{"id":{"repo_id":"u-iceland","oai_identifier":"oai:skemman.is:1946/51551"},"canonical_url":"https://search.dev.ndltd.org/etd/u-iceland/oai:skemman.is:1946/51551","repository":{"repo_id":"u-iceland","name":"University of Iceland","base_url":"https://skemman.is/oai/request"},"display":{"title":"Developing a Satellite-Based Methodology to Estimate Built-up Biocapacity for Use in the National Ecological Footprint and Biocapacity Accounts: A Comparative Study of Pléiades Neo and Sentinel-2 Imagery, Classification Algorithms, and Segmentation Approaches","abstract":"Built-up land is an important component of the ecological footprint accounting framework, yet the National Footprint and Biocapacity Accounts (NFBA) assume built-up land has the biocapacity of cropland. The NFBA uses equivalence factors and yield factors to transform raw area data into global hectares (gha), hectares of globally average productivity. This thesis aims to develop a satellite-based methodology for estimating built-up biocapacity. A 2,800-ha study area in The Hague, the Netherlands, was used to compare the effect of spatial resolution, classification algorithm, and segmentation technique on the results. Multispectral imagery from Pléiades Neo (1.2-meter resolution) and Sentinel-2 (10-meter resolution) provided the basis for classification into land cover categories. Identified land cover categories within the built-up fabric were transformed to biocapacity in gha by linking them to the NFBA land types and adapting the yield and equivalence factors from the NFBA to reflect reduced productivity in built-up conditions. Built-up biocapacity for the study area was between 2500 and 4000 gha, far below the 10325 gha using the current NFBA method. Among methodological factors, segmentation had the strongest influence, explaining 27.3% of variation for Sentinel-2 and 9.7% for Pléiades Neo. Pixel-based classifications in both Sentinel-2 and Pléiades Neo yielded remarkably consistent results across algorithms, showing a spread of 3.90% and 2.34%, respectively. However, while pixel-based classifications tended to capture more small, vegetated features, they were more prone to misclassification of shaded infrastructure as dune sands or water. Although Sentinel-2 results captured a lot of urban tree canopy, with a lower BC yield than trees in urban forests, the higher area attributed to this land cover explains the low difference in means between Sentinel-2 and Pléiades Neo results.","abstract_html":"Built-up land is an important component of the ecological footprint accounting framework, yet the National Footprint and Biocapacity Accounts (NFBA) assume built-up land has the biocapacity of cropland. The NFBA uses equivalence factors and yield factors to transform raw area data into global hectares (gha), hectares of globally average productivity. This thesis aims to develop a satellite-based methodology for estimating built-up biocapacity. A 2,800-ha study area in The Hague, the Netherlands, was used to compare the effect of spatial resolution, classification algorithm, and segmentation technique on the results. Multispectral imagery from Pléiades Neo (1.2-meter resolution) and Sentinel-2 (10-meter resolution) provided the basis for classification into land cover categories. Identified land cover categories within the built-up fabric were transformed to biocapacity in gha by linking them to the NFBA land types and adapting the yield and equivalence factors from the NFBA to reflect reduced productivity in built-up conditions. Built-up biocapacity for the study area was between 2500 and 4000 gha, far below the 10325 gha using the current NFBA method. Among methodological factors, segmentation had the strongest influence, explaining 27.3% of variation for Sentinel-2 and 9.7% for Pléiades Neo. Pixel-based classifications in both Sentinel-2 and Pléiades Neo yielded remarkably consistent results across algorithms, showing a spread of 3.90% and 2.34%, respectively. However, while pixel-based classifications tended to capture more small, vegetated features, they were more prone to misclassification of shaded infrastructure as dune sands or water. Although Sentinel-2 results captured a lot of urban tree canopy, with a lower BC yield than trees in urban forests, the higher area attributed to this land cover explains the low difference in means between Sentinel-2 and Pléiades Neo results.","abstract_has_math":false,"creators":["Johanna Louise Van Berkum 2001-"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Háskóli Íslands"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2025,"date_issued":"2025-09-30T15:30:45Z","date_published":"2025-09-30T15:30:45Z","updated_at":"2026-07-27T21:36:33Z","subjects":["Umhverfis- og