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.

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 among the 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 15 bands and 13 bands, respectively, from the blue to NIR regions to be significant for discriminating among the nine species.

A study was conducted along the Rio Grande in southwest Texas to evaluate color-infrared aerial photography combined with supervised image analysis to quantify changes in giant reed (Arundo donax L.) populations over a 6-year period. Aerial photographs from 2002 and 2008 of the same seven 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. Expansion 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.

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.

Research was conducted to evaluate the spatial distribution of black mangrove (Avicennia germinans) on the dredged-material or “spoil” islands of the Lower Laguna Madre of Texas. Aerial color-infrared (CIR) photographs revealed the presence of black mangrove stands on many of the islands located south of the Arroyo Colorado (a distributary of the Rio Grande which empties into the Laguna Madre), but failed to detect significant mangrove stands on islands located north of the Arroyo. Analysis of CIR photographs and supervised image classifications for individual islands suggested a concentration of black mangrove along western shorelines and relatively low interior areas of islands, although relatively small and localized mangrove stands were clearly evident along eastern shorelines at several locations. These observations were consistent with ground surveys which indicated significantly higher mangrove densities along western vs eastern shorelines of selected islands (0.6 and 0.1 plants/ m, respectively; P < 0.05), but no difference between ratios of small to large plants in stands located along western vs eastern shorelines (1.6 and 1.4, respectively; P>0.05). The most plausible explanation for these trends is that wave action caused by prevailing southeasterly winds during most of the year may impede or prevent the establishment of black mangrove propagules (germinated „seeds‟) along eastern shorelines of islands which otherwise constitute suitable habitat for A. germinans. If this interpretation is correct, development of planting strategies designed to facilitate establishment of black mangrove stands along shorelines subject to turbulent wave action will be a requisite to the use of this important native plant species for erosion prevention and mitigation on spoil islands in the Lower Laguna Madre. Additional

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.

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.

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.

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.

A large-plot study was conducted for five years to determine the effect of irrigation rate and fertigated nitrogen on the performance of Fibermax (FM) 989BR, Stoneville (ST) 5599BR, and Paymaster (PM) 2280 BG/RR in a field infested with the root-knot nematode (Meloidogyne incognita). The objective was to determine if irrigation rate would effect the relative ability of susceptible and root-knot nematode tolerant cultivars to perform in a root-knot nematode infested field. The split-plot design included irrigation and nitrogen rates as the whole plots (base irrigation rate = B, 75% of base rate [75% B], and 125% of base rate [125% B], with nitrogen applied through the center pivot system and being proportional to irrigation rate) and cultivars as the sub-plot with three replications. The B and 125% B rates had higher lint yield and gross loan value ha(-1) (P <= 0.05) than the 75% B rate when averaged across the three cultivars. The root-knot nematode tolerant cultivar ST 5599BR, averaged equal or higher lint yields than FM 989BR at all irrigation rates when averaged over five years. ST 5599BR and FM 989BR had similar gross loan values ha(-1) at all three irrigation rates. ST 5599BR had higher lint yields and gross loan values ha(-1) than PM 2280 BG/RR (2003 - 2006) at the B and 125% B rates. The Paymaster cultivar is susceptible to root-knot nematode. Increasing irrigation rate did not improve the profitability of susceptible cultivars in a root-knot infested field, compared to the response of a tolerant cultivar.

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.

QuickBird false color satellite imagery was evaluated for distinguishing black mangrove [Avicennia germinans (L.) L.] populations on the south Texas Gulf Coast. The imagery had three bands (green, red, and near-infrared) and contained 11-bit data. Two subsets of the satellite image were extracted and used as test sites. Supervised and unsupervised image analysis techniques were used to classify the imagery. For the supervised classification of site 1, black mangrove had a producer's accuracy of 82.1% and a user's accuracy of 95.8%, whereas for the unsupervised classification, black mangrove had a producer's accuracy of 100% and a user's accuracy of 60.9%. In the supervised classification of site 2, black mangrove had a producer's accuracy of 91.7% and a user's accuracy of 100%, whereas in the unsupervised classification, black mangrove had a producer's accuracy of 100% and a user's accuracy of 85.7%. These results indicate that QuickBird imagery combined with image analysis techniques can be used successfully to distinguish and map black mangrove along the south Texas Gulf Coast.

QuickBird (2.4 in resolution) and SPOT 5 (10 in resolution) multi-spectral satellite imagery were compared for mapping the invasive grass, giant reed (Arundo donax L.), along the Rio Grande in southwest Texas. The imagery had three bands (green, red, and near-infrared). Three subsets from both the QuickBird and SPOT 5 images were extracted and used as study sites. The same subsets were extracted from both images. The images were subjected to supervised image analysis. Accuracy assessments performed on QuickBird classification maps from the three sites had producer's and user's accuracies for giant reed that ranged from 92% to 100%. Accuracy assessments performed on SPOT 5 classification maps from the three sites had producer's and user's accuracies for giant reed ranging from 75.7% to 93.3%. The lower accuracies of the SPOT 5 image classifications were attributed to its coarser resolution.

