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Relevant bibliographies by topics / Land use – Kansas – Data processing

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Academic literature on the topic 'Land use – Kansas – Data processing'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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Journal articles on the topic "Land use – Kansas – Data processing"

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Arsenault, Kristi R., Sujay V. Kumar, James V. Geiger, Shugong Wang, Eric Kemp, David M. Mocko, Hiroko Kato Beaudoing, et al. "The Land surface Data Toolkit (LDT v7.2) – a data fusion environment for land data assimilation systems." Geoscientific Model Development 11, no. 9 (September 5, 2018): 3605–21. http://dx.doi.org/10.5194/gmd-11-3605-2018.

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Abstract. The effective applications of land surface models (LSMs) and hydrologic models pose a varied set of data input and processing needs, ranging from ensuring consistency checks to more derived data processing and analytics. This article describes the development of the Land surface Data Toolkit (LDT), which is an integrated framework designed specifically for processing input data to execute LSMs and hydrological models. LDT not only serves as a preprocessor to the NASA Land Information System (LIS), which is an integrated framework designed for multi-model LSM simulations and data assimilation (DA) integrations, but also as a land-surface-based observation and DA input processor. It offers a variety of user options and inputs to processing datasets for use within LIS and stand-alone models. The LDT design facilitates the use of common data formats and conventions. LDT is also capable of processing LSM initial conditions and meteorological boundary conditions and ensuring data quality for inputs to LSMs and DA routines. The machine learning layer in LDT facilitates the use of modern data science algorithms for developing data-driven predictive models. Through the use of an object-oriented framework design, LDT provides extensible features for the continued development of support for different types of observational datasets and data analytics algorithms to aid land surface modeling and data assimilation.

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Wong, S. N., and M. L. R. Sarker. "Land use/land cover mapping using multi-scale texture processing of high resolution data." IOP Conference Series: Earth and Environmental Science 18 (February 25, 2014): 012185. http://dx.doi.org/10.1088/1755-1315/18/1/012185.

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Guliyeva, S. H. "LAND COVER / LAND USE MONITORING FOR AGRICULTURE FEATURES CLASSIFICATION." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B3-2020 (August 21, 2020): 61–65. http://dx.doi.org/10.5194/isprs-archives-xliii-b3-2020-61-2020.

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Abstract. Remote sensing applications are directed to agricultural observation and monitoring. It has been huge of scientific papers are dedicated to the research of the contribution of remote sensing for agriculture studies. There are several global challenges needed to be considered within agriculture activities. It can be embraced by the main agriculture sector facing the obstacles impacting the production and productivity of the sector. These are the following options that can be pointed out: biomass and yield estimation; vegetation vigor and drought stress monitoring; assessment of crop phenological development; crop acreage estimation and cropland mapping; and mapping of disturbances and Land Use/Land Cover changes. In this study has been undertaken the realization of satellite-based Land Use/Land Cover monitoring based on various optical satellite data. It has been used satellite images taken from satellites AZERSKY, RapidEye, Sentinel-2B and further processed for Land Use/Land Cover classification. Following the complex approach of the supervised and unsupervised classification, the methodology has been used for satellite image processing. As the main satellite imagery for monitoring crop condition were AZERSKY taken during the growing season, from May to June of 2019 year. The study area was some part of the Sheki region, which covers the central part of the southern slope of the Greater Caucasus Mountain Range within Azerbaijan Republic. In this research work satellite imagery processing and mapping has been carried out on the basis of software application of ArcGIS Pro 2.5.

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Seo, Bumsuk, Christina Bogner, Thomas Koellner, and Bjorn Reineking. "Mapping Fractional Land Use and Land Cover in a Monsoon Region: The Effects of Data Processing Options." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 9, no. 9 (September 2016): 3941–56. http://dx.doi.org/10.1109/jstars.2016.2544802.

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Shelestov, Andrii, and Bohdan Yailymov. "The state of actual land use monitoring in the leading countries with use of satellite data." Ukrainian journal of remote sensing, no. 12 (May 11, 2017): 59–66. http://dx.doi.org/10.36023/ujrs.2017.12.93.

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This paper provides the results of the analysis of satellite data usage for monitoring the use of agricultural land in different countries. Satellite data availability, generic data processing and retrieval approaches were analyzed from practical point of view.

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Ferretti, A., D. Colombo, A. Fumagalli, F. Novali, and A. Rucci. "InSAR data for monitoring land subsidence: time to think big." Proceedings of the International Association of Hydrological Sciences 372 (November 12, 2015): 331–34. http://dx.doi.org/10.5194/piahs-372-331-2015.

