**4. Remote sensing of biomass in miombo woodlands: Opportunities and limitations for research**

Miombo display complex vegetation patterns in which dense vegetation alternates with sparsely populated or bare soil in response to environmental and disturbance (deforestation, fires and herbivory) factors. Low vegetation cover, in some places, and small-scale variations in others, can produce unpredictable errors in the quantication of biophysical and ecological properties of the vegetation. Ignoring this spatial variation can produce inaccurate results, even in fairly homogeneous environments (Aubry & Debouzie, 2001 cited by Hufkens *et al.,* 2008; Haining, 1990).

The use of remote sensing for estimations of vegetation biomass has proved to be of great importance to fill up data gaps and to estimate large-scale variations, especially in low accessible places. The technique lacks of precision when compared to detailed forest inventories. But, for miombo the lack of detailed field data and uncertainty of biomass stocks associated to disturbances, make remote sensing one important technique to address temporal and spatial variations in biomass.

Although repeated satellite imaging has improved in resolution over the years, it is still limited in detecting fine patterns within savanna vegetation. Some forms of remote sensing, such as the sub-meter resolution IKONOS, GeoEye and QUICKBIRD satellite sensors, allow individual trees to be recognized. Other, such as microwave remote sensing from radar (RADARSAT, ALOS-PALSAR, JERS-1, etc.) and LiDAR provide a three dimensional representation of vegetation, which is an improvement over optical remote sensing. However, both low-resolution optical and microwave scenes are currently too expensive for large-scale or regional studies and they require a substantive amount of processing capacities. These represent some of the major limitations for its use in the southern Africa region.

Discriminating between subtypes of savanna vegetation, even simply looking at structural differences, has proved a taxing undertaking, especially in places where field data is limited.

Remote Sensing of Biomass in

are:

change.

**6. References** 

the Miombo Woodlands of Southern Africa: Opportunities and Limitations for Research 93

2. There exist few sites in the region with detailed data on biomass variations that can be used to test improved remote sensing techniques. For example, permanent sample plots exist in almost all miombo countries. These plots are being frequently evaluated for

several parameters including biomass and carbon in different compartments; 3. There are a growing number of remote sensing specialists in the region. In addition, several networks (miombo network, safnet, saccnet, among others) are dedicated to improve remote sensing techniques and data sharing. These involve not only regional but also international senior specialists thus representing a good way of data sharing,

improve techniques, establish a link between research and decision making, etc. In face of the limitations and opportunities for the remote sensing of biomass in miombo, there are five research areas of interest in the region that can benefit from the advance of remote sensing techniques. The core areas presented below are general and can accommodate several research topics according to site particularities. The research themes

1. Land use and land cover changes and its effects on miombo biodiversity and biomass; 2. Improved techniques for spatial and temporal variations of biomass and biodiversity; 3. Contribution of miombo to the global changes. This may include several topics such as: carbon stock assessment for different ecosystem compartments (vegetation, soils, NPV, belowground); fire regimes and management; vulnerability and adaptation to climate

4. Biomass changes and effects on the availability of resources to human population;

Anaya, J.A.; Chuvieco, E. & Palacios-Orueta, A. (2009). Aboveground biomass assessment in

Andreae, M.O. (1993). The influence of tropical biomass burning on climate and the

Asner, G.P.; Wessman, C.A. & Schimel, D.S. (1998). Heterogeneity of savanna canopy

Asner, G.P. (2004). Remote Sensing of terrestrial ecosystems: Biophysical remote sensing

Asner, G.P.; Knapp, D.E.; Cooper, A.N.; Bustamante, M.M.C. & Olander, L.P. (2005).

Backéus, I.; Petterson, B.; Strömquist, L. & Ruffo, C. (2006). Tree communities and structural

Banda, T.; Schwartz, M.W. & Caro, T. (2006). Woody vegetation structure and composition

Colombia: A remote sensing approach. *Forest Ecology and Management,* Vol.257,

atmospheric environment, In: *Biogeochemistry of Global Change: Radiatively Active Trace Gases,* Oremland, R.S., (ed.), 113-150, Chapman & Hall, New York, NY, USA.

structure and function from imaging spectrometry and inverse modeling. *Ecological* 

signatures in arid and semi-arid ecosystems In: *Remote Sensing for Natural Resources Management and Environmental Monitoring,* Ustin, S.L, (ed.), 53-109, John Wiley and

Ecosystem Structure throughout the Brazilian Amazon for Landsat Observationsand Auntomated Spectral Unmixing. *Earth Interactions*, Vol.9, pp.1-30

dynamics in miombo (*Brachystegia*\_*Julbernardia*) woodland, Tanzania. *Forest Ecology* 

along a protection gradient in a miombo ecosystem of western Tanzania. *Forest* 

5. Knowledge Management: from science to policy.

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pp.1237–1246. doi:10.1016/j.foreco.2008.11.016

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 (Furley, 2010).

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 calibrated for this ecosystem.

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 completely understood yet.

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 differentiate carbon pools.
