Compare NDVI Extracted from Landsat 8 Imagery with that from Landsat 7 Imagery

Xu, Dandan; Guo, Xulin

doi:10.11648/j.ajrs.20140202.11

Cited by 72 publications

(37 citation statements)

References 18 publications

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“…On the other hand, the intercalibration analysis among different sensors within the same satellite has shown good results. As an example, in Xu and Guo [27], the cross-comparison between the Normalized Difference Vegetation Index (NDVI) extracted from Landsat 8 and Landsat 7 images, showed larger differences between NDVI calculated from the two generations of Landsat in lower vegetation covered areas; however, the difference decreases at higher vegetation cover (i.e., the NDVI values increase).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…However, due to their insufficient spatial resolution, these time-series can detect only patches of bare soil for winter land use at the field scale ([10]. High and very high spatial resolution remotely sensed data are also widely used to discriminate the main crop rotations and land uses at the field scale [11,12]. Refs.…”

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confidence: 99%

Evaluation of Using Sentinel-1 and -2 Time-Series to Identify Winter Land Use in Agricultural Landscapes

et al. 2018

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Monitoring vegetation cover during winter is a major environmental and scientific issue in agricultural areas. From an environmental viewpoint, the presence and type of vegetation cover in winter influences the transport of pollutants to water resources. From a methodological viewpoint, characterizing spatio-temporal dynamics of land cover and land use at the field scale is challenging due to the diversity of farming strategies and practices in winter. The objective of this study was to evaluate the respective advantages of Sentinel optical and SAR time-series to identify land use in winter. To this end, Sentinel-1 and -2 time-series were classified using Support Vector Machine and Random Forest algorithms in a 130 km² agricultural area. From the classification, the Sentinel-2 time-series identified winter land use more accurately (overall accuracy (OA) = 75%, Kappa index = 0.70) than that of Sentinel-1 (OA = 70%, Kappa = 0.66) but a combination of the Sentinel-1 and -2 time-series was the most accurate (OA = 81%, Kappa = 0.77). Our study outlines the effectiveness of Sentinel-1 and -2 for identify land use in winter, which can help to change agricultural practices.

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“…Satellite-borne remote sensing data are tested for monitoring drought extent in about 184. Similarly, different vegetation and drought indices were employed for drought estimation [9][10][11][12][13]. However, few studies are available in data-scare regions such as Thar Desert.…”

Section: Introductionmentioning

confidence: 99%

Spatial Drought Monitoring in Thar Desert Using Satellite-Based Drought Indices and Geo-Informatics Techniques

Bilal

Liaqat

Cheema

et al. 2017

The 2nd International Electronic Conference on Water Sciences

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Drought is a continuous process in Thar Desert, Pakistan. The extent of this drought needs to be assessed for future land use and adaptation. The effect of previous drought on vegetation cover of the Thar region was studied, through combined use of drought indices and geographic information (GIS) techniques. Five years (2002, 2005, 2008, 2011 and 2014) were selected to analyze the drought conditions and land use pattern of the Thar region. The drought indices used in this study included the Normalized Difference Vegetation Index (NDVI) and the Standard Precipitation Index (SPI). Images of past drought were compared with post-drought images of our targeted area and land use maps were developed for spatio-temporal analysis. The results of the study revealed that vegetation in Thar showed an improving trend from 2002 to 2011 and then declined from 2011 to 2014. The rainfall occurred at a below average rate and SPI values for each year were calculated to be negative, indicating below average rainfall. This actual precipitation data was then compared with the data obtained from the Tropical Rainfall Measuring Mission (TRMM) satellite and R 2 ; Pearson correlation coefficients were also calculated. The R 2 values for the years 2002 and 2014 were 0.519 and 0.670 respectively. In the same manner, the Pearson correlation coefficient values for the years 2002 and 2014 were 0.721 and 0.867 respectively. The results showed the TRMM satellite's over-estimation in calculating rainfall data. Further, the average temperature for the five years under study was analyzed by graphical representation and it was revealed that the temperature of Thar has increased by almost 1 °C during the last decade.

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Compare NDVI Extracted from Landsat 8 Imagery with that from Landsat 7 Imagery

Cited by 72 publications

References 18 publications

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

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

Evaluation of Using Sentinel-1 and -2 Time-Series to Identify Winter Land Use in Agricultural Landscapes

Spatial Drought Monitoring in Thar Desert Using Satellite-Based Drought Indices and Geo-Informatics Techniques

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