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Letteratura scientifica selezionata sul tema "Land cover"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 31 luglio 2024

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Articoli di riviste sul tema "Land cover"

Azevedo, Thiago, e Lindon Fonseca Matias. "Urban Land Use and Land Cover Mapping". Agua y Territorio / Water and Landscape, n. 23 (7 settembre 2023): e7251. http://dx.doi.org/10.17561/at.23.7251.

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The Brazilian urbanization process produced a complex urban space, with a variety of urban land use and cover as a result. The study of these forms through a classification system is essential, but most current systems don’t capture this complexity, condensing it. Urban forms are difficult to distinguish and classify, resulting in the need for a system with a high degree of detail, for a more accurate urban planning. The objective of this work is to propose a classification system for urban land use and cover, which can demonstrate the multiplicities through remote sensing, using data from CBERS 04A satellite. The methodology surveys the visual elements of remote sensing images, through visual interpretation, relating them to each proposed use and cover class. With this, a classification system was developed that covers the urban space in 17 classes, being an effective way to raise information about the different urban forms.

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TOWNSHEND, J. R. G. "Land cover". International Journal of Remote Sensing 13, n. 6-7 (aprile 1992): 1319–28. http://dx.doi.org/10.1080/01431169208904193.

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Strode, Georgianna, Victor Mesev, Benjamin Thornton, Marjorie Jerez, Thomas Tricarico e Tyler McAlear. "Geovisualization of land use and land cover using bivariate maps and Sankey flow diagrams". Proceedings of the ICA 1 (16 maggio 2018): 1–5. http://dx.doi.org/10.5194/ica-proc-1-106-2018.

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The terms ‘land use’ and ‘land cover’ typically describe categories that convey information about the landscape. Despite the major difference of land use implying some degree of anthropogenic disturbance, the two terms are commonly used interchangeably, especially when anthropogenic disturbance is ambiguous, say managed forestland or abandoned agricultural fields. Cartographically, land use and land cover are also sometimes represented interchangeably within common legends, giving with the impression that the landscape is a seamless continuum of land use parcels spatially adjacent to land cover tracts. We believe this is misleading, and feel we need to reiterate the well-established symbiosis of land uses as amalgams of land covers; in other words land covers are subsets of land use. Our paper addresses this spatially complex, and frequently ambiguous relationship, and posits that bivariate cartographic techniques are an ideal vehicle for representing both land use and land cover simultaneously. In more specific terms, we explore the use of nested symbology as ways to represent graphically land use and land cover, where land cover are circles nested with land use squares. We also investigate bivariate legends for representing statistical covariance as a means for visualizing the combinations of land use and cover. Lastly, we apply Sankey flow diagrams to further illustrate the complex, multifaceted relationships between land use and land cover. Our work is demonstrated on data representing land use and cover data for the US state of Florida.

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Morales, Narkis S., e Ignacio C. Fernández. "Land-Cover Classification Using MaxEnt: Can We Trust in Model Quality Metrics for Estimating Classification Accuracy?" Entropy 22, n. 3 (17 marzo 2020): 342. http://dx.doi.org/10.3390/e22030342.

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MaxEnt is a popular maximum entropy-based algorithm originally developed for modelling species distribution, but increasingly used for land-cover classification. In this article, we used MaxEnt as a single-class land-cover classification and explored if recommended procedures for generating high-quality species distribution models also apply for generating high-accuracy land-cover classification. We used remote sensing imagery and randomly selected ground-true points for four types of land covers (built, grass, deciduous, evergreen) to generate 1980 classification maps using MaxEnt. We calculated different accuracy discrimination and quality model metrics to determine if these metrics were suitable proxies for estimating the accuracy of land-cover classification outcomes. Correlation analysis between model quality metrics showed consistent patterns for the relationships between metrics, but not for all land-covers. Relationship between model quality metrics and land-cover classification accuracy were land-cover-dependent. While for built cover there was no consistent patterns of correlations for any quality metrics; for grass, evergreen and deciduous, there was a consistent association between quality metrics and classification accuracy. We recommend evaluating the accuracy of land-cover classification results by using proper discrimination accuracy coefficients (e.g., Kappa, Overall Accuracy), and not placing all the confidence in model’s quality metrics as a reliable indicator of land-cover classification results.

