The Experts below are selected from a list of 9720 Experts worldwide ranked by ideXlab platform
Johan Six - One of the best experts on this subject based on the ideXlab platform.
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object based Crop identification using multiple vegetation indices textural features and Crop phenology
Remote Sensing of Environment, 2011Co-Authors: J M Penabarragan, Moffatt K Ngugi, Richard E Plant, Johan SixAbstract:Abstract Crop identification on specific parcels and the assessment of soil management practices are important for agro-ecological studies, greenhouse gas modeling, and agrarian policy development. Traditional pixel-based analysis of remotely sensed data results in inaccurate identification of some Crops due to pixel heterogeneity, mixed pixels, spectral similarity, and Crop pattern variability. These problems can be overcome using object-based image analysis (OBIA) techniques, which incorporate new spectral, textural and hierarchical features after segmentation of imagery. We combined OBIA and decision tree (DT) algorithms to develop a methodology, named Object-based Crop Identification and Mapping (OCIM), for a multi-seasonal assessment of a large number of Crop types and field status. In our approach, we explored several vegetation indices (VIs) and textural features derived from visible, near-infrared and short-wave infrared (SWIR) bands of ASTER satellite scenes collected during three distinct growing-season periods (mid-spring, early-summer and late-summer). OCIM was developed for 13 major Crops cultivated in the agricultural area of Yolo County in California, USA. The model design was built in four different scenarios (combinations of three or two periods) by using two independent training and validation datasets and the best DTs resulted in an error rate of 9% for the three-period model and between 12 and 16% for the two-period models. Next, the selected DT was used for the thematic classification of the entire Cropland area and mapping was then evaluated applying the confusion matrix method to the independent testing dataset that reported 79% overall accuracy. OCIM detected intra-class variations in most Crops attributed to variability from local Crop calendars, tree-orchard structures and land management operations. Spectral variables (based on VIs) contributed around 90% to the models, although textural variables were necessary to discriminate between most of the Permanent Crop-fields (orchards, vineyard, alfalfa and meadow). Features extracted from late-summer imagery contributed around 60% in classification model development, whereas mid-spring and early-summer imagery contributed around 30 and 10%, respectively. The Normalized Difference Vegetation Index (NDVI) was used to identify the main groups of Crops based on the presence and vigor of green vegetation within the fields, contributing around 50% to the models. In addition, other VIs based on SWIR bands were also crucial to Crop identification because of their potential to detect field properties like moisture, vegetation vigor, non-photosynthetic vegetation and bare soil. The OCIM method was built using interpretable rules based on physical properties of the Crops studied and it was successful for object-based feature selection and Crop identification.
J M Penabarragan - One of the best experts on this subject based on the ideXlab platform.
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object based Crop identification using multiple vegetation indices textural features and Crop phenology
Remote Sensing of Environment, 2011Co-Authors: J M Penabarragan, Moffatt K Ngugi, Richard E Plant, Johan SixAbstract:Abstract Crop identification on specific parcels and the assessment of soil management practices are important for agro-ecological studies, greenhouse gas modeling, and agrarian policy development. Traditional pixel-based analysis of remotely sensed data results in inaccurate identification of some Crops due to pixel heterogeneity, mixed pixels, spectral similarity, and Crop pattern variability. These problems can be overcome using object-based image analysis (OBIA) techniques, which incorporate new spectral, textural and hierarchical features after segmentation of imagery. We combined OBIA and decision tree (DT) algorithms to develop a methodology, named Object-based Crop Identification and Mapping (OCIM), for a multi-seasonal assessment of a large number of Crop types and field status. In our approach, we explored several vegetation indices (VIs) and textural features derived from visible, near-infrared and short-wave infrared (SWIR) bands of ASTER satellite scenes collected during three distinct growing-season periods (mid-spring, early-summer and late-summer). OCIM was developed for 13 major Crops cultivated in the agricultural area of Yolo County in California, USA. The model design