Building a community-based open harmonised reference data repository for global crop mapping

Reference data is key to produce reliable crop type and cropland maps. Although research projects, national and international programs as well as local initiatives constantly gather crop related reference data, finding, collecting, and harmonizing data from different sources is a challenging task. Furthermore, ethical, legal, and consent-related restrictions associated with data sharing represent a common dilemma faced by international research projects. We address these dilemmas by building a community-based, open, harmonised reference data repository at global extent, ready for model training or product validation. Our repository contains data from different sources such as the Group on Earth Observations Global Agricultural Monitoring Initiative (GEOGLAM) Joint Experiment for Crop Assessment and Monitoring (JECAM) sites, the Radiant MLHub, the Future Harvest (CGIAR) centers, the National Aeronautics and Space Administration Food Security and Agriculture Program (NASA Harvest), the International Institute for Applied Systems Analysis (IIASA) citizen science platforms (LACO-Wiki and Geo-Wiki), as well as from individual project contributions. Data of 2016 onwards were collected, harmonised, and annotated. The data sets spatial, temporal, and thematic quality were assessed applying rules developed in this research. Currently, the repository holds around 75 million harmonised observations with standardized metadata of which a large share is available to the public. The repository, funded by ESA through the WorldCereal project, can be used for either the calibration of image classification deep learning algorithms or the validation of Earth Observation generated products, such as global cropland extent and maize and wheat maps. We recommend continuing and institutionalizing this reference data initiative e.g. through GEOGLAM, and encouraging the community to publish land cover and crop type data following the open science and open data principles

WorldCereal: a dynamic open-source system for global-scale, seasonal, and reproducible crop and irrigation mapping

The challenge of global food security in the face of population growth, conflict and climate change requires a comprehensive understanding of cropped areas, irrigation practices and the distribution of major commodity crops like maize and wheat. However, such understanding should preferably be updated at seasonal intervals for each agricultural system rather than relying on a single annual assessment. Here we present the European Space Agency funded WorldCereal system, a global, seasonal, and reproducible crop and irrigation mapping system that addresses existing limitations in current global-scale crop and irrigation mapping. WorldCereal generates a range of global products, including temporary crop extent, seasonal maize and cereals maps, seasonal irrigation maps, seasonal active cropland maps, and confidence layers providing insights into expected product quality. The WorldCereal product suite for the year 2021 presented here serves as a global demonstration of the dynamic open-source WorldCereal system. The presented products are fully validated, e.g., global user's and producer's accuracies for the annual temporary crop product are 88.5 % and 92.1 %, respectively. The WorldCereal system provides a vital tool for policymakers, international organizations, and researchers to better understand global crop and irrigation patterns and inform decision-making related to food security and sustainable agriculture. Our findings highlight the need for continued community efforts such as additional reference data collection to support further development and push the boundaries for global agricultural mapping from space. The global products are available at https://doi.org/10.5281/zenodo.7875104 (Van Tricht et al., 2023)

Internet of Things for Sustainable Forestry

Forests and grasslands play an important role in water and air purification, prevention of the soil erosion, and in provision of habitat to wildlife. Internet of Things has a tremendous potential to play a vital role in the forest ecosystem management and stability. The conservation of species and habitats, timber production, prevention of forest soil degradation, forest fire prediction, mitigation, and control can be attained through forest management using Internet of Things. The use and adoption of IoT in forest ecosystem management is challenging due to many factors. Vast geographical areas and limited resources in terms of budget and equipment are some of the limiting factors. In digital forestry, IoT deployment offers effective operations, control, and forecasts for soil erosion, fires, and undesirable depositions. In this chapter, IoT sensing and communication applications are presented for digital forestry systems. Different IoT systems for digital forest monitoring applications are also discussed

Mapping urban composition and green infrastructure using remote sensing in support of urban ecosystem service assessment

