@article {49889, title = {A comparison of three feature selection methods for object-based classification of sub-decimeter resolution UltraCam-L imagery}, journal = {International Journal of Applied Earth Observation and Geoinformation}, volume = {15}, year = {2012}, month = {01/2012}, pages = {70-78}, chapter = {70}, abstract = {The availability of numerous spectral, spatial, and contextual features with object-based image analysis (OBIA) renders the selection of optimal features a time consuming and subjective process. While several feature election methods have been used in conjunction with OBIA, a robust comparison of the utility and efficiency of approaches would facilitate broader and more effective implementation. In this study, we evaluated three feature selection methods, 1) Jeffreys-Matusita distance (JM), 2) classification tree analysis (CTA), and 3) feature space optimization (FSO) for object-based vegetation classifications with sub-decimeter digital aerial imagery in arid rangelands of the southwestern U.S. We assessed strengths, weaknesses, and best uses for each method using the criteria of ease of use, ability to rank and/or reduce input features, and classification accuracies. For the five sites tested, JM resulted in the highest overall classification accuracies for three sites, while CTA yielded highest accuracies for two sites. FSO resulted in the lowest accuracies. CTA offered ease of use and ability to rank and reduce features, while JM had the advantage of assessing class separation distances. FSO allowed for determining features relatively quickly, because it operates within the OBIA software used in this analysis (eCognition). However, the feature ranking in FSO is not transparent and accuracies were relatively low. While all methods offered an objective approach for determining suitable features for classifications of sub-decimeter resolution aerial imagery, we concluded that CTA was best suited for this particular application. We explore the limitations, assumptions, and appropriate uses for this and other datasets.}, keywords = {aerial imagery, classification, feature selection, object-based image analysis, very high resolution}, doi = {10.1016/j.jag.2011.05.011}, url = {/files/bibliography/12-012.pdf}, author = {Andrea S. Laliberte and Dawn M. Browning and A. Rango} } @article {49890, title = {Mapping rangelands with unmanned aircraft}, year = {2012}, month = {03/2012}, pages = {40-44}, abstract = {

At the Jornada Experimental Range in southern New Mexico, ongoing research is aimed at determining the utility of UAS for rangeland mapping and monitoring and developing an operational UAS-based remote sensing program for ecological applications.

