IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i12p3429-d241965.html
   My bibliography  Save this article

Recognizing Landscapes for the Purpose of Sustainable Development—Experiences from Poland

Author

Listed:
  • Iga Solecka

    (Department of Spatial Economy, The Faculty of Environmental Engineering and Geodesy, Wrocław University of Environmental and Life Sciences, ul. Grunwaldzka 55, 50-357 Wrocław, Poland)

  • Dietmar Bothmer

    (Department of Natural and Environmental Sciences, Institute of Ecology and Environmental Protection, University of Applied Sciences Zittau/Goerlitz, Theodor-Körner-Allee 16, 02763 Zittau, Germany)

  • Arkadiusz Głogowski

    (Institute of Environmental Protection and Development, The Faculty of Environmental Engineering and Geodesy, Wrocław University of Environmental and Life Sciences, pl. Grunwaldzki 24, 50-363 Wrocław, Poland)

Abstract

Landscape identification forms a base for landscape management and sustainable land use policy. According to the European Landscape Convention, each Member State needs to recognize the landscapes as an essential component of people’s surroundings. Poland developed a method for landscape auditing that will be conducted for landscapes in the whole country. The identification of landscape units is based on landscape type characteristics and spatial data that is layered and analyzed in order to identify landscape units. In this paper, we aim to test the possibilities of automatic landscape identification. We take the assumptions designed for landscape identification for the needs of the audit. Based on the “Typology of Poland’s current landscapes”, we design a process to identify landscape units with the use of the aggregation of land cover data and multivariable analysis. We use tools in an ArcGIS environment to design a process that will support human perception. Our approach is compared with the approach presented in the method designed for a landscape audit in order to be used for landscape unit identification at the municipal level. The case study area is the municipality of Siechnice within the suburban area of the city of Wrocław, an example of a changing landscape under suburbanization pressure. We conclude that both approaches can support each other in the landscape identification process.

