Landscaping: Natural Aesthetic Landlords Use to Double Rent & Property Prices

Landscape planning which consists of planting strategic plants and flowers, adds aesthetic enchantment and increases land valuation.

According to real estate gurus, adequately beautified properties bring in better prices from future prospective buyers. In most scenarios, the cost doubles, earning a assets even a lot more beautiful.

“Some landlords even tend to improve rental rates just after carrying out suitable beautification programmes,” Environmentally friendly Valley indicated on its internet site.

The whole landscaping process includes experience-lifting the residence generating the outdoors habitable that are typically employed for amusement, gardening, events, calming, occasional picnics, and perform by most households.

Different plants in a home garden during the beautification programme

Diverse plants in a household garden throughout the beautification programme.

Inexperienced Valley

Landscaping ideation starts straight away just after getting the architectural models.

Aspects to Think about Just before Landscaping

Local climate Investigation

A comprehensive research of the site’s climate is needed given that the survival of vegetation in the location wholly depends upon it. 

Soil Style

It is also essential for one particular to have out right soil investigation to create which plants will prosper in the location.

Utilizing Existing Crops

Recognising which crops can be integrated into the landscape is also important. Some present crops act as buffers to new vegetation, serving to them improve with out destruction.

Potential of the Landscape

Landowners are also encouraged to conceptualise how landscape layout will seem in the upcoming.

“For case in point, contemplate the situation of choosing a specific landscape style for its roomy structure and quick accessibility to every single corner of the property. 

But the plants and saplings of your option are probable to expand up into huge trees in a couple years as a result, the principal objective of picking the landscape design and style is rendered futile immediately after,” Eco-friendly Valley stated.

An apartment block in Karen Estate, Nairobi.

An apartment block in Karen Estate, Nairobi.

File

Routine maintenance Value

According to previous product Emma Way too, upkeep is crucial to take into consideration prior to landscaping. Good decision of plants will help landowners to approximate the servicing fees.

“Upkeep is the most critical element of landscaping, it’s high priced, but it’s an financial commitment that pays off in the extended crew. As a result the choice of type of crops and grass is essential to minimize the charge, people give up when it gets high-priced. Prepare for reduced servicing scapes,” Too defined.

Spicing up the outside with swimming swimming pools and other services also raises land valuation, producing house owners hike purchasing prices.

Social media and deep learning capture the aesthetic quality of the landscape

  • 1.

    Daniel, T. C. et al. Contributions of cultural services to the ecosystem services agenda. Proc. Natl. Acad. Sci. USA 109, 8812–8819. https://doi.org/10.1073/pnas.1114773109 (2012).

    ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 2.

    Gobster, P. H., Nassauer, J. I., Daniel, T. C. & Fry, G. The shared landscape: What does aesthetics have to do with ecology?. Landsc. Ecol. 22, 959–972. https://doi.org/10.1007/s10980-007-9110-x (2007).

    Article 

    Google Scholar
     

  • 3.

    Abraham, A., Sommerhalder, K. & Abel, T. Landscape and well-being: A scoping study on the health-promoting impact of outdoor environments. Int. J. Public Health 55, 59–69. https://doi.org/10.1007/s00038-009-0069-z (2010).

    Article 
    PubMed 

    Google Scholar
     

  • 4.

    Rice, W. L. et al. Changes in recreational behaviors of outdoor enthusiasts during the COVID-19 pandemic: Analysis across urban and rural communities. J. Urban Ecol. https://doi.org/10.1093/jue/juaa020 (2020).

  • 5.

    Venter, Z. S., Barton, D. N., Gundersen, V., Figari, H. & Nowell, M. Urban nature in a time of crisis: Recreational use of green space increases during the COVID-19 outbreak in Oslo, Norway. Environ. Res. Lett. 15, 104075. https://doi.org/10.1088/1748-9326/abb396 (2020).

    ADS 
    Article 
    CAS 

    Google Scholar
     

  • 6.

    Maes, J. et al. Mainstreaming ecosystem services into EU policy. Curr. Opin. Environ. Sustain. 5, 128–134. https://doi.org/10.1016/j.cosust.2013.01.002 (2013).

    Article 

    Google Scholar
     

  • 7.

    Paracchini, M. L. et al. Mapping cultural ecosystem services: A framework to assess the potential for outdoor recreation across the EU. Ecol. Indic. 45, 371–385. https://doi.org/10.1016/j.ecolind.2014.04.018 (2014).

