Academic literature on the topic 'Purdue University. Laboratory for Applications of Remote Sensing'

On 1 October 2004, the most ambitious high-performance Grid project in the United States—the TeraGrid—became fully operational. Resources at nine sites—the San Diego Supercomputer Center, the California Institute of Technology, the National Center for Supercomputing Applications, the University of Chicago/Argonne National Laboratory, Pittsburgh Supercomputing Center, Texas Advanced Computing Center, Purdue University, Indiana University and Oak Ridge National Laboratory—were joined via an ultra-fast optical network, unified policies and security procedures and a sophisticated distributed computing software environment. Funded by the National Science Foundation, the TeraGrid enables scientists and engineers to combine distributed, multiple data sources with computation at any of the sites or link massively parallel computer simulations to extreme-resolution visualizations at remote sites. A single shared utility lets multiple resources be easily leveraged and provides improved access to advanced computational capabilities. One of the demonstrations of this new model for using distributed resources, Teragyroid, linked the infrastructure of the TeraGrid with computing resources in the United Kingdom via a transatlantic data fibre link. Once connected, the software framework of the RealityGrid project was used to successfully explore lattice-Boltzmann simulations involving lattices of over one billion sites.

Abstract. Time series (TS) analysis has always been an important topic in Remote Sensing (RS). Open access to archives of Earth Observation missions, periodically acquired nation-wide aerial imagery and LiDAR point clouds, and various research datasets, together with open tools for TS data processing, have brought new possibilities in RS research but also challenges in education. The topic of TS analysis in RS has become an essential part of MSc curricula in geoinformatics, geography, and related fields. Open learning materials systematically addressing the issue of TS at the master's level are rare and their development is time and resource demanding. Thus, based on previous collaboration, the four research groups from Charles University, Heidelberg University, University of Innsbruck, and University of Warsaw joint their specific expertise and developed an open E-learning course on Time Series Analysis in RS for Understanding Human-Environment Interactions (E-TRAINEE). The course consists of four Modules covering the topics of TS from general approaches (M1) to specific methods of processing TS of satellite multispectral images (M2), 3D/4D point clouds (M3), and aerial image and laboratory spectroscopy (M4). Theoretical parts are supported with exercises/tutorials and case studies based on research activities of the involved teams. The course is accessible via a web site and is published under the CC-BY SA 4.0 license. The primary target group are MSc and PhD students of geoinformatics and geography, but it is also relevant to students of environmental studies, ecology, or geology, as well as potential users from the public and private sectors dealing with applications of RS.

Nowadays the imagery data acquired from UAV sensors are the main source of all data used in various remote sensing applications, photogrammetry projects and in imagery intelligence (IMINT) as well as in other tasks as decision support. Therefore quality assessment of such imagery is an important task. The research team from Military University of Technology, Faculty of Civil Engineering and Geodesy, Geodesy Institute, Department of Remote Sensing and Photogrammetry has designed and prepared special test field- The Portable Imagery Quality Assessment Test Field (PIQuAT) that provides quality assessment in field conditions of images obtained with sensors mounted on UAVs. The PIQuAT consists of 6 individual segments, when combined allow for determine radiometric, spectral and spatial resolution of images acquired from UAVs. All segments of the PIQuAT can be used together in various configurations or independently. All elements of The Portable Imagery Quality Assessment Test Field were tested in laboratory conditions in terms of their radiometry and spectral reflectance characteristics.

Geo-information technology plays a critical role in urban planning and management, land resource quantification, natural disaster risk and damage assessment, smart city development, land cover change modeling and touristic flow management. In particular, the development of big data mining and machine learning techniques (including deep learning) in recent years has expanded the potential applications of geo-information technology and promoted innovation in approaches to mining in different fields. In this context, the International Conference on Geo-Information Technology and its Applications (ICGITA 2019) was held in Nanchang, Jiangxi, China, 11–13 October 2019, co-organized by the Key Laboratory of Digital Land and Resources, East China University of Technology, the Institute of Remote Sensing and Digital Earth (RADI) of the Chinese Academy of Sciences (CAS), which was renamed in 2017 the Aerospace Information Research Institute (AIR), CAS, and the Institute of Space and Earth Information Science of the Chinese University of Hong Kong. The outstanding papers presented at this event and some other original articles were collected and published in this Special Issue “Geo-Information Technology and Its Applications” in the International Journal of Geo-Information. This Special Issue consists of 14 high-quality and innovative articles that explore and discuss the typical applications of geo-information technology in the above-mentioned domains.

