Relevant bibliographies by topics / Vegetation mapping – Remote sensing

Contents

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

Academic literature on the topic 'Vegetation mapping – Remote sensing'

Author: Grafiati
Published: 4 June 2021
Last updated: 11 February 2022

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Vegetation mapping – Remote sensing.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Vegetation mapping – Remote sensing"

1

Nagasawa, Ryota, and Yoshiyuki Hioki. "Vegetation Mapping using Remote Sensing and GIS." Landscape Ecology and Management 11, no. 1 (2006): 1–2. http://dx.doi.org/10.5738/jale.11.1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Hioki, Yoshiyuki, and Ryota Nagasawa. "Vegetation mapping using remote sensing and GIS." Landscape Ecology and Management 11, no. 2 (2007): 105. http://dx.doi.org/10.5738/jale.11.105.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Laidler, Gita J., and Paul Treitz. "Biophysical remote sensing of arctic environments." Progress in Physical Geography: Earth and Environment 27, no. 1 (March 2003): 44–68. http://dx.doi.org/10.1191/0309133303pp358ra.

Full text
Abstract:
Various remote sensing studies have been conducted to investigate methods and applications of vegetation mapping and analysis in arctic environments. The general purpose of these studies is to extract information on the spatial and temporal distribution of vegetation as required for tundra ecosystem and climate change studies. Because of the recent emphasis on understanding natural systems at large spatial scales, there has been an increasing interest in deriving biophysical variables from satellite data. Satellite remote sensing offers potential for extrapolating, or ‘scaling up’ biophysical measures derived from local sites, to landscape and even regional scales. The most common investigations include mapping spatial vegetation patterns or assessing biophysical tundra characteristics, using medium resolution satellite data. For instance, Landsat TM data have been shown to be useful for broad vegetation mapping and analysis, but not accurately representative of smaller vegetation communities or local spatial variation. It is anticipated, that high spatial resolution remote sensing data, now available from commercial remote sensing satellites, will provide the necessary sampling scale to link field data to remotely sensed reflectance data. As a result, it is expected that these data will improve the representation of biophysical variables over sparsely vegetated regions of the Arctic.
APA, Harvard, Vancouver, ISO, and other styles
4

Bobbe, Tom, Henry Lachowski, Paul Maus, Jerry Greer, and Chuck Dull. "A primer on mapping vegetation using remote sensing." International Journal of Wildland Fire 10, no. 4 (2001): 277. http://dx.doi.org/10.1071/wf01029.

Full text
Abstract:
This paper was presented at the conference ‘Integrating spatial technologies and ecological principles for a new age in fire management’, Boise, Idaho, USA, June 1999 The use of information based upon remotely sensed data is a central factor in our 21st Century society. Scientists in land management agencies especially require accurate and current geospatial information to effectively implement ecosystem management. The increasing need to collect data across diverse landscapes, scales, and ownerships has resulted in a wider application of remote sensing, Geographic Information Systems (GIS) and associated geospatial technologies for natural resource applications. This paper summarizes the use of digital remotely sensed data for vegetation mapping. Key steps in preparing vegetation maps are described. These steps include defining project requirements and classification schemes, use of reference data, classification procedures, and assessing accuracy. The role of field personnel and inventory data is described. Case studies and applications of vegetation mapping on national forest land are also included. remote sensing, GIS, mapping, geospatial, project planning.
APA, Harvard, Vancouver, ISO, and other styles
5

Xie, Y., Z. Sha, and M. Yu. "Remote sensing imagery in vegetation mapping: a review." Journal of Plant Ecology 1, no. 1 (March 1, 2008): 9–23. http://dx.doi.org/10.1093/jpe/rtm005.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Zhang, Liyuan, Huihui Zhang, Yaxiao Niu, and Wenting Han. "Mapping Maize Water Stress Based on UAV Multispectral Remote Sensing." Remote Sensing 11, no. 6 (March 13, 2019): 605. http://dx.doi.org/10.3390/rs11060605.

