Detection and prediction of land use/land cover change using spatiotemporal data fusion and the Cellular Automata–Markov model

Lu, Yu-Ting; Wu, Penghai; Ma, Xiaoshuang; Li, Xinghua

doi:10.1007/s10661-019-7200-2

Cited by 98 publications

(68 citation statements)

References 55 publications

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“…The work of [7] shows that the CA-MARKOV combination integrates the stochastic function of Markov Method with the stochastic spatial characteristic of Technology CA. CA-MARKOV a powerful tool that has enabled several authors including [8] [9] [10] to simulate land cover and land use changes.…”

Section: Introductionmentioning

confidence: 99%

“…Analysis of changes over the entire study period was done by post-classification comparison. It produces a change detection matrix resulting from the comparison between the pixels of two classifications between two dates[9]. From this situation, the global rate of change (T g ) and the average annual rate of spatial expansion (T c ) were calculated.Global scale changes were determined by showing the areas of different land use units for each year.…”

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confidence: 99%

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Simulation of the Dynamics of Land Cover and Land Use in the Lobo River Watershed Upstream of Nibéhibé (Center-West of Côte d’Ivoire)

Kouassı¹,

Konan-Waidhet²,

Yao³

et al. 2020

JGIS

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From medium-resolution satellite images (Landsat TM, ETM+ and OLI), the spatial dynamics of land cover and land use are highlighted. The objective of this study is to quantify the evolution of land use in the watershed of the Lobo River upstream of Nibéhibé between 1986 and 2019 in order to analyze the impacts of human activities on the landscape. The study method was based, on the one hand, on the processing of satellite images, for the analysis of the dynamics of land use and, on the other hand, on the CA-Markov model, for the prediction of land use by 2050. It emerged from this study that the land use maps produced made it possible to highlight the spatio-temporal dynamics of land use on the basin. For the period from 1986 to 2019, there is a decrease in the area of forests in favor of built-bare ground and crops and fallows. A land use scenario for the years 2019 and 2050 was simulated with an accuracy of 87.11%. The regressive trend in forests seems to continue in the future with current land use practices.

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Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

Simulation of the Dynamics of Land Cover and Land Use in the Lobo River Watershed Upstream of Nibéhibé (Center-West of Côte d’Ivoire)

Kouassı¹,

Konan-Waidhet²,

Yao³

et al. 2020

JGIS

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show abstract

“…where S is a finite, discrete states set of cells; N is the cellular neighborhood; t and t + i are different moments; and f is the cell transformation rule of the local space. Therefore, the model can simulate the spatiotemporal LULC evolution of complex systems and has been widely applied in many countries [25][26][27][28].…”

Section: St+i=pijstmentioning

confidence: 99%

“…SWAT was used to create a streamflow simulation for the NRB. It is a physically based hydrological model [28] that has been widely used and has been proven to be effective in investigating the impacts of climate and land use on water quantity and quality [29][30][31][32]. The SWAT was developed using various input data, such as topography, land use, soil properties, and weather data for the basin.…”

Section: Model Descriptionmentioning

confidence: 99%

Land Use Change Impacts on Hydrology in the Nenjiang River Basin, Northeast China

Zhang

et al. 2019

Forests

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The objectives of this study were to assess land use changes and their hydrological impacts in the Nenjiang River Basin (NRB). The Soil and Water Assessment Tool (SWAT) model was employed to evaluate the impacts of land use changes. The Cellular Automata-Markov model was used to predict a land use map in 2038. Streamflow under each land use state was simulated by the SWAT model. The results showed that there was a significant expansion of agriculture area at the expense of large areas of grassland, wetland, and forest during 1975–2000. The land use changes during the period of 1975 to 2000 had decreased the water yield (3.5%), surface runoff (1.7%), and baseflow (19%) while they increased the annual evapotranspiration (2.1%). For impacts of individual land use type, the forest proved to have reduced streamflow in the flood season (10%–28%) and increased surface runoff in the drought season (20%–38%). Conversely, grassland, dry land, and paddy land scenarios resulted in increase of streamflow during summer months by 7%–37% and a decrease of streamflow in the cold seasons by 11.7%–59.7%. When the entire basin was changed to wetland, streamflow reduced over the whole year, with the largest reduction during January to March. The 2038 land use condition is expected to increase the annual water yield, surface runoff and wet season flow, and reduce evapotranspiration and baseflow. These results could help to improve sustainable land use management and water utilization in the NRB.