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Oliveira, J. C., R. A. C. Lamparelli, G. K. D. A. Figueiredo, E. E. Campbell, J. R. Soares, L. A. Monteiro, M. Vianna et al. "PASTURE LAND COVER CHANGE IN SÃO PAULO STATE, BRAZIL". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W12-2020 (6 novembre 2020): 321–26. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w12-2020-321-2020.

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Abstract. Pastures are complex land covers with a variety of land use systems. This land cover occupies large areas in the globe and is mainly used for livestock production. Brazil is one of the largest livestock producers and has extensive pasture areas. We analyzed the pasture land cover change of the São Paulo State between the years 2000 to 2015. São Paulo was chosen as study case due to its large industrial and agricultural importance and its expressive land cover changes over past decades. It was analyzed land covers databases generated by the Brazilian Annual Land Use and Land Cover Mapping Project (MapBiomas Project) – Collection 4. Transition matrix was generated to analyze the land cover change during the period. Gain, loss, total change, net change and swap were calculated in terms of area. Total pasture area decreased but continues the largest land cover of the São Paulo State; with 79.5% of persistence in the area. Main changes were from losses of pastures and gains in agriculture. Most of the changes to pasture came from other non vegetated areas and grassland categories. These results demonstrated the relevance of pastures areas in land cover change dynamics to address land use policy and plan future land use scenarios.

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Comber, A. J. "Land use or land cover?" Journal of Land Use Science 3, n. 4 (22 novembre 2008): 199–201. http://dx.doi.org/10.1080/17474230802465140.

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Nganro, Sudirman, Slamet Trisutomo, Roland Barkey, Mukti Ali, Hidefumi Imura, Akio Onishi, Pei-I. Tsai e Mohd Amirul Mahamud. "Prediction of Future Land Use and Land Cover (LULC) in Makassar City". TATALOKA 23, n. 2 (31 maggio 2021): 183–89. http://dx.doi.org/10.14710/tataloka.23.2.183-189.

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Migration from rural area to urban area increases urban population. It increases and needs for settlements, leading to conversion of agricultural lands into settlement areas. Inconsistent land use compared with spatial planning causes change in land use. Spatial land use expansion can be monitored and predicted by modeling. NetLogo application is a software integrated with Agent-Based Modeling (ABM), which can be used to predict change of land use with various complex parameters. The present study used population growth as a parameter to predict change of land use of Makassar in 2050 based on 2017 land use classification map as the start of the prediction. The analysis result showed that the biggest change of land use happens to Settlement class which is 594.74 hectares and the smallest is Water Body class which is 8.76 hectares.

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Šetka, Josip, Petra Radeljak Kaufmann e Luka Valožić. "Modelling land use and land cover changes in the Lower Neretva Region". Hrvatski geografski glasnik/Croatian Geographical Bulletin 85, n. 1 (2023): 41–63. http://dx.doi.org/10.21861/hgg.2023.85.01.02.

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It has been shown that simulation models are reliable tools for predicting land changes, which contributes to better understanding and management of human impact on the environment. Land use and land cover changes in the Lower Neretva Region between 1990 and 2035 have been analysed and modelled in this study. The final simulation model of future changes was created based on cellular automata and artificial neural networks, implemented in the MOLUSCE plugin for QGIS. In addition, a test simulation model for 2020 was created, which showed high accuracy. Input variables for the final simulation model included a digital elevation model (DEM), slope, distance from water bodies, distance from built-up areas, and population density by settlement in 2011 and 2021. According to the results, forests and grasslands will expand and occupy almost 45% of the area. A slight increase in built-up and agricultural areas is expected, while swamps, water bodies, and sparse vegetation areas will decrease.

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Deshpande, Srinivas, e Rohan S. Gurav. "Change Detection of Land Use and Land Cover Over Ghataprabha River Basin". International Journal of Science and Research (IJSR) 12, n. 10 (5 ottobre 2023): 71–78. http://dx.doi.org/10.21275/sr23930164256.

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Anua, S. N., e W. V. C. Wong. "Utilizing Landsat 8 OLI for land cover classification in plantations area". IOP Conference Series: Earth and Environmental Science 1053, n. 1 (1 giugno 2022): 012027. http://dx.doi.org/10.1088/1755-1315/1053/1/012027.

