@proceedings {JRN54865, title = {Challenges and opportunities with standardized monitoring for management decison-making}, volume = {January 28-February 2, 2018}, year = {2018}, month = {01/28/2018}, publisher = {Society for Range Management Meeting Proceedings}, address = {Sparks, Nevada}, abstract = {

The importance of monitoring for adaptive management of rangelands has been well established. However, the actual use of monitoring data in rangeland management decisions has been modest despite extensive efforts to develop and implement monitoring programs from local to national scales. More effective use of monitoring data is critical to inform adaptive management, to empirically justify management decisions, and to ensure a return on resources invested in monitoring programs like the Bureau of Land Management{\textquoteright}s (BLM) Assessment, Inventory, and Monitoring (AIM) program. Several challenges limit the use of monitoring data in management decision making. First, there is often a disconnect between aspects of monitoring (e.g., indicators, sample design, timing) and information needs of managers for making decisions. This can arise from a lack of specific monitoring objectives tied to management decisions or from monitoring indicators being analyzed and presented in forms that do not mesh with land management workflows. Second, in many cases little information exists on how to interpret monitoring results with respect to land potential (e.g., is the amount of bare ground more than what is expected for this type of rangeland?). Third, given limited resources, monitoring data often produce estimates with large confidence intervals, which causes challenges for interpreting whether a change has occurred or a land health standard has been met. Fourth, there is a tension between flexibility to design monitoring around management needs for specific, immediate objectives and maintaining standard monitoring approaches to build long-term datasets. This has the effect of reinforcing short-term monitoring at the expense of investing in standardized efforts that could address multiple objectives over the long term. We describe these challenges in the context of the BLM AIM projects discussed during the symposium. We explore potential opportunities for addressing these challenges and how the AIM program can be leveraged for success.

}, author = {Jason W. Karl and Sarah McCord and Scott W. Miller} } @article {JRN54662, title = {A comparison of cover calculation techniques for relating point-intercept vegetation sampling to remote sensing imagery}, journal = {Ecological Indicators}, volume = {73:156-165}, year = {2017}, month = {01/2017}, abstract = {
Accurate and timely spatial predictions of vegetation cover from remote imagery are an important data source for natural resource management. High-quality in situ data are needed to develop and validate these products. Point-intercept sampling techniques are a common method for obtaining quantitative information on vegetation cover that have been widely implemented in a number of local and national monitoring programs. The use of point-intercept data in remote sensing projects, however, is complicated due to differences in how vegetation cover indicators can be calculated. Decisions on whether to use plant intercepts from any canopy layer (i.e., any-hit cover) or only the first plant intercept at each point (i.e., top-hit cover) can result in discrepancies in cover estimates which are used to train remotely-sensed imagery. Our objective in this paper was to explore the theory of point-intercept sampling relative to training and testing remotely-sensed imagery, and to test the strength of relationships between top-hit and any-hit methods of calculating vegetation cover and high-resolution satellite imagery in two study areas managed by the Bureau of Land Management in northwestern Colorado and northeastern California. We modeled top-hit and any-hit percent cover for six vegetation indicators from 5m-resolution RapidEye imagery using beta regression. Model performance was judged using normalized root mean-squared error (RMSE) from a 5-fold cross validation. Any-hit cover estimates were significantly higher (a \< 0.05) than top-hit cover estimates for forbs and grasses in the White River study area, but only marginally higher in Northern California. Pseudo-R2 values for beta regression models of vegetation cover from RapidEye image information varied from 0.1045 to 0.7681 in White River and 0.2143 to 0.5775 in Northern California, with little pattern to whether any-hit or top-hit indicators produced better model fit. However, in all cases except for annual forbs in Northern California, normalized RMSE was lower for any-hit cover indicators indicating better model performance, though for some indicators the difference was minimal. Our results do not support the idea that top-hit cover estimates from point-intercept sampling are the most appropriate for remote sensing applications in arid and semi-arid shrub-steppe environments. In fact, having two sets of different indicators calculated from the same data may cause additional confusion in a situation where there is already considerable debate on how vegetation cover should be measured and used. Ultimately, selection of indicators to use for developing remote sensing classification or predictive models should be based first on the meaning or interpretation of the indicator, and second on how well the indicator performs in modeling applications.\ 
}, doi = {10.1016/j.ecolind.2016.09.034}, url = {files/bibliography/17-004.pdf}, author = {Jason W. Karl and Sarah McCord and Hadley, Brian} } @inbook {JRN54700, title = {Monitoring protocols: Options, approaches, implementation, benefits}, booktitle = {Rangeland Systems}, year = {2017}, pages = {527-567}, publisher = {Springer}, organization = {Springer}, chapter = {16}, address = {Gewerbestrasee, Switzerland}, abstract = {
Monitoring and adaptive management are fundamental concepts to rangeland management across land management agencies and embodied as best management practices for private landowners. Historically, rangeland monitoring was limited to determining impacts or maximizing the potential of specific land uses {\textendash} typically grazing. Over the past several decades, though, the uses of and disturbances to rangelands have increased dramatically against a backdrop of global climate change that adds uncertainty to predictions of future rangeland conditions. While the multi-dimensional monitoring needs for rangeland management must be reconciled with the harsh realities of the costs to collect the requisite data, conceptual advances in rangeland ecology and management over the past 25 years, driven by developments in ecological theory, and changes in natural resource policies and societal values have facilitated new approaches to monitoring that can support rangeland management{\textquoteright}s diverse information needs. Additionally, advances in sensor technologies and remote-sensing techniques have broadened the suite of rangeland attributes that can be monitored and the temporal and spatial scales at which they can be monitored. We review some of the conceptual and technological advancements over the past 25 years and provide examples of how they have influenced rangeland monitoring. We then discuss implications of these developments for rangeland management and highlight what we see as challenges and opportunities for implementing effective rangeland monitoring. We conclude with a vision for how monitoring can contribute to rangeland information needs in the future.
}, doi = {10.1007/978-3-319-46709-2_16}, url = {files/bibliography/17-012.pdf}, author = {Jason W. Karl and Jeffrey E. Herrick and D.A. Pyke} } @proceedings {JRN54677, title = {The role of data and inference in the development and application of ecological site concepts and state-and-transition models}, volume = {Jan 29 - Feb 02, 2017}, year = {2017}, month = {01/2017}, address = {Saint George, Utah}, abstract = {
Information embodied in ecological site descriptions and their state-and-transition models is crucial to effective land management, and as such is needed now. There is not time (or money) to employ a traditional research-based approach (i.e., inductive/deductive, hypothesis driven inference) to addressing the unknowns in developing and documenting ecological site concepts. We propose that the development of ecological site products is a dynamic task of defining concepts and processes that best explain the available data (i.e., abductive reasoning), and as such a more iterative approach to their development is needed than is currently used. Under the proposed approach, ecological site concepts are never viewed as final, but only the best representation that is supported by available knowledge and data. The natural result of this way of thinking is that products like ESDs and STMs should continually be tested and improved as new data become available.
}, author = {Jason W. Karl and Curtis Talbot} } @conference {JRN54642, title = {Monitoring protocols: options, approaches, implementation, benefits}, booktitle = {69th Annual Meeting of the Society for Range Management}, year = {2016}, month = {02/2016}, publisher = {Annual Meeting of the Society for Range Management}, organization = {Annual Meeting of the Society for Range Management}, address = {Corpus Christi, TX}, abstract = {
Monitoring and adaptive management are fundamental concepts to rangeland management across land management agencies and embodied as best management practices for private landowners. Historically, rangeland monitoring was limited to determining impacts or maximizing the potential of specific land uses {\textendash} typically grazing. Over the past several decades, though, the uses of and disturbances to rangelands have increased dramatically against a backdrop of global climate change that adds uncertainty to predictions of future rangeland conditions. Thus, today{\textquoteright}s monitoring needs are more complex (or multi-dimensional) and yet still must be reconciled with the realities of costs to collect requisite data. However, conceptual advances in rangeland ecology and management and changes in natural resource policies and societal values over the past 25 years have facilitated new approaches to monitoring that can support rangeland management{\textquoteright}s diverse information needs. Additionally, advances in sensor technologies and remote-sensing techniques have broadened the suite of rangeland attributes that can be monitored and the temporal and spatial scales at which they can be monitored. We review some of the conceptual and technological advancements and provide examples of how they have influenced rangeland monitoring. We then discuss implications of these developments for rangeland management and highlight what we see as challenges and opportunities for implementing effective rangeland monitoring. We conclude with a vision for how monitoring can contribute to rangeland information needs in the future.
}, author = {Jason W. Karl and Jeffrey E. Herrick and D.A. Pyke} } @article {JRN54663, title = {The role of data and inference in the development and application of ecological site concepts and state-and-transition models}, journal = {Rangelands}, volume = {38:322-328}, year = {2016}, month = {12/2016}, abstract = {
Information embodied in ecological site descriptions and their state-and-transition models is crucial to effective land management, and as such is needed now. There is not time (or money) to employ a traditional research-based approach (i.e., inductive/deductive, hypothesis driven inference) to addressing the unknowns in developing and documenting ecological site concepts. We propose that the development of ecological site products is a dynamic task of defining concepts and processes that best explain the available data (i.e., abductive reasoning), and as such a more iterative approach to their development is needed than is currently used. Under the proposed approach, ecological site concepts are never viewed as final, but only the best representation that is supported by available knowledge and data. The natural result of this way of thinking is that products like ESDs and STMs should continually be tested and improved as new data become available.
}, doi = {10.1016/j.rala.2016.10.009}, url = {files/bibliography/16-037.pdf}, author = {Jason W. Karl and Curtis Talbot} } @conference {JRN54450, title = {Facilitating knowledge discovery and visualization through mining contextual data from published studies: lessons from JournalMap}, booktitle = {100th Annual Meeting of the Ecological Society of America}, year = {2015}, month = {08/2015}, publisher = {Ecological Society of America}, organization = {Ecological Society of America}, address = {Baltimore, MD}, abstract = {

