Semi-Automatic Method to Evaluate Ecological Value of Urban Settlements with the Biotope Area Factor Index: Sources and Logical Framework
Abstract
:1. Introduction
1.1. Aim of the Research and Current Paper Structure
1.2. State of the Art
2. Methodology
2.1. Study Area
2.2. Topographic Database
2.3. Sentinel-2 Satellite Data
2.4. Aerial Imagery
2.5. Simplified BAF
3. Analysis
3.1. BAF Calculation
3.1.1. BAF Calculation from a DBT Source
3.1.2. BAF Calculation from Sentinel Image Sources
3.1.3. BAF Calculation from an Aerial Imagery Source
4. Results
4.1. Abbiategrasso
Type of Surface | Area | Weight Factor | Ecologically Effective Surface |
---|---|---|---|
Sealed surface | 657,841 sq.m. | 0 | 0 sq.m. |
Partially sealed surface | 24,230 sq.m. | 0.1 | 2423 sq.m. |
Semi-open surface | 5472 sq.m. | 0.2 | 1094 sq.m. |
Surfaces with vegetation unconnected to soil below, shallow substrate | 12,424 sq.m. | 0.5 | 6212 sq.m. |
Extensive roof greening | 19,465 sq.m. | 0.5 | 9733 sq.m |
Surfaces with vegetation unconnected to soil below, deep substrate | 245,225 sq.m. | 0.9 | 220,703 sq.m. |
Surfaces with vegetation connected to soil below | 35,343 sq.m. | 1 | 35,343 sq.m. |
TOTAL | 1,000,000 sq.m. | 275,507 sq.m. | |
BAF | 0.28 |
Category | Area | Weight Factor | Ecologically Effective Surface |
---|---|---|---|
0–0.49 | 687,543 sq. m. | 0 | 0 sq. m. |
0.5–1 | 312,457 sq. m. | 1 | 312,457 sq. m. |
TOTAL | 1,000,000 sq. m. | 312,457 sq. m. | |
BAF | 0.31 |
Month | Category | Area | Weight Factor | Ecologically Effective Surface |
---|---|---|---|---|
April 2020 | 0–0.49 | 689,637 sq. m. | 0 | 0 sq. m. |
0.5–1 | 310,363 sq. m. | 1 | 310,363 sq. m. | |
TOTAL | 1,000,000 sq. m. | 310,363 sq. m. | ||
BAF | 0.31 | |||
July 2020 | 0–0.49 | 762,998 sq. m. | 0 | 0 sq. m. |
0.5–1 | 237,002 sq. m. | 1 | 237,002 sq. m. | |
TOTAL | 1,000,000 sq. m | 237,002 sq. m. | ||
BAF | 0.24 | |||
October 2020 | 0–0.49 | 557,211 sq. m. | 0 | 0 sq. m. |
0.5–1 | 442,789 sq. m. | 1 | 442,789 sq. m. | |
TOTAL | 1,000,000 sq. m | 442,789 sq. m. | ||
BAF | 0.44 |
April 2020 | July 2020 | October 2020 | |
---|---|---|---|
Sentinel BAF | 0.31 | 0.24 | 0.44 |
Average | 0.33 |
Category | Pixel | Area | Weight Factor | Ecologically Effective Surface |
---|---|---|---|---|
No green | 52,028 | 633,129 sq. m. | 0 | 0 sq. m. |
Green | 30,148 | 366,871 sq. m. | 1 | 366,871 sq. m. |
TOTAL | 82,176 | 1,000.00 sq. m. | 366,871 sq. m. | |
BAF | 0.37 |
4.2. Segrate
Type of Surface | Area | Weight Factor | Ecologically Effective Surface |
---|---|---|---|
Sealed surface | 516,515 sq. m. | 0 | 0 sq. m. |
Partially sealed surface | 32,851 sq. m. | 0.1 | 3285 sq. m. |
Semi open surface | 2850 sq. m. | 0.2 | 570 sq. m. |
Surfaces with vegetation unconnected to the soil below, shallow substrate | 18,085 sq. m. | 0.5 | 9042 sq. m. |
