>Carbon Stock and Sequestration

Carbon Stock and Sequestration

Carbon may be stored/sequestered in natural systems as plant biomass (above or below ground), or soil carbon as a result of partial decomposition. Flux refers to the exchange of carbon among soil, plant, water, and air compartments. From a climate change point of view, it would be best to have high carbon storage rates and negative net flux to the atmosphere of CO2 and CH4.
Ecological Processes

QT: Indicators

What is it?

For this indicator we examine two elements of the carbon budget of the Feather River Watershed, carbon sequestration amounts and net primary productivity. Both are of interest in terms of global change issues, in particular because of the potential for offsetting increases in atmospheric carbon dioxide by storage of carbon in terrestrial carbon pools (Dixon et al. 1994). In this analysis we look at carbon standing stock at a single point in time as a measure of watershed condition, and assess trends in carbon storage by examining changes in net primary productivity detected by satellite remote sensing.

Research on carbon sequestration has focused on measurements of carbon stocks and carbon flux. Measuring carbon flux requires sophisticated instrumentation making fine-scale studies difficult, but measurement of carbon stock is more amenable to landscape-scale studies. The general approach for carbon stock evaluation is to amalgamate remote sensing-based landscape classifications with vegetation plot data that includes above-ground biomass, litter accumulation on the soil floor, and below-ground carbon to estimate total carbon storage across the landscape. Typical units for the metric are in megagrams (Mg) of carbon per hectare for the stock and Mg C per hectare per year for the flux. In this analysis we use the results from a landscape-scale assessment of carbon stocks in California and compare that to a reference condition that assumes all trees are fully mature.

Why is it important?

Carbon sequestration is considered an important means to mitigate the impacts of greenhouse gases on climate change (Sedjo & Solomon 1989). Increasing the amount of carbon stored on a watershed may become an important policy goal with economic benefits accruing from the establishment of a carbon offset market (Richards & Stokes 2004).

Forest ecosystems sequester the most carbon of any terrestrial ecosystem, and most United States surveys of carbon storage to date have emphasized storage in forests, usually working with the USFS Forest Inventory and Assessment plots as a base (Woodbury et al. 2007, Blackard et al. 2008). The forests of the Pacific Northwest, including the Sierra Nevada, may have some of the highest potential to store additional carbon of any forests in the world (Hudiburg et al. 2009).

Target or Desired Condition

Prior to the industrial revolution, the planet’s carbon cycle was closer to a state of equilibrium. While an increase in solar radiation or an increase in planetary volcanism can drastically change the carbon cycle for a relatively short period of time, it has been shown that human activity has adjusted this cycle by adding more carbon and methane into the atmosphere at higher concentrations than any natural occurrence over the last 650,000 years (Siegenthaler et al., 2005). The carbon cycle is a global phenomena, so to return to a desired condition at equilibrium will be a global, population-wide, effort. To select a desired condition at a regional scale, we look at the carbon holding capacity for each region and compare it with current conditions.

We take the desired condition to be a landscape where all trees are fully mature; that is, they have grown to the point where additional carbon storage on the landscape in aboveground biomass is limited to the rate of trees dying and new ones growing. Such a landscape is at its maximum potential for mitigating climate change through storage of atmospheric carbon dioxide.

What can influence or stress condition?

Any changes in plant cover in the landscape will affect the amount of aboveground carbon storage. Most important are changes in forest cover, given that forests have the greatest amount of biomass of any habitat type. Processes that influence forest cover and hence carbon storage include fire, timber harvest, land development, and disturbances such as pest outbreaks as well as forest regrowth (Brown et al. 2004). In a recent study, scientists found that logging was the greatest impact on reduced carbon storage in forests and “no management” of forests resulted in the greatest sequestration of carbon (Nunery and Keeton, 2010). Fire can also reduce NPP, with reduction depending on fire intensity (Meigs et al., 2009). Remaining and newly-growing plants will tend to grow vigorously, so at the landscape scale, fire temporarily reduces NPP rates.

Data Sources

The primary GIS data source for the carbon stock calculations was the CalFire Multi-Source Land Cover layer (Fire and Resource Protection Program 2003), which provides 100 meter resolution habitat data for all of California. This dataset was compiled in 2002, by amalgamating the best available local sources for land cover information in California present at that time. Most of these local data sources were made available in the period from 1993 to 1998. Equations for calculating carbon stock were from Brown et al. (2004), using equations orignialy published in Smith et al. (2003).


Brown et al. (2004) provided the first comprehensive evaluation of carbon storage and greenhouse gas emissions across agricultural lands, forests, and rangelands in California. We followed their methodology at a watershed scale in this analysis. They used the CalFire, FRAP, Multi-Source Land Cover (MSLC) layer as well as Land Cover Mapping and Monitoring Program (LCMMP) change maps to assess changes in carbon stock in the 1990s, referring to Smith et al. (2003) for measures of carbon content by forest cover type.

In particular, the CalFire MSLC layer provides habitat mapping for the state to 100 meter resolution using the vegetation classification from the California Wildlife Habitat Relationships (CWHR) mapping system (Mayer and Laudenslayer 1988). In addition to the vegetation type, this dataset gives information on vegetation canopy cover and canopy size where source data was available. The methodology in Brown 2004, calls for crosswalking the CWHR vegetation types to 5 forest types given in Smith (2003), namely Douglas fir, hardwoods, redwoods, fir-spruce, and other conifers. Taken together with canopy cover information, the equations in Brown (2004) allow for estimation of the carbon content (Table 5).