Full text
Abstract:
Mapping maize water stress status and monitoring its spatial variability at a farm scale are a prerequisite for precision irrigation. High-resolution multispectral images acquired from an unmanned aerial vehicle (UAV) were used to evaluate the applicability of the data in mapping water stress status of maize under different levels of deficit irrigation at the late vegetative, reproductive and maturation growth stages. Canopy temperature, field air temperature and relative humidity obtained by a handheld infrared thermometer and a portable air temperature/relative humidity meter were used to establish a crop water stress index (CWSI) empirical model under the weather conditions in Ordos, Inner Mongolia, China. Nine vegetation indices (VIs) related to crop water stress were derived from the UAV multispectral imagery and used to establish CWSI inversion models. The results showed that non-water-stressed baseline had significant difference in the reproductive and maturation stages with an increase of 2.1 °C, however, the non-transpiring baseline did not change significantly with an increase of 0.1 °C. The ratio of transformed chlorophyll absorption in reflectance index (TCARI) and renormalized difference vegetation index (RDVI), and the TCARI and soil-adjusted vegetation index (SAVI) had the best correlations with CWSI. R2 values were 0.47 and 0.50 for TCARI/RDVI and TCARI/SAVI at the reproductive and maturation stages, respectively; and 0.81 and 0.80 for TCARI/RDVI and TCARI/SAVI at the late reproductive and maturation stages, respectively. Compared to CWSI calculated by on-site measurements, CWSI values retrieved by VI-CWSI regression models established in this study had more abilities to assess the field variability of crop and soil. This study demonstrates the potentiality of using high-resolution UAV multispectral imagery to map maize water stress.
APA, Harvard, Vancouver, ISO, and other styles
7

Pascucci, Simone, Stefano Pignatti, Raffaele Casa, Roshanak Darvishzadeh, and Wenjiang Huang. "Special Issue “Hyperspectral Remote Sensing of Agriculture and Vegetation”." Remote Sensing 12, no. 21 (November 9, 2020): 3665. http://dx.doi.org/10.3390/rs12213665.

Full text
Abstract:
The advent of up-to-date hyperspectral technologies, and their increasing performance both spectrally and spatially, allows for new and exciting studies and practical applications in agriculture (soils and crops) and vegetation mapping and monitoring atregional (satellite platforms) andwithin-field (airplanes, drones and ground-based platforms) scales. Within this context, the special issue has included eleven international research studies using different hyperspectral datasets (from the Visible to the Shortwave Infrared spectral region) for agricultural soil, crop and vegetation modelling, mapping, and monitoring. Different classification methods (Support Vector Machine, Random Forest, Artificial Neural Network, Decision Tree) and crop canopy/leaf biophysical parameters (e.g., chlorophyll content) estimation methods (partial least squares and multiple linear regressions) have been evaluated. Further, drone-based hyperspectral mapping by combining bidirectional reflectance distribution function (BRDF) model for multi-angle remote sensing and object-oriented classification methods are also examined. A review article on the recent advances of hyperspectral imaging technology and applications in agriculture is also included in this issue. The special issue is intended to help researchers and farmers involved in precision agriculture technology and practices to a better comprehension of strengths and limitations of the application of hyperspectral measurements for agriculture and vegetation monitoring. The studies published herein can be used by the agriculture and vegetation research and management communities to improve the characterization and evaluation of biophysical variables and processes, as well as for a more accurate prediction of plant nutrient using existing and forthcoming hyperspectral remote sensing technologies.
APA, Harvard, Vancouver, ISO, and other styles
8

Mohd Salleh, M. R., N. I. Ishak, K. A. Razak, M. Z. Abd Rahman, M. A. Asmadi, Z. Ismail, and M. F. Abdul Khanan. "GEOSPATIAL APPROACH FOR LANDSLIDE ACTIVITY ASSESSMENT AND MAPPING BASED ON VEGETATION ANOMALIES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W9 (October 30, 2018): 201–15. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w9-201-2018.

Full text
Abstract:
<p><strong>Abstract.</strong> Remote sensing has been widely used for landslide inventory mapping and monitoring. Landslide activity is one of the important parameters for landslide inventory and it can be strongly related to vegetation anomalies. Previous studies have shown that remotely sensed data can be used to obtain detailed vegetation characteristics at various scales and condition. However, only few studies of utilizing vegetation characteristics anomalies as a bio-indicator for landslide activity in tropical area. This study introduces a method that utilizes vegetation anomalies extracted using remote sensing data as a bio-indicator for landslide activity analysis and mapping. A high-density airborne LiDAR, aerial photo and satellite imagery were captured over the landslide prone area along Mesilau River in Kundasang, Sabah. Remote sensing data used in characterizing vegetation into several classes of height, density, types and structure in a tectonically active region along with vegetation indices. About 13 vegetation anomalies were derived from remotely sensed data. There were about 14 scenarios were modeled by focusing in 2 landslide depth, 3 main landslide types with 3 landslide activities by using statistical approach. All scenarios show that more than 65% of the landslides are captured within 70% of the probability model indicating high model efficiency. The predictive model rate curve also shows that more than 45% of the independent landslides can be predicted within 30% of the probability model. This study provides a better understanding of remote sensing data in extracting and characterizing vegetation anomalies induced by hillslope geomorphology processes in a tectonically active region in Malaysia.</p>
APA, Harvard, Vancouver, ISO, and other styles
9

