Dr. Muhammad Kamal Profile

Dr. Muhammad Kamal

at Univ Gadjah Mada

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Author

Publications (14)

Proceedings Article | 29 January 2024 Paper

Above-ground biomass mapping of mangrove forest using WorldView-3 imagery in Gili Lawang, Nusa Tenggara Barat

Edelweiss Maheswari Rinjani, Aurelia Nur Khofifah, Devandra Budi Mahendra Putra Pattiasina, Fannya Rachma Annisa, Galang Baasith Adidharma, Muhammad Kamal, Candra Sari Djati Kartika

Proceedings Volume 12977, 129771Q (2024) https://doi.org/10.1117/12.3009661

KEYWORDS: Vegetation, Biomass, Biological research, Data modeling, Carbon, Image processing, Remote sensing, Near infrared, Ecosystems, Spatial resolution

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Proceedings Article | 29 January 2024 Paper

Canopy cover mapping in Ratai Bay mangrove forests using airborne LiDAR data

Mulyanto M., Muhammad Kamal, Muhammad Sufwandika Wijaya

Proceedings Volume 12977, 129771R (2024) https://doi.org/10.1117/12.3009670

KEYWORDS: LIDAR, Data modeling, Associative arrays, Error analysis, Vegetation, Point clouds, Statistical analysis, Data acquisition, Accuracy assessment, Remote sensing

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Proceedings Article | 22 December 2021 Paper

Identification of paddy fields based on temporal phase using Spectral Angle Mapper (SAM) on Landsat-8 OLI

Mohammad Naufal Fathoni, Gelanggoro Kismo Anintika, Dariin Firda, Pronika Kricella, Prima Widayani, Sanjiwana Arjakusuma, Muhammad Kamal

Proceedings Volume 12082, 120820B (2021) https://doi.org/10.1117/12.2619435

KEYWORDS: Reflectivity, Earth observing sensors, Landsat, Visualization, Remote sensing, Vegetation, Agriculture, Mid-IR, Image classification, Spatial resolution

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Proceedings Article | 22 December 2021 Paper

An object-based approach for vegetation and non-vegetation discrimination using WorldView-2 image

Syafiq Muhammad Ridha, Muhammad Kamal

Proceedings Volume 12082, 120820U (2021) https://doi.org/10.1117/12.2619373

KEYWORDS: Vegetation, Accuracy assessment, Image classification, Image segmentation, Image processing, Spatial resolution, Remote sensing, Image processing algorithms and systems, Visualization, Atmospheric corrections

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Proceedings Article | 22 December 2021 Paper

Aboveground mangrove carbon stock mapping using WorldView-2 imagery

Panji Mahyatar, Muhammad Kamal

Proceedings Volume 12082, 120820R (2021) https://doi.org/10.1117/12.2619342

KEYWORDS: Carbon, Vegetation, Data modeling, Ecosystems, Statistical modeling, Remote sensing

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Proceedings Article | 24 December 2019 Paper

Comparison of object spectral reflectance from WorldView-2 image and field measurement

Zulfikri Isnaen, Muhammad Kamal, Denny Wijaya Kusuma

Proceedings Volume 11372, 113721U (2019) https://doi.org/10.1117/12.2541768

KEYWORDS: Reflectivity, Near infrared, Spectroscopy, Vegetation, Remote sensing, Satellites, Radiometric corrections, Satellite imaging, Earth observing sensors, Image processing

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Proceedings Article | 24 December 2019 Paper

Object-based approach for agricultural vegetation mapping using WorldView-2: a case study in part of Dieng Plateau, Central Java

Siti Martha Uly Br Sinaga, Muhammad Kamal

Proceedings Volume 11372, 113720L (2019) https://doi.org/10.1117/12.2541865

KEYWORDS: Vegetation, Agriculture, Image segmentation, Visualization, Java, Image classification, Remote sensing, Image processing algorithms and systems, Geographic information systems, Digital imaging

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Proceedings Article | 24 December 2019 Paper

Geographic object-based image analysis (GEOBIA) of Landsat 8 OLI for landform identification

Assyria Fahsya Umela, Sigit Heru Murti Budi Santosa, Muhammad Kamal

Proceedings Volume 11372, 113720C (2019) https://doi.org/10.1117/12.2541806

KEYWORDS: Image segmentation, Image processing, Image analysis, Landsat, Image classification, Remote sensing, Image fusion

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Proceedings Article | 21 November 2019 Open Access

