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Evaluating the effectiveness of landscape configuration metrics from landscape composition metrics

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Abstract

Although landscape configuration and landscape composition metrics are correlated theoretically and empirically, the effectiveness of configuration metrics from composition metrics has not been explicitly investigated. This study explored to what extent substantial information of configuration metrics increases from certain easily calculated and extensively used composition metrics and how strongly the effectiveness is influenced by different factors. The effectiveness of 12 landscape configuration metrics from the percentage of landscape (PLAND) of each land-use class and patch density (PD) was evaluated through the coefficient of determination (R 2) of multivariate stepwise linear regression analysis of 150 town-based landscape samples from three regions. The different landscape configuration metrics from PLAND and PD presented significantly different performances in terms of effectiveness [the contagion index and aggregation index possess minimal information, and the effective mesh size (MESH) and area-weighted mean patch fractal dimension possess abundant information]. Furthermore, the effectiveness of configuration metrics showed different responses to changing cell sizes and different land-use categorization in different regions (interspersion and juxtaposition index, patch cohesion index, and MESH exhibited large variations in R 2 among the different regions). No single, uniform, consistent characteristic of effectiveness was determined across different factors. This new approach to understanding the effectiveness of configuration metrics helps clarify landscape metrics and is fundamental to landscape metric assessment.

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References

  • Bailey D, Herzog F, Augenstein I et al (2007) Thematic resolution matters: indicators of landscape pattern for European agro-ecosystems. Ecol Indic 7(3):692–709

    Article  Google Scholar 

  • Braimoh AK (2006) Random and systematic land-cover transitions in northern Ghana. Agric Ecosyst Environ 113(1–4):254–263

    Article  Google Scholar 

  • Buyantuyev A, Wu J (2007) Effects of thematic resolution on landscape pattern analysis. Landsc Ecol 22(1):7–13

    Article  Google Scholar 

  • Congalton RG (1997) Exploring and evaluating the consequences of vector-to-raster and raster-to-vector conversion. Photogramm Eng Remote Sens 63(4):425–434

    Google Scholar 

  • Corry RC, Lafortezza R (2007) Sensitivity of landscape measurements to changing grain size for fine-scale design and management. Landsc Ecol Eng 3(1):47–53

    Article  Google Scholar 

  • Cushman SA, McGarigal K, Neel MC (2008) Parsimony in landscape metrics: strength, universality, and consistency. Ecol Indic 8(5):691–703

    Article  Google Scholar 

  • Dendoncker N, Schmit C, Rounsevell M (2008) Exploring spatial data uncertainties in land-use change scenarios. Int J Geogr Inf Sci 22(9):1013–1030

    Article  Google Scholar 

  • Frohn RC (2006) The use of landscape pattern metrics in remote sensing image classification. Int J Remote Sens 27(10):2025–2032

    Article  Google Scholar 

  • Geri F, Amici V, Rocchini D (2010) Human activity impact on the heterogeneity of a Mediterranean landscape. Appl Geogr 30(3):370–379

    Article  Google Scholar 

  • Griffith JA, Martinko EA, Price KP (2000) Landscape structure analysis of Kansas at three scales. Landsc Urban Plan 52(1):45–61

    Article  Google Scholar 

  • Herold M, Couclelis H, Clarke KC (2005) The role of spatial metrics in the analysis and modeling of urban land use change. Comput Environ Urban Syst 29(4):369–399

    Article  Google Scholar 

  • Huang C, Geiger EL, Kupfer JA (2006) Sensitivity of landscape metrics to classification scheme. Int J Remote Sens 27(14):2927–2948

    Article  Google Scholar 

  • Hung W-C, Chen Y-C, Cheng K-S (2010) Comparing landcover patterns in Tokyo, Kyoto, and Taipei using ALOS multispectral images. Landsc Urban Plan 97(2):132–145

    Article  Google Scholar 

  • IBM Corp R (2010) IBM SPSS statistics for windows, version 19.0. IBM, Armonk, NY

    Google Scholar 

  • Jiao L, Liu Y, Li H (2012) Characterizing land-use classes in remote sensing imagery by shape metrics. ISPRS J Photogramm Remote Sens 72:46–55

    Article  Google Scholar 

  • Kromroy K, Ward K, Castillo P, Juzwik J (2007) Relationships between urbanization and the oak resource of the Minneapolis/St. Paul Metropolitan area from 1991 to 1998. Landsc Urban Plan 80(4):375–385

