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

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.

National-scale mangrove forest mapping by using Sentinel-1 SAR and Sentinel-2 MSI imagery on the Google Earth Engine Platform

2020 ◽  
Author(s):  
Luojia Hu ◽  
Wei Yao ◽  
Zhitong Yu ◽  
Lei Wang
Keyword(s):  
Spatial Resolution ◽  
Mangrove Forest ◽  
Large Scale ◽  
Landsat Imagery ◽  
Google Earth ◽  
Accuracy Evaluation ◽  
Mangrove Forests ◽  
Forest Mapping ◽  
Google Earth Engine ◽  
Sentinel 2

<p>Mangrove forest is considered as one of the pivotal ecosystems to near-shore environment health, adjacent terrestrial ecosystems and even global climate change migration. However, for past two decades, they are declining rapidly. In order to take effective steps to prevent the extinction of mangroves, high spatial resolution information of large-scale mangrove distribution is urgent. Recent study has indicated that a suitable pixel size for extracting mangroves should be at least equal to 10 m. Hence, Sentinel imagery (Sentinel-1 C-band synthetic aperture radar (SAR) and Sentinel-2 Multi-Spectral Instrument (MSI) imagery) whose spatial resolution is 10 m may hold great potentials to achieve this goal, but there are limited researches investigating it. Therefore, in this study, we will explore the potential of Sentinel imagery to extract mangrove forests in China on the Google Earth Engine platform. Specifically, our study was mainly conducted around 3 questions: (1) Which Sentinel imagery provides a higher accuracy for mangrove forest mapping, Sentinel-1 SAR data or Sentinel-2 multi-spectral data? (2) which combination of features from Sentinel imagery provides the most accurate mangrove forest map? (3) Compared to 30-m resolution mangrove products derived from Landsat imagery, how does 10-m resolution map improve our knowledge about the distribution of mangrove forest in China?</p><p> </p><p>Our results show that: (1) The highest producer’s accuracies (the reason why using producer’s accuracy as an accuracy evaluation indicator here is that the omission errors in mangrove forest extent map are much larger than commission errors) of mangrove forest maps derived from Sentinel-1 and Sentinel-2 imagery are 91.76% and 90.39%, respectively, which means that the contributions of Sentinel-1 SAR and Sentinel-2 MSI imagery to mangrove mapping are similar; (2) The highest producer’s accuracy of mangrove forest map at 10-m resolution is 95.4%. The mangrove forest map with the highest accuracy is obtained by combining quantiles of spectral and backscatter bands, spectral index, and texture index derived from time series of Sentinel-1 and Sentinel-2 imagery, indicating that the combination of Sentinel-1 SAR and Sentinel-2 MSI imagery is more useful in mangrove forest mapping than using them separately; (3) In China, the total area of mangrove forest extent at 10-m resolution is similar to that at 30-m resolution (20003 ha vs. 19220 ha). However, compared to 30-m resolution mangrove products, the 10-m resolution mangrove map identifies 1741 ha (occupying 8.7% of total mangrove forest area in China) mangrove forests in size smaller than 1 ha, which are especially important to low-lying coastal zone. This study demonstrates the feasibility of Sentinel imagery in large-scale mangrove forest mapping and gives guidance to map global mangrove forest at 10-m resolution in the future.  </p><p> </p>

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 DEFORESTATION MAPPING USING SENTINEL-1 AND OBJECT-BASED RANDOM FOREST CLASSIFICATION ON GOOGLE EARTH ENGINE

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 865-872
Author(s):  
V. Yordanov ◽  
M. A. Brovelli
Keyword(s):  
Land Cover ◽  
Agricultural Land ◽  
Weather Conditions ◽  
Google Earth ◽  
Random Forest Classification ◽  
Machine Learning Classification ◽  
Forest Classification ◽  
Object Based ◽  
Short Period ◽  
Google Earth Engine

Abstract. Deforestation can be defined as the conversion of forest land cover to another type. It is a process that has massively accelerated its rate and extent in the last several decades. Mainly due to human activities related to socio-economic processes as population growth, expansion of agricultural land, wood extraction, etc. In the meantime, there are great efforts by governments and agencies to reduce these deforestation processes by implementing regulations, which cannot always be properly monitored whether are followed or not. In this work is proposed an approach that can provide forest loss estimations for a short period of time, by using Synthetic Aperture Radar imagery for an area in the Brazilian Amazon. SAR are providing data with almost no alteration due to weather conditions, however they may present other limitations. To mitigate the speckle effect, here was applied the dry coefficient, which is the mean of image values under the first quartile while preserving the spatial resolution. While for obtaining land cover maps containing only forest and non-forest areas an object-based machine learning classification on the Google Earth Engine platform was applied. The preliminary tests were carried out in a bitemporal manner between 2015 and 2019, followed by applying the approach monthly for the year of 2020. The outputs yielded very satisfactory and accurate results, allowing to estimate the forest dynamics for the area under consideration for each month.

