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ISBN 978-90-8585-894-2

Prof. dr M. HeroldInaugural lecture upon taking up the post of Professor of Geo-information Science with emphasis on Remote Sensing at Wageningen University on 8 September 2011

Remote Sensing Science 2.0

Remote sensing science provides

underpinnings for monitoring and

understanding of our changing

world. The Web 2.0 and sensor webs,

open satellite data and Google Earth

are democratizing the way society

and science interact; creating new

collective intelligence and ways for

shared decision making. Remote

sensing research is now interfacing

with open science, social networking,

citizen science and observatories,

location-based information services,

and societal demands for a self-aware

Earth. What are topics and inter-

disciplinary opportunities of remote

sensing science 2.0? How can citizens

more directly benefit and engage in

Earth observations?

10229_Omslag oratie Martin Herold.indd 1 4-8-2011 15:48:31

Prof. dr M. Herold

Inaugural lecture upon taking up the post of Professor of Geo-information Science with emphasis on Remote Sensing at Wageningen University on 8 September 2011


10229_binnenwerk_Oratie Martin Herold.indd 1 23-11-2011 10:03:09

Prof. dr M. Herold Remote Sensing Science 2.0

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ISBN 978-90-8585-894-2


Wageningen University

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Remote sensing science is a well-established discipline at Wageningen Univer-sity and its evolution connected to names like Martien Molenaar, Steven De Jong, Michael Schaepman, and Jan Clevers. Wherever I have been in my academic career, the remote sensing research at Wageningen University was always known, recogni-zed and valued. I would like to open this inaugural address by thanking Wagenin-gen University for maintaining long-term focus on this scientific field and for putting thrust in somebody in his mid-thirties to take on the chair of Geoinforma-tion Science with emphasis on Remote Sensing. I feel privileged for being asked to continue the good work Wageningen University is known for, but also to tackle new avenues to further develop our field of remote sensing science. So my aim today is to explore one novel area the remote sensing research community is starting to get active in.

During the last decade, I performed remote sensing research in Germany, in the US and since 2010 in the Netherlands. For example, to help to create global land cover maps such as the GLOBCOVER map (Figure 1). This map builds upon 2009 satellite data of the MERIS instrument on-board the European environmental flagship satellite ENVISAT with about 300 m spatial resolution. About 20 Terabytes of raw image data were processed, so that in less than 10 month after the last images has been acquired, the map was openly released at the end of 2010. In the first 3 months after release, this global map was downloaded more than 50.000 times from the website of the European Space Agency (ESA). Global land datasets are now created regularly from remote sensing and have a large audience from


Esteemed Rector Magnificus, dear family, friends, colleagues and collaborators, PhD students and candidates, ladies and gentlemen.



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different institutional and thematic sectors, including the research community but also governmental and commercial sector; which shows that such maps are both a science and an application subject. We are currently working on the next genera-tion global land cover datasets (Bontemps et al., 2011) responding to the need of the United Framework Convention on Climate Change for monitoring Essential Climate Variables (Herold et al., 2009a). This effort requires the processing and analysis of about 160 Terabytes of raw remote sensing data. One of the fundamen-tal advantages of remote sensing is the ability to cover large areas in great spatial and temporal detail, and provide information in consistent and transparent manner so one can compare land surfaces worldwide. Another key issue is the opportunity to potentially challenge map accuracy since the underlying original observation data are also still available even years after map production and can be reprocessed.

As you note from the map, country boundaries do not matter a lot for such remote sensing analysis; and for me as a globalized individual the same is in fact true for my academic career. I am officially still an immigrant to the Netherlands, but in reality I do not feel like one. However, I feel like an immigrant of a different

Figure 1: Global land cover map (GLOBCOVER 2009) and table with distribution of key user organizations



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type. I may be among the younger chair holders at Wageningen University but I am too old to be considered to what has been called a ‘digital native’ (defined by Prensky, 2001a, 2001b) since I did not grow up using the internet having to learn the language and concepts of the 2.0 world.

While the divide between digital natives and the aging generation of digital immigrants is declining (Prensky, 2009), the question remains on how such developments impact the world of science and higher education. The ‘World 2.0’ is information-driven and constant growth and update in information and its open exchange are fundamental. It is also known that about 80 % of all information is geospatially referenced (Franklin & Hane, 1992) and that the vast majority of information are captured visually (Ware, 2004). Remote sensing delivers both spatial and visual information and an increasing array of data and information that are also up-to-date, global, and commonly accessible. Remote sensing is a key 2.0 tool and the widespread use of Google Earth is a known starting point. But the interesting questions for science are much broader and fundamental and relate to Science 2.0, the role of remote sensing and societal interactions and networking as part of it.

How is Science 2.0 evolving?The idea of Science 2.0 started in the middle of the last decade alongside the

evolution of the Web 2.0. The Web 2.0 is about social networking and interaction, and harnessing collective intelligence driven by humans (as users and observers) and increasingly by sensors. I am sure some of you have already noticed active sensors in this room right now. Prominent Web 2.0 platforms include Facebook, Wikipedia and the various Google services. Such networks or applications become better the more people use them and when they learn from user input and contributions (O’Reilly & Battelle, 2009).

Science 2.0 is evolving around these developments and may completely change the way society and science interacts and impacts each other. Science 2.0 can help to bring life and sciences much closer together. There is great potential if, for example, scientific data and results are openly available, discussed, further develo-ped and more directly influencing decisions and choices of people through social networking. In return, scientific progress can benefit from building upon the



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collective knowledge, concerns, observations and feedback of (potentially) all Web 2.0 users and citizens. Following the ladder of citizen participation of Arnstein’s (1969), Science 2.0 helps to move from informing and consultation to more partnership, to delegated power and citizen control.

Shneiderman (2008) proposed Science 2.0 as an expansion to traditional scientific methods; while guiding strategies of traditional science are still needed, such as hypothesis testing, predictive models or needs for validity and transparency. Others think more radical (such as Anderson, 2008) and proclaim that in age of petabytes and with sensors everywhere, infinite storage, clouds of processors, we can analyse the data without hypotheses about what it might show. Our empirical basis to study the world grows tremendously and the aim is just to find patterns. In the era of big data, more isn’t just more. More is different (Anderson, 2008).