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Abstract. Satellite interferometric synthetic aperture radar (InSAR) data have proven effective and valuable in the analysis of urban subsidence phenomena based on multi-temporal radar images. Results obtained by processing data acquired by different radar sensors, have shown the potential of InSAR and highlighted the key points for an operational use of this technology, namely: (1) regular acquisition over large areas of interferometric data stacks; (2) use of advanced processing algorithms, capable of estimating and removing atmospheric disturbances; (3) access to significant processing power for a regular update of the information over large areas. In this paper, we show how the operational potential of InSAR has been realized thanks to the recent advances in InSAR processing algorithms, the advent of cloud computing and the launch of new satellite platforms, specifically designed for InSAR analyses (e.g. Sentinel-1a operated by the ESA and ALOS2 operated by JAXA). The processing of thousands of SAR scenes to cover an entire nation has been performed successfully in Italy in a project financed by the Italian Ministry of the Environment. The challenge for the future is to pass from the historical analysis of SAR scenes already acquired in digital archives to a near real-time monitoring program where up to date deformation data are routinely provided to final users and decision makers.

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Mainuri, Zachary Gichuru, John M. Mironga, and Samuel M. Mwonga. "Land Use/Land Cover Changes in a Disturbed River Watershed Kenya." European Journal of Engineering and Formal Sciences 3, no. 2 (August 31, 2019): 29. http://dx.doi.org/10.26417/ejef.v3i2.p29-36.

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Drivers of land use change were captured by the use of DPSIR model where Drivers (D) represented human needs, Pressures (P), human activities, State (S), the ecosystem, Impact (I) services from the ecosystem and Response (R), the decisions taken by land users. Land sat MSS and Land sat ETM+ (path 185, row 31) were used in this study. The Land sat ETM+ image (June 1987, May, 2000 and July, 2014) was downloaded from USGS Earth Resources Observation Systems data website. Remote sensing image processing was performed by using ERDAS Imagine 9.1. Two land use/land cover (LULC) classes were established as forest and shrub land. Severe land cover changes was found to have occurred from 1987-2000, where shrub land reduced by -19%, and forestry reduced by -72%. In 2000 – 2014 shrub land reduced by-45%, and forestry reduced by -64%. Forestry and shrub land were found to be consistently reducing.

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Cehla, Béla, Ferenc Ede Búzás, Sándor Kiss, István Szűcs, and László Posta. "Possibilities of mass valuation in land use in Hungary." Acta Agraria Debreceniensis, no. 1 (June 1, 2021): 59–68. http://dx.doi.org/10.34101/actaagrar/1/9218.

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Technological development makes it possible to simplify and accelerate decision-making processes by adequately processing and evaluating large volumes of data. Sub-data obtained from large data sets have a very important practical role in asset valuation, forecasting and valuing delineated or difficult-to-map areas, or in the context of portfolio management. Land valuation is a separate segment within asset valuation and it requires a specific methodological approach on behalf of evaluators. In this study, the authors compared the transaction data of arable land and the value of other land use categories. Based on empirical assessments, the authors developed proposals for the fast and cost-effective determination of the value of land use categories other than arable land - mainly meadows and pastures.

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Malandra, Francesco, Alessandro Vitali, Carlo Urbinati, and Matteo Garbarino. "70 Years of Land Use/Land Cover Changes in the Apennines (Italy): A Meta-Analysis." Forests 9, no. 9 (September 8, 2018): 551. http://dx.doi.org/10.3390/f9090551.

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Land use science usually adopts a case study approach to investigate landscape change processes, so we considered a meta-analysis an appropriate tool for summarizing general patterns and heterogeneous findings across multiple case studies over a large geographic area. Mountain landscapes in the Apennines (Italy) have undergone significant variations in the last century due to regional and national socio-economic changes. In this work, we reviewed 51 manuscripts from different databases and examined 57 case studies. We explored heterogeneous data sets, adopting a stepwise approach to select the case studies: Step 1, a general overview of the main studies; Step 2, an analysis of the features of the study sites and of land-use/cover transitions; Step 3, a landscape pattern analysis. We standardized the processing methods to obtain a new set of homogeneous data suitable for comparative analysis. After some pre-processing of the selected paper due to the broad heterogeneity of the data, we calculated common landscape metrics ex novo. We obtained digital images used to perform automatic segmentation with eCognition Developer 64 software. Our review indicated that most case studies were in Central and Southern Italy, 83% were examined at local scale, 77% carried out change detection, but only 38% included both change detection and landscape spatial pattern analysis. The results revealed a clear trend of forest expansion (+78%) and the reduction of croplands (−49%) and grasslands (−19%). We did not find significant changes in the landscape spatial patterns.

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Nivedita Priyadarshini, K., M. Kumar, S. A. Rahaman, and S. Nitheshnirmal. "A COMPARATIVE STUDY OF ADVANCED LAND USE/LAND COVER CLASSIFICATION ALGORITHMS USING SENTINEL-2 DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (November 19, 2018): 665–70. http://dx.doi.org/10.5194/isprs-archives-xlii-5-665-2018.

