Application of Multi-Spectral Index from Sentinel-2 Data for Extracting Build-up Land of Hanoi Area in the Dry Season

• Autorzy: Thu Ha Le Thi , Huu Long Nguyen , Van Trung Nguyen , Thi Lan Pham

Identyfikatory

DOI

10.29227/IM-2024-01-94

Warianty tytułu

PL

Zastosowanie wielospektralnego indeksu z danych Sentinel-2 do ekstrakcji terenów zabudowanych w rejonie Hanoi w sezonie suchym

• Języki publikacji: EN

Abstrakty

EN

A remote sensing index is a simple and effective way to highlight a specific land cover. Therefore, in this study, we try to increase the accuracy of the urban land map developed for Hanoi city by focusing on determining the appropriate combination of spectral indices calculated from satellite image data. To conduct the study, four spectral indices were selected including namely normalized difference tillage index (NDTI), bare soil index (BSI), dry bare soil index (DBSI) and the normalized difference vegetation index (NDVI). All these spectral indices are calculated from Sentinel-2 data acquired in the dry season. The two combinations are created from the superposition of NDTI/BSI/NDVI and NDTI/DBSI/NDVI spectral index layers. The use of the “K-means” algorithm as an unsupervised classifier provides rapid and automatic urban land detection. The results show that the BSI index performs better than using the DBSI index. As a result, the BSI index brings improvements: bare soil types and accumulation processes are better differentiated, with overall accuracy increasing by 5.82% and Kappa coefficient increasing by 11.1%. The results show that the NDTI/BSI/NDVI multi-spectral index dataset is suitable for mapping urban areas with the potential to help better urban management during the dry season.

PL

Wskaźnik zdalnego wykrywania jest prostym i skutecznym sposobem na wyróżnienie określonego pokrycia terenu. Dlatego w tym bada-niu staramy się zwiększyć dokładność mapy terenów miejskich opracowanej dla miasta Hanoi, skupiając się na określeniu odpowiedniego połączenia wskaźników spektralnych obliczanych z danych obrazów satelitarnych. Do przeprowadzenia badania wybrano cztery wskaźniki spektralne, a mianowicie znormalizowany wskaźnik różnicy uprawy (NDTI), wskaźnik gołej gleby (BSI), wskaźnik suchej gołej gleby (DBSI) i znormalizowany wskaźnik różnicy wegetacji (NDVI). Wszystkie te wskaźniki spektralne są obliczane z danych Sentinel-2 uzyskanych w sezonie suchym. Dwie kombinacje są tworzone z nakładania się warstw wskaźników spektralnych NDTI/BSI/NDVI i NDTI/DBSI/NDVI. Użycie algorytmu “K-means” jako klasyfikatora nienadzorowanego zapewnia szybkie i automatyczne wykrywanie terenów miejskich. Wyniki pokazują, że wskaźnik BSI działa lepiej niż użycie wskaźnika DBSI. W rezultacie wskaźnik BSI przynosi poprawki: typy gołej gleby i procesy akumulacji są lepiej zróżnicowane, a ogólna dokładność wzrasta o 5,82%, a współczynnik Kappa wzrasta o 11,1%. Wyniki pokazują, że zestaw danych wielospektralnych wskaźników NDTI/BSI/NDVI jest odpowiedni do mapowania obszarów miejskich z potencjałem pomocy w lepszym zarządzaniu miastem podczas sezonu suchego.

Słowa kluczowe

EN

Sentinel-2 data; K-means algorithm; Bare Soil Index (BSI); Dry Bare Soil Index (DBSI); Normalized Difference Tillage Index (NDTI)

PL

dane Sentinel-2; algorytm K-means; Bare Soil Index (BSI); Dry Bare Soil Index (DBSI); Normalized Difference Tillage Index (NDTI)

• Wydawca: Polskie Towarzystwo Przeróbki Kopalin
• Czasopismo: Inżynieria Mineralna
• Rocznik: 2024
• Tom: Vol. 2, no. 1
• Strony: 63--70
• Opis fizyczny: Bibliogr. 33 poz., tab., wykr., zdj.

Twórcy

autor

Thu Ha Le Thi

• Hanoi university of Mining and Geology
• Geomatics in Earth Sciences Research Group, Hanoi University of Mining and Geology, 18 Vien Str., Duc Thang Ward, Hanoi 100000, Vietnam

• https://orcid.org/000-0001-9459-787X

autor

Huu Long Nguyen

• Dong Thap University

• https://orcid.org/0000-0002-3032-6808

autor

Van Trung Nguyen

• Hanoi university of Mining and Geology
• Geomatics in Earth Sciences Research Group, Hanoi University of Mining and Geology, 18 Vien Str., Duc Thang Ward, Hanoi 100000, Vietnam

• https://orcid.org/0000-0003-3982-1896

autor

Thi Lan Pham

• Hanoi university of Mining and Geology

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).