Paddy field classification with MODIS-terra multi-temporal image transformation using phenological approach in Java Island

This paper presents the paddy field classification model using the approach based on periodic plant life cycle events and how these elevations in climate as well as habitat factors, such as elevation. The data used are MODIS-Terra two tiles of H28v09 and H29v09 of 2016, consist of 46 series of 8-daily data, with 500 meter resolution in Java region. The paddy field classification method based on the phenological model is done by Maximum Likelihood on the transformed annual multi-temporal image of the reflectance data, index data, and the combination of reflectance and index data. The results of the study showed that, with the reference of the Paddy Field Map from the Ministry of Agriculture (MoA), the overall accuracies of the paddy field classification results using the combination of reflectance and index data provide the highest (85.4%) among the reflectance data (83.5%) and index data (81.7%). The accuracy levels were varied; these depend on the slope and the types of paddy fields. Paddy fields on the slopes of 0-2% could be well identified by MODIS-Terra data, whereas it was difficult to identify the paddy fields on the slope >2%. Rain-fed lowland paddy field type has a lower user accuracy than irrigated paddy fields. This study also performed correlation (r2) between the analysis results and the statistical data based on district and provincial boundaries were >0.85 and >0.99 respectively. These correlations were much higher than the previous study results, which reached 0.49-0.65 (hilly-flat areas of county-level), and 0.80-0.88 (hilly-flat areas of provincial level) for China, and reached 0.44 for Indonesia

DEVELOPMENT OF ANNUAL LANDSAT 8 COMPOSITE OVER CENTRAL KALIMANTAN, INDONESIA USING AUTOMATIC ALGORITHM TO MINIMIZE CLOUD

Since January 2013, Landsat 8 data can be freely accessed from LAPAN, making it possible to use the all available Landsat 8 data to  produce the cloud-free Landsat 8 composite images. This study used Landsat 8 archive images in 2015,  Operational Land Imager (OLI) sensor in 30 meter resolution, geometric correction level of L1T. The eight data in L1T of 118-062, southern part of Central Kalimantanwere used to produce a cloud-free composite image. Radiometric correction using Top of Atmosphere (TOA) and Bidirectional Reflectance Distribution Function (BRDF) algorithm to produce reflectance images have been applied, and then the most cloud-free pixels were selected in composite result. Six composite methods base on greens, open area and haze indices were compared, and the best one was selected  using visual analysis. The analysis shows that the composite algorithm using Max (Max (NIR, SWIR1)/Green) produces the best image composite

A Minimum Cloud Cover Mosaic Image Model of the Operational Land Imager Landsat-8 Multitemporal Data using Tile based

The need for remote sensing minimum cloud cover or cloud free mosaic images is now increasing in line with the increased of national development activities based on one map policy. However, the continuity and availability of cloud and haze free remote sensing data for the purpose of monitoring the natural resources are still low. This paper presents a model of medium resolution remote sensing data processing of Landsat-8 uses a new approach called mosaic tile based model (MTB), which is developed from the mosaic pixel based model (MPB) algorithm, to obtain an annual multitemporal mosaic image with minimum cloud cover mosaic imageries. The MTB model is an approach constructed from a set of pixels (called tiles) considering the image quality that is extracted from cloud and haze free areas, vegetation coverage, and open land coverage of multitemporal imageries. The data used in the model are from Landsat-8 Operational Land Imager (OLI) covering 10 scenes area, with 2.5 years recording period from June 2015 to June 2017; covered Riau, West Sumatra and North Sumatra Provinces. The MTB model is examined with tile size of 0.1 degrees (11x11 km2), 0.05 degrees (5.5x5.5 km2), and 0.02 degrees (2.2x2.2 km2). The result of the analysis shows that the smallest tile size 0.02 gives the best result in terms of minimum cloud cover and haze (or named clear area). The comparison of clear area values to cloud cover and haze for three years (2015, 2016 and 2017) for the three mosaic images of MTB are 68.2%, 78.8%, and 86.4%, respectively