}, url = {files/bibliography/12-ProSurveyMag-Laliberte.pdf}, author = {Andrea S. Laliberte} } @article {49958, title = {Image processing and classification procedures for analysis of sub-decimeter imagery acquired with an unmanned aircraft over arid rangelands}, journal = {GIScience \& Remote Sensing}, volume = {48}, year = {2011}, pages = {4-23}, chapter = {4-23}, abstract = {Unmanned aerial systems (UAS) have great potential as a platform for acquiring very high resolution aerial imagery for vegetation mapping. However, image processing and classification techniques require adaptation to images obtained with low-cost digital cameras. We developed and evaluated an image processing workflow that included the integration of resolution-appropriate field sampling, feature selection, and object-based image analysis for the purpose of classifying rangeland vegetation from a five-centimeter-resolution UAS image mosaic. Classification tree analysis was used to determine the optimal spectral, spatial, and contextual features. Segmentation and classification rule sets were developed on a test plot and were applied to the remaining study area, resulting in an overall classification accuracy of 78\% at the species level and 81\% at the structure-group level. The image processing approach provides a roadmap for deriving quality vegetation classification products from UAS imagery with very high spatial, but low spectral resolution.}, doi = {10.2747/1548-1603.48.1.4}, url = {/files/bibliography/11-001.pdf}, author = {Andrea S. Laliberte and A. Rango} } @proceedings {JRN54529, title = {Multispectral remote sensing from unmanned aircraft: development of workflows and comparison with WorldView-2 data}, volume = {CD ROM}, year = {2011}, month = {03/2012}, publisher = {American Society for Photogrammetry and Remote Sensing}, address = {Sacramento, CA}, abstract = {Unmanned aircraft systems (UAS) have seen increasing use in remote sensing of natural resources in recent years. Relatively low operation costs, ability to rapidly revisit the same location, and very high resolution imagery offer new opportunities for remote sensing applications and comparison with satellite imagery. Most digital cameras mounted on UAS deliver high spatial resolution imagery, but the low radiometric and spectral resolution has limits for vegetation mapping. We integrated a lightweight multispectral camera (Mini MCA 6, Tetracam, Inc.) into a BAT 3 UAS. The 6-band camera acquires 10-bit data from the visible to the near infrared spectrum, controlled by interchangeable filters, here designed to acquire data with band centers at 450, 550, 650, 720, 750, and 850 nm. Imagery was acquired at a flying height of 210 m, with a nominal GSD of 14 cm, over arid shrubland in southern New Mexico. We developed a custom approach for band-to-band registration and evaluated registration errors. Batch processing algorithms were developed for conversion of raw to tif file format, band-to-band registration, bit conversion, band stacking, and radiometric correction for convenient and fast processing of hundreds of UAS images. An empirical line method was used to fit digital numbers to field measured reflectance spectra. Spectral information derived from the orthorectified UAS mosaic was compared with data derived from a WorldView-2 image corrected atmospherically using ATCOR. We obtained good agreement (R2=0.82) for vegetation and soil reflectance values for the two data sources, especially in the red and near infrared bands. This demonstrates that high resolution multispectral UAS imagery shows great promise as ground truth for WorldView-2 data. An object-based classification of the UAS mosaic showed good spectral separation between arid land shrubs and grasses. We discuss challenges encountered in the multispectral image processing workflow and potential opportunities for multispectral, multi-scale image fusion.}, author = {Andrea S. Laliberte and A. Rango} } @article {49957, title = {Multispectral remote sensing from unmanned aircraft: image processing workflows and applications for rangeland environments}, journal = {Remote Sensing}, volume = {3}, year = {2011}, month = {11/2011}, pages = {2529-2551}, chapter = {2529}, abstract = {Using unmanned aircraft systems (UAS) as remote sensing platforms offers the unique ability for repeated deployment for acquisition of high temporal resolution data at very high spatial resolution. Multispectral remote sensing applications from UAS are reported in the literature less commonly than applications using visible bands, although light-weight multispectral sensors for UAS are being used increasingly. . In this paper, we describe challenges and solutions associated with efficient processing of multispectral imagery to obtain orthorectified, radiometrically calibrated image mosaics for the purpose of rangeland vegetation classification. We developed automated batch processing methods for file conversion, band-to-band registration, radiometric correction, and orthorectification. An object-based image analysis approach was used to derive a species-level vegetation classification for the image mosaic with an overall accuracy of 87\%. We obtained good correlations between: (1) ground and airborne spectral reflectance (R2 = 0.92); and (2) spectral reflectance derived from airborne and WorldView-2 satellite data for selected vegetation and soil targets. UAS-acquired multispectral imagery provides quality high resolution information for rangeland applications with the potential for upscaling the data to larger areas using high resolution satellite imagery.}, keywords = {classification, multispectral, reflectance, Unmanned AircraftSystem (UAS)}, doi = {10.3390/rs3112529}, url = {/files/bibliography/11-034.pdf}, author = {Andrea S. Laliberte and Goforth, Mark A. and Caitriana Steele and A. Rango} } @conference {49956, title = {Orthorectification, mosaicking, and analysis of sub-decimeter resolution UAV imagery for rangeland monitoring}, booktitle = {64th Annual Society for Range Management Meeting}, year = {2011}, month = {02/09/2011}, address = {Billings, Montana}, abstract = {Unmanned aerial vehicles (UAVs) offer an attractive platform for acquiring imagery for rangeland monitoring. UAVs can be deployed quickly and repeatedly, and they can obtain sub-decimeter resolution imagery at lower image acquisition costs than with piloted aircraft. Low flying heights result in imagery highly suitable for mapping soil and vegetation types, structure, and pattern in great detail. Small UAVS are commonly equipped with lightweight digital cameras due to low payload capabilities, resulting in challenges associated with photogrammetric processing and creation of orthomosaics from large number of small footprint images. We developed a custom, semi-automated approach that is suitable for processing hundreds of UAV images into orthorectified image mosaics. A customized algorithm improves the accuracy of the UAV{\textquoteright}s exterior orientation data, comprised of position (X, Y, Z) and attitude (roll, pitch, heading) information derived from the UAV{\textquoteright}s flight computer. The corrected exterior orientation data are subsequently used as inputs for orthorectification and mosaicking with minimal or no need for tie- and/or ground control points, greatly reducing time and cost of processing. The workflow has been tested on 65 image mosaics of arid rangelands with few distinguishing features. Orthomosaics created using this process have positional accuracies of 1 m in flat terrain and 1.9 m in hilly terrain. Object-based image analysis of the image mosaics has resulted in classification accuracies of 78\%{\textendash}92\%, depending on vegetation type and number of classes. The results show that UAVs are viable remote sensing platforms and that quality products can be derived from the imagery. }, author = {Andrea S. Laliberte and Craig Winters and A. Rango} } @article {49955, title = {UAS remote sensing missions for rangeland applications}, journal = {Geocarto International}, volume = {26}, year = {2011}, month = {03/01/2011}, pages = {141-156}, chapter = {141}, abstract = {Rangelands cover about 50\% of the earth{\textquoteright}s land surface, are in remote areas and have low population densities, all of which provide an ideal opportunity for remote sensing applications from unmanned aircraft systems (UAS). In this paper, we describe a proven workflow for UAS-based remote sensing, and discuss geometric errors of image mosaics and classification accuracies at different levels of detail. We report on several UAS missions over rangelands in Idaho and New Mexico, USA, where we acquired 6-8-cm resolution aerial photography and concurrent field measurements. The geometric accuracies of the image mosaics were in the 1-2 m range, and overall classification accuracies for vegetation maps ranged from 78\% to 92\%. Despite current FAA regulations that restrict UAS operations to distances within line-of-sight of the UAS, our results show that UAS are a viable platform for obtaining very high resolution remote sensing products for applied vegetation mapping of rangelands.}, keywords = {digital camera, photogrammetry, rangelands, UAS, unmanned aircraft systems}, doi = {10.1080/10106049.2010.534557}, url = {/files/bibliography/11-002.pdf}, author = {Andrea S. Laliberte and Craig Winters and A. Rango} } @article {50043, title = {Acquisition, orthorectification, and classification of unmanned aerial vehicle (UAV) imagery for rangeland monitoring}, journal = {Photogrammetric Engineering and Remote Sensing}, volume = {76}, number = {6}, year = {2010}, pages = {661-672}, abstract = {The use of unmanned aerial vehicles (UAVs) for natural resource applications has increased considerably in recent years due to their greater availability, the miniaturization of sensors, and the ability to deploy a UAV relatively quickly and repeatedly at low altitudes. We examine in this paper the potential of using a small UAV for rangeland inventory, assessment and monitoring. Imagery with a ground resolved distance of 8 cm was acquired over a 290 ha site in southwestern Idaho. We developed a semiautomated orthorectification procedure suitable for handling large numbers of small-footprint UAV images. The geometric accuracy of the orthorectified image mosaics ranged from 1.5 m to 2 m. We used object-based hierarchical image analysis to classify imagery of plots measured concurrently on the ground using standard rangeland monitoring procedures. Correlations between imageand ground-based estimates of percent cover resulted in r-squared values ranging from 0.86 to 0.98. Time estimates indicated a greater efficiency for the image-based method compared to ground measurements. The overall classification accuracies for the two image mosaics were 83 percent and 88 percent. Even under the current limitations of operating a UAV in the National Airspace, the results of this study show that UAVs can be used successfully to obtain imagery for rangeland monitoring, and that the remote sensing approach can either complement or replace some ground-based measurements. We discuss details of the UAV mission, image processing and analysis, and accuracy assessment.}, keywords = {assessment, journal, monitoring, orthorectifiction, rangeland inventory, UAV, unmanned aerial vehicle}, url = {/files/bibliography/10-08.pdf}, author = {Andrea S. Laliberte and Jeffrey E. Herrick and A. Rango} } @proceedings {50045, title = {Feature selection methods for object-based classification of sub-decimeter resolution digital aerial imagery}, volume = {XXXVIII}, year = {2010}, pages = {4/C7}, address = {Ghent, Belgium}, abstract = {Due to the availability of numerous spectral, spatial, and contextual features, the determination of optimal features and class separabilities can be a time consuming process in object-based image analysis (OBIA). While several feature selection methods have been developed to assist OBIA, a robust comparison of the utility and efficiency of approaches could facilitate broader application. In this study, we tested feature selection methods and assessments of class separability for object-based classifications of arid land vegetation in the southwestern U.S. using sub-decimeter digital aerial imagery with a 6 cm ground resolved distance. Using Definiens Developer software, we evaluated 1) classification tree analysis (CTA), 2) feature space optimization (FSO), and 3) SEparability and THreshold (SEaTH). We assessed strengths, weaknesses and best uses for each approach using the criteria of ease of use, ability to rank and/or reduce input features, ability to obtain feature thresholds and class separation distances, and underlying statistical assumptions. We conclude that CTA is best suited for reducing a large number of input features, either for nearest neighbor classification or for deriving rules for rule-based classification. FSO is most appropriate for determining a suitable group of features relatively quickly, because it operates within the Definiens software. However, the features are not ranked and feature thresholds cannot be determined. SEaTH interfaces well with outputs from Definiens and is most appropriate for ranking input features based on class separation distances for two-class comparisons, but has the disadvantage of assuming normality, unlike the non-parametric CTA. All methods offered an objective approach for determining suitable features for classifications of hyperspatial aerial imagery. Limitations, assumptions, and appropriate uses for this and other datasets are discussed.}, keywords = {accuracy, aerial photography, classification, feature selection, high resolution, object-based image analysis (OBIA), vegetation}, url = {/files/bibliography/10-048.pdf}, author = {Andrea S. Laliberte and Dawn M. Browning and A. Rango} } @article {50044, title = {Hierarchical object-based classification of ultra-high-resolution digital mapping camera (DMC) imagery for rangeland monitoring and assessment}, journal = {Journal of Spatial Science}, volume = {55}, year = {2010}, month = {June 2010}, pages = {101-115}, chapter = {101}, abstract = {Ultra high resolution digital aerial photography has great potential to complement or replace ground measurements of vegetation cover for rangeland monitoring and assessment. We investigated object-based image analysis (OBIA) techniques for classifying vegetation in southwestern U.S. arid rangelands with 4 cm resolution digital aerial photography. We obtained high r-square values for image- and ground-based measures of percent cover (r-square values: 0.82-0.92). OBIA enabled us to automate the classification process and demonstrated potential for quantifying fine-scale land cover attributes with ultra high resolution imagery. This approach exhibits promise for nationwide application for monitoring grazing lands.}, keywords = {aerial photography, object-based image analysis, rangelands, vegetation classifications, very high resolution}, doi = {10.1080/14498596.2010.487853}, url = {/files/bibliography/10-023.pdf}, author = {Andrea S. Laliberte and Dawn M. Browning and Jeffrey E. Herrick and Peg Gronemeyer} } @conference {50042, title = {Object-based classification of hyperspatial digital mapping camera (DMC) imagery for potential integration into the National Resource Inventory (NRI) of grazing lands}, booktitle = {American Society for Photogrammetry and Remote Sensing (ASPRS) Annual Meeting}, year = {2010}, month = {April 26-30,2010}, address = {San Diego, CA}, abstract = {Land management agencies such as the Bureau of Land Management (BLM) and the Natural Resources Conservation Service (NRCS) are required to monitor and assess vegetation conditions across millions of acres of rangelands and grazing lands. Field-based assessments are very costly and inefficient over large areas; remote sensing offers the potential to increase the number of monitoring locations, automate the image classification process, and reduce monitoring costs. Hyperspatial digital aerial photography has great potential to complement or replace ground measurements of vegetation cover. We investigated object-based image analysis (OBIA) techniques for classifying vegetation in southwestern U.S. arid rangelands with 4 cm resolution digital aerial photography. We obtained high r-square values for image- and ground-based measures of percent cover (r-square values: 0.82-0.92). OBIA enabled us to automate the classification process by applying a process tree developed on one image iteratively to subsequent images of the same vegetation community. The ability to classify multiple images efficiently offers the potential to increase the precision of national level inventories by increasing sample locations and to reduce costs by requiring fewer personnel to obtain ground measurements. This approach has demonstrated potential for quantifying fine-scale land cover attributes with very high resolution digital imagery, and exhibits promise for nationwide application for monitoring grazing lands.}, keywords = {aerial photography, digital mapping camera, grazing lands, National Resource Inventory, object-based classification}, author = {Andrea S. Laliberte and Dawn M. Browning and Jeffrey E. Herrick and Peg Gronemeyer} } @conference {50041, title = {Unmanned aerial vehicles for hyperspatial remote sensing of rangelands: object-based classification and field validation}, booktitle = {American Society for Photogrammetry and Remote Sensing (ASPRS) Annual Meeting}, year = {2010}, month = {April 26-30, 201}, address = {San Diego, CA}, abstract = {UAVs are ideally suited for monitoring and assessing vegetation conditions in remote rangelands due to the relatively low operating costs, ability for fast deployment, and greater flexibility than piloted aircraft. The likelihood of obtaining FAA permission for operating a UAV is also greater in remote and unpopulated rangeland areas. Researchers at the Jornada Experimental Range have been investigating rangeland remote sensing applications with UAVs for several years, are acquiring UAV imagery on a regular basis, and have developed reliable techniques for deriving hyperspatial UAV image mosaics. However, several challenges remain. While the spatial resolution of the imagery is high (6-8 cm GSD), the spectral and radiometric resolution is low due to the low-cost digital camera used on the UAV. Field validation is also complicated due to the relatively large error of conventional GPS-collected field data in relation to the very high spatial resolution imagery. In this study, we used object-based image analysis (OBIA) techniques and tested field data collections suitable for object-based classification of hyperspatial UAV imagery. The UAV image mosaic was segmented and classified into broad land cover classes, and the segmentation outlines were used in the field as a base for collecting training samples to fine-tune the classification to the species level. The training samples were used as input for a decision tree analysis, which incorporated spectral, spatial, contextual and texture features. The digital data collection allowed for an easy transfer into the image analysis software. Results indicate that geometric and classification accuracies are suitable for routine rangeland monitoring purposes, and that unmanned aircraft are a viable tool for obtaining very high resolution vegetation maps. The field data acquisition methods in conjunction with OBIA are also applicable to very high resolution digital aerial imagery acquired with piloted aircraft. }, author = {Andrea S. Laliberte and A. Rango and Craig Winters and A. L. Slaughter and Connie J. Maxwell} } @conference {50111, title = {Rangeland remote sensing applications with unmanned aerial systems (UAS) in the national airspace: challenges and experiences.}, booktitle = {American Society for Photogrammetry and Remote Sensing (ASPRS) Annual Conference}, year = {2009}, month = {03/2009}, address = {Baltimore, MD}, abstract = {

