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Reflectance data were subjected to a variety of analysis methods to determine the utility of hyperspectral reflectance for differentiating soybean, soil, and six weed species commonly found in Mississippi agricultural fields. Weed species evaluated were hemp sesbania, palmleaf morningglory, pitted morningglory, prickly sida, sicklepod, and smallflower morningglory. Hyperspectral reflectance data were collected from mature plant leaves three times in 2002 and two times in 2003. Vegetation indices were calculated and subjected to principal component analysis (PCA) and linear discriminant analysis (LDA). The PCA, using vegetation indices, produced the poorest classification accuracies for the plant species studied, generally less than 50%, whereas LDA resulted in classification accuracies greater than those from PCA. Best spectral band combination (BSBC) provided the greatest classification accuracies, with all better than 80% for all data sets. The BSBC indicated three wavelength bands of interest for species discrimination in the short wavelength infrared portion of the electromagnetic spectrum, which are not commonly used in current vegetation indices for species differentiation. These areas of interest were located from 1,445 to 1,475 nm, 2,030 to 2,090 nm, and 2,115 to 2,135 nm. The top 10 wavelengths determined by BSBC were then added to the vegetation indices and reanalyzed using PCA and LDA. Classification accuracies increased for all species when these wavelengths were added rather than using vegetation indices alone, suggesting greater crop and weed species differentiation can be obtained when using sensors that include these wavelength regions of the short wavelength infrared portion of the electromagnetic spectrum.
A hand-held hyperspectral radiometer was used to measure differences in reflectance characteristics of 24 Palmer amaranth and 15 pitted morningglory accessions collected from the central and southern United States. A hyperspectral reflectance reading was collected from two mature leaves at 24 and 27 d after emergence (DAE) for each accession. Two analysis techniques, linear discriminant analysis and best spectral-band combination (BSBC) analysis, were performed using various vegetation indices, spectral bands, and individual wavelengths. Differentiation of individual accessions was difficult. Palmer amaranth accession classification accuracies were < 50% using both analysis techniques, except one accession collected in South Carolina (63%), when pooled over data acquisition dates. Pitted morningglory accession classification accuracies were also generally < 50%. Classification accuracies were higher using BSBC analysis at 24 DAE; however, at 27 DAE only one accession resulted in classification accuracy > 30%. These results suggest there are only slight reflectance differences within Palmer amaranth and pitted morningglory accessions. These differences may not be predictable based upon accession origin because of the genetic diversity of Palmer amaranth and pitted morningglory. However, differentiation between Palmer amaranth and pitted morningglory was 100%. Thus, spectral sensors used to differentiate between Palmer amaranth and pitted morningglory do not need to be calibrated for a particular region of the United States, and differentiation between these two species can be made using reflectance characteristics.
Field experiments were conducted in southwestern Oklahoma near Colony in 2000 and near Ft. Cobb in 2001 to quantify the effect of time of removal of a natural population of crownbeard on peanut yield. Weed densities and dry weed weights were measured at eight weed-removal times, and in-shell peanut yields were determined at harvest. Crownbeard was removed at 0 (the weed-free check), 4, 6, 8, 10, 12, 14, and 16 wk (full season) after crop emergence (WAE). Weed density was a poor predictor for dry weed weight and peanut yield; however, dry weed weight and time of removal were good predictors for peanut yield. Weed growth was minimal up to 4 WAE and increased linearly after that time. For each week of weed growth, a 0.52 kg/plot increase in dry weed weight was measured. Peanut yield decreased linearly because of crownbeard competition. For each kilogram per plot increase in dry weed weight, a 129 kg/ha or 5.1% peanut yield reduction took place. For each week of weed interference, a 75 kg/ha or 2.8% peanut yield reduction occurred. Crownbeard removal by or before 4 WAE will minimize losses in peanut yield because of interference.
In North America, terrestrial records of biodiversity and climate change that span Marine Oxygen Isotope Stage (MIS) 5 are rare. Where found, they provide insight into how the coupling of the ocean–atmosphere system is manifested in biotic and environmental records and how the biosphere responds to climate change. In 2010–2011, construction at Ziegler Reservoir near Snowmass Village, Colorado (USA) revealed a nearly continuous, lacustrine/wetland sedimentary sequence that preserved evidence of past plant communities between ~140 and 55 ka, including all of MIS 5. At an elevation of 2705 m, the Ziegler Reservoir fossil site also contained thousands of well-preserved bones of late Pleistocene megafauna, including mastodons, mammoths, ground sloths, horses, camels, deer, bison, black bear, coyotes, and bighorn sheep. In addition, the site contained more than 26,000 bones from at least 30 species of small animals including salamanders, otters, muskrats, minks, rabbits, beavers, frogs, lizards, snakes, fish, and birds. The combination of macro- and micro-vertebrates, invertebrates, terrestrial and aquatic plant macrofossils, a detailed pollen record, and a robust, directly dated stratigraphic framework shows that high-elevation ecosystems in the Rocky Mountains of Colorado are climatically sensitive and varied dramatically throughout MIS 5.
An experiment was conducted to determine the utility of multispectral imagery for identifying soybean, bare soil, and six weed species commonly found in Mississippi. Weed species evaluated were hemp sesbania, palmleaf morningglory, pitted morningglory, prickly sida, sicklepod, and smallflower morningglory. Multispectral imagery was analyzed using supervised classification techniques based upon 2-class, 3-class, and 8-class systems. The 2-class system was designed to differentiate bare soil and vegetation. The 3-class system was used to differentiate bare soil, soybean, and weed species. Finally, the 8-class system was designed to differentiate bare soil, soybean, and all weed species independently. Soybean classification accuracies classified as vegetation for the 2-class system were greater than 95%, and bare soil classification accuracies were greater than 90%. In the 3-class system, soybean classification accuracies were 70% or greater. Classification of soybean decreased slightly in the 3-class system when compared to the 2-class system because of the 3-class system separating soybean plots from the weed plots, which was not done in the 2-class system. Weed classification accuracies increased as weed density or weeks after emergence (WAE) increased. The greatest weed classification accuracies were obtained once weed species were allowed to grow for 10 wk. Palmleaf morningglory and pitted morningglory classification accuracies were greater than 90% for 10 WAE using the 3-class system. Palmleaf morningglory and pitted morningglory at the highest densities of 6 plants/m2 produced the highest classification accuracies for the 8-class system once allowed to grow for 10 wk. All other weed species generally produced classification accuracies less than 50%, regardless of planting density. Thus, multispectral imagery has the potential for weed detection, especially when being used in a management system when individual weed species differentiation is not essential, as in the 2-class or 3-class system. However, weed detection was not obtained until 8 to 10 WAE, which is unacceptable in production agriculture. Therefore, more refined imagery acquisition with higher spatial and/or spectral resolution and more sophisticated analyses need to be further explored for this technology to be used early-season when it would be most valuable.
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