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Abstract Identifying the land cover in plantations is crucial to assist the management of an area. Today, land cover classification can be achieved using free satellite data. The objective of this study is to perform a supervised classification using LANDSAT 8 OLI to differentiate the land cover in Brumas Camp which consists of non-vegetation, oil palm, forest, and forest plantations. The overall accuracy and Kappa’s coefficients were 71.64% and 0.62, respectively. We found out that the accuracy of classification for non-vegetation is relatively higher compared to vegetation land cover types. The non-vegetation land cover has distinct spectral reflectance which is useful to differentiate between non-vegetation and vegetation land covers.

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Tesi sul tema "Land cover"

Comber, Alexis. "Automated land cover change detection". Thesis, University of Aberdeen, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.248628.

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This thesis describes a generic approach for automated land cover monitoring. Knowledge about land cover is acquired through a knowledge acquisition exercise and used to augment image analysis in order to determine land cover change direction. It is demonstrated that taking a task-oriented approach to the change problem avoids the specificity of more traditional data-oriented approaches. The approach described here involves four key analyses that have contributed to the overall problem solution: Identifying the knowledge used to determine different land cover elements (elicitation and modelling); Land cover remote sensing characteristics; Land cover bio-geographic characteristics; Investigation of the most suitable approach for combining evidence. The results of these investigations were applied to semi-natural change problems: evidence about areas known to have changed was reasoned with and change directions identified by applying the knowledge and interpreter rules of thumb. The results were compared with field surveys of the change areas, and were shown to have correctly identified the land cover change direction in each case.

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Leach, Nicholas Persak. "Hydrologic response of land use and land cover changes". Thesis, University of Iowa, 2015. https://ir.uiowa.edu/etd/1870.

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Throughout the years, land use and land cover (LULC) changes have directly impacted the water cycle in Iowa. To better understand the hydrological response to LULC change, the Hydrological Simulation Program - FORTRAN (HSPF) model will be used to qualitatively evaluate the effects of different LULC’s and quantify the associated adjustments to model parameters. Anecdotal and observational evidence of the hydrologic response will be employed to define how model parameters should be adjusted to represent certain LULC changes. The hydrologic assessment will be of the Turkey River Watershed located in northeast Iowa, which covers approximately 1,693 square miles.

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Skelsey, Chris. "A system for monitoring land cover". Thesis, University of Aberdeen, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.361785.

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Underlying the majority of remotely-sensed data analysis is the assumption that geographical phenomena, such as rivers, heather-moors and the dynamics associated with such objects, can be adequately detected and identified through the use of spectral and other visual information alone. There is a common misconception that any major deficiencies of quantitative analyses are "hardware problems": that by increasing the spectral, spatial, radiometric and temporal resolutions of sensors, geographical phenomena will be identified with similarly increasing accuracy and reliability. This, however, is an unrealistic viewpoint. This thesis has developed a prototype of an automated system based on the principle that by considering the "real-world" properties of the land, a more effective and robust analysis of its dynamic nature can ensue. SYMOLAC is an automated SYstem for MOnitoring LAnd Cover based upon theories of artificial intelligence. It has been developed within a specifically designed hybrid software environment called ETORA, an Environment for Task-Orientated Analysis. This prototype environment allows SYMOLAC to utilise disparate sources of spatial data, to reason with both quantitative and qualitative knowledge, to model disparate domain uncertainties, and to exploit the functionality of third-party software components. Unlike standard approaches, it allows an automated analysis to focus on each particular domain task and how it may best be performed with the available data, knowledge and software resources. The detection of forest felling and the subsequent update of the Land Cover of Scotland (1988) dataset forms the initial application of SYMOLAC. It is concluded that the system's approach is flexible, extensible and adaptable, and demonstrates one way in which satellite imagery can offer potential to the future monitoring of complex land cover change without the need for human intervention.

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Zollet, Simona <1986&gt. "ANALYSIS OF LAND COVER AND LAND COVER CHANGE THROUGH REMOTE SENSING IN THE DOLOMITI BELLUNESI NATIONAL PARK". Master's Degree Thesis, Università Ca' Foscari Venezia, 2015. http://hdl.handle.net/10579/7015.