Extensive roof greening | 31,561 sq. m. | 0.5 | 15,781 sq. m. |
Surfaces with vegetation unconnected to the soil below, deep substrate | 261,524 sq. m. | 0.9 | 235,372 sq. m. |
Surfaces with vegetation connected to soil below | 136,614 sq. m. | 1 | 136,614 sq. m. |
TOTAL | 1,000,000 sq. m. | 400,664 sq. m. | |
BAF | 0.4 |
Category | Area | Weight Factor | Ecologically Effective Surface |
---|---|---|---|
0–0.49 | 552,216 sq. m. | 0 | 0 sq. m. |
0.5–1 | 447,784 sq. m. | 1 | 447,784 sq. m. |
TOTAL | 1,000,000 sq. m. | 447,784 sq. m. | |
BAF | 0.45 |
Month | Category | Area | Weight Factor | Ecologically Effective Surface |
---|---|---|---|---|
April 2020 | 0–0.49 | 699,451 sq. m | 0 | 0 sq. m. |
0.5–1 | 300,549 sq. m. | 1 | 300,549 sq. m. | |
TOTAL | 1,000,000 sq. m. | 300,549 sq. m. | ||
BAF | 0.30 | |||
July 2020 | 0–0.49 | 572,533 sq. m. | 0 | 0 sq. m. |
0.5–1 | 427,467 sq. m. | 1 | 427,467 sq. m. | |
TOTAL | 1,000,000 sq. m. | 427,467 sq. m | ||
BAF | 0.43 | |||
October 2020 | 0–0.49 | 586,225 sq. m. | 0 | 0 sq. m. |
0.5–1 | 413,775 sq. m. | 1 | 413,775 sq. m. | |
TOTAL | 1,000,000 sq. m. | 413,775 sq. m. | ||
BAF | 0.41 |
April 2020 | July 2020 | October 2020 | |
---|---|---|---|
Sentinel BAF | 0.30 | 0.43 | 0.41 |
Average | 0.38 |
Category | Pixel | Area | Weight Factor | Ecologically Effective Surface |
---|---|---|---|---|
No green | 47,558 | 541,157 sq. m. | 0 | 0 sq. m. |
Green | 40,324 | 458,843 sq. m. | 1 | 458,843 sq. m. |
TOTAL | 87,984 | 1,000.00 sq. m. | 458,843 sq. m. | |
BAF | 0.46 |
4.3. Results Comparison
5. Discussions
5.1. Method Evaluation: Range of Error
Mixed Pixel Errors
5.2. Replicability in Different Contexts
5.3. Scale Variation: From Local to Regional Dimensions
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- UN Department of Economic and Social Affairs, Population Division. The World’s Cities in 2018—Data Booklet (ST/ESA/SER.A/417); UN Department of Economic and Social Affairs, Population Division: New York, NY, USA, 2018. [Google Scholar]
- UN Habitat. World Cities Report 2020 the Value of Sustainable Urbanization; UN Habitat: Nairobi, Kenya, 2020. [Google Scholar]
- Xiong, Y.; Huang, S.; Chen, F.; Ye, H.; Wang, C.; Zhu, C. The Impacts of Rapid Urbanization on the Thermal Environment: A Remote Sensing Study of Guangzhou, South China. Remote Sens. 2012, 4, 2033–2056. [Google Scholar] [CrossRef] [Green Version]
- World Health Organization. WHOQOL and Measuring Quality of Life. Division of Mental Health and Prevention of Substance Abuse; World Health Organization: Geneva, Switzerland, 1998. [Google Scholar]
- OECD Better Life Index. Available online: https://www.oecdbetterlifeindex.org/#/11111111111 (accessed on 27 October 2021).