BELLUCO, E., M. CAMUFFO, S. FERRARI, L. MODENESE, S. SILVESTRI, A. MARANI, and M. MARANI. "Mapping salt-marsh vegetation by multispectral and hyperspectral remote sensing." Remote Sensing of Environment 105, no. 1 (November 15, 2006): 54–67. http://dx.doi.org/10.1016/j.rse.2006.06.006.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Rao, Mahesh, Zachary Silber-Coats, Sharon Powers, Lawrence Fox III, and Abduwasit Ghulam. "Mapping drought-impacted vegetation stress in California using remote sensing." GIScience & Remote Sensing 54, no. 2 (March 4, 2017): 185–201. http://dx.doi.org/10.1080/15481603.2017.1287397.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Vegetation mapping – Remote sensing"

1

Lymburner, Leo. "Mapping riparian vegetation functions using remote sensing and terrain analysis." Connect to thesis, 2005. http://repository.unimelb.edu.au/10187/2821.

Full text
Abstract:
Land use practices over the last 200 years have dramatically altered the distribution and amount of riparian vegetation throughout many catchments in Australia. This has lead to a number of negative impacts including a decrease in water quality, an increase in sediment transport and a decrease in the quality of terrestrial and aquatic habitats. The task of restoring the functions of riparian zones is an enormous one and requires spatial and temporal prioritisation. An analysis of the existing and historical functions of riparian zones and their spatial distribution is a major aid to this process and will enable efficient use of remediation resources. The approach developed in this thesis combines remote sensing, field measurement and terrain analysis to describe the distribution of five riparian zone functions: sediment trapping, bank stabilization, denitrification, stream shading and large woody debris production throughout a large semi-arid catchment in central Queensland.
APA, Harvard, Vancouver, ISO, and other styles
2

Heumann, Benjamin W. "Mapping vegetation phenology in the Sahel and Soudan, Africa, 1982 to 2005." Thesis, McGill University, 2006. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=101139.

Full text
Abstract:
The Sahel and Soudan regions of Africa are hot-spots for vegetation change due to climatic and anthropogenic causes. Recent studies using remote sensing have demonstrated that vegetation recovery has occurred across the region questioning the occurrence of widespread land degradation in the region. These studies have focused on proxy measurements of net primary productivity, but have not addressed seasonal characteristics of vegetation such as phenology. This thesis examines how vegetation phenology has changed from 1982--2005 in the Sahel and Soudan regions and how phenology relates to observed bio-productivity and regional precipitation patterns. This is the first research to assess multi-decadal phenology change for a tropical ecosystem. Results show that while bio-productivity has significantly increased in the Sahel, significant phenology change has primarily been detected in the Soudan region. Furthermore, the relationship between phenology and bio-productivity and precipitation differs between the Sahel and Soudan. This research demonstrates the utility of measuring phenological change of a tropical ecosystem for vegetation monitoring applications.
APA, Harvard, Vancouver, ISO, and other styles
3

Mayr, Thomas. "The evaluation of PMI data for vegetation mapping in the Somerset Levels." Thesis, Cranfield University, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.281899.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Palm, Fredrik. "Urban Vegetation Mapping Using Remote Sensing Techniques : A Comparison of Methods." Thesis, Stockholms universitet, Institutionen för naturgeografi, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-117108.

