Artificial Populations for Teaching and Testing Range Techniques

Schultz, A. M.; Gibbens, Robert P.; DeBano, Leonard F.

doi:10.2307/3894738

Cited by 23 publications

(24 citation statements)

References 2 publications

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“…The lack of correlation in other characteristics suggests that experience may not influence visual estimates, a finding in agreement with Schultz et al (1961). Our cross-check comparison of data from people in supervisory vs. technical positions indicates there can be significant data differences between these groups, depending on the method used (Table 4).…”

Section: Measurement Subjectivitysupporting

confidence: 81%

“…EVEs are quicker and physically easier to perform than a 100-point DGO classification. Schultz et al (1961) found untrained high school students from advanced-placement classes were able to estimate cover more accurately than were a random sampling of rangeland professionals attending a Society for Range Management annual meeting. The evidence suggests that even untrained people can make accurate cover estimates.…”

Section: Measurement Subjectivitymentioning

confidence: 90%

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Detection-Threshold Calibration and Other Factors Influencing Digital Measurements of Ground Cover

Booth¹,

Cox²,

Johnson³

2005

REM

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New methods of image acquisition and analysis are advancing rangeland assessment techniques. Most image-analysis programs require users to adjust detection thresholds for color or object classification, a subjective process we postulated would be influenced by human error and variation. We developed a ground-cover-measurement calibration procedure, the digital grid overlay (DGO), which is similar to image point sampling (dot grid) advanced by earlier researchers. We asked 21 rangeland professionals to measure ground cover using 2 subjective visual-estimate methods (threshold adjustment process, or TAP, and external [to the software] visual estimate, or EVE) and the DGO on 5 different nadir-view images of rangeland. We also compared cover measurements made by DGO-calibrated software in automated batch processing against DGO manual-only measurements. We found an unacceptable range of variation among rangeland professionals using TAP. The DGO and EVE values were more closely aligned. We discovered an age-related bias in bare-ground measurements: all users over 50 years of age classified more bare ground than did all users under 50 years of age when using TAP. One explanation for this bias is age-related yellowing of the eye lens. Manual DGO measurements required up to 15 minutes per image compared to about 1 second per image for automated computer analysis after software calibration. The greatest bare-ground difference between the DGOcalibrated software and manual DGO measurements for the data sets analyzed was 5.6% and the correlations imply that reasonably accurate automated measurements can be used for bare-ground measurements from digital-image data sets. The exception is where the software cannot adequately separate litter and bare ground. The digital methods we tested need improvement. However, external calibration (DGO or EVE) of current-generation image-analysis algorithms bring economical, statistically adequate monitoring of extensive land areas within the realm of practical application. ResumenLos nuevos métodos de adquisició n y aná lisis de imágenes está n avanzando las técnicas de evaluació n de los pastizales. La mayoría de los programas de aná lisis de imá genes requieren a los usuarios ajustar los umbrales de detecció n para la clasificació n de colores u objetos, un postulamos que un proceso subjetivo que debería ser influenciado por errores humanos y variació n. Desarrollamos un procedimiento de calibració n de mediciones de cobertura del suelo, la capa de cuadrícula digital (DGO), la cual es similar al muestreo de puntos en imágenes (cuadrícula de puntos) desarrollada por los primeros investigadores. Solicitamos a 21 profesionales de manejo de pastizales medir la cobertura del suelo usando dos métodos subjetivos de estimació n visual (proceso de ajuste de umbrales o TAP, y un método externo (al programa), la estimació n visual o EVE) y el DGO en cinco diferentes imá genes de visió n profunda de pastizales. También comparamos las mediciones de cobertura hechas por el programa DGO-calibrad...

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Section: Measurement Subjectivitysupporting

confidence: 81%

Section: Measurement Subjectivitymentioning

confidence: 90%

Detection-Threshold Calibration and Other Factors Influencing Digital Measurements of Ground Cover

Booth¹,

Cox²,

Johnson³

2005

REM

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“…Such artificial plots have many limitations: constancy of shape and color, restriction of no overlap, and non-random distribution at the edges, as suggested by Schultz et al (1961) andbooth et al (2006b). But even though artificial plots do not accurately reflect the real world, they do serve the purpose of demonstrating the influence of changing sampling density on accuracy when using the DGo and the PDPC methods.…”