Valuable information on the location and context of ecological studies are locked up in publications in myriad formats that are not easily machine readable. This presents significant challenges to building geographic-based tools to search for and visualize sources of ecological knowledge. JournalMap is a scientific literature search engine that was created to enable discovery of relevant research based on location and biophysical attributes along with traditional keyword searching. We will present approaches developed in JournalMap for mining, mapping and visualizing study location information from scientific articles using pattern recognition algorithms and crowdsourcing tools.\  IGN 6-5.

}, author = {Jason W. Karl and Jeffrey K. Gillan} } @conference {JRN54432, title = {JournalMap: Discovering location-relevant knowledge from published studies for sustainable land use, preventing degradation, and restoring landscapes}, booktitle = {UNCCD 3rd Scientific Conference Book of Abstracts}, year = {2015}, month = {03/2015}, address = {Cancun, MX}, abstract = {

Finding relevant knowledge and information to prevent land degradation and support restoration has historically involved researchers working from their own knowledge, querying people they know, and tediously searching topical literature reviews. Over the past two decades, vast quantities of knowledge and information have been made accessible, but finding that which is relevant often remains difficult and inefficient. The relevance of knowledge is determined by how closely the context in which it was created matches a given situation (Karl et al. 2012). In land stewardship and restoration, context includes both patterns of the biophysical (e.g., soils, climate, landform) and human environment. However, the ability to determine what is known about a land use or restoration practice in a particular landscape is hindered by current search technologies because they still rely primarily on key word, topic, text, and author searching {\textendash} concepts of cataloging and searching for published information that have changed little since the late 1800s. With the addition of geographic filters and the locations of where studies have occurred, search results could be limited to specific areas and searches could be extended to contextually-similar areas across the globe.\  Methods: To address this need we created JournalMap (http://www.journalmap.org), a map-based scientific literature database and search engine (Karl et al. 2013b). JournalMap uses study area descriptions from an article (not author affiliations) to map where the research was actually conducted. All articles in JournalMap are geotagged, either automatically using pattern recognition algorithms looking for geographic coordinates or manually from text-based descriptions. Article content in JournalMap comes from partnerships with publishers (e.g., Taylor \& Francis, Pensoft, IOP) and research organizations, existing literature georeferencing efforts, and crowdsourcing from JournalMap{\textquoteright}s users.JournalMap makes it easy to search for literature from specific places through a simple map interface. Results of JournalMap searches can be exported in different formats or saved as a collection with a unique URL for a spatial bibliography on a topic. For many topics, though, there has been little research done in many parts of the world. But, research conducted in areas with similar physical, environmental, cultural or political contexts can, in many cases, be relevant to these understudies regions (See Figure). JournalMap currently allows users to search for literature based on a few existing spatial layers including geology, soils, vegetation, through a simple overlay analysis of existing spatial data layers.\  Results: JournalMap{\textquoteright}s literature database currently contains records for over 20,000 articles on a diversity of topics, and new content is being added continually. We have found that across knowledge domains approximately 85\% of published studies are attributable to a mapable location, and around two-thirds of modern studies report geographic coordinate values that can be automatically extracted. Automated mapping of scientific knowledge could be improved by the adoption of standards for location reporting. In addition to map- and similarity-based searching, Journal Map data has been used to look at the distribution and spread of knowledge on topics over time like species conservation and to evaluate bias in the location and distribution of knowledge. To illustrate the potential for JournalMap to identify relevant knowledge for an given area, we examined research studies from the Chihuahuan Desert of the southwestern United States. We mapped the locations of more than 800 journal articles that were returned from a Web of Science{\texttrademark} search on {\textquotedblleft}Chihuahuan Desert{\textquotedblright}. Only a third of the search results were actually from the target area. Additionally, the keyword literature search missed any study in the area that didn{\textquoteright}t use those two terms. In JournalMap{\textquoteright}s existing database, we found over 100 articles in the target area that were not included in the keyword search. Outcomes:\  JournalMap{\textquoteright}s ability to search for ecological and agricultural literature thematically and geographically improves the ease of accessibility of potentially relevant research findings, promote syntheses and meta-analyses, provide mechanistic understanding to environmental patterns and causes of land degradation, facilitate evaluations of bias in ecological knowledge, and limit redundancy in conducting new studies (Karl et al. 2013a). JournalMap{\textquoteright}s database could be easily extended to include governmental reports on land degradation and restoration, providing a powerful tool for discovering and using the {\textquotedblleft}dark data{\textquotedblright} that is often hidden in these documents because they are not easily discoverable.\  Additionally, through a publically available application programming interface (API) JournalMap{\textquoteright}s geographic search capabilities and georeferenced literature database can easily be combined with other databases (Karl and Herrick 2013) of land use practices (e.g., WOCAT), conservation projects, and site-based assessments of land potential (e.g., Land Potential Knowledge System).

