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A SPATIAL MODELING AND DECISION SUPPORT SYSTEM FOR CONSERVATION OF BIOLOGICAL DIVERSITY
Annual Report--Year 3
Report Date: February 15, 1996
Submitted to:
IBM Environmental Research Program, Dr. Joe Sarsenski
Principal Investigator: Frank W. Davis
Co-Principal Investigator: Michael F. Goodchild
Institute for Computational Earth System Science
and Department of Geography, University of California
Santa Barbara, CA 93106
Phone: 805-893-3438II. ANNUAL PROGRESS
A. Problem Statement
The earth is experiencing a mass extinction of species that is unparalleled in its history. Installation of an effective reserve network to minimize future loss of biodiversity will require coordinated conservation assessments at international, national, regional and local levels. Such assessments already rely heavily on advanced mapping technologies and computing systems for spatial data analysis and display.
Progress in conservation assessment and planning is severely and unnecessarily limited by hardware and software for such mapping and spatial analysis. Specifically: 1) biogeographers and conservation biologists do not have adequate computing resources to analyze the large volumes of data involved in conservation assessments; 2) data management systems in general use are poorly designed for manipulation of heterogeneous biogeographic data; 3) there is practically no coupling among database management systems and analytical software used in biodiversity analyses; 4) it is difficult to visualize biogeographical data sets and model outputs with existing display tools; and 5) spatial modeling and decision support are constrained by inadequate hardware and cumbersome protocols for conducting sensitivity and error propagation analyses.
The goal of this project is to design and test a prototype Spatial Modeling and Decision Support System for Conservation of Biological Diversity. The project is closely tied to the U.S. Fish and Wildlife Service's Gap Analysis Program, and to related efforts at multi-species conservation planning in southern California. Our project objectives are to:
- Design and enable a prototype "regional" computing facility for storage, analysis and visualization of biodiversity data.
- Program a set of specific software applications to support national (and potentially international) Gap Analysis.
- Conduct a conservation Gap Analysis of the Intermountain Sagebrush Ecoregion over six western states.
- Develop applications for monitoring wildlife habitats using multi-temporal satellite imagery.
- Develop software to support reserve siting and reserve design and apply it to reserve design in southern California.
B. Goals and Objectives addressed during 1995
To meet the objectives listed above, the UCSB project has embarked on research into several, interrelated components of regional conservation planning, as illustrated in Figure 1. As will be described below, we made considerable progress in 1995 on the vegetation mapping and regional conservation planning components. Our challenge during Year 4 will be to bring these components together in a fully integrated spatial decision support environment.
Figure 1. Framework for a Conservation Spatial Decision Support System
C. Scientific Developments
During the past decade there has been a pronounced shift in natural resource management and conservation away from piecemeal action on single species or localities towards integrated analysis of multi-species communities, habitats, and human social and economic systems over large planning areas. There are many scientific, institutional and technological barriers to implementing such "bioregional" planning and ecosystem management. Efforts in California and elsewhere have been hampered by inadequate ecological theory, by the lack of reliable geospatial data on species and habitats, and by the sometimes insurmountable problems encountered in trying to piece together existing data and information from different agencies and organizations. These problems, which we have encountered many times in conducting our Gap Analysis of California, only worsen as one tries to bring together biological and environmental data and models for several states to conduct a regional assessment such as our IBM supported Gap Analysis of the Intermountain Sagebrush Steppe Region.
During Year 3 of our IBM-ERP project we focused our efforts on the two following unmet scientific and technical needs in bioregional planning and decision support:
- Regional vegetation classification and mapping based on existing maps and satellite imagery. In our original proposal we posed this as a problem of map generalization to bring adjacent maps into consistent taxonomic and spatial detail, but have reformulated the problem as that of 1) selecting an existing vegetation classification system or developing a new classification that is better suited to regional conservation planning, and 2) creating a new regional vegetation map from remotely sensed imagery under guidance from existing subregional vegetation maps. We have devised a new approach to image classification that appears extremely promising not only for regional vegetation mapping but for monitoring as well. Our emphasis in 1995 was to develop an alternative approach to image compositing to provide consistent, cloud-free remotely sensed data for the entire study region throughout a growing season.
