scholarly journals Wetland Mapping with Landsat 8 OLI, Sentinel-1, ALOS-1 PALSAR, and LiDAR Data in Southern New Brunswick, Canada

2020 ◽  
Vol 12 (13) ◽  
pp. 2095 ◽  
Author(s):  
Armand LaRocque ◽  
Chafika Phiri ◽  
Brigitte Leblon ◽  
Francesco Pirotti ◽  
Kevin Connor ◽  
...  
Keyword(s):  
New Brunswick ◽  
Water Levels ◽  
Aerial Photographs ◽  
Coastal Marsh ◽  
Forested Wetland ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Visual Interpretation ◽  
Thematic Accuracy ◽  
Classified Image

Mapping wetlands with high spatial and thematic accuracy is crucial for the management and monitoring of these important ecosystems. Wetland maps in New Brunswick (NB) have traditionally been produced by the visual interpretation of aerial photographs. In this study, we used an alternative method to produce a wetland map for southern New Brunswick, Canada, by classifying a combination of Landsat 8 OLI, ALOS-1 PALSAR, Sentinel-1, and LiDAR-derived topographic metrics with the Random Forests (RF) classifier. The images were acquired in three seasons (spring, summer, and fall) with different water levels and during leaf-off/on periods. The resulting map has eleven wetland classes (open bog, shrub bog, treed bog, open fen, shrub fen, freshwater marsh, coastal marsh, shrub marsh, shrub wetland, forested wetland, and aquatic bed) plus various non-wetland classes. We achieved an overall accuracy classification of 97.67%. We compared 951 in-situ validation sites to the classified image and both the 2106 and 2019 reference maps available through Service New Brunswick. Both reference maps were produced by photo-interpretation of RGB-NIR digital aerial photographs, but the 2019 NB reference also included information from LiDAR-derived surface and ecological metrics. Of these 951 sites, 94.95% were correctly identified on the classified image, while only 63.30% and 80.02% of these sites were correctly identified on the 2016 and 2019 NB reference maps, respectively. If only the 489 wetland validation sites were considered, 96.93% of the sites were correctly identified as a wetland on the classified image, while only 58.69% and 62.17% of the sites were correctly identified as a wetland on the 2016 and 2019 NB reference maps, respectively.

scholarly journals GLACIER IDENTIFICATION FROM LANDSAT8 OLI IMAGERY USING DEEP U-NET

2020 ◽  
Vol V-3-2020 ◽  
pp. 381-386
Author(s):  
Q. He ◽  
Z. Zhang ◽  
G. Ma ◽  
J. Wu
Keyword(s):  
Regional Climate ◽  
Spectral Characteristics ◽  
Semantic Segmentation ◽  
Identification Accuracy ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Visual Interpretation ◽  
Identification Methods ◽  
Identification Method ◽  
Data Source

Abstract. Glacier is one of the clearest signal of climate change, and its changes have important effects on regional climate and water resources. Glacier identification is the basic of glacial changes research. Traditional remote sensing glacier identification methods usually perform simple bands calculation based on the spectral characteristics of glacier. The identification results are greatly affected by threshold segmentation. In addition, there is a misclassification of water body and glacier. As a simple and efficient semantic segmentation network, U-Net has been widely used in many fields of image processing. This paper performs an improved semantic segmentation network Deep U-Net for glacier identification using Landsat 8 OLI image as the data source, and compares it with the traditional NDSI glacier identification method. The identification results are validated by the glacier label data produced by visual interpretation. The results indicate that the proposed method achieves an identification accuracy of 97.27%, which is higher than the NDSI glacier identification method. It can effectively exclude the interference of water bodies on glacier identification, and has a higher degree of automation.

scholarly journals EKSTRAKSI INFORMASI PENUTUP LAHAN AREA LUAS DENGAN METODE EXPERT KNOWLEDGE OBJECT-BASED IMAGE ANALYSIS (OBIA) PADA CITRA LANDSAT 8 OLI PULAU KALIMANTAN