For shrublands and grasslands, Brown et al. (2004) use estimates for carbon content derived from other literature. In their report, Brown et al. (2004) do not provide carbon content values for woodlands, so the USDA Forest Service Carbon Online Estimator (NCASI 2010) was used to give carbon estimates for different age classes of blue oak, blue oak woodland being the dominant woodland habitat in the Feather River Watershed.

In a raster GIS, the portion of the MSLC layer that covered the Feather River Watershed was selected and analysis was restricted to the boundaries of the watershed using a raster mask. Using the CWHR habitat types in the MSLC layer and the crosswalk described above, vegetation pixels within the watershed were classified to one of eight vegetation types: either the five forest types listed above, shrublands, grasslands, or oak woodlands. Agricultural lands and developed lands were also masked out. The MSLC layer provides canopy cover information using the four canopy cover classes described in CWHR, namely sparse (10-24% cover), open (25-39%), moderate (40-59%) or dense cover (60% or greater). In pixels where the MSLC layer did not identify a canopy cover value. It was assumed this value was moderate cover. Using the mean values of the canopy cover class intervals, the carbon estimation relationships described above for the eight vegetation types were used to create a lookup table from which each pixel was assigned a carbon content value. All carbon stock GIS calculations were performed in the GIS GRASS (Neteler & Mitasova 2008).

A target condition layer was calculated using the same method, except that instead of taking the canopy cover value to be the actual value from the MSLC layer, dense cover was assigned. Because the carbon estimation relationships all reach their maximum value in the dense cover condition, this forces the output layer to have the maximum stock possible while keeping vegetation types the same for each pixel. The raster data for carbon standing stock and the subwatershed boundaries were intersected to generate a mean value per subwatershed.

What did we find out/How are we doing?

There were relatively high scores for carbon standing stock, ranging from 86 for the East Branch North Fork Feather to 96 for Deer Creek (Table 1 and Figure 1). There was significant downward trends in annual NPP for the three the western, lower elevation and agricultural rich, subwatersheds (Lower Bear, Lower Feather, Lower Yuba). Despite the high absolute values of the indicator scores, scores should be as close to 100% as possible, because of the need to reach global greenhouse gas mitigation goals.

Carbon Standing Stock

The indicator value is a comparison of current standing stock to a potential maximum, which is based on a combination of underlying vegetation types and canopy closure values. Figure 2 is an intermediate layer which shows carbon storage at a 100 meter pixel-resolution and provides additional detail about the patterns in each subwatershed. Low elevation subwatersheds with a predominance of oak woodlands and chaparral have relatively low levels of carbon storage. There is a mid-elevation zone where carbon storage is particularly high, which is related to the wetter, productive conifer and mixed-conifer/hardwood forest types. On the eastern slope watersheds (Middle Fork Feather, East Branch North Fork Feather) there is a large band with relatively low carbon storage.

Temporal and spatial resolution

Carbon standing stock was measured at 100 m resolution with vegetation data that was roughly a decade old. The NPP data were at a 0.1 degree resolution, which is equivalent to roughly 10 kilometers squared, using data that are recent and updated. The standing stock is unlikely to change rapidly over areas the size of the subwatersheds, but for planning watersheds or similar units it may. NPP changes rapidly (daily to monthly) and high time-resolution is required for accurate estimations.

How sure are we about our findings? (Things to keep in mind)

Carbon stock estimation is difficult for a number of reasons, and the results above should be treated carefully. First, because estimation methodology depends upon combining synoptic land cover data from remote sensing platforms with plot-level measurements of carbon in living and dead plant material, it is important that the remote sensing-derived map has accurate information about vegetation height and cover. This is challenging because remotely sensed imagery usually only gives spectral information about the top level of the canopy and not the canopy depth, the latter corresponding more closely to volume of aboveground biomass. Also, plot-level data tends to be focused on forest stands [e.g. the Forest Inventory and Assessment plots (Woodbury et al. 2007)], with shrublands and grasslands being sampled more poorly. Carbon estimation is even difficult at the plot level, since the usual technique for estimating carbon stored in a tree is to measure diameter and height and then refer to a set of allometric equations (e.g. Jenkins et al. 2004) relating tree biomass to those parameters, and these equations may have been developed from measurements of trees located in a very different landscape than one’s study plot.

For this particular analysis of carbon stocks, a couple things to note are the following. First, using the estimator equations in Brown et al. (2004) involves reducing the land cover data to types that are not very specific to California vegetation. It would be best if this assessment was made using equations based on California vegetation types, if these were available. Second, the reference condition assumes that carbon storage will be maximized if all vegetation types are at dense cover. This introduces error because some localities will not support dense forest (e.g. sparsely forested upper elevation rocky areas).

The standard deviation measure, which was calculated from the values of all pixels within each subwatershed, is relatively high, with values ranging from 9.9 to 17.9. This reflects the fact that only four discrete canopy cover classes were used to calculate the carbon values in each pixel, leading to discrete and well spread apart bins in the output values.

Area Code Score Trend Confidence
North Fork Feather NFF 94
East Branch North Fork Feather EBNFF 86
Middle Fork Feather MFF 88
Lower Feather LF 93 Downward
North Yuba NY 93
Middle Yuba MY 89
South Yuba SY 93
Deer Creek DC 96
Lower Yuba LY 91 Downward
Upper Bear UB 91
Lower Bear LB 93 Downward