was built in four different scenarios (combinations of three or two periods) by using two independent training and validation datasets and the best DTs resulted in an error rate of 9% for the three-period model and between 12 and 16% for the two-period models. Next, the selected DT was used for the thematic classification of the entire Cropland area and mapping was then evaluated applying the confusion matrix method to the independent testing dataset that reported 79% overall accuracy. OCIM detected intra-class variations in most Crops attributed to variability from local Crop calendars, tree-orchard structures and land management operations. Spectral variables (based on VIs) contributed around 90% to the models, although textural variables were necessary to discriminate between most of the Permanent Crop-fields (orchards, vineyard, alfalfa and meadow). Features extracted from late-summer imagery contributed around 60% in classification model development, whereas mid-spring and early-summer imagery contributed around 30 and 10%, respectively. The Normalized Difference Vegetation Index (NDVI) was used to identify the main groups of Crops based on the presence and vigor of green vegetation within the fields, contributing around 50% to the models. In addition, other VIs based on SWIR bands were also crucial to Crop identification because of their potential to detect field properties like moisture, vegetation vigor, non-photosynthetic vegetation and bare soil. The OCIM method was built using interpretable rules based on physical properties of the Crops studied and it was successful for object-based feature selection and Crop identification.
Richard E Plant - One of the best experts on this subject based on the ideXlab platform.
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object based Crop identification using multiple vegetation indices textural features and Crop phenology
Remote Sensing of Environment, 2011Co-Authors: J M Penabarragan, Moffatt K Ngugi, Richard E Plant, Johan SixAbstract:Abstract Crop identification on specific parcels and the assessment of soil management practices are important for agro-ecological studies, greenhouse gas modeling, and agrarian policy development. Traditional pixel-based analysis of remotely sensed data results in inaccurate identification of some Crops due to pixel heterogeneity, mixed pixels, spectral similarity, and Crop pattern variability. These problems can be overcome using object-based image analysis (OBIA) techniques, which incorporate new spectral, textural and hierarchical features after segmentation of imagery. We combined OBIA and decision tree (DT) algorithms to develop a methodology, named Object-based Crop Identification and Mapping (OCIM), for a multi-seasonal assessment of a large number of Crop types and field status. In our approach, we explored several vegetation indices (VIs) and textural features derived from visible, near-infrared and short-wave infrared (SWIR) bands of ASTER satellite scenes collected during three distinct growing-season periods (mid-spring, early-summer and late-summer). OCIM was developed for 13 major Crops cultivated in the agricultural area of Yolo County in California, USA. The model design was built in four different scenarios (combinations of three or two periods) by using two independent training and validation datasets and the best DTs resulted in an error rate of 9% for the three-period model and between 12 and 16% for the two-period models. Next, the selected DT was used for the thematic classification of the entire Cropland area and mapping was then evaluated applying the confusion matrix method to the independent testing dataset that reported 79% overall accuracy. OCIM detected intra-class variations in most Crops attributed to variability from local Crop calendars, tree-orchard structures and land management operations. Spectral variables (based on VIs) contributed around 90% to the models, although textural variables were necessary to discriminate between most of the Permanent Crop-fields (orchards, vineyard, alfalfa and meadow). Features extracted from late-summer imagery contributed around 60% in classification model development, whereas mid-spring and early-summer imagery contributed around 30 and 10%, respectively. The Normalized Difference Vegetation Index (NDVI) was used to identify the main groups of Crops based on the presence and vigor of green vegetation within the fields, contributing around 50% to the models. In addition, other VIs based on SWIR bands were also crucial to Crop identification because of their potential to detect field properties like moisture, vegetation vigor, non-photosynthetic vegetation and bare soil. The OCIM method was built using interpretable rules based on physical properties of the Crops studied and it was successful for object-based feature selection and Crop identification.
