Land Cover Classification using Object-Based Image Analysis of SPOT-6 Imagery for Land Cover and Forest Monitoring in Nagan Raya, Aceh – Indonesia

Digital classification technique using Object-Based Image Analysis (OBIA) of SPOT-6 imagery could improve classification accuracy and provide detail type of land cover. This method is better applied for data with higher spatial resolution imagery which has high heterogeneity where the pixel size is smaller the actual size of the objects. The sequence steps were image preprocessing and pansharpening, identify the potential of landcover types using Jeffries-Matusita (JM) feature separability, determine a stratification boundary, and developing the Object-Based Orientation classification using Erdas Imagine Objective. Detail process of OBIA was image pixel segmentation with the parameters were consists of determining the minimum pixel segmentation ratio which was 1000 and applying the single feature probability and NDVI, and determine cue weight. The second segmentation was object vector classification which segmented the vector object using the empirical distribution analysis. The segmentation is based on region growing of the Multi-Bayesian network. The classification result is then assessed by comparing the classification result of Maximum Likelihood (MLC) using confusion matrix. The result shows that object-based classification technique could improve the classification by 83% compared to the MLC with only 67%. The method of segmentation used the stratification zone in order to make an optimum cue weight in detecting the object through texture, size, and shape while also applying the spectral-based method

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

DETERMINATION OF STRATIFICATION BOUNDARY FOR FOREST AND NON FOREST MULTITEMPORAL CLASSIFICATION TO SUPPORT REDD+ IN SUMATERA ISLAN

Multi-temporal classification is a method to determine forest and non-forest by considering a missing data, such as cloud cover using correlations value from the other data. This circumstances is frequently occured in a tropical area such as in Indonesia. To gain an optimum result of forest and non-forest classification, it is needed a stratification zone that describes the difference of vegetation condition due to different of vegetation type, soil type, climate, and land use/cover associations. This stratification zone will be useful to indicate the different biomass volume relating to carbon content for supporting the REDD+ project. The objective of this study was to determine stratification boundary by performing multi temporal  classification in Sumatera Island  using  Landsat  imagery  in  25 meter resolution and Quick Bird imagery in 0.6 meter. Rough stratification was made by considering land use/cover, DEM and landform, using visual interpretation of moderate spatial resolution of satellitedata. High spatial resolution data was also provided in some areas to increase the accuracy level of stratification zone. The stratification boundary was evaluated using forest classification indices, and it was  redetermined  to  obtain  the  final  stratification  zone. The  indices was generated  by CanonicalVariate Analysis (CVA) method, which was depend on training samples of forest and non-forest in each previous stratification zone. The amount of indices used in each zone were two or three indices depending on the separability of the forest and non-forest classification. The suitable indices used in each  zone  described forest  as  100, non-forest  as  0, and  uncertain  forest between  50-99. The  result showed 20 stratification zones in Sumatera spreading out in coastal, mountain, flat area, and group of small islands. The stratification zone will improve the accuracy of forest and non-forest classification result and their change based on multi temporal classification

ANALISIS PENINGKATAN KUALITAS GEOMETRI DENGAN MENGGUNAKAN TITIK IKAT BUNDLE ADJUSTMENT (STUDI KASUS DATA PLEIADES WILAYAH KABUPATEN MADIUN DAN KABUPATEN MAGETAN)

Pemanfaatan data resolusi spasial sangat tinggi seperti Pleaides saat ini mengalami permintaan yang tinggi. Salah satu pemanfaatan data ini untuk mendukung kebencanaan, dimana proses pengolahan otomatisasi dan cepat sangat diperlukan dan tidak terhindarkan. Citra Pleiades telah diakusisi oleh stasiun bumi LAPAN di tahun 2018. Penelitian ini mengkaji tentang peningkatan kualitas geometri citra Pleiades dengan metode titik ikat bundle adjustment (BA) untuk proses mosaik dengan wilayah studi di wilayah Kabupaten Madiun dan Magetan. Metode ini menggunakan parameter keterkaitan geometri antar scene. Keterkaitan tersebut dihubungkan dengan membuat titik ikat. Titik-titik ini berada di area pertampalan antar scene. Citra hasil proses koreksi geometri BA akan dilakukan penilaian kualitas hasil koreksi geometrinya dengan membandikan data koordinat pengukuran lapangan. Hasil penilaian kualitas akurasi koreksi geometri menunjukkan bahwa koreksi geometri menggunakan metode BA lebih mendekati titik koordinat pengukuran lapangan dibandingkan koreksi geometri tanpa BA