Over the years different Science 2.0 approaches and topics have been explored and implemented (Table 1). Although a significant number of scientists have been exposed or have engaged in one or the other issue, it is fair to say the ideas around Science 2.0 have not created the expected major impacts. There are of course reservations of the traditional science community and some of the proposed novel methods compare well with ones used in ecology or social sciences (Yoder, 2008). In contrast, some of the methods to use the collective intelligence of social networking are yet to be developed and tested. Key required competencies includes the analysis from unstructured data to structured datasets in social networking and citizen-acquired data (i.e. to study and analyse patterns), real-time capabilities for analysis and response, and the need to build upon input by humans and sensors col-lectively (O’Reilly & Battelle, 2009). Thus, in its first years of existence, Science 2.0 has not really evolved to a significant and renowned research field with only limited impacts to report on the initial objectives. While the topics shown in Table 1 are still valid, it is expected that some may not continue and some may further develop but just take more time and learning by doing such as the case for the developments around Open Science.



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Science 2.0 topics Key motivations Comment on status and risksOpen science data It is crucial that science data

and tools are made freely and openly available for an effective functioning of science and for society to obtain the full benefits.

A set of ‘Panton Principles’ have recently been defined for this purpose (Murray-Rust et al., 2010). Tools like Google Earth have stimulated more data to become available, but common concerns remain such as misuse or commercial use of public data and tools, and how updates and uncertainties are incorporated.

Open science collaboration

Scientific work improves while using tools provided by social networks, wikis and forums.

Existing platforms such as Science 2.0 or Research Gate are operating but limited to selective communities.

Open access and discussions

Provision of unrestricted online access to scientific publications and means to interact and further develop them and add value.

Open access journals are common. Science blogging and science social networking has largely failed as business model and remain a niche activity. Proper peer-review remains fundamen-tal.

Synthesizing scientific knowledge

Harvesting and meta-analysis of large digital databases and bibliographies to answer broader scientific questions.

Some platforms (i.e. Mendeley), analysis methods and numerous scientific studies have been developed and published in different fields. Outcomes and impacts vary.

Researching socio-technical systems

Need to study Web 2.0 activities by rigorous observations (successes and failures), interventions (changing interfaces and privacy rules), or ambitious data collection (analysing all public data and activities)

The provision and analysis of large empirical data is just starting. Computer Science is one the leading fields in this. Use of personal data can be critical. Many Web 2.0 data are owned by large cooperation’s that harvest them.

Citizen science and observers

Integrating citizens in scientific work by them adding observations, local knowledge and networking.

Some examples for specific areas (i.e. Open Street Maps) but success depends on the (continued) motivation and interest of the citizens in science. So far little consideration of society-driven topics.

Table 1: Some topics for Science 2.0 and related issues, status and risks (adapted and synthesized after Crotty, 2011; Gaggioli, 2011; Shneiderman, 2008).



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Where are the Science 2.0 opportunities?Looking at start-up lessons, it becomes clearer where the key Science 2.0

opportunities are. The interaction of science and society needs to be more mutually beneficial. Science 2.0 should be a two-way road with more consideration of society’s expectations to science and the related cyber-infrastructures (Poore, 2011). Citizens participate and contribute to social networks and, for that matter, to scientific work if they feel motivated. What are some of the motivations?1) Personal and locational details: There is a fundamental interest on where

things are, how and why things are the way they are. For example: Looking at Google Earth, what is the size of my garden compared to my neighbours? How can I get to the best Food valley lunch when I visit Wageningen?

2) Self-aware world: The notion of the self-aware world is stimulated by the criticism on restricted data, and the related need for transparency in informa-tion, and independent data and observations for specific general topics. Prominent issues include environmental problems such as tropical deforesta-tion, health issues, openness and participation in political decision processes, or updated information on disasters worldwide.

3) Delta-driven information priorities: information on dynamics and change are commonly more interesting than stability. So the interest in the “delta”; where and why things are different is intriguing to many people and relates to historical changes, real-time information on events or expected future develop-ments.

4) Specialized science interests: There are citizens and communities that have interest in specific topics (i.e. bird watching, physics experiments) and could lead to Science 2.0 in smaller, user-created and controlled networks that are much less driven by the cooperate-backed science networking tools available today (Crotty, 2011).

Science 2.0 will have a higher chance to grow in areas that are of particular interest to society and in this sense will be selective in topics and approaches. I believe that remote sensing has a lot to offer in that regard and to make the case I will next describe some of the related developments in the field of remote sensing science.



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Remote sensing science: an updateRemote sensing science provides comprehensive and unique observations and

information on the functioning and behaviour of land-ocean-atmosphere systems with global coverage and local detail. It is progressing as its own scientific field researching satellite, airborne and in-situ sensor technologies, fundamentals of the imaging and measurement process, and developing techniques to process observa-tion data and transform them into information. Remote sensing science engages in interdisciplinary research integrating observations, theories and modelling of natural systems behaviour, human activities and impacts, and developing commer-cial monitoring and assessment services.

In 2010, the International Society for Photogrammetry and Remote Sensing (ISPRS) held its annual Symposium in Vienna where the society was founded in 1910 entitled: “100 Years ISPRS - Advancing Remote Sensing Science”. The listing of the topics provides an overview of common remote sensing research issues:• Multi-spectralandhyperspectralremotesensing• Microwaveremotesensing• LIDARandlaserscanning• Geometricmodelling• Physicalmodellingandsignatures• Changedetectionandprocessmodelling• Landcoverclassification• Imageprocessingandpatternrecognition• Datafusionanddataassimilation• Earthobservationprogrammes• (Operational)remotesensingapplications

The list emphasizes that sensor-driven approaches, signal processing and image analysis, and developing fields of application are generally still in the core of remote sensing scientist’s interest.

In the Netherlands, the Earth Observation community came into existence with the establishment of NIWARS in 1971. This group of researchers focused on fundamental remote sensing research of the land and sea surface, and had a strong



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contribution from ‘Wageningen’. The National Remote Sensing Programme (NRSP) was set-up in 1986, focusing on fundamental research, operational and commercial applications and services. The research investments in the 80s and 90s of the previous century have led to an extensive growth of scientific research, applications and of the value-adding sector for land applications in the Nether-lands; including several companies in Wageningen such as WaterWatch (now called Eleaf ) and Sarvision that are well known internationally. This is a good example how funding of scientific research at a significant level is still crucial for our knowledge economy. This dedicated research programme supporting land surface remote sensing science basically stopped in 2000 (Bunnik, 2008).

