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PENGARUH RESOLUSI SPASIAL CITRA TERHADAP HASIL PEMETAAN KANDUNGAN HARA NITROGEN PERKEBUNAN KARET Saputra, Jamin; Kamal, Muhammad; Wicaksono, Pramaditya
Jurnal Penelitian Karet JPK : Volume 36, Nomor 1, Tahun 2018
Publisher : Pusat Penelitian Karet - PT. Riset Perkebunan Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22302/ppk.jpk.v36i1.545

Nitrogen merupakan salah satu unsur hara yang dibutuhkan dalam jumlah banyak oleh tanaman. Tanaman yang mengalami kekurangan unsur hara nitrogen akan menyebabkan terhambatnya pertumbuhan dan penurunan produktivitas tanaman. Penerapan sistem pertanian presisi pada kegiatan pemupukan di perkebunan karet dilakukan dengan cara dosis pemupukan dibuat berdasarkan kandungan hara tanah dan kandungan hara pada tanaman. Pada areal yang luas membutuhkan biaya analisa hara tanaman yang cukup mahal. Oleh karena itu sangat dibutuhkan suatu teknologi yang dapat mengestimasi kondisi hara tanaman dengan cepat dan biaya yang murah. Teknologi penginderaan jauh merupakan alternatif yang dapat digunakan untuk areal yang luas dan dengan waktu yang cepat serta biaya yang relatif murah. Penelitian ini bertujuan untuk mengetahui pengaruh resolusi spasial citra terhadap peta hasil estimasi kandungan nitrogen perkebunan karet. Citra multi resolusi yang digunakan antara lain GeoEye-1 (2 m) Sentinel-2A (10 dan 20 m) dan Landsat 8 OLI (30 m). Metode yang digunakan adalah membangun hubungan semi-empiris antara band tunggal dan indeks vegetasi citra dengan kandungan hara nitrogen perkebunan karet. Hasil penelitian menunjukkan bahwa peta hasil estimasi kandungan hara nitrogen perkebunan karet menggunakan citra Sentinel-2A (SE 0,369) memiliki akurasi yang lebih tinggi dibandingkan dengan menggunakan citra GeoEye-1 (SE 0,519) dan Landsat 8 OLI (SE 0,462).

ANALISIS SALURAN SPEKTRAL YANG PALING BERPENGARUH DALAM IDENTIFIKASI KESEHATAN TERUMBU KARANG: Studi Kasus Pulau Menjangan Besar dan Menjangan Kecil, Kepulauan Karimunjawa Murti, Sigit Heru; Wicaksono, Pramaditya
MAJALAH ILMIAH GLOBE Vol 16, No 2 (2014)
Publisher : Badan Informasi Geospasial

ABSTRAKSalah satu faktor yang berpengaruh dalam penggunaan teknologi penginderaan jauh untuk identifikasi tingkat kesehatan terumbu karang adalah konfigurasi saluran spektral sensor. Pemahaman tentang saluran spektral yang berpengaruh positif terhadap proses identifikasi kesehatan terumbu karang sangat penting dalam efisiensi pemetaan, baik dari segi waktu maupun akurasi yang didapatkan. Penelitian ini bertujuan untuk mencari saluran spektral yang berkontribusi positif terhadap identifikasi kesehatan terumbu karang, dengan menggunakan bantuan analisis PCA (Principle Component Analysis) dan Factor Loadings pada citra Landsat 7 ETM+ dan ASTER. Tingkat kesehatan terumbu karang dilihat dari persentase tutupan karang hidupnya dan dibagi menjadi empat kelas yaitu Sangat Baik (>75% tutupan karang hidup), Baik (50-74%), Sedang (25-49%) dan Rusak (<25%). Untuk mengetahui saluran spektral yang paling baik dalam identifikasi kesehatan terumbu karang, klasifikasi multispektral dilakukan pada kombinasi PC (Principle Component) band dan dilakukan uji akurasi. Hasil uji akurasi dipasangkan dengan hasil analisis Factor Loadings untuk melihat kontribusi tiap saluran spektral pada tiap akurasi. Hasil penelitian menunjukkan bahwa saluran hijau merupakan saluran yang berkontribusi paling tinggi dan saluran merah adalah saluran memberikan kontribusi paling rendah. Saluran biru, yang merupakan saluran dengan penetrasi tubuh air paling baik, memberikan kontribusi yang lebih rendah dibandingkan dengan saluran hijau karena tingginya hamburan Rayleigh yang terjadi pada saluran biru tersebut.Kata Kunci: Landsat 7 ETM+, ASTER, PCA, factor loadings, terumbu karangABSTRACTOne of the major factors to determine the success of remote sensing identification for coral reefs health is the spectral resolution of the sensor. The understanding about the characteristic of spectral bands contribute positively to the identification of coral reefs health is very important for the effective and satisfactory mapping results. This research aimed to identify the most effective spectral band for the coral reefs health identification, using Principle Component Analysis (PCA) and factor loadings analysis. Landsat 7 ETM+ and ASTER VNIR images were used in this research. Coral reefs health condition is determined from the percentage of live coral cover and divided into four ordinal classes: very good (>75% live coral reefs cover), good (50-74%), medium (25-49%), and bad (<25%). To find the most effective bands for coral reefs health identification, multispectral classification was applied on Principle Component (PC) bands combinations. Afterward, the mapping accuracy of each PC bands combination was assessed. Each accuracy assessment result was evaluated with factor loadings analysis result to understand the contribution of different spectral bands on the resulting mapping accuracy. The results show that green band is the most effective spectral band which provides the highest contribution to the mapping, while red band provide the lowest contribution. Blue band, which is the best water penetration band, was less efficient than green band due to the strong Rayleigh scattering that affects more significantly on shorter wavelengths.Keywords: Landsat 7 ETM+, ASTER, PCA, factor loadings, coral reefs