Giant reed (Arundo donax L.) occurs throughout the U.S. from California to Maryland. It is considered an invasive plant in some parts of this range but not others. To test the hypothesis that plants from different regions have similar growth characteristics, we grew plants from stem cuttings collected at two sites in Florida, one site in Texas, and two sites in California in a common garden experiment in Davis, California. Plants were grown outdoors in topsoil or a 90:10 sand:topsoil mix, in large plastic containers. All plants survived winter conditions in Davis, California, during 2004, when the minimum air temperature was -3.3 C. Stem width, number of stems per plant, number of leaves per stem, total leaf area per plant, and RGR(NSTEMS) did not differ among the provenances studied. Variegated plants had somewhat greater stem angles, indicating that the stems were more prostrate early in the growing season. Differences in stem height, number of internodes per stem, and mean internode distance were consistent with those expected from the inclusion of variegated plants in this study. Plant dry weight differed depending on plant origin and substrate type. Variegated plants weighed less regardless of the substrate. With the exception of a variegated form, plants from disparate geographic locations grew equally well under similar conditions, and no differences in growth characteristics were found that would suggest different invasive potential and impact on resident species.

Giant reed ( Arundo donax L . ) occurs throughout the U.S. from California to Maryland. It is considered an invasive plant in some parts of this range but not others. To test the hypothesis that plants from different regions have similar growth characteristics, we grew plants from stem cuttings collected at two sites in Florida, one site in Texas, and two sites in California in a common garden experiment in Davis, California. Plants were grown outdoors in topsoil or a 90:10 sand:topsoil mix, in large plastic containers. All plants survived winter conditions in Davis, California, during 2004, when the minimum air temperature was -3.3 C. Stem width, number of stems per plant, number of leaves per stem, total leaf area per plant, and RGR NSTEMS did not differ among the provenances studied. Variegated plants had somewhat greater stem angles, indicating that the stems were more prostrate early in the growing season. Differences in stem height, number of internodes per stem, and mean internode distance were consistent with those expected from the inclusion of variegated plants in this study. Plant dry weight differed depending on plant origin and substrate type. Variegated plants weighed less regardless of the substrate. With the exception of a variegated form, plants from disparate geographic locations grew equally well under similar conditions, and no differences in growth characteristics were found that would suggest different invasive potential and impact on resident species.

Vegetation indices (VIs) derived from remotely sensed imagery are commonly used to estimate crop yields. Spectral angle mapper (SAM) provides an alternative approach to quantifying the spectral differences among all pixels in an image and therefore has the potential for mapping yield variability. The objective of this study was to apply the SAM technique to airborne hyperspectral imagery for mapping yield variability. Airborne hyperspectral imagery was acquired from two grain sorghum fields in south Texas, and yield data were collected using a grain yield monitor SAM images were generated from the hyperspectral images based on six reference spectra extracted directly from the hyperspectral images and four reflectance spectra measured on the ground. Statistical analysis showed that the ten SAM images for each field produced similar correlation coefficients with yield. For comparison, all 5151 possible narrow-band normalized difference vegetation indices (NDVIs) were derived from the 102-band images and related to yield. Results showed that the SAM images based on the soil reference spectra provided higher correlation coefficients with yield than 75% and 92% of the 5151 narrow-band NDVIs for fields 1 and 2, respectively. Like an NDVI image, a SAM image can be easily generated from a hyperspectral image to characterize the spatial variability in yield. Moreover, since the best NDVI typically varies with yield datasets, a SAM image based on a single reference spectrum can be a better representation of yield variability if actual yield data are not available for the identification of the best NDVI. The results from this study indicate that the SAM technique can be used alone or in conjunction with other VIs for yield estimation from hyperspectral imagery.

QuickBird multispectral satellite imagery was evaluated for distinguishing giant salvinia (Salvinia molesta Mitchell) in a large reservoir in east Texas. The imagery had four bands (blue, green, red, and near-infrared) and contained 11-bit data. Color-infrared (green, red, and near-infrared bands), normal color (blue, green and red bands), and four-band composite (blue, green, red, and near-infrared bands) images were studied. Unsupervised image analysis was used to classify the imagery. Accuracy assessments performed on the classification maps of the three composite images had producer's and user's accuracies for giant salvinia ranging from 87.8 to 93.5%. Color-infrared, normal color, and four-band satellite imagery were excellent for distinguishing giant salvinia in a complex field habitat.

This paper describes a six-camera multispectral digital video imaging system designed for natural resource assessment and shows its potential as a research tool. It has five visible to near-infrared light sensitive cameras, one near-infrared to mid-infrared light sensitive camera, a monitor, a computer with a multichannel digitizing board, a keyboard, a power distributor, an amplifier, and a mouse. Each camera is fitted with a narrowband interference filter, allowing the system to obtain imagery in the blue (447 – 455 nm), green (555 – 565 nm), red (625 – 635 nm), red edge (704 – 716 nm), near-infrared (814–826 nm), and mid-infrared (1631 – 1676 nm) regions of the electromagnetic spectrum. Analogue video acquired by this system is converted to digital format. Radiometric resolution of the imagery is 8-bit (pixel values range 0 – 255). Images obtained by the system can be evaluated individually and/or in combination with each other to assess natural resources.

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.

A study as conducted on the south Texas Gulf Coast to evaluate color-infrared (CIR) aerial photography and CIR true digital imagery combined with unsupervised image analysis techniques to distinguish and map black mangrove [Auticennia germinans (L.) L.] populations. Accuracy assessments performed on computer-classified maps of photographic and digital images of the same study site had both producer's and user's accuracies of 100% for black mangrove. An accuracy assessment performed on a computer-classified map of a digital image only of a second study site had a producer's accuracy of 78.6% and a user's accuracy of 100%. These results indicate that CIR photography and digital imagery combined with image analysis techniques can be used successfully to distinguish and quantify the extent of black mangrove along the south Texas Gulf Coast.