EVALUASI REHABILITASI LAHAN KRITIS BERDASARKAN TREND NDVI LANDSAT-8 (Studi Kasus: DAS Serayu Hulu)

Pemanfaatan penginderaan jauh dalam memantau vegetasi sudah banyak dilakukan, tetapi pemanfaatannya untuk mengevaluasi rehabilitasi di lahan kritis masih sangat jarang. Kegiatan rehabiliatsi hutan dan lahan dilakukan karena makin meningkatnya lahan kritis. Kegiatan rehabilitasi tersebut perlu dievaluasi, mengingat banyak sekali dana, waktu, dan tenaga yang diperlukan. Selama ini evaluasi dilakukan dengan cara langsung mendatangi lokasi rehabilitasi dengan memantau pertumbuhan tanaman pada setiap akhir tahun sampai akhir tahun ketiga. Menurut ketentuan peraturan yang berlaku, rehabilitasi dapat dikatakan berhasil apabila 90% vegetasi yang ditanam bisa tumbuh di akhir tahun ketiga. Kegiatan evaluasi dengan cara memantau kondisi vegetasi atau kerapatannya dapat dilaksanakan dengan memanfaatkan data penginderaan jauh, karena data tersebut mempunyai sifat multi temporal dan cakupan yang luas dan ketersediannya yang berlimpah dan mudah didapat. Data penginderaan jauh yang digunakan adalah Landsat-8 tahun 2013 sampai dengan 2018 dan metode evaluasi adalah analisis NDVI dari waktu ke waktu menggunakan SIG. Hasilnya adalah bahwa dari hasil survey yang diperoleh di kawasan APL terdapat lokasi rehabilitasi di lahan tidak kritis, agak kritis, kritis, dan sangat kritis dan berturut-turut keberhasilan rehabilitasi untuk APL_TK; APL_K; APL_AK; APL_SK jika NDVI melampaui nilai 0,337; 0,465; 0,493; 0,490 setelah bulan ke 21,8; 24,5; 26, dan 25,8

Pemanfaatan Citra Pankromatik SPOT-6 untuk interpretasi penutupan lahan menggunakan analisis tekstur

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A Canonical Variate Analysis Methode To Classify Forest And Non Forest Using Landsat Image For Central Kalimantan Province In 2000-2008

The information of forest and non forest for Central Kalimantan Province in year 2000-2008 derived using Landsat 5 and 7 images. This research is conducted by Indonesia's National Carbon Accounting System (INCAS) project in order to support a mechanism scheme plan of decreasing emission from Reducing Emissions from Deforestation and Forest Degradation/REDD. The INCAS activities in LAPAN are inventoring Landsat data, scene selection, orthorectification, radiometric correction, cloud masking, mosaicing wall to wall per year, and classification. This paper is only discussed of classification of forest and non forest. The criterion of forest in this term is a trees, having canopy cover more than 20%, and 2 meter tall. The data are Landsat 5 and 7 in year 2000 and 2008. The secondary data are Ikonos, Quickbird, geology, and ground truth data. The stages of process are decided training sample, forest base probability, forest matching, and stratification zones. The methodology to make forest base probability is using canonical variate analysis (CVA) to derive the indices and threshold. The result are forest extend and changes per year

Pemanfaatan DEM ALOS Palsar, DEM SRTM, dan citra Landsat untuk mengetahui potensi longsor (Studi kasus : Kabupaten Purworejo-Provinsi Jawa Tengah)

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