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“…Currently, the similar pixel selection methods based on spectral similarity satisfy the requirements for applications such as land-cover mapping [16], since the spectral differences among different land cover types are observable. However, these methods may not perform well when they are used in vegetation-mapping applications.…”

Section: Introductionmentioning

confidence: 99%

An Object-Based Strategy for Improving the Accuracy of Spatiotemporal Satellite Imagery Fusion for Vegetation-Mapping Applications

Guan

et al. 2019

Remote Sensing

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Spatiotemporal data fusion is a key technique for generating unified time-series images from various satellite platforms to support the mapping and monitoring of vegetation. However, the high similarity in the reflectance spectrum of different vegetation types brings an enormous challenge in the similar pixel selection procedure of spatiotemporal data fusion, which may lead to considerable uncertainties in the fusion. Here, we propose an object-based spatiotemporal data-fusion framework to replace the original similar pixel selection procedure with an object-restricted method to address this issue. The proposed framework can be applied to any spatiotemporal data-fusion algorithm based on similar pixels. In this study, we modified the spatial and temporal adaptive reflectance fusion model (STARFM), the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) and the flexible spatiotemporal data-fusion model (FSDAF) using the proposed framework, and evaluated their performances in fusing Sentinel 2 and Landsat 8 images, Landsat 8 and Moderate-resolution Imaging Spectroradiometer (MODIS) images, and Sentinel 2 and MODIS images in a study site covered by grasslands, croplands, coniferous forests, and broadleaf forests. The results show that the proposed object-based framework can improve all three data-fusion algorithms significantly by delineating vegetation boundaries more clearly, and the improvements on FSDAF is the greatest among all three algorithms, which has an average decrease of 2.8% in relative root-mean-square error (rRMSE) in all sensor combinations. Moreover, the improvement on fusing Sentinel 2 and Landsat 8 images is more significant (an average decrease of 2.5% in rRMSE). By using the fused images generated from the proposed object-based framework, we can improve the vegetation mapping result by significantly reducing the “pepper-salt” effect. We believe that the proposed object-based framework has great potential to be used in generating time-series high-resolution remote-sensing data for vegetation mapping applications.

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Detection and prediction of land use/land cover change using spatiotemporal data fusion and the Cellular Automata–Markov model

Cited by 98 publications

References 55 publications

Simulation of the Dynamics of Land Cover and Land Use in the Lobo River Watershed Upstream of Nibéhibé (Center-West of Côte d’Ivoire)

Simulation of the Dynamics of Land Cover and Land Use in the Lobo River Watershed Upstream of Nibéhibé (Center-West of Côte d’Ivoire)

Land Use Change Impacts on Hydrology in the Nenjiang River Basin, Northeast China

An Object-Based Strategy for Improving the Accuracy of Spatiotemporal Satellite Imagery Fusion for Vegetation-Mapping Applications

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Detection and prediction of land use/land cover change using spatiotemporal data fusion and the Cellular Automata–Markov model

Cited by 98 publications

References 55 publications

Simulation of the Dynamics of Land Cover and Land Use in the Lobo River Watershed Upstream of Nib&#233;hib&#233; (Center-West of C&#244;te d’Ivoire)

Simulation of the Dynamics of Land Cover and Land Use in the Lobo River Watershed Upstream of Nib&#233;hib&#233; (Center-West of C&#244;te d’Ivoire)

Land Use Change Impacts on Hydrology in the Nenjiang River Basin, Northeast China

An Object-Based Strategy for Improving the Accuracy of Spatiotemporal Satellite Imagery Fusion for Vegetation-Mapping Applications

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Simulation of the Dynamics of Land Cover and Land Use in the Lobo River Watershed Upstream of Nibéhibé (Center-West of Côte d’Ivoire)

Simulation of the Dynamics of Land Cover and Land Use in the Lobo River Watershed Upstream of Nibéhibé (Center-West of Côte d’Ivoire)