Boreal Zone Forest Type and Structure From EOS Data Sets

Jon Ranson (Principal Investigator), NASA GSFC; Guoqing Sun, University of Maryland at College Park; Slava Kharuk, Sukachev Institute of Forest, Krasnoyark, Russia; Daniel S. Kimes, NASA GSFC

The boreal forest is an important component of the world’s forests covering 12x106 km2, nearly 30% of world’s forest, and 73% of its coniferous forest area. General circulation models predict regions of Canada and Siberia, especially the northern and sub-arctic parts, will experience significant warming over the foreseeable future (Hansen et al 1996, Geophys. Res. Lett. 23:1665- 1668). The magnitude of the boreal forest area suggests that it plays a critical role in the global climate system, e.g., as potential sink or source of atmospheric carbon (Stocks et al. 2002, http://www.igac.unh.edu/newsletter/ 15/boreal.php). Forest cover type and structure (e.g., height, biomass) are fundamental parameters for understanding the global carbon cycle and ecosystem dynamics in the face of changing climate.

Objectives: the purpose of the project is to map boreal forest type and structure using Terra instrument data aided by point measurements of canopy height, density and biomass inferred from satellite lidar data. The primary objective is to improve forest identification and biomass estimation by combining MODIS, MISR, and Geoscience Laser Altimeter System (GLAS) data sets.

Methods: the emphasis of this project is to map boreal forest structure parameters (i.e. age, coverage, height and biomass) using temporal, multi-angle, and vertical profile information of MODIS, MISR and GLAS data. Field samples, GLAS point data, and MISR images will be used at test sites to develop training and testing datasets, which will be used in MODIS data classification and for further algorithm development. The relationships between MODIS data and forest structure parameters will be investigated using the datasets. The relationships will then be used to map forest structure parameters from MODIS data, and the results will be compared with the map produced from MODIS classification and GLAS samples.

Expected results: methods for mapping boreal forest structure parameters from Terra and GLAS data, a validated biomass map for a portion Eurasia boreal forest. Other maps such as tree height, tree coverage, may also be produced. The results of this research will be applicable to answering several important questions posed by NASA’s Earth Science Enterprise including: How are global ecosystems changing? and, How is the Earth’s surface being transformed and how can such information be used to predict future changes? The results of this study will address these questions for a large portion of the Siberian boreal zone and help pave the way for future circumpolar boreal forest studies.

Human and Biophysical Dimensions of Land-Use/Cover Change in Amazonia: Towards a Multi-scale Synthesis and Sustainability

Emilio F. Moran (Principal Investigator), Indiana University; Mateus Batistella, EMBRAPA Satellite Monitoring, Brazil; Eduardo Brondizio, Ryan Jensen, Paul Mausel, Indiana State University; Lars Hedin, Princeton University

The goals of this project are to develop a multi-scale synthesis of LCLUC in 7 study areas over the past 25 years; develop a multi-sensor analysis of land-cover using artificial neural networks; understand landscape level controls by nitrogen and phosphorus dynamics in sustainability of forests in the Basin; and to identify trajectories of land-use most likely to be conducive to environmental and socially sustainable uses of the Amazonian tropical forest landscape.

Particular contribution of this project within LCLUC is through studies of demographic and institutional dimensions of environmental change i.e. human dimensions of global environmental change. The project contributes to larger goals of NASA and the US Carbon Cycle Program by converting our vegetation biomass to carbon pool estimates at all seven sites and by calculating the carbon emissions from deforestation and the rates of carbon sequestration. The project also examines the effects caused by export of nutrients; the patterns of dissolved nutrient losses from our research sites; characterize patterns of N and P losses across geographic gradient of soil fertility by sampling in both wet and dry seasons; and undertaking hydrologic mass balance calculations to translate measures into estimates of N and P losses from the forest ecosystem to assess sustainability.

