Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction

Jiang, Zhaoyang; Huete, Alfredo; Chen, Jin; Chen, Yunhao; Li, Jing; Yan, Guangjian; Zhang, Xiaoyu

doi:10.1016/j.rse.2006.01.003

Cited by 522 publications

(305 citation statements)

References 54 publications

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“…Usually a linear transformation of remote-sensed NDVI is used to calculate actual canopy cover (M) (Gutman and Ignatov, 1998;Jiang et al, 2006):…”

Section: Remote Sensing Datamentioning

confidence: 99%

“…Semi-empirical relationships Z. Cong et al: Ecohydrological optimality in the Northeast China Transect between NDVI and canopy cover were used to derive the possible arithmetic expression of canopy cover (Baret et al, 1995;Carlson and Ripley, 1997;Gutman and Ignatov, 1998;Jiang et al, 2006). With the rising attention of the climate change issue, studies on the relationship between canopy cover and climate factors have been conducted in different regions of the world (Zhou et al, 2001;Schultz and Halpert, 1993;Piao et al, 2011;Park and Sohn, 2010;Li et al, 2002;Wang et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

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Ecohydrological optimality in the Northeast China Transect

Cong

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. The Northeast China Transect (NECT) is one of the International Geosphere-Biosphere Program (IGBP) terrestrial transects, where there is a significant precipitation gradient from east to west, as well as a vegetation transition of forest-grassland-desert. It is remarkable to understand vegetation distribution and dynamics under climate change in this transect. We take canopy cover (M), derived from Normalized Difference Vegetation Index (NDVI), as an index to describe the properties of vegetation distribution and dynamics in the NECT. In Eagleson's ecohydrological optimality theory, the optimal canopy cover (M * ) is determined by the trade-off between water supply depending on water balance and water demand depending on canopy transpiration. We apply Eagleson's ecohydrological optimality method in the NECT based on data from 2000 to 2013 to get M * , which is compared with M from NDVI to further discuss the sensitivity of M * to vegetation properties and climate factors. The result indicates that the average M * fits the actual M well (for forest, M * = 0.822 while M = 0.826; for grassland, M * = 0.353 while M = 0.352; the correlation coefficient between M and M * is 0.81). Results of water balance also match the field-measured data in the references. The sensitivity analyses show that M * decreases with the increase of leaf area index (LAI), stem fraction and temperature, while it increases with the increase of leaf angle and precipitation amount. Eagleson's ecohydrological optimality method offers a quantitative way to understand the impacts of climate change on canopy cover and provides guidelines for ecorestoration projects.

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“…Usually a linear transformation of remote-sensed NDVI is used to calculate actual canopy cover (M) (Gutman and Ignatov, 1998;Jiang et al, 2006):…”

Section: Remote Sensing Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ecohydrological optimality in the Northeast China Transect

Cong

et al. 2017

Hydrol. Earth Syst. Sci.

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“…This study focuses on the systematic bias present in retrievals (parameters) due to differences in the spatial resolution of the data [24][25][26], called 'scaling effects'. The term 'scaling effects' has several meanings in remote sensing, depending on the issues to be addressed.…”

Section: Introductionmentioning

confidence: 99%

“…The term 'scaling effects' has several meanings in remote sensing, depending on the issues to be addressed. Chen [27] categorized the scaling effects into three types: (1) the effects of the area-averaging operation and the point spread function [28,29]; (2) the effects of surface heterogeneity and the derivation algorithms on the area-averaged retrievals [24,25]; and (3) the effects of surface heterogeneity and the correlations between the surface and atmospheric variables involved in the estimation processes [30,31]. In this study, we focused on the second type of scaling effects, which have been widely discussed in remote sensing studies.…”

Section: Introductionmentioning

confidence: 99%

“…Studies have examined scaling effects in the calculation of the vegetation index (VI) [16,24,25,30,[38][39][40][41][42][43][44], leaf area index (LAI) [27,32,35,36,45,46], fraction of vegetation cover (FVC) [33], latent [42,47], sensible heat flux [24,30], and other parameters related to surface processes [48][49][50][51][52][53][54][55]. Scaling effects in the calculation of the NDVI have been investigated in the context of empirical investigations [42,44], regression analysis [38,40,41], numerical simulations [39], and analytical studies [24,25,30,43].…”

Section: Introductionmentioning

confidence: 99%

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Scaling Effect of Area-Averaged NDVI: Monotonicity along the Spatial Resolution

et al. 2012

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Changes in the spatial distributions of vegetation across the globe are routinely monitored by satellite remote sensing, in which the reflectance spectra over land surface areas are measured with spatial and temporal resolutions that depend on the satellite instrumentation. The use of multiple synchronized satellite sensors permits long-term monitoring with high spatial and temporal resolutions. However, differences in the spatial resolution of images collected by different sensors can introduce systematic biases, called scaling effects, into the biophysical retrievals. This study investigates the mechanism by which the scaling effects distort normalized difference vegetation index (NDVI). This study focused on the monotonicity of the area-averaged NDVI as a function of the spatial resolution. A monotonic relationship was proved analytically by using the resolution transform model proposed in this study in combination with a two-endmember linear mixture model. The monotonicity allowed the inherent uncertainties introduced by the scaling effects (error bounds) to be explicitly determined by averaging the retrievals at the extrema of the resolutions. Error bounds could not be estimated, on the other hand, for non-monotonic relationships. Numerical simulations were conducted to demonstrate the monotonicity of the averaged NDVI along spatial resolution. This study provides a theoretical basis for the scaling effects and develops techniques for rectifying the scaling effects in biophysical Remote Sens. 2012, 4 161 retrievals to facilitate cross-sensor calibration for the long-term monitoring of vegetation dynamics.

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Quantifying the heat flux regulation of metropolitan land use/land cover components by coupling remote sensing modeling with in situ measurement

et al. 2015

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Quantifying the effects of urban land use/land cover with regard to surface radiation and heat flux regulation is important to ecological planning and heat stress mitigation. To retrieve the spatial pattern of heat fluxes in the Beijing metropolitan area, China, a remote sensing-based energy balance model was calibrated with synchronously measured energy fluxes including net radiation, latent heat flux (LE), and sensible heat flux (H). Our model calibration approach avoided the uncertainties due to subjective judgments in previous empirical parameterization methods. The land surface temperature (LST), H, and Bowen ratio (β) of Beijing were found to increase along the outskirt-suburban-urban gradient, with strong spatial variation. LST and H were negatively correlated with vegetation fraction cover (VFC). For example, the modern high-rise residential areas with relatively higher VFC had lower H and β than the traditional low-rise residential areas. Our findings that indicate thermal dissipation through vegetation transpiration might play an important role in urban heat regulation. Notably, the thermal dissipating strength of vegetation (calculated as LE/VFC) declined exponentially with increased VFC. For the purpose of heat stress regulation, we recommend upgrading the traditional low-rise residential areas to modern high-rise residential areas and focusing urban greenery projects in areas whose VFC < 0.1, where the heat regulating service by urban vegetation could be twice as effective as in other places.

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Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction

Cited by 522 publications

References 54 publications

Ecohydrological optimality in the Northeast China Transect

Ecohydrological optimality in the Northeast China Transect

Scaling Effect of Area-Averaged NDVI: Monotonicity along the Spatial Resolution

Quantifying the heat flux regulation of metropolitan land use/land cover components by coupling remote sensing modeling with in situ measurement

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