- Improved algorithms for reserve selection and design. After a thorough review of existing literature, we concluded that most approaches could be subsumed within a more general framework from operations research known as the "maximal covering location problem." A simple reserve siting problem was easily solved using the MCLP approach in Year 2. Our research goal in Year 3 was to elaborate the simple model to provide more realistic and useful solutions, with the ultimate goal of integrating that model into our decision support environment. An opportunity was presented to us in the Sierra Nevada Ecosystem Project (SNEP) sponsored by the U. S. Forest Service. SNEP was a multidisciplinary assessment of the current state of the Sierran ecosystem and an evaluation of management alternatives. We developed a Biodiversity Management Area Selection model we called BMAS that improved upon the less sophisticated MCLP model by incorporating land suitability factors and permitted land uses and achieving specific target levels of protection rather than simply "representing" all species at least once.
The next sections of the report provide additional detail on scientific progress in each of these areas.
1. Vegetation Classification and Mapping
a. AVHRR compositing and map guided classification
Mapping the vegetation of the Intermountain Sagebrush Steppe ecoregion will require two new developments: a multitemporal image dataset that covers the entire region and improved classification techniques for incorporating existing map information to assist in labeling the spectral clusters. Our focus during the past year has been on the multitemporal image dataset by improving upon the algorithm currently used in the international global change community for removing cloud cover.
We have chosen the NOAA Advanced Very High Resolution Radiometer (AVHRR) data for the multitemporal imagery because it is available on a daily basis over the entire growing season. This means that plant phenology can be incorporated in the classification.
Compositing methods have been used in the past to aggregate daily images into periods covering 10-14 days in order to remove the effects of cloud cover. Our previous research found that the traditional algorithm for compositing, used by the U. S. Geological Survey, tends to be biased towards off-nadir viewing. This bias has the effect of blurring the spatial resolution of the data as well as adding atmospheric and surface reflectance effects. We have explored different methods of compositing that try to favor near-nadir views that also satisfactorily remove cloud effects.
There are three criteria to consider for the selection of the best pixels for the given composite period: pixels chosen would ideally have the minimum satellite zenith angle values, the maximum vegetation index values, and the maximum apparent temperature of all candidate pixels for a single pixel on the ground or geopixel. Each of these criteria has been shown to improve the quality of AVHRR composites. Both maximizing vegetation index and maximizing apparent temperature improve composites by choosing pixels with less atmosphere and clouds, while a smaller satellite zenith angle improves the consistency of the pixel resolution across the land surface. Therefore, a multiple objective approach was utilized.
For any geopixel location, considering all candidate pixels for the composite period, the hypothetically optimal pixel has the highest vegetation index, highest apparent temperature and lowest satellite zenith angle. It is quite likely the hypothetically optimal pixel does not exist, however, but finding the pixel closest to this hypothetically optimal value maximizes the three objectives. In order to find the "best" pixel considering all three goals, the multidimensional Euclidean distance of each pixel from each image is calculated, and the one with the shortest distance to the hypothetical optimum is chosen.
The three axes, of course, are in different units and it is necessary to scale each axis independently. Weights used for apparent temperature and NDVI were varied in all combinations of 0, .25, .5, .75 and 1 and satellite zenith angle of 0 0.1 and 0.2. The best preliminary compositing algorithm, based on a comparison with higher resolution Thematic Mapper imagery from the same compositing period in September, 1990, was with a high weight for apparent temperature, a small weight for satellite zenith angle, and no weight for the vegetation index. The algorithm with a case study application for California has been submitted as a journal article (Stoms et al., 1996) and is included in the appendix.
