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Forest structure estimation is very important in geological, ecological and environmental studies. It provides the basis for the carbon stock estimation and effective means of sequestration of carbon sources and sinks. Multiple parameters are used to estimate the forest structure like above ground biomass, leaf area index and diameter at breast height. Among all these parameters, vegetation height has unique standing. In addition to forest structure estimation it provides the insight into long term historical changes and the estimates of stand age of the forests as well. There are multiple techniques available to estimate the canopy height. Light detection and ranging (LiDAR) based methods, being the accurate and useful ones, are very expensive to obtain and have no global coverage. There is a need to establish a mechanism to estimate the canopy height using freely available satellite imagery like Landsat images. Multiple studies are available which contribute in this area. The majority use Landsat images with random forest models. Although random forest based models are widely used in remote sensing applications, they lack the ability to utilize the spatial association of neighboring pixels in modeling process. In this research work, we define Convolutional Neural Network based model and analyze that model for three test configurations. We replicate the random forest based setup of Grant et al., which is a similar state-of-the-art study, and compare our results and show that the convolutional neural networks (CNN) based models not only capture the spatial association of neighboring pixels but also outperform the state-of-the-art.

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Aim: To assess the accuracy of inserting a pedicle screw percutaneously to fix thoracic and lumbar spinal fractures by minimally invasive procedures. Study design: Prospective observational study. Place and duration: This study was conducted at , Pakistan Institute of Medical Sciences Islamabad, Pakistan from March 2020 to March 2021. Methodology: A total of 50 patients were evaluated, who had suffered from spinal injuries in the form of thoracic and lumbar spine fractures and were admitted for spinal surgery. Extensive CT scans were ordered for these patients and on the basis of these scans, the clinicians authorized and assessed the positioning for the percutaneous pedicle screw. However, in cases where the scans were not enough to visualize the exact placement of the pedicle cortex, an approach known as the cortical encroachment was applied. In cases where the need for the screw to be inserted laid outside the boundaries of the pedicle cortex the clinicians utilized a method known as the frank penetration. If placement for this screw was located outside the boundaries of the pedicle these boundaries were categorized in three types. Minor: if the displacement was less than 3 millimeters, moderate: where the placement lay between 3 to 6 millimeters and severe: where the placement was greater than 6 millimeters. Results: At total of 380 screws were fitted percutaneously in 50 patients. A total of 281(74%) of these screws were fitted inside the pedicle, 53 screws (14%) were placed in such a way that they encroached the pedicle and 46 (12%) of the cases showed that the screws penetrated the pedicle. Minor screw penetration was in 32 (8.47%) cases, moderate penetration in 10 (2.58 %) and severe penetration in 3 (0.7%) cases. Among one of severe screw penetration post-operative neurological symptoms were noted. Conclusion: Our study concludes that most of the pedicle positioning of screws were ideally placed, while in few cases there were Encroachment and perforation Keywords: Thoracic spine, lumber spine, spinal injury, pedicel screw

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