- Myrtho, J.; Fahui, W.; Lei, W. GIS-based assessment of urban environmental quality in Port-au-Prince, Haiti. Habitat Int. 2014, 41, 33–40. [Google Scholar]
- Gómez-Baggethun, E.; Gren, Å.; Barton, D.N.; Langemeyer, J.; McPhearson, T.; O’farrell, P.; Andersson, E.; Hamstead, Z.; Kremer, P. Urban Ecosystem Services. In Urbanization, Biodiversity and Ecosystem Services: Challenges and Opportunities; Springer: Dordrecht, The Netherlands, 2013; pp. 175–251. [Google Scholar]
- Yu, C.; Hien, W.N. Thermal benefits of city parks. Energy Build. 2006, 38, 105–120. [Google Scholar] [CrossRef]
- Dickinson, D.C.; Hobbs, R.J. Cultural ecosystem services: Characteristics, challenges and lessons for urban green space research. Ecosyst. Serv. 2017, 25, 179–194. [Google Scholar] [CrossRef]
- Krellenberg, K.; Welz, J.; Reyes-Päcke, S. Urban green areas and their potential for social interaction–A case study of a socioeconomically mixed neighbourhood in Santiago de Chile. Habitat Int. 2014, 44, 11–21. [Google Scholar] [CrossRef]
- Schetke, S.; Qureshi, S.; Lautenbach, S.; Kabisch, N. What determines the use of urban green spaces in highly urbanized areas?—Examples from two fast growing Asian cities. Urban. For. Urban. Green. 2016, 16, 150–159. [Google Scholar] [CrossRef]
- Tost, H.; Reichert, M.; Braun, U.; Reinhard, I.; Peters, R.; Lautenbach, S.; Hoell, A.; Schwarz, E.; Ebner-Priemer, U.; Zipf, A.; et al. Neural correlates of individual differences in affective benefit of real-life urban green space exposure. Nat. Neurosci. 2019, 22, 1389–1393. [Google Scholar] [CrossRef] [PubMed]
- Houlden, V.; Weich, S.; de Albuquerque, J.P.; Jarvis, S.; Rees, K. The relationship between greenspace and the mental wellbeing of adults: A systematic review. PLoS ONE 2018, 13, e0203000. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Maas, J. Green space, urbanity, and health: How strong is the relation? J. Epidemiol. Community Health 2006, 60, 587–592. [Google Scholar] [CrossRef] [Green Version]
- Lega, C.; Gidlow, C.; Jones, M.; Ellis, N.; Hurst, G. The relationship between surrounding greenness, stress and memory. Urban. For. Urban. Green. 2021, 59, 126974. [Google Scholar] [CrossRef]
- Venter, Z.; Barton, D.; 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. 2020, 6, 104075. [Google Scholar] [CrossRef]
- Klopp, J.; Ppetretta, D.L. The urban sustainable development goal: Indicators, complexity and the politics of measuring cities. Cities 2017, 63, 92–97. [Google Scholar] [CrossRef]
- Huang, L.; Wu, J.; Yan, L. Defining and measuring urban sustainability: A review of indicators. Landsc. Ecol. 2015, 30, 1175–1193. [Google Scholar] [CrossRef]
- Bossard, M.; Feranec, J.; Otahel, J. CORINE Land Cover Technical Guide: ADDENDUM 2000; EEA: Copenhagen, Denmark, 2000. [Google Scholar]
- Seifert, F.M. Improving urban monitoring toward a European urban atlas. In Global Mapping of Human Settlement: Experiences, Datasets, and Prospects; CRC Press: Boca Raton, FL, USA, 2009. [Google Scholar]
- The Trust for Public Land: ParkServe Data Set. Available online: https://www.tpl.org/parkserve (accessed on 5 November 2021).
