The present study utilised
Landsat MSS (Multispectral Scanner System) and OLI (Operational Land Imager) datasets of 1980 and 2014 (Table 1).
After discovering that
Landsat 5 was not conducting routine orbit adjustments and scientists were faced with potential degradation in data, Williams was critical in ensuring that the issues were remedied.
Aqui cabe-se ressaltar a sinergia apresentada entre os dados
Landsat 8 OLI, Theos e cenas do Google Earth, as quais permitiram uma analise em multiplas resolucoes espaciais, facilitando a identificacao e as correcoes vetoriais para adequar o mapa de uso e cobertura da terra.
In this study, the NDVI values derived from
Landsat 8 (L8) were greater than NDVI values derived from
Landsat 7 (L7) in mid-season (Figure 3 (2014 and 2016)) and early stages (Figure 3 (2013)).
For example, WorldView-4 data includes four spectral bands, and
Landsat 8 OLI/TIRS provides 11 spectral bands.
In this paper,
Landsat 8 imagery is used as input imagery data to estimate SHI map, and also various urban and vegetation indices are calculated.
One of the reasons that led the Maxver classifier to be excellent was the method of acquisition of training samples, which was not exclusively based on the RGB false-color compositions of the
Landsat and IRS, but also on the analysis of the spectro-temporal profile of the EVI vegetation index of the MODIS sensor, considerably decreasing the spectral confusion in the mapping process.
The experiments are conducted by using the both
Landsat and TerraSAR-X database images.
In this research, the
Landsat 5 Thematic Mapper (TM) datasets of years 2000 and 2009 and
Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) datasets of year 2015 available from the United States Geological Survey (USGS) were used.
The analysis was made possible through a collaboration with colleagues from Google Earth Engine, who implemented the models developed at UMD for characterizing the
Landsat data sets.