The Experts below are selected from a list of 28251 Experts worldwide ranked by ideXlab platform
James H. Everitt - One of the best experts on this subject based on the ideXlab platform.
-
Employing airborne multispectral Digital Imagery to map Brazilian pepper infestation in South Texas
Geocarto International, 2011Co-Authors: Reginald S Fletcher, James H. Everitt, Chenghai YangAbstract:A study was conducted in south Texas to determine the feasibility of using airborne multispectral Digital Imagery for differentiating the invasive plant Brazilian pepper (Schinus terebinthifolius) from other cover types. Imagery obtained in the visible, near-infrared, and mid-infrared regions of the light spectrum and a supervised classification approach were employed to develop thematic maps of two areas infested with Brazilian pepper. Map accuracies ranged from 84.2 to 100% for the Brazilian pepper class. Findings support using airborne multispectral Digital Imagery as a tool for separating Brazilian pepper from associated land cover types and further encourage exploration of airborne multispectral Digital Imagery and image processing techniques for developing maps of Brazilian pepper infestation in Texas and abroad.
-
Mapping broom snakeweed through image analysis of color-infrared photography and Digital Imagery
Environmental Monitoring and Assessment, 2007Co-Authors: James H. Everitt, Chenghai YangAbstract:A study was conducted on a south Texas rangeland area to evaluate aerial color-infrared (CIR) photography and CIR Digital Imagery combined with unsupervised image analysis techniques to map broom snakeweed [Gutierrezia sarothrae (Pursh.) Britt. and Rusby]. Accuracy assessments performed on computer-classified maps of photographic images from two sites had mean producer’s and user’s accuracies for broom snakeweed of 98.3 and 88.3%, respectively; whereas, accuracy assessments performed on classified maps from Digital images of the same two sites had mean producer’s and user’s accuracies for broom snakeweed of 98.3 and 92.8%, respectively. These results indicate that CIR photography and CIR Digital Imagery combined with image analysis techniques can be used successfully to map broom snakeweed infestations on south Texas rangelands.
-
Mapping phymatotrichum root rot of cotton using airborne three-band Digital Imagery
Transactions of the ASAE, 2005Co-Authors: Chenghai Yang, Carlos J. Fernandez, James H. EverittAbstract:Phymatotrichum root rot, caused by the fungus Phymatotrichum omnivorum, is a serious and destructive disease that significantly reduces cotton yield and lowers lint quality. Cultural practices are commonly recommended for the control of cotton root rot, and fungicides and fumigants that may suppress the disease have also been used. Because of the high costs of these chemicals, their use may be economically feasible only when the infested portions of the field are treated. The objective of this study was to evaluate airborne multispectral Imagery for detecting and mapping root rot areas in cotton fields for site-specific management of the disease. One center-pivot irrigated field and one rainfed field near Corpus Christi, Texas, were selected for this study. Airborne three-band Digital Imagery was taken from the two fields shortly before harvest in 2001 when the infested areas with wilted and dead plants were almost fully pronounced for the season. The Imagery was georeferenced and then classified into healthy and root rot areas using unsupervised classification. Accuracy assessment on the classification maps for the two fields indicated that airborne Imagery effectively and accurately identified root rot areas within the fields. Ground samples taken from the fields showed that cotton yield and some lint quality indices were significantly lower in root rot areas than in healthy areas. Buffer zones around the root rot areas were generated to account for the spread of cotton root rot on the classification maps. The mapping procedures and maps presented in this study will be useful for site-specific management of the disease.
