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All Journal Geoplanning : Journal of Geomatics and Planning Forum Geografi JURNAL ILMIAH GEOMATIKA JURNAL GEOGRAFIKA (GEOGRAFI LINGKUNGAN LAHAN BASAH) Aerospace Engineering
Sanjiwana Arjasakusuma, Sanjiwana
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Aerospace Engineering Earth & Planetary Sciences Engineering Mathematics

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EKSTRAKSI INFORMASI PENUTUP LAHAN MENGGUNAKAN SPEKTROMETER LAPANGAN SEBAGAI MASUKAN ENDMEMBER PADA DATA HIPERSPEKTRAL RESOLUSI SEDANG Kamal, Muhammad; Arjasakusuma, Sanjiwana
GEOMATIKA Vol 16, No 2 (2010)
Publisher : Badan Informasi Geospasial in Partnership with MAPIN

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (613.921 KB) | DOI: 10.24895/JIG.2010.16-2.240

Abstract

Hyperspectral sensor captures a large number of narrow and contiguous spectral bands, mostly covering from 400 to 2500 nm of electromagnetic spectrum. This characteristics offer recognition of high-detailed object spectral reflectance, which serve as basic information on object analysis using hyperspectral data. This research aims to study the applicability of field-based endmember collection as input for land cover mapping, and assess the accuracy of resulted map. The mapping algorithm used was Spectral Angle Mapper (SAM), which compares the angle between endmember’s vector and each pixel’s vector in n-dimension space. Smaller angle values indicate higher similarity between pixels and the endmember. The result of this research is a land cover map of 26 land cover classes, with overall accuracy of 60.82% (Kappa 0.52). Overall, the utility of field-based spectrometer for endmember input is potentially high; however, the effect of time difference between data acquisition and field work and image resolution is remains problematic.Keywords: hyperspectral, endmembers, field spectrometer, spectral angle mapper, land cover.ABSTRAKSensor hiperspektral merekam saluran spektral yang sangat banyak, dengan julat tiap saluran sempit, yang umumnya beroperasi pada spektrum 400 – 2500 nm. Karakeristik ini dapat memberikan pola reflektansi spektral obyek yang sangat rinci, yang bertindak sebagai informasi dasar dalam analisis obyek menggunakan data hiperspektral. Penelitian ini bertujuan untuk mengkaji penerapan teknik pengambilan endmember berbasis lapangan sebagai masukan untuk penutup lahan, dan menilai akurasi hasilnya. Algoritma pemetaan yang digunakan adalah Spectral Angle Mapper (SAM), yaitu dengan membandingkan sudut antara vektor endmembers dan tiap vektor piksel dalam ruang n-dimensi. Semakin kecil sudut piksel terhadap suatu endmember maka semakin sesuai piksel untuk masuk ke dalam kelas endmember tersebut. Hasil klasifikasi berupa peta penutup lahan untuk 26 kelas penutup lahan, dengan akurasi keseluruhan sebesar 60,82% (Kappa 0,52). Secara keseluruhan, utilitas spektrometer lapangan untuk mengumpulkan endmember berbasis lapangan berpotensi tinggi, namun dampak yang disebabkan oleh perbedaan waktu akuisisi citra dan kerja lapangan dan resolusi citra masih menjadi problem.Kata Kunci: Hiperspektral, endmembers, spectrometer lapangan, spectral angle mapper, penggunaan lahan.
Vegetation Change Detection Analysis Using Multi-sensor Hyperspectral Imagery Nugraha, Wahyu Ananta; Wicaksono, Pramaditya; Arjasakusuma, Sanjiwana
Jurnal Geografika (Geografi Lingkungan Lahan Basah) Vol 5, No 1 (2024): GEOGRAFIKA
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jgp.v5i1.11709