Aplikasi Citra WorldView-2 Untuk Pemetaan Batimetri Di Pulau Kemujan Taman Nasional Karimunjawa Rahman, Waskito; Wicaksono, Pramaditya
Jurnal Penginderaan Jauh Indonesia Vol 1 No 1 (2019): JPJI
Publisher : Masyarakat Ahli Penginderaan Jauh Indonesia (MAPIN) /Indonesian Society of Remote Sensing (ISRS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (717.881 KB)

The Application of WorldView-2 Image for Bathymetry Mapping in Kemujan Island Karimunjawa National Park The development of remote sensing technology gives an opportunity to extract bathymetry information on the optically shallow water area. This was done by utilizing the reflectance of spectral bands with the ability to penetrate water body. The aim of this research is to map bathymetry of Kemujan Island using remote sensing empirical modeling.  Quickbird image was used in this study. It has four spectral bands namely blue, green, red and near infrared band. These bands were rationed and acquired 12 band ratios. In total, 120 samples were used to produce bathymetry model and 379 samples were used for validation. The models were created for up to the depth of 7 m.  The result showed that the model from band ratio of green and blue band produced the highest accuracy with R² of 0.632 and SE of 1.2 m. The result proved that blue band is the most effective band to be combined with other bands for band ratio input for bathymetry modeling.

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

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.

Bibliometric Analysis of Indonesian Journal of Geography from 2015-2022 Istiana, Purwani; Wicaksono, Pramaditya
Indonesian Journal of Geography Vol 57, No 1 (2025): Indonesian Journal of Geography
Publisher : Faculty of Geography, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijg.99939

This bibliometric analysis aimed to determine the growth trend of document publications, the development of journal citations, the distribution of author countries, the most influential documents, and the dominant research topics in the Indonesian Journal of Geography from 2015 to 2022. The data used were extracted from the Scopus database, comprising a total of 280 documents. A bibliographic data mapping was conducted using the VOSviewer version 1.6.18 and Analyze Search Results tools on the Scopus database. The results showed that the number of documents published in the Indonesian Journal of Geography has been increasing over time. Furthermore, journal impact on scientific development has also increased. The authors of the documents included originate from 29 countries. The most influential document addressed the topic of urban research, while key topics developed from 2015 to 2022 included remote sensing and GIS used as main support and technology for understanding and analyzing various geographical phenomena. This research serves as a reference material for prospective authors and also provides an evaluation of Indonesian Journal of Geography in the future.Received: 2024-09-15 Revised: 2025-01-31 Accepted: 2025-03-19 Published: 2025-04-28

Aplikasi Citra WorldView-2 Untuk Pemetaan Batimetri Di Pulau Kemujan Taman Nasional Karimunjawa Rahman, Waskito; Wicaksono, Pramaditya
Jurnal Penginderaan Jauh Indonesia Vol 1 No 1 (2019)
Publisher : Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/jpji.v1i1.255

