Relevant bibliographies by topics / Natural images

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Natural images'

Author: Grafiati
Published: 4 June 2021
Last updated: 26 July 2025

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Journal articles on the topic "Natural images"

1

Moorhead, Ian, and Tom Trościanko. "Natural Images." Perception 29, no. 9 (2000): 1013–15. http://dx.doi.org/10.1068/p2909ed.

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Zhang, S., C. Abbey, and M. Eckstein. "Classification Images for Search in Natural Images." Journal of Vision 10, no. 7 (2010): 1355. http://dx.doi.org/10.1167/10.7.1355.

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Ahmad, Khairul Adilah, Sharifah Lailee Syed Abdullah, and Mahmod Othman. "Natural Images Contour Segmentation." Journal of Computing Research and Innovation 2, no. 4 (2018): 39–47. http://dx.doi.org/10.24191/jcrinn.v2i4.62.

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This paper, a combination of edge detection and contour based segmentation approach for object contour delineation is proposed. The proposed approach employs a new methodology for segmenting the fruit contour from the indoor and outdoo r natural images more effectively. The overall process is carried out in five steps. The first step is to pre - process the image in order to convert the colour image to grayscale image. Second step is the adoption of Laplacian of Gaussian edge detection and a new corner template detection algorithm for adjustment of the pixels along the edge map in the interpol
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Hall, Ronald L. "Images of natural evil." International Journal for Philosophy of Religion 87, no. 3 (2020): 213–16. http://dx.doi.org/10.1007/s11153-020-09757-9.

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Banafar, Lokendra Singh, and Dr Lalita Gupta. "Text Detection from Natural Images using MSER Algorithm." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 73–81. http://dx.doi.org/10.31142/ijtsrd10806.

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V. Seeri, Shivananda, J. D. Pujari, and P. S. Hiremath. "PNN Based Character Recognition in Natural Scene Images." Bonfring International Journal of Software Engineering and Soft Computing 6, Special Issue (2016): 109–13. http://dx.doi.org/10.9756/bijsesc.8254.

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Kataoka, Hirokatsu, Kazushige Okayasu, Asato Matsumoto, et al. "Pre-Training Without Natural Images." International Journal of Computer Vision 130, no. 4 (2022): 990–1007. http://dx.doi.org/10.1007/s11263-021-01555-8.

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AbstractIs it possible to use convolutional neural networks pre-trained without any natural images to assist natural image understanding? The paper proposes a novel concept, Formula-driven Supervised Learning (FDSL). We automatically generate image patterns and their category labels by assigning fractals, which are based on a natural law. Theoretically, the use of automatically generated images instead of natural images in the pre-training phase allows us to generate an infinitely large dataset of labeled images. The proposed framework is similar yet different from Self-Supervised Learning bec
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Pronin, S. V. "Symmetrical patterns in natural images." Journal of Optical Technology 89, no. 1 (2022): 17. http://dx.doi.org/10.1364/jot.89.000017.

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Hunter, D. W., and P. B. Hibbard. "Statistics of Natural Binocular Images." i-Perception 4, no. 7 (2013): 485. http://dx.doi.org/10.1068/ig9.

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Buffat, S., C. Roumes, and J. Lorenceau. "Repetition blindness with natural images." Journal of Vision 5, no. 8 (2005): 857. http://dx.doi.org/10.1167/5.8.857.

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Dissertations / Theses on the topic "Natural images"

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Chen, Ting-Li. "On the statistics of natural images /." View online version; access limited to Brown University users, 2005. http://wwwlib.umi.com/dissertations/fullcit/3174586.

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Miflah, Hussain Ismail Ahamed. "Higher-level representations of natural images." Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/39759.

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The traditional view of vision is that neurons in early cortical areas process information about simple features (e.g. orientation and spatial frequency) in small, spatially localised regions of visual space (the neuron's receptive field). This piecemeal information is then fed-forward into later stages of the visual system where it gets combined to form coherent and meaningful global (higher-level) representations. The overall aim of this thesis is to examine and quantify this higher level processing; how we encode global features in natural images and to understand the extent to which our pe
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Tavakoli, Fatemeh. "On Visual Attention in Natural Images." Thesis, Linnéuniversitetet, Institutionen för fysik och elektroteknik (IFE), 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-48256.