-
Relationships Between Yield Monitor Data and Airborne Multidate Multispectral Digital Imagery for Grain Sorghum
Precision Agriculture, 2002Co-Authors: Chenghai Yang, James H. EverittAbstract:Remote sensing Imagery taken during a growing season not only provides spatial and temporal information about crop growth conditions, but also is indicative of crop yield. The objective of this study was to evaluate the relationships between yield monitor data and airborne multidate multispectral Digital Imagery and to identify optimal time periods for image acquisition. Color-infrared (CIR) Digital images were acquired from three grain sorghum fields on five different dates during the 1998 growing season. Yield data were also collected from these fields using a yield monitor. The images and the yield data were georeferenced to a common coordinate system. Four vegetation indices (two band ratios and two normalized differences) were derived from the green, red, and near-infrared (NIR) band images. The image data for the three bands and the four vegetation indices were aggregated to generate reduced-resolution images with a cell size equivalent to the combine's effective cutting width. Correlation analyses showed that grain yield was significantly related to the Digital image data for each of the three bands and the four vegetation indices. Multiple regression analyses were also performed to relate grain yield to the three bands and to the three bands plus the four indices for each of the five dates. Images taken around peak vegetative development produced the best relationships with yield and explained approximately 63, 82, and 85% of yield variability for fields 1, 2, and 3, respectively. Yield maps generated from the image data using the regression equations agreed well with those from the yield monitor data. These results demonstrated that airborne Digital Imagery can be a very useful tool for determining yield patterns before harvest for precision agriculture.
-
Mapping grain sorghum growth and yield variations using airborne multispectral Digital Imagery.
Transactions of the ASAE, 2000Co-Authors: Chenghai Yang, James H. Everitt, J. M. Bradford, D. E. EscobarAbstract:Airborne Digital Imagery is becoming an increasingly important data source for precision agriculture. In this study, airborne Digital Imagery and yield monitor data were used to map plant growth and yield variability. Color-infrared (CIR) images were acquired from a grain sorghum field five times during the 1998 growing season, and yield monitor data were also collected from the field during harvest. The images were georeferenced and then classified into zones of homogeneous spectral response using unsupervised classification procedures. The images and unsupervised classification maps clearly revealed the consistency and change of plant growth patterns over the growing season. Correlation analyses showed grain yield was significantly related to the individual near-infrared (NIR), red, and green bands of the CIR images and the normalized difference vegetation index (NDVI) for the five dates. Stepwise linear regression was also used to relate yield to the three bands for each of the five dates, and the three images obtained at and after the peak growth produced higher R 2 -values (0.64, 0.66, and 0.61) than the other two early season images (0.39 and 0.37). Yield maps generated from the three best images agreed well with a yield map from the yield monitor data. These results demonstrated that airborne Digital Imagery can be a very useful data source for detecting plant growth and yield variability for precision agriculture.
E.a. Ripley - One of the best experts on this subject based on the ideXlab platform.
-
Assessment of seasonal change in a young aspen (Populus tremuloides Michx.) canopy using Digital Imagery
Applied Geography, 2004Co-Authors: O. W. Archibold, E.a. RipleyAbstract:Abstract Direct and indirect measurements of leaf area distribution during the 2001-growing season were used to characterize the structure of a small aspen grove in relict grassland of the northern Great Plains. Located in the Kernen Prairie near Saskatoon, the 1.5 ha grove had last been burned in October 1986. Measurements included solar radiation inside and outside the grove; Digital hemispheric images of the tree canopy taken bi-weekly, and mean areas of individual leaves at three canopy heights. The images were processed using Idrisi® Digital-image processing software designed for geographical information system (GIS) applications. Leaf size averaged near 5 cm 2 , varying little through the summer, although larger leaves were proportionally more abundant in the upper canopy early in the growing season. Hemispherical cover increased rapidly in early May from about 40% (no leaves) to about 80% (fully leafed), dropping back to the earlier value after leaf fall in late September and early October. Solar radiation under the canopy dropped from 55% of ambient before the first leaves appeared to 25% during most of the summer, increasing to near 50% by late October. Calculated plant (leaf plus stem) area indices increased from about 0.4 in the leafless phase to near 1.7 during the full-leaf stage in late June. Digital Imagery analysis provided a rapid assessment of canopy structure. This may be useful for long-term monitoring of stand responses to different management techniques.
Kalliopi Kyrkou - One of the best experts on this subject based on the ideXlab platform.