- Burghardt, W. Soil sealing and soil properties related to sealing. In Function of Soils for Human Societies and the Environment; Frossard, E., Blum, W.E.H., Warkentin, B.P., Eds.; Special Publications 266; The Geological Society: London, UK, 2006; pp. 117–124. [Google Scholar]
- Xiao, R.; Su, S.; Zhang, Z.; Qi, J.; Jiang, D.; Wu, J. Dynamics of soil sealing and soil landscape patterns under rapid urbanization. CATENA 2013, 109, 1–12. [Google Scholar] [CrossRef]
- Salvati, L. The spatial pattern of soil sealing along the urban-rural gradient in a Mediterranean region. J. Environ. Plan. Manag. 2013, 57, 848–861. [Google Scholar] [CrossRef]
- Artmann, M. Assessment of Soil Sealing Management Responses, Strategies, and Targets Toward Ecologically Sustainable Urban Land Use Management. AMBIO 2014, 43, 530–541. [Google Scholar] [CrossRef] [Green Version]
- Kabisch, N.; Selsam, P.; Kirsten, T.; Lausch, A.; Bumberger, J. A multi-sensor and multi-temporal remote sensing approach to detect land cover change dynamics in heterogeneous urban landscapes. Ecol. Indic. 2019, 99, 273–282. [Google Scholar] [CrossRef]
- Senate Department for the Environment. Transport and Climate Protection BAF–Biotope Area Factor. Available online: https://www.berlin.de/sen/uvk/en/nature-and-green/landscape-planning/baf-biotope-area-factor/ (accessed on 28 October 2021).
- Becker, G.; Mohren, R. The Biotope Area Factor as an Ecological Parameter; Planen & Bauen: Berlin, Germany, 1990. [Google Scholar]
- Casella, V.; De Lotto, R.; Franzini, M.; Gazzola, V.; Morelli di Popolo, C.; Sturla, S.; Venco, E.M. Estimating the Biotope Area Factor (BAF) by Means of Existing Digital Maps and GIS Technology. In Proceedings of the Computational Science and Its Applications–ICCSA 2015, 15th International Conference, GEO-AND-MOD 15 Proceedings Part III. Banff, AB, Canada, 22–25 June 2015; pp. 617–632. [Google Scholar]
- Peroni, F.; Pristeri, G.; Codato, D.; Pappalardo, S.E.; De Marchi, M. Biotope Area Factor: An Ecological Urban Index to Geovisualize Soil Sealing in Padua, Italy. Sustainability 2020, 12, 150. [Google Scholar] [CrossRef] [Green Version]
- AA.VV. Nature-Based Solutions & Re-Naturing Cities, Towards an EU Research and Innovation policy agenda for Final Report of the Horizon 2020. 2015. Available online: https://op.europa.eu/it/publication-detail/-/publication/fb117980-d5aa-46df-8edc-af367cddc202 (accessed on 28 October 2021).
- Nature4Cities. Available online: https://www.nature4cities.eu (accessed on 7 January 2022).
- Battarra, R.; Pinto, F.; Tremiterra, M.R. Indicators and Actions for the Smart and Sustainable City: A study on Italian Metropolitan Cities. In Smart Planning: Sustainability and Mobility in the Age of Change; Papa, R., Fistola, R., Gargiulo, C., Eds.; Springer International Publishing: Berlin/Heidelberg, Germany, 2018; pp. 83–108. [Google Scholar]
- De Lotto, R. Nature-based Solutions: New EU topic to renature cities. Urban. Inf. 2017, 272, 798–803. [Google Scholar]
- Galderisi, A.; Mazzeo, G.; Pinto, F. Cities dealing with energy issues and climate-related impacts: Approaches, strategies and tools for a sustainable urban development. In Smart Energy in the Smart City. Urban Planning for a Sustainable Future; Papa, R., Fistola, R., Eds.; Publishing series “Green Energy and Technology”; Springer International Publishing: Berlin/Heidelberg, Germany, 2016; pp. 199–217. [Google Scholar]
- Seattle Department of Construction & Inspections. Seattle Green Factor. Available online: https://www.seattle.gov/sdci/codes/codes-we-enforce-(a-z)/seattle-green-factor (accessed on 5 November 2021).
- Urban Planning and Construction Department. Municipality of Bologna. Riduzione Impatto Edilizio–RIE. Available online: http://dru.iperbole.bologna.it/riduzione-impatto-edilizio-rie (accessed on 7 January 2022).