2016 ◽  
Vol 18 (1) ◽  
pp. 09
Author(s):  
Zylshal Zylshal ◽  
Heri Susanto ◽  
Sarip Hidayat
Keyword(s):  
Decision Tree ◽  
Expert Knowledge ◽  
Contextual Information ◽  
Acquisition Time ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Visual Interpretation ◽  
Large Area ◽  
Spectral Difference ◽  
Multiresolution Segmentation

<p class="abstrak">Ekstraksi informasi penutup/penggunaan lahan area luas seperti di Pulau Kalimantan umumnya terkendala oleh variasi nilai spektral di beberapa area yang berbeda, serta sulitnya mendapatkan hasil perekaman yang bebas dari awan. Klasifikasi visual, meski memberikan hasil yang baik, merupakan pekerjaan yang membutuhkan waktu dan tenaga yang relatif banyak, belum lagi potensi pengaruh subjektifitas interpreter. OBIA yang sudah mulai diterima dan banyak digunakan dalam klasifikasi digital bisa menjadi alternatif tambahan selain interpretasi visual maupun analisis digital berbasis piksel konvensional. Penelitian ini menggunakan data Landsat 8 OLI <em>orthorectified </em>yang telah melalui proses <em>mosaicking</em> dan <em>cloud masking</em> untuk mendapatkan citra satu Pulau Kalimantan yang bebas awan. <em>Layer </em>NDVI, MNDWI, NDBI, BSI, SAVI, dan <em>Built-up Index</em> kemudian diturunkan dari data Citra Landsat untuk dimasukkan ke dalam tahap segmentasi dan klasifikasi. Segmentasi dilakukan dengan menggunakan algoritma <em>Multiresolution Segmentation</em> dan <em>Spectral Difference Segmentation</em>. Klasifikasi dilakukan dengan menggunakan serangkaian <em>multilevel threshold</em> yang disusun dalam bentuk <em>decision tree</em>. Empat belas kelas penutup/penggunaan lahan kemudian berhasil diekstrak, dengan nilai <em>overall accuracy</em> 77,65%. Metode yang digunakan juga menunjukkan akurasi yang tinggi untuk kelas hutan lahan kering, perkebunan, kebun campur dan semak belukar dengan nilai akurasi di atas 80%. Hasil ini menunjukkan bahwa metode ini bisa dijadikan sebagai alternatif dalam mengidentifikasi dan mengekstrak informasi tutupan vegetasi untuk kegiatan pemetaan area luas.</p><p class="katakunci"><strong>Kata kunci: </strong>OBIA, area luas, perubahan penutupan/penggunaan lahan, citra landsat, <em>decision tree</em></p><p class="judulABS"><strong><em>ABSTRACT</em></strong></p><p class="Abstrakeng"><em>Large area landuse/landcover extraction such as on the island of Borneo using Landsat 8 data are generally constrained by the great variations in the spectral values, due to the vast use of different scenes with different acquisition time, as well as the fact that it almost impossible to get a completely cloud-free image of the whole island. Visual classification, despite the good results, is a labour-intensif job that requires a huge time and effort, not to mention the potential influence of interpreter’s subjectivity. While the pixel based digital classification suffer from“salt pepper” effect as well as almost exclusively relied on spectral information, OBIA has been accepted and widely used in digital classification as an alternative for the visual interpretation and conventional pixel-based classification, with its ability to use additional contextual information. This study aimed to used OBIA method on Landsat 8 OLI cloudfree mosaic dataset for the whole Borneo region to create a landuse/landcover map using both spectral and contextual information, as well as ancilarry DEM data. Additional layers of NDVI, MNDWI, NDBI, BSI, SAVI, and Built-up Index were then derived from Landsat data to be used in the segmentation and classification process. Multiresolution Segmentation algorithm and Spectral Difference Segmentation were then conducted respectively. The classification wasdone by using a series of multilevel crisp classification using thresholds in the form of a decision tree. Fourteen of landuse/landcover classes were then successfully extracted, with a value of 77.65% on overall accuracy. The proposed method showed reasonable high accuracy for the forest, plantation, mixed garden and shrub classes with the accuracy all above 80%. These results indicate that the proposed method can be used as an alternative to identify and extract information related to vegetation cover for large areamapping activities.</em></p><em><strong>Keywords: </strong>OBIA, large area, land use cover change (LULC), landsat image, decision tree</em>