    Article 

    Google Scholar
     

  • 8.

    Hein, L. et al. Progress in natural capital accounting for ecosystems. Science 367, 514–515. https://doi.org/10.1126/science.aaz8901 (2020).

    ADS 
    Article 
    PubMed 
    CAS 

    Google Scholar
     

  • 9.

    Díaz, S. et al. Assessing nature’s contributions to people. Science 359, 270–272. https://doi.org/10.1126/science.aap8826 (2018).

  • 10.

    Martínez-Harms, M. J. & Balvanera, P. Methods for mapping ecosystem service supply: A review. Int. J. Biodiv. Sci. Ecosyst. Serv. Manag. 8, 17–25. https://doi.org/10.1080/21513732.2012.663792 (2012).

    Article 

    Google Scholar
     

  • 11.

    Raymond, C. M., Kenter, J. O., Plieninger, T., Turner, N. J. & Alexander, K. A. Comparing instrumental and deliberative paradigms underpinning the assessment of social values for cultural ecosystem services. Ecol. Econ. 107, 145–156. https://doi.org/10.1016/j.ecolecon.2014.07.033 (2014).

    Article 

    Google Scholar
     

  • 12.

    Bateman, I. J. et al. Bringing ecosystem services into economic decision-making: Land use in the United Kingdom. Science 341, 45–50. https://doi.org/10.1126/science.1234379 (2013).

    ADS 
    Article 
    PubMed 
    CAS 

    Google Scholar
     

  • 13.

    Hernández-Morcillo, M., Plieninger, T. & Bieling, C. An empirical review of cultural ecosystem service indicators. Ecol. Indic. 29, 434–444. https://doi.org/10.1016/j.ecolind.2013.01.013 (2013).

    Article 

    Google Scholar
     

  • 14.

    Hermes, J., Albert, C. & von Haaren, C. Assessing the aesthetic quality of landscapes in Germany. Ecosyst. Serv. 31, 296–307. https://doi.org/10.1016/j.ecoser.2018.02.015 (2018).

    Article 

    Google Scholar
     

  • 15.

    Uuemaa, E., Antrop, M., Roosaare, J., Marja, R. & Mander, U. Landscape metrics and indices: An overview of their use in landscape research. Living Rev. Landsc. Res. 3, 1–28. https://doi.org/10.12942/lrlr-2009-1 (2009).

    Article 

    Google Scholar
     

  • 16.

    Schirpke, U., Tasser, E. & Tappeiner, U. Predicting scenic beauty of mountain regions. Landsc. Urban Plan. 111, 1–12. https://doi.org/10.1016/j.landurbplan.2012.11.010 (2013).

    Article 

    Google Scholar
     

  • 17.

    Tveit, M., Ode, Å. & Fry, G. Key concepts in a framework for analysing visual landscape character. Landsc. Res. 31, 229–255. https://doi.org/10.1080/01426390600783269 (2006).

    Article 

    Google Scholar
     

  • 18.

    Ode, Å., Tveit, M. & Fry, G. Capturing landscape visual character using indicators: Touching base with landscape aesthetic theory. Landsc. Res. 33, 89–117. https://doi.org/10.1080/01426390701773854 (2008).

    Article 

    Google Scholar
     

  • 19.

    de Groot, R. S., Alkemade, R., Braat, L., Hein, L. & Willemen, L. Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecol. Complex. 7, 260–272. https://doi.org/10.1016/j.ecocom.2009.10.006 (2010).

    Article 

    Google Scholar
     

  • 20.

    Schröter, M., Remme, R. P., Sumarga, E., Barton, D. N. & Hein, L. Lessons learned for spatial modelling of ecosystem services in support of ecosystem accounting. Ecosyst. Serv. 13, 64–69. https://doi.org/10.1016/j.ecoser.2014.07.003 (2015).

    Article 

    Google Scholar
     

  • 21.

    Tenerelli, P., Püffel, C. & Luque, S. Spatial assessment of aesthetic services in a complex mountain region: Combining visual landscape properties with crowdsourced geographic information. Landsc. Ecol. 32, 1097–1115. https://doi.org/10.1007/s10980-017-0498-7 (2017).

    Article 

    Google Scholar
     

  • 22.