We have found that the weighting scheme appears to work well over a larger region such as the Great Basin using data from several composite periods throughout the growing season. Similarly, it has been successful using recent AVHRR data acquired in Santa Barbara on the Pacific Coast where the full range of viewing angles are available for testing. We used this compositing strategy to develop time-series images of NDVI and spectral bands for classification of land cover in the Intermountain region.
2a)
2b)
Figure 2. Histograms of the frequency of satellite zenith angles for the September 14-27, 1990 biweekly composite. Satellite zenith angle at nadir is 0 degrees. a) the Maximum Value Composite strategy used by USGS, and b) the Multiobjective Composite developed at UCSB. Views closer to nadir will generally have less variation contributed by the atmosphere and surface reflectance properties.
b. Edgematching
Work was begun on edgematching land cover maps across state boundaries in two geographic regions. The first region was the entire Mojave Desert, comprised of parts of California, Nevada, Utah, and Arizona. Our co-investigator, Tom Edwards, of Utah State University, took the lead in mapjoining the state GAP maps using a common classification. We assisted Tom in crosswalking the California cover types into this common schema. There were obvious differences in spatial resolution and pattern between states caused by use of different mapping techniques. Differences in taxonomic detail also account for some of the variation in the maps. We were encouraged that the AVHRR composites, described above, for this region appear to provide some additional information for developing a more consistent regional land cover map. This was particularly heartening in this region where the vegetation cover is extremely low relative to the background surface geology and soils.
To assure regional continuity of the California GAP map along the Oregon border, we met with Blair Csuti, Principal Investigator of the Oregon GAP. Together we examined the fit of polygons across the border and the degree to which the vegetation types agreed in the two coverages. Generally agreement was good. The linework matched well, which was to be expected as we used the Oregon GAP lines as well as TM images for delineating polygons near the border. We eventually used information from the Oregon map to identify vegetation types for about 20 small polygons, the main bodies of which lay on the Oregon side of the border. Information was sent up to the Oregon project so that they could do the same with overlaps from the California side. The Oregon project agreed to amend their vegetation classification to reflect two classes identified on the California side of the border but had been overlooked initially on the Oregon side.
Oregon GAP is in the process of doing a second iteration of their vegetation map, based on a detailed satellite image classification. At the time of our meeting, only the section adjoining the Modoc Plateau was completed. We used this updated coverage for comparing types along the northeastern border. Again there was fairly good agreement between the two coverages, as there had been with the first iteration two years ago. It remains to be seen how well the new Oregon map will agree in the forested regions of the border.
2. Nature Reserve Selection and Design Methods
Over the past decade, conservation planners have developed a number of methods for selecting priority areas for potential nature reserves. The evolution of approaches has proceeded from simple scoring methods to iterative heuristic algorithms. Although these methods differ in the objectives they emphasize and the algorithms used, they all shared the objective of selecting sites in an explicit, objective, repeatable, and efficient manner. That is, to select a reserve system that accomplishes the most protection for the least cost (or area). The basic premise of our research in Year 2 was that the reserve selection problem can be reformulated as a classic maximal covering location problem (MCLP) described in the operations research and regional science literature twenty years ago. The MCLP can be solved optimally, that is, no better solution exists, and most problems of the size described for reserve selection can be solved within reasonable computer resources. We solved the MCLP model for a real application using vertebrate distribution data prepared for the Gap Analysis of southwestern California. Although the basic MCLP model did not account for other conservation objectives such as habitat quality, site configuration, emphasis on rare species or additional biodiversity elements, or flexibility, it provided a mathematically more elegant problem structure that could be enhanced in the future. The MCLP approach to reserve selection is described in greater detail in Church et al. (in press), provided in the appendix of this report. Dr. Church is continuing to explore enhancements to the MCLP model by giving different weights for endemic versus widespread species. A follow-up paper to the MCLP article that incorporates this multi-objective version is in preparation.