-
Digital Imagery/Telecytology
Acta cytologica, 1998Co-Authors: Michael J. O'brien, Masayoshi Takahashi, Gerard Brugal, H. Christen, Thomas Gahm, Roberta M. Goodell, Peter Karakitsos, Ernest Arthur Knesel, Terry Paul Kobler, Kalliopi KyrkouAbstract:ISSUES Optical Digital imaging and its related technologies have applications in cytopathology that encompass training and education, image analysis, diagnosis, report documentation and archiving, and telecommunications. Telecytology involves the use of telecommunications to transmit cytology images for the purposes of diagnosis, consultation or education. This working paper provides a mainly informational overview of optical Digital imaging and summarizes current technologic resources and applications and some of the ethical and legal implications of the use of these new technologies in cytopathology. CONSENSUS POSITION Computer hardware standards for optical Digital Imagery will continue to be driven mainly by commercial interests and nonmedical imperatives, but professional organizations can play a valuable role in developing recommendations or standards for Digital image sampling, documentation, archiving, authenticity safeguards and teleconsultation protocols; in addressing patient confidentiality and ethical, legal and informed consent issues; and in providing support for quality assurance and standardization of Digital image-based testing. There is some evidence that high levels of accuracy for telepathology diagnosis can be achieved using existing dynamic systems, which may also be applicable to telecytology consultation. Static systems for both telepathology and telecytology, which have the advantage of considerably lower cost, appear to have lower levels of accuracy. Laboratories that maintain Digital image databases should adopt practices and protocols that ensure patient confidentiality. Individuals participating in telecommunication of Digital images for diagnosis should be properly qualified, meet licensing requirements and use procedures that protect patient confidentiality. Such individuals should be cognizant of the limitations of the technology and employ quality assurance practices that ensure the validity and accuracy of each consultation. Even in an informal teleconsultation setting one should define the extent of participation and be mindful of potential malpractice liability. ONGOING ISSUES Digital Imagery applications will continue to present new opportunities and challenges. Position papers such as this are directed toward assisting the profession to stay informed and in control of these applications in the laboratory. Telecytology is an area in particular need of studies of good quality to provide data on factors affecting accuracy. New technologic approaches to addressing the issue of selective sampling in static image consultation are needed. The use of artificial intelligence software as an adjunct to enhance the accuracy and reproducibility of cytologic diagnosis of Digital images in routine and consultation settings deserves to be pursued. Other telecytology-related issues that require clarification and the adoption of workable guidelines include interstate licensure and protocols to define malpractice liability.
-
Digital Imagery telecytology
Acta Cytologica, 1998Co-Authors: Michael J Obrien, Masayoshi Takahashi, Gerard Brugal, H. Christen, Thomas Gahm, Roberta M. Goodell, Peter Karakitsos, Ernest Arthur Knesel, Terry Paul Kobler, Kalliopi KyrkouAbstract:ISSUES Optical Digital imaging and its related technologies have applications in cytopathology that encompass training and education, image analysis, diagnosis, report documentation and archiving, and telecommunications. Telecytology involves the use of telecommunications to transmit cytology images for the purposes of diagnosis, consultation or education. This working paper provides a mainly informational overview of optical Digital imaging and summarizes current technologic resources and applications and some of the ethical and legal implications of the use of these new technologies in cytopathology. CONSENSUS POSITION Computer hardware standards for optical Digital Imagery will continue to be driven mainly by commercial interests and nonmedical imperatives, but professional organizations can play a valuable role in developing recommendations or standards for Digital image sampling, documentation, archiving, authenticity safeguards and teleconsultation protocols; in addressing patient confidentiality and ethical, legal and informed consent issues; and in providing support for quality assurance and standardization of Digital image-based testing. There is some evidence that high levels of accuracy for telepathology diagnosis can be achieved using existing dynamic systems, which may also be applicable to telecytology consultation. Static systems for both telepathology and telecytology, which have the advantage of considerably lower cost, appear to have lower levels of accuracy. Laboratories that maintain Digital image databases should adopt practices and protocols that ensure patient confidentiality. Individuals participating in telecommunication of Digital images for diagnosis should be properly qualified, meet licensing requirements and use procedures that protect patient confidentiality. Such individuals should be cognizant of the limitations of the technology and employ quality assurance practices that ensure the validity and accuracy of each consultation. Even in an informal teleconsultation setting one should define the extent of participation and be mindful of potential malpractice liability. ONGOING ISSUES Digital Imagery applications will continue to present new opportunities and challenges. Position papers such as this are directed toward assisting the profession to stay informed and in control of these applications in the laboratory. Telecytology is an area in particular need of studies of good quality to provide data on factors affecting accuracy. New technologic approaches to addressing the issue of selective sampling in static image consultation are needed. The use of artificial intelligence software as an adjunct to enhance the accuracy and reproducibility of cytologic diagnosis of Digital images in routine and consultation settings deserves to be pursued. Other telecytology-related issues that require clarification and the adoption of workable guidelines include interstate licensure and protocols to define malpractice liability.