Suggested Citation

  • Iga Solecka & Dietmar Bothmer & Arkadiusz Głogowski, 2019. "Recognizing Landscapes for the Purpose of Sustainable Development—Experiences from Poland," Sustainability, MDPI, vol. 11(12), pages 1-19, June.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:12:p:3429-:d:241965
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/12/3429/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/12/3429/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yuanyuan Yang & Shuwen Zhang, 2018. "Historical Arable Land Change in an Eco-Fragile Area: A Case Study in Zhenlai County, Northeastern China," Sustainability, MDPI, vol. 10(11), pages 1-20, October.
    2. Katarzyna Pukowiec-Kurda & Urszula Myga-Piątek & Oimahmad Rahmonov, 2019. "The landscape profile method as a new tool for sustainable urban planning," Journal of Environmental Planning and Management, Taylor & Francis Journals, vol. 62(14), pages 2548-2566, December.
    3. Piotr Krajewski & Iga Solecka & Karol Mrozik, 2018. "Forest Landscape Change and Preliminary Study on Its Driving Forces in Ślęża Landscape Park (Southwestern Poland) in 1883–2013," Sustainability, MDPI, vol. 10(12), pages 1-21, November.
    4. Friedman, Jerome H. & Hastie, Trevor & Tibshirani, Rob, 2010. "Regularization Paths for Generalized Linear Models via Coordinate Descent," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 33(i01).
    5. Martin Herold & Joseph Scepan & Keith C Clarke, 2002. "The Use of Remote Sensing and Landscape Metrics to Describe Structures and Changes in Urban Land Uses," Environment and Planning A, , vol. 34(8), pages 1443-1458, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mohd Alsaleh & Muhammad Mansur Abdulwakil & Abdul Samad Abdul-Rahim, 2021. "Land-Use Change Impacts from Sustainable Hydropower Production in EU28 Region: An Empirical Analysis," Sustainability, MDPI, vol. 13(9), pages 1-19, April.
    2. Brygida Klemens & Brygida Solga & Krystian Heffner & Piotr Gibas, 2022. "Environmental and Energy Conditions in Sustainable Regional Development," Energies, MDPI, vol. 15(15), pages 1-26, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yi Zhu, 2020. "Estimating the activity types of transit travelers using smart card transaction data: a case study of Singapore," Transportation, Springer, vol. 47(6), pages 2703-2730, December.
    2. Tutz, Gerhard & Pößnecker, Wolfgang & Uhlmann, Lorenz, 2015. "Variable selection in general multinomial logit models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 207-222.
    3. Ernesto Carrella & Richard M. Bailey & Jens Koed Madsen, 2018. "Indirect inference through prediction," Papers 1807.01579, arXiv.org.
    4. Lucie Kupková & Ivan Bičík & Leoš Jeleček, 2021. "At the Crossroads of European Landscape Changes: Major Processes of Landscape Change in Czechia since the Middle of the 19th Century and Their Driving Forces," Land, MDPI, vol. 10(1), pages 1-25, January.
    5. Rui Wang & Naihua Xiu & Kim-Chuan Toh, 2021. "Subspace quadratic regularization method for group sparse multinomial logistic regression," Computational Optimization and Applications, Springer, vol. 79(3), pages 531-559, July.
    6. Mkhadri, Abdallah & Ouhourane, Mohamed, 2013. "An extended variable inclusion and shrinkage algorithm for correlated variables," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 631-644.
    7. Masakazu Higuchi & Mitsuteru Nakamura & Shuji Shinohara & Yasuhiro Omiya & Takeshi Takano & Daisuke Mizuguchi & Noriaki Sonota & Hiroyuki Toda & Taku Saito & Mirai So & Eiji Takayama & Hiroo Terashi &, 2022. "Detection of Major Depressive Disorder Based on a Combination of Voice Features: An Exploratory Approach," IJERPH, MDPI, vol. 19(18), pages 1-13, September.
    8. Susan Athey & Guido W. Imbens & Stefan Wager, 2018. "Approximate residual balancing: debiased inference of average treatment effects in high dimensions," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 80(4), pages 597-623, September.
    9. Vincent, Martin & Hansen, Niels Richard, 2014. "Sparse group lasso and high dimensional multinomial classification," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 771-786.
    10. Chen, Le-Yu & Lee, Sokbae, 2018. "Best subset binary prediction," Journal of Econometrics, Elsevier, vol. 206(1), pages 39-56.
    11. Perrot-Dockès Marie & Lévy-Leduc Céline & Chiquet Julien & Sansonnet Laure & Brégère Margaux & Étienne Marie-Pierre & Robin Stéphane & Genta-Jouve Grégory, 2018. "A variable selection approach in the multivariate linear model: an application to LC-MS metabolomics data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 17(5), pages 1-14, October.
    12. Fan, Jianqing & Jiang, Bai & Sun, Qiang, 2022. "Bayesian factor-adjusted sparse regression," Journal of Econometrics, Elsevier, vol. 230(1), pages 3-19.
    13. Jun Li & Serguei Netessine & Sergei Koulayev, 2018. "Price to Compete … with Many: How to Identify Price Competition in High-Dimensional Space," Management Science, INFORMS, vol. 64(9), pages 4118-4136, September.
    14. Sung Jae Jun & Sokbae Lee, 2020. "Causal Inference under Outcome-Based Sampling with Monotonicity Assumptions," Papers 2004.08318, arXiv.org, revised Oct 2023.
    15. Rina Friedberg & Julie Tibshirani & Susan Athey & Stefan Wager, 2018. "Local Linear Forests," Papers 1807.11408, arXiv.org, revised Sep 2020.
    16. Xiangwei Li & Thomas Delerue & Ben Schöttker & Bernd Holleczek & Eva Grill & Annette Peters & Melanie Waldenberger & Barbara Thorand & Hermann Brenner, 2022. "Derivation and validation of an epigenetic frailty risk score in population-based cohorts of older adults," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    17. Hewamalage, Hansika & Bergmeir, Christoph & Bandara, Kasun, 2021. "Recurrent Neural Networks for Time Series Forecasting: Current status and future directions," International Journal of Forecasting, Elsevier, vol. 37(1), pages 388-427.
    18. Hui Xiao & Yiguo Sun, 2020. "Forecasting the Returns of Cryptocurrency: A Model Averaging Approach," JRFM, MDPI, vol. 13(11), pages 1-15, November.
    19. Christopher J Greenwood & George J Youssef & Primrose Letcher & Jacqui A Macdonald & Lauryn J Hagg & Ann Sanson & Jenn Mcintosh & Delyse M Hutchinson & John W Toumbourou & Matthew Fuller-Tyszkiewicz &, 2020. "A comparison of penalised regression methods for informing the selection of predictive markers," PLOS ONE, Public Library of Science, vol. 15(11), pages 1-14, November.
    20. Brian Quistorff & Gentry Johnson, 2020. "Machine Learning for Experimental Design: Methods for Improved Blocking," Papers 2010.15966, arXiv.org.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:12:p:3429-:d:241965. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.