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All Journal Indonesian Journal of Geography Geo Media: Majalah Ilmiah dan Informasi Kegeografian Majalah Geografi Indonesia JURNAL AGROTEKNOLOGI Forum Geografi GEA, Jurnal Pendidikan Geografi MAJALAH ILMIAH GLOBE BHUMI: Jurnal Agraria dan Pertanahan
Iswari Nur Hidayati
Departement Of Geographic Information Science, Faculty Of Geography, Gadjah Mada University
Agriculture, Biological Sciences & Forestry Earth & Planetary Sciences Education Law, Crime, Criminology & Criminal Justice Social Sciences

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ANALISIS HARGA LAHAN BERDASARKAN CITRA PENGINDERAAN JAUH RESOLUSI TINGGI Hidayati, Iswari Nur
Jurnal Pendidikan Geografi Gea Vol 13, No 1 (2013)
Publisher : Indonesia University of Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17509/gea.v13i1.3309

Abstract

Pemetaan harga lahan di Yogyakarta sangat diperlukan, hal ini dikarenakan kenaikan harga lahan sangat pesat seiring dengan pertumbuhan ekonomi. Pemetaan harga lahan menggunakan parameter yang bisa disadap menggunakan data penginderaan jauh seperti aksesibilitas lahan, penggunaan lahan, dengan aksesibilitas negatif. Oleh karena itu, peneliti mempunyai gagasan untuk melakukan penelitian tentang harga lahan. Adapun tujuan penelitian adalah a) Mengetahui kemampuan citra Quickbird untuk mengetahui tingkat harga lahan, b) Untuk mengetahui distribusi spasial tentang harga lahan di Kecamatan Jetis. Data penginderaan jauh akan digunakan untuk ekstrasi data penggunaan lahan. Penelitian ini terdiri atas tiga tahapan yaitu tahap persiapan, tahap pemrosesan data, dan tahap analisis data. Data penggunaan lahan akan dianalisis lebih lanjut untuk mengetahui aksesibilitas lahan positif dan lahan negatif. Beberapa parameter ini digunakan untuk mendukung dalam memetakan harga lahan di Kecamatan Jetis.Untuk menambah informasi, pengumpulan data lapangan sangat diperlukan untuk mendukung pemetaan ini. Hasil dari penelitian ini menunjukkan bahwa citra Quickbird dapat digunakan untuk penelitian ini dengan akurasi pemetaan sebesar 92,49%. Hasil pemetaan harga lahan ini dibagi menjadi empat kelas dengan kelas harga lahan tertinggi terdapat 52,35%. Harga lahan tinggi terdapat 22,94%, kelas lahan sedang terdapat 21,76%, dan kelas lahan rendah mempunyai luasan sekitar 2,75%.Kata kunci : harga lahan, penginderaan jauh, pemetaan.
Developing an Extraction Method of Urban Built-Up Area Based on Remote Sensing Imagery Transformation Index Hidayati, Iswari Nur; Suharyadi, R; Danoedoro, Projo
Forum Geografi Vol 32, No 1 (2018): July 2018
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v32i1.5907

Abstract

Studying urban areas using remote sensing imagery has become a challenge, both visually and digitally. Supervised classification, one of the digital classification approaches to differentiate between built-up and non-built-up area, used to be leading in digital studies of urban area. Then the next generation uses index transformation for automatic urban data extraction. The extraction of urban built-up land can be automatically done with NDBI although it has one limitation on separating built-up land and bare land. The previous studies provide opportunities for further research to increase the accuracy of the extraction, particularly using index transformation. This study aims to obtain the maximum accuracy of the extraction by merging several indices including NDBI, NDVI, MNDWI, NDWI, and SAVI. The merging of the indices is using four stages: merging of two indices, three indices, four indexes and five indices. Several operations were experimented to merge the indices, either by addition, subtraction, or multiplication. The results show that merging NDBI and MNDWI produce the highest accuracy of 90.30% either by multiplication (overlay) or reduction. Application of SAVI, NDBI, and NDWI also gives a good effect for extracting urban built-up areas and has 85.72% mapping accuracy.
PENGARUH KETINGGIAN DALAM ANALISIS KEMASUK-AKALAN (PLAUSIBILITY FUNCTION) UNTUK OPTIMALISASI KLASIFIKASI PENGGUNAAN LAHAN Hidayati, Iswari Nur
MAJALAH ILMIAH GLOBE Vol 15, No 1 (2013)
Publisher : Badan Informasi Geospasial

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1580.29 KB) | DOI: 10.24895/MIG.2013.15-1.66

