scholarly journals Pemantauan Fase Pertumbuhan Tanaman Padi Menggunakan Citra Radarsat-2 Quad Polarimetrik

2021 ◽  
Vol 5 (1) ◽  
pp. 1-14
Author(s):  
Rian Nurtyawan ◽  
Gerryn Maulannisaa
Keyword(s):  
Rice Plant ◽  
Alpha Angle ◽  
Surface Scattering ◽  
Growth Phases ◽  
Phase Zone ◽  
Volume Scattering ◽  
Initiation Phase ◽  
Remote Sensing Technology ◽  
Sensing Technology

ABSTRAKIndramayu merupakan salah satu lumbung padi Indonesia yang ada di wilayah Jawa Barat dimana Badan Pusat Statistik mencatat pada tahun 2014, Indramayu menghasilkan padi sebesar 1.361.374 ton. Untuk memantau produksi padi, sangat diperlukan pemantauan fase pertumbuhan tanaman padi, salah satu metodenya dengan teknologi penginderaan jauh sistem RADAR menggunakan citra RADARSAT-2 quad polarimetrik. Penelitian ini bertujuan untuk mengklasifikasi daerah fase pertumbuhan tanaman padi menggunakan metode Cloude Pottier H/A/α (entropi/anisotropi/sudut alfa) dan mengevaluasi metode tersebut dalam klasifikasi fase pertumbuhan tanaman padi. Hasil dari penelitian ini yaitu peta klasifikasi fase pertumbuhan tanaman padi dimana dari keseluruhan akuisisi citra, luas lahan tertinggi adalah fase germination/laut yang berjumlah 2.368.242 m2 (22 September 2014). Hasil klasifikasi ini disesuaikan dengan bidang H-α classification plane untuk mengetahui pada zona mana yang memiliki hamburan paling dominan. Hasil pada 18 Juni 2014 dan 5 Agustus 2014 menunjukkan zona 7 (fase panicle initiation/inisiasi malai), zona 8 (fase milk stage/gabah matang susu), dan zona 9 (fase germination/perkecambahan benih atau fase seeding/pertunasan) menjadi zona yang dominan dimana ketiga mekanisme memiliki arti double-bounce scattering (Z7), volume scattering (Z8), dan surface scattering (Z9) sedangkan pada 22 September 2014 dan 16 Oktober 2014 hamburan yang paling dominan terdapat pada Z8 (fase milk stage/gabah matang susu) dengan mekanisme volume scattering dan Z9 (fase germination/perkecambahan benih atau fase seeding/pertunasan) dengan mekanisme surface scattering.Kata kunci: Pertumbuhan Padi, Klasifikasi, RADARSAT-2, H/A/α ABSTRACTIndramayu is one of Indonesia's granary in West Java where Statistic Data Center noted that in 2014 Indramayu produced 1.361.374 tons of rice. It’s necessary to monitor growth phase of rice plant for monitoring rice production, one of the method is remote sensing technology is the RADAR system with RADARSAT-2 image quad-polarimetric. This study aims to classify the phase of growth of rice plants using the Cloude Pottier H / A / α method (entropy / anisotropy / alpha angle) and evaluate these methods in classification of rice plant growth phases. The results of this study are the classification map of the rice plant phase where from the overall image acquisition, the highest land area is the germination / sea phase, which amounts to 2,368,242 m2 (22 September 2014). The classification results are adjusted with the H-α classification plane to find out which zone has the most dominant scattering. The result on 18 June 2014 and 5 August 2014 showed zone 7 (panicle initiation phase), zone 8 (milk stage phase), and zone 9 (germination/seeding) to be the dominant zone where the three mechanisms mean double-bounce scattering (Z7), volume scattering (Z8), and surface scattering (Z9) while on 22 September 2014 and 16 October 2014 the most dominant scattering is in Z8 (milk stage phase) with volume scattering mechanism and Z9 (germination/seeding phase) with surface scattering mechanismKeywords: Rice Growth , Classification, RADARSAT-2, H/A/α.

scholarly journals Overview of Hyperspectral Image Classification

2020 ◽  
Vol 2020 ◽  
pp. 1-13 ◽  
Author(s):  
Wenjing Lv ◽  
Xiaofei Wang
Keyword(s):  
Supervised Classification ◽  
Hyperspectral Image ◽  
Unsupervised Classification ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Good Classification ◽  
Semisupervised Classification

With the development of remote sensing technology, the application of hyperspectral images is becoming more and more widespread. The accurate classification of ground features through hyperspectral images is an important research content and has attracted widespread attention. Many methods have achieved good classification results in the classification of hyperspectral images. This paper reviews the classification methods of hyperspectral images from three aspects: supervised classification, semisupervised classification, and unsupervised classification.

scholarly journals Estimation of Peat Surface Carbon Stocks on The Semenanjung Kampar from Land Use Changes Using Landsat Multi-Temporal in 2009-2018

2019 ◽  
Vol 3 (1) ◽  
pp. 49-53
Author(s):  
Yudi Antomi ◽  
Ristalia Ristalia
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Oil Palm ◽  
Land Use Changes ◽  
Carbon Stocks ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Multi Temporal ◽  
Peat Surface