- Pettorelli, N.; Schulte to Bühne, H.; Tulloch, A.; Dubois, G.; Macinnis-Ng, C.; Queirós, A.M.; Keith, D.A.; Wegmann, M.; Schrodt, F.; Stellmes, M.; et al. Satellite remote sensing of ecosystem functions: Opportunities, challenges and way forward. Remote Sens. Ecol. Conserv. 2018, 4, 71–93. [Google Scholar] [CrossRef]
- Herold, M.; Couclelis, H.; Clarke, K.C. The role of spatial metrics in the analysis and modeling of urban land use change. Comput. Environ. Urban. Syst. 2005, 29, 369–399. [Google Scholar] [CrossRef]
- Wu, J.G. Effects of changing scale on landscape pattern analysis: Scaling relations. Landsc. Ecol. 2004, 19, 125–138. [Google Scholar] [CrossRef]
- Hu, X.; Xu, H. A new remote sensing index for assessing the spatial heterogeneity in urban ecological quality: A case from Fuzhou City, China. Ecol. Indic. 2018, 89, 11–21. [Google Scholar] [CrossRef]
- Liao, W.; Jiang, W. Evaluation of the Spatiotemporal Variations in the Eco-environmental Quality in China Based on the Remote Sensing Ecological Index. Remote Sens. 2020, 12, 2462. [Google Scholar] [CrossRef]
- Guo, H.; Zhang, B.; Bai, Y.; He, X. Ecological environment assessment based on Remote Sensing in Zhengzhou. IOP Conf. Ser. Earth Environ. Sci. 2017, 94, 012190. [Google Scholar] [CrossRef]
- Groom, G.; Mücher, C.A.; Ihse, M.; Wrbka, T. Remote sensing in landscape ecology: Experiences and perspectives in a European context. Landsc. Ecol. 2005, 20, 391–408. [Google Scholar] [CrossRef]
- Yue, H.; Liu, Y.; Li, Y.; Lu, Y. Eco-Environmental Quality Assessment in China’s 35 Major Cities Based On Remote Sensing Ecological Index Digital Object Identifier. IEEE Access 2019, 7, 51295–51311. [Google Scholar] [CrossRef]
- Van de Voorde, T. Spatially explicit urban green indicators for characterizing vegetation cover and public green space proximity: A case study on Brussels, Belgium. Int. J. Digit. Earth 2017, 10, 798–813. [Google Scholar] [CrossRef]
- Lakesa, T.; Kim, H.O. The urban environmental indicator “Biotope Area Ratio”—An enhanced approach to assess and manage the urban ecosystem services using high resolution remote-sensing. Ecol. Indic. 2012, 13, 93–103. [Google Scholar] [CrossRef]
- de Sherbinin, A.; Levy, M.; Zell, E.; Weber, S.; Jaiteh, M. Using satellite data to develop environmental indicators. Environ. Res. Lett. 2014, 9, 084013. [Google Scholar] [CrossRef]
- Shahtahmassebi, A.R.; Li, C.; Fan, Y.; Wu, Y.; Lin, Y.; Gan, M.; Wang, K.; Malik, A.; Blackburn, G.A. Remote sensing of urban green spaces: A review. Urban. For. Urban. Green. 2021, 57, 126946. [Google Scholar] [CrossRef]
- Stow, D.A.; Weeks, J.R.; Toure, S.; Coulter, L.L.; Lippitt, C.D.; Ashcroft, E. Urban Vegetation Cover and Vegetation Change in Accra, Ghana: Connection to Housing Quality. Prof. Geogr. 2013, 65, 451–465. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Schulz, D.; Yin, H.; Tischbein, B.; Verleysdonk, S.; Adamou, R.; Kumar, N. Land use mapping using Sentinel-1 and Sentinel-2 time series in a heterogeneous landscape in Niger, Sahel. ISPRS J. Photogramm. Remote Sens. 2021, 178, 97–111. [Google Scholar] [CrossRef]
- Lange, M.; Dechant, B.; Rebmann, C.; Vohland, M.; Cuntz, M.; Doktor, D. Validating MODIS and sentinel-2 NDVI products at a temperate deciduous forest site using two independent ground-based sensors. Sensors 2017, 17, 1855. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kopecká, M.; Szatmári, D.; Rosina, K. Analysis of urban green spaces based on Sentinel-2A: Case studies from Slovakia. Land 2017, 6, 25. [Google Scholar] [CrossRef] [Green Version]
- Frick, A.; Tervooren, S. A framework for the long-term monitoring of urban green volume based on multi-temporal and multi-sensoral remote sensing data. J. Geovis. Spat. Anal. 2019, 3, 6. [Google Scholar] [CrossRef]
- ISTAT. Istituto Nazionale di Statistica. Censimento Permanente Popolazione e Abitazioni. Available online: https://www.istat.it/it/censimenti-permanenti/popolazione-e-abitazioniIstat.it–Censimentopermanentepopolazioneeabitazioni (accessed on 27 October 2021).