Novel developments for land remote sensingWhile the established remote sensing science field is fundamentally growing,

there are developments that are of particular interest to the 2.0 topic discussed here:

a) The increasing need for land observation data and productsWhile land (cover) and change observations are not operational (in a weather

forecasting sense), there is increasing need for such data to be provided in a global, consistent, and transparent manner. The Group on Earth Observation (GEO) is a high-level political Earth Observation process with annual ministerial summits. It puts particular emphasis to improve global land cover, forest and land use observa-tions (Herold et al., 2008). This also includes the UN-Conventions on climate change, biodiversity, to combat desertification and Ramsar that require informa-tion from research and systematic observations (Overpeck et al., 2011, Pereira et al., 2010) but increasingly for the formulation and implementation of international and national policies (i.e. such as those of the post-Kyoto agreement under negotiation or the 2020 targets of the biodiversity convention). As an example, the UN Food and Agriculture Organization (FAO) is currently completing the first global forest change remote sensing survey using historical satellite data for 1990, 2000, 2005 and 2010 to paint the first consistent picture on global deforestation in addition to the national level reporting, that is often in-consistent and in-complete reporting. Results are expected later this year.



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The need for land observation is further advocated by several research commu-nities. The climate science field for example aims to integrate research communities working on the physical basis of climate change and those on adaptation, impacts and vulnerabilities. This is in line with the increasing integration among the three IPCC working groups developing the reports for the physical basis, climate change mitigation and adaptation (Hibbard et al., 2010). The land part is the key overlap among them, and increasing availability and quality in land observation can act as catalyst to stimulate more integrated climate science. b) Open access data availability and processing capabilities

Since a few years the remote sensing entered a new era with increasingly free and open access to observation data. While very-high resolution data remain widely commercial, the moderate resolution domain such as Landsat, MODIS or a series of European sensors are now available free of charge. There is not a lack of satellite observations but a lack of coordination and making the data from historical and available space assets more openly available. Technical issues with processing such large amounts of data are declining due to availability of cloud computing capabili-ties. Thus, the limited access to suitable remote sensing data that has been a major limitation to land monitoring in the past are steadily disappearing.

c) European leadership in Global Monitoring for Environment and Security (GMES)Besides Galileo, GMES (Global Monitoring for Environment and Security)

builds European leadership in the space sector and earth observations. Investments of about 3 billion Euros are made since almost 10 years now to develop the space segment of at least 3 pairs of Sentinel satellites for land monitoring that will provide an unprecedented observation data stream with a free-and-open data policy. These missions are funded from operational budgets and aim on consistency and continuity to address the needs of Earth Observation services and less so scientific novelties. In parallel, significant investments are made to develop a service portfolio mainly on the European level building upon the GMES satellite assets. A number of European policies will now be implemented with the help of operational satellite observations. This also offers great opportunities for the Dutch Earth Observation value-adding sector and for developing science and service capacities.



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d) Time-series analysis and monitoring of dynamics and changesThe remote sensing focus has been traditionally on creating maps. There is a

growing interest in information on change and dynamics. Such type of analysis becomes increasingly feasible with longer and denser time-series observation data available openly. For example, the new GMES Sentinels do not provide major improvements in terms of spatial detail and the spectral domain but on temporal revisit and continuity, and thus allow for improved assessments of changes and dynamics. Time series analysis is an active and critical field of remote sensing science. Figure 2 shows an example from the Netherlands with the vegetation index (NDVI) temporal profiles for several land cover and land use types emphasizing differences in the magnitude and temporal signature of the phenological cycles. While common remote sensing sensors detect land cover as primary observation variable, such time series data will help to move towards the assessments of land use and changes.

Figure 2: Examples of representative remote sensing time series for different land types in the Netherlands (Roerink & Danes, 2010)



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Consistent global remote sensing time series go back until the early 80ies as shown for the AVHRR time-series data. Th ese data since 1981 allow for global analysis of trends in phenological dynamics (Figure 3, De Jong and De Bruin, 2011); areas in green indicate a signifi cant trend in increasing length of the growing season – areas in red show a decreasing signifi cant trend. Th ere are however trade-off s between spatial and temporal detail provided by diff erent sensor types. Th e global analysis was performed using 8x8 km data and attribution of observed change with on the ground processes remains an issue. Th is is because land surface variations are driven by many factors including seasonality, short-term climate variations or human disturbances. Th us, the full complexity of change and dynamics need to be considered to focus the analysis on specifi c type of change processes. Th e BFAST algorithm is being developed for that purpose to analyse both trends and events in vegetation patterns (Verbesselt et al., 2010a, Verbesselt et al., 2010b) and to also detect normal and abnormal time series behaviour in near real-time. Decomposing the time-series signal helps to defi ne the stable and regular dynamics as shown in Figure 4: highlighting the original MODIS NDVI time series, the harmonic component (refl ecting regular phenology) and a trend break visible in the time series around the year 2005/06 – the time when the WU Forum

Figure : Global trend in changes of growing season length fr om AVHRR time series data (De Jong et al., 2011)



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building was constructed. In the future, such analysis can be extended to the 10-30 m scale data as time-series become denser and longer, and entering the scale where many of the human-induced change processes are operating.

Detecting change is one primary objective but to identify and label the type of change or quantify the impact is more challenging and requires more fundamental understanding and advanced analysis tools. Th e link between what is happening on the ground and what is observed from the satellite is not straightforward; which links to the issue of integrated land monitoring.

e) Sensor technologies and integrated land monitoringTh e integrated and synergistic use of diff erent observation data sources is

another aspect of remote sensing research. Combining advantages of diff erent data sources in terms of the temporal, spatial and thematic detail is essential in particular

Figure : Example of the BFAST time series analysis approach using 250 m MODIS data 2000-2011 for the area of the forum building of Wageningen University



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when it comes to analysis of dynamics and change, and for the quantitative estima-tion of land surface parameters. The schematic diagram in Figure 5 shows the situation for global vegetation monitoring, the dense time series sensors in green provide only coarser spatial resolution, Landsat-type data that allow the study of many land change processes in blue and the ground data domain in red.

The in-situ and ground data domain is currently preventing significant remote sensing progress. In fact, the need for robust ground data is increasing the more quantitative and detailed remote sensing analysis become. This is probably the largest data gap today for remote sensing science. The problem is that even “operational satel-lite” missions such as those from GMES do not include a proper ground reference network for calibration and validation of products. In case ground data are available (i.e. from inventories) they often do not provide the temporal and thematic informa-tion needed to be combined with remote sensing analysis.