Moffatt K Ngugi - One of the best experts on this subject based on the ideXlab platform.
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object based Crop identification using multiple vegetation indices textural features and Crop phenology
Remote Sensing of Environment, 2011Co-Authors: J M Penabarragan, Moffatt K Ngugi, Richard E Plant, Johan SixAbstract:Abstract Crop identification on specific parcels and the assessment of soil management practices are important for agro-ecological studies, greenhouse gas modeling, and agrarian policy development. Traditional pixel-based analysis of remotely sensed data results in inaccurate identification of some Crops due to pixel heterogeneity, mixed pixels, spectral similarity, and Crop pattern variability. These problems can be overcome using object-based image analysis (OBIA) techniques, which incorporate new spectral, textural and hierarchical features after segmentation of imagery. We combined OBIA and decision tree (DT) algorithms to develop a methodology, named Object-based Crop Identification and Mapping (OCIM), for a multi-seasonal assessment of a large number of Crop types and field status. In our approach, we explored several vegetation indices (VIs) and textural features derived from visible, near-infrared and short-wave infrared (SWIR) bands of ASTER satellite scenes collected during three distinct growing-season periods (mid-spring, early-summer and late-summer). OCIM was developed for 13 major Crops cultivated in the agricultural area of Yolo County in California, USA. The model design was built in four different scenarios (combinations of three or two periods) by using two independent training and validation datasets and the best DTs resulted in an error rate of 9% for the three-period model and between 12 and 16% for the two-period models. Next, the selected DT was used for the thematic classification of the entire Cropland area and mapping was then evaluated applying the confusion matrix method to the independent testing dataset that reported 79% overall accuracy. OCIM detected intra-class variations in most Crops attributed to variability from local Crop calendars, tree-orchard structures and land management operations. Spectral variables (based on VIs) contributed around 90% to the models, although textural variables were necessary to discriminate between most of the Permanent Crop-fields (orchards, vineyard, alfalfa and meadow). Features extracted from late-summer imagery contributed around 60% in classification model development, whereas mid-spring and early-summer imagery contributed around 30 and 10%, respectively. The Normalized Difference Vegetation Index (NDVI) was used to identify the main groups of Crops based on the presence and vigor of green vegetation within the fields, contributing around 50% to the models. In addition, other VIs based on SWIR bands were also crucial to Crop identification because of their potential to detect field properties like moisture, vegetation vigor, non-photosynthetic vegetation and bare soil. The OCIM method was built using interpretable rules based on physical properties of the Crops studied and it was successful for object-based feature selection and Crop identification.
Jesus Fernandez - One of the best experts on this subject based on the ideXlab platform.
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the potential of cynara cardunculus l for seed oil production in a perennial cultivation system
Biomass & Bioenergy, 2002Co-Authors: G. Sánchez, Jesus FernandezAbstract:Cynara cardunculus L. has been recognised as an energy Crop for rainfed lands with Mediterranean climates. For energy purposes, it is grown as a Permanent Crop and the whole aerial biomass produced over the annual growth cycle is harvested every year. In this work, the potential of C. cardunculus L. as an oil Crop is studied in a perennial cultivation system. The degree of variation of seed oil content and fatty acid oil composition is assessed in two experiments designed to study the effect of the factors: plant population, Crop year and experiment location. The range of values of seed oil content was greater for the multilocal experiment (20.0–31.6%) than for the experiment on populations (22.0–28.8%), but the effect of the agricultural year was noticed in both experiments. There were significant differences in the 18 populations experiment and a group of 5 populations was identified as rich in seed oil (>26%). Seed presscake was analysed and as a result the use of this material as fertiliser or as animal feed was proposed. Finally, the degree of variation of fatty acid oil composition—studied for the factors population, location and year—turned out to be rather small. Cynara oil profile was characterised in terms of major fatty acids as: 10.7% palmitic, 3.7% stearic, 25.0% oleic and 59.7% linoleic.