Preface

The accurate measurement of ecosystem biomass is of great importance in scientific, resource management and energy sectors. In particular, biomass is a direct measurement of carbon storage within an ecosystem and of great importance for carbon cycle science and carbon emission mitigation. Closing the global carbon budget is one of the greatest scientific and societal needs of our time. Quantifying the carbon cycle is the most important element in understanding climate change and its consequences, yet is poorly understood (Le Toan et al, 2004). As an example, globally forests store 85% of terrestrial carbon, yet the amount of carbon contained in the earth's forests is not known to even one significant figure, ranging from 385 to 650 1015g carbon (Saugier et al. 2001, Goodale et al. 2002, Houghton et al. 2009). It is therefore crucial that biomass measurements be improved.

Measurements of ecosystem biomass have far ranging societal, policy and management implications. The anticipated economic and societal burden that will result from unmitigated rises in CO2 emissions and losses in ecosystem services alone are estimated to be in the trillion of dollars by mid-century (Stern Report, 2008). Ongoing international carbon mitigation initiatives need detailed, precise and accurate measurements of carbon storage in terrestrial, coastal and aquatic ecosystems to be successful.

Remote Sensing is the most accurate tool for global biomass measurements because of the ability to measure large areas. Current biomass estimates are derived primarily from ground-based samples, as compiled and reported in inventories and ecosystem samples. By using remote sensing technologies, we are able to scale up the sample values and supply wall to wall mapping of biomass. Three separate remote sensing technologies are available today to measure ecosystem biomass: passive optical, radar, and lidar.

There are many measurement methodologies that range from the application driven to the most technologically cutting-edge. The goal of this book is to address the newest developments in biomass measurements, sensor development, field measurements and modeling. The chapters in this book are separated into five main sections.

#### XII Preface

In section I (Forests) the authors present recent developments in remote sensing using lidar in Chapter 1 *Lidar Remote Sensing for Biomass Assessment* by J. Rosette, et al., Synthetic Aperture Radar in Chapter 2 *Forest Structure Retrieval from Multi-Baseline SAR tomography*, by S. Tebaldini, and very high resolution optical imagery in Chapter 3 *Biomass Prediction in Tropical Forest: the Canopy Grain Approach* by C. Proisy et al. In Chapter 4 *Remote Sensing of Biomass in the Miombo Woodlands of Southern Africa: opportunities and limitations for research*, N. Ribeiro et al lead us through a review of the current state of biomass estimation in Woodland forests of Southern Africa.

Preface XI

**Temilola Fatoyinbo** 

USA

Hernandez et al. create and validate methods for the estimation of above ground biomass in Chile using medium spatial resolution satellite imagery, digital elevation models and geostatistical modeling. Finally, Chapter 14 *Using Remote Sensing To Estimate A Renewable Resource: Forest Residual Biomass* by A. García-Martín et al. describes a methodology developed to estimate the amount of Forest Residual Biomass potentially suitable for renewable energy production in the pine forests of Mediterranean areas at regional scales, using optical satellite images and forest

Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD,

inventory data.

Section II (Oceans) of this book addresses biomass estimation of biomass in the oceans. In chapter 5 *Remote Sensing of Marine Phytoplankton Biomass*, T. Moisan et al. provide a review of the remote sensing techniques currently used in phytoplankton biomass estimation from space. In Chapter 6 *Using SVD analysis of combined altimetry and ocean color satellite data for assessing basin scale physical-biological coupling in the Mediterranean Sea,* the authors A. Jordi and G. Basterretxea analyze patterns of phytoplankton variability at inter-annual, seasonal and intra-annual scales in the Mediterranean Sea based on satellite imagery.

Section III (Fires) addresses the remote sensing of fires and post-fire monitoring in forests. In Chapter 7 Advances in Remote Sensing of Post-Fire Monitoring, I. Gitas et al. carry out a detailed review of the current state of remote sensing of burned forest areas. Chapter 8 *The science and application of Satellite Based Fire radiative energy* by E. Elicott and E. Vermote examines the effect of forest biomass burning on the biosphere and atmosphere.

In Section IV (Models), the chapters address the combination of remote sensing and ecosystem modeling as they relate to biomass. Chapter 9. *Resilience and stability associated with conversion of boreal forest* by J.K. Shuman and H.H. Shugart examines the forest composition and biomass across Siberia and the Russian Far East from individual based modeling and remote sensing to evaluate forest response to climate change. In chapter 10 *Estimating biomass dynamics from LAI through a plant model*, M. Kang et al present a model of plant growth from noisy and incomplete remote sensing data.

Section V (Applications) composed of chapters where biomass is estimated for resource management or agricultural applications. In Chapter 11 *Mapping aboveground and foliage biomass over the Porcupine caribou habitat in northern Yukon and Alaska using Landsat and JERS-1/SAR data*, W. Chen et al. develop baseline maps of aboveground and foliage biomass of forested and non forested areas over the Porcupine caribou habitat in northern Yukon and Alaska, using Landsat and JERS-1/SAR data. Chapter 12 *Rice Crop Monitoring with Unmanned Helicopter Remote Sensing Images* by K. Swain et al. explores the use of unmanned Helicopter Remote sensing for precision agriculture and biomass yield estimations in rice plantations. In Chapter 13 *Geostatistical Estimation of Biomass Stock in Chilean Native Forests and Plantations,* the authors J. Hernandez et al. create and validate methods for the estimation of above ground biomass in Chile using medium spatial resolution satellite imagery, digital elevation models and geostatistical modeling. Finally, Chapter 14 *Using Remote Sensing To Estimate A Renewable Resource: Forest Residual Biomass* by A. García-Martín et al. describes a methodology developed to estimate the amount of Forest Residual Biomass potentially suitable for renewable energy production in the pine forests of Mediterranean areas at regional scales, using optical satellite images and forest inventory data.