    Article  Google Scholar 

  • Kweon BS, Ellis CD, Leiva PI, Rogers GO (2010) Landscape components, land use, and neighborhood satisfaction. Environ Plan B Plan Des 37(3):500

    Article  Google Scholar 

  • Lausch A, Herzog F (2002) Applicability of landscape metrics for the monitoring of landscape change: issues of scale, resolution and interpretability. Ecol Indic 2(1):3–15

    Article  Google Scholar 

  • Lausch A, Blaschke T, Haase D et al (2015) Understanding and quantifying landscape structure—a review on relevant process characteristics, data models and landscape metrics. Ecol Model 295:31–41

    Article  Google Scholar 

  • Lechner AM, Reinke KJ, Wang Y, Bastin L (2013) Interactions between landcover pattern and geospatial processing methods: effects on landscape metrics and classification accuracy. Ecol Complex 15:71–82

    Article  Google Scholar 

  • Li X, Zhou W, Ouyang Z (2013) Relationship between land surface temperature and spatial pattern of greenspace: what are the effects of spatial resolution? Landsc Urban Plan 114:1–8

    Article  Google Scholar 

  • Liu Y, Jiao L, Liu Y (2011) Analyzing the effects of scale and land use pattern metrics on land use database generalization indices. Int J Appl Earth Obs Geoinf 13(3):346–356

    Article  Google Scholar 

  • Long JA, Nelson TA, Wulder MA (2010) Characterizing forest fragmentation: distinguishing change in composition from configuration. Appl Geogr 30(3):426–435

    Article  Google Scholar 

  • Mairota P, Cafarelli B, Boccaccio L et al (2013) Using landscape structure to develop quantitative baselines for protected area monitoring. Ecol Indic 33:82–95

    Article  Google Scholar 

  • McGarigal K, Cushman S, Ene E (2012) FRAGSTATS v4: spatial pattern analysis program for categorical and continuous maps. University of Massachusettes, Amherst, MA. URL http://www.umass.edu/landeco/research/fragstats/fragstats.html

  • Morelli F, Pruscini F, Santolini R, Perna P, Benedetti Y, Sisti D (2013) Landscape heterogeneity metrics as indicators of bird diversity: determining the optimal spatial scales in different landscapes. Ecol Indic 34:372–379

    Article  Google Scholar 

  • Neel MC, McGarigal K, Cushman SA (2004) Behavior of class-level landscape metrics across gradients of class aggregation and area. Landsc Ecol 19(4):435–455

    Article  Google Scholar 

  • Pasher J, Mitchell SW, King DJ, Fahrig L, Smith AC, Lindsay KE (2013) Optimizing landscape selection for estimating relative effects of landscape variables on ecological responses. Landscape Ecol 28(3):371–383

    Article  Google Scholar 

  • Paudel S, Yuan F (2012) Assessing landscape changes and dynamics using patch analysis and GIS modeling. Int J Appl Earth Obs Geoinf 16:66–76

    Article  Google Scholar 

  • Pecher C, Tasser E, Walde J, Tappeiner U (2013) Typology of Alpine region using spatial-pattern indicators. Ecol Indic 24:37–47

    Article  Google Scholar 

  • Peng J, Wang Y, Ye M, Wu J, Zhang Y (2007) Effects of land-use categorization on landscape metrics: a case study in urban landscape of Shenzhen, China. Int J Remote Sens 28(21):4877–4895

    Article  Google Scholar 

  • Peng J, Wang Y, Zhang Y, Wu J, Li W, Li Y (2010) Evaluating the effectiveness of landscape metrics in quantifying spatial patterns. Ecol Indic 10(2):217–223

    Article  Google Scholar 

  • Plexida SG, Sfougaris AI, Ispikoudis IP, Papanastasis VP (2014) Selecting landscape metrics as indicators of spatial heterogeneity—a comparison among Greek landscapes. Int J Appl Earth Obs Geoinf 26:26–35

    Article  Google Scholar 

  • Proulx R, Fahrig L (2010) Detecting human-driven deviations from trajectories in landscape composition and configuration. Landsc Ecol 25(10):1479–1487

    Article  Google Scholar 

  • Rayburn AP, Schulte LA (2009) Landscape change in an agricultural watershed in the US Midwest. Landsc Urban Plan 93(2):132–141

    Article  Google Scholar 

  • Rempel R, Kaukinen D, Carr A (2012) Patch analyst and patch grid. Ontario Ministry of Natural Resources. Centre for Northern Forest Ecosystem Research, Thunder Bay