One other advance on the original MCLP analysis was made during Year 3. We conducted a sensitivity analysis on the size of planning units. We would expect larger planning units to generate less efficient solutions because relatively few new species are accumulated as the unit grows larger. That is, a large site would cover fewer species than four smaller sites of the same total area if those four sites were widely distributed. In conservation biology terms, the dispersed sites "complement" one another more than four contiguous sites (i.e., the larger site) does. In our analysis, we tested this hypothesis by aggregating the southwestern California vertebrate data from units equal to the 7.5' quadrangle grid into 15' and 30' grids, each representing a fourfold increase in area. At the 30' grid size, more than ten times the area was required to cover all species at least once, relative to the total area with 7.5' grids (Davis and Stoms, 1995; see Appendix). The tradeoff curve showing the relative coverage by the three grid sizes is shown in Figure 3.
In 1994 the U. S. Forest Service initiated the Sierra Nevada Ecosystem Project (SNEP) to assess the current state of the Sierran ecosystem and to evaluate alternative management strategies. As participants in SNEP, we were able to build on our understanding of the strengths and limitations of the MCLP model and develop a new model for siting conservation areas. The Sierra Nevada region differs from southern California in that biodiversity in the Sierra is not restricted to islands of protected habitat in a sea of urbanization. Rather, the landscape matrix in the Sierra Nevada, though often managed for intensive resource extraction, still provides habitat for many species. We proposed that a system of core biodiversity management areas be instituted in the region that would not serve as a comprehensive reserve system, but rather would reduce the vulnerability of native biodiversity to human activities.
Based on this goal, we reformulated the reserve selection problem with three significant refinements. First, specific target levels of biodiversity management as a percentage of the distributions of each biodiversity element were to be met, and these targets could be varied between alternatives or even between elements (e.g., rare vs. widespread). In the MCLP model, the only requirement was that each element be "covered" or represented one or more times. Second, suitability of the sites for biodiversity management was incorporated into the objective function of the model, whereas suitability was not considered in the MCLP model. This modification discouraged the model from selecting sites that were heavily impacted by roads and human settlements or that would be difficult to manage for biodiversity because they were on private land or had extensive fragmentation of public and private ownership. Third, the management class definitions were refined relative to GAP standards by incorporating data on grazing and timber management from existing land use plans from the Forest Service (Figure 4). We were able both to change assumptions between alternatives about the level of protection different management classes provided for biodiversity protection and also could compare the amounts of each class required by the solution for each alternative (Figure 5 shows one alternative solution and the suitability index of candidate sites). The MCLP model did not consider current management status, either in the solution or in its evaluation of solutions. We named the new model the Biodiversity Management Area Selection (BMAS) model.
The BMAS model will be a chapter in the final SNEP report to Congress which is in final preparation. The manuscript will be included in the next year's annual ERP report. Two journal articles and a conference paper based on this work are also in preparation. 
Figure 3. Accumulation curve showing the maximum number of species covered for three sizes of planning units. The line symbols for the curves correspond to: solid line with squares = 7.5' quadrangles, the dashed lines with triangles = 15' quadrangles, and dotted lines with circles = 30' quadrangles.
Figure 4. Comparison of the three standard levels used for GAP in other regions of California (top) and the five class schema used for the Sierra Nevada study (bottom). The maps show a small portion of the northern Sierra Nevada region. Level 1 = formally designated for biodiversity management; 2 = other public lands; 3 = private lands. Class 1 = formally designated for protection of biodiversity, grazing not permitted; 2 = not formally designated but no grazing or commercial timber harvest; 3 = within existing grazing allotments but no commercial timber harvest; 4 = allocated to commercial timber harvest on public lands; and 5 = private lands on which development, grazing, and timber harvest may be permitted.