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This work focuses on the spatial changes in semi-natural grasslands habitats over the past 60 years in the Dolomiti Bellunesi National Park, Italy. Semi-natural grasslands have been created and maintained over the centuries by human activities, resulting in biodiversity-rich landscape mosaics, but the progressive abandonment of traditional grassland management activities such as low-intensity grazing and haymaking after World War II resulted in a widespread phenomenon of forest expansion, declining biodiversity and shrinking or disappearance of grassland areas. Remote sensing technologies and Geographic Information Systems (GIS) represent invaluable tools for understanding the dynamics of this phenomenon; GIS software greatly improves image processing, data organization and quantitative analysis of the multi-temporal remote sensing da-tasets used for studying the dynamics of environmental patterns and processes at the spatial and temporal scale of interest. This in turn can provide support for decision-making and nature con-servation management, an aspect of fundamental importance for semi-natural ecosystems be-cause they require active management to sustain their ecosystem services. In the first part of this work, high-resolution maps of distribution of semi-natural grassland habitats within the Dolomiti Bellunesi National Park (Italy) was created based on two aerial image-ry datasets: the 1954 GAI flight (the oldest available photographic record of the study area, dating back to before the start of mountain abandonment) and the 2012 TELAER flight. The distribution maps were created through the visual interpretation of aerial photographs and manual digitization of grassland polygons with the aid of GIS software. The two distribution maps were subsequently combined and compared to assess the spatial distribution and magnitude of changes over the 58-years time period. The second part of the study explores the potential of studying the reduction of semi-natural grassland habitats by using Landsat satellite images, employing supervised pixel-based methods for classification and change detection and the NDVI vegetation index to increase the separability between different vegetated surfaces. First, a seasonality study was carried out to identify the most suitable period for ensuring maximum land cover separability during the vegetative season; for this purpose, a series of 5 Landsat TM images from 2005 (May-October) were classified inde-pendently through a semi-automated algorithm and their classification accuracies compared. Based on the best date identified by the seasonality study, two Landsat images (from the beginning and the end of Landsat TM data availability period) were chosen for NDVI calculation and NDVI differencing change detection. In this change detection procedure, the NDVI values of the two images were subtracted pixel-by-pixel, resulting in a ΔNDVI change image that can be classified by setting an appropriate threshold to distinguish significant changes (increase or decrease of vegetation cover, where an increase is associated to forest expansion or densification) from situations where the land cover remained essentially unchanged.

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Maluki, Peter Masavi. "MAPPING LAND COVER LAND USE CHANGE IN MBEERE DISTRICT, KENYA". Miami University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=miami1187030316.

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White, Claire McKenzie. "Analysis and Comparison of a Detailed Land Cover Dataset versus the National Land Cover Dataset (NLCD) in Blacksburg, Virginia". Thesis, Virginia Tech, 2011. http://hdl.handle.net/10919/46322.

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While many studies have completed accuracy assessments on the National Land Cover Dataset (NLCD), little research has utilized a detailed digitized land cover dataset, like that available for the Town of Blacksburg, for this comparison. This study aims to evaluate the information available from a detailed land cover dataset and compare it with the National Land Cover Dataset (NLCD) at a localized scale. More specifically, it utilizes the detailed land cover dataset for the Town of Blacksburg to analyze the land cover distribution for varying land uses including single-family residential, multi-family residential, and non-residential. In addition, an application scenario assigns an area-weighted curve number to watersheds based on each land cover dataset. This study exhibits the importance of obtaining detailed land cover datasets for cities and towns. Furthermore, it shows the comprehensive information and subsequent quantifications that can be surmised from a detailed land cover dataset.
Master of Science

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Lawrence, Peter. "Climate impacts of Australian land cover change /". [St. Lucia, Qld.], 2004. http://www.library.uq.edu.au/pdfserve.php?image=thesisabs/absthe18055.pdf.

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Rosa, Isabel Maria Duarte. "Modelling land cover change in tropical rainforests". Thesis, Imperial College London, 2013. http://hdl.handle.net/10044/1/40148.

