**6. References**

92 Remote Sensing of Biomass – Principles and Applications

Thus, there is a pressing need to intensify studies calibrating satellite with field measurements, albeit the low accessibility of some areas in the miombo ecoregion. In addition to spatial measurement, it has long been clear that temporal surveys at varied time intervals are required, particularly in biomes that are highly dynamic such as miombo

The problem of biomass retrieval in miombo is compounded by the fact that senescent and dead material (also known as NPV) associated with phenology and disturbances can be a major component of the total surface cover. These three factors play a major role in both abiotic and biotic dynamics of the miombo as explored in Section 2 of this chapter. NPV can represent an important carbon pool in this ecosystem. Many common methods for estimating vegetation cover and biomass use VIs that are insensitive to the presence of NPV. Thus, SMA, data fusion among others may be more appropriate techniques to address spatial and temporal variations of biomass in miombo, but they still need to be adapted and

Addressing large-scale and temporal variations of biomass in the different compartments of miombo (PV, NPV, soils and underground) has to be enhanced in the near future due to their varied contribution to global emissions of greenhouse gases - Carbon dioxide in particular. For example, NPV is the main source of fuel-load for fires, which causes large amounts of carbon to be lost in the form of carbon dioxide to the atmosphere. Because of this, the contribution of miombo to global climate changes may be significant but not

The southern African region is embarking in the carbon market under the Kyoto Protocol and REDD schemes of the UNFCCC. The ability to negotiate in this market and gain a better position is dependent on the capacity of a country (or region) to estimate carbon stocks with minimal errors. Thus, the advance of remote sensing measurements of biomass from space is an important step towards that achievement (Houghton, 2005 cited by Houghton, 2010). However, it is important to acknowledge that most remote sensing techniques for measurements of biomass usually miss belowground biomass and soil carbon in tropical and sub-tropical regions, which may hold a representative fraction of carbon in the whole system. Thus, future research should focus on the use of microwave radar remote sensing (P-Band and higher) or optical techniques such as SMA and data fusion, that are able to

1. Biomass variations at scales of less than 25 m associated with season and disturbances

4. Limited accessibility to improved sensors (high resolution optical, microwave and

However there are some opportunities that may be explored to improve data generation

1. Improved methods to estimate biomass in different compartments of the ecosystem

Remote sensing of biomass in miombo faces the following constraints:

2. Limited network of field plots due to low accessibility of some areas;

(fires, herbivory and slash and burn agriculture);

3. Lack of contemporary field and remote sensing data;

and analysis, thus a better understanding of this ecosystem:

(Furley, 2010).

calibrated for this ecosystem.

completely understood yet.

differentiate carbon pools.

**5. Conclusions** 

LiDAR data).

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**Oceans** 

**5** 

*1USA 2Canada, 3UK* 

**Ocean Color Remote Sensing of** 

**Phytoplankton Functional Types** 

*2Plymouth Marine Laboratory, Bedford Institute of Oceanography,* 

*1NASA Goddard Space Flight Center, Wallops Island,* 

*Prospect Place, The Hoe Plymouth,* 

Tiffany A.H. Moisan1, Shubha Sathyendranath2 and Heather A. Bouman3

Interest in phytoplankton diversity has increased in recent years due to its possible role in regulating climate by production and consumption of greenhouse gases. For example, gases can diffuse across the air-sea interface, many of which are synthesized and emitted by certain phytoplankton species or groups. It has been suggested that these variations play an important role in moderating our climate through backscattering of solar radiation and within cloud formation. Climate will ultimately control fundamental environmental conditions that regulate algal growth, including water temperature, nutrients, and light and thus can be expected to result in changes in the species composition, trophic structure and function of marine ecosystems. In the past several decades, the scientific community has

The marine phytoplankton community is diverse and includes on the order of tens of thousands of phytoplankton species (Jeffrey & Vesk 1997). On regional scales, phytoplankton biogeography is controlled by the physical, chemical, and meteorological characteristics that force ecosystem dynamics. There is a renewed impetus for new technologies to provide information about the phytoplankton community composition over global scales. Real-time, large-scale taxonomic information, if available, could open up new possibilities and approaches geared toward monitoring highly-dynamic oceanic processes and phenomena such as algal blooms (including harmful algal blooms), frontal structures, eddies, and episodic events (storms, river outflow, and wind mixing). Phytoplankton diversity information provides a valuable quantitative database for structuring sophisticated predictive models that includes taxonomic phytoplankton community information such as size spectra, probability distribution of taxa, and upper trophic level estimations including fisheries productivity (Cheson & Case 1986, De Angelis & Waterhouse 1987). There have been several reviews and books written on phytoplankton community structure, dynamics, and biogeochemistry as measured by ocean color (Mitchell 1994, Martin 2004, Mueller et al. 2004, Miller et al. 2005,

**1. Introduction** 

witnessed changes in phytoplankton distribution.

Richardson and LeDrew 2006, Longhurst 2007, Robinson 2010).

*3Department of Earth Sciences, University of Oxford, South Parks Road, Oxford,* 
