J.H. Everitt’s research while affiliated with Agricultural Research Service and other places

Cotton defoliation and post-harvest destruction are important cultural practices for cotton production. Cotton root rot is a serious and destructive disease that affects cotton yield and lint quality. This paper presents an overview and summary of the methodologies and results on the use of remote sensing technology for evaluating cotton defoliation and regrowth control methods and for assessing cotton root rot infection based on published studies. Ground reflectance spectra and airborne multispectral and hyperspectral imagery were used in these studies. Ground reflectance spectra effectively separated different levels of defoliation and airborne multispectral imagery permitted both visual and quantitative differentiations among defoliation treatments. Both ground reflectance and airborne imagery were able to differentiate cotton regrowth among different herbicide treatments for cotton stalk destruction. Airborne multispectral and hyperspectral imagery accurately identified root rot-infected areas within cotton fields. Results from these studies indicate that remote sensing can be a useful tool for evaluating the effectiveness of cotton defoliation and regrowth control strategies and for detecting and mapping root rot damage in cotton fields. Compared with traditional visual observations and ground measurements, remote sensing techniques have the potential for effective and accurate assessments of various cotton production operations and pest conditions.

In situ hyperspectral reflectance data were studied at 50 bands (10 nm bandwidth) over the 400–900 nm spectral range to determine their potential for distinguishing among nine aquatic plant species: American lotus [Nelumbo lutea (Willd.) Pers.], American pondweed (Potamogeton nodusus Poir.), giant duckweed [Spirodela polyrrhiza (L.) Schleid.], Mexican waterlily (Nymphaea mexicana Zucc.), white waterlily (Nymphaea odorata Aiton), spatterdock [Nuphar lutea (L.) Sm.], giant salvinia (Salvinia molesta Mitchell), waterhyacinth [Eichhornia crassipes (Mart.) Solms] and waterlettuce (Pistia stratiotes L.). The species were studied on three dates: 30 May, 1 July and 3 August 2009. All nine species were studied in July and August, while only eight species were studied in May; giant duckweed was not studied in May due to insufficient availability. Two procedures were used to determine the optimum bands for discriminating among species: multiple comparison range tests and stepwise discriminant analysis. Multiple comparison range tests results for May showed that most separations among species occurred at bands 795–865 nm in the near-infrared (NIR) spectral region where up to six species could be distinguished. For July, few species could be distinguished amongthe 50 bands; most separations occurred at the 715 nm red-NIR edge band where four species could be differentiated. The optimum bands in August occurred in the green (525–595 nm), red (605–635 nm) and red-NIR edge (695–705 nm) spectral regions where up to six species could be distinguished. Stepwise discriminant analysis identified 11 bands in the blue, green, red-NIR edge and NIR spectral regions to be significant to discriminate among the eight species in May. For July and August, stepwise discriminant analysis identified 15bands and 13 bands, respectively, from the blue to NIR regions to be significant for discriminating among the nine species.

Broom snakeweed (Gutierrezia sarothrae (Pursh) Britt. & Rusby) is one of the most widespread and abundant rangeland weeds in western North America. The objectives of this study were to evaluate airborne hyperspectral imagery and compare it with aerial colour-infrared (CIR) photography and multispectral digital imagery for mapping broom snakeweed infestations. Airborne hyperspectral imagery along with aerial CIR photographs and digital CIR images was acquired from a rangeland area in south Texas. The hyperspectral imagery was transformed using minimum noise fraction (MNF) and then classified using minimum distance, Mahalanobis distance, maximum likelihood, and spectral angle mapper (SAM) classifiers. The digitized aerial photographs and the digital images were respectively mosaicked as one photographic image and one digital image; these were then classified using the same classifiers. Accuracy assessment showed that the maximum likelihood classifier performed the best for the three types of images. The best overall accuracies for three-class classification maps (snakeweed, mixed woody and mixed herbaceous) were 91.0%, 92.5%, and 95.0%, respectively, for the CIR photographic image, the digital CIR image and the MNF-transformed hyperspectral image. Kappa analysis showed that there were no significant differences in maximum likelihood-based classifications among the three types of images. These results indicate that airborne hyperspectral imagery along with aerial photography and multispectral imagery can be used for monitoring and mapping broom snakeweed infestations on rangelands.

A study was conducted on the South Texas Gulf Coast to evaluate archive aerial color-infrared (CIR) photography combined with supervised image analysis techniques to quantify changes in black mangrove [Avicennia germinans (L.) L.] populations over a 26-year period. Archive CIR film from two study sites (sites 1 and 2) was studied. Photographs of site 1 from 1976, 1988, and 2002 showed that black mangrove populations made up 16.2%, 21.1%, and 29.4% of the study site, respectively. Photographs of site 2 from 1976 and 2002 showed that black mangrove populations made up 0.4% and 2.7% of the study site, respectively. Over the 26-year period, black mangrove had increases in cover of 77% and 467% on sites 1 and 2, respectively. These results indicate that aerial photographs coupled with image analysis techniques can be useful tools to monitor and quantify black mangrove populations over time.

Silverleaf sunflower (Helianthus argophyllus, Torr and Gray) is an annual weed found on rangelands in south and southeast Texas. Colour-infrared aerial photography and computer image analysis techniques were evaluated for detecting and mapping silverleaf sunflower infestations on a south Texas rangeland area. Supervised and unsupervised image analysis classification techniques were used to classify photographs from two study sites. Supervised classification of the two photographs showed that silverleaf sunflower had mean producer's and user's accuracies of 95.2% and 91.3%, respectively. Unsupervised classification of the two photographs had mean producer's and user's accuracies for silverleaf sunflower of 65.7% and 80.1%, respectively. These results indicate that the supervised technique is superior to the unsupervised technique for mapping silverleaf sunflower infestations using colour-infrared aerial photos.

