scholarly journals Fishpond Mapping by Spectral and Spatial-Based Filtering on Google Earth Engine: A Case Study in Singra Upazila of Bangladesh

2020 ◽  
Vol 12 (17) ◽  
pp. 2692
Author(s):  
Zhiqi Yu ◽  
Liping Di ◽  
Md. Shahinoor Rahman ◽  
Junmei Tang
Keyword(s):  
Google Earth ◽  
Water Bodies ◽  
Lulc Change ◽  
Important Indicator ◽  
Selection Technique ◽  
Water Index ◽  
Small Water ◽  
Normalized Difference Water Index ◽  
Google Earth Engine

Inland aquaculture in Bangladesh has been growing fast in the last decade. The underlying land use/land cover (LULC) change is an important indicator of socioeconomic and food structure change in Bangladesh, and fishpond mapping is essential to understand such LULC change. Previous research often used water indexes (WI), such as Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), to enhance water bodies and use shape-based metrics to assist classification of individual water features, such as coastal aquaculture ponds. However, inland fishponds in Bangladesh are generally extremely small, and little research has investigated mapping of such small water objects without high-resolution images. Thus, this research aimed to bridge the knowledge gap by developing and evaluating an automatic fishpond mapping workflow with Sentinel-2 images that is implemented on Google Earth Engine (GEE) platform. The workflow mainly includes two steps: (1) the spectral filtering phase that uses a pixel selection technique and an image segmentation method to automatically identify all-year-inundated water bodies and (2) spatial filtering phase to further classify all-year-inundated water bodies into fishponds and non-fishponds using object-based features (OBF). To evaluate the performance of the workflow, we conducted a case study in the Singra Upazila of Bangladesh, and our method can efficiently map inland fishponds with a precision score of 0.788. Our results also show that the pixel selection technique is essential in identifying inland fishponds that are generally small. As the workflow is implemented on GEE, it can be conveniently applied to other regions.

scholarly journals Pequenas barragens de água: definição do grau de hierarquização para fins de fiscalização.

2021 ◽  
Author(s):  
◽  
Carla Isoneide Araújo da Silva ◽  
Keyword(s):  
Google Earth ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Google Earth Engine ◽  
Sentinel 2

Dados precisos sobre a distribuição e características de pequenas barragens são importantes para fins de gestão de emergências e planejamento de recursos hídricos em bacia hidrográfica e para auxiliar o monitoramento de indicadores do Objetivo de Desenvolvimento Sustentável (ODS) 6, sobre o uso e disponibilidade dos recursos hídricos e a implementação da gestão integrada dos recursos hídricos em todos os níveis. É necessário, assim, um sistema simplificado que auxilie no processo de identificação e classificação dessas pequenas barragens. Nesse contexto, a proposta deste estudo é identificar a presença de pequenos reservatórios através de imagens do MSI/Sentinel-2 entre janeiro e dezembro de 2020 e elaborar um Grau de Hierarquização (GR) para ações de fiscalização dos órgãos gestores. Foram utilizados para identificação o Normalized Difference Water Index (NDWI), Modified Normalized Difference Water Index e o método de transformação de espaço de cores RGB para HSV. O software QGIS versão 3.10 e o Google Earth Engine foram utilizados para o processamento das imagens e composição dos mapas apresentados. Os resultados comprovaram que o método HSV apresentou melhor resultado na identificação dos alvos propostos. A partir da aplicação do GR a uma pequena barragem de água, foi possível avaliar o seu nível de risco potencial e propor uma escala de prioridade para ações de fiscalização. Por fim, pode-se concluir que o GR pode auxiliar na tomada de decisão, fornecendo aos órgãos públicos uma ferramenta de fácil utilização para avaliar a prioridade de ação em pequenos barramentos.

scholarly journals CLOUD-DESKTOP REMOTE SENSING DATA MANAGEMENT TO ENSURE TIME SERIES ANALYSIS, INTEGRATION OF QGIS AND GOOGLE EARTH ENGINE

2020 ◽  
Vol XLIII-B4-2020 ◽  
pp. 553-557
Author(s):  
E. Panidi ◽  
I. Rykin ◽  
P. Kikin ◽  
A. Kolesnikov
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Data Storage ◽  
Remote Sensing Data ◽  
Growing Season ◽  
Data Representation ◽  
Google Earth ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Google Earth Engine