O. W. Archibold - One of the best experts on this subject based on the ideXlab platform.
-
Assessment of seasonal change in a young aspen (Populus tremuloides Michx.) canopy using Digital Imagery
Applied Geography, 2004Co-Authors: O. W. Archibold, E.a. RipleyAbstract:Abstract Direct and indirect measurements of leaf area distribution during the 2001-growing season were used to characterize the structure of a small aspen grove in relict grassland of the northern Great Plains. Located in the Kernen Prairie near Saskatoon, the 1.5 ha grove had last been burned in October 1986. Measurements included solar radiation inside and outside the grove; Digital hemispheric images of the tree canopy taken bi-weekly, and mean areas of individual leaves at three canopy heights. The images were processed using Idrisi® Digital-image processing software designed for geographical information system (GIS) applications. Leaf size averaged near 5 cm 2 , varying little through the summer, although larger leaves were proportionally more abundant in the upper canopy early in the growing season. Hemispherical cover increased rapidly in early May from about 40% (no leaves) to about 80% (fully leafed), dropping back to the earlier value after leaf fall in late September and early October. Solar radiation under the canopy dropped from 55% of ambient before the first leaves appeared to 25% during most of the summer, increasing to near 50% by late October. Calculated plant (leaf plus stem) area indices increased from about 0.4 in the leafless phase to near 1.7 during the full-leaf stage in late June. Digital Imagery analysis provided a rapid assessment of canopy structure. This may be useful for long-term monitoring of stand responses to different management techniques.
Chenghai Yang - One of the best experts on this subject based on the ideXlab platform.
-
Employing airborne multispectral Digital Imagery to map Brazilian pepper infestation in South Texas
Geocarto International, 2011Co-Authors: Reginald S Fletcher, James H. Everitt, Chenghai YangAbstract:A study was conducted in south Texas to determine the feasibility of using airborne multispectral Digital Imagery for differentiating the invasive plant Brazilian pepper (Schinus terebinthifolius) from other cover types. Imagery obtained in the visible, near-infrared, and mid-infrared regions of the light spectrum and a supervised classification approach were employed to develop thematic maps of two areas infested with Brazilian pepper. Map accuracies ranged from 84.2 to 100% for the Brazilian pepper class. Findings support using airborne multispectral Digital Imagery as a tool for separating Brazilian pepper from associated land cover types and further encourage exploration of airborne multispectral Digital Imagery and image processing techniques for developing maps of Brazilian pepper infestation in Texas and abroad.
-
Mapping broom snakeweed through image analysis of color-infrared photography and Digital Imagery
Environmental Monitoring and Assessment, 2007Co-Authors: James H. Everitt, Chenghai YangAbstract:A study was conducted on a south Texas rangeland area to evaluate aerial color-infrared (CIR) photography and CIR Digital Imagery combined with unsupervised image analysis techniques to map broom snakeweed [Gutierrezia sarothrae (Pursh.) Britt. and Rusby]. Accuracy assessments performed on computer-classified maps of photographic images from two sites had mean producer’s and user’s accuracies for broom snakeweed of 98.3 and 88.3%, respectively; whereas, accuracy assessments performed on classified maps from Digital images of the same two sites had mean producer’s and user’s accuracies for broom snakeweed of 98.3 and 92.8%, respectively. These results indicate that CIR photography and CIR Digital Imagery combined with image analysis techniques can be used successfully to map broom snakeweed infestations on south Texas rangelands.
