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Keyword: MODIS

Timing It Right: Maximizing Range Management Effectiveness with PhenoMap

Documents and Media Posted on: January 27, 2021
PhenoMap is a new Web-based tool that managers can use to assess the production and location of high-quality forage. It uses satellite imagery to address the need for near-real-time information about plant life cycle events over large spatial areas. Document Type: Other Documents

Monitoring weekly changes in phenology remotely across the western United States

Science Spotlights Posted on: August 31, 2020
PhenoMap monitors weekly changes in phenology (green-up and brown-down) across the western United States via satellite.  Weekly satellite values of “greenness” successfully tracked changes in phenology documented by phenology cameras in grasslands, shrublands, deciduous broadleaf, and mixed forests but demonstrated the difficulty of tracking changes in phenology of evergreen needleleaf forests.

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

Publications Posted on: June 22, 2020
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.

Monitoring land surface phenology in near real time by using PhenoMap

Publications Posted on: February 21, 2020
Monitoring vegetation phenology is important for managers at several scales. Across decades, changes in the timing, pattern, and duration of significant life cycle events for plant groups can foreshadow shifts in species assemblages that can affect ecosystem services.

Forest degradation assessment based on trend analysis of MODIS-Leaf Area Index: A case study in Mexico

Publications Posted on: February 18, 2020
Assessing forest degradation has been a challenging task due to the generally slow-changing nature of the process, which demands long periods of observation and high frequency of records.

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

Publications Posted on: May 11, 2018
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.

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.

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.

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