AbstractBidirectional reflectance of a surface is defined as the ratio of the scattered radiation at the detector to the incident irradiance as a function of geometry. Accurate knowledge of the bidirectional reflection function for layers composed of discrete, randomly positioned scattering particles is essential for many remote sensing, engineering, and biophysical applications, as well as for different areas of astrophysics. Computations of bidirectional reflection functions for plane parallel particulate layers are usually reduced to solving the radiative transfer equation by the existing techniques. In this work we present our laboratory data on bidirectional reflectance versus phase angle for two sample sizes of alumina, 0.3 and 1 μm, for the He–Ne laser at wavelengths of 632.8 nm (red) and 543.5 nm (green). The nature of the phase curves of the asteroids depends on the parameters like particle size, composition, porosity, roughness, etc. In the present study we analyze data which are being generated using a single scattering phase function, that is, Mie theory of treating particles as a compact sphere. The well-known Hapke formula, along with different particle phase functions such as Mie and Henyey–Greenstein, will be used to model the laboratory data obtained at the asteroid laboratory of Assam University.

Abstract. Synthetic aperture radar (SAR) system based on frequency modulated continuous wave (FM-CW) transmission is a viable option for producing high-resolution ground-based imaging radars. Compared with pulsed SAR systems, the combination of FM-CW technology and SAR processing techniques have the advantages of small cubage, lightweight, cost-effectiveness, and high resolution in the SAR image. These characteristics make FM-CW SAR suitable to be deployed as payload on ground Based SARs (GB-SARs) for environmental and civilian applications. In this paper, the Range Migration Algorithm (RMA) is used in the processing chain of a Ground-Based SAR (GB-SAR) sensor. The mentioned sensor has been developed in Microwave Remote Sensing Laboratory (MReSL) at the School of Surveying and Geospatial Engineering, the University of Tehran for the generation of a complex image from the raw signal. The raw signal is acquired with that sensor working at S-band, frequency modulating from 2.26 GHz to 2.59 GHz.

In the field of remote sensing, most of the feature indexes are obtained based on expert knowledge or domain analysis. With the rapid development of machine learning and artificial intelligence, this method is time-consuming and lacks flexibility, and the indexes obtained cannot be applied to all areas. In order to not rely on expert knowledge and find the effective feature index with regard to a certain material automatically, this paper proposes a data-driven method to learn interactive features for hyperspectral remotely sensed data based on a sparse multiclass logistic regression model. The key point explicitly expresses the interaction relationship between original features as new features by multiplication or division operation in the logistic regression. Through the strong constraint of the L1 norm, the learned features are sparse. The coefficient value of the corresponding features after sparse represents the basis for judging the importance of the features, and the optimal interactive features among the original features. This expression is inspired by the phenomenon that usually the famous indexes we used in remote sensing, like NDVI, NDWI, are the ratio between different spectral bands, and also in statistical regression, the relationship between features is captured by feature value multiplication. Experiments were conducted on three hyperspectral data sets of Pavia Center, Washington DC Mall, and Pavia University. The results for binary classification show that the method can extract the NDVI and NDWI autonomously, and a new type of metal index is proposed in the Pavia University data set. This framework is more flexible and creative than the traditional method based on laboratory research to obtain the key feature and feature interaction index for hyperspectral remotely sensed data.