Figure 5. Map of one alternative solution to the Biodiversity Management Area Selection (BMAS) problem (selected watersheds shown in bold outline). This alternative specified a minimum of 10% representation for every plant community and assumed that only formally designated management areas that were ungrazed provided adequate protection. The gray tone indicates the level of suitability for biodiversity management as defined by road and human population densities, percentage of private land ownership, and density of boundary between public and private lands.
D. Computational Advances
2. Database cataloguing
Our experience with PGBIO, our first generation data cataloging tool, was that it was a very useful product for locating datasets but that it was overly complicated for others to install and operate. The original version relied on postgres, a public domain, object-oriented database management system and TK/TCL as the graphic programming language. For new installations, users had to ftp software from several sites, which were not always stable. Once the software was installed, the system manager had to reconfigure the UNIX server's operating system and maintain a database server not conforming to the SQL database language standard. The executable files for postgres are quite large, and PGBIO did not make extensive use of its database management capabilities.
Our next attempt at a data cataloguing tool was simplified to use an ASCII flat file database with a TK/TCL interface and RCS to lock the database for individual transactions. This system included more metadata fields, to comply with the Federal Geographic Data Committee metadata standards for spatial data. Adding more fields to the metadata made it more difficult to use and fill out for an in-house data catalogue system, which discouraged users from using it. Another deterrent was the need to learn a new GUI to operate the catalogue tool.
Our solution to the above two problems was to implement another version of the data catalog tool. We have chosen to write the data catalog interface in HTML with PERL CGI scripts to access the database. Since most users are comfortable with a WWW browser interface such as Netscape or Mosaic, users are comfortable with its look and feel and there is virtually no learning curve to its operation. The new version also provides more display functions to inline display many image format types and text files, while launching helper applications for map data, word processing documents or postscript files. In addition to its familiar feel, an ``autofill'' function has been implemented which automatically fills in all the metadata that can be obtained directly from the dataset. The autofill function eliminates tedious entries for information that is already contained in the dataset itself. This encourages users to take advantage of the powerful tools the data catalogue system provides.
3. WWW home page
The World Wide Web has experienced geometric growth since the writing of last year's report when we had a preliminary home page set up, coupled with an anonymous ftp site. We are now running our own httpd server and have many of our datasets online. Using a web browser such as Netscape, users can select data from our archives using two methods. The first utilizes a clickable imagemap for a region, where a user clicks on a region on a map and receives thumbnail images of the datasets available for that region along with a list of available datasets and descriptions. Each dataset can be downloaded directly with the click of a button. The second way to query the database is for the user to select both the regions and data themes of interest by clicking checkboxes which list the regions and themes (Figure 6). Once the datasets of interest have been selected and the query submitted, the user sees thumbnail images of each data theme along with a link to the real data's ftp location. The URL for our WWW site is http://www.biogeog.ucsb.edu. We expect during the final year of this project that we'll improve our WWW site and the amount of data available.
Figure 6. Web page for gap database browsing.
III. PROBLEMS/ISSUES
A. Gap Analysis of the Intermountain Sagebrush Steppe Ecoregion (ISSE)
Progress towards this goal has been slow for several reasons. Most importantly, our work must await completion of the Gap Analysis databases in the other states. The Utah database was completed in late 1994. Washington and Nevada are just nearing completion at the end of 1995. Oregon and Idaho are being remapped with Thematic Mapper data to revise their earlier pilot study maps. Also impacting the project is the publication in 1994 of a new ecoregion map by Bailey et al. of the Forest Service. The ISSE has been split into three parts. Further, the three new regions extend into Montana, Wyoming, and Colorado, while California is just barely included. Thus we are awaiting a decision from the GAP headquarters on which regionalization to use and which ecoregion(s) we should map. Because of these obstacles, we have delayed hiring a post-doctoral researcher to conduct the project. We still anticipate hosting a collaborators workshop and having a preliminary Gap Analysis completed in 1996.