This is realized by the remote sensing science community and there are now serious efforts to move towards more integrated land monitoring with solid under-pinnings and robust ground-data component. When it comes to the analysis of

Figure 5: Conceptual diagram for integrated land observations (Herold et al., 2008)



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change we still require fundamental understanding since many kinds of changes and dynamics are commonly ongoing in parallel. Figure 5 highlights an example of studying the effect of water stress on Tamarugo forests in the Atacama desert requires understanding on how such processes affect the spectral response. Changes in the plants are both structural as shown for the diurnal cycle of planophile and eroctiphile leaf conditions, and the changes in leaf area index, and in terms of leaf conditions (water content). To analyse such spectra of change we can use the plant facility at Wageningen University (Clevers et al., 2011) that allows research on the anisotropic reflectance and emittance behaviour under laboratory conditions, and appropriate radiative transfer models – in this case with good match between measured and modelled spectra.

The use of terrestrial LIDAR or Laser scanning has also proven to be a compre-hensive ground data reference source for remote sensing analysis. Commonly used for visualization and design purposes, it provides a full hemispherical representa-tion of vegetation and canopy characteristics. The three-dimensional representation of forests and relevant radiative transfer variables allows for a better integration on what is measured from satellites (which is largely a canopy signal). It also provides

Figure 6: Example for studying “spectra of change” of the effect of water stress on Tamarugo forests in the Atacama desert (Chávez and Clevers, 2011)



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suitable reference data on variables such as biomass by measuring the volume of trees, and leaf area index and gap fraction by measuring the foliage (Calders et al., 2011). Both the plant facility and the LIDAR system are expected to stimulate more cooperation and interdisciplinary research with other more in-situ oriented monitoring communities.

Another example is ongoing research to link remote sensing and sensor webs. An ongoing experiment in the South of the Netherlands explores approaches to link tractor-mounted sensors and remote sensing datasets with the objective to combine the spatial, temporal and thematic details they provide for precision farming purposes (Thessler et al., 2011).

Citizen observers also play an increasing role in the improvement of remote sensing studies through providing local information and knowledge. Citizens often do not provide measurements of high thematic precision but even simple observati-ons of the geo-location of events, overall quantity, and of good temporal detail can already provide useful information. Examples of such data include the Nature-calendar or phenology networks, biodiversity observing groups, and others that particular signalling or labelling changes in the landscape. The link between what is observed from space and on the ground is not straightforward and thus a subject to active research.

A near-real time data stream could particularly stimulate citizen participation. Such a prototype system exists using MODIS satellite data and is running for the area of Wageningen (Figure 7). Since May 2011, two main areas have been signalled with abnormal behaviour in the vegetation dynamics. Both are related to construc-tion sites (Niewe Koort Noord in the west, and constructions around WU building 119 in the North) and clearly visible as anomaly in the time series signal (shown in red in the time series graph of Figure 7). But it is the local knowledge that is critical to understand and value observations coming from space. The case also shows the opportunity how a near-real time monitor could be implemented, and together with a proper platform, can allow for active citizen participation and input. The example is for Wageningen but the underlying remote sensing time series data are available with global coverage.



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Remote sensing and Science 2.0 – Examples for monitoring deforestationThese examples highlight how remote sensing science field is particular relevant

for stimulating societal interactions and Science 2.0, in particular given: • Theneedsforuptodateinformationonlandchangeisgrowing,i.e.asadvoca-

ted by the societal benefit areas of GEO, but the direct interaction and use with society is only initial (through platforms such as Google Earth),

• Theincreasingamountoffree-and-openremotesensingdatasetsforthepastand in the future (i.e. coming from the GMES) allows synoptic, more consistent and transparent global view on the land surface and related changes on a detailed local level,

• Thatcitizenobservernetworkscanhaveanimportantroleinbridgingtheground data gap.

I will now use the case of monitoring deforestation to describe remote sensing and Science 2.0 opportunities in more detail. Deforestation is a worldwide issue

Figure 7: Detection of “abnormal” vegetation behaviour for the Wageningen area using MODIS data and the BFAST algorithm



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since both the drivers causing forest loss and the resulting impacts are related to both local and global scales. There is a value (i.e. ecosystem services, livelihoods, commercial) in stabilizing or increasing the available forest resources and this issue has been subject to societal discourse and engagement. Recently, the negotiations of the post-Kyoto climate agreement emphasize on the increasing role of develo-ping countries in reducing carbon emissions from deforestation and forest degradation (REDD, UNFCCC, 2010). While international and national REDD policies and ways to compensate developing countries for their efforts are taking shape, the need for robust and transparent monitoring, reporting and verification is essential. Many developing countries need to develop further monitoring capacities (Herold, 2009b, see Figure 8).

Remote sensing is one of the key data sources to fill existing gaps since it provides readily available datasets to compare historical and future rates of deforestation (DeFries et al., 2007). Developing countries, international donors supporting REDD activities, local implementers and NGO’s, and the interested public in general putting high expectations to satellite monitoring to help to fill this gap. Remote sensing is the transparent and consistent source for large area assessments and both the observation data and information products can be made available and reprocessed also for historical periods. So how can this be done also building upon 2.0 ideas?

Figure 8: Distribution of the capacity of developing countries to monitor forests and carbon stock changes on the national level for REDD (Herold, 2009b)



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a) Google Earth EngineGoogle.org (the technology and philanthropy driven arm of google.com)

recently established the Earth Engine that provides a Science 2.0 framework for forest monitoring. Google.org is providing the large parts of historical Landsat archive (currently more than 1 Million individual images and growing), a rapid prototyping system for scientists and analysts to implement algorithms and upload own data, and a global-scale algorithm processing centre to implement mapping and monitoring tasks (earthengine.googlelabs.com). All processing and analysis is performed in the Google-cloud processing system. Results can be created in real-time and displayed, distributed and discussed. The idea for Google.org is not to do global monitoring or scientific algorithm research, it is to provide the platform. It is simple enough and also accessible to people with limited remote sensing knowledge. The Google Earth Engine reduces some of the complicating issues in using remote sensing for a wider audience such as the easy access to processed worldwide satellite data, to advanced scientific algorithms, and to create maps in short-time for a specific area. For scientists, the platform is suitable to implement large processing tasks and perform large dataset analysis that would have not been possible otherwise (BFAST example). The Earth Engine is still a prototype but can become an important Remote Sensing Science 2.0 asset.

b) Near real-time deforestation assessmentsThe remote sensing monitoring objective for REDD is not only to map

deforestation (area) but support policy formulation and implementation. This focus is more on tracking human activities to also understand why and how forests are changing and the fate of the land and disturbance history through time series analysis. This includes approaches to detect negative developments such as active deforestation in near-real time to help to enforce policies and take countermeasures for example in the case of active fires or illegal activities. The Brazilian PRODES system provides annual Landsat-based deforestation area estimates for the whole Amazon (MCTI, 2011a) and has proven a decline in annual deforestation since 2004. The system is complemented by a near real-time monitoring system based on 250 m MODIS data (MCTI, 2011b) signalling when forest change is happening with all information publically available on the internet. Such data are then used for enforcement agencies to take action. It also commonly stimulates public debate



