Landsat-8: Science and product vision for terrestrial global change research

Roy, David P.; Wulder, Michael A.; Loveland, Thomas R.; Woodcock, Curtis E.; Allen, Richard G.; Anderson, Martha C.; Helder, Dennis L.; Irons, James R.; Johnson, David M.; Kennedy, Robert E.; Scambos, T. A.; Schaaf, Crystal B.; Schott, John R.; Sheng, Yongwei; Vermote, Éric; Belward, Alan; Bindschadler, Robert; Cohen, Warren B.; Gao, Feng; Hipple, James D.; Hostert, Patrick; Huntington, Justin L.; Justice, Christopher O.; Kiliç, Ayşe; Kovalskyy, V.; Lee, Z. P.; Lymburner, Leo; Masek, Jeffrey G.; McCorkel, Joel; Shuai, Yanmin; Trezza, Ricardo; Vogelmann, James E.; Wynne, Randolph H.; Zhu, Zhe

doi:10.1016/j.rse.2014.02.001

Cited by 1,738 publications

(912 citation statements)

References 147 publications

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“…Landsat is among the most widely used satellite remote-sensing data, and its long-term continuous availability, cost-effectiveness, and timeliness made it invaluable resource for monitoring LULC change. General technical information on Landsat data has been presented in vast literature (Chander, Markham, & Helder, 2009;Markham & Helder, 2012;Modica et al, 2016;Roy et al, 2014 (2015) images were collected from the official website of US Geological survey (USGS). The details of remote-sensing data used in this study are listed in Table 1.…”

Section: Image Pre-processing and Lulc Classificationmentioning

confidence: 99%

Spatio-temporal urban growth dynamics of Lagos Metropolitan Region of Nigeria based on Hybrid methods for LULC modeling and prediction

Wang

Maduako

2018

European Journal of Remote Sensing

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An accurate information on the amount and location of Land use and land cover (LULC) changes is necessary to develop and implement a sustainable-urban planning.This research investigates the potential of an integrated Multi-Layer Perceptron and Markov Chain Analysis (MLP-MCA) to map and accurately predict the future LULC change scenarios in Lagos Metropolitan Region of Nigeria. Multi-temporal LULC datasets derived from remotely sensed Landsat images from 1984, 2000 and 2015 were used for modeling, validation and prediction. Predicted LULC changes for 2030 and 2050 were performed based on the LULC map of 2015 using MLP-MCA method. The result reveals a significant expansion of built-up areas during the whole study period. Analysis of LULC distribution in Lagos metropolitan region shows that about 50% of urban land expansion happened beyond the administrative boundary of Lagos State during the period of 2000-2015. It is predicted that more than 75% of future urban growth will occur across the border of Lagos State, in the neighbouring Ogun State by 2050. These results imply that a strong and consistent collaboration between different states is crucial to establish an effective regional planning framework and ensure a proper planned growth of the metropolitan region.ARTICLE HISTORY

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Section: Image Pre-processing and Lulc Classificationmentioning

confidence: 99%

Spatio-temporal urban growth dynamics of Lagos Metropolitan Region of Nigeria based on Hybrid methods for LULC modeling and prediction

Wang

Maduako

2018

European Journal of Remote Sensing

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“…Landsat 8 (Operational Land Imager, OLI) records nine spectral bands with a ground sampling distance (GSD) of 30 m [63] (Table 1). RapidEye records the spectral reflectance in five spectral bands at a GSD of 6.5 m [50]:…”

Section: Satellite Imagerymentioning

confidence: 99%

Self-Guided Segmentation and Classification of Multi-Temporal Landsat 8 Images for Crop Type Mapping in Southeastern Brazil

et al. 2015

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Only well-chosen segmentation parameters ensure optimum results of object-based image analysis (OBIA). Manually defining suitable parameter sets can be a time-consuming approach, not necessarily leading to optimum results; the subjectivity of the manual approach is also obvious. For this reason, in supervised segmentation as proposed by Stefanski et al. (2013) one integrates the segmentation and classification tasks. The segmentation is optimized directly with respect to the subsequent classification. In this contribution, we build on this work and developed a fully autonomous workflow for supervised object-based classification, combining image segmentation and random forest (RF) classification. Starting from a fixed set of randomly selected and manually interpreted training samples, suitable segmentation parameters are automatically identified. A sub-tropical study site located in São Paulo State (Brazil) was used to evaluate the OPEN ACCESSRemote Sens. 2015, 7 14483proposed approach. Two multi-temporal Landsat 8 image mosaics were used as input (from August 2013 and January 2014) together with training samples from field visits and VHR (RapidEye) photo-interpretation. Using four test sites of 15 × 15 km 2 with manually interpreted crops as independent validation samples, we demonstrate that the approach leads to robust classification results. On these samples (pixel wise, n ≈ 1 million) an overall accuracy (OA) of 80% could be reached while classifying five classes: sugarcane, soybean, cassava, peanut and others. We found that the overall accuracy obtained from the four test sites was only marginally lower compared to the out-of-bag OA obtained from the training samples. Amongst the five classes, sugarcane and soybean were classified best, while cassava and peanut were often misclassified due to similarity in the spatio-temporal feature space and high within-class variabilities. Interestingly, misclassified pixels were in most cases correctly identified through the RF classification margin, which is produced as a by-product to the classification map.

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“…Landsat 8 was launched in 2013, and its Operational Land Imager (OLI) provides high quality multispectral images at the resolution of 30 metres (15 for panchromatic) and a revisiting time of 16 days [2,3]. It aims to provide data continuity to the Landsat Earth observation programme, started in the 1970s.…”

Section: Introductionmentioning

confidence: 99%

Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use

2016

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Abstract:The availability of new generation multispectral sensors of the Landsat 8 and Sentinel-2 satellite platforms offers unprecedented opportunities for long-term high-frequency monitoring applications. The present letter aims at highlighting some potentials and challenges deriving from the spectral and spatial characteristics of the two instruments. Some comparisons between corresponding bands and band combinations were performed on the basis of different datasets: the first consists of a set of simulated images derived from a hyperspectral Hyperion image, the other five consist instead of pairs of real images (Landsat 8 and Sentinel-2A) acquired on the same date, over five areas. Results point out that in most cases the two sensors can be well combined; however, some issues arise regarding near-infrared bands when Sentinel-2 data are combined with both Landsat 8 and older Landsat images.

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Landsat-8: Science and product vision for terrestrial global change research

Cited by 1,738 publications

References 147 publications

Spatio-temporal urban growth dynamics of Lagos Metropolitan Region of Nigeria based on Hybrid methods for LULC modeling and prediction

Spatio-temporal urban growth dynamics of Lagos Metropolitan Region of Nigeria based on Hybrid methods for LULC modeling and prediction

Self-Guided Segmentation and Classification of Multi-Temporal Landsat 8 Images for Crop Type Mapping in Southeastern Brazil

Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use

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