B. Reserve Selection
The BMAS model developed for SNEP was a notable advancement in reserve selection models. It incorporated a number of conservation factors that were mostly ignored in earlier studies, such as suitability and permitted land uses. Nevertheless, we identified a number of features that could make the BMAS model even more realistic and effective. Some improvements would be in the model structure or formulation, while others involve refinements in input data. The current version of the BMAS model does not consider the spatial pattern of the selected watersheds. Based on general principles of conservation biology one could argue that larger, better connected BMAs would tend to maintain biodiversity better than small, poorly connected systems. On the other hand, there is evidence that populations in several scattered sites are less vulnerable to large-scale environmental disturbances than populations in a single larger site. We want to explore analytical means of evaluating solutions more rigorously in terms of the viability of protected populations. Obviously, it would be useful to incorporate spatial considerations in the BMAS model in order to explore these issues more analytically. Contiguity is difficult to incorporate as a suitability factor, however, because it is not a property than can be measured a priori for a watershed but is dynamic in that it changes as its neighbors are selected. The BMAS model provides solutions that are the most efficient solutions only in terms of requiring the least area. Thus the solutions can be considered planning benchmarks in terms of the area requirements for representative BMA systems. Any additional constraints such as spatial design will increase the area of the solution. Further, new methods must be developed for evaluating viability of different solutions.
The BMAS model also does not handle scheduling of reserve allocation over time, variations in land costs, and trade-offs with other resources. Given that implementation of a BMA system might need to be scheduled rather than instantaneous, a method is needed to prioritize sites for allocation, analogous to a budget constraint. All private lands are currently treated as being equally unsuitable for BMA selection and less suitable than public lands in recognition of the cost of land acquisition. However, private lands can vary widely in value. We have discussed ideas with another SNEP science team member how these land costs could be estimated for each planning unit and incorporated into the suitability data of the model. Data for the Sierra Nevada were obtained for public lands allocated to grazing and commercial timber harvest. These were used in defining management classes which in turn were used to determine vulnerability of biodiversity elements. They were also used to evaluate the alternatives in regard to selection of resource management lands as BMAs. It should be possible to revise the suitability data (or add an additional objective) such that the model minimizes conflict with other resources.
Dr. Rick Church has observed that the MCLP model we developed for covering species can be reformulated as a p-median problem. This reformulation would give us the opportunity to develop an integrated tool for reserve selection using existing GIS software packages. Therefore, instead of a model requiring external optimization software that most gap analysis projects would not have access to and passing data back and forth with the GIS, conservation planners could solve the problem entirely within their GIS.
C. Improved spatial understanding and visualization of biodiversity data
Regional biodiversity databases are complex in the number of elements to understand. The Gap Analysis of California database has many map layers, each with a large number of attributes. The vegetation database, for instance, has detailed information on dominant canopy plant species, their relative abundance, the association with other species as recurring communities, canopy closure, and human impacts. Much of the information is encoded as alphanumeric codes related by lookup table to the botanical names or other more readily understandable description. For those of us familiar with the database structure and coding schemes, it is relatively straightforward to query it. Unfortunately, this richness of the database makes it more difficult for novice users to answer their conservation or biogeographical questions. A better interface is needed to address the standard kinds of queries and analyses users will most commonly pose.
IV. FUTURE GOALS AND PLANS
A. Integrating SDSS Tools
Our goals in 1996 are to 1) complete database development and conduct a gap analysis of the Intermountain Sagebrush Steppe Region, 2) integrate the various software components that we have produced over the past 3 years into a prototype spatial decision support system for regional conservation analysis and planning, 3) prepare a series of papers for peer-reviewed journals that report the major findings from our IBM ERP project, and 4) develop an interactive query and access environment to make our data and software available over the Internet and via a CD-ROM. Publication of the CD-ROM product is being financed by the National Biological Service.