DETECTION OF FOREST FIRE, SMOKE SOURCE LOCATIONS IN KALIMANTAN DURING THE DRY SEASON FOR THE YEAR 2015 USING LANDSAT 8 FROM THE THRESHOLD OF BRIGHTNESS TEMPERATURE ALGORITHM

Almost every dry season, there are large forest/land fires in several regions in Indonesia, especially in Kalimantan and Sumatra in the dry season of August to September 2015 a forest fire in 6 provinces namely West Kalimantan, Central Kalimantan, South Kalimantan, Riau, Jambi, and South Sumatra. Even some parties proposed that the Government of Indonesia declares them as a national disaster. The low-resolution remote sensing data have been widely used for monitoring the occurrence of forest/land fires (hotspots), and mapping of  burnt scars. The hotspot detection was done by utilizing the data of NOAA-AVHRR and MODIS data which have a lower spatial resolution (1 km). In order to increase the level of detail and accuracy of product information, this research is done by using Landsat 8 TIRS (Thermal Infrared Sensor) band which has a greater spatial resolution of 100 m. The purpose of this research is to find and to determine the threshold value of the brightness temperature of the TIRS data to identify the source of fire smoke. The data used is the Landsat 8 of several parts of Borneo during the period of 24 August to 18 September 2015 recorded by the LAPAN's receiving station. Landsat - 8 TIRS band was converted into brightness temperature in degrees Celsius, then dots in a region that is considered the source of the smoke if the temperature of each pixel in the region > 43oC, and given the attributes with the highest temperatures of the pixels in the region. The source of the smoke was obtained through visual interpretation of the objects in the multispectral Natural Color Composite (NCC) and True Color Composite (TCC) images. Analysis of errors (commission error) is obtained by comparing the temperature detected by TIRS band with a visual appearance of the source of the smoke. The result of the experiment showed that there were detected 9 scenes with high temperatures over 43oC from the 27 scenes Kalimantan Landsat 8 data, which include 153 sites. The accuracy (commission error) of identification results using temperature ≥ 51°C is 0%, temperature ≥ 47°C is 10%, and temperature ≥ 43°C is 30.5%

Digital Interpretability of Annual Tile-based Mosaic of Landsat-8 OLI for Time-series Land Cover Analysis in the Central Part of Sumatra

This paper presents an interoperability of annual tile-based mosaic (MTB) images, as well as a verification of the validity of the model for the time series land cover analysis purposes. The primary data used are MTB image of Landsat-8 of the central part of Sumatra, acquired from January 2015 to June 2017. The method used for the interoperability validation is the digital analysis of three-years time series land cover. The classification was performed with four band spectral groups. Training samples are taken from the image of 2016. The results are then reclassified to improve the overall accuracy score based on Jefferies Matusita (JM) distance. The interoperability can be measured by the average of overall accuracy (AOA) score, namely Good (scores > 80%), Fair (70.0% -79.9%), and Bad ( 80% for six and four classes object. Thus the most efficient for interoperability is the use of Bands 6-5 to analyze four class object of land cover.

HAZE REMOVAL IN THE VISIBLE BANDS OF LANDSAT 8 OLI OVER SHALLOW WATER AREA

Haze is one of radiometric quality parameters in remote sensing imagery. With certain atmospheric correction, haze is possible to be removed. Nevertheless, an efficient method for haze removal is still a challenge. Many methods have been developed to remove or to minimize the haze disruption. While most of the developed methods deal with removing haze over land areas, this paper tried to focus to remove haze from shallow water areas. The method presented in this paper is a simple subtraction algorithm between a band that reflected by water and a band that absorbed by water. This paper used data from Landsat 8 with visible bands as a band that reflected by water while the band that absorbed by water represented by NIR, SWIR-1, and SWIR-2 bands. To validate the method, a reference data which relatively clear of cloud and haze contamination is selected. The pixel numbers from certain points are selected and collected from data scene, results scene and reference scene. Those pixel numbers, then being compared each other to get a correlation number between data scene to reference scene and between result scene and reference scene. The comparison shows that the method using NIR, SWIR-1, and SWIR-2 all significantly improved correlations numbers between result scene with reference scene to higher than 0.9. The comparison also indicates that haze removal result using NIR band had the highest correlation with reference data.