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<p><strong>Abstract.</strong> Land Use/ Land Cover (LU/LC) is a major driving phenomenon of distributed ecosystems and its functioning. Interpretation of remote sensor data acquired from satellites requires enhancement through classification in order to attain better results. Classification of satellite products provides detailed information about the existing landscape that can also be analyzed on temporal basis. Image processing techniques acts as a platform for analysis of raw data using supervised and unsupervised classification algorithms. Classification comprises two broad ranges in which, the analyst specifies the classes by defining the training sites called supervised classification where as automatically clustering of pixels to the defined number of classes namely the unsupervised classification. This study attempts to perform the LU/LC classification for Paonta Sahib region of Himachal Pradesh which is a major industrial belt. The data obtained from Sentinel 2A, from which the stacked bands of 10<span class="thinspace"></span>m resolution are only used. Various classification algorithms such as Minimum Distance, Maximum Likelihood, Parallelepiped and Support Vector Machine (SVM) of supervised classifiers and ISO Data, K-Means of unsupervised classifiers are applied. Using the applied classification results, accuracy assessment is estimated and compared. Of these applied methods, the classification method, maximum likelihood provides highest accuracy and is considered to be the best for LU/LC classification using Sentinel-2A data.</p>

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Dissertations / Theses on the topic "Land use – Kansas – Data processing"

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陳章偉 and Cheung-Wai Jonathan Chan. "A neural network approach to land use/land cover change detection." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1998. http://hub.hku.hk/bib/B31238166.

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Miller, David B. "Decision support systems for land evaluation : theoretical and practical development." Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/24865.

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The challenge of resolving land use allocation and policy questions depends to a large degree on the conversion of data into information, and the effective integration of information into the decision process. Land evaluation is one of the fundamental means of generating information for land planning. Information products have however, been inconsistently and ineffectively used in the decision process. This thesis develops a decision centered approach to land evaluation as a response to this concern. Included in this development is a description of important theoretical concepts, as well as a practical demonstration of the use of decision support systems as a design approach. Initially, a conceptual model is introduced illustrating the technical and use components of information generation, as well as the adaptive design cycle. Various terms and techniques involved in the technical aspects of land evaluation are reviewed. Decision making concepts including decision structure, environment, analysis, and criteria are outlined. Three existing methods of land evaluation are then compared from a use or decision making perspective. Having completed a review of current approaches, Decision Support Systems are introduced as a logical progression towards a decision centered approach. Decision Support System design is demonstrated using a portion of the Central Fraser Valley Regional District as a case study area combined with an interactive microcomputer land planning tool (LANDPLAN). The demonstration emphasizes the advantages of the flexible, interactive capabilities of Decision Support Systems in aiding the decision process. Iterative design is also promoted with several needs identified if a more complete system is to be developed. In particular, data on strategic long term supply and demand factors is required, as well as continuous rating functions for assessing land performance.
Science, Faculty of
Resources, Environment and Sustainability (IRES), Institute for
Graduate

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Nodine, Dewayne J. "Spatial decision support system for evaluation of land use plans based upon storm water runoff impacts : a theoretical framework." Virtual Press, 1996. http://liblink.bsu.edu/uhtbin/catkey/1020175.

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All land uses affect storm water runoff However, different uses of the same site generate varying amounts of runoff Many communities have come to rely upon detention and/or retention basins for controlling the additional runoff resulting from land development. It is argued that this incremental approach to storm water management must be replaced with a more proactive long-term view.To achieve this, more user-friendly software capable of modeling the effect long-range land use plans have on the volume and behavior of storm water runoff is needed. This software, called a Spatial Decision Support System (SDSS), must be capable of guiding the user, who may not be an expert at runoff analysis, through the process and also capable of generating output in various formats understandable by lay persons. This study utilizes a systems analysis technique to develop a theoretical framework for the Storm Water SDSS.
Department of Urban Planning

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Riehl, Sean K. "Property Recommendation System with Geospatial Data Analytics and Natural Language Processing for Urban Land Use." Cleveland State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=csu1590513674513905.

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Kidane, Dawit K. "Rule-based land cover classification model : expert system integration of image and non-image spatial data." Thesis, Stellenbosch : Stellenbosch University, 2005. http://hdl.handle.net/10019.1/50445.