}, author = {Jason W. Karl and Jeffrey E. Herrick and Jeffrey K. Gillan} } @conference {JRN54449, title = {Protocols for vegetation and habitat monitoring with unmanned aerial vehicles: linking research to management on US public lands}, booktitle = {100th Annual Meeting of the Ecological Society of America}, year = {2015}, month = {08/2015}, publisher = {Ecological Society of America}, organization = {Ecological Society of America}, address = {Baltimore, MD}, abstract = {

Background/Question/Methods:\  Monitoring of the condition and trend of natural resources is critical for determining effectiveness of management actions and understanding ecosystem responses to broad-scale processes like climate change. While broad-scale remote sensing has generally improved the ability to monitoring ecosystems, many important fine-scale indicators must still be measured\ in situ\ through direct observation or via very-high-resolution (i.e., \< 10cm ground-sampling distance [GSD]) imaging. Recent research has shown that many field-derived indicators (e.g., cover, density, vegetation structure, canopy gaps) of ecosystems can be obtained via very-high-resolution (VHR) aerial photography or digital photogrammetric techniques. The relaxing of regulations on operating unmanned aerial systems (UAS) in national airspace in the United States has turned UAS into a viable platform for acquiring VHR imagery for natural resource monitoring programs. However, while much research has looked at using VHR imagery to estimate ecosystem indicators, the available technologies and techniques have not previously been synthesized into a protocol for integrating UAS-based image collection and analysis into a national monitoring program. Our objective was to create and test a multiple-indicator sampling protocol for UAS-based data collection that could be integrated into the Bureau of Land Management{\textquoteright}s Assessment, Inventory, and Monitoring program.Results/Conclusions: Our proposed protocol uses a double-sampling approach where statistical relationships between UAS-derived and field-measured indicators collected at the same locations are used to make indicator estimates to areas where only UAS images are collected. The protocol takes advantage of the UAS platform{\textquoteright}s ability to collect data quickly over field plot areas at GSD \< 1cm. Our protocol calls for use of a multi-rotor UAS (e.g., a {\textquotedblleft}quadcopter{\textquotedblright}) to collect downward-looking (i.e., nadir) images with 60\% forward overlap and 30\% sidelap augmented by oblique (i.e., 45{\textordmasculine} camera angle) images. The nadir images are used to assess, via image interpretation, vegetation cover by functional group, bare ground amount, and canopy gaps. The overlapping nadir and oblique images are used to create three-dimensional representations of the plot area to assess soil erosion, hydrologic connectivity, and vegetation height. We tested this protocol in Chihuahuan Desert shrubland sites and found a high degree of correspondence between field measurements and UAS-derived indicator estimates. The ability to realize the potential of UAS technologies for advancing monitoring of natural resources will depend not only on research verifying VHR imagery can be used to derive meaningful ecosystem indicators but also on being able to work these technologies into implementable protocols for monitoring programs.\  OOS-68-8.

}, author = {Jason W. Karl and Jeffrey K. Gillan} } @conference {JRN54445, title = {Weighing the costs of different errors when determining statistical significant during monitoring}, booktitle = {68th Annual Society for Range Management Meeting}, year = {2015}, month = {02/2015}, publisher = {Sociiety for Range Management}, organization = {Sociiety for Range Management}, address = {Sacramento, CA}, abstract = {

Selecting appropriate significance levels when constructing confidence intervals and performing statistical analyses with rangeland monitoring data is not a straightforward process. This process is burdened by the conventional selection of {\textquotedblleft}95\% confidence{\textquotedblright} (i.e., Type I error rate, a =0.05) as the default with no consideration as to whether, or in what circumstances, it is appropriate. The value of a =0.05 has its origins in hypothesis testing for scientific research where variation is controllable and the costs of making different errors are farily easily quantifiable. A core belief in scientific research is that the cost of committing a Type I error is much higher than committing a Type II error {\textendash} i.e., it is better to miss an experimental effect than to erroneously conclude that effect occurred. Yet, when managing rangeland resources, the costs of committing Type I and Type II errors are not the same as in research {\textendash} and often vary with objective and scale. In many rangeland resource applications making a Type II error (i.e., failing to detect a change) may actually be catastrophic {\textendash} e.g., failing to detect increases in bare ground cover may lead to continued overgrazing, soil loss and permanently decreased range health. Accordingly, for rangeland monitoring the selection of appropriate error rates should be based on: 1) the relative costs of making the two types of errors associated with statistical tests, and 2) the role the data analysis is to play in making management decisions and committing resources. We present a tiered framework for selecting appropriate error rates for rangeland monitoring designed to be sensitive to detecting change at its broadest level but have high specificity at finer levels depending on the costs of making errors at each of those levels. This approach enables statistical analyses that support proactive rather than reactive decision-making for natural resource management.