VARIABILITY OF NORMALIZED DIFFERENCE VEGETATION INDICES IN SUMATRA AND ITS RELATION TO CLIMATE ANOMALIES(KERAGAMAN INDEKS VEGETASI DI SUMATERA DAN HUBUNGANNYA DENGAN ANOMALI IKLIM)

Indeks vegetasi yang diperoleh dari data NOAA-AVHRR sudah umum digunakan sebagai indikator kehijauan dan kekeringan vegetasi. Kondisi iklim global dan regional diatas Sumatera mempengaruhi indeks vegetasi di Sumatera. Penelitian ini bertujuan mempelajari keragaman indeks vegetasi terutama di Sumatra dan hubungannya dengan El Nino - Southern Oscillation (ENSO) dan Indian Ocean Dipole Mode Event (DME). LAC NDVI periode 1996-2002 digunakan untuk menganalisa koefisien keragaman dan analisis korelasi kanonik. Keragaman NDVI yang tinggi ditemukan di pantai timur, bagian selatan dan bagian utara Sumatera, sedangkan di bagian barat dan tengah keragamannya rendah. Secara keseluruhan, keragaman NDVI selama monsun barat lebih tinggi daripada periode monsun timur. ENSO dan DME mempangaruhi indeks vegetasi di Sumatera pada lag 0,4,dan 5 bulan (nyata pada taraf 5%). Kontribusi terbesar diberikan oleh variable kanonik lag 1 (R2=70.1%), sisa 29,9 % disebabkan oleh keragaman factor-faktor lainnya. Kerana korelasi dan signifikansi dari parameter iklim secara statistik tinggi, maka dapat digunakan sebagai prediktor NDVI di Sumatera. Diantara 6 time lag , parameter dengan lag 6 bulan mempunyai keragaman yang tertinggi. Namun, uji beda nyata menunjukkan bahwz korelasi kanonik pada lag 0,4,dan 5 yang mempunyai beda nyta tertinggi (pada taraf 95%). Struktur korelasi kanonik untuk parameter iklim pada lag 0 dan 1 didominasi oleh SOI dan anomaly SST. Sedangkan korelasi pada lag 2,5, dan 6 didominasi oleh SOI, anomaly SST, dan DMI. Berdasarkan hasil analisis tersebut, kami menyimpulkan bahwa analisis korelasi kanonik merupakan metode yang optimum untuk memprediksi NDVI di Sumatera pada lag 5 bulan menggunakan SOI, SSTA, dan DMI sebagai prediktor. Hasil ini menunjukkan bahwa parameter iklim dapat digunakan untuk memprediksi NDVI 5 bulan ke depan dengan baik di Sumatera