Our strategy for constructing a conservation SDSS is to exploit existing software tools for visualization and analysis such as Arcview, ARC/INFO, and OSL, as well as taking advantage of UNIX tools such as PERL and TCL/TK and the many scripts and macros that we have developed within these various software environments. We will take advantage of the Web to build a user interface to facilitate data query, access, and analysis, and will also make use of WWW applications that have already been developed by UCSB's digital libraries project known as Project Alexandria. Data formatting and interchange among applications poses the greatest problem. We do not expect to overcome all interchange problems to create a truly seamless environment, but will work towards this goal.
V. APPENDICES
A. Technical Presentations
1. IBM-ERP Presentations by PI Davis during 1995
"Gap analysis of natural vegetation in southwestern California," Department of Integrative Biology, University of California, Berkeley, January.
"A spatial analytical hierarchy for Gap Analysis," Gap Analysis Symposium, Annual Meeting of the American Society for Photogrammetry and Remote Sensing, Charlotte, NC, March.
"Applications of Gap Analysis data in the Mojave Desert of California," Gap Analysis Symposium, Annual Meeting of the American Society for Photogrammetry and Remote Sensing, Charlotte, NC, March.
"Regional conservation planning in California," Stanford Research Institute Environmental Policy Forum, Stanford University, May.
"Gap analysis of the vegetation of southwest California," Carneggie Institute, Stanford University, May.
"Analysis of GAP data," Annual Meeting of NBS Gap Analysis Investigators, Fayetteville, Arkansas, August.
"Selecting Biodiversity Management Areas," Workshop on statewide biodiversity planning, Defenders of Wildlife, Portland, Oregon, September.
"Regional conservation planning case study: the Sierra Nevada Ecosystem Project" University of Tennessee at Knoxville, October.
"Regional conservation of global biodiversity," University of Tennessee at Knoxville, October.
"Biodiversity Management Area Selection," conference presentation (by Dr. Rick Church) at the 42nd North American meeting of the Regional Science Association International in Cincinnati, Ohio, October.
"Biodiversity Management Area Selection," presentation (by Dr. Rick Church) at the Argonne National Lab, Illinois, November.
2. Visitors to the Biogeography Lab in 1995
The following table lists the visitors to the Biogeography Lab who were shown the IBM-donated equipment being applied to conservation problems during the year.
Visitors Name Purpose of Visit Peggy Harwood, Mark Borchert, Barry Cohn, Los Padres National Forest GIS support for USFS Carl Steinitz, Steve Ervin, Mike Binford, Paul Cote from Harvard Graduate School of Design GIS data for their Camp Pendleton planning project Dave Graber, National Biological Service--Sequoia National Park GIS wildlife modeling for Sierra Nevada Ecosystem Project Janine Stenback, California Department of Forestry and Fire Protection, and Lisa Mann-Levien, USFS remote sensing for regional land use change detection Hiromichi Fukui, STB Research Institute, Japan applications of GIS for carrying capacity, urban planning Steve Beckwitt, Sierran Biodiversity Institute comparison of vegetation maps Yoon-Chul Choy, Yonsei University, Korea GIS applications Doug Updyke, California Department of Fish & Game plan a GIS wildlife modeling workshop Gerard Rushton, University of Iowa spatial decision support systems Tom Duncan, Natalie Munn, and Randy Ballew, Berkeley Museum Informatics Project integration of herbarium database with GIS and potential future research collaboration Len Gaydos, USGS-Ames Research Center Mojave GAP in conjunction with DoD/USGS planning effort NCGIA Initiative 15-global change meeting (~12 people) Biogeography Lab tour Montserrat Comelles, Spain Gap analysis as it might be applied in Spain Alex Tuyahov, Jerry Garriani, NASA HQ, and Jim Brass, NASA Ames site visit on change detection project Lance Craighead, Dan Chandler, American Wildlands GIS habitat modeling Peter Burrough, University of Utrecht, Netherlands GIS modeling Elizabeth Bowen, CSIRO Australia GIS modeling for natural resources Norm Johnson, Oregon State