Using multilevel remote sensing and ground data to estimate forest biomass resources in remote regions: a case study in the boreal forests of interior Alaska

The emergence of a new generation of remote sensing and geopositioning technologies, as well as increased capabilities in image processing, computing, and inferential techniques, have enabled the development and implementation of increasingly efficient and cost-effective multilevel sampling designs for forest inventory. In this paper, we (i) describe the conceptual basis of multilevel sampling, (ii) provide a detailed review of several previously implemented multilevel inventory designs, (iii) describe several important technical considerations that can influence the efficiency of a multilevel sampling design, and (iv) demonstrate the application of a modern multilevel sampling approach for estimating the forest biomass resources in a remote area of interior Alaska. This approach utilized a combination of ground plots, lidar strip sampling, satellite imagery (multispectral and radar), and classified land cover information. The variability in the total biomass estimate was assessed using a bootstrapping approach. The results indicated only marginal improvement in the precision of the total biomass estimate when the lidar sample was post-stratified using the classified land cover layer (reduction in relative standard error from 7.3% to 7.0%), whereas there was a substantial improvement in the precision when the estimate was based on the biomass map derived via nearest-neighbor imputation (reduction in relative standard error from 7.3% to 5.1%).This is the publisher’s final pdf. The published article is copyrighted by the Canadian Aeronautics and Space Institute and can be found at: http://www.casi.ca/cdn-journal-of-remote-sensing

Ecological legacies of drought, fire, and insect disturbance in western North American forests, The

Includes bibliographical references.2015 Fall.Temperate forest ecosystems are subject to various disturbances including insect agents, drought and fire, which can have profound effects on the structure of the ecosystem for many years after the event. Impacts of disturbance can vary widely, therefore an understanding of the legacies of an event are critical in the interpretation of contemporary forest patterns and those of the near future. The primary objective of this dissertation was to investigate the ecological legacies of drought, beetle outbreak and ensuing wildfire in two different ecosystems. A secondary objective of my research, data development, was motivated by a lack of available data which precluded ecological investigation of each disturbance. I studied the effects of drought on deciduous and coniferous forest along a forest-shrubland ecotone in the southern portion of the Wyoming Basin Ecoregion. The results show that forests in the region have experienced high levels of cumulative drought related mortality over the last decade. Negative trends were not consistent across forest type or distributed randomly across the study area. The patterns of long-term trends highlight areas of forest that are resistant, persistent or vulnerable to severe drought. In the second thread of my dissertation, I used multiple lines of evidence to retrospectively characterize a landscape scale mountain pine beetle disturbance from the 1970s in Glacier National Park. The lack of spatially explicit data on this disturbance was a major data gap since wildfire had removed some of the evidence from the landscape. I used this information to assess the influence of beetle severity on the burn severity of subsequent wildfires in the decades after the outbreak. Although many factors contribute to burn severity, my results indicate that beetle severity can positively influence burn severity of wildfire. This is likely due to the change in forest structure in the decades after the outbreak and not as a direct result of tree mortality from the outbreak. The long-term perspective of this study suggests that ecological legacies of high severity disturbance may continue to influence subsequent disturbance for many years after the initial event. This work also provides insight on future disturbance interactions associated with the recent mountain pine beetle outbreak that has impacted tens of millions of hectares in western North America over the last two decades

Triennial Report: 2006-2008

Triennial Report Purpose [Page] 2 The Geographic Information Science Center of Excellence [Page] 4 Three Years in Review [Page] 5 SDSU Faculty [Page] 6-11 EROS Faculty [Page] 12-16 Post-Doctoral Researchers [Page] 17-26 GSE Ph.D. program [Page] 27 Ph.D. Students [Page] 28-39 Center Scholars Program [Page] 40 Masters Students [Page] 41 Geospatial Analysts [Page] 42 Administrative Staff [Page] 43 Center Alumni [Page] 44 Research Funding [Page] 45-46 Ph.D. Student Scholarship Grants [Page] 47 Computing Resources [Page] 48 Looking Forward [Page] 49 Appendix I Faculty publications 2006-2008 [Page] 50-58 Appendix II Cool faculty research and locations [Page] 60-65 Appendix III GIScCE birthplace map [Page] 66 Appendix IV Telephone and email contact information [Page] 67-68 Appendix V How to get to the GIScCE [Page] 6

An Alternate Approach to Ecosystem Mapping: Fusing Orthophotography with LANDSAT ETM+ Data for a Object-Based Classification, South Eastern Arkansas.

Maintaining representative sampling of biologically rich and rare ecosystems has become an important means to preventing biodiversity loss. A limitation in indentifying and quantifying ecosystems is the cost of obtaining high resolution imagery necessary for a high resolution land cover assessment. This research shows how free, different resolution imagery (orthoimages and LANDSAT ETM+) could be combined to produce a hybrid dataset with enhanced spectral, spectral and temporal properties, and processed to obtain a object-based classification of land cover of bottomland and pine hardwood forest in south eastern Arkansas. Three classification techniques were evaluated: 1) a human derived, rule based method, 2) A nearest neighbor classification using only the infrared orthoimage (SRGB), and 3) A nearest neighbor classification using the infrared orthoimage and LANDSAT ETM+ derived multitemporal NDVI values (SNDVI). Overall accuracy of the rule based method and SNDVI were comparable, and significantly higher (~10-20%) than the SRGB. Further, when compared to existing land cover maps, both the rule based method and SNDVI had far greater visual appeal and accuracy