This is a collection of articles I have authored on different applications of spatial analysis and land health monitoring techniques in ecology.

Description

My research at the Jornada focuses on the applications of geographic information systems (GIS), remote sensing, multivariate and spatial statistics, and ecological informatics to sustainable land management. I am the lead PI on the Landscape Toolbox and JournalMap projects, and I work extensively with the Bureau of Land Management, Natural Resource Conservation Service, and other federal agencies on the development and implementation of land health monitoring strategies and techniques.

latest article added on December 2013

ArticleFirst AuthorPublished
Spatial Predictions of Cover Attributes of Rangeland Ecosystems Using Regression Kriging and Remote Sensing.Karl, Jason W2010

Spatial Predictions of Cover Attributes of Rangeland Ecosystems Using Regression Kriging and Remote Sensing.

Keywords

Bromus tectorum ; geostatistics ; Idaho ; landscape-scale assessment ; shrub cover ; statistical modeling

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 it 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 19098 m) but present in all three variables (range of 12646 m and 768 m 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.

Authors

Jason W Karl

Year Published

2010

Publication

Rangeland Ecology & Management

Locations
DOI

10.2111/REM-D-09-00074.1

A Technique for Estimating Rangeland Canopy-Gap Size Distributions From High-Resolution Digital ImageryKarl, Jason W.2012

A Technique for Estimating Rangeland Canopy-Gap Size Distributions From High-Resolution Digital Imagery

Keywords

digital aerial photography, image classification, photo interpretation, remote sensing

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.

Authors

Karl, Jason W., Duniway, Michael C. and Schrader, T. Scott

Year Published

2012

Publication

Rangeland Ecology & Management

Locations
DOI

10.2111/REM-D-11-00006.1

Multivariate correlations between imagery and field measurements across scales: comparing pixel aggregation and image segmentationKarl, Jason W.2010

Multivariate correlations between imagery and field measurements across scales: comparing pixel aggregation and image segmentation

Keywords

remote sensing, scale, multi-scale analysis, object-based image analysis, canonical correlation, Idaho, Ikonos, Landsat

Abstract

To successfully use remotely-sensed data in landscape-level management, questions as to the relevance of image data to landscape patterns and optimal scales of analysis must be addressed. Object-based image analysis, segmenting images into homogeneous regions called objects, has been suggested for increasing accuracy of remotely-sensed products, but little research has gone into determining image object size with regard to scaling of ecosystem properties. We looked at how segmentation of high-resolution Ikonos and medium-resolution Landsat images into successively coarser objects affected multivariate correlations between image data and eight percent-cover measurements of a sagebrush ecosystem. We also looked at changes in correlation as imagery was aggregated into larger square pixels. We found similar canonical correlations between field and image data at the finest scales, but higher for image segmentation than pixel aggregation for both images when scale increased. For image segmentation, correlations between the canonical variables and original field variables were invariant with respect to size of the image objects, suggesting linear scaling of vegetation cover in our study system. We detected a scaling threshold with the Ikonos segmentation and confirmed with a semi-variogram of the sample data. Below the threshold interpretation of the canonical variables was consistent: scale levels differed primarily in the amount of detail portrayed. Above the threshold, meaning of the canonical variables changed. This approach proved useful for evaluating overall utility of images to address an objective, and identified scaling limits for analysis. Selection of appropriate scale for analysis will ultimately depend on the objective being considered.

Authors

Karl, Jason W. and Maurer, Brian A.

Year Published

2010

Publication

Landscape Ecology

Locations
DOI

10.1007/s10980-009-9439-4

Tribal and state ecosystem management regimes influence forest regenerationReo, Nicholas J.2010

Tribal and state ecosystem management regimes influence forest regeneration

Keywords

Oak regeneration; Northern red oak; Tribal natural resource management; Ecosystem management; White-tailed deer; Herbivory