Full text
Abstract:
The aim of this study is to compare remote sensing methods in the context of a vegetation mapping of an urban environment. The methods used was (1) a traditional per-pixel based method; maximum likelihood supervised classification (ENVI), (2) a standard object based method; example based feature extraction (ENVI) and (3) a newly developed method; Window Independent Contextual Segmentation (WICS) (Choros Cognition). A four-band SPOT5 image with a pixel size of 10x10m was used for the classifications. A validation data-set was created using a ortho corrected aerial image with a pixel size of 1x1m. Error matrices was created by cross-tabulating the classified images with the validation data-set. From the error matrices, overall accuracy and kappa coefficient was calculated. The object-based method performed best with a overall accuracy of 80% and a kappa value of 0.6, followed by the WICS method with an overall accuracy of 77% and a kappa value of 0.53, placing the supervised classification last with an overall accuracy of 71% and a kappa value of 0.38. The results of this study suggests object-based method and WICS to perform better than the supervised classification in an urban environment.
APA, Harvard, Vancouver, ISO, and other styles
5

Miglhorance, Edmar. "Mapping Wild Leek with UAV and Satellite Remote Sensing." Thesis, Université d'Ottawa / University of Ottawa, 2019. http://hdl.handle.net/10393/38865.

Full text
Abstract:
Wild leek (Allium tricoccum) is a spring ephemeral of northeastern North America. In the Canadian province of Quebec, it is listed as threatened due to human harvesting, and in Gatineau Park its presence is used as an indicator of human impact. Wild leek grows in patches on the forest floor, and before the tree canopy develops its green leaves are clearly visible through the bare branches of deciduous forests, allowing it to be observed with optical remote sensing. This study developed and tested a new method for monitoring wild leek across large geographic areas by integrating field observations, UAV video, and satellite imagery. Three-cm resolution orthomosaics were generated for five <0.1 km2 sites from the UAV video using Structure-from-Motion, segmented, and classified into wild leek (WL) or other (OT) surface types using a simple greenness threshold. The resulting maps, validated using the field observations, had a high overall accuracy (F1-scores between 0.64 to 0.94). These maps were then used to calibrate a linear model predicting the per-pixel percentage cover of wild leek (%WL) from NDVI in the satellite imagery. The linear model calibrated for a Sentinel-2 image from 2018, covering all of Gatineau Park (~361 km2), allowed %WL to be predicted with an RMSE of 10.32. A similar model calibrated for a WorldView-2 image from 2018 was noisy (RMSE = 37.64), though much improved by resampling this image to match the spatial resolution of Sentinel-2, due to MAUP scale effect (RMSE = 13.06). Testing the potential for satellite-based monitoring of wild leek, the %WL prediction errors were similar when a new linear model was developed using the Sentinel-2 image from 2017 (RMSE = 12.84) and when the model calibrated with the 2018 Sentinel-2 image was applied to the 2017 satellite data (RMSE = 16.97). The linear models developed for the Sentinel-2 and WorldView-2 images from 2018 were used to map wild leek cover for Gatineau Park. Both images allowed production of similar wild leek maps that, based on field experience and visual inspection of the imagery, provide good descriptions of the actual distribution of wild leek at Gatineau Park.
APA, Harvard, Vancouver, ISO, and other styles
6

Hassan, Bukar. "Applications of remote sensing to arid grasslands : experimental and Nigerian case studies." Thesis, Bangor University, 1989. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.329703.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Kamalesh, Vidhya Lakshmi. "Vegetation parameter retrieval from hyperspectral, multiple view angle PROBA/CHRIS data." Thesis, Swansea University, 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.678514.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Hurst, Rebecca Jeanne. "Use of satellite imagery to measure cover of prairie vegetation for the detection of change." Thesis, Montana State University, 2006. http://etd.lib.montana.edu/etd/2006/hurst/HurstR0506.pdf.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Koon, Michael. "A spatial and temporal analysis of conifers using remote sensing and GIS." Huntington, WV : [Marshall University Libraries], 2004. http://www.marshall.edu/etd/descript.asp?ref=401.

Full text
Abstract:
Thesis (M.S.)--Marshall University, 2004.
Title from document title page. Document formatted into pages; contains viii, 40 p. including illustrations. Includes abstract. Includes bibliographical references (p. 39-40).
APA, Harvard, Vancouver, ISO, and other styles
10

Sulieman, Hussein Mohamed. "Mapping and Modelling of Vegetation Changes in the Southern Gadarif Region, Sudan, Using Remote Sensing." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2008. http://nbn-resolving.de/urn:nbn:de:bsz:14-ds-1199964393472-79860.