Abstract. Our context research is conducted to investigate the possibility of common application of the remote sensing and ground-based monitoring data to detection and observation of the dynamics and change in climate and vegetation cover parameters. We applied the analysis of the annual graphs of Normalized Difference Water Index to estimate the length and time frames of growing seasons. Basing on previously gained results, we concluded that we can use the Index-based monitoring of growing season parameters as a relevant technique. We are working on automation of computations that can be applied to processing satellite imagery, computing Normalized Difference Water Index time series (in the forms of maps and annual graphs), and estimation of growing season parameters. As currently used data amounts are big (or up-to-big) geospatial data, we use the Google Earth Engine platform to process initial datasets. Our currently described experimental work incorporates investigation of the possibilities for integration of cloud computing data storage and processing with client-side data representation in universal desktop GISs. To ensure our study needs we developed a prototype of a QGIS plugin capable to run processing in GEE and represent results in QGIS.

scholarly journals Nation-Scale Mapping of Coastal Aquaculture Ponds with Sentinel-1 SAR Data Using Google Earth Engine

2020 ◽  
Vol 12 (18) ◽  
pp. 3086
Author(s):  
Zhe Sun ◽  
Juhua Luo ◽  
Jingzhicheng Yang ◽  
Qiuyan Yu ◽  
Li Zhang ◽  
...  
Keyword(s):  
Coastal Area ◽  
Google Earth ◽  
River Delta ◽  
Water Bodies ◽  
Series Data ◽  
Radar Backscatter ◽  
Water Index ◽  
Coastal Aquaculture ◽  
Aquaculture Ponds ◽  
Google Earth Engine

Global rapid expansion of the coastal aquaculture industry has made great contributions to enhance food security, but has also caused a series of ecological and environmental issues. Sustainable management of coastal areas requires the explicit and efficient mapping of the spatial distribution of aquaculture ponds. In this study, a Google Earth Engine (GEE) application was developed for mapping coastal aquaculture ponds at a national scale with a novel classification scheme using Sentinel-1 time series data. Relevant indices used in the classification mainly include the water index, texture, and geometric metrics derived from radar backscatter, which were then used to segment and classify aquaculture ponds. Using this approach, we classified aquaculture ponds for the full extent of the coastal area in Vietnam with an overall accuracy of 90.16% (based on independent sample evaluation). The approach, enabling wall-to-wall mapping and area estimation, is essential to the efficient monitoring and management of aquaculture ponds. The classification results showed that aquaculture ponds are widely distributed in Vietnam’s coastal area and are concentrated in the Mekong River Delta and Red River delta (85.14% of the total area), which are facing the increasing collective risk of climate change (e.g., sea level rise and salinity intrusion). Further investigation of the classification results also provides significant insights into the stability and deliverability of the approach. The water index derived from annual median radar backscatter intensity was determined to be efficient at mapping water bodies, likely due to its strong response to water bodies regardless of weather. The geometric metrics considering the spatial variation of radar backscatter patterns were effective at distinguishing aquaculture ponds from other water bodies. The primary use of GEE in this approach makes it replicable and transferable by other users. Our approach lays a solid foundation for intelligent monitoring and management of coastal ecosystems.

scholarly journals Mapping of Flood Areas Using Landsat with Google Earth Engine Cloud Platform

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 866
Author(s):  
Hamid Mehmood ◽  
Crystal Conway ◽  
Duminda Perera
Keyword(s):  
Confusion Matrix ◽  
Google Earth ◽  
Data Cube ◽  
Flood Inundation ◽  
Mapping Algorithm ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Information Products ◽  
Google Earth Engine ◽  
The Cost

The Earth Observation (EO) domain can provide valuable information products that can significantly reduce the cost of mapping flood extent and improve the accuracy of mapping and monitoring systems. In this study, Landsat 5, 7, and 8 were utilized to map flood inundation areas. Google Earth Engine (GEE) was used to implement Flood Mapping Algorithm (FMA) and process the Landsat data. FMA relies on developing a “data cube”, which is spatially overlapped pixels of Landsat 5, 7, and 8 imagery captured over a period of time. This data cube is used to identify temporary and permanent water bodies using the Modified Normalized Difference Water Index (MNDWI) and site-specific elevation and land use data. The results were assessed by calculating a confusion matrix for nine flood events spread over the globe. The FMA had a high true positive accuracy ranging from 71–90% and overall accuracy in the range of 74–89%. In short, observations from FMA in GEE can be used as a rapid and robust hindsight tool for mapping flood inundation areas, training AI models, and enhancing existing efforts towards flood mitigation, monitoring, and management.