scholarly journals Geometric Accuracy Assessment for Shoreline Derived from NDWI, MNDWI, and AWEI Transformation on Various Coastal Physical Typology in Jepara Regency using Landsat 8 OLI Imagery in 2018

2019 ◽  
Vol 6 (1) ◽  
pp. 55
Author(s):  
Arief Wicaksono ◽  
Pramaditya Wicaksono
Keyword(s):  
Accuracy Assessment ◽  
Landsat 8 ◽  
Geometric Accuracy ◽  
Landsat 8 Oli ◽  
Visual Interpretation ◽  
Geospatial Information ◽  
Index Method ◽  
Water Index ◽  
Subjective Threshold ◽  
Map Accuracy

Landsat 8 OLI imagery and water index utilization is expected to be able to complete the shoreline data that is difficult to obtain by using terrestrial and hydrographic surveys. In fact, coastal areas in Indonesia have a variety of coastal physical typology so that each water index characteristic in obtaining shoreline data needs to be understood in order to use water index method effectively. The objectives of this study are to map the shoreline using NDWI, MNDWI, and AWEI transformations and assess the shoreline geometric accuracy on various coastal physical typology. The shoreline derived from water index is obtained from Landsat 8 OLI imagery, while the reference shoreline for accuracy assessment is obtained from visual interpretation on Planet Scope imagery. Threshold 0 and subjective threshold based on per coastal physical typology sample experiments are used to separate land-sea. The horizontal accuracy standard of the shoreline derived from water index uses the regulation from Geospatial Information Agency of Indonesia No.15 in 2014 on technical guidelines for basic map accuracy. The results consisted of 1:100,000 scale shoreline map and shoreline geometric accuracy per coastal physical typology. Based on the shoreline geometry accuracy assessment, NDWI has the lowest shoreline geometry accuracy on artificial coast (RMSE=24.13 m). MNDWI has the lowest shoreline geometry accuracy on land deposition coast (RMSE=15.84 m), marine deposition coast (RMSE=29.53 m), and volcanic coast (RMSE=10 m). AWEIsh has the lowest shoreline geometry accuracy on the organic coast (RMSE=13.47 m), while AWEI does not superior to any coastal physical typology.

scholarly journals Enhancement of snow cover change detection with sparse representation and dictionary learning

2014 ◽  
Vol XL-8 ◽  
pp. 543-548 ◽  
Author(s):  
D. Varade ◽  
O. Dikshit
Keyword(s):  
Sparse Representation ◽  
Snow Cover ◽  
Reference Data ◽  
Matching Pursuit ◽  
Support Vector ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Visual Interpretation ◽  
The Difference ◽  
Change Map