Full text
Abstract:
The study was conducted at the vicinity of the rural town of Doka in an area of about 55 x 40 km2. The aim of the study was to map and model the influences of the introduction of mechanized rain-fed agriculture and its rapid expansion on the natural vegetation in the southern Gadarif Region. To achieve these objectives the study utilized a series of techniques. Beside the intensive use of remote sensing imagery, interviews with key informants and farmers as well as detailed field surveys were carried out. Multi-temporal analyses of remote sensing imagery showed that during the seventies the average natural vegetation clearing rate increased most rapidly and then began to slow down. Farmers are aware that land degradation, in various forms, is taking place on their cultivated agricultural land. This is based on their perception and the interpretation of indicators such as weed infestation, reduced soil fertility and soil compaction. Continuous cropping, mono-cropping, rainfall shortage and the use of inferior seeds were the main reasons of land degradation indicated by the farmers. Abandonment of agricultural land to restore soil fertility is a common practice among farmers in the Gadarif Region. The study proved that the subsequent natural regeneration of plant species and the vegetation development on abandoned agricultural land are subject to the previous cultivation period and the duration of the fallow. The current regeneration capacity of the abandoned land may not be sufficient to reach full restoration of the previous vegetation climax except for some pockets which received more regenerative resources. Field surveys in conjunction with remotely sensed and topographic data have the potential to explain the restoration and rehabilitation patterns of degraded/abandoned agricultural land to a good extent. The findings of the study seem to be representative not only for the whole Gadarif Region or other areas in Sudan, but also for other regions in the Sahel Zone with similar problems and environmental and social conditions. One of the most practical conservation approaches is to let farmers play an active role in managing their abandoned land. Such management aims to allow for a certain level of use and benefits while maintaining the natural vegetation development on theses area in order to achieve maximal restoration. Although the study investigated the vegetation development in abandoned mechanized rainfed agricultural land, a full understanding of the path-way needs surveys that include more types of abandoned land and investigation of the effects of other local environmental factors (e.g. fire, grazing, distance from forests etc.) for more than one season.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Vegetation mapping – Remote sensing"

1

Lake, Leonard. Mapping and monitoring noxious weeds using remote sensing. [Salt Lake City, UT]: United States. Dept. of Agriculture, Forest Service Engineering, Remote Sensing Applications Center, 1997.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

ESCAP-BIOTROP Training Course on Remote Sensing Techniques Applied to Vegetation Studies (1985 Bogor, Indonesia). Remote sensing in vegetation studies: Report of the ESCAP-BIOTROP Training Course on Remote Sensing Techniques Applied to Vegetation Studies. Bangkok, Thailand: UNDP/ESCAP Regional Remote Sensing Programme, 1985.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Burgan, Robert E. Monitoring vegetation greenness with satellite data. Ogden, UT: U.S. Dept. of Agriculture, Forest Service, Intermountain Research Station, 1993.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
4

Vygodskai͡a, N. N. Teorii͡a i ėksperiment v distant͡sionnykh issledovanii͡akh rastitelʹnosti. Leningrad: Gidrometeoizdat, 1987.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Workshop, EARSeL. Microwave remote sensing applied to vegetation: Proceedings of an EARSeL Workshop. Paris, France: European Space Agency, 1985.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
6

Sobhan, Md Istiak. Species discrimination from a hyperspectral perspective. Enschede: ITC, 2007.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

Koslowsky, Dirk. Mehrjährige validierte und homogenisierte Reihen des Reflexionsgrades und des Vegetationsindexes von Landoberflächen aus täglichen AVHRR-Daten hoher Auflösung. Berlin: Reimer, 1996.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
8

Sensing, United States Bureau of Land Management Denver Service Center Branch of Remote. Rangeland inventory and monitoring: Selected bibliography of remote sensing applications. Denver, Colo: U.S. Bureau of Land Management, Denver Service Center, 1986.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
9

Jong, Steven M. de. Applications of reflective remote sensing for land degradation studies in a Mediterranean environment. [Amsterdam]: Koninklijk Nederlands Aardrijkskundig Genootschap, 1994.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
10

Agee, James K. Vegetation and fuel mapping of North Cascades National Park Service complex: Final report. Seattle, Wash: National Park Service, Cooperative Park Studies Unit, College of Forest Resources, University of Washington, 1985.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Vegetation mapping – Remote sensing"

