Staff Publications

Staff Publications

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    'Staff publications' is the digital repository of Wageningen University & Research

    'Staff publications' contains references to publications authored by Wageningen University staff from 1976 onward.

    Publications authored by the staff of the Research Institutes are available from 1995 onwards.

    Full text documents are added when available. The database is updated daily and currently holds about 240,000 items, of which 72,000 in open access.

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Recent Advances in Forest Observation with Visual Interpretation of Very High-Resolution Imagery
Schepaschenko, Dmitry ; See, Linda ; Lesiv, Myroslava ; Bastin, Jean-François ; Mollicone, Danilo ; Tsendbazar, Nandin-Erdene ; Bastin, Lucy ; McCallum, Ian ; Laso Bayas, Juan Carlos ; Baklanov, Artem ; Perger, Christoph ; Dürauer, Martina ; Fritz, Steffen - \ 2019
Surveys in Geophysics 40 (2019)4. - ISSN 0169-3298 - p. 839 - 862.
The land area covered by freely available very high-resolution (VHR) imagery has grown dramatically over recent years, which has considerable relevance for forest observation and monitoring. For example, it is possible to recognize and extract a number of features related to forest type, forest management, degradation and disturbance using VHR imagery. Moreover, time series of medium-to-high-resolution imagery such as MODIS, Landsat or Sentinel has allowed for monitoring of parameters related to forest cover change. Although automatic classification is used regularly to monitor forests using medium-resolution imagery, VHR imagery and changes in web-based technology have opened up new possibilities for the role of visual interpretation in forest observation. Visual interpretation of VHR is typically employed to provide training and/or validation data for other remote sensing-based techniques or to derive statistics directly on forest cover/forest cover change over large regions. Hence, this paper reviews the state of the art in tools designed for visual interpretation of VHR, including Geo-Wiki, LACO-Wiki and Collect Earth as well as issues related to interpretation of VHR imagery and approaches to quality assurance. We have also listed a number of success stories where visual interpretation plays a crucial role, including a global forest mask harmonized with FAO FRA country statistics; estimation of dryland forest area; quantification of deforestation; national reporting to the UNFCCC; and drivers of forest change.
Change Detection in Satellite Image Time Series for Continuous Land Cover Map Updating
Masiliunas, D. ; Tsendbazar, N.E. ; Herold, M. ; Lesiv, Myroslava ; Verbesselt, J. - \ 2019
Land cover monitoring is an integral part of land management. Many stakeholders, such as land owners, governments, NGOs and international organisations are interested in keeping track of changes on the ground, so that these changes could be managed. This can be done using land cover maps, however, most land cover maps are only produced for a single date. For example, the GLC2000 product was produced with the intention of representing the global land cover in the year 2000. Yet, the target of land cover monitoring is the land cover change over time, which is an indicator of ongoing processes on the ground.

One way to express land cover change is by producing multiple single-date maps, and analysing the differences between them. However, if the probability that the pixel belongs to a certain class is close between two classes, then even a small change in model input data may cause the prediction to shift to a different land cover class.

In this study, we propose to update land cover maps by performing change detection on time series of satellite imagery. If there is an abrupt change in seasonal pattern in the time series of a pixel, then a land cover class transition is likely to have occurred at that location. Only areas with detected change need to be updated for producing the next iteration of the map, making it more consistent over time, and taking less computational effort to reclassify the map. In addition, performing change detection allows us to avoid extracting temporal features for classification from periods of change. If no land cover change happened, then it is possible to use a longer history period for extracting temporal features, therefore increasing the robustness and accuracy of the classification model (see Figure 1). However, a limitation of using change detection for map updating is that we assume that the original land cover map is accurate and can be used as a basemap for updating, which may not always be the case.

To see if this method is viable and scalable, we compared the performance of three time series break detection algorithms: strucchange::breakpoints, BFAST Monitor and annual t-test. The algorithms were run over the time series of vegetation indices derived from MODIS 250 m data over the entire continent of Africa. A computer cluster hosted by VITO was used for this big data processing task. This was done in the context of the Copernicus Global Land System: Dynamic Land Cover (CGLS-LC100) land cover project.

In order to validate the algorithm performance, the CGLS-LC100 project is collecting reference points from Africa. Land cover change is identified through image interpretation of very high resolution imagery, as well as Sentinel-2 images and NDVI profiles. Once the reference data is collected, algorithm performance will be assessed using confusion matrix statistics, such as sensitivity, specificity, false positive rate, likelihood ratio for positive tests and positive predictive value.

Preliminary results from West African drylands show that land cover change is an uncommon phenomenon, as only 3.4% of the 1010 reference points show change. All of the tested algorithms tended to overestimate the detected change in this region. The annual t-test algorithm was the fastest, but it does not provide any temporal metrics, only whether there was a break in time series over a chosen year. In contrast, strucchange::breakpoints provides the estimated day of year when a break in the time series is detected. BFAST Monitor is in between, in that it gives a rough estimate of when the break occurred within a particular year.

Once additional reference data about land cover change is collected, we will investigate options to further tune the algorithms to detect land cover change, so that it would improve the classification accuracy of the final land cover map for a given year. This will also reduce the data amounts that need reprocessing each year to produce an updated map. Limiting the amount of data to process is becoming more important, as land cover maps move into higher spatial resolution, e.g. 20 m resolution based on Sentinel-2 imagery.