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Thesis (MSc)--Stellenbosch University, 2005.
ENGLISH ABSTRACT: Remote sensing and image processing tools provide speedy and up-to-date information on land resources. Although remote sensing is the most effective means of land cover and land use mapping, it is not without limitations. The accuracy of image analysis depends on a number of factors, of which the image classifier used is probably the most significant. It is noted that there is no perfect classifier, but some robust classifiers achieve higher accuracy results than others. For certain land cover/uses, discrimination based only on spectral properties is extremely difficult and often produces poor results. The use of ancillary data can improve the classification process. Some classifiers incorporate ancillary data before or after the classification process, which limits the full utilization of the information contained in the ancillary data. Expert classification, on the other hand, makes better use of ancillary data by incorporating data directly into the classification process. In this study an expert classification model was developed based on spatial operations designed to identify a specific land cover/use, by integrating both spectral and available ancillary data. Ancillary data were derived either from the spectral channels or from other spatial data sources such as DEM (Digital Elevation Model) and topographical maps. The model was developed in ERDAS Imagine image-processing software, using the expert engineer as a final integrator of the different constituent spatial operations. An attempt was made to identify the Level I land cover classes in the South African National Land Cover classification scheme hierarchy. Rules were determined on the basis of expert knowledge or statistical calculations of mean and variance on training samples. Although rules could be determined by using statistical applications, such as the classification analysis regression tree (CART), the absence of adequate and accurate training data for all land cover classes and the fact that all land cover classes do not require the same predictor variables makes this option less desirable. The result of the accuracy assessment showed that the overall classification accuracy was 84.3% and kappa statistics 0.829. Although this level of accuracy might be suitable for most applications, the model is flexible enough to be improved further.
AFRIKAANSE OPSOMMING: Afstandswaameming-en beeldverwerkingstegnieke kan akkurate informasie oorbodemhulpbronne weergee. Alhoewel afstandswaameming die mees effektiewe manier van grondbedekking en grondgebruikkartering is, is dit nie sonder beperkinge nie. Die akkuraatheid van beeldverwerking is afhanklik van verskeie faktore, waarvan die beeld klassifiseerder wat gebruik word, waarskynlik die belangrikste faktor is. Dit is welbekend dat daar geen perfekte klassifiseerder is nie, alhoewel sekere kragtige klassifiseerders hoër akkuraatheid as ander behaal. Vir sekere grondbedekking en -gebruike is uitkenning gebaseer op spektrale eienskappe uiters moeilik en dikwels word swak resultate behaal. Die gebruik van aanvullende data, kan die klassifikasieproses verbeter. Sommige klassifiseerders inkorporeer aanvullende data voor of na die klassifikasieproses, wat die volle aanwending van die informasie in die aanvullende data beperk. Deskundige klassifikasie, aan die ander kant, maak beter gebruik van aanvullende data deurdat dit data direk in die klassifikasieproses inkorporeer. Tydens hierdie studie is 'n deskundige klassifikasiemodel ontwikkel gebaseer op ruimtelike verwerkings, wat ontwerp is om spesifieke grondbedekking en -gebruike te identifiseer. Laasgenoemde is behaal deur beide spektrale en beskikbare aanvullende data te integreer. Aanvullende data is afgelei van, óf spektrale eienskappe, óf ander ruimtelike bronne soos 'n DEM (Digitale Elevasie Model) en topografiese kaarte. Die model is ontwikkel in ERDAS Imagine beeldverwerking sagteware, waar die 'expert engineer' as finale integreerder van die verskillende samestellende ruimtelike verwerkings gebruik is. 'n Poging is aangewend om die Klas I grondbedekkingklasse, in die Suid-Afrikaanse Nasionale Grondbedekking klassifikasiesisteem te identifiseer. Reëls is vasgestel aan die hand van deskundige begrippe of eenvoudige statistiese berekeninge van die gemiddelde en variansie van opleidingsdata. Alhoewel reëls met behulp van statistiese toepassings, soos die 'classification analysis regression tree (CART)' vasgestel kon word, maak die afwesigheid van genoegsame en akkurate opleidingsdata vir al die grondbedekkingsklasse hierdie opsie minder aantreklik. Bykomend tot laasgenoemde, vereis alle grondbedekkingsklasse nie dieselfde voorspellingsveranderlikes nie. Die resultaat van hierdie akkuraatheidsskatting toon dat die algehele klassifikasie-akkuraatheid 84.3% was en die kappa statistieke 0.829. Alhoewel hierdie vlak van akkuraatheid vir die meeste toepassings geskik is, is die model aanpasbaar genoeg om verder te verbeter.

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麥淑嫻 and Shuk-han Ann Mak. "Automating knowledge acquisition and site-selection in a generic knowledge-based GIS system: a theoreticalstudy." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1999. http://hub.hku.hk/bib/B31240720.

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Mugadza, Precious. "An assessment of the usefulness of spatial agricultural land resource digital data for agritourism and ecotourism." Thesis, Link to the online version, 2005. http://hdl.handle.net/10019/1125.

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Hammam, Yasser, and n/a. "Geographical vector agents." University of Otago. Department of Information Science, 2008. http://adt.otago.ac.nz./public/adt-NZDU20080404.150839.

