Arindam Samanta

Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011

Long-term global data sets of vegetation Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) are critical to

Comment on “Drought-Induced Reduction in Global Terrestrial Net Primary Production from 2000 Through 2009”

Zhao and Running (Reports, 20 August 2010, p. 940) reported a reduction in global terrestrial net primary production (NPP) from 2000 through 2009. We argue that the small trends, regional patterns, and interannual variations that they describe are artifacts of their NPP model. Satellite observations of vegetation activity show no statistically significant changes in more than 85% of the vegetated lands south of 70°N during the same 2000 to 2009 period.

Seasonal changes in leaf area of Amazon forests from leaf flushing and abscission

[1] A large increase in near-infrared (NIR) reflectance of Amazon forests during the light-rich dry season and a corresponding decrease during the light-poor wet season has been observed in satellite measurements. This increase has been variously interpreted as seasonal change in leaf area resulting from net leaf flushing in the dry season or net leaf abscission in the wet season, enhanced photosynthetic activity during the dry season from flushing new leaves and as change in leaf scattering and absorption properties between younger and older leaves covered with epiphylls. Reconciling these divergent views using theory and observations is the goal of this article. The observed changes in NIR reflectance of Amazon forests could be due to similar, but small, changes in NIR leaf albedo (reflectance plus transmittance) resulting from the exchange of older leaves for newer ones, but with the total leaf area unchanged. However, this argument ignores accumulating evidence from ground-based reports of higher leaf area in the dry season than the wet season, seasonal changes in litterfall and does not satisfactorily explain why NIR reflectance of these forests decreases in the wet season. More plausibly, the increase in NIR reflectance during the dry season and the decrease during the wet season would result from changes in both leaf area and leaf optical properties. Such change would be consistent with known phenological behavior of tropical forests, ground-based reports of seasonal changes in leaf area, litterfall, leaf optical properties and fluxes of evapotranspiration, and thus, would reconcile the various seemingly divergent views.

Vegetation response to extreme climate events on the Mongolian Plateau from 2000 to 2010

Climate change has led to more frequent extreme winters (aka, dzud) and summer droughts on the Mongolian Plateau during the last decade. Among these events, the 2000?2002 combined summer drought?dzud and 2010 dzud were the most severe on vegetation. We examined the vegetation response to these extremes through the past decade across the Mongolian Plateau as compared to decadal means. We first assessed the severity and extent of drought using the Tropical Rainfall Measuring Mission (TRMM) precipitation data and the Palmer drought severity index (PDSI). We then examined the effects of drought by mapping anomalies in vegetation indices (EVI, EVI2) and land surface temperature derived from MODIS and AVHRR for the period of 2000?2010. We found that the standardized anomalies of vegetation indices exhibited positively skewed frequency distributions in dry years, which were more common for the desert biome than for grasslands. For the desert biome, the dry years (2000?2001, 2005 and 2009) were characterized by negative anomalies with peak values between ?1.5 and ?0.5 and were statistically different (P?<?0.001) from relatively wet years (2003, 2004 and 2007). Conversely, the frequency distributions of the dry years were not statistically different (p?<?0.001) from those of the relatively wet years for the grassland biome, showing that they were less responsive to drought and more resilient than the desert biome. We found that the desert biome is more vulnerable to drought than the grassland biome. Spatially averaged EVI was strongly correlated with the proportion of land area affected by drought (PDSI?<??1) in Inner Mongolia (IM) and Outer Mongolia (OM), showing that droughts substantially reduced vegetation activity. The correlation was stronger for the desert biome (R2?=?65 and 60, p?<?0.05) than for the IM grassland biome (R2?=?53, p?<?0.05). Our results showed significant differences in the responses to extreme climatic events (summer drought and dzud) between the desert and grassland biomes on the Plateau.

MODIS Enhanced Vegetation Index data do not show greening of Amazon forests during the 2005 drought

Ecosystems 12: 489–502. Schoeneweiss DF. 1975. Predisposition, stress, and plant disease. Annual Review of Phytopathology 1: 19–211. Shaw MW. 2009. Preparing for changes in plant disease due to climate change. Plant Protection Science 45: S3–S10. Stevens RB. 1960. In: Horsfall JG, Dimond AE, eds. Plant pathology, an advanced treatise, Vol 3. New York, NY, USA: Academic Press, 357–429. Storer AJ, Wood DL, Gordon TR. 2002. The epidemiology of pitch canker of Monterey pine in California. Forest Science 48: 694–700. Storer AJ, Wood DL, Wikler KR, Gordon TR. 1998. Association between a native spittlebug (Homoptera: Cercopidae) on Monterey pine and an introduced tree pathogen which causes pitch canker disease. Canadian Entomologist 10: 783–792. Yamada T, Hasegawa E, Miyashita S, Aoki H. 2000. Participation of insect attack on the development of resinous stem canker of Hinoki cypress and Hiba arbor-vitae. (Abstract in) Journal of the Japanese Forestry Society 82: 141–147.