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in the media for example this year when deforestation was noted to be up again after years of decline. Such rapid assessment data also provide the platforms for environmental organizations and local community observation networks to feed in their on the ground data on where deforestation is occurring. This helps not only to tune and improve the remote sensing analysis but also to help the local organizati-ons on where to aim their activities.

c) Participatory forest monitoringREDD is a national level mechanism but can only have an effect if implementa-

tion activities work on the local scale. The issue to engage local partners and communities in forest monitoring has been put forward as approach to create ownership and responsibility, support implementation, and make monitoring more efficient. It has been shown that local communities are able to track forest change activities (location, area and type of deforestation and by whom) and provide simple measurements that relate to forest carbon (i.e. counting trees or estimate diameter at breast height supported by handheld devices, Danielsen et al., 2011; GOFC-GOLD, 2010). Still, such local-scale measurements need to be verified and feed into a national reporting system. Remote sensing approaches can support this by providing an independent data source for the verification of local measurements, and provide the framework to link the community acquired data to national monitoring and estimation and, thus, provides a good example how citizen acquired local data and remote sensing analysis can add value to each other when used in synergy.

Remote Sensing Science 2.0 topicsThe examples for monitoring deforestation showcase how combining remote

sensing and Science 2.0 ideas can work in practice. There are some subsequent and emerging topics that reflect research needs for a Remote Sensing Science 2.0:

a) Advance traditional remote sensing science as fundamentThere is no successful remote sensing science 2.0 (RSS 2.0) without continued

fundamental remote sensing research. Approaches and expertise for processing and analysis need to be constantly developed and updated with evolving technology, new sensors, new research, and growing interests from society and users. Other-



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wise, platforms like the Google Earth Engine or near-real time monitoring systems would not exist or be properly maintained. Algorithms useful for Science 2.0 activities by nature should be openly published, open source and subject to public debate. The same is true for available remote sensing and ground data. There can of course be algorithms or data that are copyrighted or subject to other restrictions but their value for RSS 2.0 will be limited.

One particular need for remote sensing science is to improve the physical underpinnings. In societal debates and conflicting interests, the remote sensing science community needs to provide explanations not only that specific features or changes are observed from remote sensing but also why and, thus, address ambigui-ties and limitations. In that context, physically-based analysis approaches are more robust than empirical ones and, there is need for fundamental remote sensing research if the initial RSS 2.0 approaches are proven successful and more topics or more details need to be addressed. In this sense, successful RSS 2.0 increases the need for fundamental remote sensing work.

b) Studying and improving RSS 2.0 approachesSpecific efforts should be focused on improving remote sensing science 2.0 tools

and methods. For example, interactions with citizen observers using Twitter for monitoring have been demonstrated (De Longueville et al., 2009) but it is important to understand what approaches and data are useful for a specific purpose and why. This includes (i) studying the existing ones to learn and further advance them; (ii) develop tools to integrate different data sources, in particular those coming from citizen observers, sensor webs and social networks and how they can be better integrated; (iii) explore new topics (i.e. beyond deforestation) and monitoring approaches to enter the Science 2.0 world – it is expected that this process should be largely driven by societal needs and knowing that such priorities change, and (iv) stimulate interdisciplinary work with Geographic Information Science and Computer Science to further develop Science 2.0 networks and interaction platforms.



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c) Addressing and managing the conflation of information and incompatible dataIncreasing opportunities and information streams with Google Earth Engine

and near real-time monitoring approaches will be created and basically everyone can create his own map with ease. Based on the same observation but multiple mapping algorithms, different maps can be created for the same area and easily creates information overload and conflation of information if the outcomes do not agree. The same is true if satellite and ground data or available official datasets do not coincide. For example, running a change analysis using automated methods implemented in Google Earth Engine may provide indications of forest change but still reflect a data driven analysis. Definitions of what constitutes a forest or deforestation may vary depending on the application or accounting framework. Simple remote sensing approaches can often not easily reflect that and thus the mapping outcomes become incomplete or incompatible. Such differences may result in critical debates and require expert moderation and mediation. The science community will have an important role in this context to provide clarification and validation. Similar to administrator system implemented for Wikipedia, expert networks or advisory panels can take on this role. These networks do exist in some instances for the case of global monitoring where international collaboration and consensus building has been already been a central topic. Global Observations of Forest Cover and Land Dynamics (GOFC-GOLD) is such an expert panel of the UN Global Terrestrial Observing System working on the issue of deforestation. It can be expected that these expert networks will play an important role in maturing of RSS 2.0.

d) Research on social interactions and spatial thinkingRSS 2.0 and the related societal process should be researched as social interac-

tion process and in the context of the evolving field of spatial thinking. Interesting questions are what kind of information are being derived and used by which community? Who is most interested and active and what is influencing the sustainability of such engagements and contributions? Which information has most impact and influence decision forming or even on policy formulation and implementation processes? Such studies should involve the social science fields.



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e) Understanding and monitoring of limitations and new potentialsThe whole Science 2.0 field is still in evolution and neither all potentials nor the

limitations have been fully explored. The same is true for the remote sensing science role and there are critical issues that should be considered. Remote sensing science 2.0 still has to become reality and will include some trial and error approaches; including that some ideas and concepts will not succeed. One key measure of success is the acceptance and level of interaction with specific communities and society that help to drive the process forward. As an additional problem, new data sources may create too much detailed and personal-related information. An example case is the establishment of Google Street View in Germany that has resulted in significant opposition by citizens and detailed information had to be blanked out. 244,000 households in the first 20 cities to be placed online submitted requests to have their homes blurred out. Understanding and monitoring these limitations will be an important factor for the overall success of Science 2.0 approaches. There is also the question about the next generation or the semantic Web (also called Web 3.0). 2.0 activities are about interaction and networking, while 3.0 is about sensor intelligence and a direct and automated response to measurements. Remote Sensing Science 2.0 contributes to a what can be called Digital Earth Nervous System (de Longueville et al., 2010) but 3.0 approaches are required to develop an Earth Sensing Skin that is also able to develop a direct response.

Figure 9: Driving forces for Remote Sensing Science 2.0 and potential life science topics of Wageningen University



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In summary of these Remote Sensing Science 2.0 topics I would like to reiterate the some of the issues I was trying to convene today (Figure 9). RSS 2.0 is driven by interactions and networking with citizens and society and their motivations and interest are instrumental; it is building upon Web 2.0 and Science 2.0 opportuni-ties and lessons learned in the last 5 years; and it benefits from the developments in Remote Sensing Science such as the processing large amount of open access data using time-series analysis in near-real time.

