Relevant bibliographies by topics / Remote sensing {Geographie}

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

Academic literature on the topic 'Remote sensing {Geographie}'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Remote sensing {Geographie}"

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Ehlers, M. "Fernerkundung und GIS bei Umweltmonitoring und Umweltmanagement." Geographica Helvetica 52, no. 1 (March 31, 1997): 5–10. http://dx.doi.org/10.5194/gh-52-5-1997.

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Abstract. Remote sensing image analysis Systems and geographie in formation Systems (GIS) show great promise for the Integra tion of a wide variety of spatial information as a support to en vironmental monitoring and managementtasks. Current and future remote sensing programs are based on a variety of sensors that will provide timely and repetitive multisensor earth Observation data on a global scale. GIS offer efficient tools for handling, manipulating, analyzing and presenting spatial data that are required for environmental ly sound deci sion making. To combine the power of both spatial techno logies, however, efficient synergistic processing techniques have to be developed to cope with large multisensor image datasets and to automatically extract information for environ mental GIS applications. These efforts have to be put in a broader perspective of an evolving discipline Geoinformatics as one component of a new and innovative interdisciplinary approach for academic education. One example of this approach is the new academic program Environmental Sciences at the University of Vechta.
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Ajar, Seno Budhi, Inna Prihartini, and Gentur Adi Tjahjono. "THE OBSTACLES FACTORS OF GEOGRAPHY INFORMATION SYSTEMS AND REMOTE SENSING PRACTICUM IN A HIGH SCHOOL LEVEL IN WONOGIRI DISTRICT." GeoEco 5, no. 2 (July 12, 2019): 151. http://dx.doi.org/10.20961/ge.v5i2.30198.

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Practicum Geographical information systems and remote sensing have become one of the basic competencies in the realm of skills in the geography learning process at the high school level The purpose of this study is to identify problems or obstacles faced by geography teachers in carrying out geographic information systems and remote sensing practicum activities in high schools in Wonogiri Regency this is a qualitative descriptive study with the use of random sampling. Data collection uses a questionnaire about the implementation of geographic information system practice and remote sensing to the teacher. This study uses descriptive statistical analysis techniques to analyze the factors that become obstacles in the application of geographic information system practice and remote sensing in high school. The result is the obstacles factor of geographic information system and remote sensing practicum in in high schools in Wonogiri is (1). Hardware devices that have not been installed software specifically for geography information systems and remote sensing The teacher has a low ability in the geography and remote sensing information system practicum skills and does not coordinate with the teacher or laboratory assistant for Information and Communication Technology (ICT) subjects (3) Insufficient time allocation for practicum and absence of geographic information system and remote sensing practice tests as evaluations
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Allaw, Kamel, Jocelyne Adjizian Gerard, Makram Zouheir Chehayeb, and Nada Badaro Saliba. "Population estimation using geographic information system and remote sensing for unorganized areas." Geoplanning: Journal of Geomatics and Planning 7, no. 2 (January 1, 2021): 75–86. http://dx.doi.org/10.14710/geoplanning.7.2.75-86.

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Population estimation using remotely sensed data has been largely discussed in the literature relative to human geography. However, the previously established models can be applied on organized areas (mainly urban areas) but they are not suitable for unorganized areas which already suffer from a lack of population data. So, the aim of this study is the establish a statistical model for population estimation based on remote sensing data and suitable for unorganized areas. To do so, the morphological characteristics have been studied and a bivariate analysis was carried out to determine factors having a strong relationship with population data as a first step. Second, factors with strongest correlations have been chosen to establish the required model. As a result, an equation has been generated which relates the population data to building volume, density of roads, number of nodes, actual urban areas, and urban trend.
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A, JOTHIBASU, and ANBAZHAGAN S. "Drought Hazard Assessment in Ponnaiyar River Basin, India Using Remote Sensing and Geographic Information System." INTERNATIONAL JOURNAL OF EARTH SCIENCES AND ENGINEERING 10, no. 02 (April 26, 2017): 247–56. http://dx.doi.org/10.21276/ijee.2017.10.0216.

