Browsing by Author "Shrestha, Rupesh"

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    Estimating biophysical parameters of individual trees in an urban environment using small footprint discrete-return imaging lidar
    Shrestha, Rupesh; Wynne, Randolph H. (MDPI, 2012-02-15)
    Quantification of biophysical parameters of urban trees is important for urban planning, and for assessing carbon sequestration and ecosystem services. Airborne lidar has been used extensively in recent years to estimate biophysical parameters of trees in forested ecosystems. However, similar studies are largely lacking for individual trees in urban landscapes. Prediction models to estimate biophysical parameters such as height, crown area, diameter at breast height, and biomass for over two thousand individual trees were developed using best subsets multiple linear regression for a study area in central Oklahoma, USA using point cloud distributional metrics from an Optech ALTM 2050 lidar system. A high level of accuracy was attained for estimating individual tree height (R2 = 0.89), dbh (R2 = 0.82), crown diameter (R2 = 0.90), and biomass (R2 = 0.67) using lidar-based metrics for pooled data of all tree species. More variance was explained in species-specific estimates of biomass (R2 = 0.68 for Juniperus virginiana to 0.84 for Ulmus parviflora) than in estimates from broadleaf deciduous (R2 = 0.63) and coniferous (R2 = 0.45) taxonomic groups-or the data set analysed as a whole (R2 = 0.67). The metric crown area performed particularly well for most of the species-specific biomass equations, which suggests that tree crowns should be delineated accurately, whether manually or using automatic individual tree detection algorithms, to obtain a good estimation of biomass using lidar-based metrics.
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    Inventorying trees in an urban landscape using small-footprint discrete return imaging lidar
    Shrestha, Rupesh (Virginia Tech, 2011-03-28)
    Automation of urban tree inventory using remote sensing is needed not only to reduce inventory costs but also to support carbon accounting for urban planners and policy-makers. However, urban areas are heterogeneous and complex, and a more sophisticated approach is needed for using remote-sensing technology like lidar for tree inventory in urban areas than is required for forested environments. Based on remote sensing and field data from a suburban residential area in the central United States, this dissertation presents a methodology for utilizing airborne small-footprint lidar data to inventory urban trees. This dissertation proposes approaches that have the potential to automate three main activities of urban tree inventory -- identifying the locations of trees, classifying the trees into taxonomic categories, and estimating biophysical parameters of individual trees -- using airborne lidar data. Mathematical morphological operations followed by a marker-controlled watershed segmentation were found to perform well (r = 0.82 to 0.92) to delineate individual tree crowns in urban areas, especially when the trees occur in relatively isolated conditions. Using various distribution metrics of lidar returns, random forests were used to classify individual trees into different taxonomic classes (broadleaves/conifers, genera, and species). A classification accuracy of 80.5% was obtained when separating trees only into broadleaf and conifer classes, 50.0% for genera, and 51.3% for species. Using spectral metrics from high-resolution satellite imagery in addition to lidar-derived predictors improved the classification accuracies by 10.4% (to 90.9%) for broadleaf and conifer, 8.4% (to 58.4%) for genera and 8.8% (to 60.1%) for species compared to using lidar metrics alone. Prediction models to estimate several biophysical parameters such as height, crown area, diameter at breast height, and biomass were developed using lidar point cloud distributional metrics from individual trees. A high level of accuracy was attained for estimating tree height (R2=0.89, RMSE=1.3m), diameter at breast height (R2=0.82, RMSE=9.1cm), crown diameter (R2=0.90, RMSE=0.7m) and biomass (R2=0.67, RMSE=1.2t). Our results indicate that, while using lidar data alone can achieve the automation of major urban forest inventory tasks to an acceptable level of accuracy, a synergistic use of lidar data with other spectral data such as hyperspectral or orthoimagery, which are usually available at least in the United States for most urban areas, can considerably improve the performance of the lidar-based method.
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    Omeprazole Inhibits Glioblastoma Cell Invasion and Tumor Growth
    Jin, Un-Ho; Michelhaugh, Sharon K.; Polin, Lisa A.; Shrestha, Rupesh; Mittal, Sandeep; Safe, Stephen (MDPI, 2020-07-28)
    Background: The aryl hydrocarbon receptor (AhR) is expressed in gliomas and the highest staining is observed in glioblastomas. A recent study showed that the AhR exhibited tumor suppressor-like activity in established and patient-derived glioblastoma cells and genomic analysis showed that this was due, in part, to suppression of CXCL12, CXCR4 and MMP9. Methods: Selective AhR modulators (SAhRMs) including AhR-active pharmaceuticals were screened for their inhibition of invasion using a spheroid invasion assay in patient-derived AhR-expressing 15-037 glioblastoma cells and in AhR-silenced 15-037 cells. Invasion, migration and cell proliferation were determined using spheroid invasion, Boyden chambers and scratch assay, and XTT metabolic assays for cell growth. Changes in gene and gene product expression were determined by real-time PCR and Western blot assays, respectively. In vivo antitumorigenic activity of omeprazole was determined in SCID mice bearing subcutaneous patient-derived 15-037 cells. Results: Results of a screening assay using patient-derived 15-037 cells (wild-type and AhR knockout) identified the AhR-active proton pump inhibitor omeprazole as an inhibitor of glioblastoma cell invasion and migration only AhR-expressing cells but not in cells where the AhR was downregulated. Omeprazole also enhanced AhR-dependent repression of the pro-invasion CXCL12, CXCR4 and MMP9 genes, and interactions and effectiveness of omeprazole plus temozolomide were response-dependent. Omeprazole (100 mg/kg/injection) inhibited and delayed tumors in SCID mice bearing patient-derived 15-037 cells injected subcutaneously. Conclusion: Our results demonstrate that omeprazole enhances AhR-dependent inhibition of glioblastoma invasion and highlights a potential new avenue for development of a novel therapeutic mechanism-based approach for treating glioblastoma.