scholarly journals SPECIFIC ALPINE ENVIRONMENT LAND COVER CLASSIFICATION METHODOLOGY: GOOGLE EARTH ENGINE PROCESSING FOR SENTINEL-2 DATA

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 663-670
Author(s):  
E. Belcore ◽  
M. Piras ◽  
E. Wozniak
Keyword(s):  
Random Forest ◽  
Land Cover ◽  
Western Alps ◽  
Computational Effort ◽  
Google Earth ◽  
Random Forest Classification ◽  
Importance Analysis ◽  
Google Earth Engine ◽  
Sentinel 2

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.

scholarly journals Broad-Leaved and Coniferous Forest Classification in Google Earth Engine Using Sentinel Imagery

2020 ◽  
Vol 3 (1) ◽  
pp. 64
Author(s):  
Gordana Kaplan
Keyword(s):  
Forest Cover ◽  
Accuracy Assessment ◽  
Coniferous Forest ◽  
Google Earth ◽  
Support Vector ◽  
Svm Classifier ◽  
Sample Collection ◽  
Forest Classification ◽  
Google Earth Engine ◽  
Sentinel 2

Forest structures knowledge is fundamental to understanding, managing, and preserving the biodiversity of forests. With the well-established need within the remote sensing community for better understanding of canopy structure, in this paper, the effectiveness of Sentinel-2 imagery for broad-leaved and coniferous forest classification within the Google Earth Engine (GEE) platform has been assessed. Here, we used Sentinel-2 image collection from the summer period over North Macedonia, when the canopy is fully developed. For the sample collection of the coniferous areas and the accuracy assessment of the classification, we used imagery from the spring period, when the broad-leaved forests are in the early green stage. A Support Vector Machine (SVM) classifier has been used for discriminating forest cover groups, namely, broadleaved and coniferous forests. According to the results, more than 90% of the canopy in North Macedonia is broad-leaved, while less than 10% is conifers. The results in this study show that, with the use of GEE, Sentinel-2 data alone can be effectively used to obtain rapid and accurate mapping of main forest types (conifers-broadleaved) with a fine resolution.

scholarly journals Thirty-Year Dynamics of LULC at the Dong Thap Muoi Area, Southern Vietnam, Using Google Earth Engine

2021 ◽  
Vol 10 (4) ◽  
pp. 226
Author(s):  
Nguyen An Binh ◽  
Huynh Song Nhut ◽  
Nguyen Ngoc An ◽  
Tran Anh Phuong ◽  
Nguyen Cao Hanh ◽  
...  
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Google Earth ◽  
Ancillary Data ◽  
Landsat Images ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Agricultural Structure ◽  
Unused Land ◽  
Google Earth Engine

The main purpose of this paper is to assess the land use and land cover (LULC) changes for thirty years, from 1990–2020, in the Dong Thap Muoi, a flooded land area of the Mekong River Delta of Vietnam using Google Earth Engine and random forest algorithm. The specific purposes are: (1) determine the main LULC classes and (2) compute and analyze the magnitude and rate of changes for these LULC classes. For the above purposes, 128 Landsat images, topographic maps, land use status maps, cadastral maps, and ancillary data were collected and utilized to derive the LULC maps using the random forest classification algorithm. The overall accuracy of the LULC maps for 1990, 2000, 2010, and 2020 are 88.9, 83.5, 87.1, and 85.6%, respectively. The result showed that the unused land was dominant in 1990 with 28.9 % of the total area, but it was primarily converted to the paddy, a new dominant LULC class in 2020 (45.1%). The forest was reduced significantly from 14.4% in 1990 to only 5.5% of the total area in 2020. Whereas at the same time, the built-up increased from 0.3% to 6.2% of the total area. This research may help the authorities design exploitation policies for the Dong Thap Muoi’s socio-economic development and develop a new, stable, and sustainable ecosystem, promoting the advantages of the region, early forming a diversified agricultural structure.

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

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.

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

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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