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Lytvynenko, N. "THE APPLYING OF GIS INFORMATION TECHNOLOGIES IN THE SPHERE OF THE REMOTE SENSING OF THE EARTH TO SOLVE MILITARY PROBLEMS." Visnyk Taras Shevchenko National University of Kyiv. Military-Special Sciences, no. 2 (39) (2018): 18–22. http://dx.doi.org/10.17721/1728-2217.2018.39.18-22.

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The rapid development of geographic information systems and means of remote sensing, observed in recent decades, requires the mutual integration of these areas. This article focuses on the integration of geographic information systems and technologies in the field of remote sensing to solve military problems. Keywords: remote sensing, geographic information system.
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BAUMGARTNER, MICHAEL F., and GABRIELA M. APFL. "Remote sensing and geographic information systems." Hydrological Sciences Journal 41, no. 4 (August 1996): 593–607. http://dx.doi.org/10.1080/02626669609491527.

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Sun, Tong He, and Guo Qing Yan. "Land Classification Method and Analysis Based on Remote Sensing Technology." Advanced Materials Research 726-731 (August 2013): 4582–86. http://dx.doi.org/10.4028/www.scientific.net/amr.726-731.4582.

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In recent years, the research about land utilization changing already became one of the research about global changing's key topics, the land utilization classification, as its sub-topic, also attract men's high attention. Remote Sensing (RS) and the Geographic Information System (GIS) as the two big spatial technology tool to support modern geography, their union arouses people's universal interest and research. Because the remote sensing information has the advantage of covering wide area, timeliness and current situation, quick speed, short cycle and reliable accurate as well as time-saving, effort-saving, low status merit expense, it is widely used in the land resource and land utilization situation investigation at present, land utilization change monitor and so on. This paper discusses land utilization classification based on remote sensing technology. Taking the Xinjiang Kulja county bureau department area remote sensing images as the basic information, and using ERDAS IMAGINE, this paper discusses non-supervised classification and supervised classification methods. The results show that remote sensing technology applies to land utilization situation and land classification, which has reference value.
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Naue, Carine Rosa, Marilia W. Marques, Nelson Bernardi Lima, and Josiclêda Domiciano Galvíncio. "Sensoriamento remoto como ferramenta aos estudos de doenças de plantas agrícolas: uma revisão (Remote Sensing as a Toll for the Study of Plant Diseases on Agriculture: a Revision)." Revista Brasileira de Geografia Física 3, no. 3 (February 21, 2011): 190. http://dx.doi.org/10.26848/rbgf.v3i3.232675.