Remote sensing has advantages in terms of temporal resolution that can be used to check changes in an object at different times. The Semenanjung Kampar peatland underwent land use change after the change in PP No. 71 of 2014 became PP No. 57 of 2016 which requires companies (paper companies) to restore the ecosystem on the Semenanjung Kampar. These changes were analyzed by utilizing remote sensing technology through multi-temporal imagery.This study aims to analyze changes in peatland use on the Semenanjung Kampar in 2009, 2013 and 2018, then estimate carbon stocks from changes in peatland use. The method used is the classification of Iso Cluster unsupervised and calculation of increase and decrease in carbon stocks (Gain and Loss). Based on this research the results of the accuracy of the classification of changes in land use on the Semenanjung Kampar were 0.72 or 72%.Changes in land use on the Semenanjung Kampar occur dynamically.The dominant land change for the 2009-2013 period was shrubs which became acacia forests 89386.31 ha and bushes from 2013-2018 to oil palm plantations 57878.47 ha. Furthermore, carbon stocks in the period 2009-2013 that have increased (acces) are 8.2% acacia forest and 13% decrease in primary peat forest while the 2013-2018 period has increased, namely 8% oil palm plantation and 21% shrub decline.

scholarly journals Indices Extraction from Multitemporal Remote Sensing Data for Mapping Urban Built-Up

2021 ◽  
Vol 887 (1) ◽  
pp. 012004
Author(s):  
A. K. Hayati ◽  
Y.F. Hestrio ◽  
N. Cendiana ◽  
K. Kustiyo
Keyword(s):  
Remote Sensing ◽  
Supervised Classification ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Sensing Data ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Multitemporal Remote Sensing ◽  
Multitemporal Data

Abstract Remote sensing data analysis in the cloudy area is still a challenging process. Fortunately, remote sensing technology is fast growing. As a result, multitemporal data could be used to overcome the problem of the cloudy area. Using multitemporal data is a common approach to address the cloud problem. However, most methods only use two data, one as the main data and the other as complementary of the cloudy area. In this paper, a method to harness multitemporal remote sensing data for automatically extracting some indices is proposed. In this method, the process of extracting the indices is done without having to mask the cloud. Those indices could be further used for many applications such as the classification of urban built-up. Landsat-8 data that is acquired during 2019 are stacked, therefore each pixel at the same position creates a list. From each list, indices are extracted. In this study, NDVI, NDBI, and NDWI are used to mapping built-up areas. Furthermore, extracted indices are divided into four categories by their value (maximum, quantile 75, median, and mean). Those indices are then combined into a simple formula to mapping built-up to see which produces better accuracy. The Pleiades as high-resolution remote sensing data is used to assist supervised classification for assessment. In this study, the combination of mean NDBI, maximum NDVI, and mean NDWI result highest Kappa coefficient of 0.771.

scholarly journals CLASSIFICATION OF RICE-PLANT GROWTH PHASE USING SUPERVISED RANDOM FOREST METHOD BASED ON LANDSAT-8 MULTITEMPORAL DATA

2020 ◽  
Vol 16 (2) ◽  
pp. 187
Author(s):  
Dwi Wahyu Triscowati ◽  
Bagus Sartono ◽  
Anang Kurnia ◽  
Dede Dirgahayu ◽  
Arie Wahyu Wijayanto
Keyword(s):  
Random Forest ◽  
Rice Plant ◽  
Vegetation Index ◽  
Classification Model ◽  
Landsat 8 ◽  
Random Forest Method ◽  
Temporal Autocorrelation ◽  
Sensing Technology ◽  
Pattern Information

Data on rice production is crucial for planning and monitoring national food security in a developing country such as Indonesia, and the classification of the growth phases of rice plants is important for supporting this data. In contrast to conventional field surveys, remote sensing technology such as Landsat-8 satellite imagery offers more scalable, inexpensive and real-time solutions. However, utilising Landsat-8 for classification of rice-plant phase required spectral pattern information from one season, because these spectral patterns show the existence of temporal autocorrelation among features. The aim of this study is to propose a supervised random forest method for developing a classification model of rice-plant phase which can handle the temporal autocorrelation existing among features. A random forest is a machine learning method that is insensitive to multicollinearity, and so by using a random forest we can make features engineering to select the best multitemporal features for the classification model. The experimental results deliver accuracy of 0.236 if we use one temporal feature of vegetation index; if we use more temporal features, the accuracy increases to 0.7091. In this study, we show that the existence of temporal autocorrelation must be captured in the model to improve classification accuracy.

Hyperspectral Remote Sensing Technology (HRST) program

1997 ◽  
Author(s):  
Tom Wilson ◽  
Rebecca Baugh ◽  
Ron Contillo ◽  
Tom Wilson ◽  
Rebecca Baugh ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Hyperspectral Remote Sensing ◽  
Remote Sensing Technology ◽  
Sensing Technology

Application of remote sensing technology to the relation of submarine pipelines and coastal morphology

1995 ◽  
Vol 32 (2) ◽  
pp. 77-83
Author(s):  
Y. Yüksel ◽  
D. Maktav ◽  
S. Kapdasli
Keyword(s):  
Remote Sensing ◽  
Sediment Transport ◽  
Surf Zone ◽  
Hydrodynamic Forces ◽  
Construction Site ◽  
Coastal Morphology ◽  
Remote Sensing Technology ◽  
Sensing Technology ◽  
Sea Bed

Submarine pipelines must be designed to resist wave and current induced hydrodynamic forces especially in and near the surf zone. They are buried as protection against forces in the surf zone, however this procedure is not always feasible particularly on a movable sea bed. For this reason the characteristics of the sediment transport on the construction site of beaches should be investigated. In this investigation, the application of the remote sensing method is introduced in order to determine and observe the coastal morphology, so that submarine pipelines may be protected against undesirable seabed movement.

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