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By visual attention process biological and machine vision systems are able to select the most relevant regions from a scene. The relevancy process is achieved either by top-down factors, driven by task, or bottom-up factors, the visual saliency, which distinguish a scene region that are different from its surrounding. During the past 20 years numerous research efforts have aimed to model bottom-up visual saliency with many successful applications in computer vision and robotics.In this thesis we have performed a comparison between a state-of-the-art saliency model and subjective test (human ey
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Kim, Kyu-Heon. "Segmentation of natural texture images using a robust stochastic image model." Thesis, University of Newcastle Upon Tyne, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.307927.

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Azzabou, Noura. "Variable Bandwidth Image Models for Texture-Preserving Enhancement of Natural Images." Paris Est, 2008. http://pastel.paristech.org/4041/01/ThesisNouraAzzabou.pdf.

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Cette thèse s’intéresse aux problèmes de restauration d’images et de préservation de textures. Cette tâche nécessite un modèle image qui permet de caractériser le signal qu’on doit obtenir. Un tel model s’appuie sur la définition de l’interaction entre les pixels et qui est caractérisé par deux aspects : (1) la similarité photométrique entre les pixels (2) la distance spatiale entre les pixels qui peut être comparée à une grandeur d’échelle. La première partie de la thèse introduit un nouveau modèle non paramétrique d’image. Ce modèle permet d’obtenir une description adaptative de l’image en u
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Viklund, Alexander, and Emma Nimstad. "Character Recognition in Natural Images Utilising TensorFlow." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208385.

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Convolutional Neural Networks (CNNs) are commonly used for character recognition. They achieve the lowest error rates for popular datasets such as SVHN and MNIST. Usage of CNN is lacking in research about character classification in natural images regarding the whole English alphabet. This thesis conducts an experiment where TensorFlow is used to construct a CNN that is trained and tested on the Chars74K dataset, with 15 images per class for training and 15 images per class for testing. This is done with the aim of achieving a higher accuracy than the non-CNN approach by de Campos et al. [1],
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Granlund, Oskar, and Kai Böhrnsen. "Improving character recognition by thresholding natural images." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208899.

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The current state of the art optical character recognition (OCR) algorithms are capable of extracting text from images in predefined conditions. OCR is extremely reliable for interpreting machine-written text with minimal distortions, but images taken in a natural scene are still challenging. In recent years the topic of improving recognition rates in natural images has gained interest because more powerful handheld devices are used. The main problem faced dealing with recognition in natural images are distortions like illuminations, font textures, and complex backgrounds. Different preprocess
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Johnson, Samuel Alan. "Articulated human pose estimation in natural images." Thesis, University of Leeds, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.598026.

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In this thesis the problem of estimating the 2-D articulated pose, or configuration of a person in unconstrained images such as consumer photographs is addressed. Contributions are split among three major chapters. In previous work the Pictorial Structure Model approach has proven particularly successful. and is appealing because of its moderate computational cost. However, the accuracy of resulting pose estimates has been limited by the use of simple representations of limb appearance. In this thesis strong discriminatively trained limb detectors combining gradient and colour segmentation cue
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Nasrallah, Alexandre James. "Statistics of gradient directions in natural images." Thesis, University College London (University of London), 2007. http://discovery.ucl.ac.uk/1444886/.

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Interest in finding statistical regularities in natural images has been growing since the advent of information theory and the advancement of the efficient coding hypothesis that the human visual system is optimised to encode natural visual stimuli. In this thesis, a statistical analysis of gradient directions in an ensemble of natural images is reported. Information-theoretic measures have been used to compute the amount of dependency which exists between triples of gradient directions at separate image locations. Control experiments are performed on other image classes: phase randomized natu
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Ma, Yufeng. "Going Deeper with Images and Natural Language." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/99993.

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One aim in the area of artificial intelligence (AI) is to develop a smart agent with high intelligence that is able to perceive and understand the complex visual environment around us. More ambitiously, it should be able to interact with us about its surroundings in natural languages. Thanks to the progress made in deep learning, we've seen huge breakthroughs towards this goal over the last few years. The developments have been extremely rapid in visual recognition, in which machines now can categorize images into multiple classes, and detect various objects within an image, with an ability t
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Books on the topic "Natural images"

1

Ian, Moorhead, and Trościanko Tom, eds. Natural images. Pion, 2000.

