Keyword: MODIS

Developing models to predict the number of fire hotspots from an accumulated fuel dryness index by vegetation type and region in Mexico

Understanding the linkage between accumulated fuel dryness and temporal fire occurrence risk is key for improving decision-making in forest fire management, especially under growing conditions of vegetation stress associated with climate change.

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

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.

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

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

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

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

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

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

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.