Exploiting big earth data from space – first experiences with the timescan processing chain

Esch, Thomas; Üreyen, Soner; Zeidler, Julian; Metz-Marconcini, Annekatrin; Hirner, Andreas; Asamer, Hubert; Tum, Markus; Böttcher, Martin; Kuchař, Štěpán; Svaton, Vaclav; Marconcini, Mattia

doi:10.1080/20964471.2018.1433790

Cited by 35 publications

(28 citation statements)

References 30 publications

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“…Accordingly, the core idea is to compute and analyse for each pixel the temporal maximum of the Normalised Difference Vegetation Index (NDVI), which depicts the status at the peak of the phenological cycle. To this purpose, the NDVI available from the TimeScan dataset [40,49] has been used, which has been derived globally from Landsat-8 scenes acquired during 2014-2015. Figure 1 shows a subset of the WSF-2015-Density layer for Toluca state in Mexico.…”

Section: Wsf-2015-density Layermentioning

confidence: 99%

New Perspectives for Mapping Global Population Distribution Using World Settlement Footprint Products

et al. 2019

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In the production of gridded population maps, remotely sensed, human settlement datasets rank among the most important geographical factors to estimate population densities and distributions at regional and global scales. Within this context, the German Aerospace Centre (DLR) has developed a new suite of global layers, which accurately describe the built-up environment and its characteristics at high spatial resolution: (i) the World Settlement Footprint 2015 layer (WSF-2015), a binary settlement mask; and (ii) the experimental World Settlement Footprint Density 2015 layer (WSF-2015-Density), representing the percentage of impervious surface. This research systematically compares the effectiveness of both layers for producing population distribution maps through a dasymetric mapping approach in nine low-, middle-, and highly urbanised countries. Results indicate that the WSF-2015-Density layer can produce population distribution maps with higher qualitative and quantitative accuracies in comparison to the already established binary approach, especially in those countries where a good percentage of building structures have been identified within the rural areas. Moreover, our results suggest that population distribution accuracies could substantially improve through the dynamic preselection of the input layers and the correct parameterisation of the Settlement Size Complexity (SSC) index.

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Section: Wsf-2015-density Layermentioning

confidence: 99%

New Perspectives for Mapping Global Population Distribution Using World Settlement Footprint Products

et al. 2019

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“…To evaluate the predictive value of C-band SAR imagery in the temporal dimension, we acquired images to create datasets representing the dry season (15 images between June 2017 and November 2017), the rainy season (11 images from May 2017 and between December 2017 and March 2018) ( Table 2) and all year (dry season and rainy season images combined). The temporal statistics of mean, maximum (max), minimum (min), standard deviation (std), and coefficient of variation (cv) of these Sentinel-1 composites were calculated and named as dry season timescan, rainy season timescan, and Annual timescan [47]. Sentinel-1 SAR images were pre-processed using Sentinel Application Platform tool (SNAP).…”

Section: Sentinel-1mentioning

confidence: 99%

Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Mauya

Koskinen

Tegel

et al. 2019

Forests

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Remotely sensed assisted forest inventory has emerged in the past decade as a robust and cost efficient method for generating accurate information on forest biophysical parameters. The launching and public access of ALOS PALSAR-2, Sentinel-1 (SAR), and Sentinel-2 together with the associated open-source software, has further increased the opportunity for application of remotely sensed data in forest inventories. In this study, we evaluated the ability of ALOS PALSAR-2, Sentinel-1 (SAR) and Sentinel-2 and their combinations to predict growing stock volume in small-scale forest plantations of Tanzania. The effects of two variable extraction approaches (i.e., centroid and weighted mean), seasonality (i.e., rainy and dry), and tree species on the prediction accuracy of growing stock volume when using each of the three remotely sensed data were also investigated. Statistical models relating growing stock volume and remotely sensed predictor variables at the plot-level were fitted using multiple linear regression. The models were evaluated using the k-fold cross validation and judged based on the relative root mean square error values (RMSEr). The results showed that: Sentinel-2 (RMSEr = 42.03% and pseudo − R2 = 0.63) and the combination of Sentinel-1 and Sentinel-2 (RMSEr = 46.98% and pseudo − R2 = 0.52), had better performance in predicting growing stock volume, as compared to Sentinel-1 (RMSEr = 59.48% and pseudo − R2 = 0.18) alone. Models fitted with variables extracted from the weighted mean approach, turned out to have relatively lower RMSEr % values, as compared to centroid approaches. Sentinel-2 rainy season based models had slightly smaller RMSEr values, as compared to dry season based models. Dense time series (i.e., annual) data resulted to the models with relatively lower RMSEr values, as compared to seasonal based models when using variables extracted from the weighted mean approach. For the centroid approach there was no notable difference between the models fitted using dense time series versus rain season based predictor variables. Stratifications based on tree species resulted into lower RMSEr values for Pinus patula tree species, as compared to other tree species. Finally, our study concluded that combination of Sentinel-1&2 as well as the use Sentinel-2 alone can be considered for remote-sensing assisted forest inventory in the small-scale plantation forests of Tanzania. Further studies on the effect of field plot size, stratification and statistical methods on the prediction accuracy are recommended.