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Parish, Steve. Australia from the heart: Words and images. S. Parish Pub., 1990.

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Adar, Pelah, ed. The vision of natural and complex images. Elsevier Science, 1997.

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Damon, James, Peter Giblin, and Gareth Haslinger. Local Features in Natural Images via Singularity Theory. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41471-3.

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Ahmed, Saad Bin, Muhammad Imran Razzak, and Rubiyah Yusof. Cursive Script Text Recognition in Natural Scene Images. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1297-1.

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Peebles, Douglas. Hawaiʻi: Images of the islands. Mutual Pub., 2005.

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1951-, Gérard Philippe, Ost François 1952-, and Kerchove Michel van de, eds. Images et usages de la nature en droit. Facultés universitaires Saint-Louis, 1993.

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MIT Committee on the Visual Arts., ed. Natural forms and forces: Abstract images in American sculpture. MIT Committee on the Visual Arts, 1986.

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American Museum of Natural History. Official guide: Images from around the American Museum of Natural History. American Museum of Natural History, 1993.

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Martin, James. North Cascades crest: Notes and images from America's Alps. Sasquatch Books, 1999.

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Book chapters on the topic "Natural images"

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Wolff, Robert S., and Larry Yaeger. "Images and Image Processing." In Visualization of Natural Phenomena. Springer US, 1993. http://dx.doi.org/10.1007/978-1-4684-0646-7_1.

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Chevrier, Vincent, Christine Bourjot, and Vincent Thomas. "Region Detection in Images." In Natural Computing Series. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-17348-6_17.

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Fleet, David J. "Application to Natural Images." In Measurement of Image Velocity. Springer US, 1992. http://dx.doi.org/10.1007/978-1-4615-3648-2_11.

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Zhu, Song-Chun, and Ying Wu. "Statistics of Natural Images." In Computer Vision. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96530-3_2.

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Verbyla, David L. "Satellite Images." In Satellite Remote Sensing of Natural Resources. CRC Press, 2022. http://dx.doi.org/10.1201/9780138740191-1.

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Zheng, Gang, Jingsong Yang, Antony K. Liu, et al. "Observing Typhoons from Satellite-Derived Images." In Springer Natural Hazards. Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-2893-9_9.

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Erdem, Erkut, and Sibel Tari. "Revisiting Skeletons from Natural Images." In Association for Women in Mathematics Series. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-16348-2_7.

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Hyvärinen, Aapo, Jarmo Hurri, and Patrik O. Hoyer. "Temporal Sequences of Natural Images." In Computational Imaging and Vision. Springer London, 2009. http://dx.doi.org/10.1007/978-1-84882-491-1_16.

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Kataoka, Hirokatsu, Kazushige Okayasu, Asato Matsumoto, et al. "Pre-training Without Natural Images." In Computer Vision – ACCV 2020. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-69544-6_35.

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Serrano, J. F., J. H. Sossa, C. Avilés, R. Barrón, G. Olague, and J. Villegas. "Scene Retrieval of Natural Images." In Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10268-4_91.

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Conference papers on the topic "Natural images"

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Li, Jizhizi, Jing Zhang, and Dacheng Tao. "Deep Automatic Natural Image Matting." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/111.

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Automatic image matting (AIM) refers to estimating the soft foreground from an arbitrary natural image without any auxiliary input like trimap, which is useful for image editing. Prior methods try to learn semantic features to aid the matting process while being limited to images with salient opaque foregrounds such as humans and animals. In this paper, we investigate the difficulties when extending them to natural images with salient transparent/meticulous foregrounds or non-salient foregrounds. To address the problem, a novel end-to-end matting network is proposed, which can predict a genera
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Qin, Shaoling, and Ning Cao. "Chroma discrimination for natural images." In 2010 3rd International Congress on Image and Signal Processing (CISP). IEEE, 2010. http://dx.doi.org/10.1109/cisp.2010.5648227.

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Mansoor, Atif Bin, and Shoab A. Khan. "Contoulet denoising of natural images." In 2008 International Conference on Audio, Language and Image Processing. IEEE, 2008. http://dx.doi.org/10.1109/icalip.2008.4590267.

