David Saah

Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates

Landsat Thematic mapper (TM) image has long been the dominate data source, and recently LiDAR has offered an important new structural data stream for forest biomass estimations. On the other hand, forest biomass uncertainty analysis research has only recently obtained sufficient attention due to the difficulty in collecting reference data. This paper provides a brief overview of current forest biomass estimation methods using both TM and LiDAR data. A case study is then presented that demonstrates the forest biomass estimation methods and uncertainty analysis. Results indicate that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems. LiDAR can overcome TM’s shortcoming providing better biomass estimation performance but has not been extensively applied in practice due to data availability constraints. The uncertainty analysis indicates that various sources affect the performance of forest biomass/carbon estimation. With that said, the clear dominate sources of uncertainty are the variation of input sample plot data and data saturation problem related to optical sensors. A possible solution to increasing the confidence in forest biomass estimates is to integrate the strengths of multisensor data.

The Effect of Refugee Inflows on Host Communities: Evidence from Tanzania

Despite the large and growing number of humanitarian emergencies, there is little economic research on the impact of refugees and internally displaced people on the communities that receive them. This analysis of the impact of the refugee inflows from Burundi and Rwanda in 1993 and 1994 on host populations in western Tanzania shows large increases in the prices of nonaid food items and more modest price effects for aid-related food items. Food aid is shown to mitigate these effects, though its impact is smaller than that of the increases in the refugee population. Examination of household assets suggests positive wealth effects of refugee camps on nearby rural households and negative wealth effects on households in urban areas.

A Global Meta-Analysis of Forest Bioenergy Greenhouse Gas Emission Accounting Studies

The potential greenhouse gas benefits of displacing fossil energy with biofuels are driving policy development in the absence of complete information. The potential carbon neutrality of forest biomass is a source of considerable scientific debate because of the complexity of dynamic forest ecosystems, varied feedstock types, and multiple energy production pathways. The lack of scientific consensus leaves decision makers struggling with contradicting technical advice. Analyzing previously published studies, our goal was to identify and prioritize those attributes of bioenergy greenhouse gas (GHG) emissions analysis that are most influential on length of carbon payback period. We investigated outcomes of 59 previously published forest biomass greenhouse gas emissions research studies published between 1991 and 2014. We identified attributes for each study and classified study cases by attributes. Using classification and regression tree analysis, we identified those attributes that are strong predictors of carbon payback period (e.g. the time required by the forest to recover through sequestration the carbon dioxide from biomass combusted for energy). The inclusion of wildfire dynamics proved to be the most influential in determining carbon payback period length compared to other factors such as feedstock type, baseline choice, and the incorporation of leakage calculations. Additionally, we demonstrate that evaluation criteria consistency is required to facilitate equitable comparison between projects. For carbon payback period calculations to provide operational insights to decision makers, future research should focus on creating common accounting principles for the most influential factors including temporal scale, natural disturbances, system boundaries, GHG emission metrics, and baselines.

Carbon Accounting for Woody Biomass from Massachusetts (USA) Managed Forests: A Framework for Determining the Temporal Impacts of Wood Biomass Energy on Atmospheric Greenhouse Gas Levels

Policies based on assumed carbon neutrality fail to address the timing and magnitude of the net greenhouse gas (GHG) changes from using wood for energy. We present a “debt-then-dividend” framework for evaluating the temporal GHG impacts of burning wood for energy. We also present a case study conducted in Massachusetts, USA to demonstrate the framework. Four key inputs are required to calculate the specific shape of the debt-then-dividend curve for a given region or individual biomass facility. First, the biomass feedstock source: the GHG implications of feedstocks differ depending on what would have happened to the material in the absence of biomass energy generation. Second, the form of energy generated: energy technologies have different generation efficiencies and thus different life cycle GHG emissions profiles. Third, the fossil fuel displaced: coal, oil, and natural gas each have different emissions per unit of energy produced. Fourth, the management of the forest: forest management decisions affect recovery rates of carbon from the atmosphere. This framework has broad application for informing the development of renewable energy and climate policies. Most importantly, this debt-then-dividend framework explicitly recognizes that GHG benefits of wood biomass energy will be specific to the forest and technology context of the region or biomass energy projects.

City Growth Under Conflict Conditions: The View from Nyala, Darfur

The developing world today is challenging conventional accounts of city growth and change. In Africa, for example, conflict and mass displacement are reconfiguring the urban landscape in ways that are hard to ignore. This paper analyzes how conflict and the arrival of a large humanitarian aid infrastructure influence the dynamics of city growth and bring about a distinct spatial structure, niche gentrification, and informal economy in Nyala, Darfur. Using data from a three–year field study, we show how the size and socio–spatial organization of the city changed, the directions in which the city grew, and the factors that drove these changes. We look at interaction patterns between residents of Nyala itself and those now residing in internally displaced person (IDP) camps on the edge of the city. We show that considerations of both insecurity and risk are vitally important to understanding the processes of conflict urbanization. Conflict generates a distinctive social structure as internal displaced people, international aid workers, and long–time urban residents all move within the city.