Integrating the representation of the territory, through airborne remote sensing activities with hyperspectral and visible sensors, and managing complex data through dimensionality reduction for the identification of cannabis plantations, in Albania, is the focus of the research proposed by the multidisciplinary group of the Benecon University Consortium. In this study, principal components analysis (PCA) was used to remove redundant spectral information from multiband datasets. This makes it easier to identify the most prevalent spectral characteristics in most bands and those that are specific to only a few bands. The survey and airborne monitoring by hyperspectral sensors is carried out with an Itres CASI 1500 sensor owned by Benecon, characterized by a spectral range of 380–1050 nm and 288 configurable channels. The spectral configuration adopted for the research was developed specifically to maximize the spectral separability of cannabis. The ground resolution of the georeferenced cartographic data varies according to the flight planning, inserted in the aerial platform of an Italian Guardia di Finanza’s aircraft, in relation to the orography of the sites under investigation. The geodatabase, wherein the processing of hyperspectral and visible images converge, contains ancillary data such as digital aeronautical maps, digital terrain models, color orthophoto, topographic data and in any case a significant amount of data so that they can be processed synergistically. The goal is to create maps and predictive scenarios, through the application of the spectral angle mapper algorithm, of the cannabis plantations scattered throughout the area. The protocol consists of comparing the spectral data acquired with the CASI1500 airborne sensor and the spectral signature of the cannabis leaves that have been acquired in the laboratory with ASD Fieldspec PRO FR spectrometers. These scientific studies have demonstrated how it is possible to achieve ex ante control of the evolution of the phenomenon itself for monitoring the cultivation of cannabis plantations.

Integrating the representation of the territory, through airborne remote sensing activities with hyperspectral and visible sensors, and managing complex data through dimensionality reduction for the identification of cannabis plantations, in Albania, is the focus of the research proposed by the multidisciplinary group of the Benecon University Consortium. In this study, principal components analysis (PCA) was used to remove redundant spectral information from multiband datasets. This makes it easier to identify the most prevalent spectral characteristics in most bands and those that are specific to only a few bands. The survey and airborne monitoring by hyperspectral sensors is carried out with an Itres CASI 1500 sensor owned by Benecon, characterized by a spectral range of 380–1050 nm and 288 configurable channels. The spectral configuration adopted for the research was developed specifically to maximize the spectral separability of cannabis. The ground resolution of the georeferenced cartographic data varies according to the flight planning, inserted in the aerial platform of an Italian Guardia di Finanza’s aircraft, in relation to the orography of the sites under investigation. The geodatabase, wherein the processing of hyperspectral and visible images converge, contains ancillary data such as digital aeronautical maps, digital terrain models, color orthophoto, topographic data and in any case a significant amount of data so that they can be processed synergistically. The goal is to create maps and predictive scenarios, through the application of the spectral angle mapper algorithm, of the cannabis plantations scattered throughout the area. The protocol consists of comparing the spectral data acquired with the CASI1500 airborne sensor and the spectral signature of the cannabis leaves that have been acquired in the laboratory with ASD Fieldspec PRO FR spectrometers. These scientific studies have demonstrated how it is possible to achieve ex ante control of the evolution of the phenomenon itself for monitoring the cultivation of cannabis plantations.

