Kombinasi Indeks Citra untuk Analisis Lahan Terbangun dan Vegetasi Perkotaan

https://doi.org/10.22146/mgi.31899

Iswari Nur Hidayati(1*), R. Suharyadi(2), Projo Danoedoro(3)

(1) Faculty of Geography Universitas Gadjah Mada, Yogyakarta Geographic Science Department, Faculty of Geography, Gadjah Mada University
(2) Fakultas Geografi, Universitas Gadjah Mada, Yogyakarta, Indonesia
(3) Fakultas Geografi, Universitas Gadjah Mada, Yogyakarta, Indonesia
(*) Corresponding Author

Abstract


Lahan terbangun di perkotaan dan area vegetasi menjadi hal yang sangat menarik untuk dikaji. Apalagi dinamika penggunaan lahan di perkotaan yang sangat cepat berubah. Berbagai metode dikembangkan untuk ekstraksi lahan terbangun di perkotaan, mulai dari klasifikasi multispektral, object based approach, hingga penelitian berbasis indeks. NDBI menjadi salah satu indeks pioner untuk ekstraksi lahan terbangun perkotaan dengan menggunakan saluran SWIR. Pengembangan indeks lahan terbangun ini masih perlu dikembangan untuk citra yang tidak mempunyai panjang gelombang SWIR. Tujuan penelitian ini adalah merumuskan kombinasi saluran terbaik dalam ekstraksi lahan terbangun dan area vegetasi serta menghitung kepadatan bangunan dan kerapatan vegetasi berbasis indeks. Penelitian ini menggunakan Citra Worldview-2 yang diperoleh dari Digital Globe Foundation untuk ekstraksi lahan terbangun dan kerapatan vegetasi. Normalized difference index digunakan sebagai formula dalam pembuatan indeks. Pemanfaatan semua saluran spektral dalam citra Worldview-2 digunakan untuk ekstraksi lahan terbangun dan kepadatan bangunan di perkotaan dengan PCA sebagai metode untuk penggabungan delapan saluran dalam Worldview-2. Saluran NIR 1 dan NIR 2 yang digabungkan dengan Saluran Merah menjadi pilihan untuk ekstraksi vegetasi. Proses trial dan error mewarnai pemilihan kombinasi saluran yang digunakan dan treshold yang digunakan untuk analisis biner dalam membedakan lahan terbangun dan non lahan terbangun serta area vegetasi dan area non vegetasi. Pemanfaatan unique identification (UID) digunakan untuk pembuatan grid berbasis raster dalam perhitungan kepadatan bangunan dan kerapatan vegetasi. Hasil penelitian menunjukkan bahwa indeks yang dibangun dengan PC2 dan NIR 1 serta PC2 dan NIR 2 mempunyai akurasi tinggi yaitu 94,43% untuk bangunan dan kombinasi indeks dari NIR1_Red mempunyai akurasi optimal yaitu 99,51% dan NIR2_Red mempunyai akurasi 92,87 untuk ekstraksi data vegetasi.

 

 

Urban phenomenon becomes a very interesting thing to be studied. The urban land use, land conversion, urban green space, are rapidly changing. Various methods were developed for urban built-up data extraction, such as multispectral classification, object-based approach, and index-based research. NDBI became one of pioneer indices for urban-built urban land extraction using SWIR band. The development of this built-up index is still required for images that do not have SWIR wavelengths. The study objectives were to select the best methods for built-up land and vegetation extraction and to calculate building density and index-based vegetation density. Worldview-2 image obtained from Digital Globe Foundation tested for built-up land data extracting and vegetation density analyzing. Normalized difference index formula is applied for combining and setting built-up land and vegetation indexes. Merger of Worldview-2 spectral imagery were using PCA method for extracting built-up land and calculating building density. Combining eight bands into eight new images that have different information from original images was done by PCA method.  NIR 1, NIR2, and Red bands are the perfect choice for vegetation extraction because near infrared characteristics have high reflections on vegetation. Selection of band combinations and selection of threshold values through trial and error processes to perceive the best index combinations and reasonable threshold values. Binary analysis is particularly useful for separating the built-up and non-built-up areas as well as vegetation and non-vegetation. The Unique identification (UID) technique used in estimating built-up and vegetation density from precisely classified images provided better and accurate assessment of built-up and vegetation density.  The results show that the built-up index involving PC2_NIR 1 and PC2_NIR 2 for the urban built land research achieved an optimal accuracy of 94, 43%. The best accuracy for vegetation data extraction was obtained from the combined NIR1_Red index with 99,51% and NIR2_Red values with an overall accuracy of 92,87%.

 

 

 


Keywords


kombinasi indeks; analisis grid; kepadatan bangunan; kerapatan vegetasi

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DOI: https://doi.org/10.22146/mgi.31899

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Accredited Journal, Based on Decree of the Minister of Research, Technology and Higher Education, Republic of Indonesia Number 30e/KPT/2018

Volume 31 No 2 the Year 2017 for Volume 35 No 2 the Year 2021

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