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Para que estudos epidemiológicos e medidas de controle de doenças de plantas fossem realizados foi necessário o desenvolvimento de métodos de quantificação de doenças. As doenças podem ser avaliadas por métodos diretos ou indiretos e dentre os métodos diretos encontram-se a estimativa dos parâmetros de incidência e severidade e as técnicas de sensoriamento remoto. Em estudos de doenças de plantas, o sensoriamento remoto, além de ser utilizado para quantificação, também poderá servir para a detecção de plantas infectadas. A detecção de doenças de plantas ou até mesmo sua quantificação, através do sensoriamento remoto, baseia-se na radiação refletida das folhagens. As diferenças de reflectância podem ser obtidas pelo sensoriamento remoto multispectral, que tem sido utilizado de forma eficiente para controlar a incidência de um número de patógenos de plantas e atualmente por medidas hiperespectrais. O objetivo deste artigo é apresentar uma revisão sobre o uso do sensoriamento remoto na detecção e análise de doenças de plantas. Os estudos apresentados aqui mostram que o sensoriamento remoto é uma ferramenta que pode ser utilizada para detectar plantas doentes de forma rápida e eficiente em pequenas e grandes áreas geográficas. Além disso, pode detectar plantas infectadas, dispensar a coleta e o processamento de amostras em laboratório permitindo levantamentos precisos e confiáveis, em curto espaço de tempo, independente do tamanho da área em questão. Além disso, pode proporcionar diversos estudos na área de fitopatologia e afins.Palavras-chave: Sensoriamento remoto, doenças de plantas, hiperespectral Remote Sensing as a Toll for the Study of Plant Diseases on Agriculture: a Revision ABSTRACT For epidemiological studies and measures to control plant diseases were carried out was necessary to develop methods of quantifying disease. The diseases can be assessed by direct or indirect methods and among the direct methods are estimating the incidence and severity parameters and remote sensing techniques. In studies of plant diseases, remote sensing is used to quantify, can also serve for detection of infected plants. The detection of plant diseases or even to quantify, through remote sensing, based on reflected radiation from foliage. The differences in reflectance can be obtained by multispectral remote sensing, which has been used effectively to control incidence a number of plant pathogens and recently, hyperspectral measurements. The aim of this paper is present a review on use of remote sensing in detection and analysis of plant diseases. The studies presented here show that remote sensing is a tool that can be used to detect diseased plants quickly and efficiently in large and small geographic areas. Moreover, it can detect infected plant, waive the collection and processing of lab samples allowing accurate and reliable surveys in a short space of time, regardless of size of the area. In addition, several studies can provide in the area of plant pathology and related areas. Key-words: remote sensing, diseased plants, hyperspectral.
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Han, X., and J. Wu. "DISASTER EMERGENCY RAPID ASSESSMENT BASED ON REMOTE SENSING AND BACKGROUND DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3 (April 30, 2018): 481–83. http://dx.doi.org/10.5194/isprs-archives-xlii-3-481-2018.

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The period from starting to the stable conditions is an important stage of disaster development. In addition to collecting and reporting information on disaster situations, remote sensing images by satellites and drones and monitoring results from disaster-stricken areas should be obtained. Fusion of multi-source background data such as population, geography and topography, and remote sensing monitoring information can be used in geographic information system analysis to quickly and objectively assess the disaster information. According to the characteristics of different hazards, the models and methods driven by the rapid assessment of mission requirements are tested and screened. Based on remote sensing images, the features of exposures quickly determine disaster-affected areas and intensity levels, and extract key disaster information about affected hospitals and schools as well as cultivated land and crops, and make decisions after emergency response with visual assessment results.
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Mohommad, Shahid, and Shambhu Prasad Joshi. "ROLE OF REMOTE SENSING AND GEOGRAPHIC INFORMATION SYSTEM TO ANALYZE THE IMPACT OF CLIMATE CHANGE ON FOREST ECOSYSTEMS." International Journal of Research -GRANTHAALAYAH 3, no. 8 (August 31, 2015): 61–68. http://dx.doi.org/10.29121/granthaalayah.v3.i8.2015.2959.

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Climate change is an inevitable process impacting the forest ecosystem. Various impacts like treeline shift, forest fires, and Species distribution are due to the effect of climate change. Green House Gases concentration in the atmosphere is increasing day by day due to anthropogenic activities. The pace of climate change is very alarming which will have the substantial impact on the forest ecosystem. Role of remote sensing and geographic information system in observing the forest ecosystem was reviewed. Spatio-temporal analysis of change in forest structure can be proficiently done with the help of remote sensing and geographic information system. Climate Change Mitigation programmes like Reducing Emissions from Deforestation and Forest Degradation (REDD-plus) can be implemented with the help of remote sensing and geographic information system. Baseline data generation using remote sensing and geographic information system can be useful in designing the policies for forest management and monitoring.
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Dissertations / Theses on the topic "Remote sensing {Geographie}"

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Propastin, Pavel. "Remote sensing based study on vegetation dynamics in drylands of Kazakhstan." Doctoral thesis, Stuttgart Ibidem-Verl, 2007. http://hdl.handle.net/11858/00-1735-0000-0006-B26A-A.

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Jago, Rosemary Alison. "Remote sensing of contaminated land." Thesis, University of Southampton, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.243094.