Why Is Remote Sensing of Amazon Forest Greenness So Challenging

The prevalence of clouds and aerosols and their impact on satellite-measured greenness levels of forests in southern and central Amazonia are explored in this article using 10 years of NASA Moderate Resolution Imaging Spectroradiometer (MODIS) greenness data: normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). During the wet season (October-March), cloud contamination of greenness data is pervasive;

Decadal variations in NDVI and food production in India

In this study we use long-term satellite, climate, and crop observations to document the spatial distribution of the recent stagnation in food grain production affecting the water-limited tropics (WLT), a region where 1.5 billion people live and depend on local agriculture that is constrained by chronic water shortages. Overall, our analysis shows that the recent stagnation in food production is corroborated by satellite data. The growth rate in annually integrated vegetation greenness, a measure of crop growth, has declined significantly (p < 0.10) in 23% of the WLT cropland area during the last decade, while statistically significant increases in the growth rates account for less than 2%. In

Divergent Arctic-Boreal Vegetation Changes between North America and Eurasia over the Past 30 Years

Arctic-Boreal region—mainly consisting of tundra, shrub lands, and boreal forests—has been experiencing an amplified warming over the past 30 years. As the main driving force of vegetation growth in the north, temperature exhibits tight coupling with the Normalized Difference Vegetation Index (NDVI)—a proxy to photosynthetic activity. However, the comparison between North America (NA) and northern Eurasia (EA) shows a weakened spatial dependency of vegetation growth on temperature changes in NA during the past decade. If this relationship holds over time, it suggests a 2/3 decrease in vegetation growth under the same rate of warming in NA, while the vegetation response in EA stays the same. This divergence accompanies a circumpolar widespread greening trend, but 20 times more browning in the Boreal NA compared to EA, and comparative greening and browning trends in the Arctic. These observed spatial patterns of NDVI are consistent with the temperature record, except in the Arctic NA, where vegetation exhibits a similar long-term trend of greening to EA under less warming. This unusual growth pattern in Arctic NA could be due to a lack of precipitation velocity compared to the temperature velocity, when taking velocity as a measure of northward migration of climatic conditions.

Interpretation of variations in MODIS-measured greenness levels of Amazon forests during 2000 to 2009

This work investigates variations in satellite-measured greenness of Amazon forests using ten years of NASA Moderate Resolution Imaging Spectroradiometer (MODIS) enhanced vegetation index (EVI) data. Corruption of optical remote sensing data with clouds and aerosols is prevalent in this region; filtering corrupted data causes spatial sampling constraints, as well as reducing the record length, which introduces large biases in estimates of greenness anomalies. The EVI data, analyzed in multiple ways and taking into account EVI accuracy, consistently show a pattern of negligible changes in the greenness levels of forests both in the area affected by drought in 2005 and outside it. Small random patches of anomalous greening and browning—especially prominent in 2009—appear in all ten years, irrespective of contemporaneous variations in precipitation, but with no persistence over time. The fact that over 90% of the EVI anomalies are insignificantly small—within the envelope of error (95% confidence interval) in EVI—warrants cautious interpretation of these results: there were no changes in the greenness of these forests, or if there were changes, the EVI data failed to capture these either because the constituent reflectances were saturated or the moderate resolution precluded viewing small-scale variations. This suggests a need for more accurate and spatially resolved synoptic views from satellite data and corroborating comprehensive ground sampling to understand the greenness dynamics of these forests.

Using hyperspectral vegetation indices to estimate the fraction of photosynthetically active radiation absorbed by corn canopies

The fraction of photosynthetically active radiation (FPAR) absorbed by vegetation – a key parameter in crop biomass and yields as well as net primary productivity models – is critical to guiding crop management activities. However, accurate and reliable estimation of FPAR is often hindered by a paucity of good field-based spectral data, especially for corn crops. Here, we investigate the relationships between the FPAR of corn (Zea mays L.) canopies and vegetation indices (VIs) derived from concurrent in situ hyperspectral measurements in order to develop accurate FPAR estimates. FPAR is most strongly (positively) correlated to the green normalized difference vegetation index (GNDVI) and the scaled normalized difference vegetation index (NDVI*). Both GNDVI and NDVI* increase with FPAR, but GNDVI values stagnate as FPAR values increase beyond 0.75, as previously reported according to the saturation of VIs – such as NDVI – in high biomass areas, which is a major limitation of FPAR-VI models. However, NDVI* shows a declining trend when FPAR values are greater than 0.75. This peculiar VI–FPAR relationship is used to create a piecewise FPAR regression model – the regressor variable is GNDVI for FPAR values less than 0.75, and NDVI* for FPAR values greater than 0.75. Our analysis of model performance shows that the estimation accuracy is higher, by as much as 14%, compared with FPAR prediction models using a single VI. In conclusion, this study highlights the feasibility of utilizing VIs (GNDVI and NDVI*) derived from ground-based spectral data to estimate corn canopy FPAR, using an FPAR estimation model that overcomes limitations imposed by VI saturation at high FPAR values (i.e. in dense vegetation).