    Google Scholar 

  • Saura S (2002) Effects of minimum mapping unit on land cover data spatial configuration and composition. Int J Remote Sens 23(22):4853–4880

    Article  Google Scholar 

  • Shortridge AM (2004) Geometric variability of raster cell class assignment. Int J Geogr Inf Sci 18(6):539–558

    Article  Google Scholar 

  • Šímová P, Gdulová K (2012) Landscape indices behavior: a review of scale effects. Appl Geogr 34:385–394

    Article  Google Scholar 

  • Su S, Jiang Z, Zhang Q, Zhang Y (2011) Transformation of agricultural landscapes under rapid urbanization: a threat to sustainability in Hang-Jia-Hu region, China. Appl Geogr 31(2):439–449

    Article  Google Scholar 

  • Su S, Xiao R, Jiang Z, Zhang Y (2012) Characterizing landscape pattern and ecosystem service value changes for urbanization impacts at an eco-regional scale. Appl Geogr 34:295–305

    Article  Google Scholar 

  • Szabó S, Túri Z, Márton S (2014) Factors biasing the correlation structure of patch level landscape metrics. Ecol Indic 36:1–10

    Article  Google Scholar 

  • Tian G, Qiao Z, Zhang Y (2012) The investigation of relationship between rural settlement density, size, spatial distribution and its geophysical parameters of China using Landsat TM images. Ecol Model 231:25–36

    Article  Google Scholar 

  • Tian Y, Jim CY, Wang H (2014) Assessing the landscape and ecological quality of urban green spaces in a compact city. Landsc Urban Plan 121:97–108

    Article  Google Scholar 

  • Uuemaa E, Mander Ü, Marja R (2013) Trends in the use of landscape spatial metrics as landscape indicators: a review. Ecol Indic 28:100–106

    Article  Google Scholar 

  • Wu J (2006) Landscape ecology, cross-disciplinarity, and sustainability science. Landsc Ecol 21(1):1–4

    Article  CAS  Google Scholar 

  • Wu J, Shen W, Sun W, Tueller PT (2002) Empirical patterns of the effects of changing scale on landscape metrics. Landsc Ecol 17(8):761–782

    Article  Google Scholar 

  • Zaragozí B, Belda A, Linares J, Martínez-Pérez J, Navarro J, Esparza J (2012) A free and open source programming library for landscape metrics calculations. Environ Model Softw 31:131–140

    Article  Google Scholar 

  • Zhou W, Huang G, Cadenasso ML (2011) Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landsc Urban Plan 102(1):54–63

    Article  Google Scholar 

Download references

Acknowledgements

This paper is supported by the Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology (Grant No. DLLJ201610), the Doctoral Scientific Research Foundation of East China University of Technology (Grant No. DHBK2015311) and the key laboratory of watershed ecology and geographical environment monitoring, NASG (Grant No. WE2016018).

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Authors and Affiliations

  1. School of Geomatics, East China University of Technology, 418 Guanglan Road, 330013, Nanchang, China

    Xiaojian Wei

  2. Key Laboratory for Digital Land and Resources of Jiangxi Province, East China University of Technology, 330013, Nanchang, China

    Xiaojian Wei

  3. Key Laboratory of Watershed Ecology and Geographical Environment Monitoring, NASG, Nanchang, China

    Xiaojian Wei

  4. Journal of Natural Science of Wuhan University, 430072, Wuhan, China

    Zhanchun Xiao

  5. School of Resource and Environment Science, Wuhan University, 129 Luoyu Road, 430079, Wuhan, China

    Qingling Li & Peiling Li

  6. Organizational Department of the CPC Jinniu District Committe, 610081, Chengdu, China

    Peiling Li

  7. Jiangxia Land Resources and Planning Bureau, 430200, Wuhan, China

    Chao Xiang

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  1. Xiaojian Wei
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  2. Zhanchun Xiao
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  3. Qingling Li
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  4. Peiling Li
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  5. Chao Xiang
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Correspondence to Zhanchun Xiao.

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Wei, X., Xiao, Z., Li, Q. et al. Evaluating the effectiveness of landscape configuration metrics from landscape composition metrics. Landscape Ecol Eng 13, 169–181 (2017). https://doi.org/10.1007/s11355-016-0314-6

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  • DOI: https://doi.org/10.1007/s11355-016-0314-6

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