Comparison of the sensor dependence of vegetation indices based on Hyperion and CHRIS hyperspectral data

Chen, Xiaoping; Zhang, Lifu; Xia, Zhang; Liu, Bo

doi:10.1080/01431161.2012.742216

Cited by 8 publications

(5 citation statements)

References 35 publications

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“…VIUPD produced the best R 2 values, indicating that it was less sensor dependent. Previous studies of VIUPD consistency between Hyperion and the Compact High Resolution Imaging Spectrometer (CHRIS), and between the Moderate Resolution Imaging Spectrometer (MODIS) and thematic mapper (TM) have been reported [21,34]. Both studies reported that VIUPD had a high consistency among the different platform sensors, which is consistent with our results.…”

Section: Discussionsupporting

confidence: 82%

“…However, due to the various sensor characteristics, there are differences among VIs derived from multiple sensors for the same target [10][11][12][13][14]. Therefore, multi-sensor VI continuity and compatibility are critical but complicated issues in the application of multi-sensor vegetation observations [11,[15][16][17][18][19][20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types

She

Zhang

et al. 2015

Remote Sensing

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Landsat 8, the most recently launched satellite of the series, promises to maintain the continuity of Landsat 7. However, in addition to subtle differences in sensor characteristics and vegetation index (VI) generation algorithms, VIs respond differently to the seasonality of the various types of vegetation cover. The purpose of this study was to elucidate the effects of these variations on VIs between Operational Land Imager (OLI) and Enhanced Thematic Mapper Plus (ETM+). Ground spectral data for vegetation were used to simulate the Landsat at-senor broadband reflectance, with consideration of sensor band-pass differences. Three band-geometric VIs (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI)) and two band-transformation VIs (Vegetation Index based on the Universal Pattern Decomposition method (VIUPD), Tasseled Cap Transformation Greenness (TCG)) were tested to evaluate the performance of various VI generation algorithms in relation to multi-sensor continuity. Six vegetation types were included to evaluate the continuity in different vegetation types. Four pairs of data during four seasons were selected to evaluate continuity with respect to seasonal variation. The simulated data showed that OLI largely inherits the band-pass characteristics of ETM+. Overall, the continuity of band-transformation derived VIs was higher than OPEN ACCESSRemote Sens. 2015, 7 13486 band-geometry derived VIs. VI continuity was higher in the three forest types and the shrubs in the relatively rapid growth periods of summer and autumn, but lower for the other two non-forest types (grassland and crops) during the same periods.

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Section: Discussionsupporting

confidence: 82%

Section: Introductionmentioning

confidence: 99%

Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types

She

Zhang

et al. 2015

Remote Sensing

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“…In this case, NDVI from the bandwidth of specific satellite sensor (MODIS, in this study) may not best reflect the vegetation water status in some particular regions. VIUPD has been proved to be a sensor-independent index which can greatly avoid the effect of difference in spectral bandwidth [22,23].…”

Section: Facters Which Affect the Performance Of Viupd-derived Vcimentioning

confidence: 99%

“…Unlike the traditional broadband vegetation indices that are usually computed using the near-infrared and red bands, VIUPD is based on all observed bands. Narrowband hyperspectral data-based VIUPD has been proven to be sensitive to spectral operations and was more sensor-independent than the other 11 vegetation indices, including the NDVI and EVI [23]. VIUPD also showed great potential to discern variations in urban LST when it was used to analyze the urban heat island effect in Shijiazhuang, China [24].…”

Section: Introductionmentioning

confidence: 99%

Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States

et al. 2016

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Drought is a complex hazard, and it has an impact on agricultural, ecological, and socio-economic systems. The vegetation condition index (VCI), which is derived from remote-sensing data, has been widely used for drought monitoring. However, VCI based on the normalized difference vegetation index (NDVI) does not perform well in certain circumstances. In this study, we examined the utility of the vegetation index based on the universal pattern decomposition method (VIUPD) based VCI for drought monitoring in various climate divisions across the continental United States (CONUS). We compared the VIUPD-derived VCI with the NDVI-derived VCI in various climate divisions and during different sub-periods of the growing season. It was also compared with other remote-sensing-based drought indices, such as the temperature condition index (TCI), precipitation condition index (PCI) and the soil moisture condition index (SMCI). The VIUPD-derived VCI had stronger correlations with long-term in situ drought indices, such as the Palmer Drought Severity Index (PDSI) and the standardized precipitation index (SPI-3, SPI-6, SPI-9, and SPI-12) than did the NDVI-derived VCI, and other indices, such as TCI, PCI and SMCI. The VIUPD has considerable potential for drought monitoring. As VIUPD can make use of the information from all the observation bands, the VIUPD-derived VCI can be regarded as an enhanced VCI.

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“…The SG smoothing and MA (smoothing window of five points) techniques were adopted to reduce the influence of high-frequency noise and baseline translation noise while retaining the unique characteristics of the samples (Chen et al 2012; Zhang et al 2020). SG is the most commonly used smoothing algorithm.…”

Section: Methodsmentioning

confidence: 99%

Characterization and classification of urban weed species in northeast China using terrestrial hyperspectral images

Wang,

Chen,

et al. 2023

Weed Sci

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Weeds contribute to biodiversity and a wide range of ecosystem functions. It is crucial to map different weed species and analyze their physiological activities. Remote sensing techniques for plant identification, especially hyperspectral imaging, are being developed using spectral response patterns to vegetation for detection and species identification. A library of hyperspectral images of 40 urban weed species in northeast China was established in this study. A terrestrial hyperspectral camera was used to acquire 435 hyperspectral images. The hyperspectral information for each weed species was extracted and analyzed. The spectral characteristics and vegetation indices of different weeds revealed the differences between weed species in the cities of northeast China and indirectly characterized the growth and physiological activity levels of different species, but could not effectively distinguish different species. Five methods—first derivative spectrum (FDS), second derivative spectrum (SDS), standard normal variate (SNV), moving averages (MA), and Savitzky-Golay (SG) smoothing—were used to pretreat the spectral curves to maximize the retention of spectral characteristics while removing the influence of noise. We investigated the application of a convolutional neural network (CNN) with terrestrial hyperspectral remote sensing to identify urban weeds in northeast China. A CNN classification model was established to distinguish weeds from the hyperspectral images and demonstrated a test accuracy of 95.32% to 98.15%. The accuracy of the original spectrum was 97.45%; SNV had the best accuracy (98.15%) and SG was the least accurate (95.32%). This provides a baseline for understanding the hyperspectral characteristics of urban weed species and monitoring their growth. It also contributes to the development of a hyperspectral imaging database with global applicability.

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Comparison of the sensor dependence of vegetation indices based on Hyperion and CHRIS hyperspectral data

Cited by 8 publications

References 35 publications

Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types

Comparison of the Continuity of Vegetation Indices Derived from Landsat 8 OLI and Landsat 7 ETM+ Data among Different Vegetation Types

Evaluating an Enhanced Vegetation Condition Index (VCI) Based on VIUPD for Drought Monitoring in the Continental United States

Characterization and classification of urban weed species in northeast China using terrestrial hyperspectral images

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