Digital technologies in support of flood resilience: A case study for Nepal

This paper presents ongoing efforts to support flood resilience in the Karnali basin in Nepal through the provision of different forms of digital technology. Flood Risk Geo-Wiki is an online visualization and crowdsourcing tool, which has been adapted to display flood risk maps at the global scale as well as information of relevance to planners and the community at the local level. Community-based flood risk maps, which have traditionally been drawn on paper, are being digitized and integrated with OpenStreetMap to provide better access to this collective knowledge base. Mobile phones, using the GeoODK (Geographical Open Data Kit) questionnaire builder, are being deployed to collect georeferenced information on flood risks and vulnerability, which can be used to validate flood models and design action plans and strategies for coping with future flood events. These types of digital technologies are simple to implement yet together can help support flood prone communities

Comment on “The extent of forest in dryland biomes”

Bastin et al. (Reports, 12 May 2017, p. 635) claim to have discovered 467 million hectares of new dryland forest. We would argue that these additional areas are not completely “new” and that some have been reported before. A second shortcoming is that not all sources of uncertainty are considered; the uncertainty could be much higher than the reported value of 3.5%

Validation of Automatically Generated Global and Regional Cropland Data Sets: The Case of Tanzania

There is a need to validate existing global cropland maps since they are used for different purposes including agricultural monitoring and assessment. In this paper we validate three recent global products (ESA-CCI, GlobeLand30, FROM-GC) and one regional product (Tanzania Land Cover 2010 Scheme II) using a validation data set that was collected by students through the Geo-Wiki tool. The ultimate aim was to understand the usefulness of these products for agricultural monitoring. Data were collected wall-to-wall for Kilosa district and for a sample across Tanzania. The results show that the amount of and spatial extent of cropland in the different products differs considerably from 8% to 42% for Tanzania, with similar values for Kilosa district. The agreement of the validation data with the four different products varied between 36% and 54% and highlighted that cropland is overestimated by the ESA-CCI and underestimated by FROM-GC. The validation data were also analyzed for consistency between the student interpreters and also compared with a sample interpreted by five experts for quality assurance. Regarding consistency between the students, there was more than 80% agreement if one difference in cropland category was considered (e.g., between low and medium cropland) while most of the confusion with the experts was also within one category difference. In addition to the validation of current cropland products, the data set collected by the students also has potential value as a training set for improving future cropland products

FotoQuest Go: A Citizen Science Approach to the Collection of In-Situ Land Cover and Land Use Data for Calibration and Validation

The Land Use/Cover Area frame Survey (LUCAS) is a harmonized data collection exercise on land cover and land use, which employs a systematic sample across EU member countries. The survey is undertaken every three years by trained surveyors and is a rich data set of land use and land cover, including geotagged photographs. LUCAS has been used to validate the CORINE land cover map, which is generated for EU member countries every 6 years, and it represents one of the only publicly available in situ data sets for the calibration and validation of products derived from Earth Observation for Europe. However, the LUCAS exercise is undertaken at a considerable cost to the taxpayer. Given that citizen science is becoming more popular, i.e., the involvement of citizens in scientific research including data collection, we set out to determine whether citizens could help in gathering in situ data on land use and land cover. Advantages of this approach include data collection that is at a denser sample in some areas, the potential for more up-to-date information, since LUCAS is only carried out every 3 years, and as a cost effective way to complement and enrich LUCAS data collection. To test out this idea of land use and land cover data collection by citizens, the FotoQuest Go app was developed. FotoQuest Go is one of many tools that are part of the H2020-funded LandSense Citizen Observatory for land cover and land use. FotoQuest Go (shown in Figure 1) leads any citizen taking part in our crowdsourcing campaigns to pre-specified locations shown on the map. In some cases, these locations overlap with LUCAS points so that quality assurance can be undertaken, comparing the land cover and land use data from the citizens with that of the professional surveyors. As a location on the map is reached, users are asked to take 4 photographs in 4 cardinal directions away from the location and one at the actual point. The map guides the users, e.g., only allowing them to take a photograph if the compass direction is S, N, E or W, and providing advice regarding how the photos should be taken, e.g. two-thirds land and one-third sky. The citizens are then asked to classify the land cover using a simple, visual decision tree, followed by the land use. The app has been designed to be easy-to-use. For example, it is visually attractive and intuitive as the map interface provides guidance on reaching locations, and the app helps users in taking optimal photographs. The decision tree for determining land use and land cover has also been designed in a simple user-friendly fashion. A number of different campaigns have been run with FotoQuest Go, where incentives for participation have ranged from prizes at the end of the campaign to small, monetary rewards for each point captured that was deemed to be of sufficient quality. These gamification elements have helped to motivate the crowd and make the crowdsourcing experience more fun. An analysis of the data showed good agreement between the citizens and the surveyors at the LUCAS locations when considering high level land cover classes, e.g. forest, urban, water, etc., i.e., accuracies greater than 80%. Thus, using an app such as FotoQuest Go, citizens can collect land cover and land use data that could be used for calibration and validation of land cover and land use maps. Moreover, many geotagged photographs have been collected, which could additionally be interpreted and used for calibration and validation purposes. More recently, the main functionality contained within FotoQuest Go has been moved into the PAYSAGES mobile app for crowdsourcing data on land cover and land use. The idea is to involve citizens in the validation and improvement of the French land cover map developed by the French Mapping Agency (IGN). The PAYSAGES app has been developed within the H2020 LandSense Citizen Observatory as part of a demonstration case in urban areas. The app will be used in data collection campaigns during the summer of 2019