auðlindafræði","Fjarkönnun","Þéttbýli","Mannvirki"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1946/51551","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Háskóli Íslands"]},{"key":"dc:creator","label":"Author","values":["Johanna Louise Van Berkum 2001-"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2025-09-30T15:30:34Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2025-09-30T15:30:34Z"]},{"key":"dc:date.issued","label":"Date","values":["2025-09-30T15:30:45Z"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Umhverfis- og auðlindafræði","Fjarkönnun","Þéttbýli","Mannvirki"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/1946/51551"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Built-up land is an important component of the ecological footprint accounting framework, yet the National Footprint and Biocapacity Accounts (NFBA) assume built-up land has the biocapacity of cropland. The NFBA uses equivalence factors and yield factors to transform raw area data into global hectares (gha), hectares of globally average productivity. This thesis aims to develop a satellite-based methodology for estimating built-up biocapacity. A 2,800-ha study area in The Hague, the Netherlands, was used to compare the effect of spatial resolution, classification algorithm, and segmentation technique on the results. Multispectral imagery from Pléiades Neo (1.2-meter resolution) and Sentinel-2 (10-meter resolution) provided the basis for classification into land cover categories. Identified land cover categories within the built-up fabric were transformed to biocapacity in gha by linking them to the NFBA land types and adapting the yield and equivalence factors from the NFBA to reflect reduced productivity in built-up conditions. Built-up biocapacity for the study area was between 2500 and 4000 gha, far below the 10325 gha using the current NFBA method. Among methodological factors, segmentation had the strongest influence, explaining 27.3% of variation for Sentinel-2 and 9.7% for Pléiades Neo. Pixel-based classifications in both Sentinel-2 and Pléiades Neo yielded remarkably consistent results across algorithms, showing a spread of 3.90% and 2.34%, respectively. However, while pixel-based classifications tended to capture more small, vegetated features, they were more prone to misclassification of shaded infrastructure as dune sands or water. Although Sentinel-2 results captured a lot of urban tree canopy, with a lower BC yield than trees in urban forests, the higher area attributed to this land cover explains the low difference in means between Sentinel-2 and Pléiades Neo results.","Þéttbýli og mannvirki eru mikilvægur þáttur í vistsporabókhaldi, en NFBA (National Footprint and Biocapacity Accounts) gera ráð fyrir að mannvirki hafi líffræðilega getu sambærilega við ræktarland. NFBA nota jöfnunar- og framleiðnistuðla til að umreikna flatarmál ákveðinna landgerða yfir í jarðhektara (jha), þ.e. hektara af meðalframleiðni á heimsvísu. Fjarkönnun var beitt við að reikna líffræðilega getu þéttbýlis og mannvirkja. Rannsóknarsvæðið, Haag í Hollandi, spannaði 2.800 hektara af fjölbreyttu borgarlandi. Fjölrófsmyndir frá Pléiades Neo (1,2 metra staðupplausn) og Sentinel-2 (10 metra staðupplausn) voru flokkaðar eftir landgerðum. Landgerðarflokkarnir voru umreiknaðir í líffræðilega getu (í jha) að höfðu tilliti til framleiðni- og jöfnunarstuðla. Könnuð voru áhrif mismunandi staðupplausnar, flokkunargerðar og sundrunartækni á niðurstöðurnar. Líffræðileg geta rannsóknarsvæðisins reyndist vera á bilinu 2500 til 4000 jha, langt undir 10.325 jha sem núverandi reikniaðferð NFBA gefur. Sundrunartæknin hafði þar mest áhrif og skýrði 27,3% af breytileikanum fyrir Sentinel-2 og 9,7% fyrir Pléiades Neo. Flokkun byggð á myndeiningum gaf áþekkar niðurstöður fyrir báðar myndgerðir, og sýndi dreifingu upp á 3,90% og 2,34%. Þó flokkun byggð á myndeiningum hafi tilhneigingu til að ná yfir fleiri smærri, gróðureiningar, voru þær líklegri til að rangflokka skyggða innviði sem sandöldur eða vatn. Sentinel-2 myndir komu betur út við greiningu á trjákrónum í þéttbýli, sem hafa lægri líffræðilega getu en skógar, en minni staðupplausn útskýrir hve lítil áhrif þetta hafði á mun milli myndgerða í útreikningunum."]},{"key":"dc:title","label":"Title","values":["Developing a Satellite-Based Methodology to Estimate Built-up Biocapacity for Use in the National Ecological Footprint and Biocapacity Accounts: A Comparative Study of Pléiades Neo and Sentinel-2 Imagery, Classification Algorithms, and Segmentation Approaches","Þróun fjarkönnunaraðferða við mat á vistspori og líffræðilegri getu þéttbýlissvæða: Samanburður á Sentinel-2 og Pléiades Neo fjölrófsmyndum, flokkunaraðferðum og sundrunartækni."]