In recent years, civilian applications of unmanned aerial systems (UAS) have increased considerably due to their greater availability and the miniaturization of sensors, GPS, inertial measurement units, and other hardware. UAS are well suited for rangeland remote sensing applications, because of the remoteness and low population density of rangelands, and the need of land management agencies to monitor and assess millions of acres of rangeland, a task that is not feasible with ground monitoring techniques alone. While UAS offer reduced operational costs, faster deployment, and more flexibility than piloted aircraft, the operator also has to conform to the Federal Aviation Administrations\’s (FAA) regulations on operating a UAS in the National Airspace, limiting the area and altitude of the UAS mission. We report here on 1) our experiences with obtaining Certificates of Authorization to operate a UAS for the purpose of image acquisition for rangeland monitoring, 2) on the UAS mission, and 3) on image processing and analysis. We acquired 6-cm resolution true color aerial photography over rangelands in southern New Mexico and southwestern Idaho. Concurrently, field measurements of vegetation were collected. The imagery was orthorectified, mosaicked, and analyzed using object-based image analysis. Geometric and classification accuracies were sufficient for rangeland monitoring and assessment purposes, and allowed for mapping the structure and patterns of vegetation and soil in great detail. The results show that UAS are a viable tool for rangeland monitoring, given the current constraints of FAA regulations. Those regulations are in flux, and it is anticipated that in the future, policies for UAS operations will be implemented, including the separation of large and small UAS.

}, keywords = {abstract, National Airspace, remote sensing, UAS, unmanned aerial systems}, author = {Andrea S. Laliberte and A. Rango and Craig Winters and Connie J. Maxwell and A. L. Slaughter} } @article {50113, title = {Texture and scale in object-based analysis of subdecimeter resolution unmanned aerial vehicle (UAV) imagery}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {47}, number = {3}, year = {2009}, month = {03/2009}, pages = {761-770}, chapter = {761}, abstract = {

Imagery acquired with unmanned aerial vehicles (UAVs) has great potential for incorporation into natural resource monitoring protocols due to their ability to be deployed quickly and repeatedly and to fly at low altitudes. While the imagery may have high spatial resolution, the spectral resolution is low when lightweight, off-the-shelf digital cameras are used, and the inclusion of texture measures can potentially increase the classification accuracy. Texture measures have been used widely in pixel-based image analysis, but their use in an object-based environment has not been well documented. Our objectives were to determine the most suitable texture measures and the optimal image analysis scale for differentiating rangeland vegetation using UAV imagery segmented at multiple scales. A decision tree was used to determine the optimal texture features for each segmentation scale. The error rate of the decision tree was lower, prediction success was higher, class separability was greater, and overall accuracy was higher (high 90\% range) at coarser segmentation scales, between 55 and 70 (range 10-80). The inclusion of texture measures increased classification accuracies at nearly all segmentation scales, and entropy was the texture measure with the highest score in most decision trees. The results demonstrate that UAVs are viable platforms for rangeland monitoring and that the drawbacks of low-cost off-the-shelf digital cameras can be overcome by including texture measures and using object-based image analysis highly suitable for very high resolution imagery.

