Integration of Sentinel-1 and Sentinel-2 for Classification of Small Urban Areas in Rural Landscape aided by Google Earth Engine

Abstract. Land Cover (LC) plays a key role in many disciplines and its classification from optical imagery is one of the prevalent applications of remote sensing. Besides years of researches and innovation on LC, the classification of some areas of the World is still challenging due to environmental and climatic constraints, such as the one of the mountainous chains. In this contribution, we propose a specific methodology for the classification of the Land Cover in mountainous areas using Sentinel 2, 1C-level imagery. The classification considers some specific high-altitude mountainous classes: clustered bare soils that are particularly prone to erosion, glaciers, and solid-rocky areas. It consists of a pixel-based multi-epochs classification using random forest algorithm performed in Google Earth Engine (GEE). The study area is located in the western Alps between Italy and France and the analyzed dataset refers to 2017–2019 imagery captured in the summertime only. The dataset was pre-processed, enriched of derivative features (radiometric, histogram-based and textural). A workflow for the reduction of the computational effort for the classification, which includes correlation and importance analysis of input features, was developed. Each image of the dataset was separately classified using random forest classification algorithm and then aggregated each other by the most frequent pixel value. The results show the high impact of textural features in the separation of the mountainous-specific classes the overall accuracy of the final classification achieves 0.945.

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Mapping National Mangrove Cover for Belize Using Google Earth Engine and Sentinel-2 Imagery

Applied Sciences ◽

10.3390/app11094258 ◽

2021 ◽

Vol 11 (9) ◽

pp. 4258

Author(s):

Jordan R. Cissell ◽

Steven W. J. Canty ◽

Michael K. Steinberg ◽

Loraé T. Simpson

Keyword(s):

Google Earth ◽

Mangrove Forests ◽

Random Forest Classification ◽

High Resolution Mapping ◽

Mangrove Ecosystems ◽

Forest Classification ◽

Resolution Mapping ◽

Google Earth Engine ◽

Terrestrial Biodiversity ◽

Sentinel 2

In this paper, we present the highest-resolution-available (10 m) national map of the mangrove ecosystems of Belize. These important ecosystems are increasingly threatened by human activities and climate change, support both marine and terrestrial biodiversity, and provide critical ecosystem services to coastal communities in Belize and throughout the Mesoamerican Reef ecoregion. Previous national- and international-level inventories document Belizean mangrove forests at spatial resolutions of 30 m or coarser, but many mangrove patches and loss events may be too small to be accurately mapped at these resolutions. Our 10 m map addresses this need for a finer-scale national mangrove inventory. We mapped mangrove ecosystems in Belize as of 2020 by performing a random forest classification of Sentinel-2 Multispectral Instrument imagery in Google Earth Engine. We mapped a total mangrove area of 578.54 km2 in 2020, with 372.04 km2 located on the mainland and 206.50 km2 distributed throughout the country’s islands and cayes. Our findings are substantially different from previous, coarser-resolution national mangrove inventories of Belize, which emphasizes the importance of high-resolution mapping efforts for ongoing conservation efforts.

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The rapid Land Use and Land Cover change analysis using the Sentinel-2 images in Google Earth Engine: A Case Study of Xiong’an New Area from 2017 to 2020

10.1109/agro-geoinformatics50104.2021.9530306 ◽

2021 ◽

Author(s):

Jiansong Luo ◽

Rujin Yuan ◽

Yujia Cao ◽

Min Xie ◽

Yikai Wang

Keyword(s):

Land Use ◽

Land Cover ◽

Land Cover Change ◽

Google Earth ◽

Change Analysis ◽

Google Earth Engine ◽

Land Cover Change Analysis ◽

Sentinel 2

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ANALISIS TEMPORAL PERUBAHAN HUTAN MANGROVE MENGGUNAKAN CITRA SATELIT SENTINEL-2 (Studi Kasus di Pulau Tanakeke, Kabupaten Takalar)

Seminar Nasional Geomatika ◽

10.24895/sng.2020.0-0.1193 ◽

2021 ◽

pp. 777

Author(s):

Andi Tenri Waru ◽

Athar Abdurrahman Bayanuddin ◽

Ferman Setia Nugroho ◽

Nita Rukminasari

Keyword(s):

Random Forest ◽

Google Earth ◽

Multi Temporal ◽

Google Earth Engine ◽

Sentinel 2

Pulau Tanakeke merupakan salah satu pulau dengan hutan mangrove yang luas di pesisir Sulawesi Selatan. Hutan mangrove ini menjadi ekosistem penting bagi masyarakat sekitar karena nilai ekologi maupun ekonominya. Namun, dalam kurun waktu sekitar tahun 1980-2000, keberadaan mangrove tersebut terancam oleh perubahan penggunaan lahan dan juga pemanfaatan yang berlebihan. Penelitian ini bertujuan untuk menganalisis perubahan temporal luas dan tingkat kerapatan hutan mangrove di Pulau Tanakeke antara tahun 2016 dan 2019. Metode analisis perubahan luasan hutan mangrove menggunakan data citra satelit Sentinel-2 multi temporal berdasarkan hasil klasifikasi hutan mangrove dengan menggunakan random forest pada platform Google Earth Engine. Akurasi keseluruhan hasil klasifikasi hutan mangrove tahun 2016 dan 2019 sebesar 91% dan 98%. Berdasarkan hasil analisis spasial diperoleh perubahan penurunan luasan mangrove yang signifikan dari 800,21 ha menjadi 640,15 ha. Kerapatan mangrove di Pulau Tanakeke sebagian besar tergolong kategori dalam kerapatan tinggi.

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Wetland Mapping of Northern Provinces of Iran Using Sentinel-1 and Sentinel-2 in Google Earth Engine

10.1109/igarss47720.2021.9554984 ◽

2021 ◽

Author(s):

Mohammad Ali Hemati ◽

Mahdi Hasanlau ◽

Masaud Mahdianpari ◽

Fariba Mohammadimanesh

Keyword(s):

Google Earth ◽

Wetland Mapping ◽

Google Earth Engine ◽

Sentinel 2

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Spruce Crown Transparency Levels Detected from Sentinel-2 Using Google Earth Engine

10.1109/igarss47720.2021.9553506 ◽

2021 ◽

Author(s):

Carsten Montzka ◽

Bagher Bayat ◽

Andreas Tewes ◽

David Mengen ◽

Harry Vereecken

Keyword(s):

Google Earth ◽

Crown Transparency ◽

Google Earth Engine ◽

Sentinel 2

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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

<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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Estimating Satellite-Derived Bathymetry (SDB) with the Google Earth Engine and Sentinel-2

Remote Sensing ◽

10.3390/rs10060859 ◽

2018 ◽

Vol 10 (6) ◽

pp. 859 ◽

Cited By ~ 55

Author(s):

Dimosthenis Traganos ◽

Dimitris Poursanidis ◽

Bharat Aggarwal ◽

Nektarios Chrysoulakis ◽

Peter Reinartz

Keyword(s):

Google Earth ◽

Google Earth Engine ◽

Sentinel 2

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

PLoS ONE ◽

10.1371/journal.pone.0253209 ◽

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.

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