Spectral Reflectance and Digital Image Relations Among Five Aquatic Weeds

This study reports on the use of an artificial quartz halogen lighting source to facilitate the acquisition of spectral light reflectance measurements and digital imaging of invasive aquatic weeds. Spectral leaf or leaf/stem reflectance measurements were made on five aquatic weeds: Eurasian watermilfoil (Myriophyllum spicatum L.), hydrilla [Hydrilla verticillata (L. F.) Royle], parrotfeather [Myriophyllum aquaticum (Vall.), waterhyacinth [Eichhornia crassipes (Mart.) Solms], and waterlettuce (Pistia stratiotes L.). Reflectance measurements were studied at five wavelengths of the electromagnetic spectrum: 450 nm (visible blue), 550 nm (visible green), 650 nm (visible red), 680 nm (visible red edge), and 850 nm (near-infrared). Reflectance values differed significantly (P= 0.05) among the species at all five wavelengths. However, more distinct separations among species occurred at the 550 nm, 650 nm, 680 nm, and 850 nm wavelengths. Reflectance differences among species were attributed to variable foliage coloration and vegetative density. Close range conventional color and color-infrared digital images of leaves or leaves/stems of the five species showed they differed in image tonal response. Reflectance measurements were related to the image tonal response of the plant species on both types of imagery. Supervised image classifications performed on both conventional color and color-infrared images showed the computer generally did an adequate job in identifying the image tonal responses of the weed species

Salvia Miltiorrhiza Ameliorates Liver Fibrosis by Activating Hepatic Natural Killer Cells in Vivo and in Vitro

Natural killer (NK) cells are known for their ability to kill activated hepatic stellate cells (HSCs), which has been confirmed both in patients and animal models. But the killing function is depressed in period of advanced liver injury. Salvia Miltiorrhiza (SM), a Chinese herbal medicine for invigorating blood circulation and eliminating stasis, is widely used to treat liver fibrosis in clinic. Nevertheless, the immunological mechanism remains unclearly. Here, we put forward the hypothesis that the anti-fibrotic effect of SM is concerned with boosting the activation of hepatic NK cells. Liver fibrosis was induced with carbon tetrachloride (CCl4) and effects of SM on NK cells and HSC (JS-1 cell line, HSC) were investigated in vivo and in vitro. Hepatic NK cells were isolated from C57BL/6 mice, and pre-incubated with SM before they were co-cultured with HSCs. We found that SM increased frequency of NK cells, enhanced activities of NKG2D and Nkp46 on NK cells and inhibited activation of HSCs in vivo and in vitro. SM could promote the activities of NK cells by increasing the expressions of NKG2D and IFN-γ before or after co-cultured with HSCs in vitro. Besides, SM could partially antagonize ASGM-1-induced NK cell depletion and enhance the cell activities to inhibit HSCs activation in vitro. Therefore, our work provided a new insight into the anti-fibrotic mechanism that SM could enhance the activities of NK cell to reduce liver fibrosis in vivo and in vitro

Features of the Three Dimensional Structure in the Pacific Sub-surface Layer in Summer

The anomaly of the summer sea temperature is analyzed by a spatial-temporal synthetically rotated orthogonal function (REOF) at three different depths (0 m, 40 m, and 120 m) over the area 110°E~100°W and 30°S~60°N. The spatial-temporal distribution shows that the “signal” of annual anomaly is stronger in the sub-surface layer than the surface layer, and it is stronger in the eastern equatorial Pacific than in the western area. The spatial structure of the sea temperature anomaly at different layers is related to both the ocean current and the interaction of ocean and atmosphere. The temporal changing trend of the sub-surface sea temperature in different areas shows that the annual mean sea temperature increases and the annual variability evidently increases from the 1980s, and these keep the same trend with the increasing El Nino phenomenon very well

Spectrally derived values of community leaf dry matter content link shifts in grassland composition with change in biomass production

Leaf traits link environmental effects on plant species abundances to changes in ecosystem processes but are a challenge to measure regularly and over large areas. We used measurements of canopy reflectance from grassland communities to derive a regression model for one leaf trait, leaf dry matter content (LDMC). Partial least squares regression (PLSR) analysis was used to model community‐weighted (species abundance‐weighted) values of LDMC as a function of canopy reflectance in visible and near‐infrared (NIR) wavebands. The PLSR model then was applied to airborne measurements of canopy reflectance to determine how community LDMC interacts with inter‐annual variation in precipitation to influence the normalized difference vegetation index (NDVI), a surrogate of aboveground biomass production, of restored grassland during spring over 4 years. LDMC was well‐described by a PLSR model that included reflectance measurements located primarily in red edge and NIR portions of the spectrum. Community LDMC decreased as annual forb species became more abundant and was negatively correlated with maximum values of NDVI. Decreased precipitation reduced NDVI (biomass production) both by increasing community LDMC (LDMC response) and reducing the slope of the NDVI‐LDMC relationship (LDMC effect on NDVI). We find that grassland LDMC is well‐described by a regression model using canopy reflectance in red edge and NIR wavebands. Our results demonstrate the utility of spectral estimates of LDMC for discerning shifts in grassland composition and predicting consequences for production‐related ecosystem functions