1

Zonneveld, I. S. "Interpretation of Remote Sensing Images." In Vegetation mapping, 265–68. Dordrecht: Springer Netherlands, 1988. http://dx.doi.org/10.1007/978-94-009-3083-4_25.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Smith, Milton O., John B. Adams, and Don E. Sabol. "Mapping Sparse Vegetation Canopies." In Eurocourses: Remote Sensing, 221–35. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-0-585-33173-7_12.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Klemas, Victor V. "Remote Sensing of Submerged Aquatic Vegetation." In Seafloor Mapping along Continental Shelves, 125–40. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25121-9_5.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Schmidtlein, Sebastian, Ulrike Faude, Stefanie Stenzel, and Hannes Feilhauer. "Remote Sensing of Vegetation for Nature Conservation." In Land Use and Land Cover Mapping in Europe, 203–15. Dordrecht: Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-7969-3_13.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Bakos, Karoly Livius, Prashanth Reddy Marpu, and Paolo Gamba. "Decision Fusion of Multiple Classifiers for Vegetation Mapping and Monitoring Applications by Means of Hyperspectral Data." In Optical Remote Sensing, 147–70. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-14212-3_9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Lausch, Angela, Marco Heurich, Paul Magdon, Duccio Rocchini, Karsten Schulz, Jan Bumberger, and Doug J. King. "A Range of Earth Observation Techniques for Assessing Plant Diversity." In Remote Sensing of Plant Biodiversity, 309–48. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-33157-3_13.

Full text
Abstract:
AbstractVegetation diversity and health is multidimensional and only partially understood due to its complexity. So far there is no single monitoring approach that can sufficiently assess and predict vegetation health and resilience. To gain a better understanding of the different remote sensing (RS) approaches that are available, this chapter reviews the range of Earth observation (EO) platforms, sensors, and techniques for assessing vegetation diversity. Platforms include close-range EO platforms, spectral laboratories, plant phenomics facilities, ecotrons, wireless sensor networks (WSNs), towers, air- and spaceborne EO platforms, and unmanned aerial systems (UAS). Sensors include spectrometers, optical imaging systems, Light Detection and Ranging (LiDAR), and radar. Applications and approaches to vegetation diversity modeling and mapping with air- and spaceborne EO data are also presented. The chapter concludes with recommendations for the future direction of monitoring vegetation diversity using RS.
APA, Harvard, Vancouver, ISO, and other styles
7

Williams, David J., Nancy B. Rybicki, Alfonso V. Lombana, Tim M. O’Brien, and Richard B. Gomez. "Preliminary Investigation of Submerged Aquatic Vegetation Mapping Using Hyperspectral Remote Sensing." In Coastal Monitoring through Partnerships, 383–92. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-017-0299-7_32.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Trigo Boix, Nuri, Aurora Chimal Hernandez, Gerrit W. Heil, Roland Bobbink, and Betty Verduyn. "Classification and mapping of the vegetation using field observations and remote sensing." In Ecology and Man in Mexico’s Central Volcanoes Area, 19–48. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-007-0969-0_2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Collin, Antoine, Natasha Lambert, and Samuel Etienne. "Satellite-based salt marsh elevation, vegetation height, and species composition mapping using the superspectral WorldView-3 imagery." In Fine Resolution Remote Sensing of Species in Terrestrial and Coastal Ecosystems, 23–41. London: Routledge, 2021. http://dx.doi.org/10.4324/9781003191193-2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Aschbacher, Josef, Rey Ofren, Jean Pierre Delsol, Tri Binarko Suselo, Suvit Vibulsresth, and Thongchai Charrupat. "An integrated comparative approach to mangrove vegetation mapping using advanced remote sensing and GIS technologies: preliminary results." In Asia-Pacific Symposium on Mangrove Ecosystems, 285–94. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-0289-6_32.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Vegetation mapping – Remote sensing"