scholarly journals Monitoring of coastline change using Sentinel-2A and Landsat 8 data, a case study of Cam Pha city - Quang Ninh province

2021 ◽  
Author(s):  
Duong Thi Loi ◽  
Dang Vu Khac ◽  
Dao Ngoc Hung ◽  
Nguyen Thanh Dong ◽  
Dinh Xuan Vinh ◽  
...  
Keyword(s):  
Google Earth ◽  
Landsat 8 ◽  
Small Areas ◽  
Water Index ◽  
Coastline Change ◽  
Combined Use ◽  
Normalized Difference Water Index ◽  
Sentinel 2A ◽  
Quang Ninh

The main purpose of this study is to evaluate the performance of Sentinel - 2A and Landsat 8 data in monitoring coastline change from 1999 to 2018 at Cam Pha city, Quang Ninh province. Both data were collected under similar conditions of time and weather features to minimize the differences in interpretation results caused by these factors. The coastline was extracted from Sentinel-2A and Landsat 8 in 2018 by using the Normalized Difference Water Index (NDWI). Coastline map from Quang Ninh Department of Natural Resources and Environment with a scale of 1: 50.000 in 1999 was used as a reference of the same mask and overlaid on coastline maps in 2018 to identify the changes in the study area. The data from fieldwork and Google Earth was used to evaluate the accuracy and make comparative comments. The results presented that changes dramatically occurred between 1999 and 2018 with the accretion process prevailing. This process took place quite strongly on the East and Southeast coast while the erosion process only occurred with small areas at scattered points in the study area. The results also showed that the overall classification accuracy of Sentinel-2A imagery (95.0%) was slightly higher than that of Landsat-8 (87.5%). The combined use of Landsat-Sentinel-2 imagery is expected to generate reliable data records for continuous detecting of coastline changes.

Fusion of Sentinel-1 radar and Sentinel-2 MSI imagery for water extraction in Tibetan plateau

2020 ◽  
Author(s):  
Dan Li ◽  
Baosheng Wu ◽  
Bowei Chen ◽  
Yanjun Wang ◽  
Yi Zhang ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Radar Data ◽  
Google Earth ◽  
Water Extraction ◽  
Tibet Plateau ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Google Earth Engine ◽  
Optical Imagery ◽  
Sentinel 2

<p><strong>Abstract:</strong> Water plays a vital role in plants, animals and human survival, as well as water resources planning and protection. The spatial and temporal changes of rivers have a profound impact on climate change and the scientific protection of the regional ecological environment in Qingzang-Tibet plateau. Due to the influence of snow and cloud cover, optical remote sensing images in this region have less effective coverage. Many researches in the past mainly faced the challenge of misclassification caused by shadows from cloud and mountain. In this study, we proposed a method to improve the extraction of rivers by reducing the effect of shadows by fusing Sentinel-1 radar data and Sentinel-2 optical imagery. For the optical imagery, water indices including MNDWI (Modified Normalized Difference Water Index) and RNDWI (Revised Normalized Difference Water Index) and morphological operations were used to extract the river coverage. In addition, radar data is used to extract water in areas where there is no optical image coverage or where optical images are misclassified by using a combination of both the histogram and Otsu threshold methods. The GEE (Google Earth Engine) platform is used to implement the analysis using two classification datasets at a regional level. Relevant results from Sentinel-1 and Sentinel-2 data showed that the RNDWI has a more accurate water extraction results in this region. We further compared the final river width results with the manually measured samples from Google Earth and situ data of hydrological stations for accuracy assessment. The R<sup>2 </sup>value is 0.90, and the standard deviation is 18.663m. The river width can be estimated well by this method, which can provide basic data for the study of water in depopulated zone.</p><p><strong>Keywords: </strong>Remote sensing, shadow removal, water extraction, water index, Otsu threshold, Google Earth Engine</p>

scholarly journals Sentinel-1-Imagery-Based High-Resolution Water Cover Detection on Wetlands, Aided by Google Earth Engine

2020 ◽  
Vol 12 (10) ◽  
pp. 1614
Author(s):  
András Gulácsi ◽  
Ferenc Kovács
Keyword(s):  
Clustering Algorithm ◽  
Correlation Coefficients ◽  
Google Earth ◽  
Landsat 8 ◽  
Nature Protection ◽  
Water Index ◽  
Normalized Difference Water Index ◽  
Satellite Sensors ◽  
Google Earth Engine ◽  
Water Cover

