8-Band Image Data Processing of the Worldview-2 Satellite in a Wide Area of Applications

Tarantino, Cristina; Adamo, Maria; Pasquariello, G.; Lovergine, Francesco P.; Blonda, P.; Tomaselli, Venera

doi:10.5772/27499

Cited by 13 publications

(6 citation statements)

References 10 publications

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“…Each of the WV2 and WV3 panchromatic and multispectral Digital Number (DN) images was converted to Top of Atmosphere (TOA) radiance based on radiometric calibration parameters [20] and standard correction formula [21]. For each band, surface reflectance was generated using Second Simulation of a Satellite Signal in the Solar Spectrum Vector (6SV) [22] radiative transfer algorithm based on a sensor spectral response function and specified atmospheric condition.…”

Section: Data Preprocessingmentioning

confidence: 99%

Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images

et al. 2015

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Urban tree species mapping is an important prerequisite to understanding the value of urban vegetation in ecological services. In this study, we explored the potential of bi-temporal WorldView-2 (WV2, acquired on 14 September 2012) and WorldView-3 images (WV3, acquired on 18 October 2014) for identifying five dominant urban tree species with the object-based Support Vector Machine (SVM) and Random Forest (RF) methods. Two study areas in Beijing, China, Capital Normal University (CNU) and Beijing Normal University (BNU), representing the typical urban environment, were evaluated. Three classification schemes-classification based solely on WV2; WV3; and bi-temporal WV2 and WV3 images-were examined. Our study showed that the single-date image did not produce satisfying classification results as both producer and user accuracies of tree species were relatively low (44.7%-82.5%), whereas those derived from bi-temporal images were on average 10.7% higher. In addition, the overall accuracy increased substantially (9.7%-20.2% for the CNU area and 4.7%-12% for BNU). A thorough analysis concluded that near-infrared 2, red-edge and green bands are always more important than the other bands to classification, and spectral features always contribute more than textural features. Our results also showed that the scattered distribution of trees and a more complex surrounding environment reduced classification accuracy. Comparisons between SVM and RF classifiers suggested that SVM is more effective for urban tree species classification as it outperforms RF when working with a smaller amount and imbalanced distribution of samples.

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Section: Data Preprocessingmentioning

confidence: 99%

Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images

et al. 2015

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“…This distribution is likely related to the vegetation which is high in chlorophyll and includes both shrubs and graminoids. WorldView‐2 bands 1 and 2 (400 to 450 nm and 450 to 510 nm) have been documented to improve classification of bright green vegetation data in other studies [ Tarantino et al , ].…”

Section: Discussionmentioning

confidence: 98%

Remote sensing of water tracks

Trochim

Prakash

Kane

et al. 2016

Earth and Space Science

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Water tracks are an intrinsic part of the surficial drainage network in the foothills of the Brooks Range, Alaska. They preferentially transport water off hillslopes and represent the interplay between hydrology, vegetation, geomorphology, and permafrost characteristics. This research on mapping the location of water tracks builds on previous work which demonstrated that different types of water tracks exist due to difference primarily driven by geomorphology. We used a combination method where spectral classifications, texture, and topography were fed into random forests to identify the water track classes. The most accurate distributions were obtained for the organic-rich and wide water track classes. The distinct linear shapes of the water tracks could also be visualized for many of the classes, especially in areas where the water tracks were particularly discrete. The biggest challenges to mapping the water tracks were due to class imbalances and high variability within and overlapping between classes. This research presents a significant step forward in understanding periglacial landscape dynamics.

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“…Kekurangan dari data Worldview-2 yaitu data citra yang dihasilkan masih termasuk data komersil sehingga untuk mendapatkan data ini adalah dengan membeli diprovidernya. Beberapa permasalahan terjadi ketika terjadi perubahan kebijakan yaitu tidak mengijinkan pengiriman data ke Indonesia sehingga perlu orang ketiga, atau perantara untuk mengadakan transaksi data (Tarantino et al, 2012).…”

Section: Metode Ndviunclassified

Pemetaan Vegetasi Hutan Mangrove Menggunakan Metode Normalized Difference Vegetation Index (NDVI) di Desa Arakan, Minahasa Selatan, Sulawesi Utara. SOIJST Vol. 1 (2):211-222

Philiani¹

2018

Preprint

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AbstrakKecamatan Tatapaan di Minahasa Utara memiliki hutan mangrove seluas 8.736.000 m 2 . Salah satu desa di Kecamatan Tatapaan adalah Arakan. Penelitian ini bertujuan untuk memetakan kerapatan mangrove di Desa Arakan dan menentukan hasil Normalized Difference Vegetation Index (NDVI) terbaik dari band-band yang digunakan. Metode (NDVI) memperhitungkan besaran nilai kehijauan vegetasi yang diperoleh dari pengolahan sinyal digital data nilai kecerahan beberapa kanal data sensor satelit dari citra satelit. NDVI mengukur slope antara nilai asli band merah dan band infra merah di angkasa dengan nilai yang ada dalam tiap piksel citra. Citra yang digunakan adalah citra satelit WorldView2 perekaman tanggal 19 Juni 2014. Berdasarkan kombinasi band-band yang digunakan, hasil NDVI terbaik adalah kombinasi band Red dan NIR 2 dengan nilai tingkat penyimpangan kesalahan terkecil, yaitu 0,3. Tingkat kerapatan dari yang terluas adalah kelas rapat (824.566,01m2 ), kelas sedang (133.622,41m2 ), kelas jarang (12.004,92m 2 ), kelas sangat jarang (10.494,23m 2 ), dan yang terkecil pada kelas sangat rapat (24,45m 2 ). PendahuluanHutan mangrove adalah tipe hutan yang tersusun dari berbagai jenis mangrove yang hidup di daerah pertemuan antara sungai dan laut. Mangrove tersebar di sepanjang pantai di daerah tropik dan subtropik yang terlindung dari gerakan gelombang dan angin atau daerah belakang terumbu karang.

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8-Band Image Data Processing of the Worldview-2 Satellite in a Wide Area of Applications

Cited by 13 publications

References 10 publications

Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images

Object-Based Urban Tree Species Classification Using Bi-Temporal WorldView-2 and WorldView-3 Images

Remote sensing of water tracks

Pemetaan Vegetasi Hutan Mangrove Menggunakan Metode Normalized Difference Vegetation Index (NDVI) di Desa Arakan, Minahasa Selatan, Sulawesi Utara. SOIJST Vol. 1 (2):211-222

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