**5. References**

226 Remote Sensing of Biomass – Principles and Applications

Using the estimated hidden GreenLab parameters in recovering the series of LAI, one can simulate the dynamics of biomass production and partitioning, as shown in Fig. 6(a), and the 3D model of the observed plants, as shown in Fig. 6(b). As for a maize plant, Fig. 6(a) gives the total plant biomass (g) in black line, and the allocation to leaf blade (green), sheath (blue), internode (brown), cob (red) and male flower (purple). Recall that empirical

(a) (b)

**3.3 Plant growth reconstruction** 

0

**4. Conclusion** 

aim of estimating LAI.

500

1000

1500

Biomass

2000

2500

total blade petiel pith female flower male flower ring root

geometrical parameters are used in building the 3D structure.

Accumulated biomass production and repartition

0 5 10 15 20 25 30 35

Plant Age

among different type of organs; (b) a 3D maize plant.

Fig. 6. Computed result from calibrated model. (a) biomass production and its allocation

In this chapter, we have presented a framework of estimating LAI series from remote sensing images using filtering techniques and GreenLab model. Computational experiment was done on synthetic data to show the feasibility of full process. The result shows that by embedding a dynamic model, the LAI series can be recovered even if the source data from remote sensing images are very noisy and sparse. And in doing so, the GreenLab model can be calibrated partially to simulate biomass production and allocation. The advantage of embedding a crop model is that the knowledge on crop development and growth can be used in recovering LAI series, and the link between LAI and biomass production make it possible to estimate biomass production from remote sensing data, which is the ultimate

Yet this theoretical work needs to be further tested by real remote sensing sources. Challenges include the initialization of model parameters, such as the setting on topological parameters and initial source and sink parameter. Detection of crop type can help to solve this issue by providing empirical parameters. Yet their values are not necessary to be accurate, and other information from remote sensing, such as leaf chlorophyll content and leaf water content, may compensate. On the other hand, the combination of a functionalstructural plant model as GreenLab brings many possibilities. For example, as the threedimensional structures of crop are built, it is possible to run radiative transfer model in virtual canopy. Although the result will be dependent on the definition of geometrical


**Part 5** 

**Applications** 


**Part 5** 

**Applications** 

228 Remote Sensing of Biomass – Principles and Applications

Yan G., Mas J.-F., Maathuis B.H.P., Xiangmin Z., Van Dijk P.M., Comparison of pixel- based

Yan H.P., Kang M.Z., de Reffye P., Dingkuhn M. 2004. A dynamic, architectural plant model simulating resource- dependent growth. Annals of Botany 93: 591–602. Verhoef W. 1984. Light scattering by leaf layers with application to canopy reflectance modeling: the SAIL model. Remote Sensing of Environment, 16: 123.

18, p. 4039-4055, September, 2006.

and object-oriented image classification approaches - a case study in a coal fire area, Wuda, Inner Mongolia, China, International Journal of Remote Sensing, vol. 27, no.

**11** 

*Canada* 

**JERS-1/SAR Data** 

**Mapping Aboveground and Foliage Biomass** 

Wenjun Chen, Weirong Chen, Junhua Li, Yu Zhang, Robert Fraser,

**Yukon and Alaska Using Landsat and** 

Ian Olthof, Sylvain G. Leblanc and Zhaohua Chen *Canada Centre for Remote Sensing, Natural Resources Canada* 

**Over the Porcupine Caribou Habitat in Northern** 

The linkage between caribou and the aboriginal people in the North America has existed for thousands of years. Caribou have played a critical role in the economy, culture, and way of life of the aboriginal people (Hall, 1989; Madsen, 2001). Currently, there are 60 major migratary tundra caribou herds circum-arctic, of which 30 are located in North America, including the Porcupine caribou herd in northern Yukon, Canada and northern Alaska, USA (Russell et al., 1992; Russell et al., 1993; Russell & McNeil, 2002; Russell et al., 2002; Griffith et al., 2002). The Porcupine caribou herd has been at the centre of debate between wildlife habitat conservation and industrial development in the Arctic because of the potential oil drilling in the Arctic National Wildlife Refuge (ANWR) 1002 area, which happens to largely overlap with the calving ground of the Porcupine caribou herd (Griffith et al., 2002; Kaiser,

One of the objectives of the Canadian International Polar Year (IPY) project entitled "Climate Change Impacts on Canadian Arctic Tundra Ecosystems (CiCAT): Interdisciplinary and Multi-scale Assessments" was to assess the impact of climate change on caribou habitats over Canada's north, in close collaboration with the CircumArctic Ranfiger Monitoring and Assessment network (CARMA) (http://www.rangifer.net /carma/). Because of the vastness and remoteness of the arctic landmass, inherent logistic difficulty, and high cost of conducting field measurements, an approach that is solely based on field inventory is clearly impractical for monitoring and assessing the impact of climate on caribou habitats. Satellite remote sensing can monitor land surfaces from space repeatedly and consistently over large areas. Therefore, remote sensing provides a powerful tool for monitoring and assessing the impact of climate on caribou habitats, when calibrated

In this study, we report the development of baseline maps of aboveground and foliage biomass over the Porcupine caribou habitat in northern Yukon and Alaska, using Landsat and JERS-1/SAR data. Specifically, we will (1) describe aboveground and foliage biomass measurement, (2) establish and validate relationships between measurements and remote sensing indices, and

and validated against the field measurements and other independent data.

(3) map aboveground and foliage biomass for the Porcupine caribou habitat.

**1. Introduction** 

2002; National Research Council, 2003; Heuer, 2006).