I understand Remote Sensing Science 2.0 as an add-on to the traditional remote sensing science that still remains fundamental but can significantly expand in areas that are of particular societal interest. Many of the basic ideas presented here for the case of deforestation may also apply for other monitoring issues such as habitats and biodiversity, precision agriculture, (green) urban development, disaster response, or for different types of environmental pollution where remote sensing analysis can help and improve when combined with interests and activities of society, social networks and local partners. Science 2.0 is generally very selective in terms of topics. I believe, however that many of these interesting topics to society are central to Wageningen University research.

Research Agenda and Approaches‘Science for Impact’ in the scope of ‘Life sciences’ is the ‘Leitbild’ of Wageningen

University. I believe that investing in Science 2.0 can bring science closer to life, increase its impact and, thus, benefit for its own good. Public participation and interaction with society is important to many research groups at Wageningen University. Science 2.0 can even further stimulate interaction and feedback mechanisms and our University is a good position to take a leadership role in this context. Potential Life Sciences 2.0 topics are manifold and remote sensing research can be a catalyst to underpin the interactions with society.

So please be aware that there is a Wageningen University remote sensing chair group aiming at fundamental research, integrative and interdisciplinary activities, and interactions and impacts with society. These are highlighted in our research priority areas:



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(1) Remote Sensing Science: with focus to advance the foundations for quantita-tive land remote sensing (using time series analysis, imaging spectroscopy and the study of plant biochemistry and material properties), and to improve in-situ data analysis for next generation remote sensing data and products.

(2) Integrated Land Monitoring: to develop novel approaches for the assessment of land dynamics on multiple scales and the integration of earth observation data and products in interdisciplinary research, models and applications.

(3) Global Context and Societal Benefits: concerns international scientific leader-ship and coordination in land monitoring, and for solutions and interactions with society and policy with earth observation based monitoring, reporting and verification.

These topics are addressed by our remote sensing team of 6 senior scientists and currently 15 PhD students, and with external support from several EU 7th Frame-work Program and Marie Curie projects, the European Space Agency, the Centre for International Forest Research (CIFOR), the Governments of Norway, Germany, and the United Kingdom - all of them are gratefully acknowledged at this point. We are a central part of the WUR Centre for Geoinformation and provide our share to the Centre’s overall objective on developing spatial competen-ces for a sustainable world. We work closely with the Alterra remote sensing team under the lead of Sander Mucher. Together our groups combine 35 remote sensing researchers working on land monitoring; which makes it a leading cluster of expertise in Europe and beyond.

Wageningen University and Research Centre provides excellent support in remote sensing science equipment. The Plant Facility (operated together with Plant Research International) is a key asset and used by researcher such as Jan Clevers and Harm Bartholomeus to study different types of surfaces, and changes and dynamics (for example vegetation water stress) to improve physical underpinnings for change analysis under experimental conditions. Our terrestrial hemispherical LIDAR system provides post-docs like Jan Verbesselt with ground reference data on LAI, gap fraction and biomass to support the monitoring of vegetation and carbon dynamics using and advancing time-series analysis approaches. Lammert Kooistra is performing research in the Netherlands to integrate in-situ sensor webs and remote



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sensing for precision agriculture and to progress in the integration of earth observa-tion data in interdisciplinary research and vegetation models. A particular focus is on the remote sensing monitoring approaches for tropical forests and REDD, where our group works in projects in Guyana, Vietnam, Ethiopia, Indonesia, and Brazil. Our post-doc Valerio Avitabile focuses on integration of ground observations and remote sensing data for biomass monitoring and carbon emission estimation from tropical forest changes.

On issues of global monitoring and societal benefits from Earth Observations our group is taking international scientific leadership in land monitoring by coordinating the activities of Global Observations of Forest Cover and Land Dynamics (GOFC-GOLD) as an expert panel of the UN Global Terrestrial Observing System. The work includes close cooperation with UN Conventions, the Group on Earth Observation (GEO) and scientists worldwide. The panel has provided consensus-based best practice methods for land cover validation (Strahler et al., 2006), independent harmonization mechanisms for land cover datasets (Herold et al., 2006) and a Sourcebook on methods REDD monitoring (GOFC-GOLD, 2010).

The work of GOFC-GOLD panel and the engagement in a series of globally relevant projects provide our foundation to engage in the Science 2.0 world. Although our current team is for the most part RSS 1.0-focused, it is an area we will be more active. When it comes to social interactions, sensor-webs and spatial thinking we are working closely with the Geographic Information Science chair group of Arnold Bregt. Here we are aiming to integrate well with research topics on spatial data infra-structures and sensors; and space, society and decision.

Other interdisciplinary research activities have also been initiated with other WU groups. Of particular interest is the evolving WUR network on the issue on REDD (www.redd.wur.nl). This network involves about 80 WUR researchers working on forest management, ecosystem services, environmental policy, and the remote sensing and carbon monitoring side active in many tropical countries with a focus on Guyana, Brazil and Suriname in Latin America, Cameroon and Ethiopia in Africa, and Indonesia and Vietnam in Asia (Figure 10). REDD is a dynamic and active



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process that have, not surprisingly, resulted in activities in many WUR research groups. The REDD network has materialized through initial INREF support and is active in specific activities such as regular exchange, a REDD-PhD exchange forum, and an evolving collaborative research agenda that should next year result in a larger INREF proposal.

The Dutch research and remote sensing arena is not completely new to me but I am still in the process of finding my way around. The Dutch National Space Office has been and will be an important partner to improve presence and networking of remote sensing science within the Netherlands. I look forward to jointly foster Dutch contributions to international activities such as the GEO forest observation initiative, and to perhaps stimulate a dedicated GMES national land monitoring program. While many other European countries are already investing into research and development for national land monitoring and related GMES national services; a program in the Netherlands is missing since the early 2000’s. As remote sensing scientist working in the Netherlands, it is worrying since lack of national investments into such innovation will hurt both the value-adding and the research sector, and eventually prevent that Dutch contributions into GMES space assets can not materialize for national service providers, researchers, applications and users.

Figure 10: Number of researchers of the REDD@WUR network active in specific developing countries



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Teaching and educationRemote sensing education is well established at Wageningen University but there is

need to adapt to changing boundary conditions. The number of digital natives we are educating on different levels is only increasing. They are used to receiving information really fast. They prefer their graphics before their text rather than the opposite. They thrive on instant gratification and frequent rewards. They prefer games to “serious” work (Prensky, 2001a), which directly impacts the role of educational technologies in today’s higher education institutions (Waycott et al., 2010).

