143
6.6 Using RS to Inform Forest Management
The application of RS for vegetation stress detection has advanced rapidly, evolving from classical aerial survey and photointerpretation techniques to digital image
processing, where manual interpretation has been replaced with machine learning
to identify subtle signatures humans are incapable of seeing with the naked eye.
This technological evolution has effectively transferred these tools to the sustainable management of forest resources, but limitations remain in their widespread
use. Monitoring, detecting, and reporting on forest health threats has always been
a priority of federal and state forestry agencies. Conversion of forest land and
changes in land use; climate change, intensified storms, higher frequency and
intensity of forest fires and concerns of host range recession; and the threat of
introduction and establishment from invasive insects and diseases have created an
even more urgent demand for improved near-real-time tools and products. The
capabilities of most sensors and the applications on which they have been tested
are impressive, and more promising techniques and approaches continue to build
on field application.
Recently, several programs have been developed with the goal of advancing and
improving RS applications for forest management, including online tools developed
to bring RS products to the forest health management community in near real time.
Here we present some examples of online resources developed to transfer RS products to end users on time scales useful to inform management and planning.
World Vegetation Health Index https://www.star.nesdis.noaa.gov/smcd/emb/vci/
VH/vh_browse.php The National Oceanic and Atmospheric Administration
(NOAA)-National Environmental Satellite, Data, and Information Service
( NESDIS) has developed several RS products designed specifically to assess vegetation health across the globe. Their Center for Satellite Applications and Research
(STAR) Vegetation Health Index (Fig. 6.10) uses Advanced Very High-Resolution
Radiometer (AVHRR) imagery produced from the NOAA/NESDIS Global Area
Coverage (GAC) data set from 1981 to the present, with 4 km spatial and 7-day
composite temporal resolution. Common vegetation indices are used to estimate
vegetation health, moisture, and temperature and serve as a proxy to monitor vegetation cover, density, productivity, and drought conditions, as well as phenological
stages such as the start/end of the growing season. Outputs are scaled to a range (0
to 100), providing a relative assessment of vegetation condition rather than a prediction of actual decline symptoms or identification of stress agents. However, these
products are useful for examining short-term changes in vegetation that can be used
to identify widespread decline events such as drought, land degradation, or fire.
ForWarn Online Mapper http://forwarn.forestthreats.org/; https://forwarn.forestthreats.org/fcav2/ ForWarn Satellite-Based Change Recognition and Tracking
(Fig. 6.11) is a near-real-time product from the US Forest Service that uses 250 m
MODIS data to compare current NDVI to seasonally similar historic NDVI values
to identify disturbance such as wildfires, windstorms, insects, disease outbreaks,
6 Remote Sensing for Early, Detailed, and Accurate Detection of Forest Disturbance…
6.6 Using RS to Inform Forest Management
The application of RS for vegetation stress detection has advanced rapidly, evolving from classical aerial survey and photointerpretation techniques to digital image
processing, where manual interpretation has been replaced with machine learning
to identify subtle signatures humans are incapable of seeing with the naked eye.
This technological evolution has effectively transferred these tools to the sustainable management of forest resources, but limitations remain in their widespread
use. Monitoring, detecting, and reporting on forest health threats has always been
a priority of federal and state forestry agencies. Conversion of forest land and
changes in land use; climate change, intensified storms, higher frequency and
intensity of forest fires and concerns of host range recession; and the threat of
introduction and establishment from invasive insects and diseases have created an
even more urgent demand for improved near-real-time tools and products. The
capabilities of most sensors and the applications on which they have been tested
are impressive, and more promising techniques and approaches continue to build
on field application.
Recently, several programs have been developed with the goal of advancing and
improving RS applications for forest management, including online tools developed
to bring RS products to the forest health management community in near real time.
Here we present some examples of online resources developed to transfer RS products to end users on time scales useful to inform management and planning.
World Vegetation Health Index https://www.star.nesdis.noaa.gov/smcd/emb/vci/
VH/vh_browse.php The National Oceanic and Atmospheric Administration
(NOAA)-National Environmental Satellite, Data, and Information Service
( NESDIS) has developed several RS products designed specifically to assess vegetation health across the globe. Their Center for Satellite Applications and Research
(STAR) Vegetation Health Index (Fig. 6.10) uses Advanced Very High-Resolution
Radiometer (AVHRR) imagery produced from the NOAA/NESDIS Global Area
Coverage (GAC) data set from 1981 to the present, with 4 km spatial and 7-day
composite temporal resolution. Common vegetation indices are used to estimate
vegetation health, moisture, and temperature and serve as a proxy to monitor vegetation cover, density, productivity, and drought conditions, as well as phenological
stages such as the start/end of the growing season. Outputs are scaled to a range (0
to 100), providing a relative assessment of vegetation condition rather than a prediction of actual decline symptoms or identification of stress agents. However, these
products are useful for examining short-term changes in vegetation that can be used
to identify widespread decline events such as drought, land degradation, or fire.
ForWarn Online Mapper http://forwarn.forestthreats.org/; https://forwarn.forestthreats.org/fcav2/ ForWarn Satellite-Based Change Recognition and Tracking
(Fig. 6.11) is a near-real-time product from the US Forest Service that uses 250 m
MODIS data to compare current NDVI to seasonally similar historic NDVI values
to identify disturbance such as wildfires, windstorms, insects, disease outbreaks,
6 Remote Sensing for Early, Detailed, and Accurate Detection of Forest Disturbance…