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Wolfinbarger, Susan Rae. "People Make the Pixels: Remote Sensing Analysis for Human Rights-Based Litigation." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1337790916.

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Villeneuve, Julie. "Delineating wetlands using geographic information system and remote sensing technologies." Texas A&M University, 2005. http://hdl.handle.net/1969.1/3135.

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During the last century wetlands have considerably decreased. The principal cause is urbanization, especially in large urban regions such as the Houston area. In order to protect the remaining wetlands, they have to be monitored carefully. However monitoring wetland is a difficult and time-demanding task because it has to be done repetitively on large areas to be effective. This study was conducted to determine if Geographical Information System (GIS) and remote sensing technologies would allow accurate monitoring of wetland as a less time-consuming method. With this idea, a suitability model was developed to delineate wetlands in the Houston area. This model combined GIS and remote sensing technologies. The data used for this study were as high spatial resolution as possible and were generally easy to obtain. This suitability model consisted of four submodels: hydrology, soil, vegetation and multi- attribute. Each submodel generated a Wetland Suitability Index (WSI). Those WSI were summed to obtain a general WSI. The suitability model was calibrated using half of the study area. During calibration, the general model was evaluated as well as each individual index. Generally, the model showed a lack of sensitivity to changes. However, the model was slightly modified to improve the delineation of upland wet- lands by increasing the weight of the soil submodel. This model was validated using the second half of the study area. The validation results improved a bit compared to the calibration results; however they remained weak. It was demonstrated that the model does not favor riverine wetlands over upland wetlands, nor large size wetlands. The model ground truth data were evaluated and were suffciently proven to be up to date. Those results indicated that the weakness of the model must come from inac- curacy in the input data. Therefore, the study showed that while existing computing capacity supports remote delineation, spatial accuracy is still insuffcient to perform correct wetland delineation using remote sensing and GIS technologies.
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Kim, Kee-Tae. "Satellite mapping and automated feature extraction geographic information system-based change detection of the Antarctic coast /." Connect to this title online, 2003. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1072898409.

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Thesis (Ph. D.)--Ohio State University, 2003.
Title from first page of PDF file. Document formatted into pages; contains xiv, 157 p.; also includes graphics. Includes bibliographical references (p. 143-148).
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Tyoda, Zipho. "Landslide susceptibility mapping : remote sensing and GIS approach." Thesis, Stellenbosch : Stellenbosch University, 2013. http://hdl.handle.net/10019.1/79856.

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Thesis (MSc)--Stellenbosch University, 2013.
Landslide susceptibility maps are important for development planning and disaster management. The current synthesis of landslide susceptibility maps largely applies GIS and remote sensing techniques. One of the most critical stages on landslide susceptibility mapping is the selection of landslide causative factors and weighting of the selected causative factors, in accordance to their influence to slope instability. GIS is ideal when deriving static factors i.e. slope and aspect and most importantly in the synthesis of landslide susceptibility maps. The integration of landslide causative thematic maps requires the selection of the weighting method; in order to weight the causative thematic maps in accordance to their influence to slope instability. Landslide susceptibility mapping is based on the assumption that future landslides will occur under similar circumstances as historic landslides. The weight of evidence method is ideal for landslide susceptibility mapping, as it calculates the weights of the causative thematic maps using known landslides points. This method was applied in an area within the Western Cape province of South Africa, the area is known to be highly susceptible to landslide occurrences. A prediction rate of 80.37% was achieved. The map combination approach was also applied and achieved a prediction rate of 50.98%. Satellite remote sensing techniques can be used to derive the thematic information needed to synthesize landslide susceptibility maps and to monitor the variable parameters influencing landslide susceptibility. Satellite remote sensing techniques can contribute to landslide investigation at three distinct phases namely: (1) detection and classification of landslides (2) monitoring landslide movement and identification of conditions leading up to an event (3) analysis and prediction of slope failures. Various sources of remote sensing data can contribute to these phases. Although the detection and classification of landslides through the remote sensing techniques is important to define landslide controlling parameters, the ideal is to use remote sensing data for monitoring of areas susceptible to landslide occurrence in an effort to provide an early warning. In this regard, optical remote sensing data was used successfully to monitor the variable conditions (vegetation health and productivity) that make an area susceptible to landslide occurrence.
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Cobbing, Benedict Louis. "The use of Landsat ETM imagery as a suitable data capture source for alien acacia species for the WFW programme." Thesis, Rhodes University, 2007. http://hdl.handle.net/10962/d1005532.