Abstract

ABSTRAKTujuan penelitian ini adalah: (1) mengkaji aspek kemasuk-akalan untuk mendapatkan informasi penggunaan lahandari data penutup lahan yang diperoleh dari citra penginderaan jauh; dan (2) mengkaji pengaruh informasi ketinggiandalam pengambilan keputusan untuk pemetaan penggunaan lahan. Metode yang digunakan dalam penelitian iniadalah pembuatan klasifikasi maximum likelihood untuk pemetaan penutup lahan yang dibuat dari citra LandsatETM+. Dari hasil klasifikasi kemudian dilakukan perkalian terhadap nilai plausibilitas untuk penggunaan lahan danplausibilitas lereng sehingga menghasilkan klasifikasi penggunaan lahan optimal. Penggunaan lahan optimal adalahpenggunaan lahan yang sesuai dengan keadaan di lapangan dengan memperhatikan ”local knowledge” dan aspekketidak-pastian. Teori Dempster-Shaffer menawarkan alternatif berdasarkan teori probabilitas yang direpresentasikanmelalui ketidakpastian dengan mencari nilai terbaik dari plausibilitas penutup lahan untuk penggunaan lahan, yangdiperoleh dari perkalian antara nilai terbaik dari plausibilitas penutup lahan dengan plausibilitas elevasi optimum untukpenggunaan lahan. Penelitian ini merupakan modifikasi Teori Dempster-Shaffer untuk pemetaan Penggunaan LahanOptimal. Hasil penelitian menunjukkan bahwa metode tersebut dapat digunakan untuk optimalisasi penggunaanlahan. Hasil akurasi dari metode ini adalah 92,40% dan koefisien kappa sebesar 0,93.Kata Kunci: Ketinggian, Faktor Kemasuk-Akalan, Penggunaan Lahan, Teori Dempster-Shaffer.ABSTRACTThe aims of the research were: (1) to study the plausibility effect on land use classification, and (2) to study theeffect of elevation that is used as evidence for optimalisation of land use classification. The method applied in thisresearch was maximum likelihood classification for land cover mapping using Landsat ETM+ image. Dempster-ShaferTheory offers an alternative to traditional probabilistic theory for the mathematical representation of uncertainty.Dempster-Shafer Theory does not require an assumption regarding the probability of the individual constituents of theset or interval. Dempster-Shaffer Theory of evidence is used to find the best land use classification. Plausibility valueswere combined to find optimum land use. A plausibility class is made out of signature classes. A signature class madeout of land cover plausibilities. The method was optimum land cover plausibility multiplied by elevation plausibility. Themethod used in this study was a modification of Dempster-Shaffer Theory of evidence to optimum land useclassification. The results of this study show that the first method was very good in producing optimum land useclassification. The accuracy of land use classification was 92.40% and the kappa coefficient was 0.93.Keywords: Elevation, Plausibility Factor, Landuse, Dempster-Shaffer Theory.
A COMPARATIVE STUDY OF VARIOUS INDICES FOR EXTRACTION URBAN IMPERVIOUS SURFACE OF LANDSAT 8 OLI Hidayati, Iswari Nur; Suharyadi, R
Forum Geografi Vol 33, No 2 (2019): December 2019
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v33i2.9179

Abstract

Impervious surface is one of the major land cover types of urban and suburban environment. Conversion of rural landscapes and vegetation area to urban and suburban land use is directly related to the increase of the impervious surface area. The impervious surface expansion is straight-lined with decreasing green spaces in urban areas. Impervious surface is one of indicator for detecting urban heat islands. This study compares various indices for mapping impervious surfaces using Landsat 8 OLI imagery by optimizing the different spectral characteristics of Landsat 8 OLI imagery. The research objectives are (1) to apply various indices for impervious surface mapping and (2) identifies impervious surfaces in urban areas based on multiple indices and provide recommendations and find the best index for mapping impervious surface in urban areas. In addition to utilizing the index, land use supervised classification method, maximum likelihood classification used for extracting built-up, and non-built-up areas. Accuracy assessment of this research used field data collection as primary data for calculating kappa coefficient, producer accuracy, and user accuracy. The study can also be extended to find the land surface temperature and correlate the impervious surface extraction data with urban heat islands.
Aplikasi Penginderaan Jauh untuk Analisis Pengaruh Ruang Terbuka Hijau Terhadap Iklim Mikro di Kawasan Perkotaan Klaten Eni Susanti; Iswari Nur Hidayati
Majalah Geografi Indonesia Vol 29, No 2 (2015): Majalah Geografi Indonesia
Publisher : Fakultas Geografi, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (2725.879 KB) | DOI: 10.22146/mgi.13113