Citra penginderaan jauh telah berkembang dan menyediakan peluang untuk dapat menyediakan informasi kedalaman di perairan dangkal melalui pemodelan empiris, dengan memanfaatkan energi yang dipantulkan oleh objek di dasar perairan dan direkam oleh sensor penginderaan jauh. Tujuan penelitian ini adalah memetakan batimetri di Pulau Kemujan menggunakan pemodelan empiris penginderaan jauh. Penelitian ini menggunakan citra WorldView-2 sebagian Pulau Kemujan dengan empat saluran multispektral, yaitu band biru, hijau, merah dan inframerah dekat, yang dikombinasikan menjadi 12 band rasio dan diintegrasikan dengan data kedalaman hasil pengukuran lapangan sebanyak 369 titik sampel untuk uji akurasi dan 120 titik sampel untuk pemodelan batimetri pada rentang kedalaman 0-7 m. Hasil pemodelan menunjukkan bahwa model empiris batimetri terbaik adalah rasio band hijau dan band biru dengan nilai R² sebesar 0,632 dan standard error of estimate (SE) sebesar 1,2 m. Penelitian ini menunjukkan bahwa band biru yang dikombinasikan dengan band lain memiliki kontribusi yang signifikan dalam pemodelan batimetri.

Multi-spatial Resolution Imagery to Estimate Above-Ground Carbon Stocks in Mangrove Forests Purnamasari, Eva; Kamal, Muhammad; Wicaksono, Pramaditya; Hidayatullah, Muhammad Faqih; Susetyo, Bigharta Bekti
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2237

Mangroves are a type of vegetation that can absorb carbon and have an essential role in controlling CO2 levels in the atmosphere. Mangroves can absorb carbon better than terrestrial ecosystems because of their ability to bury carbon in sediment. This research aims to compare and measure the carbon stock content above the surface of mangroves in the field using multi-spatial resolution imagery, namely, Landsat 8 OLI, Sentinel 2A, and Planetscope. Field carbon calculations were carried out using the allometric method based on mangrove species. The calculation results are then linked through regression analysis with the vegetation index Difference Vegetation Index (DVI) results. The total carbon obtained from PlanetScope imagery was 535.27 tons, Sentinel 2A imagery was 549.23 tons, and Landsat 8 OLI imagery was 533.57 tons. Among the three images used, based on Sentinel 2A statistical analysis reflects the possibility of overfitting or the best with higher r and R2 values in the calculations. However, based on SE accuracy tests, PlanetScope has better accuracy than the other two images. Apart from that, the accuracy test results using a 1:1 goodness of fit plot for each image, the distribution pattern of mangrove carbon stock estimates shows that the entire model in mapping mangrove carbon stocks is over-estimated. The overestimated results are possible because more objects around the mangrove, especially canopy density, are recorded by remote sensing sensors compared to tree diameter as input for field carbon results.

Random Forests Algorithm for Two Levels of Coral Reef Ecosystem Mapping Using Planetscope Image in Malalayang Beach, Manado Cera, Fela Pritian; Danoedoro, Projo; Wicaksono, Pramaditya; Yasir, Moh
JURNAL GEOGRAFI Vol. 15 No. 2 (2023): JURNAL GEOGRAFI
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/jg.v15i2.30795

The coral reef ecosystem has a significant physical and biological function and is also one of the coastal ecosystem components apart from the seagrass and mangrove ecosystem. Besides their ecological function, the coral reef also has an economic function. The condition of the coral reef ecosystem in Malalayang Beach has been changing for years. The utilization of remote sensing images can monitor current conditions. This research aims to map the coral reef ecosystem mapping in Malalayang Beach, Manado and conduct a test for the accuracy of coral reef ecosystem mapping using field survey data as a classification and validation sample. PlanetScope multispectral image has four channels to detect underwater objects: red, green, blue and near infrared. PlanetScope level 3B image for the research has a surface reflectance value for its pixel. The image processing stages of this research consist of sunglint correction, water column correction, and then continue to classify the coral reef ecosystem using random forests algorithm. Classification and accuracy training sample data were obtained using the photo transect technique. The sunglint correction regression equation is between 0.27 “ 0.38. The coefficient of attenuation ratio in B1 is 0.927797938, B2 is 0.168841585, and B3 is 0.29033029. This value then becomes the input for the Lyzenga formula. The classification accuracy for level one using random forests is 72,54%, and the accuracy for level two mapping is 37,61%.Keywords: Coral Reef Ecosystem, Planetscope, Random Forests

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