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Geographic Information System technology today allows for the rapid analysis of vast amounts of spatial and non-spatial data. The power of a GIS can only be effected with the rapid collection of accurate input data. This is particularly true in the case of the South African National Working for Water (WFW) Programme where large volumes of spatial data on alien vegetation infestations are captured throughout the country. Alien vegetation clearing contracts cannot be generated, for WFW, without this data, so that the accurate capture of such data is crucial to the success of the programme. Mapping Invasive Alien Plant (IAP) data within WFW is a perennial problem (Coetzee, pers com, 2002), because not enough mapping is being done to meet the annual requirements of the programme in the various provinces. This is re-iterated by Richardson, 2004, who states that there is a shortage of accurate data on IAP abundance in South Africa. Therefore there is a need to investigate alternate methods of data capture; such as remote sensing, whilst working within the existing WFW data capture standards. The aim of this research was to investigate the use of Landsat ETM imagery as a data capture source for mapping alien vegetation for the WFW Programme in terms of their approved mapping methods, for both automated and manual classification techniques. The automated and manual classification results were compared to control data captured by differential Global Positioning Systems (DGPS). The research tested the various methods of data capture using Landsat ETM images over a range of study sites of varying complexity: a simple grassland area, a medium complexity grassy fynbos site and a complicated indigenous forest site. An important component of the research was to develop a mapping (classification) Ranking System based upon variables identified by WFW as fundamental in data capture decision making: spatial and positional accuracy, time constraints and cost constraints for three typical alien invaded areas. The mapping Ranking System compared the results of the various mapping methods for each factor for the study sites against each other. This provided an indication of which mapping method is the most efficient or suitable for a particular area.
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Gwenzi, David. "Lidar remote sensing of savanna biophysical attributes." Thesis, Colorado State University, 2015. http://pqdtopen.proquest.com/#viewpdf?dispub=3720536.

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Although savanna ecosystems cover approximately 20 % of the terrestrial land surface and can have productivity equal to some closed forests, their role in the global carbon cycle is poorly understood. This study explored the applicability of a past spaceborne Lidar mission and the potential of future missions to estimate canopy height and carbon storage in these biomes.

The research used data from two Oak savannas in California, USA: the Tejon Ranch Conservancy in Kern County and the Tonzi Ranch in Santa Clara County. In the first paper we used non-parametric regression techniques to estimate canopy height from waveform parameters derived from the Ice Cloud and land Elevation Satellite’s Geoscience Laser Altimeter System (ICESat-GLAS) data. Merely adopting the methods derived for forests did not produce adequate results but the modeling was significantly improved by incorporating canopy cover information and interaction terms to address the high structural heterogeneity inherent to savannas. Paper 2 explored the relationship between canopy height and aboveground biomass. To accomplish this we developed generalized models using the classical least squares regression modeling approach to relate canopy height to above ground woody biomass and then employed Hierarchical Bayesian Analysis (HBA) to explore the implications of using generalized instead of species composition-specific models. Models that incorporated canopy cover proxies performed better than those that did not. Although the model parameters indicated interspecific variability, the distribution of the posterior densities of the differences between composition level and global level parameter values showed a high support for the use of global parameters, suggesting that these canopy height-biomass models are universally (large scale) applicable.

