Field experiments to test the use of the normalized-difference vegetation index for phenology detection

Nagai, Shin; Nasahara, Kenlo Nishida; Muraoka, Hiroyuki; Akiyama, Tsuyoshi; Tsuchida, Seigi

doi:10.1016/j.agrformet.2009.09.010

Cited by 76 publications

(51 citation statements)

References 33 publications

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“…We must consider the presence or absence of snow/water cover and understory vegetation for each target when we use NDVI, as pointed out by several studies [24][25][26][27]. Furthermore, as Nagai et al [13] have reported, the canopy condition of autumn coloring corresponded to different NDVI values from year to year. These results suggest that a single NDVI threshold cannot offer us to detect the vegetation phenology with a simple and stable algorithm.…”

Section: Applicability Of Grvi As An Indicator Of Vegetation Phenologymentioning

confidence: 99%

“…Spectral irradiance data and photographs of sky and canopy surface were taken periodically and automatically at each site by a typical Phenological Eyes Network system (e.g., [13,21,22]), which mainly consists of a Hemi-Spherical Spectro-Radiometer (HSSR) and an Automatic-capturing Digital Fish-eye Camera (ADFC). Both HSSR and ADFC were installed atop a tower or a mast at each site.…”

Section: Observation Systemmentioning

confidence: 99%

“…However, according to the results of time-series ground observation of the spectral indices and seasonal changes of a forest canopy, the conditions of the canopy in autumn indicated by NDVI are not identical among years; therefore, it is difficult to monitor leaf autumn coloring by using time-series NDVI [13]. One simple and possible reason for this problem is that NDVI is not sensitive enough to the leaf-color change from green to yellow or red because green reflectance is not used in the calculation of NDVI.…”

Section: Introductionmentioning

confidence: 99%

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Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology

et al. 2010

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Abstract:We evaluated the use of the Green-Red Vegetation Index (GRVI) as a phenological indicator based on multiyear stand-level observations of spectral reflectance and phenology at several representative ecosystems in Japan. The results showed the relationships between GRVI values and the seasonal change of vegetation and ground surface with high temporal resolution. We found that GRVI has the following advantages as a phenological indicator: (1) "GRVI = 0" can be a site-independent single threshold for detection of the early phase of leaf green-up and the middle phase of autumn coloring, and (2) GRVI can show a distinct response to subtle disturbance and the difference of ecosystem types.

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Section: Applicability Of Grvi As An Indicator Of Vegetation Phenologymentioning

confidence: 99%

Section: Observation Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology

et al. 2010

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“…These measurements provided a set of hyper-spectral reflectances of the ground surface and hemispherical photos of the cloud conditions in the sky at the satellite overpass time. Several papers have demonstrated the usefulness of these monitoring systems when evaluating ecological remote sensing [20][21][22][23]. We evaluated the residual sub-pixel cloud noise by comparing the MODIS data with the in situ data.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Sub-Pixel Cloud Noises on MODIS Daily Spectral Indices Based on in situ Measurements

et al. 2011

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Cloud contamination is one of the severest problems for the time-series analysis of optical remote sensing data such as vegetation phenology detection. Sub-pixel clouds are especially difficult to identify and remove. It is important for accuracy improvement in various terrestrial remote sensing applications to clarify the influence of these residual clouds on spectral vegetation indices. This study investigated the noises caused by residual sub-pixel clouds on several frequently-used spectral indices (NDVI, EVI, EVI2, NDWI, and NDII) by using in situ spectral data and sky photographs at the satellite overpass time. We conducted in situ continuous observation at a Japanese deciduous forest for over a year and compared the MODIS spectral indices with the cloud-free in situ spectral indices. Our results revealed that residual sub-pixel clouds potentially contaminated about 40% of the MODIS data after cloud screening by the state flag of MOD09 product. These residual clouds significantly decreased NDVI values during the leaf growing season. However, such noises did not appear in the other indices. This result was thought to be caused by the different combination of wavelengths among spectral indices. Our results suggested that the noises by residual sub-pixel clouds can be reduced by using EVI, NDWI, or NDII in place of NDVI. OPEN ACCESSRemote Sens. 2011, 3 1645

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“…Fitted curves for vegetation index (VI) time series are used (e.g., determining the transition dates from the local maxima and minima in the fitted function), including the enhanced vegetation index (EVI) [4,18,21], the normalized difference vegetation index (NDVI) [2,20,22], and the MERIS Terrestrial Chlorophyll Index (MTCI) [23]. The advantage of remote-sensing data is that they are uniquely spatially explicit, and are useful for evaluating and validating models and assumptions with a wider spatial coverage compared to flux measurements.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Multiple Spring Phenological Indicators of Yearly GPP and NEP at Three Canadian Forest Sites

Wang

Zhang

et al. 2014

Remote Sensing

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Phenological shifts in events such as flowering and bud break are important indicators of ecosystem processes, and are therefore of particular significance for carbon (C) cycle research. Using long-term flux data from three contrasting plant functional type (evergreen and deciduous) boreal forest sites, we evaluated and compared the responses of annual C fluxes to multiple spring phenological indicators, including the C-uptake period onset (CUP onset), spring temperature (average value from March to May), and satellite-derived enhanced vegetation index (EVI) (average value from March to May). We found that the CUP onset was negatively correlated with annual gross primary production (GPP) for all three sites, but that its predictive strength for annual net ecosystem production (NEP) differed substantially among plant functional types. Spring temperature demonstrated particularly good potential for predicting both annual GPP and NEP for the evergreen sites, but not for the deciduous site. Spring EVI was demonstrated to have potential for predicting annual NEP for all sites. However, both plant functional types confounded the correlation of annual NEP with annual GPP. Although none of these phenological indicators provided consistent insight into annual C fluxes, using various currently available datasets our results remain potentially useful for the assessment of forest C cycling with future climate change. Previous analyses using only a single phenological metric should be considered with caution. OPEN ACCESSRemote Sens. 2014Sens. , 6 1992

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Field experiments to test the use of the normalized-difference vegetation index for phenology detection

Cited by 76 publications

References 33 publications

Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology

Applicability of Green-Red Vegetation Index for Remote Sensing of Vegetation Phenology

Evaluation of Sub-Pixel Cloud Noises on MODIS Daily Spectral Indices Based on in situ Measurements

Evaluation of Multiple Spring Phenological Indicators of Yearly GPP and NEP at Three Canadian Forest Sites

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