Conghe Song, Ph.D. - US grants

The overall goal of this project is to improve our understanding of the impacts of the multi-dimensional forest structures on scaling up forest ecosystem carbon cycle from stand to landscape through the integrative use of remote sensing, ecological models and ground observations. Though funding is requested for UNC Chapel Hill only, this is a collaborative research between the Department of Geography at UNC Chapel Hill and the School of Environment at Duke University. The expertise and research facilities from the two campuses are complementary. The proposed research will expands the scope and depth of the existing projects in Duke Forest. The project will take the necessary steps to transform the understanding of mass and energy exchange between forest ecosystems and the atmosphere at Duke Forest to a regional understanding. The project will use the AmeriFlux and FACE sites in the Duke Forest as the anchor points for scaling up through the integrative use of remote sensing and ecosystem models. Currently the global FLUXNET has over 200-flux towers world wide, and the up-scaling strategy investigated in this project will provide in-depth understanding of how to scale up from FLUXNET measurements to improve our understanding in global carbon cycle. The project proposes to use multisensor remotely sensed data to map multi-dimensional forest structures, including tree size and density, stand ages, leaf area index, and subpixel tree cover. The remotely sensed data include high-resolution (=1m) digital orthophoto quads and space-born images from Ikonos/QuickBird, medium resolution (30 m) Landsat images, and coarse-resolution (250m) MODIS/MISR images. The project will develop algorithms to use information from spatial, spectral/temporal and directional domains of remotely sensed data. The project can substantially enhance the use of remote sensing to extract detailed spatial vegetation information. A series of well-established ecological models will be used in the project, each of which will take forest structure at the appropriate scale to simulate terrestrial ecosystem carbon cycle. In addition to quantifying the errors caused by omitting forest structures in simulating carbon cycle, the project will lead to major improvements to the these models.

Perverse economic effects can create strong negative feedbacks between natural and human systems. For example, short-term, fine-scale, net economic benefits from uses of natural resources that compromise the future supply of related resources can reduce long-term, large-scale economic benefits. Numerous governmental programs have effectively tested the hypothesis that such negative feedbacks can be eliminated with economic counter-incentives, but few if any of these programs have been suitable for and subjected to the rigorous scientific analysis needed to determine the true results of the test and help generalize results. This project will analyze what is probably the largest program within the most widely used type of counter-incentive, the Sloping Lands Conversion Program of China, a program of payment for environmental services. Under this program, the government pays farmers to convert cropland on sloping or otherwise ecologically sensitive areas to forest or grassland. Researchers will survey farmers and local governmental agencies in three provinces to determine how the program was implemented and affected the decisions of farmers, detect changes in land cover using satellite imagery, and model carbon storage and water availability based on field measurements.

Results of this project will be of great value to policy makers and land use managers in the U.S., where similar programs have been tried and are envisioned in the context of ecological restoration and protection. The research also will help inform the global discourse on reducing carbon emissions from deforestation and forest degradation. This project will strengthen scientific collaboration in both social and natural science between the U.S. and China, and train numerous undergraduate and graduate students.

Forests are extremely important because they serve as homes for millions of animals and plants. Those organisms live, provide people with wood for building houses and for cooking, and they cool the climate by absorbing carbon dioxide. Unfortunately, forests continue to be cut down, especially in poor countries. To prevent forests from further decline, people in some communities are protecting them with what is called Community Forestry. In community forestry programs people in a village work together to decide what to do with their forests. About a third of the forests in poor countries are managed this way. However, community forestry does not always work. This project will find out why some villages in Nepal are successful in community forestry while others not. The researchers will work closely with local stakeholders to improve their forest management strategies that can benefit communities worldwide. In addition to this impact on forestry the project will train graduate and undergraduate students and leave a lasting legacy in Nepal. The United States will economically benefit from this research as a result of better community forest management through resource preservation and reduced carbon dioxide levels.

The main goal of this project is to study how can be improved to better preserve the forests and support the lives of forest-dependent people. To achieve this goal, this research will address the following questions: (1) How do community forestry practices affect people’s livelihoods and their social interactions? (2) How do those practices influence rural out-migration? (3) How do they affect land-use? (4) How has COVID-19 influenced rural people’s livelihoods and their dependence on community forestry? (5) How has community forestry influenced the ecosystem’s provision of goods and services? The researchers will interview households about their forest management practices, the origin of management rules, and the role of community members in making rules. They will also be surveyed to determine detailed information about their agricultural practices, out migration patterns, and if community forestry has helped buffer COVID-19 impacts. Remote sensing data collected by satellites, in situ hydrological data on the ground, and statistical, ecological and hydrological models will also be used to estimate how much water forests use, and how much carbon dioxide they absorb from the atmosphere. Eventually an Integrated Modeling System will be developed to study the interactions among forests, human activities, and the ecosystem goods and services the environment provides. This project will advance theory on common pool resource management, rural out migration, as well as the land use and forest-ecosystem service relationship. The new knowledge to be gained from this research will be highly valuable when developing new policies for sustainable community forestry in Nepal and other countries.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.