Abstract

Vegetation is a fundamental component of ecosystems that maintains carbon levels, hydrological cycles, mitigating greenhouse gases, and ensures climate stability. In recent years, the impacts of global climate change have led to changes in vegetation cover at various levels. Efforts to monitor changes in vegetation are important and beneficial for various fields such as forest monitoring, agriculture, and plantations, among others. The main objective of this research is to detect changes both increase and decrease in vegetation using multi-sensor hyperspectral imagery. The hyperspectral images used in this study are Hyperion 2014 and PRISMA 2021. The method involves creating different levels of spectral resolution simulations from hyperspectral images to detect vegetation changes. Meanwhile, the vegetation change Clustering method employs unsupervised (k-means) techniques. The cluster results can indicate vegetation changes such as vegetation degradation, vegetation, devegetation, or no change, though they currently have low accuracy. The highest accuracy is by Simulated RapidEye image simulations, is 33.5%. The low accuracy results attributed insufficient preprocessing, particularly in topographic correction. Additionally, this research indicates that the spectral resolution levels do not have a significant impact on vegetation change detection, as the differences in change classes at each level are very small.
Mapping of Invasive Species Acacia Decurrens in Part of Mount Merbabu National Park Using Prisma Hyperspectral Imagery Prayoga, Fuad Rosyadi; Kamal, Muhammad; Arjasakusuma, Sanjiwana
Jurnal Geografika (Geografi Lingkungan Lahan Basah) Vol 5, No 1 (2024): GEOGRAFIKA
Publisher : Universitas Lambung Mangkurat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20527/jgp.v5i1.12751

Abstract

Mount Merbabu National Park (TNGMb) is a forest area in the Mount Merbabu. Management and planting changes have caused many changes to the types of plants in TNGMb. Acacia decurrens is an invasive species and its presence in TNGMb can result in a decrease in the diversity of native vegetation types. This research aims to (1) map the distribution of Acacia decurrens plants in TNGMb using PRISMA hyperspectral imagery with the Spectral Angle Mapper (SAM) and Spectral Information Divergence (SID) methods. (2) determine and test the level of accuracy of mapping Acacia decurrens plants in TNGMb using PRISMA hyperspectral imagery. Classification results were carried out using the SAM and SID methods on PRISMA images. The best A. decurrens mapping results were SAM classification at a maximum angle of 0.3 radians with PRISMA Imagery. The distribution of Acacia decurrens in the TNGMb area is in the southwest and northwest. The best accuracy test results were from mapping Acacia decurrens with PRISMA images with an accuracy test value of 56.36%.
Classification of Mangrove Vegetation Structure using Airborne LiDAR in Ratai Bay, Lampung Province, Indonesia Wijaya, Muhammad Sufwandika; Kamal, Muhammad; Widayani, Prima; Arjasakusuma, Sanjiwana
Geoplanning: Journal of Geomatics and Planning Vol 10, No 2 (2023)
Publisher : Department of Urban and Regional Planning, Diponegoro University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/geoplanning.10.2.123-134

Abstract

Mapping and inventory of the distribution and composition of mangrove vegetation structures are crucial in managing mangrove ecosystems. The availability of airborne LiDAR remote sensing technology provides capability of mapping vegetation structures in three dimensions. It provides an alternative data source for mapping and inventory of the distribution of mangrove ecosystems. This study aims to test the ability of airborne LiDAR data to classify mangrove vegetation structures conducted in Ratai Bay, Pesawaran District, Lampung Province. The classification system applied integrates structure attributes of lifeforms, canopy height, and canopy cover percentage. Airborne LiDAR data are derived into canopy height models (CHM) and canopy cover percentage models, then grouped by examining statistics and the zonation distribution of mangroves in the study area. The results of this study show that airborne LiDAR data are able to map vegetation structures accurately. The canopy height model derived using a pit-free algorithm can represent the maximum tree height with an error range of 3.17 meters and 82.3-88.6% accuracy. On the other hand, the canopy cover percentage model using LiDAR Fraction Cover (LFC) tends to be overestimate, with an error range of 16.6% and an accuracy of 79.6-94.7%. Meanwhile, the classification results of vegetation structures show an overall accuracy of 77%.
Kajian Multitemporal Tingkat Keparahan Kebakaran Hutan dan Lahan di Kabupaten Muaro Jambi Menggunakan Penginderaan Jauh Arrafi, Muhammad; Widayani, Prima; Arjasakusuma, Sanjiwana
Aerospace Engineering Vol. 1 No. 3 (2024): July
Publisher : Indonesian Journal Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47134/aero.v1i3.2498