The study aims to assess the rate of erosion that occurred in Manjunto Watershed and financial loss using Geographic Information System and Remote Sensing. Model used to determine the erosion is E30 models. The basis for the development of this model is to integrate with the slope of the slope between NDVI. The value of NDVI obtained from satellite imagery. Slope factor obtained through the DEM processing. To determine the amount of economic losses caused by erosion used the shadow prices. The amount of nutrients lost converted to fertilizer price. The results showed that the eroded catchment area has increased significantly. The rate of average annual erosion in the watershed Manjunto in 2000 amounted to 3 Mg ha-1 yr-1. The average erosion rate in the watershed Manjunto annual increase to 27 Mg ha-1 yr-1 in the year 2009. Economic losses due to erosion in 2009 was Rp200,000,- for one hectare. Total losses due to erosion for the total watershed area is Rp15,918,213,133, -. The main factor causing the high rate of erosion is high rainfall, slope and how to grow crops that do not pay attention to the rules of conservation.Keywords: Soil erosion, digital elevation model, GIS, remote sensing, valuation erosion[How to Cite: Gunawan G, D Sutjiningsih, H Soeryantono and S Widjanarko. 2013.Soil Erosion Prediction Using GIS and Remote Sensing on Manjunto Watershed Bengkulu-Indonesia. J Trop Soils 18 (2): 141-148. Doi: 10.5400/jts.2013.18.2.141][Permalink/DOI: www.dx.doi.org/10.5400/jts.2013.18.2.141]REFERENCESAksoy E, G Ozsoy and MS Dirim. 2009. Soil mapping approach in GIS using Landsat satellite imagery and DEM data. Afr J Agric Res 4: 1295-1302.Ananda J and G Herath. 2003. Soil erosion in developing countries: a socio-economic appraisal. J Environ Manage 68: 343-353.Ananda J, G Herath and A Chisholm. 2001. Determination of yield and Erosion Damage Functions Using Subjectivly Elicited Data: application to Smallholder Tea in Sri Lanka. 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Estimation of soil loss by using combination of Erosion Model and GIS: case of study watersheds in Tunisia. J Arid Land Stud 19: 287-290.Lal R. 1998. Soil erosion impact on agronomic productivity and environment quality: Critical Review. Plant Sci 17: 319-464.Lal. 2001. Soil Degradation by Erosion. Land Degrad Develop12: 519-539.Lanya I. 1996. Evaluasi Kualitas lahan dan Produktivitas Lahan Kering Terdegradasi di Daerah Transmigrasi WPP VII Rengat Kabupaten Indragiri Hulu, Riau. [Disertasi Doktor]. Program Pasca Sarjana IPB, Bogor (in Indonesian).Mermut AR and H Eswaran. 2001. Some major developments in soil science since the mid 1960s. Geoderma 100: 403-426.Mongkolsawat C, P Thurangoon and Sriwongsa.1994. Soil erosion mapping with USLE and GIS. Proc. Asian Conf. Rem. Sens., C-1-1 to C-1-6.Morgan RPC, Morgan DDV and Finney HJ. 1984. A predictive model for the assessment of erosion risk. J Agric Eng Res 30: 245-253.Morgan RPC. 2005. Soil Erosion and Conservation. 3rd ed. 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Knowledge of the global scale distribution of atmospheric ozone and its temporal variability can only be achieved through implementation of a satellite-based remote sensing technique. Development of a space-based tropospheric ozone remote sensor has remained a difficult task to accomplish due to the challenges associated with such a measurement. Nevertheless, the ability to monitor the global distribution of tropospheric ozone is of much interest for enhancement of scientific understanding as well as to potentially lessen the ill-health impacts associated with exposure to elevated concentrations in the lower atmosphere. Such a capability can be achieved using a satellite-based nadir-viewing device making high spectral resolution measurements with high signal-to-noise ratios;1 this would enable observation in the pressure-broadened wings of strong O3 lines while minimizing the impact of undesirable signal contributions associated with, for example, the terrestrial surface and interfering species. The Fabry-Perot interferometer (FPI) provides high spectral resolution and high throughput capabilities that are essential for this measurement task. The periodic nature of the Fabry-Perot instrument function can be advantageous when observation of periodic spectra is desired.2 However, for most applications, additional optical elements are necessary to reduce the effect of unwanted passbands. This is frequently accomplished using additional Fabry-Perot etalons in a series configuration3 in conjunction with a bandpass filter. This paper will discuss a Fabry-Perot interferometer conceptual instrument design to achieve tropospheric and total ozone monitoring capability from a satellite-based nadir-viewing geometry. The design involves a double-etalon fixed-gap series configuration FPI along with an ultra-narrow bandpass filter to achieve single-order operation with an overall spectral resolution of approximately .068 cm−1. The impact of inter-etalon reflections has been reduced to acceptable levels by placement of a slightly attenuating medium in between the etalons. A passive device is selected for low power consumption; and continuous day/night coverage, independent of solar zenith angle, is enabled by observing within the strong 9.6 μm ozone infrared band. The IR-FPI detection will be performed through implementation of the new Circle to Line Interferometer Optical (CLIO) system4 (U.S. patent #4893003, 1990), developed by researchers at the Space Physics Research Laboratory (SPRL) of the University of Michigan, to accomplish focal plane fringe detection; the CLIO system converts the circular interferometric fringes into a linear pattern which then can be detected by conventional linear array detectors. A multiplex signal advantage is achievable as all necessary frequencies can be measured simultaneously using a multichannel configuration. Through proper selection of channel spectral regions, the FPI optimized for tropospheric O3 measurements can simultaneously observe a stratospheric component and thus the total O3 column abundance. Details of the preliminary instrument design are given by Larar1.