FotoQuest Go: A citizen science tool for in-situ land use and land cover monitoring

Every three years, dating back to 2006, Eurostat conducts an exhaustive Land Use/Cover Area frame Survey (LUCAS), where professional surveyors visit approximately 270,000 locations across EU countries to acquire photos and record detailed in-situ information on land use/cover. This conventional approach to ground-based calibration/ validation data acquisition is rather costly and is limited to detecting changes on a fixed 3-year cycle. As such, within the EU’s Earth Observation (EO) monitoring framework, there is a need for low-cost solutions for acquiring high quality ground-based data to support the delivery of timely, accurate and well-validated environmental monitoring products. By leveraging the proliferation of mobile devices the FotoQuest Go mobile application offers a citizen-centric tool to mapping land use and land cover dynamics. FotoQuest Go engages citizens and crowdsources the needed information in a more participatory approach while directly complementing the LUCAS survey findings. This paper describes the recent results of a 2017 FotoQuest Go crowdsourcing campaign conducted in Austria, where 100+ participants recorded land use and land cover observations from over 895 LUCAS locations. When visiting a location, the application guides the participants through a series of tasks (i.e. photo acquisition, questionnaire) that follows a subset of the standard LUCAS surveyor data collection protocol. Once the protocol is completed, participants upload their observations for quality check. Experts would then review each submission and provide feedback directly to the participants within 24hrs. Combined with the feedback was a monetary incentive of 1 EUR for each successfully completed location or quest. It was discovered that the quality control and assurance process was very effective in not only ensuring useful and high-quality citizen science data, but also providing a means to facilitate learning among the participants. In other words, within the 2017 FotoQuest Go campaign, we learned that timely and detailed feedback helped to improve the data collected by participants when they visited subsequent locations. This paper will elaborate the added value of quality-assured citizen science data to the domain of traditionally-collected data for land use and land cover monitoring. FotoQuest Go has considerable potential to lower expenditure costs on in-situ data collection and greatly extend the current sources of such data for earth system science research, thereby realizing citizen-powered innovations in the processing chain of land use/cover monitoring activities both within and beyond Europe

N2O emissions from a loamy soil cropped with winter wheat as affected by N-fertilizer amount and nitrification inhibitor

Nitrogen (N) fertilization leads to the release of reactive N species, which can be detrimental to the environment. Nitrification inhibitors (NIs) are substances capable of retarding the oxidation of ammonium to nitrate, which can increase N use efficiency of applied N fertilizer and decrease N losses such as the release of the greenhouse gas nitrous oxide (N2O). Adaption of N fertilizer amount to plant demand might also decrease N surpluses and thus lower N2O emissions. We investigated the effects of N fertilizer amount (0, 120, 180, and 240 kg N ha−1 a−1) and the use of the NI 3,4-dimethylpyrazol phosphate, DMPP, on annual N2O emission from a soil cropped with winter wheat in a 2 year field experiment. N2O fluxes were affected by N level and by use of DMPP with higher fluxes under high N amounts and treatments without NI. Application of DMPP led to a reduction of annual emissions by 45%. Interestingly, also winter emissions (8–12 months after N fertilization) were decreased by DMPP. In this period, a complete degradation of DMPP was assumed. The reason for this effect remains unclear. Wheat yield and quality were unaffected by DMPP, whereas grain yield was increased with N fertilizer amount in the first year. Nevertheless, response curves of grain yield-related N2O emissions over all data showed lower optimal N fertilizer doses when DMPP was used. Application of DMPP at suboptimal N rates could help to achieve a better profitability with simultaneous reduction of the product scaled emission

