Mapping & Monitoring
Remote Sensing Tools in Boreal Regions
Satellite sensors measure the intensity of specific wave lengths of light being reflected from the earth's surface and atmosphere. In their raw, unprocessed state, these reflectances tell very little about the surface from which they originate. In order to make these measurements useful, the spectral information contained in an image (i.e. the intensities of the different wavelengths) are related to direct field-based measurements of the specific field parameter of interest. Once these relationships are established, a model, or algorithm, is developed which uses the raw reflectance values as inputs and produces estimates of variables such as LAI or FPAR. This approach can subsequently be applied wherever reflectance values are recorded.
Estimates of ecosystem properties (e. g. Leaf Area Index ), resulting from the analysis and processing of reflectance values, are known as satellite products. Satellite products can be anything from continuous fields of ecosystem process rates to classified indices of landscape type.
As part of ongoing projects in the boreal and arctic tundra region, Woods Hole Research Center scientists and their collaborators are examining a number of different satellite vegetation products across a wide range of burned areas and assess them in the context of interannual variability in carbon sequestration by vegetative regrowth. Using imagery acquired from the Land Remote Sensing Satellite (Landsat), the IKONOS satellite, and the Advanced Very High Resolution Radiometer (AVHRR), WHRC researchers are computing a normalized difference vegetation index (NDVI) product, a fraction of absorbed photosynthetically active radiation (FPAR) product, and a net primary production product (NPP). In addition to these products, the moderate resolution imaging spectroradiometer (MODIS) introduces a large suite of vegetation products that are regularly processed and released by the MODIS land science team. Center scientists have been collecting and analyzing a number of these products in order to assess their quality and applicability to monitoring forest regrowth in the boreal region.
|MODIS Vegetation Products|
|Vegetation Indices (NDVI and EVI)||Monitoring Global Vegetation Conditions||250m - 1km||16-day, 1-month|
|LAI and FPAR||Monitoring Canopy Light Interception||1km, 0.05 Degree||8-day, 32-day|
|Net Primary Production (NPP)||Monitoring Global Carbon Exchange||1km||Yearly|
|Percent Tree Cover (vegetation continuous fields)||Monitoring Change in Forest Cover||500m||Yearly|
|MODIS vegetation products utilized in the North American Boreal Program|
Normalized difference vegetation index in the Delta Junction region, derived from Landsat ETM+ imagery.
Vegetation indices (VI) are commonly used to calculate and map vegetation characteristics. A number of VI's exist in addition to the NDVI, including the soil adjusted vegetation index (SAVI), atmospherically resistant vegetation index (ARVI), and the enhanced vegetation index (EVI). However, the NDVI has been the most widely used to explore spatial and temporal variation in vegetative properties. The NDVI is a normalized ratio of the near infrared and red bands of a satellite sensor:
NDVI = (NIR - RED) / (NIR + RED)
The ratio of bandwidths provides a simple method for reducing interference due to atmospheric absorption and scattering. This is the basic theory behind all vegetation indices. Due to the simplicity and relative ease of calculating VI's, they are widely used as inputs to models of other "higher level" vegetation variables such as primary production and leaf area index.
Fraction of absorbed photosynthetically active radiation (FPAR) derived from IKONOS 4m imagery.
As discussed in the section on field measurements, the fraction of absorbed photosynthetically active radiation is a measure of canopy light interception which relates directly to photosynthesis and forest carbon exchange. Because NDVI is a measure of vegetation concentration, FPAR exhibits a direct relationship to NDVI. Most models exploit this relationship in calculating FPAR, and use NDVI as the primary input. However, there are other factors which affect FPAR such as landcover type and angle of incident light.