As the spatial coverage of spaceborne lidar will remain limited for the immediate future, our objective in paper 3 was to explore the best means of extrapolating plot level biomass into wall-to-wall maps that provide more ecological information. We evaluated the utility of three spatial modeling approaches to address this problem: deterministic methods, geostatistical methods and an image segmentation approach. Overall, the mean pixel biomass estimated by the 3 approaches did not differ significantly but the output maps showed marked differences in the estimation precision and ability of each model to mimic the primary variable’s trend across the landscape. The results emphasized the need for future satellite lidar missions to consider increasing the sampling intensity across track so that biomass observations are made and characterized at the scale at which they vary.

We used data from the Multiple Altimeter Beam Experimental Lidar (MABEL), an airborne photon counting lidar sensor developed by NASA Goddard to simulate ICESat-2 data. We segmented each transect into different block sizes and calculated canopy top and mean ground elevation based on the structure of the histogram of the block’s aggregated photons. Our algorithm was able to compute canopy height and generate visually meaningful vegetation profiles at MABEL’s signal and noise levels but a simulation of the expected performance of ICESat-2 by adjusting MABEL data's detected number of signal and noise photons to that predicted using ATLAS instrument model design cases indicated that signal photons will be substantially lower. The lower data resolution reduces canopy height estimation precision especially in areas of low density vegetation cover.

Given the clear difficulties in processing simulated ATLAS data, it appears unlikely that it will provide the kind of data required for mapping of the biophysical properties of savanna vegetation. Rather, resources are better concentrated on preparing for the Global Ecosystem Dynamics Investigation (GEDI) mission, a waveform lidar mission scheduled to launch by the end of this decade. In addition to the full waveform technique, GEDI will collect data from 25 m diameter contiguous footprints with a high across track density, a requirement that we identified as critically necessary in paper 3. (Abstract shortened by UMI.)

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Thompson, James. "Identifying Subsurface Tile Drainage Systems Utilizing Remote Sensing Techniques." University of Toledo / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=toledo1290141705.

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Oswald, David. "Estimating resilience of Amazonian ecosystems using remote sensing." Thesis, McGill University, 2008. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=18801.

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A model for ecological resilience of Amazonian ecosystems was developed integrating processes such as atmosphere-biosphere coupling with disturbance factors such as fire and climate change. The focus of the study was on the status of ecosystems in the state of Mato Grosso and the possibility of forest to savannah transition was examined using remote sensing data. There was a consistent reduction in EVI during the dry season in Mato Grosso – May through August of each year. The 2005 drought demonstrated a greater dry-season reduction in EVI than normal and there was also a higher frequency of fires (48, 682) than in 2006 (28, 466). There was an increase in fires with distance from the major highways – which is contrary to the results of previous studies. It was estimated that there was a reduction in the amount of forest ecosystems from 2001 to 2006.
Un modèle de résilience écologique de l'écosystème amazonien a été développé, intégrant des processus tels que le couplage atmosphère-biosphère avec des facteurs de perturbation tels que le feu et les changements climatiques. L'objectif de cette étude était d'évaluer l'état des écosystèmes dans l'état du Mato Grosso. Une possible transition de la forêt à la savane a été examinée en utilisant des données de télédétection. Il y a eu une réduction de l'EVI pendant la saison sèche dans le Mato Grosso, de mai à août pour chaque année d'étude. La sécheresse de 2005 a provoqué une réduction de l'EVI plus importante que la normale, en plus d'augmenter la fréquence des feux (48, 682) par rapport à 2006 (28, 466). Il y a eu une augmentation des incendies avec la distance par rapport aux principales autoroutes, ce qui est contraire aux résultats des études précédentes. Il a été estimé qu'il y a eu une réduction du nombre d'écosystèmes forestiers entre 2001 et 2006.
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Books on the topic "Remote sensing {Geographie}"

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Khorram, Siamak. Remote sensing. Berkeley, CA: Springer, 2012.

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C, Nelson Stacy A., Koch Frank H, van der Wiele, Cynthia F., and SpringerLink (Online service), eds. Remote Sensing. Boston, MA: Springer US, 2012.