Spectral Heterogeneity Predicts Local-Scale Gamma and Beta Diversity of Mesic Grasslands

Plant species diversity is an important metric of ecosystem functioning, but field assessments of diversity are constrained in number and spatial extent by labor and other expenses. We tested the utility of using spatial heterogeneity in the remotely-sensed reflectance spectrum of grassland canopies to model both spatial turnover in species composition and abundances (β diversity) and species diversity at aggregate spatial scales (γ diversity). Shannon indices of γ and β diversity were calculated from field measurements of the number and relative abundances of plant species at each of two spatial grains (0.45 m2 and 35.2 m2) in mesic grasslands in central Texas, USA. Spectral signatures of reflected radiation at each grain were measured from ground-level or an unmanned aerial vehicle (UAV). Partial least squares regression (PLSR) models explained 59–85% of variance in γ diversity and 68–79% of variance in β diversity using spatial heterogeneity in canopy optical properties. Variation in both γ and β diversity were associated most strongly with heterogeneity in reflectance in blue (350–370 nm), red (660–770 nm), and near infrared (810–1050 nm) wavebands. Modeled diversity was more sensitive by a factor of three to a given level of spectral heterogeneity when derived from data collected at the small than larger spatial grain. As estimated from calibrated PLSR models, β diversity was greater, but γ diversity was smaller for restored grassland on a lowland clay than upland silty clay soil. Both γ and β diversity of grassland can be modeled by using spatial heterogeneity in vegetation optical properties provided that the grain of reflectance measurements is conserved

GIS-based volunteer cotton habitat prediction and plant-level detection with UAV remote sensing

Volunteer cotton plants germinate and grow at unwanted locations like transport routes and can serve as hosts for a harmful cotton pests called cotton boll weevils. The main objective of this study was to develop a geographic information system (GIS) framework to efficiently locate volunteer cotton plants in the cotton production regions in southern Texas, thus reducing time and economic cost for their removal. A GIS network analysis tool was applied to estimate the most likely routes for cotton transportation, and a GIS model was created to identify and visualize potential areas of volunteer cotton growth. The GIS model indicated that, of the 31 counties in southern Texas that may have habitat for volunteer cotton, Hidalgo, Cameron, Nueces, and San Patricio are the counties at the greatest risk. Moreover, a method based on unmanned aerial vehicle (UAV) remote sensing was proposed to detect the precise locations of volunteer cotton plants in potential areas for their subsequent removal. In this study, a UAV was used to scan limited samples of potential volunteer cotton growth areas identified with the GIS model. The results indicated that UAV remote sensing coupled with the proposed image analysis methods could accurately identify the precise locations of volunteer cotton and could potentially assist in the elimination of volunteer cotton along transport routes

Registration for Optical Multimodal Remote Sensing Images Based on FAST Detection,Window Selection, and Histogram Specification

In recent years, digital frame cameras have been increasingly used for remote sensing applications. However, it is always a challenge to align or register images captured with different cameras or different imaging sensor units. In this research, a novel registration method was proposed. Coarse registration was first applied to approximately align the sensed and reference images. Window selection was then used to reduce the search space and a histogram specification was applied to optimize the grayscale similarity between the images. After comparisons with other commonly-used detectors, the fast corner detector, FAST (Features from Accelerated Segment Test), was selected to extract the feature points. The matching point pairs were then detected between the images, the outliers were eliminated, and geometric transformation was performed. The appropriate window size was searched and set to one-tenth of the image width. The images that were acquired by a two-camera system, a camera with five imaging sensors, and a camera with replaceable filters mounted on a manned aircraft, an unmanned aerial vehicle, and a ground-based platform, respectively, were used to evaluate the performance of the proposed method. The image analysis results showed that, through the appropriate window selection and histogram specification, the number of correctly matched point pairs had increased by 11.30 times, and that the correct matching rate had increased by 36%, compared with the results based on FAST alone. The root mean square error (RMSE) in the x and y directions was generally within 0.5 pixels. In comparison with the binary robust invariant scalable keypoints (BRISK), curvature scale space (CSS), Harris, speed up robust features (SURF), and commercial software ERDAS and ENVI, this method resulted in larger numbers of correct matching pairs and smaller, more consistent RMSE. Furthermore, it was not necessary to choose any tie control points manually before registration. The results from this study indicate that the proposed method can be effective for registering optical multimodal remote sensing images that have been captured with different imaging sensors