    Wood, S. A., Guerry, A. D., Silver, J. M. & Lacayo, M. Using social media to quantify nature-based tourism and recreation. Sci. Rep. 3, 1–7. https://doi.org/10.1038/srep02976 (2013).

    Article 

    Google Scholar
     

  • 23.

    van Zanten, B. T. et al. Continental-scale quantification of landscape values using social media data. Proc. Natl. Acad. Sci. USA 113, 12974–12979. https://doi.org/10.1073/pnas.1614158113 (2016).

    ADS 
    Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 24.

    Tenerelli, P., Demšar, U. & Luque, S. Crowdsourcing indicators for cultural ecosystem services: A geographically weighted approach for mountain landscapes. Ecol. Indic. 64, 237–248. https://doi.org/10.1016/j.ecolind.2015.12.042 (2016).

    Article 

    Google Scholar
     

  • 25.

    Richards, D. R. & Tunçer, B. Using image recognition to automate assessment of cultural ecosystem services from social media photographs. Ecosyst. Serv. 31, 318–325. https://doi.org/10.1016/j.ecoser.2017.09.004 (2018).

    Article 

    Google Scholar
     

  • 26.

    Sinclair, M., Mayer, M., Woltering, M. & Ghermandi, A. Valuing nature-based recreation using a crowdsourced travel cost method: A comparison to onsite survey data and value transfer. Ecosyst. Serv. 45, 101165. https://doi.org/10.1016/j.ecoser.2020.101165 (2020).

    Article 

    Google Scholar
     

  • 27.

    Antoniou, V. et al. Investigating the feasibility of geo-tagged photographs as sources of land cover input data. ISPRS Int. J. Geo-Inf.https://doi.org/10.3390/ijgi5050064 (2016).

    Article 

    Google Scholar
     

  • 28.

    Mancini, F., Coghill, G. M. & Lusseau, D. Quantifying wildlife watchers’ preferences to investigate the overlap between recreational and conservation value of natural areas. J. Appl. Ecol. 56, 387–397. https://doi.org/10.1111/1365-2664.13274 (2019).

  • 29.

    Hollenstein, L. & Purves, R. Exploring place through user-generated content: Using Flickr tags to describe city cores. J. Spatial Inf. Sci. 1, 21–48. https://doi.org/10.5311/JOSIS.2010.1.3 (2010).

    Article 

    Google Scholar
     

  • 30.

    Donahue, M. L. et al. Using social media to understand drivers of urban park visitation in the Twin Cities, MN. Landsc. Urban Plan. 175, 1–10. https://doi.org/10.1016/j.landurbplan.2018.02.006 (2018).

    Article 

    Google Scholar
     

  • 31.

    LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444. https://doi.org/10.1038/nature14539 (2015).

    ADS 
    Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 32.

    Naik, N., Kominers, S. D., Raskar, R., Glaeser, E. L. & Hidalgo, C. A. Computer vision uncovers predictors of physical urban change. Proc. Natl. Acad. Sci. USA 114, 7571–7576. https://doi.org/10.1073/pnas.1619003114 (2017).

  • 33.

    Toivonen, T. et al. Social media data for conservation science: A methodological overview. Biol. Conserv. 233, 298–315. https://doi.org/10.1016/j.biocon.2019.01.023 (2019).

    Article 

    Google Scholar
     

  • 34.

    Zhang, F., Zhou, B., Ratti, C. & Liu, Y. Discovering place-informative scenes and objects using social media photos. R. Soc. Open Sci. 6, 181375. https://doi.org/10.1098/rsos.181375 (2019).

    ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 35.

    Srivastava, S., Vargas Muñoz, J. E., Lobry, S. & Tuia, D. Fine-grained landuse characterization using ground-based pictures: A deep learning solution based on globally available data. Int. J. Geogr. Inf. Sci. 34, 1117–1136. https://doi.org/10.1080/13658816.2018.1542698 (2020).

    Article 

    Google Scholar
     

  • 36.

    Egarter Vigl, L. et al. Harnessing artificial intelligence technology and social media data to support cultural ecosystem service assessments. People Nat. 3, 673–685. https://doi.org/10.1002/pan3.10199 (2021).

    Article 

    Google Scholar
     

  • 37.

    ScenicOrNot. ScenicOrNot Dataset (2015). http://scenicornot.datasciencelab.co.uk.