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Bhatta, Basudeb. Remote sensing and GIS. Delhi: Oxford University Press, 2008.

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Remote sensing and GIS. 2nd ed. New Delhi, India: Oxford University Press, 2011.

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Karin, Reinke, and SpringerLink (Online service), eds. Innovations in Remote Sensing and Photogrammetry. Berlin, Heidelberg: Springer-Verlag Berlin Heidelberg, 2009.

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1964-, Lubin Dan, ed. Polar remote sensing. Berlin: Springer, in association with Praxis Publishing, 2004.

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Remote sensing for GIS managers. Redlands, Ca: ESRI Press, 2005.

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Singh, Sarnam, and Indian Institute of Remote Sensing, eds. Biodiversity & environment: Remote sensing & geographic information system perspectives. Dehra Dun: Indian Institute of Remote Sensing, National Remote Sensing Agency, 2000.

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Geoinformation: Remote sensing, photogrammetry and geographic information systems. London: Taylor & Francis, 2003.

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Barrett, Eric C. Introduction to environmental remote sensing. 3rd ed. London: Chapman & Hall, 1992.

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Book chapters on the topic "Remote sensing {Geographie}"

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Piovan, Silvia Elena. "Remote Sensing." In Springer Geography, 171–97. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42439-8_7.

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Weir, Michael J. C. "Errors in Geographic Information Systems." In Eurocourses: Remote Sensing, 349–55. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-017-2879-9_19.

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Valenzuela, Carlos R. "Basic Principles of Geographic Information Systems." In Eurocourses: Remote Sensing, 279–95. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-017-2879-9_14.

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Weir, Michael J. C. "Computer Systems for Geographic Information Systems." In Eurocourses: Remote Sensing, 297–300. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-017-2879-9_15.

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Zinck, J. Alfred, and Carlos R. Valenzuela. "Soil geographic database: structure and application examples." In Eurocourses: Remote Sensing, 443–75. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-017-2879-9_23.

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Valenzuela, Carlos R., Hans de Brouwer, and Allard Meijerink. "Land Use Model Using a Geographic Information System." In Eurocourses: Remote Sensing, 425–41. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-017-2879-9_22.

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Balzarini, Raffaella, and Nadine Mandran. "Designing Geographic Information for Mountains: Mixed Methods Research." In Remote Sensing and Cognition, 111–35. Boca Raton, FL : Taylor & Francis, 2018.: CRC Press, 2018. http://dx.doi.org/10.1201/9781351040464-6.

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Chen, Liangfu, Jin Chen, Guangjian Yan, Wenjie Fan, Xiaozhou Xin, Chaoyang Wu, Tianjie Zhao, Shenglei Zhang, and Xiaoying Li. "Remote Sensing Modelling and Parameter Inversion." In Springer Geography, 323–38. Singapore: Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-1884-8_14.

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Lambrechts, Johannes, and Saurabh Sinha. "Geographic Information Systems and Remote Sensing." In Microsensing Networks for Sustainable Cities, 165–99. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-28358-6_6.

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Chan, Yupo. "Remote Sensing and Geographic Information Systems." In Location Theory and Decision Analysis, 281–362. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-15663-2_6.

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Conference papers on the topic "Remote sensing {Geographie}"

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Dherete, Pierre, and Jacky Desachy. "Extraction of geographic features using multioperator fusion." In Remote Sensing, edited by Sebastiano B. Serpico. SPIE, 1998. http://dx.doi.org/10.1117/12.331887.

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Bonnefon, Regis, Pierre Dherete, and Jacky Desachy. "Automatic geographic information system upgrading using remote detection images." In Europto Remote Sensing, edited by Sebastiano B. Serpico. SPIE, 2001. http://dx.doi.org/10.1117/12.413900.

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Arvelo-Valencia, Luis, Manuel Arbelo, and Pedro A. Hernandez-Leal. "Effects of geographic distribution of data used to derive satellite SST algorithms." In Remote Sensing, edited by Charles R. Bostater, Jr. and Rosalia Santoleri. SPIE, 2004. http://dx.doi.org/10.1117/12.514198.