Global Rates and Extent of Tropical Deforestation, Forest Regeneration, Selective Logging and Fragmentation

David Skole, Michigan State University (Principal Investigator)

This project is aimed at reducing the uncertainty in the global estimates of tropical deforestation using massive-scale analysis of Landsat data. It takes advantage of key new global Landsat datasets that have been acquired in recent years, and which now provide a comprehensive baseline for continental scale measurements of deforestation, regeneration, and fragmentation rates. The project has spent considerable time accumulating the needed global databases. The combination of the NASA orthorectified dataset and the existing TRFIC dataset make the overall holdings the largest single Landsat archive in the world outside the US federal government, with unique data not found elsewhere. These data are available at www.landsat.org. The efficacy of a global sample approach to deforestation monitoring has been tested.

The solution to this problem is to use a combination of complete “wall-to -wall” inventories in benchmark or census years (approximately every 5 years) and use these inventories to develop a low-density stratification on an annual basis between census years. Sampling alone without the advantage of a stratification based on inventories is inaccurate. The project has also been examining of the full suite of tropical forest disturbances, ranging from logging to outright deforestation. The story for the Amazon is now clear: early logging in the 1990s has not been as important as some studies suggest, but the rate has been increasing. Today the effect of logging and fragmentation is an important additional form of forest disturbance, but has yet to have a total area impact as important as deforestation.

Mapping Height and Biomass of Mangrove Forests in Everglades National Park with SRTM Elevation Data

Marc Simard (Principal Investigator), Caltech- Jet Propulsion Laboratory; Keqi Zhang, Florida International University; Victor H. Rivera-Monroy, Louisiana State University; Michael S. Ross, Pablo L. Ruiz, Florida International University; Edward Castañeda-Moya, Robert R. Twilley, Louisiana State University

Figures: A) Map of tree height in the Everglades National Park produced with SRTM elevation data. This map only includes the mangrove forests and the mean tree height estimation over the 30m pixel has an error of 2m. The zoom shows the mouth of the Shark river where most tall forests are found. B) This map shows the spatial distribution of standing biomass contained in the mangrove forests of the ENP.

An interdisciplinary team from the Jet Propulsion Laboratory, the Louisiana State University and Florida International University, has been working on this project since 2004. Their efforts revealed, for the first time, the 3D complexity of the mangrove forests at the landscape scale in the Everglades National Park (ENP). A map was produced of tree height of mangrove forests in ENP using the elevation data from the Shuttle Radar Topography Mission (SRTM). The SRTM data were calibrated using airborne LIDAR data and a high resolution USGS Digital Elevation Model (DEM). The resulting mangrove height map has an error of 2.0m (RMSE) over a pixel of 30m. In addition, field data was collected to derive a relationship between mean forest stand height and biomass in order to map the spatial distribution of standing biomass of mangroves for the entire National Park. The total mangrove standing biomass in ENP was estimated to be 5.6Mt. These maps will serve as a base to evaluate landscape changes resulting from the ongoing Comprehensive Everglades Restoration Plan (CERP) as well as impacts of hurricanes and sea level rise. More details about this investigation are published in the SRTM Special Issue of Photogrammetric Engineering and Remote Sensing of March 2006.

Other LCLUC Detection and Monitoring Projects:

• Bowling, Laura - Purdue University. Multisensor/Multiscale Assessment of Urban Impacts in the Great Lakes Region
• Christensen, Philip - Arizona State University. Investigation of rapid urbanization processes using ASTER, MODIS and Landsat data
• French, Nancy /Brown, Dan - Altarum Institute/ University of Michigan. Using Remote Sensing-Based Measures to Assess NRCS Impacts in Michigan
• Geerken, Roland - Yale University. Ecological Monitoring in Semi-Arid Central and West Asia: Drivers and Trajectories
• Hansen, Matthew - Geographic Information Science Center of Excellence SDSU. Establishing a global forest monitoring capability using multi-resolution and multi-temporal remotely sensed data sets
• Kling, Cathy - Iowa State University of Science & Technology. Interactive Drivers of Land-Use/Land-Cover Change in Agricultural Areas: Climate and Land-Manager Choices
• Ramankutty, Navin - University of Wisconsin-Madison. Land Use mapping
• Turner, Billie - Clark University. Landscape Vulnerability-Resilience in the Southern Yucatan Peninsular Region [SYPR]
• Walker, Skip - University of Alaska Fairbanks. Application of space-based technologies to examine land-cover/land-use change along a transect on the Yamal Peninsula and Novaya Zemlya, Russia