Section: Technical Considerations For Using the Plot Digital Photogramentioning

confidence: 99%

Digital Photograph Analysis for Measuring Percent Plant Cover in the Arctic

Chen¹,

Chen²,

Leblanc³

et al. 2010

ARCTIC

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AbStrACt. long-term satellite remote sensing data, when properly calibrated and validated against ground monitoring, could provide valuable data sets for assessing climate change impacts on ecosystems, wildlife, and other important aspects of life in the Arctic. Percent plant cover is ideal for seasonal and long-term ground monitoring because it can be observed non-destructively and is closely related to other key ecosystem variables, such as biomass and leaf area index (lAi). Accurately measuring percent plant cover in the Arctic, however, has been a challenge. Advances in digital photography and imageprocessing techniques have provided the potential to measure vegetation cover accurately. in this paper we report an adapted method for quantifying percent plant cover based on plot digital photograph classification (PDPC). In this digital image analysis, the red, green, and blue image channels and the intensity, hue, and saturation image channels were used together to ensure more accurate cover measurement and labeling of plant species. We evaluated the accuracy of the PDPC method and two other techniques, visual estimate and digital grid overlay, by testing against artificial plots with known percent cover, by comparing with destructively measured lAi, and by comparing results of the three methods. our evaluation indicates that the PDPC method is the most accurate. In addition, PDPC has the advantages of being objective, quick in the field, and suitable for measuring percent plant cover in the Arctic at the level of functional types or species groups.Key words: percent plant cover, Arctic, visual estimate, digital photograph, image classification, LAI rÉSuMÉ. lorsqu'elles sont bien calibrées et qu'elles sont validées contre le dépistage terrestre, les données résultant de la télédétection satellitaire à long terme pourraient fournir d'importants ensembles de données en vue de l'évaluation des incidences du changement climatique sur les écosystèmes, la faune et d'autres aspects-clés de la vie dans l'Arctique. le pourcentage de couverture végétale est idéal pour le dépistage saisonnier et le dépistage terrestre à long terme parce qu'il peut être observé sans qu'il n'y ait de destruction et parce qu'il est étroitement lié à d'autres variables-clés se rapportant aux écosystèmes, comme la biomasse et l'indice de surface foliaire (iSF). toutefois, dans l'Arctique, la mesure exacte du pourcentage de couverture végétale représente un défi. Les progrès réalisés dans les domaines de la photographie numérique et des techniques de traitement d'images fournissent la possibilité de mesurer la couverture végétale avec précision. Dans cette communication, nous faisons état d'une méthode adaptée permettant de quantifier le pourcentage de couverture végétale en fonction de la classification de photographies numériques de parcelles. Dans le cadre de l'analyse d'images numériques, les canaux rouges, verts et bleus des images ainsi que les canaux d'intensité, de tonalité et de saturation des images ont été utilisés pour donner lieu ...

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“…The ERDAS Images were then printed at actual size (scale ¼ 1:1) on white paper using a poster printer. These posters were used as models that avoided nonrandomness at the edge of the field and constancy of shape, problems that Schultz et al (1961) noted as shortcomings of their artificial population board. Averaged over the set of 20 posters, gray was the most, and black the least, prevalent color (Table 1).…”

Section: A Standard For Comparisonmentioning

confidence: 99%

“…Artificial populations are known quantities, but ours and those of Schultz et al (1961) are 2-dimensional and therefore considerably simpler than the real world. All methods tested with our 2-dimensional models had an overage bias for certain colors (we suspect color-detection variability among different-aged persons participating in data collection may have reduced the power of our test for color influences, but it did not hide it) and at the higher coverage classes, facts that would seem to influence significantly cover estimation by the ocular method.…”

Section: Implications Of Findingsmentioning

confidence: 99%

The Accuracy of Ground-Cover Measurements

Booth¹,

Cox²,

Meikle³

et al. 2006

REM

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Ground cover is a key indicator of rangeland condition and influences rangeland management decisions, yet there have been few advances in ground-cover measurement methods. The advent of digital photography and automated image processing promise a revolution in the way ground cover is measured. To assess the potential for automation we compared conventional and automated methods for measuring ground cover against known artificial populations. The known populations were created from 20 nadir images of a Wyoming big sagebrush (Artemisia tridentata Nutt. ssp. wyomingensis Beetle & Young) vegetation type acquired with a 5-megapixel Olympus E20 digital single lens reflex camera mounted on an aluminum camera frame at 2 m above ground level. The images were converted to color, 2-dimensional images that no longer represented real-world conditions but had known cover values and conserved a simplified form of the pattern and spatial context of the plant community. These images were then printed at 1:1 scale to a 1 3 1-m poster. Posters were evaluated for color cover under laboratory conditions using the conventional techniques of steel-point frame, laser-point frame, line-point intercept, ocular estimation, and line intercept. Photographs of the posters were measured for color cover using standard and custom-created algorithms within the VegMeasure image analysis framework, and using the Digital Grid Overlay method. Results indicate that conventional techniques had significantly greater correlation (! 92% agreement of measured to known) than measurements from the algorithms used in the VegMeasure analysis (70%). The critical factor influencing accuracy of point-sampling methods was the area of the contact point for the given method. These findings provide an important measure of relative accuracy among methods for land managers and for researchers seeking to improve rangeland monitoring methods. ResumenLa cobertura basal es un indicador clave de la condició n del pastizal e influye en las decisiones de manejo del pastizal; sin embargo, ha habido pocos avances en los métodos de medició n de la cobertura basal. El advenimiento de la fotografía digital y el procesamiento automá tico de imá genes promete un revolució n en la forma en la que la cobertura basal es medida. Pare evaluar el potencial de automatizació n, comparamos métodos convencionales y automatizados para medir la cobertura basal, la comparació n se realizó en poblaciones artificiales de cobertura conocida. Las poblaciones artificiales fueron creadas a partir de 20 imá genes de baja altura de un tipo de vegetació n de ''Wyoming big sagebrush'' (Artemisia tridentata Nutt. ssp. wyomingensis Beetle & Young) tomadas con una cá mara digital Olympus E20 de 5 megapixeles con lente reflex simple montada en un marco de aluminio a dos metros de altura del nivel del suelo. Las imá genes se convirtieron a color y en imágenes de dos dimensiones que ya no representaban el mundo real, pero tenían valores conocidos de cobertura y conservaron una forma simplificada del pa...

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Artificial Populations for Teaching and Testing Range Techniques

Cited by 23 publications

References 2 publications

Detection-Threshold Calibration and Other Factors Influencing Digital Measurements of Ground Cover

Detection-Threshold Calibration and Other Factors Influencing Digital Measurements of Ground Cover

Digital Photograph Analysis for Measuring Percent Plant Cover in the Arctic

The Accuracy of Ground-Cover Measurements

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