University, and Chris Riper, USFS biodiversity allocation modeling Gregory Helms, Environmental Defense Center, and Jim Eaton GIS applications to the Wildlands Project Marco Painho, University of Lisbon, Portugal GIS applications Patrick Bourgeron, The Nature Conservancy collaboration between UCSB and TNC Al Watkins, USGS National Mapping Division Gap analysis and biodiversity allocation modeling Capt. Jim Lewis, Point Mugu Naval Weapons Center Gap analysis database for China Lake Naval Weapons Center Chris Cogan, UC Santa Cruz GIS applications for biodiversity modeling Carolyn Hunsaker, Oak Ridge National Lab error analysis in ecological modeling Arun Mani Dixit, Wildlife Institiute of India Gap analysis for India Stephen Van Scoyk and Ed Thomas, Lockheed/Martin Marietta remote sensing and GIS applications Jon Krummel and Benj Schoepfle, Argonne National Lab remote sensing and decision support-GIS modeling for biodiversity conservation Danny Marks, USGS Corvallis biophysical modeling in GIS Dave Mouat, EPA/ Biodiversity Research Consortium, Corvallis data for Camp Pendleton planning project Tom Edwards, Utah State University regional mapping for Mojave/Great Basin regional gap analysis Mike Stevens, Hammon-Jenson-Wallen, Oakland GIS projects on Los Padres National Forest Peng Gong, UC Berkeley and Hui Lin, Chinese University of Hong Kong GIS applications for conservation planning Uzoma Okereke, Fluor Daniel Inc. GIS for hydrologic modeling John Palmer and Jim Young, Southern California Edison contents and applications of gap analysis database for regional environmental studies Julie Cox, Centro Internacional de Agricultura Tropical, Columbia integration of socio-economic data with remote sensing Jim Keating, Kansas State University Global Positioning Systems applications Jimmy Johnston, National Biological Service Louisiana gap analysis American Planning Association conference tour (~40 planners) GIS methods for conservation planning Steve Polasky, Oregon State University optimization methods for reserve selection Scott Miller, Bishop Museum, Hawaii use of museum specimen data in reserve selection and modeling distributions of non-native species Doug Mende, Chambers Group, Inc., and Forest Shepherd, Utah State University datasets for interagency Mojave GIS project Valentino Sorani, Geographic Institute, National Autonomous University of Mexico collaborative projects on biodiversity Richard Thackway, Australian Nature Conservancy (ERIN) nature reserve selection and gap analysis Judy Elert, Lockheed Martin Corp. and Jane Kuhar, CIA database development issues and methods Sherry Teresa and Brenda Pace, Center for Natural Lands Management applications of the GAP database to local conservation planning and management Terry Done, Australian Institute for Marine Sciences GIS applications for risk assessment Paul Schreilechner, Universitaet Salzburg, Austria GIS applications in biogeography Lenard Olson and 60 geography students from Hong Kong overview of GIS and remote sensing applications in biogeography and conservation planning Ricardo Sturaro, Universidade Estadual Paulista, Brazil GIS software recommendations Norm Haverman, American Express and UCSB Foundation research projects in the Biogeography Lab B. Student Involvement
1. Graduate theses and dissertations during 1995
Odion, D., 1995. Effects of variation in soil heating during fire on patterns of plant establishment and regrowth in maritime chaparral. Ph.D. dissertation, University of California, Santa Barbara.
2. Other graduate students who have been employed by or received training and computing support through the IBM-ERP
3. Undergraduate Training
The following UCSB Biology and Geography students (or recent graduates) were trained in GIS and remote sensing applications in the IBM-ERP lab during 1995: Dave Court, Melissa Simpson, Laurie Schwalm, and Katherine Warner.
C. Other Technology Transfer
None in 1995.
D. Refereed Publications in 1995
The following refereed publications that were developed through the grant from IBM during the previous year are attached in the appendix. References marked with an asterisk (*) were listed in last year's report but their publication status has changed since that time.
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