}, keywords = {journal, monitoring, remote sensing, texture, UAV}, doi = {10.1109/TGRS.2008.2009355}, url = {/files/bibliography/09-010.pdf}, author = {Andrea S. Laliberte and A. Rango} } @conference {50114, title = {UAS-derived imagery and terrain models for rangeland mapping and monitoring}, booktitle = {Unmanned Vehicle Systems (UVS) Canada Conference}, year = {2009}, month = {11/2/2009}, address = {Victoria, BC, Canada}, abstract = {

Images from satellites and piloted aircraft have been used extensively for mapping and monitoring rangelands, which comprise approximately 50\% of the world\’s land area. Unmanned aircraft systems (UAS) are ideally suited for monitoring these vast and remote areas, and derived data can bridge the gap between coarser resolution imagery and ground-based information. Small UAS (\<50 kg) offer the advantage of low operating costs, fast and repeated deployment, and low flying heights resulting in very high resolution imagery. However, image processing approaches require adaptation or customization to handle the large number of small-footprint images, account for low accuracy GPS/IMU data, and classify images from low-cost sensors into quality vegetation maps. We are presenting a proven workflow for orthorectification, mosaicking, and classification of 6 cm resolution UAS imagery acquired with a low-cost digital camera. The geometric accuracies of orthorectified image mosaics comprised of 200-300 images were in the 1-2 m range. Classification accuracies for rangeland vegetation maps ranged from 78\%-92\%, depending on number of classes. Digital surface models (DSM) extracted from the imagery can be used to estimate parameters for hydrologic and erosion models. Dense DSMs extracted at the pixel level have potential for deriving vegetation heights. A multispectral sensor acquiring 10-bit data in 6 narrow bands ranging from blue to near infrared is currently undergoing testing. Current FAA regulations in the U.S. limit the potential for widespread UAS image acquisition missions, but the image processing and analysis methods developed on smaller areas are scalable to larger areas.

}, author = {Andrea S. Laliberte and A. Rango} } @conference {50112, title = {Unmanned aircraft mission for rangeland remote sensing applications in the US national airspace}, booktitle = {Unmanned Vehicle Systems (UVS) Canada Conference}, year = {2009}, month = {11/02/2009}, address = {Victoria, BC, Canada}, abstract = {In recent years, civilian applications of unmanned aerial systems (UAS) have increased considerably due to their greater availability and the miniaturization of sensors, GPS, inertial measurement units, and other hardware. UAS are well suited for rangeland remote sensing applications, because of the remoteness and low population density of rangelands, and the need of land management agencies for monitoring and assessing these areas, a task that is not feasible with ground monitoring techniques alone. While UAS offer reduced operational costs, faster deployment, and more flexibility than piloted aircraft, the operator also has to conform to the Federal Aviation Administration{\textquoteright}s (FAA) regulations on operating a UAS in the National Airspace, limiting the area and altitude of the UAS mission. We report here on 1) our experiences in operating a UAS under Certificates of Authorization for the purpose of image acquisition for rangeland monitoring, 2) on the UAS missions, and 3) on image processing and analysis for vegetation mapping. We acquired 6-cm resolution true color aerial photography over rangelands in southern New Mexico and southwestern Idaho. Concurrently, field measurements of vegetation were collected. The imagery was orthorectified, mosaicked, and analyzed using object-based image analysis. Geometric and classification accuracies were sufficient for rangeland monitoring and assessment purposes, and allowed for mapping the structure and patterns of vegetation and soil in great detail. The results show that in spite of the current constraints of FAA regulations, UAS are a viable tool for rangeland monitoring.}, author = {Andrea S. Laliberte} } @proceedings {50231, title = {Correlation of object-based texture measures at mutliple scales in sub-decimeter resolution aerial photography}, volume = {XXXVII4/C1}, year = {2008}, month = {August 6, 2008}, address = {Calgary}, abstract = {Texture measures are commonly used to increase the number of input bands in order to improve classification accuracy, especially for panchromatic or true color imagery. While the use of texture measures in pixel-based analysis has been well documented, this is not the case for texture measures calculated in an object-based environment. In addition, the latter application is computer intensive, and may occur at multiple segmentation scales. Therefore fewer variables are preferred and a knowledge of correlation and how it changes across segmentation scales is required. The objectives of this study were to assess correlations between texture measures as a function of segmentation scale while mapping rangeland vegetation structure groups. A 5-cm resolution true-color aerial photo mosaic was segmented at 15 consecutively coarser scales using the object-based image analysis Definiens Professional. We investigated 10 gray-level co-occurrence matrix (GLCM) texture measures, determined the optimal texture measures for each scale with a decision tree, and calculated correlation coefficients for all texture pairs. Entropy, mean and correlation were least correlated with other texture measures at all scales. The highest correlation that remained stable across all segmentation scales was found for contrast and dissimilarity. We observed both increasing and decreasing correlation coefficients for texture pairs as segmentation scale increased, and there was larger variability from one scale to the next at finer segmentations and more consistency in correlation at medium to coarse scales. This was attributed to the fact that at finer segmentation scales, smaller objects were more numerous, and the ratio of edge to interior pixels for an image object was higher than at coarser scales. At medium to coarse scales, vegetation patches were delineated more precisely, and as a result, classification accuracy and class separability were highest. Due to the sensitivity of texture to scale, an optimal segmentation scale is important in object-based analysis. This approach allowed for determining the most suitable and uncorrelated texture measures at the optimal image analysis scale, was less computer intensive than a series of test classifications, and shows promise for incorporation into rangeland monitoring protocols with very high resolution imagery.}, keywords = {imagery, object-based, panchromatic, proceedings, sub-decimeter}, url = {/files/bibliography/08-045.pdf}, author = {Andrea S. Laliberte and A. Rango} } @proceedings {50232, title = {Incorporation of texture, intensity, hue, and saturation for rangeland monitoring with unmanned aircraft imagery}, volume = {XXXVII}, year = {2008}, month = {08/2008}, pages = {4/C1}, address = {Calgary, Canada}, abstract = {Aerial photography acquired with unmanned aerial vehicles (UAVs) has great potential for incorporation into rangeland health monitoring protocols, and object-based image analysis is well suited for this hyperspatial imagery. A major drawback, however, is the low spectral resolution of the imagery, because most lightweight cameras suitable for UAVs only acquire imagery in the red, green, and blue bands (RGB). Potential solutions include the incorporation of intensity, hue, and saturation (IHS) and/or texture measures. The use of texture had improved classification results in a related study, but we wanted to investigate whether IHS would yield similar results, because texture calculations in object-based analysis are computer intensive. Our objectives were to determine the optimal analysis scale and optimal band combinations: RGB, RGB+texture, RGB+IHS, or RGB+IHS+texture. Eight aerial photos were mosaicked and segmented at 15 consecutively coarser scale parameters (from 10 to 80) using the object-based image analysis program Definiens Professional. Class separation distances, classification accuracies and Kappa Index of Agreement were used to assess the classifications. The highest classification accuracies were achieved at segmentation scales between 50 and 70 and were consistently in the high 90\% range, regardless of which bands were included. The inclusion of texture measures increased classification accuracies at nearly all segmentation scales, but the use of RGB+IHS alone resulted in comparable accuracies at most scales and with considerably less computation time. Techniques used in this study offer an objective approach for determining segmentation scale, and for selecting bands useful for rangeland mapping with hyperspatial, low spectral resolution imagery.}, keywords = {aerial, hyperspatial, imagery, monitoring, object-based, proceedings, rangeland health, UAV}, url = {/files/bibliography/08-046.pdf}, author = {Andrea S. Laliberte and A. Rango} } @proceedings {50233, title = {A procedure for orthorectification of sub-decimeter resolution imagery obtained with an unmanned aerial vehicle (UAV)}, year = {2008}, month = {April 28, 2009}, address = {Portland, OR}, abstract = {Digital aerial photography acquired with unmanned aerial vehicles (UAVs) has great value for resource management due to the flexibility and relatively low cost for image acquisition, and very high resolution imagery (5 cm) which allows for mapping bare soil and vegetation types, structure and patterns in great detail. While image acquisition is relatively straightforward, the creation of orthorectified, GIS-ready image mosaics presents multiple challenges. Those include relatively small image footprints, image distortion due to the use of low-cost digital cameras, difficulty in locating ground control points and in automatic generation of tie points, and relatively large errors in exterior orientation (camera position and attitude information from the UAV{\textquoteright}s GPS/IMU). We developed an automated procedure to improve the accuracy of the exterior orientation by matching the UAV images to an orthorectified reference image. Using the UAV reported exterior orientation and camera geometry, combined with the reference image and DEM, the algorithm simulates image acquisition and then computes the covariance between camera image and simulated image pixels. With this evaluation function, a heuristic search algorithm finds successive improvements to the external orientation, ultimately producing a corrected exterior orientation that allows orthorectification with minimal input of tie points and/or ground control points. The RMS error for a 5-cm resolution, 257-image mosaic was 48 cm. Cost and turnaround time for production of orthorectified mosaics from UAV imagery are considerable reduced due to less time and money spent on ground control point and manual tie point collection.}, keywords = {aerial photography, digital, high resolution, imagery, UAV}, url = {/files/bibliography/08-047.pdf}, author = {Andrea S. Laliberte and Craig Winters and A. Rango} } @conference {50353, title = {Approaches for mapping and monitoring arid rangelands with object-based image analysis and hyperspatial imagery}, booktitle = {2nd Annual Symposium on Object-Based Image Analysis}, year = {2007}, month = {June 7-8, 2007}, address = {Berkeley, CA}, abstract = {At the USDA Agricultural Research Service Jornada Experimental Range (JER) in southern New Mexico, remote sensing research is focused on finding new methods for mapping and monitoring rangelands, and on relating ground-based surveys to remotely sensed information. This presentation will give an overview or recent research at JER involving object-based image analysis and hyperspatial imagery ranging from QuickBird satellite imagery, aerial photography, imagery acquired with unmanned aircraft, to ground-based plot photography. The QuickBird image was segmented at multiple scales to map shrubs at a fine scale and other vegetation communities at coarser scales. We were able to identify and map 87\% of shrubs greater than 2 m2. A decision tree was used as an effective tool for sorting through the numerous input features and reducing them to a manageable rule set for classification. The overall accuracy was 80\% for the optimal segmentation scale. In this case, we used ground-based plot photography as ground truth for the QuickBird remote sensing analysis. We compared image-based estimates of vegetation cover with line-point-intercept (LPI) measures for 50 plots (2.5 m x 3.5 m). The images were transformed from the RGB (red, green, blue) to the IHS (intensity, hue, saturation) color space. Object-based image analysis was used to classify the images into soil, shadow, green vegetation, and senescent vegetation using a masking approach and combination of membership rules and nearest neighbor classification. The correlation coefficients between LPI- and image-based estimates for the four classes ranged from 0.88 to 0.95. The object-based image approach was less labor and time intensive than the LPI method and has the potential to be incorporated into rangeland monitoring protocols. Our latest tool for rangeland monitoring is an unmanned aerial vehicle (UAV), capable of acquiring 5 cm resolution imagery from a 150 m above ground flying height. While the imagery presents some challenges for orthorectification and mosaicking, object-based image analysis has already proven to be highly successful. The very high-resolution imagery allows for identification of individual plants, patches, gaps, and patterns not previously possible, and will allow for assessment of rangeland health and ecosystem change at multiple scales. While this is a project in progress, initial mapping results indicate accuracies in the high 90\% range. Based on this recent research, it is apparent that object-based image analysis will play a major part in future rangeland mapping and monitoring at multiple scales. It is also apparent that new tools in object-based image analysis will be needed, specifically in the area of object-based accuracy assessment.}, keywords = {analysis, image, mapping, object-basaed}, author = {Andrea S. Laliberte and A. Rango} } @article {50355, title = {Combining Decision Trees with Hierarchical Object-Oriented Image Analysis for Mapping Arid Rangelands}, journal = {Photogrammetric Engineering and Remote Sensing}, volume = {73}, number = {2}, year = {2007}, month = {02/2007}, pages = {197-207}, abstract = {

