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Keyword: MODIS

Near real-time burned area mapping with VIIRS

Projects Posted on: April 05, 2018
Wildland fires emit significant amounts of greenhouse gases, particulate matter, and ozone precursors. This can have a significant negative effect on public health at multiple scales.

Using MODIS NDVI phenoclasses and phenoclusters to characterize wildlife habitat: Mexican spotted owl as a case study

Publications Posted on: February 12, 2018
Most uses of remotely sensed satellite data to characterize wildlife habitat have used metrics such as mean NDVI (Normalized Difference Vegetation Index) in a year or season. These simple metrics do not take advantage of the temporal patterns in NDVI within and across years and the spatial arrangement of cells with various temporal NDVI signatures.

Scaling Gross Primary Production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation.

Publications Posted on: May 12, 2016
The Moderate Resolution Imaging Radiometer (MODIS) is the primary instrument in the NASA Earth Observing System for monitoring the seasonality of global terrestrial vegetation. Estimates of 8-day mean daily gross primary production (GPP) at the 1 km spatial resolution are now operationally produced by the MODIS Land Science Team for the global terrestrial surface using a production efficiency approach.

Measuring radiant emissions from entire prescribed fires with ground, airborne and satellite sensors - RxCADRE 2012

Publications Posted on: October 06, 2015
Characterising radiation from wildland fires is an important focus of fire science because radiation relates directly to the combustion process and can be measured across a wide range of spatial extents and resolutions.

Novel Kalman filter algorithm for statistical monitoring of extensive landscapes with synoptic sensor data

Publications Posted on: September 29, 2015
Wall-to-wall remotely sensed data are increasingly available to monitor landscape dynamics over large geographic areas. However, statistical monitoring programs that use post-stratification cannot fully utilize those sensor data. The Kalman filter (KF) is an alternative statistical estimator. I develop a new KF algorithm that is numerically robust with large numbers of study variables and auxiliary sensor variables.

Mapping day-of-burning with coarse-resolution satellite fire-detection data

Publications Posted on: September 19, 2014
Evaluating the influence of observed daily weather on observed fire-related effects (e.g. smoke production, carbon emissions and burn severity) often involves knowing exactly what day any given area has burned.

A remote sensing protocol for identifying rangelands with degraded productive capacity

Publications Posted on: August 07, 2014
Rangeland degradation is a growing problem throughout the world. An assessment process for com-paring the trend and state of vegetation productivity to objectively derived reference conditions wasdeveloped. Vegetation productivity was estimated from 2000 to 2012 using annual maximum Normalized Difference Vegetation Index (NDVI) from the MODIS satellite platform.

Wildland fire emissions, carbon, and climate: wildland fire detection and burned area in the United States

Publications Posted on: April 16, 2014
Biomass burning is a major source of greenhouse gases, aerosols, black carbon, and atmospheric pollutants that affects regional and global climate and air quality. The spatial and temporal extent of fires and the size of burned areas are critical parameters in the estimation of fire emissions.

Carrying capacity for species richness as context for conservation: a case study of North American birds

Publications Posted on: July 03, 2013
We evaluated the leading hypotheses on biophysical factors affecting species richness for Breeding Bird Survey routes from areas with little influence of human activities.We then derived a best model based on information theory, and used this model to extrapolate SK across North America based on the biophysical predictor variables.

Prediction of Peromyscus maniculatus (deer mouse) population dynamics in Montana, USA, using satellite-driven vegetation productivity and weather data

Publications Posted on: May 23, 2012
Deer mice (Peromyscus maniculatus) are the main reservoir host for Sin Nombre virus, the primary etiologic agent of hantavirus pulmonary syndrome in North America. Sequential changes in weather and plant productivity (trophic cascades) have been noted as likely catalysts of deer mouse population irruptions, and monitoring and modeling of these phenomena may allow for development of early-warning systems for disease risk.