- PGT. Piano di Governo del Territorio di Abbiategrasso (MI). Comune di Abbiategrasso–PGT–Piano di Governo del Territorio. Available online: https://www.comune.abbiategrasso.mi.it/aree-tematiche/pgt-piano-di-governo-del-territorio.html (accessed on 27 October 2021).
- PGT. Piano di Governo del Territorio di Segrate (MI). Available online: https://www.comune.segrate.mi.it/servizi/catasto-e-urbanistica/piano-di-governo-del-territorio/ComunediSegrate–PianodiGovernodelTerritorio (accessed on 27 October 2021).
- Legge Regionale della Lombardia. Legge per il Governo del Territorio LR 12/2005. 2005. Available online: https://normelombardia.consiglio.regione.lombardia.it/NormeLombardia/Accessibile/main.aspx?view=showdoc&iddoc=lr002005031100012 (accessed on 4 October 2021).
- Database Topografico. Specifiche di Contenuto Semplificate. Available online: https://www.geoportale.regione.lombardia.it/documents/10180/0/Allegato+2_III_Specifiche/19458997-44c0-4d54-b07d-d26ae5d4c6d8 (accessed on 28 October 2021).
- Geoportale Lombardia. Available online: https://www.geoportale.regione.lombardia.it/ (accessed on 26 October 2021).
- González del Campo, A. GIS in environmental assessment: A review of current issues and future needs. J. Environ. Assess. Policy Manag. 2012, 14, 121–143. [Google Scholar]
- European Space Agency. Sentinel Online. Available online: https://sentinels.copernicus.eu/web/sentinel/home (accessed on 25 October 2021).
- European Space Agency. Copernicus Open Access Hub. Available online: https://scihub.copernicus.eu/ (accessed on 25 October 2021).
- Meraner, A.; Ebel, P.; Zhu, X.X.; Schmitt, M. Cloud removal in Sentinel-2 imagery using a deep residual neural network and SAR-optical data fusion. ISPRS J. Photogramm. Remote Sens. 2020, 166, 333–346. [Google Scholar] [CrossRef]
- Gao, J.; Yuan, Q.; Li, J.; Zhang, H.; Su, X. Cloud Removal with Fusion of High Resolution Optical and SAR Images Using Generative Adversarial Networks. Remote Sens. 2020, 12, 191. [Google Scholar] [CrossRef] [Green Version]
- Cheng, Q.; Shen, H.; Zhang, L.; Yuan, Q.; Zeng, C. Cloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model. ISPRS J. Photogramm. Remote Sens. 2014, 92, 54–68. [Google Scholar] [CrossRef]
- Tseng, D.C.; Tseng, H.T.; Chien, C.L. Automatic cloud removal from multi-temporal SPOT images. Appl. Math. Comput. 2008, 205, 584–600. [Google Scholar] [CrossRef]
- Lin, C.H.; Tsai, P.H.; Lai, K.H.; Chen, J.Y. Cloud removal from multitemporal satellite images using information cloning. IEEE Trans. Geosci. Remote Sens. 2013, 51, 232–241. [Google Scholar] [CrossRef]
- Campbell, J.B. Mapping the Land Aerial Imagery for Land Use Information; Resource Publications in Geography; Association of American Geographers: Washington, DC, USA, 1983. [Google Scholar]
- Saito, S.; Aoki, Y. Building and road detection from large aerial imagery. Proc. SPIE 9405 Image Processing: Mach. Vis. Appl. VIII 2015, 9405, 94050K. [Google Scholar]
- Congedo, L. Semi-Automatic Classification Plugin Documentation Release 7.9.7.1, Institute for Environmental Protection and Research (ISPRA). Available online: https://buildmedia.readthedocs.org/media/pdf/semiautomaticclassificationmanual/latest/semiautomaticclassificationmanual.pdf (accessed on 6 November 2021).