Abstract

Wild ungulates such as white-tailed deer (Odocoileus virginianus) are highly valued wildlife assets that provide subsistence, economic and cultural benefits to hunters and rural communities. Yet, high density populations of these herbivores can contribute significantly to regeneration failures in a wide range of forest types. Pre-European settlement white-tailed deer densities were estimated to have been approximately 2–4 deer km−2, and similar densities have been recommended to balance contemporary forest regeneration and wildlife objectives. We studied northern red oak (Quercus rubra L.) regeneration on neighboring tribal and state forests where socio-cultural differences have led to distinct hunting management practices and subsequent differences in wildlife-plant interactions. Tribes such as the Lac du Flambeau Chippewa have kept deer populations relatively low on reservation lands through active hunting practices. We used an observational study approach to compare in situ ungulate herbivory under low (2–3 deer km−2) and high (>10 deer km−2) population densities. We measured northern red oak regeneration on tribal and state forests in two management unit types: contiguous stands of oak >15 ha in area and small residual “pockets” of oak <3 ha left by foresters as a source of seed and wildlife mast. Herbivory levels were significantly higher on state forests than tribal forests and were closely correlated with the density of larger seedlings, particularly in oak pockets. If herbivory levels are too high, even with adequate light, our results suggest that seedlings may not survive in densities sufficient to maintain northern red oak as a co-dominant species in mixed forests. However, when deer densities are kept at 2–4 deer km−2, our results suggest that northern red oak seedlings can survive beyond browseable heights in sufficient numbers for maintaining oak. Tribal lands can provide contemporary examples of longstanding low to intermediate deer densities and sustainable deer–forest relationships.

Authors

Karl, Jason W. and Reo, Nicholas J.

Year Published

2010

Publication

Forest Ecology and Management

Locations
DOI

10.1016/j.foreco.2010.05.030

Using spatial statistics and point-pattern simulations to assess the spatial dependency between greater sage-grouse and anthropogenic featuresGillan, Jeffrey K.2013

Using spatial statistics and point-pattern simulations to assess the spatial dependency between greater sage-grouse and anthropogenic features

Keywords

Centrocercus urophasianus, Monte Carlo, pair correlation function, point pattern, Ripley’s K, sage-grouse, spatial statistics

Abstract

The greater sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse), a candidate species for listing under the Endangered Species Act, has experienced population declines across its range in the sagebrush (Artemisia spp.) steppe ecosystems of western North America. One factor contributing to the loss of habitat is the expanding human population with associated development and infrastructure. Our objective was to use a spatial-statistical approach to assess the effect of roads, power transmission lines, and rural buildings on sage-grouse habitat use. We used the pair correlation function (PCF) spatial statistic to compare sage-grouse radiotelemetry locations in west-central Idaho, USA, to the locations of anthropogenic features to determine whether sage-grouse avoided these features, thus reducing available habitat. To determine significance, we compared empirical PCFs with Monte Carlo simulations that replicated the spatial autocorrelation of the sampled sage-grouse locations. We demonstrate the implications of selecting an appropriate null model for the spatial statistical analysis by comparing results using a spatially random and a clustered null model. Results indicated that sage-grouse avoided buildings by 150 m and power transmission lines by 600 m, because their PCFs were outside the bounds of a 95% significance envelope constructed from 1,000 iterations of a null model. Sage-grouse exhibited no detectable avoidance of major and minor roads. The methods used here are broadly applicable in conservation biology and wildlife management to evaluate spatial relationships between species occurrence and landscape features. Our results can directly inform planning of infrastructure and other development projects in or near sage-grouse habitat.

Authors

Gillan, Jeffrey K., Strand, Eva K., Karl, Jason W., Reese, Kerry P. and Laninga, Tamara

Year Published

2013

Publication

Wildlife Society Bulletin

Locations
DOI

10.1002/wsb.272

Spatial dependence of predictions from image segmentation: A variogram-based method to determine appropriate scales for producing land-management informationKarl, Jason W.2010

Spatial dependence of predictions from image segmentation: A variogram-based method to determine appropriate scales for producing land-management information

Keywords

Object-based image analysis; Scale; Variogram; Kriging; Geostatistics

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.

Authors

Karl, Jason W. and Maurer, Brian A.