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Tropical deforestation is one of the most important drivers of biodiversity loss and carbon emissions. This thesis seeks to analyse the dynamics of tropical deforestation and develop a probabilistic model that predicts land cover change (LCC) in the tropics. The main findings from the analysis of the Brazilian Amazon deforestation dynamics are that large clearings comprised progressively smaller amounts of total annual deforestation while the number of smaller clearings remained unchanged over time. These changes were coincident with the implementation of conservation policies by the government. The review of LCC models presented here showed that this modelling community would benefit from improving: the openness to share model inputs, code and outputs; model validations; and standardised frameworks to be used for model comparisons. The modelling framework developed aimed to tackle the limitations found before and two scenarios of deforestation in the Brazilian Amazon were simulated. For both scenarios forest next to roads and areas already deforested were found to be more likely to be deforested. States in the south and east of the region showed high predicted probability of losing nearly all forest outside of protected areas by 2050. The release of carbon to the atmosphere is an important consequence of tropical deforestation. Even if deforestation had ended in 2010 there would still be large quantities of carbon to be released. The amount of carbon released immediately is higher than the one committed for future release in the first few years of analysis, but presently these accounted for at least two-thirds of total carbon emissions. Finally, the drivers of LCC were found to vary among transition types, but less so through time. The accuracy of the model predictions was heavily dependent on the year calibrated, suggesting that a widespread reliance on single calibration time period may be providing biased predictions of future LCC.

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Makido, Yasuyo Kato. "Land cover mapping at sub-pixel scales". Diss., Connect to online resource - MSU authorized users, 2006.

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陳章偉 e 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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Libri sul tema "Land cover"

Harrison, Mukunda, e Kenya. Dept. of Resource Surveys and Remote Sensing., a cura di. Land cover/land use in Kiambu District. Nairobi, Kenya: Dept. of Resource Surveys and Remote Sensing, Ministry of Environment and Natural Resources, 2008.

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Lambin, Eric F., e Helmut Geist, a cura di. Land-Use and Land-Cover Change. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-32202-7.

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Mölders, Nicole. Land-Use and Land-Cover Changes. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-1527-1.

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Meiner, Andrus. Eesti maakate: CORINE Land Cover projekti täitmine Eestis = Land cover of Estonia : implementation of CORINE Land Cover project in Estonia. Tallinn: EV Keskkonnaministeeriumi Info- ja Tehnokeskus, 1999.

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National Workshop for India (2005 Indian Institue of Technology). National Workshop for India: 15-19 February 2005 : workshop report. Rome: Food and Agriculture Organization of the United Nations, 2006.

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1959-, Taniguchi Makoto, e Western Pacific Geophysics Meeting (1996 : Brisbane, Qld.), a cura di. Subsurface hydrological responses to land cover and land use changes. Boston: Kluwer Academic Publishers, 1997.

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author, Jiang Luguang, e Zhang Jinghua author, a cura di. Lancang Jiang liu yu yu da Xianggelila di qu tu di li yong yu tu di fu bei bian hua kao cha yan jiu. Beijing: Ke xue chu ban she, 2017.

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Gaur, Mahesh K. Impact of changing pattern of landuse/land cover on the desert eco-system of Rajasthan: A study based on remote sensing technique of Pokaran Tehsil, Jaisalmer district. Jaisalmer: Mahesh Kumar Gaur, 2001.

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author, Kabir Uddin, Shrestha Basanta Raj author, International Centre for Integrated Mountain Development e HKKH Partnership Project, a cura di. Land cover mapping in the HKKH region: Cases from three mountain protected areas. Kathmandu: Published on behalf of the HKKH Partnership Project by International Centre for Integrated Mountain Development, 2009.

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Geological Survey (U.S.), a cura di. Land cover characterization program. [Washington, D.C.?]: U.S. Dept. of the Interior, U.S. Geological Survey, 1997.

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Capitoli di libri sul tema "Land cover"

Mölders, Nicole. "Impact of Land-Cover and Land-Cover Changes". In Land-Use and Land-Cover Changes, 39–115. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-1527-1_3.

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Büttner, György. "CORINE Land Cover and Land Cover Change Products". In Land Use and Land Cover Mapping in Europe, 55–74. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-7969-3_5.

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Maude, Alaric. "Land cover change". In Thinking Geographically, 219–46. London: Routledge, 2023. http://dx.doi.org/10.4324/9781003376668-11.

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Chu, Duo. "Land-Cover Change". In Remote Sensing of Land Use and Land Cover in Mountain Region, 133–54. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7580-4_7.

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Chu, Duo. "Land-Cover Classification". In Remote Sensing of Land Use and Land Cover in Mountain Region, 181–94. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-7580-4_9.

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Feranec, Ján, e Tomáš Soukup. "Land Cover and Land Use". In Recent Landform Evolution, 39–44. Dordrecht: Springer Netherlands, 2011. http://dx.doi.org/10.1007/978-94-007-2448-8_4.

