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 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 Change Detection and Impact of Climate Changes to Iraqi Southern Marshes Using Landsat 2 MSS, Landsat 8 OLI and Sentinel 2 MSI Data and GIS Applications

2019 ◽  
Vol 9 (10) ◽  
pp. 2016 ◽  
Author(s):  
Bassim Mohammed Hashim ◽  
Maitham Abdullah Sultan ◽  
Mazin Najem Attyia ◽  
Ali A. Al Maliki ◽  
Nadhir Al-Ansari
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Maximum Temperature ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Large Area ◽  
Gis Applications ◽  
Average Maximum ◽  
Water Cover ◽  
Sentinel 2

Marshes represent a unique ecosystem covering a large area of southern Iraq. In a major environmental disaster, the marshes of Iraq were drained, especially during the 1990s. Since then, droughts and the decrease in water imports from the Tigris and Euphrates rivers from Turkey and Iran have prevented them from regaining their former extent. The aim of this research is to extract the values of the normalized difference vegetation index (NDVI) for the period 1977–2017 from Landsat 2 MSS (multispectral scanner), Landsat 8 OLI (operational land imager) and Sentinel 2 MSI (multi-spectral imaging mission) satellite images and use supervised classification to quantify land and water cover change. The results from the two satellites (Landsat 2 and Landsat 8) are compared with Sentinel 2 to determine the best tool for detecting changes in land and water cover. We also assess the potential impacts of climate change through the study of the annual average maximum temperature and precipitation in different areas in the marshes for the period 1981–2016. The NDVI analysis and image classification showed the degradation of vegetation and water bodies in the marshes, as vast areas of natural vegetation and agricultural lands disappeared and were replaced with barren areas. The marshes were influenced by climatic change, including rising temperature and the diminishing amount of precipitation during 1981–2016.

scholarly journals Retrieval of Urban Aerosol Optical Depth from Landsat 8 OLI in Nanjing, China

2021 ◽  
Vol 13 (3) ◽  
pp. 415
Author(s):  
Yangyang Jin ◽  
Zengzhou Hao ◽  
Jian Chen ◽  
Dong He ◽  
Qingjiu Tian ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Absolute Error ◽  
Meteorological Conditions ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Large Area ◽  
The North ◽  
Deep Blue ◽  
Seasonal Characteristics

Aerosol is an essential parameter for assessing the atmospheric environmental quality, and accurate monitoring of the aerosol optical depth (AOD) is of great significance in climate research and environmental protection. Based on Landsat 8 Operational Land Imager (OLI) images and MODIS09A1 surface reflectance products under clear skies with limited cloud cover, we retrieved the AODs in Nanjing City from 2017 to 2018 using the combined Dark Target (DT) and Deep Blue (DB) methods. The retrieval accuracy was validated by in-situ CE-318 measurements and MOD04_3K aerosol products. Furthermore, we analyzed the spatiotemporal distribution of the AODs and discussed a case of high AOD distribution. The results showed that: (1) Validated by CE-318 and MOD04_3K data, the correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) of the retrieved AODs were 0.874 and 0.802, 0.134 and 0.188, and 0.099 and 0.138, respectively. Hence, the combined DT and DB algorithms used in this study exhibited a higher performance than the MOD04_3K-obtained aerosol products. (2) Under static and stable meteorological conditions, the average annual AOD in Nanjing was 0.47. At the spatial scale, the AODs showed relatively high values in the north and west, low in the south, and the lowest in the center. At the seasonal scale, the AODs were highest in the summer, followed by spring, winter, and autumn. Moreover, changes were significantly higher in the summer than in the other three seasons, with little differences among spring, autumn, and winter. (3) Based on the spatial and seasonal characteristics of the AOD distribution in Nanjing, a case of high AOD distribution caused by a large area of external pollution and local meteorological conditions was discussed, indicating that it could provide extra details of the AOD distribution to analyze air pollution sources using fine spatial resolution like in the Landsat 8 OLI.

scholarly journals Identification of Water Bodies in a Landsat 8 OLI Image Using a J48 Decision Tree

Sensors ◽  
2016 ◽  
Vol 16 (7) ◽  
pp. 1075 ◽  
Author(s):  
Tri Acharya ◽  
Dong Lee ◽  
In Yang ◽  
Jae Lee
Keyword(s):  
Decision Tree ◽  
Water Bodies ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
J48 Decision Tree

scholarly journals Canopy Cover Estimation in Lowland Forest in South Sumatera, Using LiDAR and Landsat 8 OLI imagery

2021 ◽  
Vol 27 (1) ◽  
pp. 50-58
Author(s):  
M B Saleh ◽  
◽  
R W Dewi ◽  
L B Prasetyo ◽  
N A Santi
Keyword(s):  
Dimensional Space ◽  
Vegetation Indices ◽  
Three Dimensional ◽  
Canopy Cover ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Estimation Model ◽  
Large Area ◽  
Lowland Forest ◽  
Three Dimensional Space

Canopy cover is one of the most important variables in ecology, hydrology, and forest management, and useful as a basis for defining forests. LiDAR is an active remote sensing method that provides the height information of an object in three-dimensional space. The method allows for the mapping of terrain, canopy height and cover. Its only setback is that it has to be integrated with Landsat to cover a large area. The main objective of this study is to generate the canopy cover estimation model using Landsat 8 OLI and LiDAR. Landsat 8 OLI vegetation indices and LiDAR-derived canopy cover estimation, through First Return Canopy Index (FRCI) method, were used to obtain a regression model. The performance of this model was then assessed using correlation, aggregate deviation, and raster display. Lastly, the best canopy cover estimation was obtained using equation, FRCI = 2.22 + 5.63Ln(NDVI), with R2 at 0.663, standard deviation at 0.161, correlation between actual and predicted value at 0.663, aggregate deviation at -0.182 and error at 56.10%.

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.

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.

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