ForWarn II

Satellite-Based Change Recognition and Tracking

Insects

Defoliation in southern LA

Mon, 05/01/2017 - 16:38 -- wchristie

Forest defoliation by baldcypress leafroller and forest tent caterpillar detected in these southern Louisiana counties: Ascension, St. James, Assumption, n. Lafourche, n. Terrebonne, St. Martin and e. Iberia County. Public lands affected appear to be the western district of Maurepas Swamp Wildlife Management Area.

Insect defoliation in Fremont National Forest, OR

Wed, 09/14/2016 - 09:14 -- wchristie

ForWarn potentially indicates that an area in the Fremont National Forest, Oregon experienced a decline in green vegetation due to the Mountain Pine Beetle from 2004 through 2010 and still may not have recovered in terms of vegetation canopy greenness. The anomaly may also be associated with climate change initiating a shift in vegetation type in the area.

Monitoring the state of Rhode Island's forests

Wed, 08/10/2016 - 16:35 -- stevenorman
Gypsy Moth defoliation affected a large part of Rhode Island in 2016

Remote sensing technologies provide an increasingly efficient way to monitor large tracts of forest canopy conditions in near-real-time and seasonally. Observational systems, such as ForWarn's MODIS-based product line, provide a continuous weekly stream of observational data that can be readily processed in ways that are useful for summary reports on changing forest conditions. Remote sensing approaches to regional monitoring are particularly powerful when they use consistent measures, sophisticated baselines for defining "normal", and cross jurisdictional boundaries.

Tracking Gypsy Moth emergence and severity with magnitude and duration

Wed, 07/27/2016 - 11:48 -- stevenorman
Gypsy Moth defoliation was extensive in 2015 in northeastern Pennsylvania

Remote sensing is adept at identifying and qualifying many forest disturbances, but there remains a substantial need to further quantify actual impacts in many cases. Traditionally, vegetation change-detection approaches, such as that used by ForWarn, identify and measure disturbance severity as the magnitude of change of some measure of reflected light between two dates or periods. ForWarn tracks the percent change in NDVI relative the prior year or years, with this index falling or rising as foliage cover shifts.

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