Urbanization presents one of the major challenges to humankind in the current century. Our cities are true drivers of global environmental change, but at the same time also represent the most susceptible areas to be suffering from the local impacts of these ongoing changes (e.g. through heat waves, flooding and air pollution). Sustainable urban management and development therefore focuses on safeguarding the local quality of life by reducing both the local and global impacts of urbanization. Urban green is increasingly recognized as a valuable means in this respect, given the many benefits (or ecosystem services) it may provide to human society. Mapping these services in a quantitative and spatially-explicit way, would enable urban planners and managers to identify those zones within a city's boundaries that should be prioritized in future urban development and would allow for a critical and objective evaluation of different urban planning scenarios. Generating such ecosystem service maps however requires detailed information about urban land cover composition in general, and concerning the specific type, properties and state of urban green in particular. By measuring the interaction of solar radiation with the earth's surface in high spatial and spectral detail, airborne hyperspectral remote sensing in theory allows for the detailed characterization of the urban environment. However, the high spatial and spectral complexity of urban areas still actively impedes such detailed assessments. In this PhD dissertation, we therefore looked into the concept of data fusion to overcome specific remaining issues in this respect. Our study area comprises the eastern part of the Brussels Capital Region (Belgium), which has been covered by 2 m resolution hyperspectral data in Summer 2015. The highly heterogeneous nature of our cities lead to the phenomenon of mixed pixels in remotely sensed imagery. Several spectral unmixing approaches have therefore been proposed in the past, aiming to determine the true composition of individual image pixels. Multiple Endmember Spectral Mixture Analysis (MESMA) is amongst the most commonly used techniques and relies on a spectral library (i.e. a collection of pure material spectra, or endmembers), ideally covering all endmember variability present within a given scene. Despite the advent of several automated endmember extraction techniques, building such libraries represents a time-consuming, yet crucial task. In Chapter 2, we therefore proposed fusing already existing urban spectral libraries as the basis for an alternative solution. As this generic urban spectral library is expected to contain a high proportion of irrelevant spectra with regard to any particular image to be processed, we developed an automated endmember selection technique (AMUSES) allowing to refine a given spectral library in function of a given image. Several experiments on simulated and real hyperspectral imagery with libraries of increasing generic nature confirmed the potential of AMUSES in this respect and have additionally shown a significant increase in subsequent mapping accuracies compared to more traditional library pruning techniques. Despite these improvements, considerable classification errors were still observed, which could be attributed to the high spectral similarity between land cover classes (e.g. roof versus pavement and grass versus tree). In Chapter 3, we therefore integrated height information extracted from airborne LiDAR data into the MESMA algorithm and successfully reduced confusion between spectrally similar, but structurally different land cover classes. In particular, height distribution information within single image pixels was employed as an additional endmember selection constraint and as fraction constraints during the unmixing procedure, thereby also reducing computation times by up to 85 %. Band selection (i.e. using different, relevant subsets of spectral bands for each individual land cover class) did not further improve classification results, but resulted in an additional decrease in computation times by 50 %. The added value of the proposed techniques for processing imagery featuring lower spectral and/or spatial resolution has been demonstrated for both the library pruning technique and the integration with LiDAR. Given the increasing availability of (hyper-)spectral data and the associated need for highly automated and efficient processing algorithms, both chapters are expected to contribute towards the development of more universal urban mapping workflows. In order to facilitate the mapping of ecosystem services provided by urban green, a functional urban green typology, covering 23 distinct types, was established in Chapter 4. Given the high spectral and structural similarities between the proposed types, object-based image analysis, combined with Random Forest classification, was employed as a more advanced image fusion technique to further explore the complementarity between airborne hyperspectral and LiDAR data. Height and intensity derived from LiDAR data were found to be the most important features overall, but required additional spectral information to accomplish good classification results at a thematically detailed level. In this respect, hyperspectral data was found to be more useful compared to multispectral data, although the latter did feature a higher spatial resolution. Despite these encouraging results, spatially continuous mapping of urban green still was severely impeded by shadow and adjacency effects, resulting in class-wise kappa values below 0.5 for detailed shrub and herbaceous vegetation types. Additionally incorporating phenological information and adopting multi-scale segmentation approaches, is expected to further increase the potential of remote sensing for detailed urban green mapping. Finally, in Chapter 5, chlorophyll concentration and Leaf Area Index (LAI) of urban trees were determined using hyperspectral and LiDAR data, and were subsequently combined into an objective estimation of tree health. Similar to the findings in Chapter 4, mixed pixel effects significantly complicated the analysis. As a result, Partial Least Squares regression, being able to learn from local calibration data and employing the full hyperspectral signal, highly outperformed existing spectral indices. As our tree health assessment showed good agreement with visual tree assessment data obtained on the ground, the proposed workflow could be used as a basis for further research focusing on revealing the drivers of urban tree health. Further efforts should additionally be devoted to the early detection of stress in urban trees in order to optimize the utility of this tool for urban green managers. Although the concept of ecosystem services has certainly increased the awareness on the importance of urban green for safeguarding the quality of life in our future cities, implementation of these concepts into urban management and design is still lagging behind. The research conducted throughout this PhD dissertation has confirmed the potential of remote sensing data and technology to contribute to the detailed assessment of urban ecosystem services, as such providing an important stepping stone towards their operational use. Due to the typically high spatial and spectral complexity of our cities, the urban remote sensing community is highly encouraged to continue the search for complementary information derived from both new (e.g. social media, sensor networks) and existing data sources in order to optimize the workflows proposed here.status: publishe