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Simulating geographic phenomena in a realistic and plausible way requires real-world entities to be abstracted based on the dynamic physical characteristics they exhibit, and treated as individuals in a simulation domain. These processes cannot be adequately supported by the traditional spatial model based on cellular-space such as Cellular Automata (CA). Although this approach has received a great attention as a most favoured technique for simulating the geographic phenomena from different aspects, the need for a generic spatial model to overcome the limitations encountered in such an approach has been raised. This applies particularly to the way real-world entities are represented in a simulation domain regarding their physical characteristics and temporal aspects. In this thesis, a new computational approach for a spatial model suitable for simulating geographic phenomena is presented: the vector agents model. The vector agent is goal-oriented, adaptable, physically defined by an Euclidean geometry and able to change its own geometric characteristics while interacting with other agents in its neighbourhood using a set of rules. The agent is modelled with sensor, state, and strategies. The successful implementation of the model�s architecture allows the representation of the physical characteristics of real-world entities and to observe their complex and dynamic behaviour in a simulation domain. Vector agents have developed out of a need to create a systematic basis for the geometric components of Geographic Automata Systems (GAS), as outlined by Torrens and Benenson (2005). A generic vector agents model was built, then tested and validated from different aspects, from which results demonstrated the model�s efficiency. It is confirmed that vector agents are flexible in producing different complex shapes and patterns for recreating real geographic phenomena through the generic use of three algorithms of geometric manipulation: midpoint displacement by using the relaxed Brownian Motion (fractal-like) algorithm, edge displacement and vertex displacement. The effectiveness of this was initially ascertained visually. A simple heuristic to govern shape growth rate and complexity was derived based on the interplay of the three algorithms. There was a further abstract model comparison against the cellular-agents environment, with the result that vector agents have the ability to emerge patterns similar to what can be produced by cellular-agents with the advantage of representing entities as individuals with their own attributes with realistic geometric boundaries. On the other hand, the city as a complex geographic phenomenon was used as a specific domain for validating the model with a real-world system. The results of the urban land use simulations (driven by simple rules based on three classical urban theories) confirmed that: (a) the model is flexible enough to incorporate various external rules based on real-world systems and (b) the model has a sufficient capability in emerging a variety of patterns under several environments close to actual patterns. The agent environment also proved to be an effective way of easily combining the rules associated with each urban theory (different agents behaved according to different theories). Finally, limitations raised through the development of this work are addressed leading to outline possible extensions of both model computation and the domain of applications.

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Yeung, Kwok-wai Albert, and 楊國偉. "A photogrammetric land information system for urban analysis: a study of the development of Kowloon from1964 to 1979." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1987. http://hub.hku.hk/bib/B31231445.

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Breytenbach, Andre. "GIS-based land suitability assessment and allocation decision-making in a degraded rural environmen." Thesis, Stellenbosch : University of Stellenbosch, 2011. http://hdl.handle.net/10019.1/16599.

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Thesis (MSc)--University of Stellenbosch, 2006.
ENGLISH ABSTRACT: Rural development problems faced by the impoverished communities in the Transkei, South Africa, are numerous, and environmental degradation has already taken much of its toll. By working at a micro-catchment-level both the socio-economic and biophysical appreciation of the land resources were captured as encapsulated in the concept of resource management domains. Participatory decision-making allowed functional land use goals and evaluation criteria to be incorporated into computerised multi-criteria evaluation and multi-objective land use allocation models in order to reach an idealised or more sustainable land use situation. In the execution of the decision-making process seven procedural steps were followed, which are discussed in detail and applied in the case study. Synthesis of the results emphasised the envisaged rural planning potential of the methods used.
AFRIKAANSE OPSOMMING: In terme van plattelandse ontwikkeling staar talle probleme die behoeftige gemeenskappe van Transkei, Suid-Afrika, in die gesig en omgewingsdegradering neem ongehinderd sy tol. Deur op ‘n mikro-opvangsgebied vlak te werk kon beide die sosio-ekonomiese en biofisiese waarde van die gebied se hulpbronne bepaal word en uitgebeeld word in hulpbron bestuursdomeine. Deur deelnemende besluitneming is funksionele grondgebruiksdoelwitte en evaluasie kriteria gebruik in gerekenariseerde meervoudige kriteria evaluering en veeldoelige grondgebruiksaanwysingsmodelle ten einde die ideale of ‘n meer volhoubare grondgebruik situasie te verkry. Vir die uitvoering van die besluitnemingsproses is van sewe opeenvolgende stappe gebruik gemaak en die uitvoering daarvan word in diepte bespreek in hierdie gevallestudie. Sintese van die resultate het die potensiaal van hierdie beoogde landelike beplanningsmetodes beklemtoon.

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Books on the topic "Land use – Kansas – Data processing"

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D, McLaughlin John, ed. Land administration. Oxford: Oxford University Press, 1999.

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Subcommittee, Wisconsin Land Records Committee Institutional Arrangements. Institutional arrangements for land information management in Wisconsin. [Madison, WI]: The Committee, 1987.

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Subcommittee, Wisconsin Land Records Committee Classification and Standards. Report on land records classification and standards. [Madison, WI]: The Committee, 1987.