1

Garcia-Haro, F. J., Fernando Camacho-de Coca, and Joaquin Melia. "Global mapping of vegetation parameters from SEVIRI/MSG data." In Remote Sensing, edited by Manfred Owe, Guido D'Urso, Jose F. Moreno, and Alfonso Calera. SPIE, 2004. http://dx.doi.org/10.1117/12.511046.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Lee, Krista, Charles M. Bachmann, Robert A. Fusina, Marcos J. Montes, Rong-Rong Li, John C. Fry, C. Reid Nichols, Christopher Parrish, and Jon Sellars. "Coastal Vegetation Mapping from Hyperspectral Imagery." In Optical Remote Sensing of the Environment. Washington, D.C.: OSA, 2010. http://dx.doi.org/10.1364/orse.2010.jtua28.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Nabucet, Jean, Laurence Hubert-Moy, Thomas Corpetti, Patrick Launeau, Dimitri Lague, Cyril Michon, and Herve Quenol. "Evaluation of bispectral LIDAR data for urban vegetation mapping." In SPIE Remote Sensing, edited by Thilo Erbertseder, Thomas Esch, and Nektarios Chrysoulakis. SPIE, 2016. http://dx.doi.org/10.1117/12.2241731.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Chabrier, Sebastien, Benoit Stoll, and Jean-Baptiste Goujon. "SVM texture classification for tropical vegetation mapping." In SPIE Asia-Pacific Remote Sensing, edited by Allen M. Larar, Hyo-Sang Chung, Makoto Suzuki, and Jian-yu Wang. SPIE, 2012. http://dx.doi.org/10.1117/12.977182.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Heege, Thomas, Anke Bogner, and Nicole Pinnel. "Mapping of submerged aquatic vegetation with a physically based process chain." In Remote Sensing, edited by Charles R. Bostater, Jr. and Rosalia Santoleri. SPIE, 2004. http://dx.doi.org/10.1117/12.514054.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Betbeder, Julie, Sébastien Rapinel, Thomas Corpetti, Eric Pottier, Samuel Corgne, and Laurence Hubert Moy. "Multi-temporal classification of TerraSAR-X data for wetland vegetation mapping." In SPIE Remote Sensing, edited by Christopher M. U. Neale and Antonino Maltese. SPIE, 2013. http://dx.doi.org/10.1117/12.2029092.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Hall, Carlton R., Charles R. Bostater, Jr., and Robert Virnstein. "Plant pigment types, distributions, and influences on shallow water submerged aquatic vegetation mapping." In Remote Sensing, edited by Charles R. Bostater, Jr. and Rosalia Santoleri. SPIE, 2004. http://dx.doi.org/10.1117/12.565765.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Garguet-Duport, Bruno, Jacky Girel, Jean-Marc Chassery, and Guy Pautou. "New merging method of multispectral and panchromatic SPOT images for vegetation mapping." In Satellite Remote Sensing, edited by Jacky Desachy. SPIE, 1994. http://dx.doi.org/10.1117/12.196763.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Mudele, Oladimeji, and Paolo Gamba. "Mapping vegetation in urban areas using Sentinel-2." In 2019 Joint Urban Remote Sensing Event (JURSE). IEEE, 2019. http://dx.doi.org/10.1109/jurse.2019.8809019.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Gu, Xiaohe, Yancang Wang, Xiaoyu Song, and Xingang Xu. "Mapping paddy biomass with multiple vegetation indexes by using multispectral remotely sensed image." In SPIE Remote Sensing, edited by Christopher M. U. Neale and Antonino Maltese. SPIE, 2016. http://dx.doi.org/10.1117/12.2241240.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Vegetation mapping – Remote sensing"

1

Ager, Alan A., and Karen E. Owens. Characterizing meadow vegetation with multitemporal Landsat thematic mapper remote sensing. Portland, OR: U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 2004. http://dx.doi.org/10.2737/pnw-rn-544.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Legleiter, Carl J. Measurement and Mapping of Riverine Environments by Optical Remote Sensing. Fort Belvoir, VA: Defense Technical Information Center, September 2011. http://dx.doi.org/10.21236/ada557208.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Tamondong, A., C. Cruz, K. D. Ticman, G. A. Go, R. Peralta, M. V. Vergara, I E Cadalzo, et al. Nationwide mapping of coastal resources in the Philippines using remote sensing. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2017. http://dx.doi.org/10.4095/305933.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Yu, Bin. Statistical Problems in Remote Sensing, Image Compression, and Mapping of Human Chromosomes. Fort Belvoir, VA: Defense Technical Information Center, May 2002. http://dx.doi.org/10.21236/ada413806.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Metz, L., and A. N. Bear-Crozier. Landslide susceptibility mapping: a remote sensing based approach using QGIS 2.2 (Valmiera): technical manual. Geoscience Australia, 2014. http://dx.doi.org/10.11636/record.2014.056.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Wells, Aaron, Tracy Christopherson, Gerald Frost, Matthew Macander, Susan Ives, Robert McNown, and Erin Johnson. Ecological land survey and soils inventory for Katmai National Park and Preserve, 2016–2017. National Park Service, September 2021. http://dx.doi.org/10.36967/nrr-2287466.