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Tseng, Ting-En, Wei-Yi Chang, Chu-Song Chen, and Yu-Chiang Frank Wang. "Style retrieval from natural images." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7471939.

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Wu, Qi, and Peter Hall. "Prime Shapes in Natural Images." In British Machine Vision Conference 2012. British Machine Vision Association, 2012. http://dx.doi.org/10.5244/c.26.45.

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Ushiku, Yoshitaka, Tatsuya Harada, and Yasuo Kuniyoshi. "Understanding images with natural sentences." In the 19th ACM international conference. ACM Press, 2011. http://dx.doi.org/10.1145/2072298.2072417.

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Almeida, Mariana S. C., and Luis B. Almeida. "Blind deblurring of natural images." In ICASSP 2008 - 2008 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2008. http://dx.doi.org/10.1109/icassp.2008.4517846.

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Hasler, David, and Sabine E. Suesstrunk. "Measuring colorfulness in natural images." In Electronic Imaging 2003, edited by Bernice E. Rogowitz and Thrasyvoulos N. Pappas. SPIE, 2003. http://dx.doi.org/10.1117/12.477378.

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Su, Ya-Fan, and Homer H. Chen. "Shadow removal from natural images." In 2010 IEEE International Symposium on Circuits and Systems - ISCAS 2010. IEEE, 2010. http://dx.doi.org/10.1109/iscas.2010.5537886.

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Thai, Thanh Hai, Florent Retraint, and Remi Cogranne. "Statistical model of natural images." In 2012 19th IEEE International Conference on Image Processing (ICIP 2012). IEEE, 2012. http://dx.doi.org/10.1109/icip.2012.6467412.

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Reports on the topic "Natural images"

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Liu, Yong, and Harel Shouval. Principal Components of Natural Images: An Analytical Solution. Defense Technical Information Center, 1993. http://dx.doi.org/10.21236/ada264800.

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Midak, Lilia Ya, Ivan V. Kravets, Olga V. Kuzyshyn, Jurij D. Pahomov, Victor M. Lutsyshyn, and Aleksandr D. Uchitel. Augmented reality technology within studying natural subjects in primary school. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/3746.

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The purpose of the research is creation of mobile app (supported by Android) for visualization of chemical structure of water and to display video- data of laboratory experiments that can be used by the teacher and pupils for an effective background for learning natural cycle subjects and performance of laboratory experiments in the elementary school using lapbook. As a result of work, aimed at visualizing the education material, a free mobile app LiCo.STEM was developed; it can be downloaded from the overall-available resource Google Play Market. Representation of the developed video material
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Tao, Yang, Amos Mizrach, Victor Alchanatis, Nachshon Shamir, and Tom Porter. Automated imaging broiler chicksexing for gender-specific and efficient production. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7594391.bard.

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Extending the previous two years of research results (Mizarch, et al, 2012, Tao, 2011, 2012), the third year’s efforts in both Maryland and Israel were directed towards the engineering of the system. The activities included the robust chick handling and its conveyor system development, optical system improvement, online dynamic motion imaging of chicks, multi-image sequence optimal feather extraction and detection, and pattern recognition. Mechanical System Engineering The third model of the mechanical chick handling system with high-speed imaging system was built as shown in Fig. 1. This syst
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Thoma, David, and Eliot Rendleman. Landscape phenology, vegetation condition, and relations with climate at Natural Bridges National Monument, 2000–2019. National Park Service, 2025. https://doi.org/10.36967/2313610.

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Climate plays a major role in determining vegetation composition in parks, and changes in climate will affect vegetation conditions and composition in the future. This study, which examined changes in climate, water availability, and vegetation from 2000 to 2019 in Natural Bridges National Monument (NM) in Utah, USA, evaluates vegetation sensitivity to weather and climate to understand climate-vegetation relationships that can be used to predict what vegetation types may change. Specifically, it evaluates where change is likely in the monument, when change may happen, and why change may occur.
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Choe, B.-H., A. Blais-Stevens, S. Samsonov, and J. Dudley. RADARSAT Constellation Mission (RCM) InSAR preliminary observations of slope movements in British Columbia, Alberta, and Nunavut. Natural Resources Canada/CMSS/Information Management, 2022. http://dx.doi.org/10.4095/331099.