Abstract

Kabupaten Muaro Jambi merupakan kabupaten dengan intensitas kebakaran hutan dan lahan tertinggi di Provinsi Jambi. Pada tahun 2019 terjadi kebakaran hutan dan lahan terbesar di Provinsi Jambi, khususnya Kabupaten Muaro Jambi yang mengakibatkan fenomena langit memerah akibat tebalnya kabut asap kebakaran. Pemetaan keparahan kebakaran hutan dapat dilakukan secara efisien memanfaatkan teknologi penginderaan jauh karena mampu menghemat waktu, biaya, dan tenaga. Citra Landsat 8 digunakan pada penelitian ini sebagai input untuk melakukan transformasi indeks Normalized Burn Ratio (NBR). Indeks NBR memanfaatkan saluran NIR dan SWIR yang mampu membedakan area terbakar dengan baik. Selisih antara nilai NBR sebelum dan NBR sesudah kebakaran akan menghasilkan informasi berupa tingkat keparahan kebakaran hutan dan lahan di Kabupaten Muaro Jambi. Hasil penelitian ini menunjukkan bahwa tingkat keparahan kebakaran pada tahun 2019 – 2020 didominasi oleh kelas keparahan rendah namun terjadi masif. Selanjutnya pada periode tahun 2019 – 2021 area-area yang terbakar sudah mengalami proses pertumbuhan vegetasi yang tinggi.
Machine Learning-Based Rice Field Mapping in Kulon Progo using a Fusion of Multispectral and SAR Imageries Khoirurrizqi, Yusri; Sasongko, Rohmad; Utami, Nur Laila Eka; Irbah, Amanda; Arjasakusuma, Sanjiwana
Forum Geografi Vol 37, No 2 (2023): December 2023
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23917/forgeo.v37i2.20304

Abstract

The land-conversion of rice fields can reduce rice production and negatively impact food security. Consequently, monitoring is essential to prevent the loss of productive agricultural land. This study uses a combination of Sentinel-2 MSI, Sentinel-1 SAR, along with SRTM (elevation and slope data) to monitor rice fields land-conversion. NDVI, NDBI and NDWI indices are transformed from the annual median composite Sentinel-2 MSI images used to identify different rice fields with another object. A monthly median composite of SAR images from Sentinel-1 data are used to identify cropping patterns of rice fields in the inundation phase. The classification is performed by using the Random Forest machine learning algorithm in the Google Earth Engine (GEE) platform. Random Forest classification is run using 1000 trees, with a 70:30 ratio of training and testing data from sample features extracted by visual interpretation of high resolution Google Earth imagery. In this study, Random Forest classification is effective in computing a high amount of multi-temporal and multi-sensory data to map rice-field land conversion with an accuracy rate of 96.16% (2021) and 95.95% (2017) for mapping paddy fields. From the multitemporal rice field maps in 2017—2021, a conversion of 826.66 hectares of rice-fields to non-rice fields was identified. Based on the spatial distribution, the conversion from rice-field to non-rice field is higher at the area near the roads, built area and Yogyakarta International Airport. Therefore, it is important to assess and ensure that National Strategic Projects are managed with due regard to environmental impacts and food security. 
Co-Authors Arrafi, Muhammad Irbah, Amanda Kamal, Muhammad Khoirurrizqi, Yusri Muhammad Kamal Muhammad Sufwandika Wijaya Nugraha, Wahyu Ananta Pramaditya Wicaksono, Pramaditya Prayoga, Fuad Rosyadi Prima Widayani Sasongko, Rohmad Utami, Nur Laila Eka