Sparse representation and decoding is often used for denoising images and compression of images with respect to inherent features. In this paper, we adopt a methodology incorporating sparse representation of a snow cover change map using the K-SVD trained dictionary and sparse decoding to enhance the change map. The pixels often falsely characterized as "changes" are eliminated using this approach. The preliminary change map was generated using differenced NDSI or S3 maps in case of Resourcesat-2 and Landsat 8 OLI imagery respectively. These maps are extracted into patches for compressed sensing using Discrete Cosine Transform (DCT) to generate an initial dictionary which is trained by the K-SVD approach. The trained dictionary is used for sparse coding of the change map using the Orthogonal Matching Pursuit (OMP) algorithm. The reconstructed change map incorporates a greater degree of smoothing and represents the features (snow cover changes) with better accuracy. The enhanced change map is segmented using kmeans to discriminate between the changed and non-changed pixels. The segmented enhanced change map is compared, firstly with the difference of Support Vector Machine (SVM) classified NDSI maps and secondly with a reference data generated as a mask by visual interpretation of the two input images. The methodology is evaluated using multi-spectral datasets from Resourcesat-2 and Landsat-8. The k-hat statistic is computed to determine the accuracy of the proposed approach.

Analisis Pengaruh Kerapatan Vegetasi Terhadap Suhu Permukaan dan Keterkaitannya Dengan Fenomena UHI

2020 ◽  
Vol 21 (1) ◽  
pp. 99
Author(s):  
Dewi Miska Indrawati ◽  
Suharyadi Suharyadi ◽  
Prima Widayani
Keyword(s):  
Urban Heat Island ◽  
Landsat 8 ◽  
Heat Island ◽  
Landsat 8 Oli ◽  
Urban Heat ◽  
Split Window Algorithm

Kota Mataram adalahpusat dan ibukota dari provinsi Nusa Tenggara Barat yang tentunya menjadi pusat semua aktivitas masyarakat disekitar daerah tersebut sehingga menyebabkan peningkatan urbanisasi. Semakin meningkatnya peningkatan urbanisasi yan terjadi di perkotaan akan menyebabkan perubahan penutup lahan, dari awalnya daerah bervegetasi berubah menjadi lahan terbangun. Oleh karena itu, akan memicu peningkatan suhu dan menyebabkan adanya fenomena UHI dikota Mataram.Tujuan dari penelitian ini untuk mengetahui hubungan kerapatan vegetasi dengan kondisi suhu permukaan yang ada diwilayah penelitian dan memetakan fenomena UHI di Kota Mataram. Citra Landsat 8 OLI tahun 2018 yang digunakan terlebih dahulu dikoreksi radiometrik dan geometrik. Metode untuk memperoleh data kerapatan vegetasi menggunakan transformasi NDVI, LST menggunakan metode Split Window Algorithm (SWA) dan identifikasi fenomena urban heat island. Hasil penelitian yang diperoleh menunjukkan kerapatan vegetasi mempunyai korelasi dengan nilai LST. Hasil korelasi dari analisis pearson yang didapatkan antara kerapatan vegetasi terhadap suhu permukaan menghasilkan nilai -0,744. Fenomena UHIterjadi di pusat Kota Mataram dapat dilihat dengan adanya nilai UHI yaitu 0-100C. Semakin besar nilai UHI, semakin tinggi perbedaan LSTnya.

PEMANFAATAN DATA PENGINDERAAN JAUH DAN TEKNOLOGI SIG UNTUK MENGIDENTIFIKASI BUILT UP AREA DAN KAITANNYA DENGAN RTRW DI KABUPATEN KENDAL

2019 ◽  
Vol 3 ◽  
pp. 521
Author(s):  
Mailendra Mailendra
Keyword(s):  
Supervised Classification ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Landsat 5 Tm

Integrasi data penginderaan jauh dengan sistem informasi geografis telah banyak dikembangkan, dan salah satunya dalam melihat perkembangan lahan terbangun. Tujuan penelitian ini adalah untuk melihat perkembangan lahan terbangun dan kesesuaiannya dengan Rencana Pola Ruang Kabupaten Kendal. Kemudian metode yang digunakan yaitu metode supervised classification dengan memanfaatkan data citra landsat 5 TM dan landsat 8 OLI yang selanjutnya dihitung luas dari masing lahan terbangun berdasarkan data temporal tahun 1990, tahun 2015 dan tahun 2017. Setelah diketahui luas lahan terbangun selanjutnya dioverlay dengan peta rencana pola ruang Kabupaten Kendal untuk melihat sesuai atau tidaknya penempatan lahan terbangun tersebut. Adapun hasil penelitiannya yaitu setiap tahunnya lahan terbangun terus meningkat di Kabupaten Kendal, terjadi peningkatan yang cukup signifikan dalam dua tahun terakhir yaitu tahun 2015 hingga tahun 2017. Selanjutnya diperkirakan 88 % lahan terbangun tersebut telah sesuai dengan RTRW karena sudah berada pada kawasan budidaya.

