Land use and landscape pattern changes of Weihai, China based on object-oriented SVM classification from Landsat MSS/TM/OLI images

Lin, Qingying; Guo, Jinyun; Yan, Jian; Wang, Heng

doi:10.1080/22797254.2018.1534532

Cited by 19 publications

(14 citation statements)

References 43 publications

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“…We also calculated user accuracy (UA), producer accuracy (PA) and Kappa coefficient, respectively [45,46]. The dynamic degrees of LULC can directly reflect the range and speed of changes in LULC types [47]. In this study, the comprehensive LULC dynamic degrees model proposed by Gao et al…”

Section: Classification and Accuracy Assessment Methodsmentioning

confidence: 99%

Ecological Risk Assessment and Impact Factor Analysis of Alpine Wetland Ecosystem Based on LUCC and Boosted Regression Tree on the Zoige Plateau, China

Hou

Gao

et al. 2020

Remote Sensing

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The Zoige Plateau is typical of alpine wetland ecosystems worldwide, which play a key role in regulating global climate and ecological balance. Due to the influence of global climate change and intense human activities, the stability and sustainability of the ecosystems associated with the alpine marsh wetlands are facing enormous threats. It is important to establish a precise risk assessment method to evaluate the risks to alpine wetlands ecosystems, and then to understand the influencing factors of ecological risk. However, the multi-index evaluation method of ecological risk in the Zoige region is overly focused on marsh wetlands, and the smallest units of assessment are relatively large. Although recently developed landscape ecological risk assessment (ERA) methods can address the above limitations, the final directionality of the evaluation results is not clear. In this work, we used the landscape ERA method based on land use and land cover changes (LUCC) to evaluate the ecological risks to an alpine wetland ecosystem from a spatial pixel scale (5 km × 5 km). Furthermore, the boosted regression tree (BRT) model was adopted to quantitatively analyze the impact factors of ecological risk. The results show the following: (1) From 1990 to 2016, the land use and land cover (LULC) types in the study area changed markedly. In particular, the deep marshes and aeolian sediments, and whereas construction land areas changed dramatically, the alpine grassland changed relatively slowly. (2) The ecological risk in the study area increased and was dominated by regions with higher and moderate risk levels. Meanwhile, these areas showed notable spatio-temporal changes, significant spatial correlation, and a high degree of spatial aggregation. (3) The topographic distribution, climate changes and human activities influenced the stability of the study area. Elevation (23.4%) was the most important factor for ecological risk, followed by temperature (16.2%). Precipitation and GDP were also seen to be adverse factors affecting ecological risk, at levels of 13.0% and 12.1%, respectively. The aim of this study was to provide more precise and specific support for defining conservation objectives, and ecological management in alpine wetland ecosystems.

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Section: Classification and Accuracy Assessment Methodsmentioning

confidence: 99%

Ecological Risk Assessment and Impact Factor Analysis of Alpine Wetland Ecosystem Based on LUCC and Boosted Regression Tree on the Zoige Plateau, China

Hou

Gao

et al. 2020

Remote Sensing

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“…They noted that the model is meant for characterizing urban land cover but can also be used for land-use study because there is a relationship between V-I-S components and land use. Lin, Guo, Yan, and Heng (2018) used the V-I-S model in classifying the land-use components of an urban area in China to landscape pattern changes.…”

Section: Related Workmentioning

confidence: 99%

Using multisource data and the V-I-S model in assessing the urban expansion of Riyadh city, Saudi Arabia

Aina

Adam

Ahmed

et al. 2019

European Journal of Remote Sensing

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This paper examines the application of remote sensing, based on the Vegetation-Impervious surface-Soil (V-IS) model and spatial metrics, in an urban analysis for promoting sustainability and understanding urban growth theory. In order to improve the accuracy of land-cover classification, spectral angle mapping (SAM), spectral mixture analysis (SMA) and band ratioing were applied on satellite images for land-cover classification and comparison of the discrimination efficiency of these techniques. For the SMA, subsets of the Landsat (2, 3, 4, 5, and 7) and ASTER (1, 2, and 3N) images were selected. After endmember extraction and purification, the Bayesian probability of each component was computed and used for spectral unmixing. The classified images of different years were compared to analyze the changes in land-use and spatial pattern using V-IS , a form of percentage of landscape (PLAND) and annualized urban sprawl index (AUSI). The result indicates that the performance of band ratioing (69% accuracy) is not asgood as that of SAM (75%) and SMA (86%) in discriminating between vegetationand agricultural land. It is concluded from the land-use analysis that the growth dynamics substantiate the urban theory of diffusion and coalescence and urban growth management strategies have not been completely successful.

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“…Figure 2 shows the procedure of each step. The parameters were set based on repeated experiments and details are provided by Lin et al (Lin et al 2018). The classification products contained four classes, including bare soil, urban land, vegetation and water area.…”

Section: Object-oriented Svm Classificationmentioning

confidence: 99%

Assessing 40 years of spatial dynamics and patterns in megacities along the Belt and Road region using satellite imagery

Sun

Guo

et al. 2020

International Journal of Digital Earth

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The Belt and Road (B&R) region, a vital area with historical, economic, cultural and political significance, has undergone rapid urbanization in the past several decades, especially in the form of urban expansion. In this study, 20 megacities in the B&R region were selected to explore different spatiotemporal patterns of urban expansion. Object-oriented support vector machines (SVM), annual growth rate (AGR) models, and landscape metrics were employed to delineate the urban areas and characterize spatiotemporal characteristics and landscape patterns of these megacities during 1975-2015. All urban maps presented high overall accuracies (80.70%-95.90%) and overall Kappa coefficients (0.76-0.95). The study revealed that megacities in the B&R region have undergone different types of urban sprawl, mainly adopting a 'concentric circle' pattern in inland areas and a 'sector' pattern in coastal areas. Besides, six expansion modes were summarized according to the AGRs of individual megacities. Differences existed in megacities of the developing and developed countries and among five sub-regions. Moreover, 'dispersion, gathering, and re-dispersion' and 'coalescence' were two major landscape patterns of megacities in developing and developed countries. Results of this study can provide a scientific reference for urban planning and aid in sustainable development of local areas.

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Land use and landscape pattern changes of Weihai, China based on object-oriented SVM classification from Landsat MSS/TM/OLI images

Cited by 19 publications

References 43 publications

Ecological Risk Assessment and Impact Factor Analysis of Alpine Wetland Ecosystem Based on LUCC and Boosted Regression Tree on the Zoige Plateau, China

Ecological Risk Assessment and Impact Factor Analysis of Alpine Wetland Ecosystem Based on LUCC and Boosted Regression Tree on the Zoige Plateau, China

Using multisource data and the V-I-S model in assessing the urban expansion of Riyadh city, Saudi Arabia

Assessing 40 years of spatial dynamics and patterns in megacities along the Belt and Road region using satellite imagery

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