare.5765967 (available at https://doi.org/10.6084/m9.figshare.5765967.v1). Near real-time weekly GeoTiff products and the entire dataset from 2005 on are also continuously uploaded to a Google Cloud Storage bucket at https://console.cloud.google.com/storage/wffr-preds/V1, and also available free of charge with a Google account. Near real-time products and the long-term archive are also available to registered Google Earth Engine (GEE) users as public GEE assets, and can be accessed with the image collection ID "users/mgray/wffr-preds" within GEE.

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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