X Preface

Southern Africa.

based on satellite imagery.

and atmosphere.

data.

In section I (Forests) the authors present recent developments in remote sensing using lidar in Chapter 1 *Lidar Remote Sensing for Biomass Assessment* by J. Rosette, et al., Synthetic Aperture Radar in Chapter 2 *Forest Structure Retrieval from Multi-Baseline SAR tomography*, by S. Tebaldini, and very high resolution optical imagery in Chapter 3 *Biomass Prediction in Tropical Forest: the Canopy Grain Approach* by C. Proisy et al. In Chapter 4 *Remote Sensing of Biomass in the Miombo Woodlands of Southern Africa: opportunities and limitations for research*, N. Ribeiro et al lead us through a review of the current state of biomass estimation in Woodland forests of

Section II (Oceans) of this book addresses biomass estimation of biomass in the oceans. In chapter 5 *Remote Sensing of Marine Phytoplankton Biomass*, T. Moisan et al. provide a review of the remote sensing techniques currently used in phytoplankton biomass estimation from space. In Chapter 6 *Using SVD analysis of combined altimetry and ocean color satellite data for assessing basin scale physical-biological coupling in the Mediterranean Sea,* the authors A. Jordi and G. Basterretxea analyze patterns of phytoplankton variability at inter-annual, seasonal and intra-annual scales in the Mediterranean Sea

Section III (Fires) addresses the remote sensing of fires and post-fire monitoring in forests. In Chapter 7 Advances in Remote Sensing of Post-Fire Monitoring, I. Gitas et al. carry out a detailed review of the current state of remote sensing of burned forest areas. Chapter 8 *The science and application of Satellite Based Fire radiative energy* by E. Elicott and E. Vermote examines the effect of forest biomass burning on the biosphere

In Section IV (Models), the chapters address the combination of remote sensing and ecosystem modeling as they relate to biomass. Chapter 9. *Resilience and stability associated with conversion of boreal forest* by J.K. Shuman and H.H. Shugart examines the forest composition and biomass across Siberia and the Russian Far East from individual based modeling and remote sensing to evaluate forest response to climate change. In chapter 10 *Estimating biomass dynamics from LAI through a plant model*, M. Kang et al present a model of plant growth from noisy and incomplete remote sensing

Section V (Applications) composed of chapters where biomass is estimated for resource management or agricultural applications. In Chapter 11 *Mapping aboveground and foliage biomass over the Porcupine caribou habitat in northern Yukon and Alaska using Landsat and JERS-1/SAR data*, W. Chen et al. develop baseline maps of aboveground and foliage biomass of forested and non forested areas over the Porcupine caribou habitat in northern Yukon and Alaska, using Landsat and JERS-1/SAR data. Chapter 12 *Rice Crop Monitoring with Unmanned Helicopter Remote Sensing Images* by K. Swain et al. explores the use of unmanned Helicopter Remote sensing for precision agriculture and biomass yield estimations in rice plantations. In Chapter 13 *Geostatistical Estimation of Biomass Stock in Chilean Native Forests and Plantations,* the authors J.

#### **Temilola Fatoyinbo**

Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA

**Part 1** 

**Forests** 

**Part 1** 

**Forests** 

**1** 

*1Swansea University, 2Forest Research* 

*1,2United Kingdom* 

*3,4USA* 

**Lidar Remote Sensing for Biomass Assessment** 

Optical remote sensing provides us with a two dimensional representation of land-surface vegetation and its reflectance properties which can be indirectly related to biophysical parameters (e.g. NDVI, LAI, fAPAR, and vegetation cover fraction). However, in our interpretation of the world around us, we use a three-dimensional perspective. The addition of a vertical dimension allows us to gain information to help understand and interpret our surroundings by considering features in the context of their size, volume and

In contrast to estimates of vegetation parameters which can be obtained from passive optical data, active lidar remote sensing offers a unique means of directly estimating biophysical parameters using physical interactions of the emitted laser pulse with the vegetation structure being illuminated. This enables the vertical profile of the vegetation canopy to be represented, not only permitting canopy height, metrics and cover to be calculated but also

This chapter provides an overview of this technology, giving examples of how lidar data have been applied for forest biomass assessment at different scales from the perspective of satellite, airborne and terrestrial platforms. The chapter concludes with a discussion of further applications of lidar data and a look to the future towards emerging lidar developments.

Aside from destructive sampling, traditional methods of calculating biomass for forest inventory, monitoring and management often rely on taking field measurements within sample plots, such as diameter at breast height (DBH) or Top/Lorey's height. This effort can be time, cost and labour intensive. Extrapolation of field measurements to larger areas relies on representative sampling of trees within a land-cover type and correct classification of

Lidar remote sensing complements traditional field methods through data analysis which enables the extraction of vegetation parameters that are commonly measured in the field.

enabling these to be related to other biophysical parameters such as biomass.

land cover over large areas; both of which have inherent uncertainties.

**1. Introduction** 

**1.1 Context** 

spatial relation to each other.

Jacqueline Rosette1,3,4, Juan Suárez2,4, Ross Nelson3,

Sietse Los1, Bruce Cook3 and Peter North1

*3NASA Goddard Space Flight Center, 4University of Maryland College Park* 