Using field data to assess model predictions of surface and ground fuel consumption by wildfire in coniferous forests of California

Inventories of greenhouse gas (GHG) emissions from wildfire provide essential information to the state of California, USA, and other governments that have enacted emission reductions. Wildfires can release a substantial amount of GHGs and other compounds to the atmosphere, so recent increases in fire activity may be increasing GHG emissions. Quantifying wildfire emissions however can be difficult due to inherent variability in fuel loads and consumption and a lack of field data of fuel consumption by wildfire. We compare a unique set of fuel data collected immediately before and after six wildfires in coniferous forests of California to fuel consumption predictions of the first-order fire effects model (FOFEM), based on two different available fuel characterizations. We found strong regional differences in the performance of different fuel characterizations, with FOFEM overestimating the fuel consumption to a greater extent in the Klamath Mountains than in the Sierra Nevada. Inaccurate fuel load inputs caused the largest differences between predicted and observed fuel consumption. Fuel classifications tended to overestimate duff load and underestimate litter load, leading to differences in predicted emissions for some pollutants. When considering total ground and surface fuels, modeled consumption was fairly accurate on average, although the range of error in estimates of plot level consumption was very large. These results highlight the importance of fuel load input to the accuracy of modeled fuel consumption and GHG emissions from wildfires in coniferous forests.

Chapter 9 Community Involvement in Wildfire Hazard Mitigation and Management: Community Based Fire Management, Fire Safe Councils and Community Wildfire Protection Plans

Community-based fire management (CBFiM) integrates community action with the standard elements of fire management and mitigation, such as prescribed fire (managed beneficial fires for reducing hazardous fuel loads, controlling weeds, preparing land for cultivation, reducing the impact of pests and diseases, etc.), mechanical fuel treatment, defensible space planning, wildfire awareness and prevention, preparedness planning, and suppression of wildfires. In developed examples of CBFiM, communities are empowered to have effective input into land and fire management and problem solving and to self regulate to respond to fire and other emergencies. Its premise is that local people usually have most at stake in the event of a harmful fire, so they should clearly be involved in mitigating these unwanted events.

A Word to the Wise: Advice for Scientists Engaged in Collaborative Adaptive Management

Collaborative adaptive management is a process for making decisions about the environment in the face of uncertainty and conflict. Scientists have a central role to play in these decisions. However, while scientists are well trained to reduce uncertainty by discovering new knowledge, most lack experience with the means to mitigate conflict in contested situations. To address this gap, we drew from our efforts coordinating a large collaborative adaptive management effort, the Sierra Nevada Adaptive Management Project, to offer advice to our fellow environmental scientists. Key challenges posed by collaborative adaptive management include the confusion caused by multiple institutional cultures, the need to provide information at management-relevant scales, frequent turnover in participants, fluctuations in enthusiasm among key constituencies, and diverse definitions of success among partners. Effective strategies included a dedication to consistency, a commitment to transparency, the willingness to communicate frequently via multiple forums, and the capacity for flexibility. Collaborative adaptive management represents a promising, new model for scientific engagement with the public. Learning the lessons of effective collaboration in environmental management is an essential task to achieve the shared goal of a sustainable future.

A Global Capacity Building Vision for Societal Applications of Earth Observing Systems and Data: Key Questions and Recommendations

Capacity building using Earth observing (EO) systems and data (i.e., from orbital and nonorbital platforms) to enable societal applications includes the network of human, nonhuman, technical, nontechnical, hardware, and software dimensions that are necessary to successfully cross the valley [of death; see NRC (2001)] between science and research (port of departure) and societal application (port of arrival). In many parts of the world (especially where ground-based measurements are scarce or insufficient), applications of EO data still struggle for longevity or continuity for a variety of reasons, foremost among them being the lack of resilient capacity. An organization is said to have resilient capacity when it can retain and continue to build capacity in the face of unexpected shocks or stresses. Stresses can include intermittent power and limited Internet bandwidth, constant need for education on ever-increasing complexity of EO systems and data, communication challenges between the ports of departure and arrival (especially across time zones), and financial limitations and instability. Shocks may also include extreme events such as disasters and losing key staff with technical and institutional knowledge.

Use of Remotely Sensed Climate and Environmental Information for Air Quality and Public Health Applications

Earth’s environment has direct and dramatic effects on its inhabitants in the realms of health and air quality. The climate, even in an unaltered state, poses great challenges but also presents great opportunity for the mankind to survive and flourish. Anthropogenic factors lead to even greater stress on the global ecosystem and to mankind, particularly with respect to air quality and the concomitant health issues. While the use of remote sensing technology to address issues is in its infancy, there is tremendous potential for using remote sensing as part of systems that monitor and forecast conditions that directly or indirectly affect health and air quality. This chapter discusses current status and future prospects in this field and presents three case studies showing the great value of remote sensing assets in distinct disciplines.