Full text
Abstract:
This study was conducted to inventory, classify, and map soils and vegetation within the ecosystems of Katmai National Park and Preserve (KATM) using an ecological land survey (ELS) approach. The ecosystem classes identified in the ELS effort were mapped across the park, using an archive of Geo-graphic Information System (GIS) and Remote Sensing (RS) datasets pertaining to land cover, topography, surficial geology, and glacial history. The description and mapping of the landform-vegetation-soil relationships identified in the ELS work provides tools to support the design and implementation of future field- and RS-based studies, facilitates further analysis and contextualization of existing data, and will help inform natural resource management decisions. We collected information on the geomorphic, topographic, hydrologic, pedologic, and vegetation characteristics of ecosystems using a dataset of 724 field plots, of which 407 were sampled by ABR, Inc.—Environmental Research and Services (ABR) staff in 2016–2017, and 317 were from existing, ancillary datasets. ABR field plots were located along transects that were selected using a gradient-direct sampling scheme (Austin and Heligers 1989) to collect data for the range of ecological conditions present within KATM, and to provide the data needed to interpret ecosystem and soils development. The field plot dataset encompassed all of the major environmental gradients and landscape histories present in KATM. Individual state-factors (e.g., soil pH, slope aspect) and other ecosystem components (e.g., geomorphic unit, vegetation species composition and structure) were measured or categorized using standard classification systems developed for Alaska. We described and analyzed the hierarchical relationships among the ecosystem components to classify 92 Plot Ecotypes (local-scale ecosystems) that best partitioned the variation in soils, vegetation, and disturbance properties observed at the field plots. From the 92 Plot Ecotypes, we developed classifications of Map Ecotypes and Disturbance Landscapes that could be mapped across the park. Additionally, using an existing surficial geology map for KATM, we developed a map of Generalized Soil Texture by aggregating similar surficial geology classes into a reduced set of classes representing the predominant soil textures in each. We then intersected the Ecotype map with the General-ized Soil Texture Map in a GIS and aggregated combinations of Map Ecotypes with similar soils to derive and map Soil Landscapes and Soil Great Groups. The classification of Great Groups captures information on the soil as a whole, as opposed to the subgroup classification which focuses on the properties of specific horizons (Soil Survey Staff 1999). Of the 724 plots included in the Ecotype analysis, sufficient soils data for classifying soil subgroups was available for 467 plots. Soils from 8 orders of soil taxonomy were encountered during the field sampling: Alfisols (<1% of the mapped area), Andisols (3%), Entisols (45%), Gelisols (<1%), Histosols (12%), Inceptisols (22%), Mollisols (<1%), and Spodosols (16%). Within these 8 Soil Orders, field plots corresponded to a total of 74 Soil Subgroups, the most common of which were Typic Cryaquents, Typic Cryorthents, Histic Cryaquepts, Vitrandic Cryorthents, and Typic Cryofluvents.
APA, Harvard, Vancouver, ISO, and other styles
7

Harris, J. R., B. Eddy, A. Rencz, E. de Kemp, P. Budkewitsch, and M. Peshko. Remote sensing as a geological mapping tool in the Arctic: preliminary results for Baffin Island, Nunavut. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2001. http://dx.doi.org/10.4095/212696.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Suir, Glenn, Kevin Suir, and Sijan Sapkota. Use of remote sensing to detect and predict aquatic nuisance vegetation growth in coastal Louisiana : summary of findings. Environmental Laboratory (U.S.), April 2018. http://dx.doi.org/10.21079/11681/26649.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Blais, A., W. R. Stevens, D. F. Graham, and V. H. Singhroy. A preliminary assessment of remote sensing as a tool for mapping surficial sediments in the southern Canadian Prairies. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1995. http://dx.doi.org/10.4095/202807.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

MacLeod, R. F., J. R. Harris, M. C. Williamson, and C. G. Kingsbury. Remote sensing datasets for mapping of HALIP rocks in the vicinity of East Fiord, western Axel Heiberg Island. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 2015. http://dx.doi.org/10.4095/297373.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Vegetation mapping – Remote sensing' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography