Integration of Spectral and Textural Features from Ikonos Image to Classify Vegetation Cover in Mountainous Area

Studi ini mengevaluasi penggunaan fitur spektral dan tekstur secara terintegrasi yang didapat dari citra IKONOS untuk mengindentifikasi tipe-tipe tutupan lahan pertanian di daerahpegunungan. Studi meliputi pra pengolahan citra, pengembangan metode kuantisasi citra, penghitungan nilai tekstur, pembuatan dataset dan penilaian akurasi. Pra pengolahan citra berfokus pada registrasi citra dan normalisasi topografis. Dalam studi ini dikembangkan dua metodekuantisasi citra yaitu segmentasi citra dan filter rata-rata. Segmentasi citra mengklasifikasi citra kedalam beberapa segmentasi berdasarkan determinasi jumlah total piksel setiap kelas, sedangkan filter rata-rata mengelompokkan citra berdasarkan rata-rata nilai angka dijital dalam ukuranwindow tertentu. Empat ukuran tekstur yaitu inverse difference moment, contrast, entropy dan energy dihitung dengan grey level co-occurrence matrix (GLCM). Hasil studi menunjukkankombinasi aspek spektral dan tekstur meningkatkan akurasi klasifikasi secara signifikan dibandingkan klasifikasi hanya menggunakan fitur spektral saja. Segmentasi citra dan filter rata-rata dapat memberikan bentuk-bentuk spasial tipe tutupan lahan pertanian yang lebih efektif dibanding menggunakan citra dengan derajat keabuan 256. Ketelitian keseluruhan meningkat 11,33% ketika menggunakan integrasi spektral dan fitur tekstur inverse difference moment (5x5) danenergy (9x9)

Mapping Vegetation Cover in Mountainous Area with Linear Mixture Modeling of Ikonos Satellite Image: a Case Study in Pangalengan, West Java, Indonesia

Tujuan studi untuk mengidentifikasi tutupan vegetasi di area pegunungan menggunakan pemodelan campuran linier (linear mixture modeling). Studi mengkombinasikan data reflektansispektral yang diukur dari fotometer tipe 2703 dengan citra satelit resolusi spasial sangat tinggi yaitu IKONOS. Ada empat tahapan yang dilakukan dalam menganalisis efek topografis (area pegunungan) dalam pengolahan citra dan masalah campuran piksel dalam mengestimasi fitur sayur mayur termasuk teh. Pertama, citra IKONOS dikoreksi dengan cara menormalisasi nilai kecerahan sehingga efek topografisnya dapat direduksi. Tahap kedua mencakup analisis karakteristikreflektansi spektral yang diperoleh dari survei lapangan dan hubungannya dengan citra satelit. Tahap ketiga mendefinisikan jumlah end-member yang digunakan dalam pemodelan campuran (mixture modeling). Akhirnya, fraksi citra teh dinilai dengan hasil klasifikasi metode tak terbimbing(unsupervised). Hasil studi mengindikasi pemodelan campuran dapat digunakan untuk mengidentifikasi teh dalam piksel campuran dengan lebih baik dibandingkan metode klasifikasitradisional.Kata kunci: pemetaan, tutupan vegetasi, area pegunungan, pemodelan campuran linier, citra IKONO

Mapping Vegetation Cover in Mountainous Area with Linear Mixture Modeling of Ikonos Satellite Image: a Case Study in Pangalengan, West Java, Indonesia

Tujuan studi untuk mengidentifikasi tutupan vegetasi di area pegunungan menggunakan pemodelan campuran linier (linear mixture modeling). Studi mengkombinasikan data reflektansispektral yang diukur dari fotometer tipe 2703 dengan citra satelit resolusi spasial sangat tinggi yaitu IKONOS. Ada empat tahapan yang dilakukan dalam menganalisis efek topografis (area pegunungan) dalam pengolahan citra dan masalah campuran piksel dalam mengestimasi fitur sayur mayur termasuk teh. Pertama, citra IKONOS dikoreksi dengan cara menormalisasi nilai kecerahan sehingga efek topografisnya dapat direduksi. Tahap kedua mencakup analisis karakteristikreflektansi spektral yang diperoleh dari survei lapangan dan hubungannya dengan citra satelit. Tahap ketiga mendefinisikan jumlah end-member yang digunakan dalam pemodelan campuran (mixture modeling). Akhirnya, fraksi citra teh dinilai dengan hasil klasifikasi metode tak terbimbing(unsupervised). Hasil studi mengindikasi pemodelan campuran dapat digunakan untuk mengidentifikasi teh dalam piksel campuran dengan lebih baik dibandingkan metode klasifikasitradisional.Kata kunci: pemetaan, tutupan vegetasi, area pegunungan, pemodelan campuran linier, citra IKONO

Integration of Spectral and Textural Features from Ikonos Image to Classify Vegetation Cover in Mountainous Area

Studi ini mengevaluasi penggunaan fitur spektral dan tekstur secara terintegrasi yang didapat dari citra IKONOS untuk mengindentifikasi tipe-tipe tutupan lahan pertanian di daerahpegunungan. Studi meliputi pra pengolahan citra, pengembangan metode kuantisasi citra, penghitungan nilai tekstur, pembuatan dataset dan penilaian akurasi. Pra pengolahan citra berfokus pada registrasi citra dan normalisasi topografis. Dalam studi ini dikembangkan dua metodekuantisasi citra yaitu segmentasi citra dan filter rata-rata. Segmentasi citra mengklasifikasi citra kedalam beberapa segmentasi berdasarkan determinasi jumlah total piksel setiap kelas, sedangkan filter rata-rata mengelompokkan citra berdasarkan rata-rata nilai angka dijital dalam ukuranwindow tertentu. Empat ukuran tekstur yaitu inverse difference moment, contrast, entropy dan energy dihitung dengan grey level co-occurrence matrix (GLCM). Hasil studi menunjukkankombinasi aspek spektral dan tekstur meningkatkan akurasi klasifikasi secara signifikan dibandingkan klasifikasi hanya menggunakan fitur spektral saja. Segmentasi citra dan filter rata-rata dapat memberikan bentuk-bentuk spasial tipe tutupan lahan pertanian yang lebih efektif dibanding menggunakan citra dengan derajat keabuan 256. Ketelitian keseluruhan meningkat 11,33% ketika menggunakan integrasi spektral dan fitur tekstur inverse difference moment (5x5) danenergy (9x9)

Green Open Space and Barren Land Mapping for Flood Mitigation in Jakarta, the Capital of Indonesia

High levels of rainfall, tidal flooding, land subsidence, intensified urban development, scarce barren land and a shortage of green open spaces (GOS) are contributing factors to the persistent flooding in Jakarta. Therefore, this study was conducted to map the GOS, built-up, and barren land in the city in order to calculate the biopore infiltration hole (LRB) potential for water infiltration as part of Jakarta's flood mitigation efforts using the Landsat 8 operational land imager (OLI). The Landsat data acquired on September 11, 2019, with path/row 122/064 were processed using the Fast Line-of-Sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) method for the radiometric correction, and geometric correction with a root mean square error (RMSE) of 7.57 meters. Moreover, the normalized difference vegetation index (NDVI) was applied to classify the GOS, the normalized difference built-up index (NDBI) for the built-up areas, and the normalized difference barren land index (NDBaI) for barren land areas which were further confirmed using NDBI to distinguish them from the built-up areas. It is also important to note that the LRB potential was calculated by adding the GOS and barren land, dividing the result by the ideal land area multiplied by the ideal number of holes. The results showed that the GOS, built-up area, and barren land were 8.34%, 85.29%, and 2.48%, respectively. Furthermore, the LRB potential through the optimization of GOS and barren land was found to be 70.06 km2 and produced 16,816,248 LRB (18.27% of total needed). The realization of this value is expected to reduce the potential inundation in Jakarta by 15.6%

The Simple Method to Assess Land Quality of Paddy Field Using Spectral, Soil pH and Statistical Regression Technique (Case Study of Paddy Field in Majalaya Subdistrict, Bandung Region)

Assessing land quality has important use in understanding the capability of soil in producing food. The area of paddy fields in Majalaya Subdistrict is located around the industrial zone and this situation is urgent to understand the land quality of paddy field due to the influence effect of industrial waste to its growth. A combination of regression model and Landsat 8 image to estimate soil pH distribution is used to predict the land quality. The result of this study is shown that the regression model of red and near infrared (NIR) band combination is used to predict soil pH has been successfully given the smallest error (RMSe) as the soil pH accuracy is 1.18 and related to the land quality assessment based on predicted soil pH is shown that in the whole area of paddy field has the acid situation of soil pH.Keywords: Spectral, Soil pH; Regression, Land Quality; Land  Suitabilit

Monitoring Sugarcane Growth Phases Based on Satellite Image Analysis (A Case Study in Indramayu and its Surrounding, West Java, Indonesia)

This study is intended to examine the growing phases and the harvest of sugarcane crops. The growing phases is analyzed with remote sensing approaches. The remote sensing data employed is Landsat 8. The vegetation indices of Normalized Difference Vegetation Index (NDVI) and Enhanced Normalized Difference Vegetation Index (ENDVI) are employed to analyze the growing phases and the harvest of sugarcane crops. Field survey was conducted in March and August 2017. The research results shows that March is the peak of the third phase (Stem elonging phase or grand growth phase), the period from May to July is the fourth phase (maturing or ripening phase), and the period from August to October is the peak of harvest. In January, the sugarcane crops begin to grow and some sugarcane crops enter the third phase again. The research results also found the sugarcane plants that do not grow well near the oil and gas field. This condition is estimated due as the impact of hydrocarbon microseepage. The benefit of this research is to identify the sugarcane growth cycle and harvest. Having knowing this, it will be easier to plan the seed development and crops transport

Land Degradation Model Based on Vegetation and Erosion Aspects Using Remote Sensing Data

The study of land degradation in various geographic conditions in the world using remote sensing is still become a concern amongst researchers because it has been proven as one of the most effective ways. In Indonesia, East Kalimantan province is one of the experiencing land area degradation due to intensive exploitation of natural resouces since 1970. The degradation model proposed in this study is modeled using a combination of ASTER and Landsat ETM+ imagery, both taken on February 27, 2001. The model composed of both two aspects: erosion aspect and vegetation aspect. Vegetation aspect is a function of suppression of vegetation from Crippen and Blom method and spectral angle a of Spectral Angle Mapper (SAM) algorithm. The erosion aspect is calculated from erosion prediction and depends on the constant factors of b as well, and the latter is said as a function of Normalized Difference Vegetation Index (NDVI) value. Based on the validation using spectral based degradation map and Land Degradation Index of Chikhaoui et al, our model proves the ability to map land degradation, especially to better distinguish the classification of land degradation at very-slightly to very-severe intensity and the ability to differentiate water body, swamp or river

The Identification of Fishing Ground Area with MODIS Satellite Image (Case Study: South Coast of West Java)

 According to UNCLOS, Indonesian marine territorial covers an area equal to around 2.8 million square kilometers inner archipelagic seas. Though the Indonesian water region is very wide, the resource within it is not yet been exploited optimally. Indonesia still has problems that have to be copped with, including identification of marine fishing ground areas. This report proposes a technology to make the fish-catching be more efficient and effective with the help of MODIS satellite image in term of Surface Temperature and chlorophyll-a computation. Data conversion from digital number to Water Brightness Temperature are performed. The determination of potential fishing ground area were conducted based on temperature and chlorophyll-a parameters which serve as an indicator of upwelling and observations were carried out on parameters which show this phenomenon. Based on the result, during May 2004 the upwelling process were not happened yet, and it seems to occur in June 2004. It showes by the decreasing of water temperature in South Coast of West Java particularly between the border of West Java and Central of Java. This phenomenon acts as an indicator for the raising of primer productivity and will takes about one month after upwelling to the bloom of phytoplankton