Development of a two-band enhanced vegetation index without a blue band

Jiang, Zhaoyang; Huete, Alfredo; Didan, Kamel; Miura, Tomoaki

doi:10.1016/j.rse.2008.06.006

Cited by 1,466 publications

(727 citation statements)

References 74 publications

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“…The Normalized Difference Vegetation Index (NDVI) is an estimate of primary productivity which is based on the difference in intensity of reflected light in the near infra-red (N) and red (R) spectrum by photosynthesizing plants and humidity (Jiang et al 2008). NDVI is calculated as NDVI = NIR−R NIR+R (Hayes 1985).…”

Section: Primary Productivitymentioning

confidence: 99%

Spatial distribution in Norwegian lemming Lemmus lemmus in relation to the phase of the cycle

et al. 2018

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Competition between individuals of the same or different species affects spatial distribution of organisms at any given time. Consequently, a species geographical distribution is related to population dynamics through density-dependent processes. Small Arctic rodents are important prey species in many Arctic ecosystems. They commonly show large cyclic fluctuations in abundance offering a potential to investigate how landscape characteristics relates to density-dependent habitat selection. Based on long-term summer trapping data of the Norwegian lemming (Lemmus lemmus) in the Scandinavian Mountain tundra, we applied species distribution modeling to test if the effect of environmental variables on lemming distribution changed in relation to the lemming cycle. Lemmings were less habitat specific during the peak phase, as their distribution was only related to primary productivity. During the increase phase, however, lemming distribution was, in addition, associated with landscape characteristics such as hilly terrain and slopes that are less likely to get flooded. Lemming habitat use varied during the cycle, suggesting density-dependent changes in habitat selection that could be explained by intraspecific competition. We believe that the distribution patterns observed during the increase phase show a stronger ecological signal for habitat preference and that the less specific habitat use during the peak phase is a result of lemmings grazing themselves out of the best habitat as the population grows. Future research on lemming winter distribution would make it possible to investigate the year around strategies of habitat selection in lemmings and a better understanding of a fundamental actor in many Arctic ecosystems.

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Section: Primary Productivitymentioning

confidence: 99%

Spatial distribution in Norwegian lemming Lemmus lemmus in relation to the phase of the cycle

et al. 2018

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“…Monitoring changes in crop GPP with satellite remote sensing data advances the capability to understand and manage global food security, sustainability practices, and environmental impacts, and to study global carbon cycle and global water cycle. The three-band Enhanced Vegetation Index (EVI) (Huete et al, 1997) and the two-band EVI (called EVI2, Jiang et al, 2008) have also been utilized to predict terrestrial GPP in a similar way as GPP = ε*EVI*PAR (Jin et al, 2013;Kalfas et al, 2011;King et al, 2011;Li et al, 2007;Mahadevan et al, 2008;Schubert et al, 2012;Sjöström et al, 2011;Wu et al, 2008Wu et al, , 2010Wu et al, , 2011Wu et al, , 2012Xiao et al, 2004;Yan et al, 2009). In addition, Gitelson and colleagues also explored the application of the green band Wide Dynamic Range Vegetation Index (WDRVI green ) and the green band Chlorophyll Index (CI green ) for crop GPP estimation, in addition to the NDVI and EVI (Gitelson et al, 2008Gitelson, 2011, 2012;.…”

Section: Introductionmentioning

confidence: 99%

Estimation of crop gross primary production (GPP): II. Do scaled MODIS vegetation indices improve performance?

Zhang

Cheng

Lyapustin

et al. 2015

Agricultural and Forest Meteorology

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a b s t r a c tSatellite remote sensing estimates of gross primary production (GPP) have routinely been made using spectral vegetation indices (VIs) over the past two decades. The Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the green band Wide Dynamic Range Vegetation Index (WDRVI green ), and the green band Chlorophyll Index (CI green ) have been employed to estimate GPP under the assumption that GPP is proportional to the product of VI and photosynthetically active radiation (PAR) (where VI is one of four VIs: NDVI, EVI, WDRVI green , or CI green ). However, the empirical regressions between VI*PAR and GPP measured locally at flux towers do not pass through the origin (i.e., the zero X-Y value for regressions). Therefore they are somewhat difficult to interpret and apply. This study investigates (1) what are the scaling factors and offsets (i.e., regression slopes and intercepts) between the fraction of PAR absorbed by chlorophyll of a canopy (fAPAR chl ) and the VIs and (2) whether the scaled VIs developed in (1) can eliminate the deficiency and improve the accuracy of GPP estimates. Three AmeriFlux maize and soybean fields were selected for this study, two of which are irrigated and one is rainfed. The four VIs and fAPAR chl of the fields were computed with the MODerate resolution Imaging Spectroradiometer (MODIS) satellite images. The GPP estimation performance for the scaled VIs was compared to results obtained with the original VIs and evaluated with standard statistics: the coefficient of determination (R 2 ), the root mean square error (RMSE), and the coefficient of variation (CV). Overall, the scaled EVI obtained the best performance. The performance of the scaled NDVI, EVI and WDRVI green was improved across sites, crop types and soil/background wetness conditions. The scaled CI green did not improve results, compared to the original CI green . The scaled green band indices (WDRVI green , CI green ) did not exhibit superior performance to either the scaled EVI or NDVI in estimating crop daily GPP at these agricultural fields. The scaled VIs are more physiologically meaningful than original un-scaled VIs, but scaling factors and offsets may vary across crop types and surface conditions.

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“…For each subarray, we used the SciDB apply function to calculate the enhanced vegetation index [19] associated to each cell and stored the results in a new subarray. Next, we used the filter operation to select from each resulting subarray those cells whose red value was greater than 100 and stored the results.…”

Section: Fields Operations In Array Databasesmentioning

confidence: 99%

Fields as a Generic Data Type for Big Spatial Data

Câmara

Egenhofer

Ferreira

et al. 2014

Geographic Information Science

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Abstract. This paper defines the Field data type for big spatial data. Most big spatial data sets provide information about properties of reality in continuous way, which leads to their representation as fields. We develop a generic data type for fields that can represent di↵erent types of spatiotemporal data, such as trajectories, time series, remote sensing and, climate data. To assess its power of generality, we show how to represent existing algebras for spatial data with the Fields data type. The paper also argues that array databases are the best support for processing big spatial data and shows how to use the Fields data type with array databases.

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Development of a two-band enhanced vegetation index without a blue band

Cited by 1,466 publications

References 74 publications

Spatial distribution in Norwegian lemming Lemmus lemmus in relation to the phase of the cycle

Spatial distribution in Norwegian lemming Lemmus lemmus in relation to the phase of the cycle

Estimation of crop gross primary production (GPP): II. Do scaled MODIS vegetation indices improve performance?

Fields as a Generic Data Type for Big Spatial Data

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