MODIS enhanced vegetation index predicts tree species richness across forested ecoregions in the contiguous U.S.A.

Waring, Richard H.; Coops, Nicholas C.; Fan, Weihong; Nightingale, Joanne

doi:10.1016/j.rse.2006.05.007

Cited by 144 publications

(94 citation statements)

References 38 publications

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“…Various linear and non-linear relationships between satellite-derived SVIs and f PAR have been found for different vegetation types and climatic conditions (e.g., Asrar et al, 1984;Badhwar et al, 1986;Fassnacht and Gower, 1997) where NIR and Red is the reflectance in the near infrared and red, respectively (Tucker 1979). SVIs have also been used to follow seasonal dynamics of vegetation using temporal profile analysis (Badhwar and Henderson, 1981;Henderson and Badhwar, 1984), and, when seasonally integrated, have been shown to be correlated with aboveground net primary production (NPP, defined as the difference between GPP and plant respiration) on an annual basis (Goward and Dye, 1987;Waring et al, 2006).…”

Section: Empirical Determination Of Absorbed Parmentioning

confidence: 99%

The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements

Hilker

Coops

Wulder

et al. 2008

Science of The Total Environment

261

187

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Section: Empirical Determination Of Absorbed Parmentioning

confidence: 99%

The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements

Hilker

Coops

Wulder

et al. 2008

Science of The Total Environment

261

187

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“…Many approaches have been developed to interpret phenological events from temporal variations in vegetation indices or, in this situation, sequential digital imagery. Information on key dates, such as the start and end of the growing season are possible (Waring et al 2006). One key method to extract these dates is based on the seasonal-midpoint (or half-maximum) approach, which was designed to predict the initial leaf expansion of broadleaf forests (White et al 1999;Schwartz et al 2002).…”

Section: Image Analysismentioning

confidence: 99%

“…The method first calculates the annual minimum and maximum value for each pixel and the midpoint is then calculated and added to the minimum. This calculated value has the advantage over other formulations in that it is sensitive to site-specific variations in the range of values and may be more sensitive to local variation in canopy leaf area and chlorophyll concentrations (Waring et al 2006). Once 2G-RBi values were calculated for each region of interest within the images for the duration of the observation period, curves were fitted to the data to estimate green-up and senescence.…”

Section: Image Analysismentioning

confidence: 99%

Using digital time-lapse cameras to monitor species-specific understorey and overstorey phenology in support of wildlife habitat assessment

Bater

Coops

Wulder

et al. 2010

Environ Monit Assess

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Critical to habitat management is the understanding of not only the location of animal food resources, but also the timing of their availability. Grizzly bear (Ursus arctos) diets, for example, shift seasonally as different vegetation species enter key phenological phases. In this paper, we describe the use of a network of seven ground-based digital camera systems to monitor understorey and overstorey vegetation within species-specific regions of interest. Established across an elevation gradient in western Alberta, Canada, the cameras collected true-colour (RGB) images daily from 13 April 2009 to 27 October 2009. Fourth-order polynomials were fit to an RGB-derived index, which was then compared to field-based observations of phenological phases. Using linear regression to statistically relate the camera and field data, results indicated that 61% (r 2 = 0.61, df = 1, F = 14.3, p = 0.0043) of the variance observed in the field phenological phase data is captured by the cameras for the start of the growing season and 72% (r 2 = 0.72, df = 1, F = 23.09, p = 0.0009) of the variance in length of growing season. Based on the linear regression models, the mean absolute differences in residuals between predicted and observed start of growing season and length of growing season were 4 and 6 days, respectively. This work extends upon previous research by demonstrating that specific understorey and overstorey species can be targeted for phenological monitoring in a forested environment, using readily available digital camera technology and RGB-based vegetation indices.

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“…On the one hand, a number of studies have associated the biodiversity of different sites with the information obtained by passive remote sensors [23][24][25][26][27], correlating biological diversity directly with spectral reflectance values [28], with different spectral vegetation indexes [19,25,[29][30][31], and different types of feature extraction like principal components analysis (PCA) [32] or minimum noise fraction (MNF) [26]. Taking this into account, it is expected that hyperspectral sensors, which have great ability to detect characteristics associated with the biochemical, physiological, and structural spectral variability of the vegetation in the electromagnetic spectrum, will provide valuable information for the evaluation of biodiversity [33,34].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Airborne LiDAR and Satellite Hyperspectral Remote Sensing to Estimate Vascular Plant Richness in Deciduous Mediterranean Forests of Central Chile

et al. 2015

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Abstract:The Andes foothills of central Chile are characterized by high levels of floristic diversity in a scenario, which offers little protection by public protected areas. Knowledge of the spatial distribution of this diversity must be gained in order to aid in conservation management. Heterogeneous environmental conditions involve an important number of niches closely related to species richness. Remote sensing information derived from satellite hyperspectral and airborne Light Detection and Ranging (LiDAR) data can be used as proxies to generate a spatial prediction of vascular plant richness. This study aimed to estimate the spatial distribution of plant species richness using remote sensing in the Andes foothills of the Maule Region, Chile. This region has a secondary deciduous forest dominated by Nothofagus obliqua mixed with sclerophyll species. Floristic measurements were performed using a nested plot design with 60 plots of 225 m 2 each. Multiple predictors were evaluated: 30 topographical and vegetation structure indexes from LiDAR data, and 32 spectral indexes and band transformations from the EO1-Hyperion sensor. A random forest algorithm was used to identify relevant variables in richness prediction, and these variables were used in turn to obtain a final multiple linear regression predictive model (Adjusted R 2 = 0.651; RSE = 3.69). An independent validation survey was performed with significant results (Adjusted R 2 = 0.571, RMSE = 5.05). Selected variables were statistically significant: OPEN ACCESSRemote Sens. 2015, 7 2693 catchment slope, altitude, standard deviation of slope, average slope, Multiresolution Ridge Top Flatness index (MrRTF) and Digital Crown Height Model (DCM). The information provided by LiDAR delivered the best predictors, whereas hyperspectral data were discarded due to their low predictive power.

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MODIS enhanced vegetation index predicts tree species richness across forested ecoregions in the contiguous U.S.A.

Cited by 144 publications

References 38 publications

The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements

The use of remote sensing in light use efficiency based models of gross primary production: A review of current status and future requirements

Using digital time-lapse cameras to monitor species-specific understorey and overstorey phenology in support of wildlife habitat assessment

Comparison of Airborne LiDAR and Satellite Hyperspectral Remote Sensing to Estimate Vascular Plant Richness in Deciduous Mediterranean Forests of Central Chile

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