A review on change detection method and accuracy assessment for land use land cover

Chughtai, A. H.; Abbasi, H. U.; Karaş, İsmail Rakıp

doi:10.1016/j.rsase.2021.100482

Cited by 112 publications

(66 citation statements)

References 74 publications

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“…There are various classification techniques which give excellent accuracies such as Random Forests (RF), Support Vector Machine (SVM) and Maximum Likelihood Classifier (MLC) (Alshari and Gawali 2021). Among these techniques, MLC is the most commonly used classification technique for various remote sensing applications (Allam et al 2019;Chughtai et al 2021).…”

Section: Input Layer Preparationmentioning

confidence: 99%

Urban modelling and forecasting of landuse using SLEUTH model

Kumar

Agrawal

2022

Int. J. Environ. Sci. Technol.

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The present study performs landcover modelling using the SLEUTH model. The urban land use changing factors are calibrated to predict the Land Use Land Cover (LULC) for a densely populated and developing smart city, Prayagraj, India. This research aims to use the SLEUTH model for simulating the future urban growth with the help of historical LULC (1990LULC ( -2020, road network and elevation data. The influence of road gravity and slope resistant coefficients is very significant in this study's outcome. The built-up area of the region increased from 40.22 km 2 (5.10% of total area) in 1990 to 85.89 km 2 (10.89%) in 2020. According to prediction results, in the next 20 years, the built-up growth rate would be 1.9% and approximated built-up area would be 118.66 km 2 (14.98%) in the year 2040. The quality of the result has been quantified in terms of best fit value of Optimal SLEUTH Metric (OSM) and validated against the existing LULC. The study utilises a spatially explicit urban growth model with 30 m resolution remote sensing data and provides future landuse of Prayagraj city.

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Section: Input Layer Preparationmentioning

confidence: 99%

Urban modelling and forecasting of landuse using SLEUTH model

Kumar

Agrawal

2022

Int. J. Environ. Sci. Technol.

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“…Validation is an important process through which users understand the accuracy of the land cover maps prepared through classification technique [28] [29]. Error Matrix or Contingency Table is the most commonly adopted technique to validate the land cover maps through overall accuracy (OA) and kappa co-efficient (k) value.…”

Section: Validationmentioning

confidence: 99%

Spatio-Temporal Land Cover Analysis and the Impact of Land Cover Variability Indices on Land Surface Temperature in Greater Accra, Ghana Using Multi-Temporal Landsat Data

Devendran¹,

Banon²

2022

JGIS

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Urbanization, as a result of anthropogenic activities, reduces the vegetated and green spaces and thereby increases the impervious surfaces in cities. This in turn increases the surface temperature of cities when compared to the rural regions resulting in the formation of urban heat island. Especially, in under-developed countries, it is very crucial to obtain timely and accurate information on the urban trend and its development due to the higher increasing rate of population growth and lack of infrastructural facilities and regulations to mitigate the adverse consequences of urbanization. The current study analyzes the urban development of Greater Accra, Ghana using Landsat 7 dataset acquired in 2002, 2013, and 2020. Further, the influence of urban growth on the land surface temperature (LST) and land cover variability (LCV) indices including NDVI (Normalized Difference Vegetation Index), NDBI (Normalized Difference Built-Up Index), and NDWI (Normalized Difference Water Index) is identified during the study periods. Results suggest that the area of built-up quadrupled between 2002 and 2020 in the study region. Shannon's entropy-based analysis suggests that the urban development in the region is heterogeneous or dispersive in nature. Further, the regression analysis highlights a strong positive relation between LST and NDBI (0.755) and a negative relation is found associated between LST and NDVI (0.4417) and LST and NDWI (0.76). Results of the study could be useful to design sustainable urban socio-economic and environmental planning policies in the study region.

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“…Spatial modeling can be used to investigate the complexities of potential agricultural production, associated LULC change, and environmental consequences. Anthropogenic and environmental mechanisms need to be thoroughly understood concerning the temporal dynamics and possible changes in land cover (Chughtai et al, 2021;Silva et al, 2020). Urban growth would be bene cial for urban planners and engineers; furthermore, recognizing the future trends and extent of urban development would be crucial for open space and natural habitat protection (L. Singh et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Assessment of Land Use And Land Cover Change Detection And Prediction Using Remote Sensing And CA Markov In The Northern Coastal Districts of Tamil Nadu, India

Abijith

Saravanan

2021

Preprint

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Land use and land cover (LULC) change analysis and forecasting aids the upcoming generation in research and evaluate the global climate change for managing and controlling environmental sustainability. This research analyzes the Northern TN coast, which is under both natural and anthropogenic stress. The analysis of LULC changes and LULC projections for the region between 2009-2019 and 2019-2030 was performed utilizing Google Earth Engine (GEE), TerrSet, and Geographical Information System (GIS) tools. LULC image is generated from Landsat images and classified in GEE using Random Forest (RF). LULC maps were then framed with the CA- Markov model to forecast future LULC change. The CA-Markov’s Land change modeler (LCM) was set up to create future LULC. It was carried out in four steps: (1) Change analysis, (2) Transition potential, (3) Change prediction, and (4) Model validation. For analyzing change statistics, the study region is divided into zone 1 and zone 2. In both zones, the water body shows a decreasing trend, and built-up areas are in increasing trend. Barren land and vegetation classes are under stress and developing into built-up. The overall accuracy was above 89%, and the kappa coefficient was above 87% for all three years. This region is highly susceptible to inland floods, coastal floods, and other natural disasters; thus, this study’s results support future development plans and decision-making.

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A review on change detection method and accuracy assessment for land use land cover

Cited by 112 publications

References 74 publications

Urban modelling and forecasting of landuse using SLEUTH model

Urban modelling and forecasting of landuse using SLEUTH model

Spatio-Temporal Land Cover Analysis and the Impact of Land Cover Variability Indices on Land Surface Temperature in Greater Accra, Ghana Using Multi-Temporal Landsat Data

Assessment of Land Use And Land Cover Change Detection And Prediction Using Remote Sensing And CA Markov In The Northern Coastal Districts of Tamil Nadu, India

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