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Wisconsin Land Records Committee. Subcommittee on Property Records. Final report of the Subcommittee on Property Records. [Madison, WI]: Wisconsin Land Records Committee, 1987.

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Jaroondhampinij, Wattana. A model of computerized parcel-based land information system for the Department of Lands, Thailand. Kensington, N.S.W., Australia: School of Surveying, University of New South Wales, 1989.

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Rainis, Ruslan. The role of geographic information system (GIS) in USAID programme impact evaluation in Zaire: A case of agricultural sector in Bandundu region. [Kinshasa: s.n., 1990.

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Holt, G. A. E. Computer application to real estate: A Canadian utility company application. [Vancouver]: B.C. Hydro, 1985.

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D. D. van der Stelt-Scheele. Formulation and characteristics of GOAL. The Hague: Wetenschappelijke Raad voor het Regeringsbeleid, 1992.

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Deggau, Michael. Pilotstudie Statistisches Informationssystem zur Bodennutzung (STABIS): Voruntersuchung. Bonn-Bad Godesberg: Der Bundesminister, 1989.

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Hitt, Kerie J. Refining 1970's land-use data with 1990 population data to indicate new residential development. Reston, VA: U.S. Geological Survey, 1994.

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Book chapters on the topic "Land use – Kansas – Data processing"

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Arsanjani, Jamal Jokar. "Data Preparation and Processing." In Dynamic land use/cover change modelling, 59–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-23705-8_4.

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Kidwell, Katherine B. "AVHRR Data Acquisition, Processing and Distribution at NOAA." In Advances in the Use of NOAA AVHRR Data for Land Applications, 433–53. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-009-0203-9_18.

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Arino, O. "AVHRR Data Acquisition, Processing and Distribution at the European Space Agency (ESA)." In Advances in the Use of NOAA AVHRR Data for Land Applications, 395–432. Dordrecht: Springer Netherlands, 1996. http://dx.doi.org/10.1007/978-94-009-0203-9_17.

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Mohamed-Ghouse, Zaffar Sadiq, Cheryl Desha, and Luis Perez-Mora. "Digital Earth in Australia." In Manual of Digital Earth, 683–711. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-32-9915-3_21.

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Abstract Australia must overcome a number of challenges to meet the needs of our growing population in a time of increased climate variability. Fortunately, we have unprecedented access to data about our land and the built environment that is internationally regarded for its quality. Over the last two decades Australia has risen to the forefront in developing and implementing Digital Earth concepts, with several key national initiatives formalising our digital geospatial journey in digital globes, open data access and ensuring data quality. In particular and in part driven by a lack of substantial resources in space, we have directed efforts towards world-leading innovation in big data processing and storage. This chapter highlights these geospatial initiatives, including case-uses, lessons learned, and next steps for Australia. Initiatives addressed include the National Data Grid (NDG), the Queensland Globe, G20 Globe, NSW Live (formerly NSW Globe), Geoscape, the National Map, the Australian Geoscience Data Cube and Digital Earth Australia. We explore several use cases and conclude by considering lessons learned that are transferrable for our colleagues internationally. This includes challenges in: 1) Creating an active context for data use, 2) Capacity building beyond ‘show-and-tell’, and 3) Defining the job market and demand for the market.

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Fonte, Cidália Costa, Joaquim António Patriarca, Marco Minghini, Vyron Antoniou, Linda See, and Maria Antonia Brovelli. "Using OpenStreetMap to Create Land Use and Land Cover Maps." In Geospatial Intelligence, 1100–1123. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-8054-6.ch047.

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OpenStreetMap (OSM) is a bottom up community-driven initiative to create a global map of the world. Yet the application of OSM to land use and land cover (LULC) mapping is still largely unexploited due to problems with inconsistencies in the data and harmonization of LULC nomenclatures with OSM. This chapter outlines an automated methodology for creating LULC maps using the nomenclature of two European LULC products: the Urban Atlas (UA) and CORINE Land Cover (CLC). The method is applied to two regions in London and Paris. The results show that LULC maps with a level of detail similar to UA can be obtained for the urban regions, but that OSM has limitations for conversion into the more detailed non-urban classes of the CLC nomenclature. Future work will concentrate on developing additional rules to improve the accuracy of the transformation and building an online system for processing the data.

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Fonte, Cidália Costa, Joaquim António Patriarca, Marco Minghini, Vyron Antoniou, Linda See, and Maria Antonia Brovelli. "Using OpenStreetMap to Create Land Use and Land Cover Maps." In Advances in Geospatial Technologies, 113–37. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-2446-5.ch007.