}, author = {Jason W. Karl and Unnasch, Robert S.} } @article {JRN53970, title = {A double-sampling approach to deriving training and validation data for remotely-sensed vegetation products}, journal = {International Journal of Remote Sensing}, volume = {35}, year = {2014}, month = {02/2014}, pages = {1936-1955}, chapter = {1936}, abstract = {

The need for large sample sizes to train, calibrate, and validate remote-sensing products has driven an emphasis toward rapid, and in many cases, qualitative field methods. Double-sampling is an option for calibrating less-precise field measurements with data from a more precise method collected at a subset of sampling locations. While applicable to the creation of training and validation datasets for remote-sensing products, double-sampling has rarely been used in this context. Our objective was to compare vegetation indicators developed from a rapid ocular-estimation field protocol with the quantitative field protocol used by the Bureau of Land Management{\textquoteright}s Assessment, Inventory and Monitoring (AIM) program to determine if double-sampling could improve the relationship between field data and high-resolution satellite imagery.\  We used beta-regression to establish the relationship between AIM- and ocular-protocol estimates of vegetation cover from 50 field sites in the Piceance Basin of northwestern Colorado, USA. Using the defined regression models for eight vegetation indicators we adjusted the ocular-protocol estimates and compared the results, along with the original measurements, to 5m-resolution RapidEye satellite imagery. We found good correlation between AIM- and ocular-protocol estimates for dominant site components like shrub cover and bare ground, but low correlations for minor site components (e.g., annual grass cover) or indicators where observers were required to estimate over multiple life forms (e.g., total canopy cover). Correcting the ocular-protocol estimates with the AIM-protocol data significantly improved correlation with the RapidEye imagery for most indicators. As a means of improving training data for remote sensing projects, double-sampling should be used where a strong relationship exists between quantitative and qualitative field techniques. Accordingly, ocular techniques should be used only when they can generate reliable estimates of vegetation cover.

}, doi = {10.1080/01431161.2014.880820}, url = {/files/bibliography/14-019.pdf}, author = {Jason W. Karl and Jason Taylor and Matthew Bobo} } @article {JRN53965, title = {Interpretation of high-resolution imagery for detecting vegetation cover composition change after fuels reduction treatments in woodlands}, journal = {Ecological Indicators}, volume = {45}, year = {2014}, month = {06/2014}, pages = {570-578}, chapter = {570}, abstract = {

The use of very high resolution (VHR; ground sampling distances \< ~5cm) aerial imagery to estimate site vegetation cover and to detect changes from management has been well documented. However, as the purpose of monitoring is to document change over time, the ability to detect changes from imagery at the same or better level of accuracy and precision as those measured in situ must be assessed for image-based techniques to become reliable tools for ecosystem monitoring. Our objective with this study was to quantify the relationship between field-measured and image-interpreted changes in vegetation and ground cover measured one year apart in a Pi{\~n}on and Juniper (P-J) woodland in southern Utah, USA. The study area was subject to a variety of fuel removal treatments between 2009 and 2010. We measured changes in plant community composition and ground cover along transects in a control area and three different treatments prior to and following P-J removal. We compared these measurements to vegetation composition and change based on photo-interpretation of ~4 cm ground sampling distance imagery along similar transects. Estimates of cover were similar between field-based and image-interpreted methods in 2009 and 2010 for woody vegetation, no vegetation, herbaceous vegetation, and litter (including woody litter). Image-interpretation slightly overestimated cover for woody vegetation and no-vegetation classes (average difference between methods of 1.34\% and 5.85\%) and tended to underestimate cover for herbaceous vegetation and litter (average difference of -5.18\% and 0.27\%), but the differences were significant only for litter cover in 2009. Level of agreement between the field-measurements and image-interpretation was good for woody vegetation and no-vegetation classes (r between 0.47 and 0.89), but generally poorer for herbaceous vegetation and litter (r between 0.18 and 0.81) likely due to differences in image quality by year and the difficulty in discriminating fine vegetation and litter in imagery. Our results show that image interpretation to detect vegetation changes has utility for monitoring fuels reduction treatments in terms of woody vegetation and no-vegetation classes. The benefits of this technique are that it provides objective and repeatable measurements of site conditions that could be implemented relatively inexpensively and easily without the need for highly specialized software or technical expertise. Perhaps the biggest limitations of image interpretation to monitoring fuels treatments are challenges in estimating litter and herbaceous vegetation cover and the sensitivity of herbaceous cover estimates to image quality and shadowing.

}, keywords = {change detection, high-resolution, image interpretation, land cover, rangeland monitoring, remote sensing}, doi = {10.1016/j.ecolind.2014.05.017}, url = {/files/bibliography/14-017.pdf}, author = {Jason W. Karl and Jeffrey K. Gillan and N. Barger and Jeffrey E. Herrick and Danielle Duni} } @article {JRN53966, title = {Discovering ecologically relevant knowledge from published studies through geosemantic searching}, journal = {BioScience}, volume = {63}, year = {2013}, month = {08/2013}, pages = {674-682}, chapter = {674}, abstract = {

It is easier to search the globe for research on genes of a local plant or animal than to find local field research on that plant{\textquoteright}s ecology.\  While internet applications can find the closest coffee shop, it is difficult to find where the nearest relevant research was conducted.\  As a result, ecologists are often unaware of published local research, and are unlikely to find relevant studies from similar environments worldwide.\  Location information in ecological studies can be harnessed to enable geographic knowledge searches, and could be standardized to make searches more fruitful. To demonstrate this potential, we developed the JournalMap website (http://www.journalmap.org). Easy access to geographic distributions of knowledge opens new possibilities for using ecological research to detect and interpret ecological patterns and trends, evaluate current ecological knowledge, and facilitate knowledge creation. We call on journals and publishers to support standard reporting of study locations in publications and metadata, and we advocate for geo-referencing of past studies.

}, keywords = {georeferencing, knowledge discovery, metadata, semantic search, spatial distribution}, doi = {10.1525/bio.2013.63.8.10}, url = {/files/bibliography/13-038.pdf}, author = {Jason W. Karl and Jeffrey E. Herrick and Unnasch, Robert S. and Jeffrey K. Gillan and Ellis, Erie and Lutters, Wayne and Martin, Laura} } @article {JRN53967, title = {Geographic searching for ecological studies: A new frontier}, journal = {Trends in Ecology and Evolution}, volume = {28}, year = {2013}, month = {07/2013}, pages = {383-384}, type = {Review Article}, chapter = {383}, abstract = {

Important ecological knowledge contained in published literature is often overlooked by researchers, resource managers, and policymakers in part because most literature search tools are thematically focused and do not explicitly consider the geographic location of the research. Searching for ecological literature thematically and geographically would improve the ease of accessibility of potentially relevant research findings that could promote syntheses and meta-analyses, provide mechanistic understanding to environmental patterns, facilitate evaluations of bias in ecological knowledge, and limit redundancy in conducting new studies. New tools are beginning to enable geographic literature searching. For example, JournalMap (http://www.journalmap.org) is a simple geographic search engine for ecological literature based on study area descriptions. The online data repository Pangaea (http://www.pangaea.de) effectively maps journal articles by the locations of their data. The GLOBE project (http://globe.umbc.edu) supports searching for, and synthesis of, user-contributed georeferenced studies for land-change science. Additionally, several journals published by Elsevier offer the ability to include supplemental geographic data and interactive maps. All these efforts, however, suffer from the same shortcomings: they do not contain enough content to realize the potential of geographic literature searching, contributions are either voluntary or limited to specific groups, and most importantly, these efforts operate independently of each other. Consequently, the lack of accessible and searchable georeferenced content is currently limiting the potential of geographic literature searching. Achieving a meaningful spatial representation of ecological knowledge will require changing how and what information is published in the future, as well as confronting the challenge of georeferencing past studies. In the short term, two simple behaviors are imperative: (i) authors should take the initiative to include geographic coordinates and descriptive study area information in their study area descriptions; and (ii) journal editorial boards and publishers should begin discussions on how broad-scale geographic searching can become standard practice in scientific knowledge discovery.

