Scrutinising MODIS and GIMMS Vegetation Indices for Extracting Growth Rhythm of Natural Vegetation in India

Jeganathan, C.; Nishant, Nilay

doi:10.1007/s12524-013-0337-5

Cited by 15 publications

(11 citation statements)

References 38 publications

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“…For the natural vegetation located in the central parts of the Indian monsoon region (14°N-26°N, 70°E-88°E), the extracted SOS in the GLOBMAP-IndianSOS product varied from the DOY of 120 to 180, which corresponded with SOS derived from the average MODIS-NDVI and the average MODIS-EVI from 2001 to 2010 as shown in Figure 3b,c in the study of Jeganathan and Nishant [19]. Figure 11a shows the counts of valid SOS retrievals in the MCD12Q2 product from 2001 to 2014.…”

Section: Comparison With the Meris Phenology Productmentioning

confidence: 76%

“…The GLOBMAP-IndianSOS product was also compared with the phenology results that were derived from MOD13C1 NDVI and EVI (5.6 km, 16 day) [19]. Figure 3b,c in the study of Jeganathan and Nishant [19].…”

Section: Comparison With the Meris Phenology Productmentioning

confidence: 99%

“…The GLOBMAP-IndianSOS product was also compared with the phenology results that were derived from MOD13C1 NDVI and EVI (5.6 km, 16 day) [19]. For the natural vegetation located in the central parts of the Indian monsoon region (14°N-26°N, 70°E-88°E), the extracted SOS in the GLOBMAP-IndianSOS product varied from the DOY of 120 to 180, which corresponded with SOS derived from the average MODIS-NDVI and the average MODIS-EVI from 2001 to 2010 as shown in Figure 3b,c in the study of Jeganathan and Nishant [19].…”

Section: Comparison With the Meris Phenology Productmentioning

confidence: 99%

“…Logistic was used for generating the MODIS LSP product (MCD12Q2) [13][14][15], but it was inapplicable for modeling the vegetation growth trajectories with a two-stage greenness increase [18] or sharp curve valleys. Fourier was chosen to extract phenology from the MERIS Terrestrial Chlorophyll Index (MTCI, 4.6 km) [16], MOD13C1 NDVI and EVI (5.6 km, 16 day), and GIMMS NDVI (8 km, 15 day) [19] in India. However, Fourier would appear as spurious oscillations in the long stable part of the vegetation growth trajectory.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Determining the Start of the Growing Season from MODIS Data in the Indian Monsoon Region: Identifying Available Data in the Rainy Season and Modeling the Varied Vegetation Growth Trajectories

Shang

Liu

et al. 2018

Remote Sensing

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Abstract:In the Indian monsoon region, frequent cloud cover in the rainy season results in less valid satellite observations during the vegetation growth period, making it difficult to extract land surface phenology (LSP). Even worse, many valid but humid observations were misidentified as clouds in the MODIS cloud mask, causing severe gaps in the LSP product. Using a refined cloud detection approach to separate clear-sky and cloudy observations, this study found that potentially valid observations during the vegetation growth period could be identified. Furthermore, the varied vegetation growth trajectories cannot be well-fitted by a global curve-fitting approach, but can be modelled by using the locally adjusted cubic-spline capping approach, which performed well for any seasonal patterns. Applying this approach, the start of growing season (SOS) was determined with 9.18% of vegetation growth amplitude between the maximum and minimum NDVI to generate the SOS product (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015)(2016). The valid percentage of this regional product largely increased from 29.30% to 69.76% compared with the MCD12Q2 product, and its reliability was approximate to that of deciduous broadleaf forest in North America and Europe. This product could serve as a basis for understanding the response of terrestrial ecosystems to the changing Indian monsoon.