APLIKASI CITRA LANDSAT 8 OLI UNTUK IDENTIFIKASI STATUS TROFIK WADUK GAJAH MUNGKUR WONOGIRI, JAWA TENGAH

2017 ◽  
Vol 19 (2) ◽  
pp. 113
Author(s):  
Kusuma Wardani Laksitaningrum ◽  
Wirastuti Widyatmanti
Keyword(s):  
Chlorophyll A ◽  
Total Phosphorus ◽  
Trophic State ◽  
Water Clarity ◽  
Coefficient Of Determination ◽  
Physical Factors ◽  
Landsat 8 ◽  
Trophic State Index ◽  
Landsat 8 Oli ◽  
State Index

<p align="center"><strong>ABSTRAK</strong></p><p class="abstrak">Waduk Gajah Mungkur (WGM) adalah bendungan buatan yang memiliki luas genangan maksimum 8800 ha, terletak di Desa Pokoh Kidul, Kecamatan Wonogiri, Kabupaten Wonogiri. Kondisi perairan WGM dipengaruhi oleh faktor klimatologis, fisik, dan aktivitas manusia yang dapat menyumbang nutrisi sehingga mempengaruhi status trofiknya. Tujuan dari penelitian ini adalah mengkaji kemampuan citra Landsat 8 OLI untuk memperoleh parameter-parameter yang digunakan untuk menilai status trofik, menentukan dan memetakan status trofik yang diperoleh dari citra Landsat 8 OLI, dan mengevaluasi hasil pemetaan dan manfaat citra penginderaan jauh untuk identifikasi status trofik WGM. Identifikasi status trofik dilakukan berdasarkan metode <em>Trophic State Index</em> (TSI) Carlson (1997) menggunakan tiga parameter yaitu kejernihan air, total fosfor, dan klorofil-a. Model yang diperoleh berdasar pada rumus empiris dari hasil uji regresi antara pengukuran di lapangan dan nilai piksel di citra Landsat 8 OLI. Model dipilih berdasarkan nilai koefisien determinasi (R<sup>2</sup>) tertinggi. Hasil penelitian merepresentasikan bahwa nilai R<sup>2</sup> kejernihan air sebesar 0,813, total fosfor sebesar 0,268, dan klorofil-a sebesar 0,584. Apabila nilai R<sup>2 </sup>mendekati 1, maka semakin baik model regresi dapat menjelaskan suatu parameter status trofik. Berdasarkan hasil kalkulasi diperoleh distribusi yang terdiri dari kelas eutrofik ringan, eutrofik sedang, dan eutrofik berat yaitu pada rentang nilai indeks 50,051 – 80,180. Distribusi terbesar adalah eutrofik sedang. Hal tersebut menunjukkan tingkat kesuburan perairan yang tinggi dan dapat membahayakan makhluk hidup lain.</p><p><strong>Kata kunci: </strong>Waduk Gajah Mungkur, citra Landsat 8 OLI, regresi, TSI, status trofik</p><p class="judulABS"><strong>ABSTRACT</strong></p><p class="Abstrakeng">Gajah Mungkur Reservoir is an artificial dam that has a maximum inundated areas of 8800 ha, located in Pokoh Kidul Village, Wonogiri Regency. The reservoir’s water conditions are affected by climatological and physical factors, as well as human activities that can contribute to nutrients that affect its trophic state. This study aimed to assess the Landsat 8 OLI capabilities to obtain parameters that are used to determine its trophic state, identifying and mapping the trophic state based on parameters derived from Landsat 8 OLI, and evaluating the results of the mapping and the benefits of remote sensing imagery for identification of its trophic state. Identification of trophic state is based on Trophic State Index (TSI) Carlson (1997), which uses three parameters there are water clarity, total phosphorus, and chlorophyll-a. The model is based on an empirical formula of regression between measurements in the field and the pixel values in Landsat 8 OLI. Model is selected on the highest value towards coefficient of determination (R<sup>2</sup>). The results represented that R<sup>2</sup> of water clarity is 0.813, total phosphorus is 0.268, and chlorophyll-a is 0.584. If R<sup>2</sup> close to 1, regression model will describe the parameters of the trophic state better. Based on the calculation the distribution consists of mild eutrophic, moderate eutrophic, and heavy eutrophic that has index values from 50.051 to 80.18. The most distribution is moderate eutrophication, and it showed the high level of trophic state and may harm other living beings.</p><p><strong><em>Keywords: </em></strong><em>Gajah Mungkur Reservoir, </em><em>L</em><em>andsat 8 OLI satellite imagery, regression, TSI, trophic state</em></p>