Effects of accession, spacing and pruning management on in-situ leaf litter decomposition of Jatropha curcas L. in Zambia

Jatropha curcas L. leaf litter decomposition and subsequent nutrient release was monitored in three experimental J. curcas plantations in Zambia, comparing accessions from six countries, pruned versus non-pruned and different plant spacings. Leaf litter production was low (267-536 kg ha-1 at the end of the growing season) and contained, on average, 1.23% N, 0.14% P and 2.61% K. Litter decomposed rapidly, losing 80% of total mass by 70 to 105 days after incubation in the field and followed a negative exponential pattern with an average decomposition constant, k, of 0.08 week-1. No significant effects of plant accession, plant spacing or pruning on the decomposition rate were detected. K, P, Mg and Na had nutrient release rates exceeding mass loss, explained by their high mobility and solubility, together with high soil temperature and rainfall conditions. Others, such as Ca and Mn, were initially retained in the decaying leaf litter before later release. The rate of N release closely approached that of mass loss. Jatropha curcas litter can be a supplemental source of nutrients in areas known for nutrient deficiency and low organic matter, which represents an additional input in intercropping systems above biofuel production. In addition J. curcas, sheds its leaves during the dry season and these can be used as mulch to minimize soil desiccation at least during the first dry period. Considering that the total primary nutrient input through J. curcas litterfall to the soil is limited (for example, for nitrogen between 9.7 and 14.2 g kg–1 and for phosphorus between 0.8 and 1.9 g kg–1), organic or mineral fertilizer application remains crucial to satisfy fully the nutrient requirements of surrounding crops

WorldCereal open global harmonized reference data repository (CC-BY-SA licensed data sets)

Within the ESA funded WorldCereal project we have built an open harmonized reference data repository at global extent for model training or product validation in support of land cover and crop type mapping. Data from 2017 onwards were collected from many different sources and then harmonized, annotated and evaluated. These steps are explained in the harmonization protocol (10.5281/zenodo.7584463). This protocol also clarifies the naming convention of the shape files and the WorldCereal attributes (LC, CT, IRR, valtime and sampleID) that were added to the original data sets. This publication includes those harmonized data sets of which the original data set was published under the CC-BY-SA license or a license similar to CC-BY-SA. See document "_In-situ-data-World-Cereal - license - CC-BY-SA.pdf" for an overview of the original data sets

WorldCereal open global harmonized reference data repository (CC-BY licensed data sets)

Within the ESA funded WorldCereal project we have built an open harmonized reference data repository at global extent for model training or product validation in support of land cover and crop type mapping. Data from 2017 onwards were collected from many different sources and then harmonized, annotated and evaluated. These steps are explained in the harmonization protocol (10.5281/zenodo.7584463). This protocol also clarifies the naming convention of the shape files and the WorldCereal attributes (LC, CT, IRR, valtime and sampleID) that were added to the original data sets. This publication includes those harmonized data sets of which the original data set was published under the CC-BY license or a license similar to CC-BY. See document "_In-situ-data-World-Cereal - license - CC-BY.pdf" for an overview of the original data sets

Erosion modelling towards, and sediment transport modelling in unnavigable watercourses in Flanders, Belgium

Antea Group and KULeuven were awarded a project in Flanders to identify the regions exporting high sediment loads to unnavigable watercourses and the sedimentation zones within them. Two types of models are applied: hydrological sediment export models (SEM) and hydraulic sediment transport models (STM). The influence of erosion control measures on sediment export as well as river engineering measures needs to be taken into account. A concept will be developed to connect the SEM and STM, enabling the sediment to be routed from upstream to the sedimentation zones. Results of the study will be used by the Flemish government to plan erosion control measures, estimate future sedimentation volumes, steer sedimentation and optimize river engineering and dredging works. Finally, model results could also be used to obtain better insights to the re-suspension risks of contaminated sediment in watercourses

Imaging Spectroscopy of Urban Environments

Future spaceborne imaging spectroscopy data will offer new possibilities for mapping ecosystems globally, including urban environments. The high spectral information content of such data is expected to improve accuracies and thematic detail of maps on urban composition and urban environmental condition. This way, urgently needed information for environmental models will be provided, for example, for microclimate or hydrological models. The diverse vertical structures, highly frequent spatial change and a great variety of materials cause challenges for urban environmental mapping with Earth observation data, especially at the 30Â m spatial resolution of data from future spaceborne imaging spectrometers. This paper gives an overview of the state-of-the-art in urban imaging spectroscopy considering decreasing spatial resolution, the related user requirements and existing knowledge gaps, as well as expected future directions for the work with new data sets