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Section: Comparison With the Meris Phenology Productmentioning

confidence: 76%

Section: Comparison With the Meris Phenology Productmentioning

confidence: 99%

Section: Comparison With the Meris Phenology Productmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Determining the Start of the Growing Season from MODIS Data in the Indian Monsoon Region: Identifying Available Data in the Rainy Season and Modeling the Varied Vegetation Growth Trajectories

Shang

Liu

et al. 2018

Remote Sensing

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“…Recently, GIMMS and MODIS data was used to extract phenological parameters across the country (Jeganathan and Nishant, 2013).…”

Section: Introductionmentioning

confidence: 99%

Study of temporal variation of vegetation indices and phenology of tropical deciduous broadleaf forest in eastern India

Gupta

2014

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In the recent years study of vegetation variation has taken significance as a quintessential part of a bigger research agenda of climate change. This study has attempted to (a) Analyse the inter-annual and seasonal variation of vegetation (b) Observe the variation in phenological transition dates of tropical deciduous broadleaf forest (DBF) in Eastern India from 2003 to 2012. The study was conducted on 1500 sq. km area of dense forest in the districts of West Singhbhum and Sundargarh. MODIS EVI data of 250 meter resolution was used. Land cover mask was used to study only the DBF pixels. Least squares convolution method proposed by Savitzky and Golay was used for noise reduction and smoothing. For the determination of phenological parameters iterative Savitzky-Golay method followed by threshold method was used in TIMESAT software. The results showed that (i) EVI varied between 0.41 in early April and 0.71 in mid October (ii) The overall trend was decreasing with a slope of 0.0022, representing a degradation trend (iii) EVI of summer season was found to be more stable than of rainy and winter season (iv) Summer and rainy months showed a decreasing trend whereas the winter months showed an overall increase (v) Day of start of season varied between 2 nd May and 20 th June whereas the day of end of season varied between 1 st February and 7 th March (vi) Length of season was longest for 2007 at 302 days and shortest for 2010 at 235 days and showed an overall decreasing trend.

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The relationship between central Indian terrestrial vegetation and monsoon rainfall distributions in different hydroclimatic extreme years using time-series satellite data

Singh

Jeganathan

Rathore

et al. 2023

Theor Appl Climatol

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The study explored the dependence of the Spatio-temporal pattern of rainfall and its variability on the spatial distribution of forests in Central Indian Landscape, which covers ~1Million Km 2 , includes five states, and supports a population of 329 million people. The monsoon rainfall is, thus, a crucial source of freshwater for these population. We analyzed the relationship between rainfall and satellite-derived vegetation vigour, vegetation fraction, and elevation across 22 experimental zones across central India (i.e., forested, non-forested, and agricultural regions; buffer zones within and outside forests). Around 87% of annual rainfall is received during monsoon, with maximum rainfall (~1600 mm) in Odisha and minimum (~900 mm) in Maharashtra. The average rainfall was greater (~1500 mm) inside forests than in non-forested regions (~1000 mm). Moreover, 245 mm km -2 yr -1 of rainfall observed over forests during monsoon, but only 215 mm km -2 yr -1 in non-forested areas. Overall, rainfall increases from forest edge towards forest core logarithmically at a rate of ~10 mm km -1 yr -1 , and it decreases exponentially when moving away from forest edge at an average rate of -20 mm km -1 yr -1 over 0-to-50 km range, and at a rate of -7.5 mm km -1 yr -1 over 50-to-100 km range. This rate of decrease was maximum in Madhya Pradesh and Jharkhand and minimum in Chhattisgarh. The results confirmed a crucial role of forests in the distribution of monsoon rainfall, but in the elevated and Western-Ghats regions orographic effect is dominant.These findings are of great concern to forest policymakers to conserve and protect the central Indian forests.

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Scrutinising MODIS and GIMMS Vegetation Indices for Extracting Growth Rhythm of Natural Vegetation in India

Cited by 15 publications

References 38 publications

Determining the Start of the Growing Season from MODIS Data in the Indian Monsoon Region: Identifying Available Data in the Rainy Season and Modeling the Varied Vegetation Growth Trajectories

Determining the Start of the Growing Season from MODIS Data in the Indian Monsoon Region: Identifying Available Data in the Rainy Season and Modeling the Varied Vegetation Growth Trajectories

Study of temporal variation of vegetation indices and phenology of tropical deciduous broadleaf forest in eastern India

The relationship between central Indian terrestrial vegetation and monsoon rainfall distributions in different hydroclimatic extreme years using time-series satellite data

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