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The Geological Survey of Canada (GSC)mp;gt;'s Public Safety Geoscience Program (PSGP) has collaborated with the Canada Centre for Remote Sensing (CCRS) to assess the performance of new RCM data for monitoring slope movements. The PSGP has the mandate to study natural hazards and provide baseline geoscience information to help stakeholders and decision-makers mitigate against potential risk. This report provides preliminary results observed from new RCM InSAR data acquired over 21 sites in British Columbia (BC), Alberta (AB), and Nunavut (NU) from April 2020 to September 2021. , In some cases,
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Struthers, Kim. Natural resource conditions at Fort Pulaski National Monument: Findings and management considerations for selected resources. National Park Service, 2023. http://dx.doi.org/10.36967/2300064.

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The National Park Service (NPS) Water Resources Division’s Natural Resource Condition Assessment (NRCA) Program initiated an NRCA project with Fort Pulaski National Monument (FOPU) in 2022. The purpose of an NRCA is to synthesize information related to the primary drivers and stressors affecting natural resource conditions at a park and to report conditions for natural resource topics selected by park managers. Resource conditions are evaluated as either a condition assessment or a gap analysis, depending on data availability. For FOPU’s NRCA, managers selected salt marsh, shorebirds, Eastern
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McCoy-Sulentic, Miles, Diane Menuz, Denise Culver, and Elisabeth Stimmel. Common Wetland Plants of Utah’s Central Basin and Range Ecoregion. Utah Geological Survey, 2024. http://dx.doi.org/10.34191/mp-178.

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The goal of this field guide is to provide an easy-to-use plant identification resource for both amateur and experienced natural resource professionals working in the Central Basin and Range ecoregion of Utah. This field guide can help users identify wetland plants, compare species with similar and easily confused species, and learn about plant attributes such as native status and affinity for wetlands. This book would not have been possible without the generous support of the Colorado Natural Heritage Program (CNHP), especially Joanna Lemly and co-author Denise Culver, whose Colorado wetland
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Lasko, Kristofer, and Sean Griffin. Monitoring Ecological Restoration with Imagery Tools (MERIT) : Python-based decision support tools integrated into ArcGIS for satellite and UAS image processing, analysis, and classification. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/40262.

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Monitoring the impacts of ecosystem restoration strategies requires both short-term and long-term land surface monitoring. The combined use of unmanned aerial systems (UAS) and satellite imagery enable effective landscape and natural resource management. However, processing, analyzing, and creating derivative imagery products can be time consuming, manually intensive, and cost prohibitive. In order to provide fast, accurate, and standardized UAS and satellite imagery processing, we have developed a suite of easy-to-use tools integrated into the graphical user interface (GUI) of ArcMap and ArcG
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Khan, Mahreen. The Environmental Impacts of War and Conflict. Institute of Development Studies, 2022. http://dx.doi.org/10.19088/k4d.2022.060.

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In modern warfare, the first widely acknowledged scientific study and documented case of environmental damage during conflict was the (direct and deliberate) use of Agent Orange and other toxic chemicals by US forces, from 1961-1971, during the Vietnam War in a policy known as herbicide. The Vietnam War has been relatively well documented for the sheer horror and magnitude of the devastation to natural habitats and because it was the first war where television and global media brought vivid images and accounts into people’s homes, making the war a matter of political and public conscience This
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Keller, Stephen, and Alexander Marr. OF-22-08 Geologic Map of the Windsor Quadrangle, Larimer and Weld Counties, Colorado. Colorado Geological Survey, 2024. https://doi.org/10.58783/cgs.of2208.lfhf7370.

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The Windsor quadrangle, situated in the northern part of the Colorado Piedmont, features relatively low topographic relief with a 94-meter variance between its highest and lowest points. Notable elongated outcrop areas of weathered bedrock are observed along the dissected southwest side of the Cache la Poudre River valley. While fresh bedrock is mainly found in lower gravel pit faces in the Big Thompson River valley, unconsolidated Quaternary deposits cover most of the quadrangle with few natural exposures along river cut banks. The landscape is primarily characterized by a broad upland swath
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