Comparison of Landscape Metrics for Three Different Level Land Cover/Land Use Maps

Sertel, Elif; Topaloğlu, Raziye Hale; Şallı, Betül; Algan, Irmak Yay; Aksu, Gül Aslı

doi:10.3390/ijgi7100408

Cited by 59 publications

(34 citation statements)

References 38 publications

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“…The "Urban Field" PLAND (UFPLAND) could depict the process of urban spatial growth as diffusing or coalescing just like the PLAND (Dietzel et al, 2005). If the growth is diffusing, it is expected that there will be scattered "seeds" of centers of growth over the landscape and UFPLAND value will be low (Sertel et al, 2018). On the other hand, if the process is in a state of coalescence, urban extent will be more compact and UFPLAND value will be high (Sertel et al, 2018).…”

Section: Image Classification and Analysismentioning

confidence: 99%

“…If the growth is diffusing, it is expected that there will be scattered "seeds" of centers of growth over the landscape and UFPLAND value will be low (Sertel et al, 2018). On the other hand, if the process is in a state of coalescence, urban extent will be more compact and UFPLAND value will be high (Sertel et al, 2018). The UFPLAND might not be sensitive to spatial pattern and processes within the urban field especially if the UFPLAND is low.…”

Section: Image Classification and Analysismentioning

confidence: 99%

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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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Section: Image Classification and Analysismentioning

confidence: 99%

Section: Image Classification and Analysismentioning

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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“…Square shape window with a side length of 100 m was used, and 8-cell neighbour rule was applied for all standard analysis. The FRAGSTATS was used by several studies for understanding the configuration of the landscape 85,86 . After making the model for the year 2018, we projected the same for past decades year 1998, 2008 and also for the future year 2028.…”

Section: Model Preparation Evaluation and Change Dynamics Of Ensemblmentioning

confidence: 99%

Past, Present and Future: Combining habitat suitability and future landcover simulation for long-term conservation management of Indian rhino

Mukherjee

Sharma

Saha

et al. 2020

Sci Rep

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the indian rhino (Rhinoceros unicornis) is susceptible to habitat change and fragmentation due to illegal logging, rapid urbanization and non-forest use and therefore were confined in to isolated areas throughout its distribution. the present study was conducted in Gorumara landscape which is composed of two protected areas (pAs) viz., Gorumara national park (Gnp) and chapramari Wildlife Sanctuary. Both pAs were separated by a territorial forest range (Bridge Area), which is between both the pAs and under high anthropogenic disturbance. the study was designed to understand the impacts of landcover change on habitat suitability of R. unicornis in a multi-temporal scenario from 1998 to 2018 using ensemble approach and also to simulate the future habitat suitability for 2028. Our result suggests a significant increase in woodland cover inside the PAs, whereas the grassland cover has increased outside the pA in territorial range. We found a strong positive association of R. unicornis with grasslands. The Comparison of the future suitability model of 2028 with that of 2018 indicates a substantial increase in rhino suitable area by 13% in the territorial forest. Hence, bringing the territorial forest into the pA network, will be a crucial step to increase the fodder availability and better connectivity for the long term survival of the species.

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“…For example, the single large or several small reserves problem in landscape ecology queries whether species diversity is better preserved by one large reserve or by several small reserves when the total protected area is equivalent [28]. Using identical data sources and methods, Sertel et al [29] generated three land cover maps (primary, secondary, and tertiary) and then used landscape indices analysis to compare the landscape fineness to the landscape indices. It was shown that the finer the land cover data, the more accurately the landscape features were defined.…”

Section: Introductionmentioning

confidence: 99%

Spatial Pattern Consistency among Different Remote-Sensing Land Cover Datasets: A Case Study in Northern Laos

Sui

Yang

Wang

et al. 2019

IJGI

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Comparisons of the accuracy and consistency of different remote-sensing land cover datasets are important for the rational application of multi-source land cover datasets to regional development, or to studies of global or local environmental change. Existing comparisons of accuracy or spatial consistency among land cover datasets primarily use confusion or transfer matrices and focus on the type and area consistency of land cover. However, less attention has been paid to the consistency of spatial patterns, and quantitative analyses of spatial pattern consistency are rare. However, when proportions of land cover types are similar, spatial patterns are essential for studies of the ecological functions of a landscape system. In this study, we used classical landscape indices that quantifies spatial patterns to analyze the spatial pattern consistency among different land cover datasets, and chose three datasets (GlobeLand30-2010, FROM-GLC2010, and SERVIR MEKONG2010) in northern Laos as a case study. We also analyzed spatial pattern consistency at different scales after comparing the landscape indices method with the confusion matrix method. We found that the degree of consistency between GlobeLand30-2010 and SERVIR MEKONG2010 was higher than that of GlobeLand30-2010 and FROM-GLC2010, FROM-GLC2010, and SERVIR MEKONG2010 based on the confusion matrix, mainly because of the best forest consistency and then water. However, the spatial consistency results of the landscape indices analysis show that the three datasets have large differences in the number of patches (NP), patch density (PD), and landscape shape index (LSI) at the original scale of 30 m, and decrease with the increase of the scale. Meanwhile, the aggregation index (AI) shows different changes, such as the changing trend of the forest aggregation index increasing with the scale. Our results suggested that, when using or producing land cover datasets, it is necessary not only to ensure the consistency of landscape types and areas, but also to ensure that differences among spatial patterns are minimized, especially those exacerbated by scale. Attention to these factors will avoid larger deviations and even erroneous conclusions from these data products.

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Comparison of Landscape Metrics for Three Different Level Land Cover/Land Use Maps

Cited by 59 publications

References 38 publications

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

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

Past, Present and Future: Combining habitat suitability and future landcover simulation for long-term conservation management of Indian rhino

Spatial Pattern Consistency among Different Remote-Sensing Land Cover Datasets: A Case Study in Northern Laos

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