Monitoring and prediction of land use/land cover changes using CA-Markov model: a case study of Ravansar County in Iran

Karimi, Houshang; Jafarnezhad, Javad; Khaledi, Jabbar; Ahmadi, Parisa

doi:10.1007/s12517-018-3940-5

Cited by 96 publications

(62 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is bordered by Turkey from the north, Iran from the east, Syria from the north-west and from the south and southwest is Iraqi part. KRGI located between latitudes 33°57'58.5" -37°20'33.55"N and longitudes 42°20'25.36" -46°19' 16.475 E (Fig. 1), and cover an area around 50325.82 km 2 with elevation ranges (88 m -3600 m).…”

Section: Study Areamentioning

confidence: 99%

See 1 more Smart Citation

Spatiotemporal Analysis of Vegetation Cover in Kurdistan Region-Iraq using MODIS Image Data

Mustafa¹

2020

JASTT

View full text Add to dashboard Cite

The rapidly and wide use of remote sensing and accurately obtain information on the spatiotemporal distribution of large-scale vegetation is of great significance for improving and managing the Environment. To assess and analyze the spatiotemporal variation of vegetation status in Kurdistan Region of Iraq (KRGI), we used time series NDVI-based vegetation that are extracted from MOD13Q1 MODIS product over 20 years (2000 - 2019). The results showed that vegetation was mainly distributed in the north-east to south-east of the KRGI, while west region has less distributed and almost no vegetation. This is clearly remarkable in the south-west part of the region (Garmian administration). While, the most dominated vegetation province was Duhok province in KRGI during study period. There is a noticeable temporal variation in vegetation over a period of 20-year in the KRGI. The lower vegetated cover area is observed in the years 2000, 2008, and 2009. The increase/decrease of vegetated cover area is not only effected by climate conditions. The anthropogenic resource is also one of the main resources that has a major influence on the increase/decrease of vegetation.

show abstract

Section: Study Areamentioning

confidence: 99%

“…Several researchers were utilized satellite images to reveal the status of the vegetation coverage (forest, grass, crops, … etc.) [12][13][14][15][16][17]. One of the satellite that provide continues data (spatially and temporally) is Moderate Resolution Imaging Spectroradiometer (MODIS).…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Analysis of Vegetation Cover in Kurdistan Region-Iraq using MODIS Image Data

Mustafa¹

2020

JASTT

View full text Add to dashboard Cite

show abstract

“…Many researchers around the world have carried out LU/LC change analysis research over many years. Some of the study areas for LU/LC analysis have been North-eastern Latvia, 1 forests, 7 Northern Ethiopia, 9 Mexico, 14 Germany, 18 China, 23 Iran, 25 Egypt, 27 United Arab Emirates, 31 India, 32 Iraq, 37 Malaysia, 41 south-western Australia, 44 Eastern Region of Ghana, 50 Dubai, 52 south-western Nigeria, 54 Indonesia, 55 Pakistan 56 and Sri Lanka. 57 The main objective of researchers in choosing their study area depends on the causes of LU/LC change ( Figure 2), and their work assists government, forest departments, land resource and urban planners in taking the necessary actions to protect the Earth's environment.…”

Section: Lu/lc Applications and Study Area Selectionmentioning

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%

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

“…In the field of remote sensing, the LU/LC classes are classified and the LU/LC change was analyzed by using the classified map of different time periods. The investigation of both spatial and temporal LU/LC changes helps in processing and managing the natural resources [7]. For monitoring the natural resources and for mapping the forest cover area the satellite data were used across the world.…”

mentioning

confidence: 99%

Land use Land Cover Change Detection using K-means Clustering and Maximum Likelihood Classification Method in the Javadi Hills, Tamil Nadu, India

Navin¹,

Agilandeeswari²

2019

IJEAT

View full text Add to dashboard Cite

Land use/Land cover (LU/LC) change analysis is the present-day challenging task for the researchers in defining the environmental change across the world in the field of remote sensing and GIS (Geographic Information System). This paper analyzes the LU/LC changes between the years 2009 and 2019 in the region of Javadi Hills located in Tamil Nadu, India. Images from the Indian remote sensing satellite Resourcesat-1 LISS III and American earth observation satellite Landsat-8 were used for analyzing the LU/LC change for the study area. In this work, the classification was performed by using the hybrid approach of unsupervised and supervised classifiers. The classified LU/LC map for the study area defines forest and non-forest covered region. The key objective of this work was to identify the percentage of LU/LC change occurred in our study area for the years 2009 to 2014 and 2014 to 2019. Observing and examining the changes occurred in the study area provides a clear view to the land resources management to take effective measures in protecting the environment.

show abstract

Monitoring and prediction of land use/land cover changes using CA-Markov model: a case study of Ravansar County in Iran

Cited by 96 publications

References 30 publications

Spatiotemporal Analysis of Vegetation Cover in Kurdistan Region-Iraq using MODIS Image Data

Spatiotemporal Analysis of Vegetation Cover in Kurdistan Region-Iraq using MODIS Image Data

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

Land use Land Cover Change Detection using K-means Clustering and Maximum Likelihood Classification Method in the Javadi Hills, Tamil Nadu, India

Contact Info

Product

Resources

About