The carbon pool that is most accurately estimated through remote sensing is above-ground biomass, which is described in this chapter. It is possible to estimate below-ground biomass from the data on above-ground biomass derived from remote sensing (see Chapters 4 and 11).
Depending on the type of land-use system being monitored for estimating carbon stock, different features of change in vegetation over time have to be considered (Rosenqvist et al. 2003); these include different types of harvesting and seasonal changes. For repeated carbon inventories, it is also important to select the same period of the year for estimation, usually the peak vegetation period.
Various methods are available to interpret and analyse satellite data for measuring changes in land cover and hence biomass. However, no consistent method or technique is available for estimating carbon through remote sensing (Rosenqvist et al. 2003). The methods range from visual interpretation of photographs to sophisticated digital analyses and from "wall-to-wall" mapping (covering a contiguous stretch of land such as a province, country or continent) to analysis of hot spots and statistical sampling. A variety of methods can be applied depending on technical capabilities and characteristics of the land-use pattern. There are several conventional methods to estimate above-ground biomass through remote sensing (Labrecque et al. 2004), such as radiometric relationships between satellite reflectance or spectral indices and biomass values measured on forest inventory plots, nearest-neighbour approaches, unsupervised classification of land cover and forest structures characteristics and forest sample plot databases. Two methods including the steps in estimating carbon stocks by using remote sensing are presented in the following section.
It is not possible to directly measure the total stocks in above-ground biomass or changes in the stocks through remote sensing. In quantifying biomass and thus carbon stocks, remote sensing data are used in combination with empirical data, either directly using allometric relationships or indirectly based on features such as canopy cover. Reflectance in different parts of the spectrum, either alone or in combination such as indices or principal components, with strong empirical relationship, can also be used for estimating biomass. Indirect estimates using empirical relationships including canopy cover, indices from several bands, photosynthetically active radiation (PAR) or net primary production (NPP) that combine environmental data with remotely sensed data are usually necessary. Additional methods include quantification of productivity through the use of light use efficiency (e.g. Brogaard et al. 2005).
In combination with empirical biomass measurements, remote sensing data can be used to extrapolate over larger areas and also over longer time frames. Usually, this is done by developing a regression model between the empirical data and the remote sensing data (e.g. Dong et al. 2003).
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