Classification of Vegetation and Analysis of its Recent Trends at Camp Williams, Utah Using Remote Sensing and Geographic Information System Techniques

Current vegetation classes were generated from remotely sensed data to provide coarse-level information for an ecosystem management plan developed at Camp Williams, Utah. Vegetation trend from 1973 - 1993 was also examined via satellite imagery. The data set consisted of Landsat Multispectral Scanner (MSS) and Thematic Mapper (TM) images from July or August of 1973, 1975, 1980, 1988, and 1993. Two approaches were used to detect vegetation change. The first approach determined overall and cover type trend from standard digital image differencing of soil-adjusted vegetation index (SAVI) images. The second approach used an unsupervised classification of a composite SAVI image of all dates. The first approach defined areas of increase, decrease, and no significant change in SAVI and differences in trend for tree versus shrub cover types. The second approach resulted in an ecological classification that defined new environmental patterns based on vegetation trend

Remote Sensing Of Rice-Based Irrigated Agriculture: A Review

The ‘Green Revolution’ in rice farming of the late 1960’s denotes the beginning of the extensive breeding programs that have led to the many improved rice varieties that are now planted on more than 60% of the world’s riceland (Khush, 1987). This revolution led to increases in yield potential of 2 to 3 times that of traditional varieties (Khush, 1987). Similar trends have also been seen in the Irrigation Areas and Districts of southern New South Wales (NSW) as the local breeding program has produced many improved varieties of rice adapted to local growing conditions since the 1960’s (Brennan et al., 1994). Increases in area of rice planted, rice quality, and paddy yield resulted (Brennan et al., 1994). Increased rice area, however, has led to the development of high water tables and risk of large tracts of land becoming salt-affected in southern NSW (Humphreys et al., 1994b). These concerns have led to various environmental regulations on rice in the region, culminating in 1994 when restrictions on rice area, soil suitability, and water consumption were fully enacted (Humphreys et al., 1994b). Strict environmental restrictions in combination with large areas of land make the management of this region a difficult task. Land managers require, among other things, a way of regulating water use, assessing or predicting crop area and productivity, and making management decisions in support of environmentally and economically sustainable agriculture. In the search for more time and cost effective methods for attaining these goals, while monitoring complex management situations, many have turned to remote sensing and Geographic Information System (GIS) technologies for assistance. The spectral information and spatial density of remote sensing data lends itself well to the measurement of large areas. Since the launch of LANDSAT-1 in 1972, this technology has been used extensively in agricultural systems for crop identification and area estimation, crop yield estimation and prediction, and crop damage assessment. The incorporation of remote sensing and GIS can also help integrate management practices and develop effective management plans. However, in order to take advantage of these tools, users must have an understanding of both what remote sensing is and what sensors are now available, and how the technology is being used in applied agricultural research. Accordingly, a description of both follows: first a description of the technology, and then how it is currently being applied. The applications of remote sensing relevant to this discussion can be separated into crop type identification; crop area measurement; crop yield; crop damage; water use/ moisture availability (ma) mapping; and water use efficiency monitoring/mapping. This report focuses on satellite remote sensing for broad-scale rice-based irrigation agricultural applications. It also discusses related regional GIS analyses that may or may not include remote sensing data, and briefly addresses other sources of finer-scale remote sensing and geospatial data as they relate to agriculture. Since a complete review of the remote sensing research was not provided in the rice literature alone, some generic agricultural issues have been learned from applications not specifically dealing with rice. Remote sensing specialists may wish to skip to section 2

Assessing and Improving Positional Accuracy and its Effects on Areal Estimation at Coleambally Irrigation Area

If management decisions are made with geospatial data that have not been assessed for positional accuracy, then debate about both methodologies of measurement and management decisions can occur. This debate, in part, can be avoided by assessing the positional accuracy of geospatial data, leading to increased confidence (decreased uncertainty) in both the data and the decisions made from the data. In this study, we assessed the positional accuracy of two Geographic Information System (GIS) baseline datasets at the Coleambally Irrigation Area (CIA); high-resolution digital aerial photography acquired in January 2000, and the Digital Topographic Data Base (DTDB) roads data. We also assessed areal error of paddock measurements from an improved accuracy version of the high-resolution digital aerial photography. Positional accuracies were assessed by comparing well-defined features from both baseline datasets (original aerial photography and DTDB roads) to high-level accuracy Differential Global Positioning System (DGPS) data for the same features. This assessment showed that neither baseline dataset met the National Mapping Council of Australia’s standards of map accuracy. Consequently, we processed the original digital photography to create an improved dataset, which was over 2.5 times more accurate than the original photography, and over 4 times more accurate than the DTDB data. The improved dataset also met the map accuracy standard for Australia. We also assessed areal error by comparing paddock boundaries delineated from the improved dataset to those delineated from a DGPS associated with paddock soil surveys. The 90% confidence interval measured from the improved data for any individual paddock is approximately at the ± 5% target error set by Coleambally Irrigation Limited (CIL). The 95% confidence interval is roughly ± 6%. Overall areal error of multiple paddocks is much lower than the individual case with the 95% confidence interval for 2 paddocks being from about ± 4% error reducing to less than ± 2% for 8 or more paddocks. Knowledge of both positional and areal accuracies of the improved high-resolution digital aerial photography provides a means to more effectively manage environmental compliance of rice farmers at CIA and gives the CIL justification for making management decisions from this spatial data

