A SOAP Web Service for accessing MODIS land product subsets

Vannan, S. K. Santhana; Cook, R.B.; Pan, Jerry; Wilson, Bruce E.

doi:10.1007/s12145-011-0079-2

Cited by 12 publications

(5 citation statements)

References 11 publications

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“…However, the default LAI and albedo with coarser spatiotemporal features and without interannual variation do not display realistic conditions. In this study, the albedo for updating the lower boundary condition of WRF was derived from MODIS Collection 6 land products based on Terra and Aqua, with a 1 day temporal resolution and about 1 km horizontal resolution [43,44]. The LAI for updating the lower boundary condition of WRF was also derived from MODIS Collection 6 land products based on Terra and Aqua, with a 4 day temporal resolution and about 500 m horizontal resolution [43,44].…”

Section: Datamentioning

confidence: 99%

Integrating Remote-Sensing and Assimilation Data to Improve Air Temperature on Hot Weather in East China

Liu

Pan

et al. 2021

Remote Sensing

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Land-surface characteristics (LSCs) and land-soil moisture conditions can modulate energy partition at the land surface, impact near-surface atmosphere conditions, and further affect land–atmosphere interactions. This study investigates the effect of land-surface-characteristic parameters (LSCPs) including albedo, leaf-area index (LAI), and soil moisture (SM) on hot weather by in East China using the numerical model. Simulations using the Weather Research and Forecasting (WRF) Model were conducted for a hot weather event with a high spatial resolution of 1 km in domain 3 by using ERA-Interim forcing fields on 20 July 2017 until 16:00 UTC on 25 July 2017. The satellite-based albedo and LAI, and assimilation-based soil-moisture data of high temporal–spatial resolution, which are more accurate to match fine weather forecasts and high-resolution simulations, were used to update the default LSCPs. A control simulation with the default LSCPs (WRF_CTL), a main sensitivity simulation with the updated LSCP albedo, LAI and SM (WRF_CHAR), and a series of other sensitivity simulations with one or two updated LSCPs were performed. Results show that WRF_CTL could reproduce the spatial distribution of hot weather, but overestimated air temperature (Ta) and maximal air temperature (Tamax) with a warming bias of 1.05 and 1.32 °C, respectively. However, the WRF_CHAR simulation reduced the warming bias, and improved the simulated Ta and Tamax with reducing relative biases of 33.08% and 29.24%, respectively. Compared to the WRF_CTL, WRF_CHAR presented a negative sensible heat-flux difference, positive latent heat flux, and net radiation difference of the area average. LSCPs modulated the partition of available land-surface energy and then changed the air temperature. On the basis of statistical-correlation analysis, the soil moisture of the top 10 cm is the main factor to improve warming bias on hot weather in East China.

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Section: Datamentioning

confidence: 99%

Integrating Remote-Sensing and Assimilation Data to Improve Air Temperature on Hot Weather in East China

Liu

Pan

et al. 2021

Remote Sensing

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“…NDVI is a general measure of the greenness of the top layer of the Earth's surface and generally correlates well with ground-based measures of vegetation biomass, primary productivity and leaf area index, and has been widely used in models of animal performance and movement (reviewed in Pettorelli et al, 2011Pettorelli et al, , 2005. We obtained measures of NDVI from the NASA Terra-MODIS (Moderate Resolution Imaging Spectroradiometer) mission accessed via the Oak Ridge National Laboratory web service (Vannan et al, 2011). Terra-MODIS was used rather than Aqua-MODIS because of the longer NDVI time-series available (Terra operational since 2000, Aqua since 2002).…”

Section: Estimating Food Availabilitymentioning

confidence: 99%

Individual-based modelling of elephant population dynamics using remote sensing to estimate food availability

Boult

Quaife

Fishlock

et al. 2018

Ecological Modelling

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Strategies for the conservation and management of many wild species requires an improved understanding of how population dynamics respond to changes in environmental conditions, including key drivers such as food availability. The development of mechanistic predictive models, in which the underlying processes of a system are modelled, enables a robust understanding of these demographic responses to dynamic environmental conditions. We present an individual-based energy budget model for a mega-herbivore, the African elephant (Loxodonta africana), which relates remotely measured changes in food availability to vital demographic rates of birth and mortality. Elephants require large spaces over which to roam in search of seasonal food, and thus are vulnerable to environmental changes which limit space use or alter food availability. The model is constructed using principles of physiological ecology; uncertain parameter values are calibrated using approximate Bayesian computation. The resulting model fits observed population dynamics data well. The model has critical value in being able to project elephant population size under future environmental conditions and is applicable to other mammalian herbivores with appropriate parameterisation.

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“…We used 16-day composite values of NDVI retrieved from Terra-MODIS (Moderate Resolution Imaging Spectroradiometer) to infer timespecific values of vegetation quantity. Specifically we used the MOD13Q1 product accessed via the Oak Ridge National Laboratory Web Service (Vannan, Cook, Pan, & Wilson, 2011). Median NDVI values were calculated for each individual's dispersal area (i.e.…”

Section: Vegetation Quantitymentioning

confidence: 99%

Modelling large herbivore movement decisions: Beyond food availability as a predictor of ranging patterns

et al. 2018

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The ability of animals to adapt to their changing environment will depend in part on shifts in their ranging patterns, but when and why individuals choose to move requires detailed understanding of their decision-making processes. We develop a simple decision-making model accounting for resource availability in habitually used ranges. We suggest that disparities between model predictions and animal tracking data indicate additional factors influencing movement decisions, which may be identified given detailed system-specific knowledge. The model was evaluated using movement data from satellite-tracked elephants (Loxodonta africana) inhabiting the Amboseli Ecosystem in Kenya, moving from savannah areas with low quality but constant resource availability, to areas with temporally-constrained higher nutrient availability. Overall, the model fit the data well: there was a good correlation between predicted and observed locations for the combined data from all elephants, but variation between individuals in how well the model fits. For those elephants where model predictions were less successful, additional factors likely to affect movement decisions, reproduction, anthropogenic threats, memory and perception, are suggested. This protocol for building and testing decision-making models should contribute to success in attempts to preserve sufficient space for large herbivores in their increasingly human-dominated ecosystems.

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A SOAP Web Service for accessing MODIS land product subsets

Abstract: Remote sensing data from satellites have provided valuable information on the state of the earth for several decades.

Cited by 12 publications

References 11 publications

Integrating Remote-Sensing and Assimilation Data to Improve Air Temperature on Hot Weather in East China

Integrating Remote-Sensing and Assimilation Data to Improve Air Temperature on Hot Weather in East China

Individual-based modelling of elephant population dynamics using remote sensing to estimate food availability

Modelling large herbivore movement decisions: Beyond food availability as a predictor of ranging patterns

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