}, doi = {10.1016/j.tree.2013.05.001}, url = {/files/bibliography/13-039.pdf}, author = {Jason W. Karl and Jeffrey K. Gillan and Jeffrey E. Herrick} } @conference {JRN53995, title = {JournalMap: Research. Reimagned.}, booktitle = {International Association of Scientific, Technical and Medical Publishers 2013 Innovations Seminar}, year = {2013}, month = {12/2013}, address = {London, UK}, abstract = {

JournalMap is a scientific literature search engine that empowers you to find relevant research based on location and biophysical variables as well as traditional keyword searches. All publications are geotagged based on reported location information and plotted on a world map showing where the research was conducted allowing you to easily visualize the research occurring in specific areas and show you where data gaps might exist. In addition to being able to search for literature geographically, JournalMap opens up the possibility to search for literature from similar areas. In many parts of the world, there has been little formal study of the structure and dynamics of local environments. However, research that has been conducted on landscapes that share similar soils and climates can, in many cases, be relevant to these understudied regions.

}, url = {http://www.stm-assoc.org/events/innovations-seminar-2013/?presentations}, author = {Jason W. Karl} } @conference {JRN54510, title = {JournalMap: Geo-semantic searching for relevant knowledge}, booktitle = {97th Ecological Society of America Meetings}, year = {2012}, month = {05/10/2012}, publisher = {Ecological Society of America}, organization = {Ecological Society of America}, address = {Portland, OR}, abstract = {

Ecologists struggling to understand rapidly changing environments and evolving ecosystem threats need quick access to relevant research and documentation of natural systems. The advent of semantic and aggregation searching (e.g., Google Scholar, Web of Science) has made it easier to find useful literature across disciplines and publishers. However, locating information about a specific ecosystem, species or landscape remains hindered by current search technologies. The science community still relies on concepts of publication cataloging and searching dating back to the late 1800{\textquoteright}s (e.g., keyword, topic, text, and author searching). The vast majority of published research conducted on ecosystems around the world is tied to specific places, and these locations can be exploited to search for literature based on geography in addition to traditional terms. Our objective was to develop an integrated geographic and thematic (i.e., geo-semantic) system for searching for published ecological literature. We parsed article text from back issues of ecological journals to extract reported study area locations (place names as well as geographic coordinates). Here we look at only papers reporting geographic coordinates. Citation information, keywords, abstracts, and standardized geographic coordinates were added to a database. A web interface was created to search for literature based on geo-semantic queries. Results are available for the last six years of two journals, Rangeland Ecology and Management (REM) and Journal of Arid Environments (JAE). Additional journals are being added to the database. Of the 2,335 studies published in REM and JAE from 2005 to 2011, 76.5\% of studies reporting a study area included geographic coordinates; the rest included only place names. Of the 1,721 REM and JAE studies reporting geographic coordinates, eight different coordinate formats were used. Obvious location errors were found in 36 studies (2.1\% of studies reporting geographic coordinates). Beyond easily seeing what has been published for specific areas, georeferenced publication databases open new possibilities for discovering and using ecological knowledge. Users can analyze distributions of published knowledge about a species or topic, or identify relevant knowledge sources from similar areas based on themes like soils and climate. To promote robust geo-semantic literature searching in the future we recommend: 1) adopting standard geographic coordinate formats in journals to increase ease of location extraction and validate coordinates, 2) requiring authors to report geographic coordinates for studies unless privacy or proprietary concerns dictate otherwise, and 3) making geographic information publically available as a part of basic article citation metadata.

}, author = {Jason W. Karl and Unnasch, Robert S. and Jeffrey E. Herrick and Jeffrey K. Gillan} } @article {49887, title = {A strategy for rangeland management based on best available knowledge and information}, journal = {Rangeland Ecology and Management}, volume = {65}, year = {2012}, month = {11/2012}, pages = {638-646}, chapter = {638}, abstract = {

Changes to rangeland systems are happening at spatial and temporal scales beyond the capability of our current knowledge and information systems. In this paper we look at how Web 2.0 tools such as wikis and crowd-sourcing and new technologies including mobile devices and massive online databases are currently being used in rangeland management and consider the potential of these tools for furthering understanding of rangeland ecosystems and improving management decision making in the future. Some potential problems and barriers associated with implementing Web 2.0 tools to support a new rangeland knowledge and information management paradigm are discussed.\  We conclude with a proposed roadmap for moving the discipline of rangeland ecology and management forward toward a more robust and effective use of information. This roadmap focuses on interoperability of data and systems, improving data consistency and documentation, geographic referencing of information, the use of structured and unstructured information, and targeted learning and teaching. Our proposal is an incremental step on top of our current ways of collecting, using, and disseminating information that moves us toward the kinds of knowledge systems we need to address the management problems of today and tomorrow.

}, keywords = {ecological site, hierarchy theory, information system, integrated knowledge system, scale}, doi = {10.2111/REM-D-12-00021.1}, url = {/files/bibliography/12-033.pdf}, author = {Jason W. Karl and Jeffrey E. Herrick and Dawn M. Browning} } @article {JRN54622, title = {A Technique for Estimating Rangeland Canopy-Gap Size Distributions From High-Resolution Digital Imagery}, journal = {Rangeland Ecology \& Management}, volume = {65}, year = {2012}, month = {01/2012}, pages = {196 - 207}, chapter = {196}, abstract = {

The amount and distribution of gaps in vegetation canopy is a useful indicator of multiple ecosystem processes and functions. In this paper, we describe a semiautomated approach for estimating canopy-gap size distributions in rangelands from high-resolution (HR) digital images using image interpretation by observers and statistical image classification techniques. We considered two different classification methods (maximum-likelihood classification and logistic regression) and both pixel-based and object-based approaches to estimate canopy-gap size distributions from 2- to 3-cm resolution UltraCamX color infrared aerial photographs for arid and semiarid shrub sites in Idaho, Nevada, and New Mexico. We compare our image-based estimates to field-based measurements for the study sites. Generally, percent of input points correctly classified and kappa coefficients of agreement for plot image classifications was very high. Plots with low kappa values yielded canopy gap estimates that were very different from field-based estimates. We found a strong relationship (R2 \> 0.9 for all four methods evaluated) between image- and field-based estimates of the total percent of the plot in canopy gaps greater than 50\ cm for plots with a classification kappa of greater than 0.5. Performance of the remote sensing techniques varied for small canopy gaps (25 to 50\ cm) but were very similar for moderate (50 to 200\ cm) and large (\> 200\ cm) canopy gaps. Our results demonstrate that canopy-gap size distributions can be reliably estimated from HR imagery in a variety of plant community types. Additionally, we suggest that classification goodness-of-fit measures are a potentially useful tool for identifying and screening out plots where precision of estimates from imagery may be low. We conclude that classification of HR imagery based on observer-interpreted training points and image classification is a viable technique for estimating canopy gap size distributions. Our results are consistent with other research that has looked at the ability to derive monitoring indicators from HR imagery.