Year Published

2010

Publication

Ecological Informatics

Locations
DOI

10.1016/j.ecoinf.2010.02.004

Rangeland and pasture monitoring: an approach to interpretation of high-resolution imagery focused on observer calibration for repeatabilityDuniway, Michael C.2012

Rangeland and pasture monitoring: an approach to interpretation of high-resolution imagery focused on observer calibration for repeatability

Keywords

remote sensing, image interpretation, aerial photography, repeatability, assessment and monitoring, large-scale

Abstract

Collection of standardized assessment and monitoring data is critically important for supporting policy and management at local to continental scales. Remote sensing techniques, including image interpretation, have shown promise for collecting plant community composition and ground cover data efficiently. More work needs to be done, however, evaluating whether these techniques are sufficiently feasible, cost-effective, and repeatable to be applied in large programs. The goal of this study was to design and test an image-interpretation approach for collecting plant community composition and ground cover data appropriate for local and continental-scale assessment and monitoring of grassland, shrubland, savanna, and pasture ecosystems. We developed a geographic information system image-interpretation tool that uses points classified by experts to calibrate observers, including point-by-point training and quantitative quality control limits. To test this approach, field data and high-resolution imagery (∼3 cm ground sampling distance) were collected concurrently at 54 plots located around the USA. Seven observers with little prior experience used the system to classify 300 points in each plot into ten cover types (grass, shrub, soil, etc.). Good agreement among observers was achieved, with little detectable bias and low variability among observers (coefficient of variation in most plots  0.9), suggesting regression-based adjustments can be used to relate image and field data. This approach could extend the utility of expensive-to-collect field data by allowing it to serve as a validation data source for data collected via image interpretation.

Authors

Karl, Jason W., Duniway, Michael C., Schrader, Scott, Baquera, Noemi and Herrick, Jeffrey E.

Year Published

2012

Publication

Environmental Monitoring and Assessment

Locations
DOI

10.1007/s10661-011-2224-2

SENSITIVITY OF SPECIES HABITAT-RELATIONSHIP MODEL PERFORMANCE TO FACTORS OF SCALEKarl, J. W.2000

SENSITIVITY OF SPECIES HABITAT-RELATIONSHIP MODEL PERFORMANCE TO FACTORS OF SCALE

Keywords

avian habitat, bird counts, GIS, Idaho, species habitat-relationship models

Abstract

Researchers have come to different conclusions about the usefulness of habitat-relationship models for predicting species presence or absence. This difference frequently stems from a failure to recognize the effects of spatial scales at which the models are applied. We examined the effects of model complexity, spatial data resolution, and scale of application on the performance of bird habitat relationship (BHR) models on the Craig Mountain Wildlife Management Area and on the Idaho portion of the U.S. Forest Service's Northern Region. We constructed and tested BHR models for 60 bird species detected on the study areas. The models varied by three levels of complexity (amount of habitat information) and three spatial data resolutions (0.09 ha, 4 ha, 10 ha). We tested these models at two levels of analysis: the site level (a homogeneous area <0.5 ha) and cover-type level (an aggregation of many similar sites of a similar land-cover type), using correspondence between model predictions and species detections to calculate kappa coefficients of agreement. Model performance initially increased as models became more complex until a point was reached where omission errors increased at a rate greater than the rate at which commission errors were decreasing. Heterogeneity of the study areas appeared to influence the effect of model complexity. Changes in model complexity resulted in a greater decrease in commission error than increase in omission error. The effect of spatial data resolution on the performance of BHR models was influenced by the variability of the study area. BHR models performed better at cover-type levels of analysis than at the site level for both study areas. Correct-presence estimates (1 − minus percentage omission error) decreased slightly as number of species detections increased on each study area. Correct-absence estimates (1 − percentage commission error) increased as number of species detections increased on each study area. This suggests that a large number of detections may be necessary to achieve reliable estimates of model accuracy.

Authors

Karl, Jason W., Heglund, P. J., Garton, E. O., Scott, J. M., Wright, N. M. and Hutto, R. L.

Year Published

2000

Publication

Ecological Applications

Locations
DOI

10.1890/1051-0761(2000)010[1690:SOSHRM]2.0.CO;2

This article contributed by:

Ecological Society of America

An assessment of Idaho's wildlife management areas for the protection of wildlife.Karl, Jason W.2005

An assessment of Idaho's wildlife management areas for the protection of wildlife.