Saline wetlands experience large temporal fluctuations in water supply during the year and are recharged only or mainly through precipitation, meaning they are vulnerable to climate-change-induced aridification. Most passive satellite sensors are unsuitable for continuous wetland monitoring due to cloud cover and their relatively low temporal resolution. However, active satellite sensors such as the C-band synthetic aperture radar of Sentinel-1 satellites offer free, cloud-independent data. We examined surface water cover changes from October 2014 to November 2018 in the strictly protected area (13,000 ha) of the Upper-Kiskunság Alkaline Lakes region in the Danube–Tisza Interfluve in Hungary, with the aim of helping with nature protection planning. Changes and sensitivity can be defined based on the knowledge of variability. We developed a method for water cover detection based on automatic classification, applying the so-called WEKA K-Means clustering algorithm. For satellite data processing and analysis, we used the Google Earth Engine cloud processing platform. In terms of validation, we compared our results with the multispectral Modified Normalized Difference Water Index (MNDWI) derived from Landsat 8 and Sentinel-2 top-of-atmosphere reflectance images using a threshold-based binary classifier (receiver operator characteristics) for the MNDWI data. Using two completely distinct methods operating in distinct wavelength ranges, we obtained adequately matching results, with Spearman’s correlation coefficients (ρ) ranging from 0.54 to 0.80.

scholarly journals Migratory Birds Monitoring of India’s Largest Shallow Saline Ramsar Site with Big Geospatial Data Using Google Earth Engine for Restoration

2021 ◽  
Author(s):  
Rajashree Naik ◽  
L.K. Sharma
Keyword(s):  
Migratory Birds ◽  
Diversity Index ◽  
Google Earth ◽  
Water Index ◽  
Research Article ◽  
Index Analysis ◽  
Normalized Difference Water Index ◽  
Future Prediction ◽  
Google Earth Engine ◽  
The Cost

Globally, saline lakes occupying 23% by area 44% by volume among all the lakes might desiccate by 2025 due to agricultural diversion, illegal encroachment, pollution, and invasive species. India’s largest saline lake, Sambhar is currently shrinking at the rate of 4.23% due to illegal saltpan en-croachment. This research article aims to identify the trend of migratory birds and monthly wetland status. Birds survey was conducted for 2019, 2020 and 2021 and combined with literature data of 1994, 2003, and 2013 for visiting trend, feeding habit, migratory and resident ratio, and ecological diversity index analysis. Normalized Difference Water Index was scripted in Google Earth Engine. Results state that it has been suitable for 97 species. Highest NDWI values for the was whole study period was 0.71 in 2021 and lowest 0.008 in 2019 which is highly fluctuating. The decreasing trend of migratory birds coupled with decreasing water level indicates the dubious status for its existence. If the causal factors are not checked, it might completely desiccate by 2059 as per its future prediction. Certain steps are suggested that might help conservation. Least, the cost of restoration might exceed the revenue generation.

scholarly journals Google Earth Engine as Multi-Sensor Open-Source Tool for Supporting the Preservation of Archaeological Areas: The Case Study of Flood and Fire Mapping in Metaponto, Italy

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1791
Author(s):  
Carmen Fattore ◽  
Nicodemo Abate ◽  
Farid Faridani ◽  
Nicola Masini ◽  
Rosa Lasaponara
Keyword(s):  
Open Source ◽  
Archaeological Site ◽  
Unsupervised Classification ◽  
Google Earth ◽  
Heritage Sites ◽  
Cultural Properties ◽  
Google Earth Engine ◽  
Archaeological Areas ◽  
The Impact

In recent years, the impact of Climate change, anthropogenic and natural hazards (such as earthquakes, landslides, floods, tsunamis, fires) has dramatically increased and adversely affected modern and past human buildings including outstanding cultural properties and UNESCO heritage sites. Research about protection/monitoring of cultural heritage is crucial to preserve our cultural properties and (with them also) our history and identity. This paper is focused on the use of the open-source Google Earth Engine tool herein used to analyze flood and fire events which affected the area of Metaponto (southern Italy), near the homonymous Greek-Roman archaeological site. The use of the Google Earth Engine has allowed the supervised and unsupervised classification of areas affected by flooding (2013–2020) and fire (2017) in the past years, obtaining remarkable results and useful information for setting up strategies to mitigate damage and support the preservation of areas and landscape rich in cultural and natural heritage.

Sign in / Sign up

Export Citation Format

Share Document