-
Mapping phymatotrichum root rot of cotton using airborne three-band Digital Imagery
Transactions of the ASAE, 2005Co-Authors: Chenghai Yang, Carlos J. Fernandez, James H. EverittAbstract:Phymatotrichum root rot, caused by the fungus Phymatotrichum omnivorum, is a serious and destructive disease that significantly reduces cotton yield and lowers lint quality. Cultural practices are commonly recommended for the control of cotton root rot, and fungicides and fumigants that may suppress the disease have also been used. Because of the high costs of these chemicals, their use may be economically feasible only when the infested portions of the field are treated. The objective of this study was to evaluate airborne multispectral Imagery for detecting and mapping root rot areas in cotton fields for site-specific management of the disease. One center-pivot irrigated field and one rainfed field near Corpus Christi, Texas, were selected for this study. Airborne three-band Digital Imagery was taken from the two fields shortly before harvest in 2001 when the infested areas with wilted and dead plants were almost fully pronounced for the season. The Imagery was georeferenced and then classified into healthy and root rot areas using unsupervised classification. Accuracy assessment on the classification maps for the two fields indicated that airborne Imagery effectively and accurately identified root rot areas within the fields. Ground samples taken from the fields showed that cotton yield and some lint quality indices were significantly lower in root rot areas than in healthy areas. Buffer zones around the root rot areas were generated to account for the spread of cotton root rot on the classification maps. The mapping procedures and maps presented in this study will be useful for site-specific management of the disease.
-
Relationships Between Yield Monitor Data and Airborne Multidate Multispectral Digital Imagery for Grain Sorghum
Precision Agriculture, 2002Co-Authors: Chenghai Yang, James H. EverittAbstract:Remote sensing Imagery taken during a growing season not only provides spatial and temporal information about crop growth conditions, but also is indicative of crop yield. The objective of this study was to evaluate the relationships between yield monitor data and airborne multidate multispectral Digital Imagery and to identify optimal time periods for image acquisition. Color-infrared (CIR) Digital images were acquired from three grain sorghum fields on five different dates during the 1998 growing season. Yield data were also collected from these fields using a yield monitor. The images and the yield data were georeferenced to a common coordinate system. Four vegetation indices (two band ratios and two normalized differences) were derived from the green, red, and near-infrared (NIR) band images. The image data for the three bands and the four vegetation indices were aggregated to generate reduced-resolution images with a cell size equivalent to the combine's effective cutting width. Correlation analyses showed that grain yield was significantly related to the Digital image data for each of the three bands and the four vegetation indices. Multiple regression analyses were also performed to relate grain yield to the three bands and to the three bands plus the four indices for each of the five dates. Images taken around peak vegetative development produced the best relationships with yield and explained approximately 63, 82, and 85% of yield variability for fields 1, 2, and 3, respectively. Yield maps generated from the image data using the regression equations agreed well with those from the yield monitor data. These results demonstrated that airborne Digital Imagery can be a very useful tool for determining yield patterns before harvest for precision agriculture.
-
Mapping grain sorghum growth and yield variations using airborne multispectral Digital Imagery.
Transactions of the ASAE, 2000Co-Authors: Chenghai Yang, James H. Everitt, J. M. Bradford, D. E. EscobarAbstract:Airborne Digital Imagery is becoming an increasingly important data source for precision agriculture. In this study, airborne Digital Imagery and yield monitor data were used to map plant growth and yield variability. Color-infrared (CIR) images were acquired from a grain sorghum field five times during the 1998 growing season, and yield monitor data were also collected from the field during harvest. The images were georeferenced and then classified into zones of homogeneous spectral response using unsupervised classification procedures. The images and unsupervised classification maps clearly revealed the consistency and change of plant growth patterns over the growing season. Correlation analyses showed grain yield was significantly related to the individual near-infrared (NIR), red, and green bands of the CIR images and the normalized difference vegetation index (NDVI) for the five dates. Stepwise linear regression was also used to relate yield to the three bands for each of the five dates, and the three images obtained at and after the peak growth produced higher R 2 -values (0.64, 0.66, and 0.61) than the other two early season images (0.39 and 0.37). Yield maps generated from the three best images agreed well with a yield map from the yield monitor data. These results demonstrated that airborne Digital Imagery can be a very useful data source for detecting plant growth and yield variability for precision agriculture.