}, keywords = {digital aerial phtoography, image classification, photo interpretations, remote sensing}, issn = {15507424}, doi = {10.2111/REM-D-11-00006.1}, url = {/files/bibliography/12-043.pdf}, author = {Jason W. Karl and Danielle Duni and Scott T. Schrader} } @article {JRN53930, title = {Using very-large-scale aerial imagery for rangeland monitoring and assessment: some statistical considerations}, journal = {Rangeland Ecology and Management}, volume = {65}, year = {2012}, month = {07/01/2012}, pages = {330-339}, chapter = {330}, abstract = {

The availability of very-high-resolution (VHR) imagery and techniques for processing those data into indicators of ecosystem function has opened the door for VHR imagery to be used in rangeland monitoring and assessment. However, VHR imagery can be expensive and, like any survey measurement, studies that rely on VHR-image-based indicators will be subject survey errors that lead to biased results or inflated standard errors. To yield defensible monitoring and assessment data for managers, indicators derived from VHR imagery must be implemented with a statistically valid sampling design. We discuss the use of VHR imagery from the perspectives of achieving unbiased estimates with minimal survey errors in the context of rangeland monitoring and assessment. Our discussion includes study design considerations such as the importance of probability-based sampling for generating credible estimates; measurement errors in VHR-derived indicators; and model-assisted estimation to integrate data from multiple sources, improve precision and adjust for sources of errors. We demonstrate the effects of these design considerations using example datasets where VHR imagery and concurrent field data were collected. Finally, we propose practical ways to conduct statistically-sound rangeland monitoring and assessment using VHR imagery.

}, keywords = {accuracy, monitoring, precision, sample design, statistics, very-large-scale aerial image}, doi = {10.2111/REM-D-11-00102.1}, url = {/files/bibliography/12-041.pdf}, author = {Jason W. Karl and Danielle Duni and Nusser, Sarah and Opsomer, Jean and Unnasch, Robert S.} } @article {49951, title = {DIMA quick start; database for inventory, monitoring and assessment}, year = {2011}, pages = {1-37}, publisher = {USDA ARS Jornada Experimental Range}, abstract = {The Database for Inventory, Monitoring and Assessment (DIMA) is a highly-customized Microsoft Access database for collecting data electronically in the field and for organizing, storing and reporting those data for monitoring and assessment.\  While DIMA can be used for any number of different monitoring data collection needs, it is particularly well suited to collecting data for the BLM\’s Assessment, Inventory and Monitoring (AIM) Strategy following the BLM Core Indicators and Methods.\  It is much easier to use DIMA if you have a basic understanding of what data base being stored in it and how they are organized.}, url = {/files/bibliography/11-027.pdf}, author = {Jason W. Karl and Jeffrey E. Herrick and Ericha Courtright} } @article {49950, title = {Rangeland assessment and monitoring methods guide}, journal = {Rangelands}, volume = {33}, year = {2011}, month = {08/2011}, pages = {48-54}, chapter = {48}, abstract = {A common concern expressed by land managers and biologists is that they do not know enough about the strengths and weaknesses of different field and remote-sensing methods for rangeland assessment and monitoring. The Methods Guide is a web-based tool and resource that provides researchers and managers information necessary to make informed decisions about which field and remote-sensing method or combination of methods could be most useful and cost effective for their management needs. The Methods Guide helps users make more informed decisions about which method or combination of methods best fits their individual assessment and monitoring information needs.bib}, doi = {10.2111/1551-501x-33.4.48}, url = {/files/bibliography/11-026.pdf}, author = {Jason W. Karl and Colson, Karen and Swartz, Heather} } @proceedings {JRN54544, title = {A technique for estimating rangeland canopy-gap size distributions from high resolution digital imagery}, volume = {p. 0255}, year = {2011}, month = {01/2012}, publisher = {Society for Range Management}, address = {Spokane, Washington}, abstract = {The amount and distribution of gaps in vegetation canopy is a useful indicator of multiple ecosystem processes and functions. We describe a semi-automated approach for estimating canopy-gap size distributions in rangelands from high-resolution (HR) digital images using image interpretation by observers and statistical image classification techniques. We considered two different classification methods (maximum-likelihood classification and logistic regression) and both pixel-based and objectbased approaches to estimate canopy-gap size distributions from 2-3cm resolution color infrared aerial photographs for arid and semi-arid shrub sites in Idaho, Nevada, and New Mexico. We compare our image-based estimates to field-based measurements for the study sites. We found a strong relationship (R2 > 0.9 for all four methods evaluated) between image- and field-based estimates of the total percent of the plot in canopy gaps greater than 50cm for plots with a classification kappa of greater than 0.5. Performance of the four remote sensing techniques varied for small canopy gaps (25 to 50cm), but were very similar for moderate (50 to 200cm) and large (>200cm) canopy gaps. Our results demonstrate that canopy-gap size distributions can be reliably estimated from HR imagery in a variety of plant community types. Additionally, we suggest that classification goodness-of-fit measures are a potentially useful tool for identifying and screening out plots where precision of estimates from imagery may be low. Our results are consistent with other research that has looked at the ability to derive monitoring indicators from HR imagery.}, author = {Jason W. Karl and Danielle Duni and Scott T. Schrader} } @article {49949, title = {Turning information into knowledge for rangeland management}, journal = {Rangelands}, volume = {33}, year = {2011}, month = {08/2011}, pages = {3-5}, chapter = {3}, abstract = {The kind of knowledge system that will be capable of meeting the needs of rangeland managers will evolve as scientists, technology specialists, managers, and biologists find ways to integrate the ever expanding array of information systems and tools to meet their needs. The tools and techniques highlighted in this issue of Rangelands were selected because they have immediate application, and illustrate the various types of tools necessary to support management decision making. The challenge is figuring out how to combine such new tools and knowledge sources in a way that increases understanding of rangeland systems and promotes good decision making.}, doi = {10.2111/1551-501x-33.4.3}, url = {/files/bibliography/11-022.pdf}, author = {Jason W. Karl} } @conference {JRN54490, title = {Cumulative effects analysis (CEA) tools}, booktitle = {Society for Range Management}, year = {2010}, month = {02/2010}, publisher = {Society for Range Managment}, organization = {Society for Range Managment}, address = {Denver, CO}, abstract = {