Crop Classification and LAI Estimation Using Original and Resolution-Reduced Images from Two Consumer-Grade Cameras

Consumer-grade cameras are being increasingly used for remote sensing applications in recent years. However, the performance of this type of cameras has not been systematically tested and well documented in the literature. The objective of this research was to evaluate the performance of original and resolution-reduced images taken from two consumer-grade cameras, a RGB camera and a modified near-infrared (NIR) camera, for crop identification and leaf area index (LAI) estimation. Airborne RGB and NIR images taken over a 6.5-square-km cropping area were mosaicked and aligned to create a four-band mosaic with a spatial resolution of 0.4 m. The spatial resolution of the mosaic was then reduced to 1, 2, 4, 10, 15 and 30 m for comparison. Six supervised classifiers were applied to the RGB images and the four-band images for crop identification, and 10 vegetation indices (VIs) derived from the images were related to ground-measured LAI. Accuracy assessment showed that maximum likelihood applied to the 0.4-m images achieved an overall accuracy of 83.3% for the RGB image and 90.4% for the four-band image. Regression analysis showed that the 10 VIs explained 58.7% to 83.1% of the variability in LAI. Moreover, spatial resolutions at 0.4, 1, 2 and 4 m achieved better classification results for both crop identification and LAI prediction than the coarser spatial resolutions at 10, 15 and 30 m. The results from this study indicate that imagery from consumer-grade cameras can be a useful data source for crop identification and canopy cover estimation

The chinese herbal decoction danggui buxue tang inhibits angiogenesis in a rat model of liver fibrosis.

In this study, we investigated the anti-angiogenic effect of the Chinese herbal decoction Danggui Buxue Tang (DBT; Radix Astragali and Radix Angelicae sinensis in 5 : 1 ratio) in a rat model of liver fibrosis, in order to elucidate its mechanisms of action against liver fibrosis. Liver fibrosis was induced with CCl(4) and high-fat food for 6 weeks, and the rats were treated with oral doses of DBT (6 g raw herbs/kg/d) and N-Acetyl-L-cysteine (NAC; 0.1 g/kg/d). The results showed that both DBT and NAC attenuated liver fibrosis and neo-angiogenesis. Furthermore, DBT and NAC improved SOD activity but decreased MDA content and 8-OH-dG in fibrotic livers, with DBT being more effective than NAC. DBT decreased the expression of VEGF, Ang1 and TGF-β1 and their signaling mediators, whereas NAC had no effect on VEGF and VEGFR2 expression. Both DBT and NAC reduced HIF-1α gene and protein expression in fibrotic livers, with DBT being more effective. These data clearly demonstrate that the anti-fibrotic properties of DBT are related to its ability to inhibit angiogenesis and its anti-angiogenic mechanisms are associated with improving oxidative stress, regulating the expression and signaling of angiogenic factors, and especially modulating HIF-1α in fibrotic livers

Crop Classification and LAI Estimation Using Original and Resolution-Reduced Images from Two Consumer-Grade Cameras

Consumer-grade cameras are being increasingly used for remote sensing applications in recent years. However, the performance of this type of cameras has not been systematically tested and well documented in the literature. The objective of this research was to evaluate the performance of original and resolution-reduced images taken from two consumer-grade cameras, a RGB camera and a modified near-infrared (NIR) camera, for crop identification and leaf area index (LAI) estimation. Airborne RGB and NIR images taken over a 6.5-square-km cropping area were mosaicked and aligned to create a four-band mosaic with a spatial resolution of 0.4 m. The spatial resolution of the mosaic was then reduced to 1, 2, 4, 10, 15 and 30 m for comparison. Six supervised classifiers were applied to the RGB images and the four-band images for crop identification, and 10 vegetation indices (VIs) derived from the images were related to ground-measured LAI. Accuracy assessment showed that maximum likelihood applied to the 0.4-m images achieved an overall accuracy of 83.3% for the RGB image and 90.4% for the four-band image. Regression analysis showed that the 10 VIs explained 58.7% to 83.1% of the variability in LAI. Moreover, spatial resolutions at 0.4, 1, 2 and 4 m achieved better classification results for both crop identification and LAI prediction than the coarser spatial resolutions at 10, 15 and 30 m. The results from this study indicate that imagery from consumer-grade cameras can be a useful data source for crop identification and canopy cover estimation