Decision tree analysis is a statistical approach for developing a rule base used for image classification developed a unique approach using object-based rather than pixel-based image information as input for a classification for mapping arid land vegetation. A QuickBird satellite image was segmented at four different scales, resulting in hierarchical netowrk of image objects representing the image information in different spatial resolutions. This allowed differentiation of individual shrubs at a fine scale and delineation of broader vegetation classes at coarser scales. Input variables included spectral, textural and contextual image information, and the variables chosen by the decision tree included many features not available or as easily determined with pixel based image analysis. Spectral information was selected near the top of the classification trees, while contextual and textural variables were more common closer to the terminal nodes of the classification tree. The combination of multi resolution image segmentation and deceision tree analysis facilitated the selection of input variables and helped in determining the appropriate image analysis scale.

}, keywords = {hierarchical, journal, journal articles, mapping, object-oriented image}, doi = {10.14358/PERS.73.2.197 }, url = {/files/bibliography/07-002.pdf}, author = {Andrea S. Laliberte and E.L. Fredrickson and A. Rango} } @article {50352, title = {An object-based image analysis approach for determining fractional cover of senescent and green vegetation with digital plot photography}, journal = {Journal of Arid Environments}, volume = {69}, year = {2007}, month = {2007}, pages = {1-14}, abstract = {

Research into automatic image processing of digital plot photography has increased in recent years. However, in most studies only overall vegetation cover is estimated. In arid regions of the southwestern US, grass cover is typically a mixture of green and senescent plant material and it is important to be able to quantify both types of vegetation. Our objectives were to develop an image analysis approach for estimating fractional cover of green and senescent vegetation using very high-resolution ground photography, and to compare image- and ground-based estimates. We acquired ground photography for fifty plots using an eight megapixel digital camera. The images were transformed from the RGB (red, green, blue) color space to the IHS (intensity, hue, saturation) color space. We used an object-based image analysis approach to classify the images into soil, shadow, green vegetation, and senescent vegetation. Shadow and soil were effectively masked out by using the intensity and saturation bands, and a nearest neighbor classification was used to separate green and senescent vegetation using intensity, hue and saturation as well as visible bands. Correlation coefficients between ground- and image-based estimates for green and senescent vegetation were 0.88 and 0.95 respectively. Image analysis underestimated total and senescent vegetation by approximately 5\%. The object-based image-processing approach is less labor and time intensive than the ground-based plot method, is a viable alternative to these methods, and has the potential to be incorporated into rangeland monitoring protocols.

