Cross-Comparison between Landsat 8 (OLI) and Landsat 7 (ETM+) Derived Vegetation Indices in a Mediterranean Environment

Abstract

Landsat 8 is the most recent generation of Landsat satellite missions that provides remote sensing imagery for earth observation. The Landsat 7 Enhanced Thematic Mapper Plus (ETM+) images, together with Landsat-8 Operational Land Imager (OLI) and Thermal Infrared sensor (TIRS) represent fundamental tools for earth observation due to the optimal combination of the radiometric and geometric images resolution provided by these sensors. However, there are substantial differences between the information provided by Landsat 7 and Landsat 8. In order to perform a multi-temporal analysis, a cross-comparison between image from different Landsat satellites is required. The present study is based on the evaluation of specific intercalibration functions for the standardization of main vegetation indices calculated from the two Landsat generation images, with respect to main land use types. The NDVI (Normalized Difference Vegetation Index), NDWI (Normalized Difference Water Index), LSWI (Land Surface Water Index), NBR (Normalized Burn Ratio), VIgreen (Green Vegetation Index), SAVI (Soil Adjusted Vegetation Index), and EVI (Enhanced Vegetation Index) have been derived from August 2017 ETM+ and OLI images (path: 188; row: 32) for the study area (Basilicata Region, located in the southern part of Italy) selected as a highly representative of Mediterranean environment. Main results show slight differences in the values of average reflectance for each band: OLI shows higher values in the near-infrared (NIR) wavelength for all the land use types, while in the short-wave infrared (SWIR) the ETM+ shows higher reflectance values. High correlation coefficients between different indices (in particular NDVI and NDWI) show that ETM+ and OLI can be used as complementary data. The best correlation in terms of cross-comparison was found for NDVI, NDWI, SAVI, and EVI indices; while according to land use classes, statistically significant differences were found for almost all the considered indices calculated with the two sensors.

Spatio-Temporal Response of Vegetation Indices to Rainfall and Temperature in A Semiarid Region

“…These improvements are based on the introduction of the blue band to reduce the effects of the atmospheric aerosols in the red band, and on some correction coefficients to reduce the effect of soil reflectance. According to these differences, the NDVI is more sensitive to the chlorophyll content, whilst the EVI is more sensitive to the structural characteristics of the vegetation cover [59]. Some studies used the NDVI and EVI in semiarid climates and reported that the EVI can provide greater dynamic range than the NDVI [60].…”

Modelling of Expansion Changes of Vilnius City Area and Impacts on Landscape Patterns Using an Artificial Neural Network

“…The OLI sensor contains enhanced bands due to new linear detector arrays which gather images in a push-broom scanner mode providing a superior signal with a high signal to noise proportion. The basic discrepancy between TM and OLI sensor refer to the overall image quality, to the different number of spectral bands, width and their spatial resolutions [38]. We used the RGB combination to better classify satellite images.…”

Small fires, frequent clouds, rugged terrain and no training data: a methodology to reconstruct fire history in complex landscapes

“…After extracting these five indices for every image of the time-series, a sequential image differencing process where each year is subtracted by the following year's values (resulting in dNBR, dNDMI, dBRI, dGRE, and dWET) is executed. Bi-temporal image differencing is a common change detection approach, widely used in burned area and burn severity assessments (Lu et al 2004;Key and Benson 2006), which can also be successfully applied to image pairs from different sensors provided that their spectral characteristics are similar, such as between Landsat TM, ETM+, and OLI, and proper calibration is performed (Oguro et al 2003;Steven et al 2003;Li et al 2013;Roy et al 2016;Mancino et al 2020). No calibration coefficients were applied but a normalized version of the difference images using z-score standardization was produced.…”

“…Calibration coefficients for single bands (Roy et al 2016) and for several spectral indices (Steven et al 2003;Mancino et al 2020) based on linear relationships between the values derived from different sensors, have been proposed. However, among the different spectral indices analyzed, the most recent work on this topic (Mancino et al 2020) showed that NBR mean values between the ETM+ and OLI sensors are the least affected and their relationship is not linear, limiting their calibration potential. For the present version of the algorithm, we decided to not apply calibration coefficients.…”