- Aslahishahri, M.; Stanley, K.G.; Duddu, H.; Shirtliffe, S.; Vail, S.; Bett, K.; Pozniak, C.; Stavness, I. From RGB to NIR: Predicting of Near Infrared Reflectance from Visible Spectrum Aerial Images of Crops. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, Online event, 12–15 October 2021; pp. 1312–1322. [Google Scholar]
- Abdulridha, J.; Batuman, O.; Ampatzidis, Y. UAV-Based Remote Sensing Technique to Detect Citrus Canker Disease Utilizing Hyperspectral Imaging and Machine Learning. Remote Sens. 2019, 11, 1373. [Google Scholar] [CrossRef] [Green Version]
- Yang, L.; Wu, X.; Praun, E.; Ma, X. Tree detection from aerial imagery. In Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (GIS ‘09). Association for Computing Machinery, New York, NY, USA, 4–6 November 2009; pp. 131–137. [Google Scholar]
- Liknes, G.C.; Perry, C.H.; Meneguzzo, D.M. Assessing tree cover in agricultural landscapes using high-resolution aerial imagery. J. Terr. Obs. 2010, 2, 38–55. [Google Scholar]
- Abdollahi, A.; Pradhan, B. Urban Vegetation Mapping from Aerial Imagery Using Explainable AI (XAI). Sensors 2021, 21, 4738. [Google Scholar] [CrossRef]
- Altman, D.G.; Bland, J.M. Standard deviations and standard errors. BMJ Br. Med. J. 2005, 331, 903. [Google Scholar] [CrossRef] [Green Version]
- Butt, A.; Shabbir, R.; Ahmad, S.S.; Aziz, N. Land use change mapping and analysis using Remote Sensing and GIS: A case study of Simly watershed, Islamabad, Pakistan. Egypt. J. Remote Sens. Space Sci. 2015, 18, 251–259. [Google Scholar] [CrossRef] [Green Version]
- Segarra, J.; Buchaillot, M.L.; Araus, J.L.; Kefauver, S.C. Remote Sensing for Precision Agriculture: Sentinel-2 Improved Features and Applications. Agronomy 2020, 10, 641. [Google Scholar] [CrossRef]
- Le Texier, M.; Schiel, K.; Caruso, G. The provision of urban green space and its accessibility: Spatial data effects in Brussels. PLoS ONE 2018, 13, e0204684. [Google Scholar] [CrossRef]
- Stein, A.; Hamm, N.; Ye, Q. Handling uncertainties in image mining for remote sensing studies. Int. J. Remote Sens. 2009, 30, 5365–5538. [Google Scholar] [CrossRef]
- European Parliament and of the Council. Directive 2001/42/CE on the Assessment of the Effects of Certain Plans and Programmes on the Environment; European Parliament and of the Council: Strasbourg, France, 2001. [Google Scholar]
Types of Surfaces | Weighting Factors |
---|---|
Sealed surfaces | 0.0 |
Partially sealed surfaces (e.g., clinker brick, mosaic paving, etc.) | 0.1 |
Semi-open surfaces (e.g., sand, gravel, etc.) | 0.2 |
Greened surfaces (e.g., gravel with grass, wooden cobbles, etc.) | 0.4 |
Surfaces with vegetation, unconnected to the soil below, shallow substrate thickness (20–40 cm of soil coverage) | 0.5 |
Surfaces with vegetation, unconnected to the soil below, medium substrate thickness (41–80 cm of soil coverage) | 0.6 |