}, keywords = {object-based image analysis, rangeland monitoring, rangeland vegetation, very high-resolution images}, doi = {10.1016/j.jaridenv.2006.08.016}, url = {/files/bibliography/07-001.pdf}, author = {Andrea S. Laliberte and A. Rango and Jeffrey E. Herrick and E.L. Fredrickson and Laura M. Burkett} } @proceedings {50354, title = {Unmanned aerial vehicles for rangeland mapping and monitoring: a comparison of two systems}, year = {2007}, month = {05/2007}, pages = {CDROM}, address = {Tampa, FL}, abstract = {Aerial photography from unmanned aerial vehicles (UAVs) bridges the gap between ground-based observations and remotely sensed imagery from aerial and satellite platforms. UAVs can be deployed quickly and repeatedly, are less costly and safer than piloted aircraft, and can obtain very high-resolution imagery. At the Jornada Experimental Range in New Mexico, ongoing research is aimed at determining the utility of UAVs for rangeland mapping and monitoring. Digital images of arid rangelands were acquired with two UAVs that differed in size/weight, payload capacity, flight duration, GPS guidance capability and cost. The first system was a modified model airplane equipped with GPS and able to fly along preloaded waypoints and acquire images with a digital camera. The second UAV was a BAT 3 (MLB Systems) with fully autonomous flight capability and equipped with color video and digital cameras. Both units provide a data file containing GPS and elevation for each image, but the BAT also records roll, pitch and yaw data. Both systems acquired high quality, high-resolution images of approximately 5 cm ground resolution (150 m flying height). Because the images have a small footprint (152 m x 114 m), mosaicking is required for further image analysis. Inclusion of camera calibration parameters (lens distortion, focal length, principal point) greatly increased the accuracy of the aero triangulation and resulting orthophotos. Due to the greater stability of the BAT and its longer flight range, the BAT imagery is better suited for analysis of larger areas than the imagery from the model airplane. However, the model airplane offers comparable image resolution and a cost effective alternative to the larger and more expensive UAV systems. Details of both systems, image acquisition and image processing results are discussed and compared.}, keywords = {aerial, monitoring, UAV, unmanned, vehicles}, url = {/files/bibliography/07-033.pdf}, author = {Andrea S. Laliberte and A. Rango and Jeffrey E. Herrick} } @proceedings {50468, title = {Comparison of nearest neighbor and rule-based decision tree classification in an object-oriented environment}, volume = {CDROM}, year = {2006}, month = {July 31-August 4}, address = {Denver, CO}, abstract = {Object-oriented classification is a useful tool for analysis of high-resolution imagery due to the incorporation of spectral, textural and contextual variables. However, feature selection and incorporation of appropriate training sites can be difficult. We compared two object-oriented image classification approaches, one using a decision tree (DT), the other a nearest neighbor classification (NN) with regard to classification accuracy, effort involved and feasibility for mapping similar areas. We used a QuickBird satellite image to map arid rangeland vegetation in a 1200 ha pasture in southern New Mexico. In the DT approach, we used ground truth data from plots (8.75 m2) as input for a decision tree to create a rule base for classification. In the NN approach, larger polygons (mean=100 m2) served as training areas for a nearest neighbor classification. Overall accuracy was 80\% using the DT and 77\% using the NN classification. The DT was a superior tool for reducing the number of input features, but this technique required more field data, export to a decision tree program and was more time consuming. With the NN approach, input features were selected within the image analysis program and were applied to the classification immediately. The use of larger polygons for training and test samples was more appropriate for use in an object-oriented environment than the small plots. We concluded that for arid rangeland classification from QuickBird data, the NN technique required less time in the field and for image analysis, had comparable accuracy to the DT approach, and would be appropriate for mapping similar areas. A combination of both methods would incorporate the advantages of feature selection in a DT with the object-oriented nature of the analysis.}, keywords = {imagery, object-oriented}, url = {/files/bibliography/06-074Proc.pdf}, author = {Andrea S. Laliberte and J. Koppa and E.L. Fredrickson and A. Rango} } @article {50469, title = {Rangeland Mapping; Ease Classification with an Object-oriented Approach and Satellite Imagery}, journal = {Earth Imaging Journal}, volume = {3}, number = {1}, year = {2006}, month = {2006}, pages = {30-32}, abstract = {

Determining detailed vegetation characteristics to classify arid rangelands often presents unique problems due to the high reflectance of the soil background, a mixture of green and senescent grasses, and the prevalence of shrubs in grasslands. These components can make it difficult to determine the proportion of grass cover. On the Jornada Experimental Range (JER), operated by the U.S. Department of Agriculture Agricultural Research Service near Las Cruces, NM, ongoing research is aimed at determining the relationship between ground-based observations and remotely sensed data.