ANALISIS MISALIGNMENT CITRA MULTISPEKTRAL TERHADAP CITRA PANKROMATIK PADA DATA WORLDVIEW-2

The standard data of Worldview-2 owned by LAPAN is Ortho-Ready Standard level 2 (OR2A) data consisting of 4 multispectral bands (blue, green, red, NIR) and one panchromatic band each 2 m and 0,5 m spatial resolution. Both images have different metadata and RPC, making it possible to perform geometric corrections separately. This paper discusses the analysis of the inaccuracies of multispectral image positions to panchromatic images compared to those that have been systematically geometric corrected. The method used is fast fourier transform phase matching by taking 500 binding points between the two images. The measurement results prove that the multispectral image of the Worldview-2 data of the OR2A level has a larger shift compared with multispectral image that has been systematically geometric corrected. The multispectral image of the OR2A data shifts are 2,14 m on the X-axis and 0,42 m on the Y-axis. While the multispectral image that has been systematically geometric corrected shifts are 1,72 m on the X-axis and 0,54 m on the Y-axis.ABSTRAKData standar Worldview-2 yang dimiliki oleh LAPAN merupakan data Ortho-Ready Standard level 2 (OR2A) yang terdiri dari 4 kanal multispektral (biru, hijau, merah, NIR) dan satu kanal pankromatik masing-masing memiliki resolusi spasial 2 meter dan 0,5 meter. Kedua kanal tersebut memiliki metadata dan RPC yang berbeda, sehingga memungkinkan untuk melakukan koreksi geometrik secara terpisah. Tulisan ini membahas tentang analisis misalignment citra multispektral terhadap citra pankromatik dibandingkan dengan yang telah terkoreksi geometrik sistematik. Metode yang digunakan adalah fast fourier transform phase matching dengan mengambil 500 titik ikat antara kedua citra tersebut. Hasil pengukuran membuktikan bahwa citra multispektral data Worldview-2 level OR2A memiliki pergeseran yang lebih besar dibandingkan dengan citra multispektral yang terkoreksi geometrik sistematik. Citra multispektral data OR2A bergeser 2,14 meter pada sumbu X dan 0,42 meter pada sumbu Y. Sedangkan citra multispektral data terkoreksi geometrik sistematik bergeser 1,72 meter pada sumbu X dan 0,54 meter pada sumbu Y

A TWO-STEPS RADIOMETRIC CORRECTION OF SPOT-4 MULTISPECTRAL AND MULTITEMPORAL FOR SEAMLESS MOSAIC IN CENTRAL KALIMANTAN

This research analyzed the radiometric correction method using SPOT-4 imageries to produce the same reflectance for the same land cover. Top of Atmosphere (TOA) method was applied in previous radiometric correction approach, this TOA approach was upgraded with the reflectance effect from difference satellite viewing angle. The 250 scene of Central Kalimantan SPOT-4 imageries from 2006 until 2012 with varies viewing angle was used. This research applied two-step approaches, the first step is TOA correction, and the second step is normalization using a linear function of reflectance and satellite viewing angle. Gain and offset coefficient of this linear function was calculated using an iterative approach to producing the same reflectance in the forest area. The target of iterative processed is to minimize the standard deviation of a digital number from a forest area in the selected region. The result shows that the standard deviation of a digital number from a forest area in the two steps approach are 8.6, 16.5, and 16.8 for band 1, band 3 and band 4. These values are smaller compared with the standard deviation of digital number result from TOA approach are 15.0, 28,3 and 34.7 for band 1, band 3 and band 4.  Decreasing the standard deviation shows the homogeneity of forest reflectance that could be seen in the seamless result. This algorithm can be applied for making seamless SPOT-4 mosaic whole of Indonesia

Identifikasi Daun Shorea Menggunakan KNN dengan Ekstraksi Fitur 2DPCA

Shorea adalah jenis meranti yang memiliki nilai ekonomis yang tinggi. Shorea tergolong dalam famili Dipterocarpaceae yang memiliki 194 spesies yang tumbuh di daerah tropis. Shorea merupakan jenis yang sulit untuk diidentifikasi karena memiliki banyak kemiripan. Untuk mengatasi kesulitan tersebut, penelitian ini mengidentifikasi Shorea berdasarkan citra daun. Jumlah spesies yang digunakan penelitian ini adalah 10 jenis Shorea. Metode ekstraksi fitur yang digunakan adalah 2 dimensional principal component analysis (2D-PCA) dengan metode klasifikasi KNN. Penelitian ini memiliki 4 percobaan yang dibagi menjadi komponen R, G, B, dan grayscale. Hasil rata-rata akurasi terbaik sebesar 75% pada komponen G dengan kontribusi nilai eigen 85%