}]}],"canonical_facts":{"dc:contributor":["Háskóli Íslands"],"dc:creator":["Johanna Louise Van Berkum 2001-"],"dc:date.accessioned":["2025-09-30T15:30:34Z"],"dc:date.available":["2025-09-30T15:30:34Z"],"dc:date.issued":["2025-09-30T15:30:45Z"],"dc:description.abstract":["Built-up land is an important component of the ecological footprint accounting framework, yet the National Footprint and Biocapacity Accounts (NFBA) assume built-up land has the biocapacity of cropland. The NFBA uses equivalence factors and yield factors to transform raw area data into global hectares (gha), hectares of globally average productivity. This thesis aims to develop a satellite-based methodology for estimating built-up biocapacity. A 2,800-ha study area in The Hague, the Netherlands, was used to compare the effect of spatial resolution, classification algorithm, and segmentation technique on the results. Multispectral imagery from Pléiades Neo (1.2-meter resolution) and Sentinel-2 (10-meter resolution) provided the basis for classification into land cover categories. Identified land cover categories within the built-up fabric were transformed to biocapacity in gha by linking them to the NFBA land types and adapting the yield and equivalence factors from the NFBA to reflect reduced productivity in built-up conditions. Built-up biocapacity for the study area was between 2500 and 4000 gha, far below the 10325 gha using the current NFBA method. Among methodological factors, segmentation had the strongest influence, explaining 27.3% of variation for Sentinel-2 and 9.7% for Pléiades Neo. Pixel-based classifications in both Sentinel-2 and Pléiades Neo yielded remarkably consistent results across algorithms, showing a spread of 3.90% and 2.34%, respectively. However, while pixel-based classifications tended to capture more small, vegetated features, they were more prone to misclassification of shaded infrastructure as dune sands or water. Although Sentinel-2 results captured a lot of urban tree canopy, with a lower BC yield than trees in urban forests, the higher area attributed to this land cover explains the low difference in means between Sentinel-2 and Pléiades Neo results.","Þéttbýli og mannvirki eru mikilvægur þáttur í vistsporabókhaldi, en NFBA (National Footprint and Biocapacity Accounts) gera ráð fyrir að mannvirki hafi líffræðilega getu sambærilega við ræktarland. NFBA nota jöfnunar- og framleiðnistuðla til að umreikna flatarmál ákveðinna landgerða yfir í jarðhektara (jha), þ.e. hektara af meðalframleiðni á heimsvísu. Fjarkönnun var beitt við að reikna líffræðilega getu þéttbýlis og mannvirkja. Rannsóknarsvæðið, Haag í Hollandi, spannaði 2.800 hektara af fjölbreyttu borgarlandi. Fjölrófsmyndir frá Pléiades Neo (1,2 metra staðupplausn) og Sentinel-2 (10 metra staðupplausn) voru flokkaðar eftir landgerðum. Landgerðarflokkarnir voru umreiknaðir í líffræðilega getu (í jha) að höfðu tilliti til framleiðni- og jöfnunarstuðla. Könnuð voru áhrif mismunandi staðupplausnar, flokkunargerðar og sundrunartækni á niðurstöðurnar. Líffræðileg geta rannsóknarsvæðisins reyndist vera á bilinu 2500 til 4000 jha, langt undir 10.325 jha sem núverandi reikniaðferð NFBA gefur. Sundrunartæknin hafði þar mest áhrif og skýrði 27,3% af breytileikanum fyrir Sentinel-2 og 9,7% fyrir Pléiades Neo. Flokkun byggð á myndeiningum gaf áþekkar niðurstöður fyrir báðar myndgerðir, og sýndi dreifingu upp á 3,90% og 2,34%. Þó flokkun byggð á myndeiningum hafi tilhneigingu til að ná yfir fleiri smærri, gróðureiningar, voru þær líklegri til að rangflokka skyggða innviði sem sandöldur eða vatn. Sentinel-2 myndir komu betur út við greiningu á trjákrónum í þéttbýli, sem hafa lægri líffræðilega getu en skógar, en minni staðupplausn útskýrir hve lítil áhrif þetta hafði á mun milli myndgerða í útreikningunum."],"dc:identifier.uri":["https://hdl.handle.net/1946/51551"],"dc:language.iso":["en"],"dc:subject":["Umhverfis- og auðlindafræði","Fjarkönnun","Þéttbýli","Mannvirki"],"dc:title":["Developing a Satellite-Based Methodology to Estimate Built-up Biocapacity for Use in the National Ecological Footprint and Biocapacity Accounts: A Comparative Study of Pléiades Neo and Sentinel-2 Imagery, Classification Algorithms, and Segmentation Approaches","Þróun fjarkönnunaraðferða við mat á vistspori og líffræðilegri getu þéttbýlissvæða: Samanburður á Sentinel-2 og Pléiades Neo fjölrófsmyndum, flokkunaraðferðum og sundrunartækni."],"dc:type":["Thesis"]},"updated_at":"2026-07-27T21:36:33Z"}