Assessing the accuracy of blending Landsat-MODIS surface reflectances in two landscapes with contrasting spatial and temporal dynamics: A framework for algorithm selection

Blending algorithms model land cover change by using highly resolved spatial data from one sensor and highly resolved temporal data from another. Because the data are not usually observed concurrently, unaccounted spatial and temporal variances cause error in blending algorithms, yet, to date, there has been no definitive assessment of algorithm performance against spatial and temporal variances. Our objectives were to: (i) evaluate the accuracy of two advanced blending algorithms (STARFM and ESTARFM) and two simple benchmarking algorithms in two landscapes with contrasting spatial and temporal variances; and (ii) synthesise the spatial and temporal conditions under which the algorithms performed best. Landsat-like images were simulated on 27 dates in total using the nearest temporal cloud-free Landsat-MODIS pairs to the simulation date, one before and one after. RMSD, bias, and r2 estimates between simulated and observed Landsat images were calculated, and overall variance of Landsat and MODIS datasets were partitioned into spatial and temporal components. Assessment was performed over the whole study site, and for specific land covers. Results addressing objective (i) were that: ESTARFM did not always produce lower errors than STARFM; STARFM and ESTARFM did not always produce lower errors than simple benchmarking algorithms; and land cover spatial and temporal variances were strongly associated with algorithm performance. Results addressing objective (ii) indicated ESTARFM was superior where/when spatial variance was dominant; and STARFM was superior where/when temporal variance was dominant. We proposed a framework for selecting blending algorithms based on partitioning variance into the spatial and temporal components and suggested that comparing Landsat and MODIS spatial and temporal variances was a practical method to determine if, and when, MODIS could add value for blending

Characterization of early ultrastructural changes in the cerebral white matter of CADASIL small vessel disease using high pressure freezing/freeze-substitution

AIMS: The objective of this study was to elucidate the early white matter changes in CADASIL small vessel disease. METHODS: We used high pressure freezing and freeze substitution (HPF/FS) in combination with high resolution electron microscopy (EM), immunohistochemistry and confocal microscopy of brain specimens from control and CADASIL (TgNotch3R169C ) mice aged 4 to 15 months to study white matter lesions in the corpus callosum. RESULTS: We first optimized the HPF/FS protocol in which samples were chemically prefixed, frozen in a sample carrier filled with 20% polyvinylpyrrolidone and freeze-substituted in a cocktail of tannic acid, osmium tetroxide and uranyl acetate dissolved in acetone. EM analysis showed that CADASIL mice exhibit significant splitting of myelin layers and enlargement of the inner tongue of small calibre axons from the age of 6 months, then vesiculation of the inner tongue and myelin sheath thinning at 15 months of age. Immunohistochemistry revealed an increased number of oligodendrocyte precursor cells, although only in older mice, but no reduction in the number of mature oligodendrocytes at any age. The number of Iba1 positive microglial cells was increased in older but not in younger CADASIL mice, but the number of activated microglial cells (Iba1 and CD68 positive) was unchanged at any age. CONCLUSION: We conclude that early WM lesions in CADASIL affect first and foremost the myelin sheath and the inner tongue, suggestive of a primary myelin injury. We propose that those defects are consistent with a hypoxic/ischaemic mechanism

Blending Landsat and MODIS Data to Generate Multispectral Indices: A Comparison of “Index-then-Blend” and “Blend-then-Index” Approaches

The objective of this paper was to evaluate the accuracy of two advanced blending algorithms, Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) and Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to downscale Moderate Resolution Imaging Spectroradiometer (MODIS) indices to the spatial resolution of Landsat. We tested two approaches: (i) "Index-then-Blend" (IB); and (ii) "Blend-then-Index" (BI) when simulating nine indices, which are widely used for vegetation studies, environmental moisture assessment and standing water identification. Landsat-like indices, generated using both IB and BI, were simulated on 45 dates in total from three sites. The outputs were then compared with indices calculated from observed Landsat data and pixel-to-pixel accuracy of each simulation was assessed by calculating the: (i) bias; (ii) R; and (iii) Root Mean Square Deviation (RMSD). The IB approach produced higher accuracies than the BI approach for both blending algorithms for all nine indices at all three sites. We also found that the relative performance of the STARFM and ESTARFM algorithms depended on the spatial and temporal variances of the Landsat-MODIS input indices. Our study suggests that the IB approach should be implemented for blending of environmental indices, as it was: (i) less computationally expensive due to blending single indices rather than multiple bands; (ii) more accurate due to less error propagation; and (iii) less sensitive to the choice of algorithm