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Probeck, Markus, Anja Colgan, Tatjana Krimly, Marcelo Zárate e Karl Schneider. "Land Use and Land Cover". In Regional Assessment of Global Change Impacts, 83–89. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-16751-0_9.

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Wang, Jing’ai, Shunlin Liang e Peijun Shi. "Land Cover and Land Use". In World Regional Geography Book Series, 121–52. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04158-7_6.

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Paudel, Basanta, e Basanta Raj Adhikari. "Land Use and Land Cover". In The Soils of Nepal, 41–51. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80999-7_5.

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Saikia, Anup. "Land-Use Land-Cover Change". In Over-Exploitation of Forests, 29–52. Cham: Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-01408-1_5.

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Atti di convegni sul tema "Land cover"

Fukue, Kiyonari, Haruhisa Shimoda e Toshibumi Sakata. "Spatial land cover classification based on land cover elements". In Europto Remote Sensing, a cura di Sebastiano B. Serpico. SPIE, 2001. http://dx.doi.org/10.1117/12.413901.

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Hongri Zhang, Xingyuan Xiao, Ying Li e Min Wang. "Study on land use/land cover change". In 2011 International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE). IEEE, 2011. http://dx.doi.org/10.1109/rsete.2011.5965806.

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Boriah, Shyam, Vipin Kumar, Michael Steinbach, Christopher Potter e Steven Klooster. "Land cover change detection". In the 14th ACM SIGKDD international conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1401890.1401993.

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Reis, Mariane S., Sidnei J. S. Sant'Anna, Luciano V. Dutra, Maria Isabel S. Escada e Eliana Pantaleao. "The use of land cover change likelihood for improving land cover classification". In 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IEEE, 2017. http://dx.doi.org/10.1109/igarss.2017.8127683.

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Wang, Yi-Chen, e Chen-Chieh Feng. "Visualizing research of land use land cover change". In Geoinformatics 2008 and Joint conference on GIS and Built Environment: The Built Environment and its Dynamics, a cura di Lin Liu, Xia Li, Kai Liu, Xinchang Zhang e Xinhao Wang. SPIE, 2008. http://dx.doi.org/10.1117/12.812709.

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Hou, Ankai, Abrado Blankson Samuel, Mujie Li, Zezhong Zheng, Jun Xia, Xiang Zhang e Guoqing Zhou. "Land Use and Land Cover Change of Ghana". In IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2020. http://dx.doi.org/10.1109/igarss39084.2020.9323303.

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Waghela, Harsh, Saurin Patel, Pooja Sudesan, Soham Raorane e Rohan Borgalli. "Land Use Land Cover Classification using Machine Learning". In 2022 International Conference on Automation, Computing and Renewable Systems (ICACRS). IEEE, 2022. http://dx.doi.org/10.1109/icacrs55517.2022.10029176.

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Lymburner, Leo, Peter Tan, Alexis McIntyre, Adam Lewis e Medhavy Thankappan. "Dynamic Land Cover Dataset version 2: 2001-now…a land cover odyssey". In IGARSS 2013 - 2013 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2013. http://dx.doi.org/10.1109/igarss.2013.6723532.

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Muad, Anuar M., e Giles M. Foody. "Super-resolution analysis for accurate mapping of land cover and land cover pattern". In IGARSS 2010 - 2010 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2010. http://dx.doi.org/10.1109/igarss.2010.5649083.

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Berry, M. W., e K. S. Minser. "Distributed land-cover change simulation". In the fifth ACM international workshop. New York, New York, USA: ACM Press, 1997. http://dx.doi.org/10.1145/267825.267843.

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Rapporti di organizzazioni sul tema "Land cover"

Wang, S. Land use/cover. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2016. http://dx.doi.org/10.4095/298873.

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Latifovic, R. Canada's land cover. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2019. http://dx.doi.org/10.4095/313355.

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Latifovic, R. Canada's land cover. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2019. http://dx.doi.org/10.4095/315659.

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Hansen, Leslie. Los Alamos Land Cover. Office of Scientific and Technical Information (OSTI), marzo 2022. http://dx.doi.org/10.2172/1855122.