ANALISIS POTENSI DAERAH RESAPAN AIR KOTA PADANG

2019 ◽  
Vol 3 ◽  
pp. 671
Author(s):  
Quinoza Guvil ◽  
Dwi Marsiska Driptufany ◽  
Syahri Ramadhan
Keyword(s):  
Landsat 8 ◽  
Landsat 8 Oli

Kota Padang adalah kota dengan kekerapan hujan dan curah hujan yang cukup tinggi. Pembangunan di Kota Padang berbanding terbalik dengan daerah resapan air sehingga air hujan tergenang dan terjadi banjir. Tujuan dari penelitian ini adalah untuk mengestimasi sebaran kawasan resapan air berbasis penggunaan lahan aktual di Kota Padang berdasarkan data parameter spasial seperti curah hujan, kemiringan lereng, peta jenis tanah, dan penggunaan lahan yang diperoleh dari data citra landsat 8 OLI dengan metode klasifikasi berbasis objek. Metode yang digunakan dalam penelitian ini adalah metode skoring dan tumpang susun atau overlay. Penelitian ini memetakan sebaran kondisi daerah resapan air berdasarkan kondisi saat ini, penentuan daerah yang ditetapkan sebagai zona resapan air Kota Padang menggunakan metode kombinasi skoring dan aritmatik dalam analisis spasial. Hasil analisis menghasilkan enam kelas kondisi potensi daerah resapan air, yang terdiri dari kondisi baik, normal alami, mulai kritis, agak kritits, kritis dan sangat kritis. Kondisi kawasan resapan air dengan luasan terbesar yaitu seluas 69,79% dari luas wilayah daerah penelitian terdapat pada kondisi resapan baik. Kawasan ini tersebar di wilayah timur Kota Padang yang merupakan wilayah pegunungan dengan ketinggian bervariasi dan sangat curam yaitu >1000 mdpl. Kawasan potensi resapan air Kota Padang masih berfungsi baik dengan luasan terbesar terdapat di Kecamatan Koto Tangah seluas 16870,288 ha. Semakin baik infiltrasi suatu parameter maka semakin baik pula resapan air suatu kawasan.

Lựa chọn tham số phân đoạn phù hợp để phân loại ảnh vệ tinh đa phổ Landsat 8 OLI

2018 ◽  
Vol 54(4) ◽  
pp. 22
Author(s):  
Ngô Thị Thùy Phương ◽  
Nguyễn Thị Thanh Hương ◽  
Võ Quang Minh
Keyword(s):  
Landsat 8 ◽  
Landsat 8 Oli
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