}, keywords = {object-oriented, rangeland mapping, satellite imagery}, url = {/files/bibliography/06-002.pdf}, author = {Andrea S. Laliberte and A. Rango and E.L. Fredrickson} } @proceedings {50467, title = {Separating Green and Senescent Vegetation in Very High Resolution Photography Using an Intensity-Hue-Saturation Transformation and Object Based Classification}, year = {2006}, month = {May 1-5, 2006}, pages = {CDROM}, address = {Reno, NV}, abstract = {
In arid regions of the southwestern US, grass cover is typically a mixture of green and senescent plant material. It is important that both types of vegetation can be quantified for land management purposes and for assessing the nutritional value of grasses. Traditional ground sampling procedures are commonly used but are time consuming. Our goal was to develop an image analysis approach for separating and quantifying green and senescent grasses in the same plot using very high resolution ground photography. The study was conducted in New Mexico at the Jornada Experimental Range (JER), operated by the USDA Agricultural Research Service. We used an eight megapixel digital camera to acquire ground photography from a height of 2.8 m above ground for fifty plots. The images were transformed from the RGB (red, green, blue) color space to the IHS (intensity, hue, saturation) color space. We used an object-based image analysis approach to classify the images into soil, shadow, green vegetation, and senescent vegetation. Shadow and soil were masked out by using the intensity and saturation bands, and a nearest neighbor classification was used to separate green and senescent vegetation using intensity, hue and saturation as well as visible bands. Correlation coefficients between ground-and image-based estimates for green and senescent vegetation were 0.88 and 0.95 respectively, and image analysis underestimated total and senescent vegetation by approximately 5\%. The image-based approach is a viable alternative to and less labor and time intensive than ground based plot measures. Research into further automation of the image analysis procedures is ongoing.
}, keywords = {intensity-hue-saturation, senescent, vegetation}, url = {/files/bibliography/06-071Proc.pdf}, author = {Andrea S. Laliberte and A. Rango and E.L. Fredrickson} } @proceedings {50572, title = {Classification of arid rangelands using an object-oriented and multiscale approach with QuickBird imagery}, year = {2005}, month = {March 7, 2005}, address = {Baltimore, Maryland}, abstract = {The classification of arid rangelands often presents unique problems due to the high reflectance of the soil background, a mixture of green and senescent grasses, and the prevalence of shrubs in grasslands, which can make it difficult to determine the proportion of grass cover from high resolution imagery. On the Jornada Experimental Range (JER), operated by the USDA Agricultural Research Service, ongoing research is aimed at determining the relationship between ground-based observations and remotely sensed data. Specific objectives of this study were to use near-earth photography and QuickBird satellite imagery to develop a detailed vegetation classification of a 1200 ha pasture in order to ascertain the extent of grassland and identify locations, extent and percent cover values for several grass species. We conducted extensive field sampling by taking photos of the ground vegetation from a height of 2.8 m and used thresholding techniques to determine percent vegetation cover and percent bare soil. The QuickBird image was analyzed with an object-oriented approach using the software eCognition. The segmentation was performed at 2 different scales, which was used to construct a hierarchical network of image objects representing the image information in different spatial resolutions simultaneously. This allowed for differentiation of individual shrubs on the lower level, and delineation of broader landscape classes on a higher level. For each image object containing the field plot, several spectral, spatial, and texture characteristics were extracted from the image. Ancillary information included soils, elevation, aspect and slope layers. The data was analyzed using classification and regression trees to determine correlations between features of the segmented image objects and the measured field plot parameters. The object-oriented classification of the QuickBird image worked favorably in this study, because shrubs could be classified separately at a finer scale, while the shrub-interspace vegetation could be analyzed at a coarser scale. This allowed us to get a reliable estimate of grass cover and shrub density in the pasture as well as shrub density within different grass species. The rule base derived from the decision tree proved to be successful at differentiating between the dominant grass species as well as defining several classes of percent grass cover. This approach shows promise for identifying detailed vegetation characteristics on arid rangelands. Future research will include refining the predictive ability of the decision tree and determining the possibility of applying this model to other locations and/or to other scales.}, keywords = {approach, arid rangelands, multiscale, object-oriented, QuickBird imagery}, url = {/files/bibliography/05-037ABS.pdf}, author = {Andrea S. Laliberte and A. Rango and E.L. Fredrickson} } @proceedings {50571, title = {Multi-scale, Objected-oriented analysis of Quickbird Imagery for Determining Percent Cover in Arid Land Vegetation}, year = {2005}, month = {October 4-6, 200}, pages = {CDROM}, address = {Weslaco, TX}, abstract = {Efforts to remotely sense arid land vegetation are often encumbered by high reflectance of the soil background, mixtures of green and senescent grasses, and the prevalence of shrubs in grasslands. These issues make it difficult to classify vegetation or estimate percent vegetation cover. Objectives of this study were to derive estimates of percent cover for several vegetation classes in a 1200 ha pasture at the USDA, Agricultural Research Service{\textquoteright}s Jornada Experimental Range. A stratified random sample approach was used to determine percent cover for 322 field plots. A QuickBird satellite image was segmented at different scales which resulted in image objects for which a multitude of spectral, spatial, and texture characteristics were extracted. We used regression trees to develop a rule base for image classification and performed discrete and fuzzy accuracy assessments. For classes with discrete boundaries map accuracy was 73\%, while accuracy values ranged from 81-86\% using a 2.5-5\% cover boundary around each class. This object-oriented multi-scale approach allowed us to extract shrubs at a fine scale and determine percent cover values for the shrub-interspace at a coarser scale. In addition, shrub density and percent cover for different grass species could be extracted using this method. The regression tree was an excellent tool for reducing the number of input variables derived from the image. Future research will include refining the predictive ability of the decision tree and determining the possibility of applying this model to other locations and/or to other scales.}, keywords = {object-oriented}, url = {/files/bibliography/05-055Proc.pdf}, author = {Andrea S. Laliberte and A. Rango and E.L. Fredrickson} } @conference {50680, title = {Human influences on range contractions of North American carnivores and ungulates}, booktitle = {Carnivores 2004 Conference: Defenders of Wildlife}, year = {2004}, month = {November 15, 200}, pages = {132}, address = {Santa Fe, NM}, abstract = {Knowledge of the geographical distribution of species is important for conservation biology. However, few studies have examined the dynamics of species ranges and changes over time and space. In addition, we know very little about human influences on species range changes at the continental scale. We compared the historic and current geographical ranges of 43 North American carnivores and ungulates to identify large-scale patterns in range contractions and expansions. Our objectives were to determine the degree of human influence on species range changes, to describe range changes with regard to biomes and elevation, and to describe changes in species richness. Seventeen of the species had experienced range contractions over more than 20\% of their historic range. Electivity indices showed a strong correlation between human influence and species range contractions. In areas of higher human influence, species were more likely to contract and less likely to persist. Species richness had also declined considerably since historic times. The temperate grasslands and temperate broadleaf{\textquoteright}mixed forest biomes lost the highest average number of species, while the boreal forest and tundra showed fewer numbers of species lost. Our spatial quantification at the continental scale showed that species contractions have been widespread, crossing regional and country boundaries and that this rapid collapse occurred over the course of only one to two centuries. The results of this study can be used to improve scientists{\textquoteright} knowledge of historical reference conditions and to provide input for wildlife reintroductions and for the creation of wildlife reserves.}, keywords = {biomes, carnivores, elevation, human influences, North America, range contractions, species range changes, ungulates}, author = {Andrea S. Laliberte and Ripple, W.J.} } @conference {50682, title = {Lewis and Clark{\textquoteright}s encounters with wildlife and Native Americans: A GIS analysis}, booktitle = {89th Annual Meeting, Ecological Society of America}, year = {2004}, month = {August 1, 2004}, pages = {Abstract p. 286}, address = {Portland, OR}, abstract = {It is often difficult to determine human influences on ecosystems before Euro-American settlement. The Lewis and Clark journals contain some of the earliest and detailed written descriptions of a large part of the United States and offer one way to assess the past. We used the journal entries to map Lewis and Clark{\textquoteright}s encounters with wildlife and the human population and performed a GIS analysis to determine human influences on wildlife. Species richness was higher in areas with few human settlements, such as the west central part of the Plains, while the fewest number of species were encountered in areas with denser human population, along the Columbia River and west to the Pacific coast. With increasing distance from human settlements and with decreasing settlement densities, abundance of large mammals increased. Bison, elk, grizzly and wolves were predominantly found in the plains. Elk, bison, bighorn sheep and wolves were absent from the Columbia Basin. The results show that humans had considerable influence over wildlife distribution and abundance, even under relatively low human population densities. This insight into historical wildlife distribution and abundance will offer a better understanding of the historical reference condition on which current management regimes are often based. The study also shows that human influences should not be underestimated when investigating historical ecological conditions.}, keywords = {ecosystems, GIS, human influendes, Lewis and Clark, Native Americans, wildlife}, author = {Andrea S. Laliberte and Ripple, W.J.} } @proceedings {50681, title = {Mapping shrub encroachment from 1937-2003 in the Jornada Basin of southern New Mexico}, year = {2004}, month = {May 23-28, 2004}, pages = {Abstract No. 120}, address = {Denver, CO}, abstract = {Shrub encroachment into arid and semi-arid grass-dominated landscapes has been noted in many parts of the Southwestern United States. Although it is well known that shrubs have increased over time, we have little quantitative information related to the rate of vegetation change over a particular time period. Our objective was to use aerial photos to measure the rate of shrub advance which is influenced by outside factors such as persistent drought periods and, in turn, has a direct influence on changes in the water, carbon, and energy cycles of these arid lands. In the Jornada basin of southern New Mexico, shrub increase has been measured with various ground survey techniques extending back to 1858. Researchers at two rangeland research field stations, the Jornada Experimental Range (JER) (USDA-ARS) (783 km2) established in 1912 and the adjacent Chihuahuan Desert Rangeland Research Center (CDRRC) (New Mexico State University) (259 km2) established in 1927, have been attempting to remediate the shrub-invaded grasslands with a number of rangeland treatments since the early 1900s. Initially, manual and mechanical removal techniques were used along with experimentation with various chemicals and fire in certain pastures. Starting in the 1950s, herbicides were applied to many pastures through the 1970s. Herbicides were effective in controlling the shrub advance if used on a regular basis. Record keeping was not consistent, but it seems that herbicide treatments were applied to much of the basin where shrubs were present. This widespread use of herbicides has made it difficult to assess how rapidly the shrubs were invading grassland in the last 60+ years. According to CDRRC records, only Pasture 2 has not been treated with herbicides. We have chosen Pasture 2 to monitor the shrub advance unaffected by herbicide treatments since the mid 1930s when aerial photographs wereacquired over the Jornada basin for the first time. Since 1936, thirty-seven separate aerial photography missions have been flown over parts of JER/CDRRC. Recently, the area has been imaged with high resolution commercial satellites. We selected 11 aerial photos that covered the study area and had sufficient resolution for shrub detection. A QuickBird satellite image was used for the 2003 coverage of the area. All photos were scanned, georectified and resampled to a common resolution of 86 cm. For our analysis, we used an object-oriented classification approach. In a pixel-based classification, the difficulty with analyzing single band aerial photos is associated with the low spectral resolution. This makes it often impossible to differentiate shrubs from the background in different areas of the image. An object-oriented approach allows the user to apply locally different strategies for analysis. We used the fractal net evolution approach (FNEA), which is an object-oriented multi-scale image analysis method embedded in the software eCognition (Baatz and Schaepe 2000, Definiens 2003). The first step is a segmentation of the image based on 3 parameters: scale, color (spectral information) and shape, which segments the image into object primitives based on the chosen parameters. Classification is then performed using those objects rather than single pixels. The multiresolution segmentation approach allows for segmentation at different scales, which is used to construct a hierarchical network of image objects representing the image information in different spatial resolutions simultaneously. This allows for differentiation of individual shrubs on the lower level, and determination of different shrub density classes on a higher level. The classification is based on fuzzy logic theory combined with user-defined rules. Spectral, shape and statistical characteristics as well as relationships between linked levels of the image object primitives can be used to combine them into meaningful classes. The total area covered by individual shrubs in pasture 2 increased from 0.9\% in 1937 to 13.1\% in 2003. In 1960, shrub cover increased to 10.9\%, then decreased in 1967 before resuming a gradual increase until 2003. Most of the shrub cover increase occurred during and just after the 1951-1957 drought which is believed to be the most severe in the past 350 years. The high shrub cover in 1960 may be partially attributed to image quality. Although all images were resampled to the same pixel size, shrubs appeared larger and separate shrubs tended to merge together in the 1960 image. In the 2003 QuickBird image, shrubs appeared to be less defined, however, visual inspection confirmed that a larger proportion of small shrubs were captured. This is due to the larger bit depth (11 bits) in the QuickBird image compared to the 8 bit range in aerial photography. Due to limitations in pixel size, the smallest shrub we could classify was 0.74 m2 (1 pixel), although many of the small shrubs were not captured due to lack of contrast. Therefore, our shrub cover estimate is likely an underestimation of the actual shrub cover. In this Pasture 2 study area, the average rate of increase of shrub cover percentage is about 0.2\% per year over the 66 year period. More rapid increases are noted in specific years, such as after the 1951-1957 severe drought. Certain years illustrate that shrub cover can also decline. It is difficult to say if this is a real effect or one resulting from varying image quality. The object-oriented multi-scale image analysis method used in this study has several advantages over a pixel based classification approach when the goal is the extraction of relatively small objects such as shrubs from panchromatic aerial photos and high resolution satellite images. This method approximates the way humans interpret information visually from aerial photos, but has the advantage of an automated classification routine. Combining several scales of analysis in a hierarchical segmentation method is appropriate in an ecological sense. The difficulties we encountered were related to varying image qualities (resolution, contrast, sun angle) as well as separating image quality from ecological variations (moisture conditions, time of year). Nevertheless, historical photos provide an important record of shrub increase over time. This technique shows promise for evaluating shrub encroachment using historical aerial photography and high resolution satellite imagery.}, keywords = {drainage basin, Jornada Basin, mapping, New Mexico, shrub encroachment, snow cover}, author = {Andrea S. Laliberte and A. Rango} } @article {50683, title = {Object-oriented image analysis for mapping shrub encroachment from 1937-2003 in southern New Mexico}, journal = {Remote Sensing of Environment}, volume = {93}, year = {2004}, month = {September 20, 20}, pages = {198-210}, abstract = {