Downscaling land surface temperature on multi-scale image for drought monitoring

A. Sediyo Adi Nugraha, Totok Gunawan, Muhammad Kamal

Proceedings Volume 11311, 113110A (2019) https://doi.org/10.1117/12.2544550

KEYWORDS: Earth observing sensors, Landsat, MODIS, Principal component analysis, Remote sensing, Temperature metrology, Clouds, Algorithm development, Image processing, Spatial resolution

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Proceedings Article | 21 November 2019 Open Access

Image segmentation for vegetation types extraction using WorldView-2: a case study in parts of Dieng Plateau, Central Java

Siti Martha Uly Br Sinaga, Muhammad Kamal

Proceedings Volume 11311, 113110R (2019) https://doi.org/10.1117/12.2543480

KEYWORDS: Image segmentation, Vegetation, Agriculture, Visualization, Java, Image processing algorithms and systems, Image fusion, Algorithm development, Surface plasmons, Image visualization

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Proceedings Article | 24 October 2018 Paper

Machine-learning regression for coral reef percentage cover mapping

Pramaditya Wicaksono, Wahyu Lazuardi, Afif Al Hadi, Muhammad Kamal

Proceedings Volume 10778, 107780F (2018) https://doi.org/10.1117/12.2324028

KEYWORDS: Image quality, Remote sensing, Machine learning, Signal to noise ratio, Spatial resolution, Principal component analysis

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Proceedings Article | 9 October 2018 Paper

Combining field and image spectral reflectance for mangrove species identification and mapping using WorldView-2 image

Muhammad Kamal, Muhammad U. Ningam, Finni Alqorina, Pramaditya Wicaksono, Sigit Murti

Proceedings Volume 10790, 107901P (2018) https://doi.org/10.1117/12.2325629

KEYWORDS: Reflectivity, Spectroscopy, Remote sensing, Image processing, Near infrared, Calibration, Spectral resolution, Sensors, Analytical research, Vegetation

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Proceedings Article | 2 November 2017 Presentation + Paper

Spectral response of healthy and damaged leaves of tropical seagrass Enhalus acoroides, Thalassia hemprichii, and Cymodocea rotundata

Pramaditya Wicaksono, Muhammad Kamal

Proceedings Volume 10421, 104210L (2017) https://doi.org/10.1117/12.2278027

KEYWORDS: Remote sensing, Ecosystems, Spectroscopy, Reflectivity, Hyperspectral imaging

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Proceedings Article | 5 October 2017 Paper

Explicit area-based accuracy assessment for mangrove tree crown delineation using Geographic Object-Based Image Analysis (GEOBIA)

Muhammad Kamal, Kasper Johansen

Proceedings Volume 10428, 104280I (2017) https://doi.org/10.1117/12.2278179

KEYWORDS: Accuracy assessment, Image analysis, Image segmentation, Ecosystems, Associative arrays, Shape analysis, Visualization, Image resolution, Photography, Environmental monitoring

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Effective mangrove management requires spatially explicit information of mangrove tree crown map as a basis for ecosystem diversity study and health assessment. Accuracy assessment is an integral part of any mapping activities to measure the effectiveness of the classification approach. In geographic object-based image analysis (GEOBIA) the assessment of the geometric accuracy (shape, symmetry and location) of the created image objects from image segmentation is required. In this study we used an explicit area-based accuracy assessment to measure the degree of similarity between the results of the classification and reference data from different aspects, including overall quality (OQ), user’s accuracy (UA), producer’s accuracy (PA) and overall accuracy (OA). We developed a rule set to delineate the mangrove tree crown using WorldView-2 pan-sharpened image. The reference map was obtained by visual delineation of the mangrove tree crowns boundaries form a very high-spatial resolution aerial photograph (7.5cm pixel size). Ten random points with a 10 m radius circular buffer were created to calculate the area-based accuracy assessment. The resulting circular polygons were used to clip both the classified image objects and reference map for area comparisons. In this case, the area-based accuracy assessment resulted 64% and 68% for the OQ and OA, respectively. The overall quality of the calculation results shows the class-related area accuracy; which is the area of correctly classified as tree crowns was 64% out of the total area of tree crowns. On the other hand, the overall accuracy of 68% was calculated as the percentage of all correctly classified classes (tree crowns and canopy gaps) in comparison to the total class area (an entire image). Overall, the area-based accuracy assessment was simple to implement and easy to interpret. It also shows explicitly the omission and commission error variations of object boundary delineation with colour coded polygons.

Showing 5 of 14 publications

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