Abstract

ABSTRAK Keberadaan RTH Hijau mulai tergusur oleh adanya pembangunan bangunan-bangunan baru di kawasan perkotaan. Salah satu fungsi dari RTH adalah sebagai pengatur dan penyeimbang iklim mikro. Citra ALOS Pan-Sharpened digunakan untuk interpretasi RTH dan penggunaan lahan. Hasil akhir penelitian ini adalah peta distribusi suhu udara, kelembaban relatif, dan kecepatan angin di kawasan perkotaan Klaten, tingkat hubungan antara kerapatan RTH terhadap iklim mikro, pola RTH terhadap iklim mikro, dan penggunaan lahan terhadap iklim mikro. Hasil penelitian menunjukkan bahwa citra ALOS Pan-Sharpened memberikan ketelitian interpretasi kerapatan RTH sebesar 81,05%, untuk interpretasi pola RTH sebesar 93,68%, dan interpretasi penggunaan lahan sebesar 94,97%. Kerapatan RTH berpengaruh terhadap suhu udara 60,6%, kelembaban udara 80%, kecepatan angin 5,2%. Pola vegetasi RTH kurang memberikan pengaruh yang signifikan terhadap iklim mikro. Pengaruh penggunaan lahan terhadap iklim mikro adalah rendah, dimana lebih dipengaruhi oleh ketersediaan RTH , tingkat kepadatan bangunan, geometri bangunan, dan emisi gas kendaraan bermotor. ABSTRACT The presence of Open Green Space is threatened by conversion to new buildings in urban areas. One of the function of Open Green Space is as a regulator and  balancing of micro climate. ALOS Pan-Sharpened images was used to Open Green Space and land use interpretation. The final result of this research is a distribution map of air temperature, relative humidity, and wind speed in Klaten’s urban areas, relations level between Open Green Space density to the micro climate, Open Green Space pattern to the micro climate, and land use to the micro climate. The result of this research showed that interpretation accuracy of Open Green Space density provided by ALOS Pan-Sharpened was 81,05%, interpretation of Open Green Space pattern was 93,68%, and interpretation of land use was 94,97%. On the other hand, Open Green Space density affect to the air temperature was 60,6%,  to the air humidity was 80%, and to the wind speed was 5,2%. Vegetation patterns of Open Green Space gave less significant influence to the micro climate which marked by very weak correlation between many vegetation patterns of Open Green Space with air temperature, air humidity, and wind speed. Land use impact to the micro climate were low,  where air temperature, air humidity, and wind speed were more influenced by buildings density level, building geometry, gas emissions from vehicles, and Open Green Space availability.
Pemanfaatan Citra Quickbird dan SIG untuk Pemetaan Tingkat Kenyamanan Permukiman di Kecamatan Semarang Barat dan Kecamatan Semarang Utara Alke Caroline Helena Maru; Iswari Nur Hidayati
Majalah Geografi Indonesia Vol 30, No 1 (2016): Majalah Geografi Indonesia
Publisher : Fakultas Geografi, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (3177.134 KB) | DOI: 10.22146/mgi.15600

Abstract

Kombinasi Indeks Citra untuk Analisis Lahan Terbangun dan Vegetasi Perkotaan Iswari Nur Hidayati; R. Suharyadi; Projo Danoedoro
Majalah Geografi Indonesia Vol 32, No 1 (2018): Majalah Geografi Indonesia
Publisher : Fakultas Geografi, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (865.503 KB) | DOI: 10.22146/mgi.31899

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%.   
Penyusunan Basis Data Spasial Fasilitas Bangunan Gedung bagi Penyandang Disabilitas (Universitas Gadjah Mada Menuju Kampus Inklusi) R. Suharyadi; Iswari Nur Hidayati; Wuri Handayani
Majalah Geografi Indonesia Vol 34, No 1 (2020): Majalah Geografi Indonesia
Publisher : Fakultas Geografi, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/mgi.50199