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SBARDELLA, P., and R. BARICHELLO. "USING REMOTE SENSING TO UPDATE GEOGRAPHIC INFORMATION SYSTEM." In Proceedings of the First International Workshop on Multitemp 2001. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812777249_0049.

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Czaja, Wojciech, Neil Fendley, Michael Pekala, Christopher Ratto, and I.-Jeng Wang. "Adversarial examples in remote sensing." In SIGSPATIAL '18: 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM, 2018. http://dx.doi.org/10.1145/3274895.3274904.

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Maleki Najafabadi, Saedeh, Alireza Soffianian, and Vahid Rahdari. "Investigation of landscape patterns of the Mouteh Wildlife Refuge using geographic information systems." In Remote Sensing, edited by Ulrich Michel and Daniel L. Civco. SPIE, 2010. http://dx.doi.org/10.1117/12.863184.

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Lizarazo, Ivan, and Paul Elsner. "Fuzzy segmentation for geographic object-based image analysis." In SPIE Europe Remote Sensing, edited by Ulrich Michel and Daniel L. Civco. SPIE, 2009. http://dx.doi.org/10.1117/12.830477.

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Mezzadri-Centeno, Tania, D. Saint-Joan, Jacky Desachy, and F. Vidal. "Approach of the spatiotemporal prediction using vectorial geographic data." In Satellite Remote Sensing III, edited by Daniel Arroyo-Bishop, Roberto Carla, Joan B. Lurie, Carlo M. Marino, A. Panunzi, James J. Pearson, and Eugenio Zilioli. SPIE, 1996. http://dx.doi.org/10.1117/12.262455.

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Perälä, Henna, Juha Jylhä, Minna Väilä, and Ari Visa. "Merging radar data with geographic data for visual land clutter source recognition." In SPIE Remote Sensing, edited by Ulrich Michel, Daniel L. Civco, Manfred Ehlers, and Hermann J. Kaufmann. SPIE, 2008. http://dx.doi.org/10.1117/12.800329.

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Oladi, Jafar, and Delavar Bozorgnia. "Evaluating the ecotourism potentials of Naharkhoran area in Gorgan using remote sensing and geographic information system." In Remote Sensing, edited by Ulrich Michel and Daniel L. Civco. SPIE, 2010. http://dx.doi.org/10.1117/12.860095.

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Reports on the topic "Remote sensing {Geographie}"

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Cihlar, J., L. St-Laurent, M. D'Iorio, and D. Mullins. Remote sensing/geographic information system database for monitoring Canadian landmass. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1994. http://dx.doi.org/10.4095/193932.

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Suhartono, Suhartono, Agoes Soegianto, and Achmad Amzeri. Mapping of land potentially for maize plant in Madura Island-Indonesia using remote sensing data and geographic information systems (GIS). EM International, November 2020. http://dx.doi.org/10.21107/amzeri.2020.1.

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Abstract:
Maize productivity in Indonesia was still low (5.241 tons/ha) compared to the average of the ten largest maize producing countries in the world (6.179 tons/ha). The potential for maize on the island of Madura is approximately 360,000 hectares. The potential for maize cultivation in Madura continues to decrease in land quality due to improper land clearing and land-use change. The purpose of this research was to make a map of land suitability for maize using Remote Sensing Data and Geographic Information System (GIS). The land suitability method for maize plants used satellite imagery as a data source, supported by fieldwork and secondary data. Data analysis using Geographic Information Systems (GIS). The results of the analysis of land suitability modeling based on agroecosystem potential found that most of the Madura area was suitable for maize cultivation. Madura island had a land area of 456,622.3ha for maize cultivation, where 170.379.5 (15.4%) was very appropriate, 211.412.3 ha (46.3%) was appropriate, 160,098.6 (35.1%) was less appropriate, and 14,732.0 ha (3.2%) was not appropriate.
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