  • 38.

    Seresinhe, C. I., Moat, H. S. & Preis, T. Quantifying scenic areas using crowdsourced data. Environ. Plan. B Urban Anal. City Sci. 45, 567–582. https://doi.org/10.1177/0265813516687302 (2017).

    Article 

    Google Scholar
     

  • 39.

    Chesnokova, O., Nowak, M. & Purves, R. S. A crowdsourced model of landscape preference. In 13th International Conference on Spatial Information Theory (COSIT 2017), vol. 86. https://doi.org/10.4230/LIPIcs.COSIT.2017.19 (2017).

  • 40.

    Seresinhe, C. I., Tobias, P. & Moat, H. S. Using deep learning to quantify the beauty of outdoor places. R. Soc. Open Sci.. https://doi.org/10.1098/rsos.170170 (2017).

    MathSciNet 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • 41.

    Workman, S., Souvenir, R. & Jacobs, N. Understanding and mapping natural beauty. In 2017 IEEE International Conference on Computer Vision (ICCV), vol. 4, 5590–5599. https://doi.org/10.1109/ICCV.2017.596 (2017).

  • 42.

    Marcos, D. et al. Contextual semantic interpretability. In Proceedings of the Asian Conference on Computer Vision (2020).

  • 43.

    Arendsen, P., Marcos, D. & Tuia, D. Concept discovery for the interpretation of landscape scenicness. Mach. Learn. Knowl. Extract.https://doi.org/10.3390/make2040022 (2020).

    Article 

    Google Scholar
     

  • 44.

    Levering, A., Marcos, D. & Tuia, D. On the relation between landscape beauty and land cover: A case study in the U.K. at Sentinel-2 resolution with interpretable AI. ISPRS J. Photogram. Remote Sens. 177, 194–203. https://doi.org/10.1016/j.isprsjprs.2021.04.020 (2021).

    ADS 
    Article 

    Google Scholar
     

  • 45.

    Havinga, I., Bogaart, P. W., Hein, L. & Tuia, D. Defining and spatially modelling cultural ecosystem services using crowdsourced data. Ecosyst. Serv. 43, 101091. https://doi.org/10.1016/j.ecoser.2020.101091 (2020).

    Article 

    Google Scholar
     

  • 46.

    Oteros-Rozas, E., Martín-López, B., Fagerholm, N., Bieling, C. & Plieninger, T. Using social media photos to explore the relation between cultural ecosystem services and landscape features across five European sites. Ecol. Indic. 94, 74–86. https://doi.org/10.1016/j.ecolind.2017.02.009 (2018).

    Article 

    Google Scholar
     

  • 47.

    Englund, O., Berndes, G. & Cederberg, C. How to analyse ecosystem services in landscapes—A systematic review. Ecol. Indic. 73, 492–504. https://doi.org/10.1016/j.ecolind.2016.10.009 (2017).

    Article 

    Google Scholar
     

  • 48.

    Zhou, B., Lapedriza, A., Khosla, A., Oliva, A. & Torralba, A. Places: A 10 million image database for scene recognition. IEEE Trans. Pattern Anal. Mach. Intell. 40, 1452–1464. https://doi.org/10.1109/TPAMI.2017.2723009 (2017).

    Article 
    PubMed 

    Google Scholar
     

  • 49.

    Patterson, G., Xu, C., Su, H. & Hays, J. The SUN attribute database: Beyond categories for deeper scene understanding. Int. J. Comput. Vis. 108, 59–81. https://doi.org/10.1007/s11263-013-0695-z (2014).

    Article 

    Google Scholar
     

  • 50.

    Lee, H., Seo, B., Koellner, T. & Lautenbach, S. Mapping cultural ecosystem services 2.0—Potential and shortcomings from unlabeled crowd sourced images. Ecol. Indic. 96, 505–515. https://doi.org/10.1016/j.ecolind.2018.08.035 (2019).

    Article 

    Google Scholar
     

  • 51.

    Ulrich, R. S. Visual landscapes and psychological well-being. Landsc. Res. 4, 17–23. https://doi.org/10.1080/01426397908705892 (1979).

    Article 

    Google Scholar
     

  • 52.

    Cordingley, J. E., Newton, A. C., Rose, R. J., Clarke, R. T. & Bullock, J. M. Habitat fragmentation intensifies trade-offs between biodiversity and ecosystem services in a heathland ecosystem in Southern England. PLOS ONE 10, e0130004. https://doi.org/10.1371/journal.pone.0130004 (2015).

    Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 53.

    Newton, A. C. et al. Impacts of grazing on lowland heathland in north-west Europe. Biol. Conserv. 142, 935–947. https://doi.org/10.1016/j.biocon.2008.10.018 (2009).

    Article 

    Google Scholar
     

  • 54.

    Tveit, M. S. Indicators of visual scale as predictors of landscape preference; A comparison between groups. J. Environ. Manag. 90, 2882–2888. https://doi.org/10.1016/j.jenvman.2007.12.021 (2009).

    Article 

    Google Scholar
     

  • 55.

    Frank, S., Fürst, C., Koschke, L., Witt, A. & Makeschin, F. Assessment of landscape aesthetics—validation of a landscape metrics-based assessment by visual estimation of the scenic beauty. Ecol. Indic. 32, 222–231. https://doi.org/10.1016/j.ecolind.2013.03.026 (2013).

    Article 

    Google Scholar
     

  • 56.

    Graham, L. J. & Eigenbrod, F. Scale dependency in drivers of outdoor recreation in England. People Nat. 1, 406–416. https://doi.org/10.1002/pan3.10042 (2019).

    Article 

    Google Scholar
     

  • 57.

    Ryo, M. & Rillig, M. C. Statistically reinforced machine learning for nonlinear patterns and variable interactions. Ecosphere 8, e01976. https://doi.org/10.1002/ecs2.1976 (2017).

    Article 

    Google Scholar
     

  • 58.

    Karasov, O., Vieira, A. A. B., Külvik, M. & Chervanyov, I. Landscape coherence revisited: GIS-based mapping in relation to scenic values and preferences estimated with geolocated social media data. Ecol. Indic. 111, 105973. https://doi.org/10.1016/j.ecolind.2019.105973 (2020).

    Article 

    Google Scholar
     

  • 59.

    Foltête, J.-C., Ingensand, J. & Blanc, N. Coupling crowd-sourced imagery and visibility modelling to identify landscape preferences at the panorama level. Landsc. Urban Plan. 197, 103756. https://doi.org/10.1016/j.landurbplan.2020.103756 (2020).

    Article 

    Google Scholar
     

  • 60.

    Labib, S. M., Huck, J. J. & Lindley, S. Modelling and mapping eye-level greenness visibility exposure using multi-source data at high spatial resolutions. Sci. Total Environ. 755, 143050. https://doi.org/10.1016/j.scitotenv.2020.143050 (2021).

    ADS 
    Article 
    PubMed 
    CAS 

    Google Scholar
     

  • 61.

    Li, H. & Wu, J. Use and misuse of landscape indices. Landsc. Ecol. 19, 389–399. https://doi.org/10.1023/B:LAND.0000030441.15628.d6 (2004).

    Article 

    Google Scholar
     

  • 62.

    Weather, U. K. UK seasonal weather summary—Winter 2009/2010. Weather 65, 99. https://doi.org/10.1002/wea.601 (2010).

    Article 

    Google Scholar
     

  • 63.

    Lenormand, M. et al. Multiscale socio-ecological networks in the age of information. PLOS ONE 13, e0206672. https://doi.org/10.1371/journal.pone.0206672 (2018).

    Article 
    PubMed 
    PubMed Central 
    CAS 

    Google Scholar
     

  • 64.

    Li, L., Goodchild, M. F. & Xu, B. Spatial, temporal, and socioeconomic patterns in the use of Twitter and Flickr. Cartogr. Geogr. Inf. Sci. 40, 61–77. https://doi.org/10.1080/15230406.2013.777139 (2013).

    Article 
    CAS 

    Google Scholar
     

  • 65.

    Uuemaa, E., Mander, Ü. & Marja, R. Trends in the use of landscape spatial metrics as landscape indicators: A review. Ecol. Indic. 28, 100–106. https://doi.org/10.1016/j.ecolind.2012.07.018 (2013).

    Article 

    Google Scholar
     

  • 66.

    Daniel, T. C. Whither scenic beauty? Visual landscape quality assessment in the 21st century. Landsc. Urban Plan. 54, 267–281. https://doi.org/10.1016/S0169-2046(01)00141-4 (2001).

    Article 

    Google Scholar