Shrub encroachment into arid and semiarid grasslands in the southwestern United States is of concern because increased shrub cover leads to declines in species diversity, water availability, grazing capacity, and soil organic matter. Although it is well known that shrubs have increased over time, we have little quantitative information related to the nonlinear nature of this vegetation change over a particular time period. On the USDA-ARS Jornada Experimental Range and the adjacent Chihuahuan Desert Rangeland Research Center (CDRRC)(New Mexico State University) in southern New Mexico, shrub increase has been measured with various ground survey techniques extending back to 1858. For this study, we used 11 aerial photos taken between 1937 and 1996 that covered a 150-ha study area and had sufficient resolution for shrub detection. A QuickBird satellite image provided coverage for 2003. We used image segmentation and object-based classification to monitor vegetation changes over time. Shrub cover increased from 0.9\% in 1937 to 13.1\% in 2003, while grass cover declined from 18.5\% to 1.9\%. Vegetation dynamics reflected changes in precipitation patterns, in particular effects of the 1951-1956 drought. Accuracy assessment showed that shrub and grass cover was underestimated due to the constraint of the pixel size. Eighty-seven percent of all shrubs \>2m2 were detected. The use of object-based classification has advantages over pixel-based classification for the extraction of shrubs from panchromatic aerial and high-resolution satellite imagery. Incorporating both spectral and spatial image information approximates the way humans interpret information visually from aerial photos but has the benefit of an automated classification routine. Combining several scales of analysis in a hierarchical segmentation method is appropriate in an ecological sense and allowed for determining shrub density in coarser level classes. Despite encountering difficulties in analyzing a greatly varying aerial photo dataset, including variability in spectral and spatial resolutions, moisture conditions, time of year of observation, and appearance of grass cover, aerial photos provide an invaluable historic record for monitoring shrub encroachment into a desert grassland.

}, keywords = {aerial photography, desert grassland, mapping, object-based classification, satellite image, segmentation, shrub encroachment}, doi = {10.1016/j.rse.2004.07.011}, url = {/files/bibliography/04-053.pdf}, author = {Andrea S. Laliberte and A. Rango and Kris Havstad and Paris, J.F. and Reldon F. Beck and R.P. McNeely and Amy L. Gonzalez} } @conference {50684, title = {Through the eyes of Lewis and Clark: wildlife abundance and distribution 200 years ago}, booktitle = {68th Annual Meeting of the Soil Science Society of America}, year = {2004}, month = {October 1, 2004}, pages = {121}, abstract = {The Lewis and Clark journals contain some of the earliest and detailed written descriptions of a large part of the United States before Euro-American settlement. We used the journal entries to map Lewis and Clark{\textquoteright}s encounters with wildlife and the human population and performed a GIS analysis to determine human influences on wildlife. Human influence was calculated by distance to the nearest settlement and settlement density in a 50-km radius. More species were present in areas with few human settlements, such as the west-central portion of the Plains. The fewest number of species were seen in areas with denser human population, along the Columbia River and west to the Pacific coast. Each species became more abundant with increasing distance from human settlements and with decreasing settlement densities. Bison, elk, grizzly, and wolves were predominantly found in the plains because the predators followed the then abundant prey population. Elk, bison, bighorn sheep, and wolves were absent from the Columbia Basin. The results show that humans had considerable influence over wildlife distribution and abundance, even under relatively low human population densities. This study offers an improved understanding of the historical reference condition and shows that human influences should not be underestimated when investigating historical ecological conditions.}, keywords = {200 years ago, abundance, distribition, Lewis and Clark, wildlife}, author = {Andrea S. Laliberte and Ripple, W.J.} }