Google earth engine and landsat data for detecting inundation changes in Limboto lake

Four burned area tools were implemented in Google Earth Engine (GEE), to obtain regular processes related to burned area (BA) mapping, using medium spatial resolution sensors (Landsat and Sentinel-2). The four tools are (i) the BA Cartography tool for supervised burned area over the user-selected extent and period, (ii) two tools implementing a BA stratified random sampling to select the scenes and dates for validation, and (iii) the BA Reference Perimeter tool to obtain highly accurate BA maps that focus on validating coarser BA products. Burned Area Mapping Tools (BAMTs) go beyond the previously implemented Burned Area Mapping Software (BAMS) because of GEE parallel processing capabilities and preloaded geospatial datasets. BAMT also allows temporal image composites to be exploited in order to obtain BA maps over a larger extent and longer temporal periods. The tools consist of four scripts executable from the GEE Code Editor. The tools’ performance was discussed in two case studies: in the 2019/2020 fire season in Southeast Australia, where the BA cartography detected more than 50,000 km2, using Landsat data with commission and omission errors below 12% when compared to Sentinel-2 imagery; and in the 2018 summer wildfires in Canada, where it was found that around 16,000 km2 had burned.

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Rapid and robust monitoring of flood events using Sentinel-1 and Landsat data on the Google Earth Engine

Remote Sensing of Environment ◽

10.1016/j.rse.2020.111664 ◽

2020 ◽

Vol 240 ◽

pp. 111664 ◽

Cited By ~ 14

Author(s):

Ben DeVries ◽

Chengquan Huang ◽

John Armston ◽

Wenli Huang ◽

John W. Jones ◽

...

Keyword(s):

Google Earth ◽

Landsat Data ◽

Flood Events ◽

Google Earth Engine

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Multi-temporal analysis of land cover changes using Landsat data through Google Earth Engine platform

Eighth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2020) ◽

10.1117/12.2571228 ◽

2020 ◽

Author(s):

Alessandra Capolupo ◽

Cristina Monterisi ◽

Mirko Saponaro ◽

Eufemia Tarantino

Keyword(s):

Land Cover ◽

Temporal Analysis ◽

Google Earth ◽

Land Cover Changes ◽

Landsat Data ◽

Multi Temporal ◽

Google Earth Engine

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Brazilian Mangrove Status: Three Decades of Satellite Data Analysis

Remote Sensing ◽

10.3390/rs11070808 ◽

2019 ◽

Vol 11 (7) ◽

pp. 808 ◽

Cited By ~ 16

Author(s):

Cesar Diniz ◽

Luiz Cortinhas ◽

Gilberto Nerino ◽

Jhonatan Rodrigues ◽

Luís Sadeck ◽

...

Keyword(s):

Mangrove Forest ◽

Regional Scale ◽

Google Earth ◽

Mangrove Forests ◽

Landsat Data ◽

Upward Trend ◽

Mangrove Area ◽

Scientific Task ◽

Google Earth Engine ◽

Second Period

Since the 1980s, mangrove cover mapping has become a common scientific task. However, the systematic and continuous identification of vegetation cover, whether on a global or regional scale, demands large storage and processing capacities. This manuscript presents a Google Earth Engine (GEE)-managed pipeline to compute the annual status of Brazilian mangroves from 1985 to 2018, along with a new spectral index, the Modular Mangrove Recognition Index (MMRI), which has been specifically designed to better discriminate mangrove forests from the surrounding vegetation. If compared separately, the periods from 1985 to 1998 and 1999 to 2018 show distinct mangrove area trends. The first period, from 1985 to 1998, shows an upward trend, which seems to be related more to the uneven distribution of Landsat data than to a regeneration of Brazilian mangroves. In the second period, from 1999 to 2018, a trend of mangrove area loss was registered, reaching up to 2% of the mangrove forest. On a regional scale, ~85% of Brazil’s mangrove cover is in the states of Maranhão, Pará, Amapá and Bahia. In terms of persistence, ~75% of the Brazilian mangroves remained unchanged for two decades or more.

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