The framework set in this study is independent of a particular satellite sensor, and so the findings are applicable to all land cover maps that are in an operational phase, i.e. are regularly updated. Due to the scalability of the approach, the methods have the potential to improve the accuracy of both local-scale and global-scale land cover classification products, as well as make them easier to update.
Conflation of expert and crowd reference data to validate global binary thematic maps
Waldner, François ; Schucknecht, Anne ; Lesiv, Myroslava ; Gallego, Javier ; See, Linda ; Pérez-Hoyos, Ana ; andrimont, Raphaël D'; Maet, Thomas De; Bayas, Juan Carlos Laso ; Fritz, Steffen ; Leo, Olivier ; Kerdiles, Hervé ; Díez, Mónica ; Tricht, Kristof Van; Gilliams, Sven ; Shelestov, Andrii ; Lavreniuk, Mykola ; Simões, Margareth ; Ferraz, Rodrigo ; Bellón, Beatriz ; Bégué, Agnès ; Hazeu, Gerard ; Stonacek, Vaclav ; Kolomaznik, Jan ; Misurec, Jan ; Verón, Santiago R. ; Abelleyra, Diego De; Plotnikov, Dmitry ; Mingyong, Li ; Singha, Mrinal ; Patil, Prashant ; Zhang, Miao ; Defourny, Pierre - \ 2019
Remote Sensing of Environment 221 (2019). - ISSN 0034-4257 - p. 235 - 246.
With the unprecedented availability of satellite data and the rise of global binary maps, the collection of shared reference data sets should be fostered to allow systematic product benchmarking and validation. Authoritative global reference data are generally collected by experts with regional knowledge through photo-interpretation. During the last decade, crowdsourcing has emerged as an attractive alternative for rapid and relatively cheap data collection, beckoning the increasingly relevant question: can these two data sources be combined to validate thematic maps? In this article, we compared expert and crowd data and assessed their relative agreement for cropland identification, a land cover class often reported as difficult to map. Results indicate that observations from experts and volunteers could be partially conflated provided that several consistency checks are performed. We propose that conflation, i.e., replacement and augmentation of expert observations by crowdsourced observations, should be carried out both at the sampling and data analytics levels. The latter allows to evaluate the reliability of crowdsourced observations and to decide whether they should be conflated or discarded. We demonstrate that the standard deviation of crowdsourced contributions is a simple yet robust indicator of reliability which can effectively inform conflation. Following this criterion, we found that 70% of the expert observations could be crowdsourced with little to no effect on accuracy estimates, allowing a strategic reallocation of the spared expert effort to increase the reliability of the remaining 30% at no additional cost. Finally, we provide a collection of evidence-based recommendations for future hybrid reference data collection campaigns.
Developing and applying a multi-purpose land cover validation dataset for Africa
Tsendbazar, N.E. ; Herold, M. ; Bruin, S. de; Lesiv, M. ; Fritz, S. ; De Kerchove, R. Van; Buchhorn, M. ; Duerauer, M. ; Szantoi, Z. ; Pekel, J.F. - \ 2018
Remote Sensing of Environment 219 (2018). - ISSN 0034-4257 - p. 298 - 309.
The production of global land cover products has accelerated significantly over the past decade thanks to the availability of higher spatial and temporal resolution satellite data and increased computation capabilities. The quality of these products should be assessed according to internationally promoted requirements e.g., by the Committee on Earth Observation Systems-Working Group on Calibration and Validation (CEOS-WGCV) and updated accuracy should be provided with new releases (Stage-4 validation). Providing updated accuracies for the yearly maps would require considerable effort for collecting validation datasets. To save time and effort on data collection, validation datasets should be designed to suit multiple map assessments and should be easily adjustable for a timely validation of new releases of land cover products. This study introduces a validation dataset aimed to facilitate multi-purpose assessments and its applicability is demonstrated in three different assessments focusing on validating discrete and fractional land cover maps, map comparison and user-oriented map assessments. The validation dataset is generated primarily to validate the newly released 100 m spatial resolution land cover product from the Copernicus Global Land Service (CGLS-LC100). The validation dataset includes 3617 sample sites in Africa based on stratified sampling. Each site corresponds to an area of 100 m × 100 m. Within site, reference land cover information was collected at 100 subpixels of 10 m × 10 m allowing the land cover information to be suitable for different resolution and legends. Firstly, using this dataset, we validated both the discrete and fractional land cover layers of the CGLS-LC100 product. The CGLS-LC100 discrete map was found to have an overall accuracy of 74.6 ± 2.1% (at 95% confidence level) for the African continent. Fraction cover products were found to have mean absolute errors of 9.3, 8.8, 16.2, and 6.5% for trees, shrubs, herbaceous vegetation and bare ground, respectively. Secondly, for user-oriented map assessment, we assessed the accuracy of the CGLS-LC100 map from four user groups' perspectives (forest monitoring, crop monitoring, biodiversity and climate modelling). Overall accuracies for these perspectives vary between 73.7% ± 2.1% and 93.5% ± 0.9%, depending on the land cover classes of interest. Thirdly, for map comparison, we assessed the accuracy of the Globeland30-2010 map at 30 m spatial resolution. Using the subpixel level validation data, we derived 15,252 sample pixels at 30 m spatial resolution. Based on these sample pixels, the overall accuracy of the Globeland30-2010 map was found to be 66.6 ± 2.4% for Africa. The three assessments exemplify the applicability of multi-purpose validation datasets which are recommended to increase map validation efficiency and consistency. Assessments of subsequent yearly maps can be conducted by augmenting or updating the dataset with sample sites in identified change areas.
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