Distinct RNA profiles in subpopulations of extracellular vesicles: apoptotic bodies, microvesicles and exosomes

Introduction: In recent years, there has been an exponential increase in the number of studies aiming to understand the biology of exosomes, as well as other extracellular vesicles. However, classification of membrane vesicles and the appropriate protocols for their isolation are still under intense discussion and investigation. When isolating vesicles, it is crucial to use systems that are able to separate them, to avoid cross-contamination. Method: EVs released from three different kinds of cell lines: HMC-1, TF-1 and BV-2 were isolated using two centrifugation-based protocols. In protocol 1, apoptotic bodies were collected at 2,000×g, followed by filtering the supernatant through 0.8 µm pores and pelleting of microvesicles at 12,200×g. In protocol 2, apoptotic bodies and microvesicles were collected together at 16,500×g, followed by filtering of the supernatant through 0.2 µm pores and pelleting of exosomes at 120,000×g. Extracellular vesicles were analyzed by transmission electron microscopy, flow cytometry and the RNA profiles were investigated using a Bioanalyzer®. Results: RNA profiles showed that ribosomal RNA was primary detectable in apoptotic bodies and smaller RNAs without prominent ribosomal RNA peaks in exosomes. In contrast, microvesicles contained little or no RNA except for microvesicles collected from TF-1 cell cultures. The different vesicle pellets showed highly different distribution of size, shape and electron density with typical apoptotic body, microvesicle and exosome characteristics when analyzed by transmission electron microscopy. Flow cytometry revealed the presence of CD63 and CD81 in all vesicles investigated, as well as CD9 except in the TF-1-derived vesicles, as these cells do not express CD9. Conclusions: Our results demonstrate that centrifugation-based protocols are simple and fast systems to distinguish subpopulations of extracellular vesicles. Different vesicles show different RNA profiles and morphological characteristics, but they are indistinguishable using CD63-coated beads for flow cytometry analysis

Down-Regulation of miR-92 in Human Plasma Is a Novel Marker for Acute Leukemia Patients

BACKGROUND: MicroRNAs are a family of 19- to 25-nucleotides noncoding small RNAs that primarily function as gene regulators. Aberrant microRNA expression has been described for several human malignancies, and this new class of small regulatory RNAs has both oncogenic and tumor suppressor functions. Despite this knowledge, there is little information regarding microRNAs in plasma especially because microRNAs in plasma, if exist, were thought to be digested by RNase. Recent studies, however, have revealed that microRNAs exist and escape digestion in plasma. METHODOLOGY/PRINCIPAL FINDINGS: We performed microRNA microaray to obtain insight into microRNA deregulation in the plasma of a leukemia patient. We have revealed that microRNA-638 (miR-638) is stably present in human plasmas, and microRNA-92a (miR-92a) dramatically decreased in the plasmas of acute leukemia patients. Especially, the ratio of miR-92a/miR-638 in plasma was very useful for distinguishing leukemia patients from healthy body. CONCLUSIONS/SIGNIFICANCE: The ratio of miR-92a/miR-638 in plasma has strong potential for clinical application as a novel biomarker for detection of leukemia

Rab4A organizes endosomal domains for sorting cargo to lysosome-related organelles

Sorting endosomes (SEs) are the regulatory hubs for sorting cargo to multiple organelles, including lysosome-related organelles such as melanosomes in melanocytes. In parallel, melanosome biogenesis is initiated from SEs with the processing and sequential transport of melanocyte-specific proteins toward maturing melanosomes. However, the mechanism of cargo segregation on SEs is largely unknown. RNAi screening in melanocytes revealed that knockdown of Rab4A results in defective melanosome maturation. Rab4A-depletion increases vacuolar endosomes and disturbs the cargo sorting, which in turn lead to the mislocalization of melanosomal proteins to lysosomes, cell surface and exosomes. Rab4A localizes to the SEs and forms endosomal complex with AP-3 adaptor, Rabenosyn-5 effector and KIF3 motor, which possibly coordinates cargo segregation on SEs. Consistently, inactivation of Rabenosyn-5 or KIF3A/B phenocopied the defects observed in Rab4A-knockdown melanocytes. Further, Rabenosyn-5 associates with Rabaptin-5 or Rabip4/4' and differentially regulate cargo sorting from SEs. Thus, Rab4A acts a key regulator of cargo segregation on SEs