Today I was aiming to convene the message that Remote Sensing Science 2.0 is about open data, sensors and real-time monitoring, about tracking changes, humans and interactions, and you maybe in the middle of it. As you can see in this animation (Figure 11), we are not short on observations even of today’s inaugural address which is also published on WUR.TV. These data were acquired using the LIDAR system that you can see to the right. The question is which information is most interesting? It is normal that people usually look at themselves first? But there may also be more general questions? How many people were in the room and who was her? We have performed repeated measurements so one could ask who came late, who moved or left early? Did somebody take a notably comfortable position or was even sleeping? If

Figure 11: Examples of the animated sequence presented during the lecture that was acquired with the terrestrial LIDAR in near-real time (http://www.youtube.com/watch?v=sllQOjMxTnw&feature=mfu_in_order&list=UL)



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we now arrange an interaction where people are interested to comment, contribute, add own observations and interact on this data; we would have an example on Remote Sensing 2.0. Also, we could study it as a scientific experiment linking what can be observed versus what type of social interaction and outcomes it has resulted in; we would have Remote Sensing Science 2.0 case.

Our body of students, in particular those interested in Geographic Information and Observation technologies, tend to appreciate these kinds of interactions as part of their higher education. Within our Master of Science in Geo-information Science we are also aiming to bring students closer to research activities. The terrestrial LIDAR together with the Plant Facility and other observation systems, are central tools for the new Advanced Earth Observation course that was offered this year for the first time.

It is also clear that remote sensing as a discipline is becoming more mainstream. Thus, for the fundamental remote sensing education I suggest a stronger role in the BSc-level education. It is important to realize that in the context of significant European investments in satellite assets and related operational services in GMES, the demand for remote sensing basic knowledge is growing in many applications and decision making Wageningen University is offering education on. Knowledge on basic methods and understanding in remote sensing and derived products are becoming more essential to a larger audience of students, and to society in general – considering that this is the key objective of RSS 2.0.

Words of gratitudeIn the tradition of inaugural lectures I would like to take this opportunity to issue

a series of thanks. I would not be standing here today without the guidance and the help of a large number of people. These people stood by my side, gave me encourage-ment in many different ways, and also made the right decisions at the right time. In that context, I thank the Rector Magnificus, the members of the Executive Board and the appointment committee for providing me the opportunity to come to Wagenin-gen University.



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I am extremely grateful to my scientific supervisors over many years including Christiane Schmullius, Volker Hochschild, Jack Estes, Keith Clarke, Dar Roberts, Helen Couclelis, John Townshend, Pierre Defourny and Tom Loveland. They have supported and guided me with their valuable suggestions, motivations, and fruitful discussions all along my research career.

I would like to thank our Wageningen team at the Centre of Geoinformation. Arnold Bregt, Jan Clevers, Lammert Kooistra, Harm Bartholomeus and the many other researchers, PhD students and staff, I am thankful about the way you accepted me in Wageningen and I look forward to fruitful collaborations in the future.

I would also like to express gratitude to my parents and my family. To my dad Lothar and Brigitta: I am happy that you can be with us today. Not here today is, understandably, our 8 month-old baby girl Louisa. Her birth in January this year is very much linked with our family move to Wageningen and not only in that context, my wife Nadine, has been a strong supporter for many years, has inspired and encouraged me in many ways, and, most importantly, makes our life most happy and enjoyable together.

Finally, I would like to acknowledge the PhD students and junior researchers in the room. Over the course of today the third Remote Sensing Symposium of the Dutch remote sensing PhD student network was held here in Wageningen with more than 80 participants. This by itself is a signal of a strong research community. You are the next generation of remote sensing scientists and I assume several of you would consider themselves as digital natives and very familiar with 2.0 world. So it is particularly up to you to carry the field of Remote Sensing Science 2.0 forward. I hope I was able to stimulate your interest today.

Meneer rector, dames en heren – Ik dank U allen voor Uw aandacht

Ik heb gezegd



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ReferencesAnderson C (2008) The End of Theory: The Data Deluge Makes the Scientific

Method Obsolete. Wired Magazine, 16.07, http://www.wired.com/science/discoveries/magazine/16-07/pb_theory. Retrieved: August 2011Arnstein S (1969) A Ladder Of Citizen Participation. Journal of the American

Institute of Planners, 35, 216-224.Bontemps S, Herold M, Kooistra L et al. (2011) Revisiting land cover observations

to address the needs of the climate modelling community. Biogeosciences Discussions, 8, 7713-7740.

Bunnik NJJ (2008) Ontwikkelingen in Nederland inzake Remote Sensing vanuit Satellieten. Ministerie van Verkeer en Waterstaat. 191 pp, ISBN 987-90-36917-04-9.

Calders K, Verbesselt J, Bartholomeus H, Herold M (2011) Applying terrestrial LiDAR to derive gap fraction distribution time series during bud break. In:SilviLaser 2011. Hobart, Tasmania.

Chávez R, Clevers JGPW (2011) Object-based analysis of 8-bands WorldView2 imagery for assessing health condition of desert trees. 8-Band Research Challenge. Digital Globe. Phase 2, 8.

Clevers JGPW, Bartholomeus HM, Schaepman ME et al. (2011) A new lab facility for measuring bidirectional reflectance/emittance distribution functions of soils and small canopies. In: 7th EARSeL Workshop of the Special Interest Group in Imaging Spectroscopy. Edinburgh, Scotland.

Crotty D (2011) Not With A Bang: The First Wave of Science 2.0 Slowly Whimpers to an End. From:

http://scholarlykitchen.sspnet.org/2011/04/27/not-with-a-bang-the-first-wave-of-science-2-0-slowly-fizzles-out/. Retrieved: August 2011

Danielsen F, Skutsch M, Burgess ND et al. (2011) At the heart of REDD+: a role for local people in monitoring forests? Conservation Letters, 4, 158-167.

De Longueville B, Annoni A, Schade S, Ostlaender N, Whitmore C (2010) Digital earth’s Nervous System for crisis events: Real-time Sensor Web Enablement of Volunteered Geographic Information. International Journal of Digital Earth, 3, 242-259.

De Longueville B, Smith RS, Luraschi G (2009) “OMG, from here, I can see the



33

flames!”: A use case of mining location based social networks to acquire spatio-temporal data on forest fires. In: GIS: Proceedings of the ACM Interna-tional Symposium on Advances in Geographic Information Systems. Seattle, WA.