Abstract

Sebagai universitas terkemuka di tanah air, Universitas Gadjah Mada harus siap menjelma sebagai kampus inklusi. Artinya, kampus yang ramah terhadap penyandang diasbilitas. Untuk menjadi kampus inklusi, maka pengelola kampus harus menyediakan fasilitas yang ramah terhadap penyandang disabilitas. Untuk itu penyusunan database spasial bangunan Gedung dengan informasi fasilitas penyandang disabilitas perlu diwujudkan. Basis data yang disusun akan bersumber dari data penginderaan jauh, dan memanfaatkan fasilitas sistem informasi geografis untuk pemanfaatan data spasial. Tujuan penelitian adalah (1) inventarisasi dan menyusun basis data bangunan gedung dan bangunan fasilitas untuk penyandang disabilitas di Kampus Universitas Gadjah Mada, (2) memberikan rekomendasi penyediaan bangunan fasilitas untuk penyandang disabilitas kampus UGM. Interpretasi visual digunakan sebagai metode untuk ekstraksi bangunan Gedung di UGM. Citra penginderaan jauh yang digunakan berupa mosaic foto udara format kecil yang dipotret dengan menggunakan wahana tanpa awak. Survey lapangan dilakukan dengan cara sensus pada seluruh bangunan Gedung di kampus UGM. Penyusunan basis data spasial menggunakan fasilitas sistem informasi geografi. Hasil penelitian berupa data base spasial bangunan Gedung dan bangunan fasilitas untuk penyandang disabilitas di kampus UGM. Atribut yang menyertadi data base tersebut antara lain aksesibilitas bagi penyandang disabilitas berupa ramp atau lift, guiding block, dan fasilitas toilet.
EKSTRAKSI DATA INDEKS VEGETASI UNTUK EVALUASI RUANG TERBUKA HIJAU BERDASARKAN CITRA ALOS DI KECAMATAN NGAGLIK KABUPATEN SLEMAN YOGYAKARTA Iswari Nur Hidayati
Jurnal Agroteknologi Vol 3, No 2 (2013): Februari 2013
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ja.v3i2.85

Abstract

The population growth is make conversion of green space area to be settlement. This situation supports degradation of environmental quality in urban areas whereas the function of urban forest is affecting the surrounding air directly or indirectly by altering the atmospheric conditions. Conversion of green space area to non-green green space area is a frequently encountered problem lately. Therefore, this research showed that relationship between vegetation index with urban green space. The objectives of this research are: (a) to assess the ability of remote sensing data especially ALOS AVNIR-2 imagery for extraction of vegetation density through vegetation index analysis, (b) to analyze the availability of green space using remote sensing data; and (c) to analyze the density of vegetation on land-use planning based on Urban Land Use Planning (RDTRK) in Ngaglik District.This study was conducted in Ngaglik area using ALOS imagery ANVIR-2 recording in 2009. Distribution of green open space transformation used Normalized Difference Transformation Index (NDVI) and RDTRK documents. The results of this study indicated that urban green space and NDVI can be extracted using ALOS AVNIR-2 imagery. The formula of NDVI was 188.1 x (NDVI)) -0.5617. The vegetation densities can be divided into five classes, non-vegetated area was 13,398,739.48 m² (34.24%), very low vegetation density was 5,381,133.12 m² (13.75%), low vegetation density was 8,143,116.62 m² (20.81%), medium vegetation density was 10,022,040.95 m² (25.61%), high vegetation density was 1,878,236.10 m² (4.80%), and very high vegetation density was 7181.22 m² (0.02%). The area of green open space was in conformity with the laws in force in the amount of 25.480.722 m² (64.86 %).
Exploring Spectral Index Band and Vegetation Indices for Estimating Vegetation Area Iswari Nur Hidayati; R. Suharyadi; Projo Danoedoro
Indonesian Journal of Geography Vol 50, No 2 (2018): Indonesian Journal of Geography
Publisher : Faculty of Geography, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (907.794 KB) | DOI: 10.22146/ijg.38981

Abstract

Visual analysis and transformation of vegetation indices have been widely applied in studies of vegetation density using remote sensing data. However, visual analysis is time intensive compared to index transformation. On the other hand, the index transformation from medium resolution imagery is not fully representative for urban vegetation studies. Meanwhile, the spectral range of high-resolution imagery is usually limited to visible wavelengths for the image transformation. Worldview-2 imagery provides a new breakthrough with a high spatial resolution and supports various spectral resolutions. This study aims to explore the spectral value of the Worldview-2 image index for estimation of vegetation density. Normalized indices were made for 56 band combinations and Otsu thresholding was implemented for the threshold selection to separate vegetation and non-vegetation areas. This thresholding was done by minimizing classes’ variances between two groups of pixels which are distinguished by system or classification. The image binarization process was performed to differentiate between vegetation and non-vegetation. For the accuracy testing, a total of 250 samples was produced by a stratified random sampling method. Our results show that the combination of indices from red channel, red-edge, NIR-1, and NIR-2 provides the best accuracy for semantic accuracy. Vegetation area extracted from the index was then compared with the results of the visual analysis. Although the index results in area difference of 2.32 m2 compared to visual analysis, the combination of NIR-2 and red bands can give an accuracy of 96.29 %.
Co-Authors Achmad Fadhilah Alke Caroline Helena Maru Eni Susanti Eni Susanti Gian Felix Ramadhan Prima Widayani Projo Danoedoro Projo Danoedoro Projo Danoedoro R. Suharyadi Suharyadi, R Suharyadi, R Westi Utami Wuri Handayani