Prediction Models for Landscape Development in GIS

Nwaogu, Chukwudi; Benc, Antonín; Pechanec, Vilém

doi:10.1007/978-3-319-61297-3_21

Cited by 6 publications

(10 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An often-used hybrid model for LU/LC change analysis is the MC-CA model. [5][6][7]13,14,[22][23][24][25][26][27]31,33,35,53,55,58,74,78 The LCM is an innovative prediction tool frequently used by researchers for LU/LC change analysis. This simulation model in IDRISI software simulates the LU/LC change trends by using different methods like MC, MLP, LR and SimWeight, and the modified K-nearest neighbour machine learning algorithm.…”

Section: Landscape Change Modelsmentioning

confidence: 99%

“…This simulation model in IDRISI software simulates the LU/LC change trends by using different methods like MC, MLP, LR and SimWeight, and the modified K-nearest neighbour machine learning algorithm. 6,7,26,35,55 QGIS is an open-source tool that helps in analysing LU/LC changes across the world. 43,54 Evaluation metrics…”

Section: Landscape Change Modelsmentioning

confidence: 99%

“…Many researchers have used hybrid models to predict LU/LC changes, including Multilayer Perceptron-Markov Chain (MLP-MC), Regression Tree-CA, CLUE-MC, CA-MC, ANN-CA and LR-MC. 5,6 LU/LC change analysis in the field of remote sensing has been studied and observed by many researchers around the world. Time series LU/LC analysis of the Zagros forest was observed between 1992 and 2016 using MSP satellite images.…”

Section: Introductionmentioning

confidence: 99%

“…In order to analyse the LU/LC change, researchers used the MC model to compute the transition probability between the LU/LC maps of 2002 and 2012. 6 Continuous classification of LU/LC changes using MSP satellite data of the Qingliu River catchment in southeast China was achieved with the Continuous Change Detection and Classification (CCDC) algorithm. The correlation between forest coverage and climatic factors was determined by calculating the Enhanced Vegetation Index (EVI).…”

Section: Introductionmentioning

confidence: 99%

“…1,3,4, CA and Markov Chain Analysis are the most used hybrid models for monitoring features that change in time and space. 6,[22][23][24][25][26][27]31,33,53,58 The quality of spectral unmixing results mainly depends on the spectral library. 59,60 The authors performed LU/LC classification for HSP images of Singapore and the coastal Jambi province on the island of Sumatra in Indonesia.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Comprehensive review on land use/land cover change classification in remote sensing

Navin

Agilandeeswari

2020

J. Spectral Imaging

View full text Add to dashboard Cite

Research in the field of remote sensing of the environment is valuable and informative. Hyperspectral (HSP) and multispectral (MSP) satellite images have been used for different remote sensing applications. Land Use/Land Cover (LU/LC) change classification has been considered as important research in the field of remote sensing environment. This review aims to identify the various LU/LC applications, remote sensing satellites, geospatial software, pre-processing techniques, LU/LC classification, clustering, spectral unmixing, landscape change models and evaluation metrics. The main objective of this review is to present the more frequently used techniques for analysing LU/LC change with MSP and HSP satellite images. An aim of this review is to motivate future researchers to work efficiently with MSP and HSP satellite images in the field of remote sensing.

show abstract

Section: Landscape Change Modelsmentioning

confidence: 99%

Section: Landscape Change Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Comprehensive review on land use/land cover change classification in remote sensing

Navin

Agilandeeswari

2020

J. Spectral Imaging

View full text Add to dashboard Cite

show abstract

A model of spatial location: New data for the Gor River megalithic landscape (Spain) from LiDAR technology and field survey

González

Almonacid

Esquivel

et al. 2022

Archaeological Prospection

View full text Add to dashboard Cite

The megalithic cluster of the Gor River valley (Andalusia, Spain) is one of the biggest dolmenic groups in Europe, made up of 151 preserved megaliths. In spite of this high number of known monuments, increasing loss and destruction of many of the graves has taken place during the last decades due to enormous soil erosion and anthropogenic activities. With the aim of recording the location of these lost megaliths, Digital Terrain Models and LiDAR data have been used to analyse the terrain showing a high quantity of structures that seem similar to those actually documented in the zone but that were not noticed until now. These possible new burial mounds have been tested by archaeological surface survey, choosing three contrasting areas as samples.Results have shown a high success rate for this methodology, even allowing the discovery of new megalithic graves in heavily researched areas. We interpret the likely higher number of burial mounds in the area to indicate greater territorial control in boundary areas between 4th and 3rd millennium BC.

show abstract

Survey on Land Use/Land Cover (LU/LC) change analysis in remote sensing and GIS environment: Techniques and Challenges

Navin

Agilandeeswari

Manoharan

2020

Environ Sci Pollut Res

118

View full text Add to dashboard Cite

Prediction Models for Landscape Development in GIS

Cited by 6 publications

References 27 publications

Comprehensive review on land use/land cover change classification in remote sensing

Comprehensive review on land use/land cover change classification in remote sensing

A model of spatial location: New data for the Gor River megalithic landscape (Spain) from LiDAR technology and field survey

Survey on Land Use/Land Cover (LU/LC) change analysis in remote sensing and GIS environment: Techniques and Challenges

Contact Info

Product

Resources

About