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OpenStreetMap (OSM) is a bottom up community-driven initiative to create a global map of the world. Yet the application of OSM to land use and land cover (LULC) mapping is still largely unexploited due to problems with inconsistencies in the data and harmonization of LULC nomenclatures with OSM. This chapter outlines an automated methodology for creating LULC maps using the nomenclature of two European LULC products: the Urban Atlas (UA) and CORINE Land Cover (CLC). The method is applied to two regions in London and Paris. The results show that LULC maps with a level of detail similar to UA can be obtained for the urban regions, but that OSM has limitations for conversion into the more detailed non-urban classes of the CLC nomenclature. Future work will concentrate on developing additional rules to improve the accuracy of the transformation and building an online system for processing the data.

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Chowdhury, Rinku Roy, and Laura C. Schneider. "Land Cover and Land Use: Classification and Change Analysis." In Integrated Land-Change Science and Tropical Deforestation in the Southern Yucatan. Oxford University Press, 2004. http://dx.doi.org/10.1093/oso/9780199245307.003.0015.

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Despite its international designation as a hotspot of biodiversity and tropical deforestation (Achard et al. 1988), the micro-scale land-cover mapping of southern Yucatán peninsular region remains surprisingly incomplete, hindering various kinds of research, including that proposed in the SYPR project. This chapter details the methodology for the thematic classification and change detection of land use and cover in the tropical sub-humid environment of the region. A hybrid approach using principal components and texture analyses of Landsat TM data enabled the distinction of land-cover classes at the local scale, including mature and secondary forest, savannas, and cropland/pasture. Results indicate that texture analysis increases the statistical separability of cover class signatures, the magnitude of improvement varying among pairs of land-cover classes. At a local level, the availability of exhaustive training site data over recent history (10–13 years) in a repository of highly detailed land-use sketch maps allows the distinction of greater numbers of land-cover classes, including three successional stages of vegetation. At the regional scale, finely detailed land-cover classes are aggregated for greater ability to generalize in a terrain wherein vegetation exhibits marked regional and seasonal variation in intra-class spectral properties. Post-classification change detection identifies the quantities and spatial pattern of major land-cover changes in a ten-year period in the region. Change analysis results indicate an average annual rate of deforestation of 0.4 per cent, with much regional variation and most change located at three subregional hotspots. Deforestation as well as successional regrowth is highest in a southern hotspot located in the newly colonized southern part of the region, an area where commercial chili production is large. The objectives of this chapter are to describe and evaluate: (1) an experimental methodology that iteratively combines three suites of image-processing techniques (PCA, texture transformation, and NDVI); (2) the statistical separability of distinct land-cover signatures; and (3) a post-classification change detection for the region from 1987 to 1997 in order to derive regional deforestation rates, and identify the spatial pattern of deforestation and secondary forest succession. Specifically, a region encompassing 18,700km2 (those land units completely within the defined region; Fig. 7.1) was mapped using a maximum likelihood supervised classification of lower-order principal components of Landsat TM imagery after tasseled-cap and texture transformations.

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Dale, Peter, and John McLaughlin. "Land Information Management." In Land Administration. Oxford University Press, 2000. http://dx.doi.org/10.1093/oso/9780198233909.003.0012.

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A key component of land administration is the management of land and property related data. Such data may be held in manual or digital form although, increasingly, all land related records are being computerized for ease of storage and retrieval. Data are raw collections of facts that, from a land administration perspective, may be gathered and written down as numbers and text, for instance in a surveyor’s field book, or collected and stored digitally through the use of ‘data loggers’ and computers. They may also be held graphically as on maps or aerial photographs. Data become information when processed into a form meaningful to a decision-maker. The usefulness of this information will depend upon the quality of the data and especially on the extent to which they are up to date, accurate, complete, comprehensive, understandable, and accessible. Even then, good data do not necessarily produce good management decisions since other factors may be involved, such as the qualities of the data user; the converse is however true, namely that poor quality data will almost certainly result in bad decision-making. Land and property related data are increasingly managed within formal land information systems (LIS). As with all information systems, LIS use a combination of human and technical resources, together with a set of organizing procedures, to produce information in support of management activities (Dale and McLaughlin 1988). Increasingly, the technologies that drive the data processing are components of geographic information systems (GIS). There has been much debate about the nature of GIS, some seeing them as sets of hardware, software, and data while others have seen them as all-embracing institutional arrangements of which the technology is only part. In the following discussion, GIS will be treated as the former and restricted to the acquisition and assembly of spatial data; their processing, storage, and maintenance; and their retrieval, analysis, and dissemination. By analogy with the motor car, GIS are the engines that power the car and data are the fuel; operating a transportation system is, however, a more complex process.

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El Mansouri, Loubna, Said Lahssini, Rachid Hadria, Nadia Eddaif, Tarik Benabdelouahab, and Asmae Dakir. "Time Series Multispectral Images Processing for Crops and Forest Mapping." In Geospatial Technologies for Effective Land Governance, 83–106. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-5939-9.ch006.

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This chapter highlights time series image processing for accurate agriculture characterization through two Moroccan experiences. The first case aims at crop mapping. A new classification approach based on multiple classifiers combination (MCC) was developed and applied to multi-temporal enhanced vegetation index (EVI) bands. The whole process is performed in three stages: (1) Landsat data preparation and multi-temporal staked EVI image extraction, (2) MCC construction from six advanced and supervised classifiers, and (3) stacked EVI image classification using the build-up MCC. Some post-classification contextual rules were also added in order to optimize the crops classification and the final parcel shape. In the second case, a post-classification change detection process was implemented to detect changes in forest area. Many classification schemes with different vegetation and texture indices were investigated. The two experiences are cost-effective, reproducible, and transferable. Consequently, they can regularly be used to produce up-to-date land use maps.

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Isbaex, Crismeire, and Ana Margarida Coelho. "The Potential of Sentinel-2 Satellite Images for Land-Cover/Land-Use and Forest Biomass Estimation: A Review." In Forest Biomass - From Trees to Energy. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.93363.

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Mapping land-cover/land-use (LCLU) and estimating forest biomass using satellite images is a challenge given the diversity of sensors available and the heterogeneity of forests. Copernicus program served by the Sentinel satellites family and the Google Earth Engine (GEE) platform, both with free and open services accessible to its users, present a good approach for mapping vegetation and estimate forest biomass on a global, regional, or local scale, periodically and in a repeated way. The Sentinel-2 (S2) systematically acquires optical imagery and provides global monitoring data with high spatial resolution (10–60 m) images. Given the novelty of information on the use of S2 data, this chapter presents a review on LCLU maps and forest above-ground biomass (AGB) estimates, in addition to exploring the efficiency of using the GEE platform. The Sentinel data have great potential for studies on LCLU classification and forest biomass estimates. The GEE platform is a promising tool for executing complex workflows of satellite data processing.

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Conference papers on the topic "Land use – Kansas – Data processing"

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Deng, Dongpo. "Measurement of semantic similarity for land use and land cover classification systems." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.815965.

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Xie, Yunlin, and Mingjun Peng. "Monitoring land use change using remote sensing and GIS." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.815747.

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Yang, Guang, and Gang Qiao. "Data Processing Method for Current Land Use Using GIS Technology." In 2010 Second International Workshop on Education Technology and Computer Science. IEEE, 2010. http://dx.doi.org/10.1109/etcs.2010.336.

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Zeng, Chen, and Yanfang Liu. "Urban land-use intensity extraction based on Quickbird high resolution image." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.815998.

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Hirose, Akira. "Big SAR data processing: Topographic and vegetation/land-use discovery for SAR data structurization." In 2019 6th Asia-Pacific Conference on Synthetic Aperture Radar (APSAR). IEEE, 2019. http://dx.doi.org/10.1109/apsar46974.2019.9048429.

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Niu, Jiqiang, Yaolin Liu, Feng Xu, and Lijun Wei. "Study on optimization of land use structure based on RS and ecological green equivalent." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.815976.

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Lan, Zeying, Yanfang Liu, and Dan Chen. "Extraction of land-use information within rural residential area from high-resolution RS images." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.816112.

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Villalon-Turrubiates, Ivan E. "Distributed land use classification with improved processing time using high-resolution multispectral data." In IGARSS 2012 - 2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012. http://dx.doi.org/10.1109/igarss.2012.6351963.

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Ma, Jianwei, Yayong Sun, Guohui Deng, Shifeng Huang, Yiting Tao, He Zhu, Qiang Teng, and Xianchao Meng. "Evaluation of Different Approaches of Convolutional Neural Networks for Land Use and Land Cover Classification Based on High Resolution Remote Sensing Images." In 2019 IEEE International Conference on Signal, Information and Data Processing (ICSIDP). IEEE, 2019. http://dx.doi.org/10.1109/icsidp47821.2019.9173451.

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Liu, Fang, Zengxiang Zhang, Wenbin Tan, Tingting Dong, and Xianhu Wei. "Dynamic change analyses on the land use/land cover in arid areas of northwest China with RS and GIS: the case of Liangzhou County in Wuwei City." In International Conference on Earth Observation Data Processing and Analysis, edited by Deren Li, Jianya Gong, and Huayi Wu. SPIE, 2008. http://dx.doi.org/10.1117/12.811843.

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Reports on the topic "Land use – Kansas – Data processing"

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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.), April 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 ArcGIS Pro as well as open-source solutions using NodeOpenDroneMap. We built the Monitoring Ecological Restoration with Imagery Tools (MERIT) using Python and leveraging third-party libraries and open-source software capabilities typically unavailable within ArcGIS. MERIT will save US Army Corps of Engineers (USACE) districts significant time in data acquisition, processing, and analysis by allowing a user to move from image acquisition and preprocessing to a final output for decision-making with one application. Although we designed MERIT for use in wetlands research, many tools have regional or global relevancy for a variety of environmental monitoring initiatives.

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