Effective rangeland management requires careful consideration of the possible cumulative effects of different management options prior to making major management decisions. State-and-transition (S/T) models, based on ecological sites, capture our understanding ecosystem functioning and can be used to predict the potential cumulative impacts of different management regimes.\  However, management units, such as grazing allotments or ranches, contain many ecological sites.\  Thus, using S/T to assess cumulative effects at the scale of the allotment or ranch or larger has been difficult, if not impossible.\  We have developed a dynamic modeling interface, the Cumulative Effects Analysis (CEA) Tool, which allows for S/T models to be used for cumulative impact assessment across these large landscapes. We will present this tool, and demonstrate its use across two large (\>125,000 acres each) Bureau of Land Management (BLM) study areas in southern Idaho. We developed a set of S/T models based on groupings of similar ecological sites, and mapped the model states from field samples and satellite imagery. We defined a range of management scenarios related to juniper encroachment in one area and post-fire restoration in the other. Using repeated Monte Carlo simulations, we used the CEA Tool to estimate the range of potential future outcomes for each management scenario and the uncertainty in the projections. This ecological-site-based assessment allowed us to weigh the effectiveness of different scenarios in meeting management objectives.

}, author = {Jason W. Karl and Unnasch, Robert S. and Kanga, Megan and Frid, Leonardo} } @conference {50039, title = {The devil is in the details: overcoming the challenge of implementing consistent ecosystem indicators for cross-scale ecosystem understanding}, booktitle = {7th International Conference on Ecological Informatics (ISEI7)}, year = {2010}, month = {12/2010}, address = {Ghent, Belgium}, abstract = {Addressing environmental challenges requires understanding and monitoring of ecosystem responses to direct and indirect human impacts from local to global scales. Because it is generally not possible to sample at a sufficiently high density across a large spatial extent with a single data program, achieving a cross-scale understanding of ecosystem responses to global change requires the collection and synthesis of data from a number of sources, including broad-scale monitoring efforts, sensor arrays, networks of long-term research sites, and locally-collected datasets. Previous studies have highlighted the importance of data accessibility, metadata and data ontologies, database structures, and scientific workflows for discovering and integrating data. However, two critical aspects of data semantics (consistency in definitions of basic ecosystem indicators observational units, compatibility of methods of measurement) can greatly affect the ability to combine datasets measuring the same attribute. Consistency and compatibility among datasets can be achieved if there is general agreement and coordination on how observational units are defined and measured. While the need for standard indicators and methods is broadly agreed upon, the coordination and implementation of such standards has proved challenging. We review different approaches to achieving consistency and compatibility in ecological data along a continuum of observer control, from completely dispersed, independent data collection without standard protocols across sites (e.g., U.S. Long Term Ecological Research sites) to a mix of independent and standardized data collections (e.g., EcoTrends Project) and then completely centralized data collection and storage according to formal protocols (e.g., U.S. Natural Resource Inventory, National Climate Data Center). In the context of a national, multi-scale monitoring effort being developed for the U.S. Bureau of Land Management, we then consider external factors that have necessitated adoption of standard indicators and methods and the impact these are having on the degree to which datasets can be integrated to answer questions across a range of spatial scales. Finally, we provide recommendations for implementing minimum standard indicators and methods for ecological data collection that can be supplemented for local needs. Improving data consistency and compatibility through standard indicators and methods will support a broad-scale framework for synthesis of ecological information that is necessary to link different sources of data across scales to address pressing environmental challenges at scales at which they are occurring.}, author = {Jason W. Karl and Debra C. Peters} } @conference {JRN54492, title = {Making spatial predictions of rangeland ecosystem attributes using regression kriging}, booktitle = {Society for Range Management}, year = {2010}, month = {02/2010}, publisher = {Society for Range Managment}, organization = {Society for Range Managment}, address = {Denver, CO}, abstract = {Sound rangeland management requires accurate information on condition over large landscapes. Typical approaches to making spatial predictions rangeland condition attributes (e.g., shrub or bare ground cover) are via regression between field and remotely-sensed data. This works well in some situations but has limited utility when correlations between field and image data are low and does not take advantage of all information contained in the field data. I compared, for three rangeland attributes (percent cover of shrubs, bare ground, and cheatgrass [Bromus tectorum L.]) in a southern Idaho study area, spatial predictions from generalized least-squares (GLS) regression to a geostatistical interpolator, regression kriging (RK) that combines GLS regression with spatial interpolation of the residuals to improve predictions of rangeland condition attributes over large landscapes. I employed a remote-sensing technique, object-based image analysis (OBIA), to segment Landsat TM 5 images into polygons (i.e., objects) because previous research has shown that OBIA yields higher image-to-field data correlations. Spatial dependence, the decrease in autocorrelation with increasing distance, was strongest for bare ground (samples auto correlated up to a distance [i.e., range] of 12,646m), but present in all three variables (range of 3,653m and 768m for shrub and cheatgrass cover, respectively). As a result, RK produced more accurate results than GLS regression alone for all three variables measured by cross-validated root mean-squared error. Additionally, the ability to map how prediction confidence changes with distance from field samples is a significant benefit of regression kriging and makes this approach suitable for landscape-level assessments and management planning.}, author = {Jason W. Karl} } @article {50036, title = {Monitoring and assessment based on ecological sites}, journal = {Rangelands}, volume = {32}, year = {2010}, month = {12/2010}, pages = {60-64}, chapter = {60}, abstract = {

The importance of monitoring and assessment of rangeland systems is generally accepted, buteh importanc eand benefits of monitoring based on a foundation of ecological site potential is less well understook. The concept of ecological sitesand their current implementation via NRCS ecological site descriptions and associated GIS spatial data layers provides a robust and scalable framework for rangeland monitoring and assessment. An ecological site provides a standard reference for land management, research and monitoring. The ecological site description informs managers as to what kinds of changes can be expected in response to management or disturbance and provides a reference for the interpretation of monitoring and assessment data. In addition, there are many practical uses of ecological sites for designing programs and collecting and analyzing data for rangeland monitoring and assessment. In this paper we describe a monitoring and assessment framework based on ecological sites and present examples for several current and potential applications of ecological sites to rangeland monitoring and assessment.