A study was conducted along the Rio Grande in southwest Texas to evaluate color-infrared aerial photography com-bined with supervised image analysis to quantify changes in giant reed (Arundo donax L.) populations over a 6-year peri-od. Aerial photographs from 2002 and 2008 of the same sev-en fixed study sites were studied. Coverage of giant reed increased in all sites from 2002 to 2008, including increases ranging from 21.9 to 49.9% in six of the seven sites. Expan-sion of giant reed resulted primarily from its displacement of mixed herbaceous vegetation and encroachment on bare soil areas. These results indicate that color-infrared aerial photographs coupled with image analysis techniques can be useful tools to monitor and quantify changes in giant reed populations over time.

This study evaluated linear spectral unmixing techniques for mapping the variation in crop yield. Both unconstrained and constrained linear spectral unmixing models were applied to airborne hyperspectral imagery recorded from a grain sorghum field and a cotton field. A pair of plant and soil spectra derived from each image was used as endmember spectra to generate unconstrained and constrained plant and soil cover fractions. Yield was positively related to plant fractions and negatively related to soil fractions. For comparison, all 5151 possible narrow-band normalized difference vegetation indices (NDVIs) were calculated from the 102-band images and related to yield. Plant fractions provided better correlations with yield than the majority of the NDVIs. These results indicate that plant cover fraction maps derived from hyperspectral imagery can be used as relative yield maps to characterize crop yield variability.

High resolution satellite imagery has the potential to map within-field variation in crop growth and yield. This study examined SPOT 5 satellite multispectral imagery for estimating grain sorghum yield. A 60 km × 60 km SPOT 5 scene and yield monitor data from three grain sorghum fields were recorded in south Texas. The satellite scene contained four spectral bands (green, red, near-infrared and mid-infrared) with a 10-m spatial resolution. Subsets were extracted from the scene that covered the three fields. Images with pixel sizes of 20 and 30 m were also generated from the individual field images to simulate coarser resolution satellite imagery. Vegetation indices and principal components were derived from the images at the three spatial resolutions. Grain yield was related to the vegetation indices, the four bands and the principal components for each field, and for all the fields combined. The effect of the mid-infrared band on estimates of yield was examined by comparing the regression results from all four bands with those from the other three bands. Statistical analysis showed that the 10-m, four-band image and the aggregated 20-m and 30-m images explained 68, 76 and 83%, respectively, of the variation in yield for all the fields combined. The coefficient of determination between yield and the imagery increased with pixel size because of the smoothing effect. The inclusion of the mid-infrared band slightly improved the R 2 values. These results indicate that high resolution SPOT 5 multispectral imagery can be a useful data source for determining within-field yield variation for crop management.

Ashe juniper (Juniperus ashei Buchholz) in excessive coverage reduces forage production, interferes with livestock management, and degrades watersheds and wildlife habitat on infested rangelands. The objective of this study was to apply minimum noise fraction (MNF) transformation and different classification techniques to airborne hyperspectral imagery for mapping Ashe juniper infestations. Hyperspectral imagery with 98 usable bands covering a spectral range of 475–845 nm was acquired from two Ashe juniper infested sites in central Texas. MNF transformation was applied to the hyperspectral imagery and the transformed imagery with the first 10 and 20 MNF bands was classified using four hard classifiers: minimum distance, Mahalanobis distance, maximum likelihood and spectral angle mapper (SAM). For comparison, the 10‐ and 20‐band MNF imagery was inversely transformed to noise‐reduced 98‐band imagery in the original data space, which was also classified using the four classifiers. Accuracy assessment showed that the first 10 MNF bands were sufficient for distinguishing Ashe juniper from associated plant species (mixed woody species and mixed herbaceous species) and other cover types (bare soil and water). Although the 20‐band MNF imagery provided better results for some classifications, the increase in overall accuracy was not statistically significant. Overall accuracy on the 10‐band MNF imagery varied from 88% for SAM to 93% for minimum distance for site 1 and from 84% for SAM to 94% for maximum likelihood for site 2. The 98‐band imagery derived from the 10‐band MNF imagery resulted in overall accuracy ranging from 91% for both SAM and Mahalanobis distance to 97% for maximum likelihood for site 1 and from 87% for SAM to 93% for minimum distance for site 2. Although both approaches produced comparable classification results, the MNF imagery required smaller storage space and less computing time. These results indicate that airborne hyperspectral imagery incorporated with image transformation and classification techniques can be a useful tool for mapping Ashe juniper infestations.

Both multispectral and hyperspectral images are being used to monitor crop conditions and map yield variability, but limited research has been conducted to compare these two types of imagery for assessing crop growth and yields. The objective of this study was to compare airborne multispectral imagery with airborne hyperspectral imagery for mapping yield variability in grain sorghum fields. Airborne color-infrared (CIR) imagery and airborne hyperspectral imagery along with yield monitor data collected from four fields were used in this study. Three-band imagery with wavebands corresponding to the collected CIR imagery and four-band imagery with wavebands similar to QuickBird satellite imagery were generated from the 102-band hyperspectral imagery. All four types of imagery (two actual and two simulated) were aggregated to increase pixel size to match the yield data resolution. Principal components and all possible normalized difference vegetation indices (NDVIs) were derived from each type of imagery and related to yield. Statistical analysis showed that the hyperspectral imagery accounted for more variability in yield than the other three types of multispectral imagery and that the best narrow-band NDVIs among the 5151 NDVIs derived from each hyperspectral image explained more variability than the best NDVIs derived from any of the actual or simulated multispectral images. These results indicate that hyperspectral imagery has the potential for improving yield estimation accuracy.