Keywords

conservation, GAP, GIS, Idaho, modeling, wildlife management areas

Abstract

Since 1940, Idaho Department of Fish and Game has developed a network of 31 Wildlife Management Areas (WMAs) across the state. The program has been focused mostly on conservation of game species and their habitats. We assessed the contribution of Idaho's WMAs to conservation of all Idaho's wildlife and other aspects of ecological diversity. Predicted occurrences of species' breeding habitats and other data were used to evaluate the representation of wildlife habitat and other ecological conditions.k We found 33 of 39 natural land cover types were mapped as occurring in WMAs. WMAs occurred in 10 of 15 of Bailey's ecoregion sections, absent only from two sections that occupy greater than 1% of Idaho. Percent area of WMAs be elevation followed a pattern similar to percent area of Idaho; hoever, mean elevation for WMAs was lower than for the state and other protected areas in Idaho. We predicted breeding habitat for 98.4% of Idaho's wildlife and all federal and state listed threatened, endangered, or candidate terrestrial vertebrates to occur in at least one WMA. We predicted habitat for 39 species to occur on five or fewer WMAs, and predicted no habitat on WMAs for five species. We found that a system of WMAs established mainly to protect game species potentially conserves many other aspects of Idaho's ecological diversity, may provide habitat for more than 98% of Idaho's wildlife and complements other protected areas in the state.

Authors

Karl, Jason W., Scott, J. Michael, and Eva Strand

Year Published

2005

Publication

Natural Areas Journal

Locations
Nest site characteristics of sharp-shinned hawks in Idaho's Frank Church-River of No Return WildernessKarl, Jason W.1994

Nest site characteristics of sharp-shinned hawks in Idaho's Frank Church-River of No Return Wilderness

Keywords

No keywords available

Abstract

Topographic and vegetative site characteristics were compared for six sharp-shinned hawk (Accipiter striatus) nests in the Big Creek area of Idaho's Frank Church-River of No Return Wilderness. Nesting birds were located by detection of responses to broadcasted sharp-shinned hawk alarm calls. Observed nests were built in live Douglas-fir (Pseudotsuga menziesii) trees within Douglas-fir/ninebark (Physocarpus malvaceus) plant communities. All nest trees were infected by dwarf mistletoe (Arceuthobium douglasii), a common parasite of Douglas-fir. Nests were commonly located in a tree that was of above-average height or the tallest tree in the stand. All nests were within 50 meters (m) of the stand edge. In addition, riparian vegetation was found within approzimately 20 m of four of the six nests.

Authors

Karl, Jason W.

Year Published

1994

Publication

Idaho Forest, Wildlife and Range Experiment Station

Locations

Recent Articles

Modeling Vegetation Heights from High Resolution Stereo Aerial Photography: an Application for Broad-Scale Rangeland Monitoring

by Gillan, Jeffrey K., Karl, Jason W., Duniway, Michael and Elaksher, Ahmed

Vertical vegetation structure in rangeland ecosystems can be a valuable indicator for assessing rangeland health and monitoring riparian areas, post-fire recovery, available forage for livestock, and wildlife habitat. Federal land management agencies are directed to monitor and manage rangelands at landscapes scales, but traditional field methods for measuring vegetation heights are often t...

published 2014 in Journal of Environmental Management

Interpretation of High-Resolution Imagery for Detecting Woodland Cover Composition Change After Fuels Reduction Treatments

by Karl, Jason W., Gillan, Jeffrey K., Barger, Nichole N., Herrick, Jeffrey E. and Duniway, Michael

The use of very high resolution (VHR; ground sampling distances &lt; ~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 assesse...

published 2014 in Ecological Indicators


A Double-Sampling Approach to Deriving Training and Validation Data for Remotely-Sensed Vegetation Products

by Karl, Jason W., Taylor, Jason and Bobo, Matt

The need for large sample sizes to train, calibrate, and validate remote-sensing products has driven an emphasis towards 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 data...

published 2014 in International Journal of Remote Sensing

Using Spatial Statistics and Point-Pattern Simulations to Assess the Spatial Dependency Between Greater Sage-Grouse and Anthropogenic Features

by Gillan, Jeffrey K., Strand, Eva K., Karl, Jason W., Reese, Kerry P. and Laninga, Tamara

The greater sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse), a candidate species for listing under the Endangered Species Act, has experienced population declines across its range in the sagebrush (Artemisia spp.) steppe ecosystems of western North America. One factor contributing to the loss of habitat is the expanding human population with associated development and infrast...

published 2013 in Wildlife Society Bulletin