}, url = {/files/bibliography/10-039.pdf}, author = {Jason W. Karl and Jeffrey E. Herrick} } @proceedings {50037, title = {Spatial dependence of predictions from image segmentation: a methods to determine appropriate scales for producing land-management information}, volume = {No. XXXVIII-4/C7}, year = {2010}, address = {Ghent, Belgium}, abstract = {A challenge in ecological studies is defining scales of observation that correspond to relevant ecological scales for organisms or processes. Image segmentation has been proposed as an alternative to pixel-based methods for scaling remotely-sensed data into ecologically-meaningful units. However, to date, selection of image object sets has been largely subjective. Changing scale of image segmentation affects the variance and spatial dependence (amount and range of spatial autocorrelation) of measured variables, and this information can be used to determine appropriate levels of image segmentation. Our objective was to examine how scaling via image segmentation changes spatial dependence of regression-based predictions of landscape features and to determine if these changes could identify appropriate segmentation levels for a given objective. We segmented an Ikonos image for southern Idaho (USA) into successively coarser scales and evaluated goodness-of-fit and spatial dependence of regression predictions of invasive western juniper (Juniperus occidentalis) density. Correlations between juniper density estimates and imagery increased with scale initially, but then decreased as scale became coarser. Scales with highest correlations generally exhibited the most spatial dependence in the regression predictions and residuals. Aggregating original juniper density estimates by image objects changed their spatial dependence, and the point at which spatial dependence began to diverge from the original observations coincided with the highest correlations. Looking at scale effects on spatial dependence of observations may be a simple method for selecting appropriate segmentation levels. The robustness of ecological analyses will increase as methods are devised that remove the subjectivity of selecting scales.}, keywords = {geostatistics, image segmentation, scale, spatial dependence, variogram}, url = {/files/bibliography/10-047.pdf}, author = {Jason W. Karl and Andrea S. Laliberte and A. Rango} } @article {50040, title = {Spatial dependence of predictions from image segmentation: A variogram-based method to determine appropriate scales for producing land-management information}, journal = {Ecological Informatics}, volume = {5}, number = {3}, year = {2010}, pages = {194-202}, abstract = {

A significant challenge in ecological studies has been defining scales of observation that correspond to the relevant ecological scales for organisms or processes of interest. Remote sensing has become commonplace in ecological studies and management, but the default resolution of imagery often used in studies is an arbitrary scale of observation. Segmentation of images into objects has been proposed as an alternative method for scaling remotely-sensed data into units having ecological meaning. However, to date, the selection of image object sets to represent landscape patterns has been largely subjective. Changes in observation scale affect the variance and spatial dependence of measured variables, and may be useful in determining which levels of image segmentation are most appropriate for a given purpose. We used observations of percent bare ground cover from 346 field sites in a semi-arid shrub-steppe ecosystem of southern Idaho to look at the changes in spatial dependence of regression predictions and residuals for 10 different levels of image segmentation. We found that the segmentation level whose regression predictions had spatial dependence that most closely matched the spatial dependence of the field samples also had the strongest predicted-to-observed correlations. This suggested that for percent bare ground cover in our study area an appropriate scale could be defined. With the incorporation of a geostatistical interpolator to predict the value of regression residuals at unsampled locations, however, we achieved consistently strong correlations across many segmentation levels. This suggests that if spatial dependence in percent bare ground is accounted for, a range of appropriate scales could be defined. Because the best analysis scale may vary for different ecosystem attributes and many inquiries consider more than one attribute, methods that can perform well across a range of scales and perhaps not at a single, ideal scale are important. More work is needed to develop methods that consider a wider range of ways to segment images into different scales and select sets of scales that perform best for answering specific management questions. The robustness of ecological landscape analyses will increase as methods are devised that remove the subjectivity with which observational scales are defined and selected.

}, url = {/files/bibliography/10-019.pdf}, author = {Jason W. Karl and Maurer, Brain A.} } @article {50038, title = {Spatial Predictions of Cover Attributes of Rangeland Ecosystems Using Regression Kriging and Remote Sensing}, journal = {Rangeland Ecology \& Management}, volume = {63}, number = {3}, year = {2010}, pages = {335-349}, abstract = {

Sound rangeland management requires accurate information on rangeland condition over large landscapes. A commonly-applied approach to making spatial predictions of attributes related to rangeland condition (e.g., shrub or bare ground cover) from remote sensing is via regression between field and remotely-sensed data. This has worked well in some situations but has limited utility when correlations between field and image data are low and does not take advantage of all information contained in the field data. I compared spatial predictions from generalized least-squares (GLS) regression to a geostatistical interpolator, regression kriging (RK), for three rangeland attributes (percent cover of shrubs, bare ground, and cheatgrass [Bromus tectorum L.]) in a southern Idaho study area. The RK technique combines GLS regression with spatial interpolation of the residuals to improve predictions of rangeland condition attributes over large landscapes. I employed a remote-sensing technique, object-based image analysis (OBIA), to segment Landsat 5 Thematic Mapper images into polygons (i.e., objects) because previous research has shown that OBIA yields higher image-to-field data correlations and can be used to select appropriate scales for analysis. Spatial dependence, the decrease in autocorrelation with increasing distance, was strongest for percent shrub cover (samples autocorrelated up to a distance [i.e., range] of 19,098 m), but present in all three variables (range of 12,646 m and 768m for bare ground and cheatgrass cover, respectively). As a result, RK produced more accurate results than GLS regression alone for all three attributes when predicted versus observed values of each attribute were measured by leave-one-out cross-validation. The results of RK could be used in assessments of rangeland conditions over large landscapes. The ability to create maps quantifying how prediction confidence changes with distance from field samples is a significant benefit of regression kriging and makes this approach suitable for landscape-level management planning.

}, doi = {10.2111/REM-D-09-00074.1}, url = {/files/bibliography/10-016.pdf}, author = {Jason W. Karl} } @conference {JRN54491, title = {Using object-based image analysis to guide the selection of field sample locations}, booktitle = {GEOBIA 2010}, year = {2010}, month = {06/2010}, publisher = {GEOBIA}, organization = {GEOBIA}, address = {Ghent, Belgium}, abstract = {One of the most challenging tasks for resource management and research is designing field sampling schemes to achieve unbiased estimates of ecosystem parameters as efficiently as possible. This study focused on the potential of fine-scale image objects from object-based image analysis (OBIA) to be used as sampling units. If image objects represent homogeneous areas on the ground with respect to sampling objectives, then their use as sampling units may have the potential to decrease sample variances, increase efficiency, and incorporate landscape information for sampling proportional to objectives while achieving unbiased estimates of rangeland parameters. We evaluated the use of image objects to define sampling units for measuring percent cover of plants in a southern Idaho sagebrush (Atremisia spp.) ecosystem. Using a fine-scale segmentation of an IKONOS image, we selected 24 pairs of adjacent image objects that spanned a range from very similar to very different with respect to the spectral mean and standard deviation of each object in the pair. In the field, 10 downward-looking photos were taken at random locations within each object (20 per pair). Percent cover of five functional groups was calculated from each photo, and mean and variance of percent cover was calculated for each object in the pair and for the pair as a whole (i.e., ignoring the boundary between the two objects). We found that variances in percent cover measurements were lower for individual objects than for object pairs. Additionally, we found that the mean in percent cover between adjacent image objects increased with the spectral difference between objects. These results suggest that objects derived from OBIA can be used as sampling units and that placing sample sites within objects can help avoid sampling across ecological boundaries.}, author = {Jason W. Karl and MacFarlane, David W.} }