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Latifovic, R., Z. L. Zhu, J. Cihlar, C. Giri e I. Olthof. Land Cover Mapping of North and Central America - Global Land Cover 2000. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2004. http://dx.doi.org/10.4095/219994.

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Bauer, Marvin, Brian Loeffelholz e Doug Shinneman. Border Lakes land-cover classification. Newtown Square, PA: U.S. Department of Agriculture, Forest Service, Northern Research Station, 2009. http://dx.doi.org/10.2737/nrs-rmap-1.

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Gould, William A., Sebastian Martinuzzi e Olga M. Ramos Gonzalez. Developed land cover of Puerto Rico. San Juan, PR: U.S. Department of Agriculture, Forest Service, International Institute of Tropical Forestry, 2008. http://dx.doi.org/10.2737/iitf-rmap-10.

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Cihlar, J., R. Latifovic, J. M. Chen, J. Beaubien, Z. Li e S. Magnussen. Selecting Representative High Resolution Sample Images for Land Cover Studies. Part 2: Application to Estimating Land Cover Composition. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2000. http://dx.doi.org/10.4095/219492.

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Cleary, Summers. Land Cover Summary Statistics for National Capital Region Park Units. National Park Service, 2024. http://dx.doi.org/10.36967/2301309.

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Abstract (sommario):

This report documents the acquisition of source data, and calculation of land cover summary statistics datasets for ten National Park Service National Capital Region park units and three custom areas of analysis: Catoctin Mountain Park, Chesapeake & Ohio Canal National Historical Park, George Washington Memorial Parkway, Harpers Ferry National Historical Park, Manassas National Battlefield Park, Monocacy National Battlefield, National Capital Parks - East, Prince William Forest Park, Rock Creek Park, Wolf Trap National Park for the Performing Arts, and the three custom areas of analysis - National Capital Parks - East: Oxon Cove Park, Oxon Hill Farm, Piscataway Park, National Capital Parks - East: Greenbelt Park and Baltimore-Washington Parkway, and National Capital Parks - East: DC and Suitland Parkway. The source data and land cover calculations are available for use within the National Park Service (NPS) Inventory and Monitoring Program. Land cover summary statistics datasets can be calculated for all geographic regions within the extent of the NPS; this report includes statistics calculated for the conterminous United States. The land cover summary statistics datasets are calculated from multiple sources, including Multi-Resolution Land Characteristics Consortium products in the National Land Cover Database (NLCD) and United States Geological Survey?s (USGS) Earth Resources Observation and Science (EROS) Center products in the Land Change Monitoring, Assessment, and Projection (LCMAP) raster dataset. These summary statistics calculate land cover at up to three classification scales: Level 1, modified Anderson Level 2, and Natural versus Converted land cover. The output land cover summary statistics datasets produced here for the ten National Capital Region park units and three custom areas of analysis utilize the most recent versions of the source datasets (NLCD and LCMAP). These land cover summary statistics datasets are used in the NPS Inventory and Monitoring Program, including the NPS Environmental Settings Monitoring Protocol and may be used by networks and parks for additional efforts.

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Cleary, Summers, Michelle Kinseth e Michelle Uchitel. Land Cover Summary Statistics for Antietam National Battlefield. National Park Service, 2023. http://dx.doi.org/10.36967/2301818.

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Abstract (sommario):

This technical report documents the acquisition of source data, and calculation of land cover summary statistics datasets for Antietam National Battlefield. The source data and land cover calculations are available for use within the National Park Service (NPS) Inventory & Monitoring (I&M) Program. Land cover summary statistics datasets can be calculated for all geographic regions within the extent of the NPS; this report includes statistics calculated for the conterminous United States. The land cover summary statistics datasets are calculated from multiple sources, including Multi-Resolution Land Characteristics Consortium products in the National Land Cover Database (NLCD) and United States Geological Survey?s (USGS) Earth Resources Observation and Science (EROS) Center products in the Land Change Monitoring, Assessment, and Projection (LCMAP) raster dataset. These summary statistics calculate land cover at up to three classification scales: Level 1, modified Anderson Level 2, and Natural versus Converted land cover. The output land cover summary statistics datasets produced here for Antietam National Battlefield utilize the most recent versions of the source datasets (NLCD and LCMAP). These land cover summary statistics datasets are used in the NPS I&M Program, including the NPS Environmental Settings Monitoring Protocol and may be used by networks and parks for additional efforts.

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