Defries R, Achard F, Brown S, Herold M, Murdiyarso D, Schlamadinger B, De Souza Jr C (2007) Earth observations for estimating greenhouse gas emissions from deforestation in developing countries. Environmental Science and Policy, 10, 385-394.

Franklin C, Hane P (1992) An introduction to GIS: linking maps to databases. Database 15 (2), 17-22.

Gaggioli A (2011) Science 2.0. From: http://gaggio.blogspirit.com/archive/2011/05/21/science-2-0.html. Retrieved:

August 2011Gofc-Gold (2010) A sourcebook of methods and procedures for monitoring and

reporting anthropogenic greenhouse gas emissions and removals caused by deforestation, gains and losses of carbon stocks in forest remaining forests, and forestation. GOFC-GOLD Report version COP16-1 pusblished Nov. 2010. Available at www.gofc-gold.uni-jena.de/redd/

Herold M et al. (2009a) GTOS land cover ECV standardization document: Assessment of the status of the development of the standards for the Terres-trial Essential Climate Variables. Global Terrestrial Observing System. Available at: http://www.fao.org/gtos/doc/ECVs/T09/T09.pdf.

Herold M (2009b) An assessment of national forest monitoring capabilities in tropical non-Annex I countries: Recommendations for capacity building. Report for The Prince’s Rainforests Project and The Government of Norway.

Herold M, Latham JS, Di Gregorio A, Schmullius CC (2006) Evolving standards in land cover characterization. Journal of Land Use Science, 1, 157-168.

Herold M, Woodcock CE, Loveland TR, Townshend J, Brady M, Steenmans C, Schmullius CC (2008) Land-cover observations as part of a global earth observation system of systems (GEOSS): Progress, activities, and prospects. IEEE Systems Journal, 2, 414-423.

Hibbard K, Janetos A, Van Vuuren DP et al. (2010) Research priorities in land use and land-cover change for the Earth system and integrated assessment model-ling. International Journal of Climatology, 30, 2118-2128.

Jong, R. de; Bruin, S. de; Wit, A.J.W. de; Schaepman, M.E.; Dent, D.L. (2011)



34

Analysis of monotonic greening and browning trends from global NDVI time-series, Remote Sensing of Environment 115 (2). - p. 692 - 702.

MCTI (Ministério da Ciência, Tecnologia e Inovação) (2011a) Projeto PRODES: Monitoramento da floresta amazônica brasileira por satélite. From:

http://www.obt.inpe.br/prodes/. Retrieved: August 2011MCTI (Ministério da Ciência, Tecnologia e Inovação) (2011b) Sistema DETER.

From: http://www.obt.inpe.br/deter/index.html. Retrieved: August 2011O’Reilly T, Battelle J (2009) Web Squared: Web 2.0 Five Years On. Available at:

http://www.web2summit.com/web2009/public/schedule/detail/10194. Re-trieved August 2011.

Overpeck JT, Meehl GA, Bony S, Easterling DR (2011) Climate data challenges in the 21st century. Science, 331, 700-702.

Pereira HM, Leadley PW, Proença V et al. (2010) Scenarios for global biodiversity in the 21st century. Science, 330, 1496-1501.

Poore BS (2011) Users as essential contributors to spatial cyberinfrastructures. Proceedings of the National Academy of Sciences of the United States of America, 108, 5510-5515.

Prensky M (2001a) Digital natives, digital immigrants. On the Horizon 9 (5): 1-6. From: http://www.marcprensky.com/writing/Prensky%20-%20Digital%20Natives,%20Digital%20Immigrants%20-%20Part1.pdf. Retrieved: August 2011

Prensky M (2001b) Do they really think differently? On the Horizon, 9(6), 1-6. From: http://www.marcprensky.com/writing/Prensky%20-%20Digital%20Natives,%20Digital%20Immigrants%20-%20Part2.pdf. Retrieved: August 2011.

Prensky M (2009) Sapiens Digital: From Digital Immigrants and Digital Natives to Digital Wisdom. Innovate, Vol. 5, Issue 3, Febr/March 2009

Roerink GJ, Danes MHGI (2010) Possibilities of GIS and Remote Sensing for General Surveillance of Genetically Modified Crops. Wageningen, The Netherlands, Alterra-report 2023 (ISSN 1566-7197).

Shneiderman B (2008) Science 2.0. Science, 319, 1349-1350.Strahler AS, Boschetti L, Foody GM et al. (2006) Global Land Cover Validation:

Recommendations for Evaluation and Accuracy Assessment of Global Land Cover Maps. Luxembourg: Office for Official Publication of the European



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Communities, EUR 22156 EN, 48 pp.Thessler S, Kooistra L, Teye F, Huitu H, Bregt A (2011) Geosensors to Support

Crop Production: Current Applications and User Requirements. Sensors, 11, 6656-6684.

UNFCC (2010) Outcome of the work of the Ad Hoc Working Group on long-term Cooperative Action under the Convention - Policy approaches and positive incentives on issues relating to reducing emissions from deforestation and forest degradation in developing countries; and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries, UNFCCC COP 16 Cancun.

Verbesselt J, Hyndman R, Newnham G, Culvenor D (2010a) Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114, 106-115.

Verbesselt J, Hyndman R, Zeileis A, Culvenor D (2010b) Phenological change detection while accounting for abrupt and gradual trends in satellite image time series. Remote Sensing of Environment, 114, 2970-2980.

Ware C (2004) Information Visualization: Perception for Design, Morgan Kaufman Publishers, San Francisco, CA - USA.

Waycott J, Bennett S, Kennedy G, Dalgarno B, Gray K (2010) Digital divides? Student and staff perceptions of information and communication technolo-gies. Computers and Education, 54, 1202-1211.

Yoder JB (2008) Science 2.0: Not So New? Science, 320, 1290-1291.



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ISBN 978-90-8585-894-2

Prof. dr M. HeroldInaugural lecture upon taking up the post of Professor of Geo-information Science with emphasis on Remote Sensing at Wageningen University on 8 September 2011


Remote sensing science provides

underpinnings for monitoring and

understanding of our changing

world. The Web 2.0 and sensor webs,

open satellite data and Google Earth

are democratizing the way society

and science interact; creating new

collective intelligence and ways for

shared decision making. Remote

sensing research is now interfacing

with open science, social networking,

citizen science and observatories,

location-based information services,

and societal demands for a self-aware

Earth. What are topics and inter-

disciplinary opportunities of remote

sensing science 2.0? How can citizens

more directly benefit and engage in

Earth observations?

10229_Omslag oratie Martin Herold.indd 1 4-8-2011 15:48:31

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