Surfaces with vegetation, unconnected to the soil below, deep substrate thickness (81–150 cm of soil covering) | 0.7 |
Surfaces with vegetation, unconnected to the soil below, very deep substrate thickness (>150 cm of soil covering) | 0.9 |
Surfaces with vegetation, connected to the soil below | 1 |
Rainwater infiltration per m2 of roof area | 0.2 |
Water surface (rainwater-fed water surface. Through the establishment of vegetation, the BAF can increase to 0.6) | 0.5 |
Vertical greenery with connection to the ground | 0.5 |
Vertical greenery without connection to the ground | 0.7 |
Urban Planning Parameters | Abbiategrasso | Milano Due |
---|---|---|
Buildings | 237,363 sq. m. | 217,042 sq. m. |
Primary urbanization | 180,937 sq. m. | 268,211 sq. m. |
Green areas | 307,081 sq. m. | 439,321 sq. m. |
Remaining areas | 274,618 sq. m. | 75,426 sq. m. |
Territorial surface | 1,000,000 sq. m. | 1,000,000 sq. m. |
Built-up area | 237,363 sq. m. | 217,042 sq. m. |
Coverage ratio | 0.24 sq. m./sq. m. | 0.22 sq. m./sq. m. |
Volume | 1,380,127 cu. m. | 2,905,755 cu. m. |
Volumetric density | 1.38 cu. m./sq. m. | 2.91 cm/sq. m. |
DBT Categories | DBT Specifications | BAF Weighting Factors | |
---|---|---|---|
Level 1 | Level 2 | ||
010101 | 0201 | Vehicle circulation area | 0 |
020102 | 0101 | Buildings | 0 |
020204 | 0103 | Tennis court | 0.2 |
050393 | 0105 | Gravel | 0.2 |
020104 | 0104 | Flat roof (green) | 0.5 |
060401 | 0104 | Flowerbed (in roads) | 0.5 |
060401 | 0101 | Green urban areas | 0.9 |
060105 | 010102 | Wild grassland | 1 |
Typology | N° Pixel | Area | Coefficients | Ecologically Effective Surface |
---|---|---|---|---|
No green | 35,564 | 432,778 sq.m. | 0 | 0 sq.m. |
Green | 46,612 | 567,222 sq.m. | 1 | 567,222 sq.m. |
TOTAL | 82,176 | 1,000,00 sq.m. | 567,222 sq.m. | |
BAF | 0.57 |
Sentinel BAF Map | |||
April | July | 55% | |
October | April | 60% | |
July | October | 51% | |
April | July | October | 40% |
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Lotto, R.D.; Sessi, M.; Venco, E.M. Semi-Automatic Method to Evaluate Ecological Value of Urban Settlements with the Biotope Area Factor Index: Sources and Logical Framework. Sustainability 2022, 14, 1993. https://doi.org/10.3390/su14041993
Lotto RD, Sessi M, Venco EM. Semi-Automatic Method to Evaluate Ecological Value of Urban Settlements with the Biotope Area Factor Index: Sources and Logical Framework. Sustainability. 2022; 14(4):1993. https://doi.org/10.3390/su14041993
Chicago/Turabian StyleLotto, Roberto De, Matilde Sessi, and Elisabetta M. Venco. 2022. "Semi-Automatic Method to Evaluate Ecological Value of Urban Settlements with the Biotope Area Factor Index: Sources and Logical Framework" Sustainability 14, no. 4: 1993. https://doi.org/10.3390/su14041993