Opening up FAIR in-situ land-use reference data: current gaps, obstacles and future challenges

It is becoming increasingly obvious that in order to address current global challenges and achieve the SDGs in the land-use sector, monitoring and evaluation using remote sensing technologies are essential. In particular, with the Copernicus program of the European Union, unprecedented free and open Earth observation data are becoming available. However, in order to improve our remotely sensed based machine learning models, training data in the form of in-situ or annotated land-use or land cover data which are based on the visual interpretation of aerial photographs or very high resolution satellite data are of utmost importance. Without sufficient training data, many land-use and land cover maps lack sufficient quality. The presentation will provide an overview of existing and open in-situ data in the field of land-use science. It will highlight what land-use data are currently available including data collected though crowdsourcing and the Geo-Wiki toolbox. In particular, it will provide insights into current gaps in land cover, land-use, livestock, forest as well as crop type information globally. It will draw on existing global data products such as those from the Copernicus global land monitoring service, and more recently generated products such as WorldCover and WorldCereal. Furthermore, tools to close those data gaps will be shown. The presentation will furthermore explore current obstacles and limitations to data sharing and debunk current arguments that are often put forth for not sharing in-situ data. These arguments include limited resources, quality issues, competition, as well as time constraints, etc. Specific attention will be given to the role of doners and funders in more clearly defining open and FAIR requirements for in-situ data. The presentation will close by making the audience aware of the LUCKINet consortium, which is trying to make more reference data openly accessible and to build a consistent global land-use change dataset as well as work done on in-situ data within the EU LAMASUS and OEMC project

LACO-Wiki: A New Online Land Cover Validation Tool Demonstrated Using GlobeLand30 for Kenya

Accuracy assessment, also referred to as validation, is a key process in the workflow of developing a land cover map. To make this process open and transparent, we have developed a new online tool called LACO-Wiki, which encapsulates this process into a set of four simple steps including uploading a land cover map, creating a sample from the map, interpreting the sample with very high resolution satellite imagery and generating a report with accuracy measures. The aim of this paper is to present the main features of this new tool followed by an example of how it can be used for accuracy assessment of a land cover map. For the purpose of illustration, we have chosen GlobeLand30 for Kenya. Two different samples were interpreted by three individuals: one sample was provided by the GlobeLand30 team as part of their international efforts in validating GlobeLand30 with GEO (Group on Earth Observation) member states while a second sample was generated using LACO-Wiki. Using satellite imagery from Google Maps, Bing and Google Earth, the results show overall accuracies between 53% to 61%, which is lower than the global accuracy assessment of GlobeLand30 but may be reasonable given the complex landscapes found in Kenya. Statistical models were then fit to the data to determine what factors affect the agreement between the three interpreters such as the land cover class, the presence of very high resolution satellite imagery and the age of the image in relation to the baseline year for GlobeLand30 (2010). The results showed that all factors had a significant effect on the agreement

Characterizing the Spatial and Temporal Availability of Very High Resolution Satellite Imagery in Google Earth and Microsoft Bing Maps as a Source of Reference Data

Very high resolution (VHR) satellite imagery from Google Earth and Microsoft Bing Maps is increasingly being used in a variety of applications from computer sciences to arts and humanities. In the field of remote sensing, one use of this imagery is to create reference data sets through visual interpretation, e.g., to complement existing training data or to aid in the validation of land-cover products. Through new applications such as Collect Earth, this imagery is also being used for monitoring purposes in the form of statistical surveys obtained through visual interpretation. However, little is known about where VHR satellite imagery exists globally or the dates of the imagery. Here we present a global overview of the spatial and temporal distribution of VHR satellite imagery in Google Earth and Microsoft Bing Maps. The results show an uneven availability globally, with biases in certain areas such as the USA, Europe and India, and with clear discontinuities at political borders. We also show that the availability of VHR imagery is currently not adequate for monitoring protected areas and deforestation, but is better suited for monitoring changes in cropland or urban areas using visual interpretation