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“…Here, the actual acquisition dates of the single scenes in fact frequently differ by several years while still showing local data gaps due to cloud coverage. Nevertheless, with the TimeScan approach, Esch et al [38] have recently introduced a methodology based on multitemporal data collections that helps to compensate for this limiting factor in multispectral data. Indeed, a global TimeScan layer derived from >450.000 Landsat images acquired within a two-year period was already successfully used as correction layer for the false alarms identification and removal in the context of the GUF post-processing (see Section 2.1.6).…”

Section: Discussionmentioning

confidence: 99%

“…Two of them, TimeScan-ASAR (DLR-TSA) and TimeScan-Landsat (DLR-TSL), are GeoTIFF in float formatting [38], and the others (OSM-Settlements, OSM-Roads, GL30-Settlements, DLR-ReliefMap, DLR-RoadCluster, CIL, and NLCD) are binary masks derived from defined thresholds or specific classes of selected source data sets, namely Open Street Map, GL30, SRTM/ASTER, Copernicus Imperviousness Layer, and US National Land Cover Dataset. The reference layers are merged by summing up the number of positive reference counts: a value of 1 representing a built-up area is assigned if at least two out of seven binary masks are positive; otherwise, it is discarded and set to 0.…”

Section: Automated Post-editingmentioning

confidence: 99%

Where We Live—A Summary of the Achievements and Planned Evolution of the Global Urban Footprint

et al. 2018

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Abstract:The TerraSAR-X (TSX) mission provides a distinguished collection of high resolution satellite images that shows great promise for a global monitoring of human settlements. Hence, the German Aerospace Center (DLR) has developed the Urban Footprint Processor (UFP) that represents an operational framework for the mapping of built-up areas based on a mass processing and analysis of TSX imagery. The UFP includes functionalities for data management, feature extraction, unsupervised classification, mosaicking, and post-editing. Based on >180.000 TSX StripMap scenes, the UFP was used in 2016 to derive a global map of human presence on Earth in a so far unique spatial resolution of 12 m per grid cell: the Global Urban Footprint (GUF). This work provides a comprehensive summary of the major achievements related to the Global Urban Footprint initiative, with dedicated sections focusing on aspects such as UFP methodology, basic product characteristics (specification, accuracy, global figures on urbanization derived from GUF), the user community, and the already initiated future roadmap of follow-on activities and products. The active community of >250 institutions already working with the GUF data documents the relevance and suitability of the GUF initiative and the underlying high-resolution SAR imagery with respect to the provision of key information on the human presence on earth and the global human settlements properties and patterns, respectively.

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Exploiting big earth data from space – first experiences with the timescan processing chain

Cited by 35 publications

References 30 publications

New Perspectives for Mapping Global Population Distribution Using World Settlement Footprint Products

New Perspectives for Mapping Global Population Distribution Using World Settlement Footprint Products

Modelling and Predicting the Growing Stock Volume in Small-Scale Plantation Forests of Tanzania Using Multi-Sensor Image Synergy

Where We Live—A Summary of the Achievements and Planned Evolution of the Global Urban Footprint

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