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Global Pasture Watch - Annual horse density layers at 1-km for 2000–2022 (including 95% prediction interval)
No full textGlobal annual layers of horse density at 1-km spatial resolution convering the period of 2000–2022. The layers were produced using harmonized and used as reference data (55,336 census polygons and 170,628 individual data entries), random forest predictive models and a large stack of multi-source harmonized gridded/raster spatial layers (128 individual raster spatial layers harmonized at 1 km spatial resolution).
Raster cell values represent heads km2 including:
Mean predicted values (_m_)
Upper prediction interval based on 97.5th percentiles (_p.975_)
Lower prediction interval based on 2.5th percentiles (_p.025_)
Based on 95% probability quantiles, prediction intervals are relatively wide; therefore, for a more effective use, we recommend converting them to standard deviation by dividing the range (p.975 - p.025) by four.
Raw/Uncalibrated headcounts are also provided and were computed by multiplying the density values by the actual area of potential land for livestock production.
In line with a request from our funders, livestock Layers will remain under embargo in Zenodo until the final acceptance of peer-reviewed publication. They can be accessed during the reviewing process by filling-in a form via Global Pasture Watch Early Access data program (https://survey.alchemer.com/S3/7859804/Pasture-Early-Adopters). All modeling framework presented in this work is publicly available at: https://github.com/wri/global-pasture-watch. We are currently preparing the data to be ingested in STAC and Google Earth Engine
WorldCereal harmonized reference datasets - extended and updated
No full textWithin the ESA WorldCereal project we have built a global, community-based and open repository of harmonized reference data on land cover, crop type and irrigation information. These datasets are typically used to construct and validate global cropland and crop type maps. We define reference data as all data which can either be used for calibrating classification algorithms or validating the resulting products. As such, reference data should contain location- and time-specific information about land cover and/or crop type. Reference data can include:
In-situ field data gathered through dedicated field surveys
Farmers declarations through parcel registration systems
Data derived from visual or automated interpretation of very high-resolution satellite imagery or in-situ photographs (e.g. streetview or mapillary)
Existing high-quality classified maps based on analysis of satellite imagery
Reference data is typically available in different formats depending on the source of the dataset. To ensure multiple reference datasets can be readily combined and can serve as input for a dedicated calibration/validation task, all datasets have been structured, harmonized, annotated and evaluated. The datasets adhere to the same (meta)data standards and formats. See for more information the PDF document 'WorldCereal_Reference_dataset_naming_convention_v2'.
Each individual dataset has its own data license defined by the original data holder, which can be retrieved from the accompanying Excel metadata file. The license will be one of the different licenses listed in this publication. The user must check the data license of each of these datasets before starting to use or redistribute the data.
Individual datasets have been grouped per year and continent into a separate .zip archive. The title of individual datasets matches the Collection ID as specified in the WorldCereal Reference Data Module, our dedicated user interface for serving reference data to the community.
A complete overview on WorldCereal reference data documentation can be found here. Information on WorldCereal reference data harmonization procedures, data standards and legend can be found here: and in the accompanying PDF document 'WorldCereal_Reference_dataset_naming_convention_v2' hosted in this repository
Opportunities for citizen science within the Global Urban Monitoring Framework
No full textThe Global Urban Monitoring Framework (UMF) is one of several international frameworks for monitoring progress in urban sustainable development with indicators from the Sustainable Development Goals (SDGs) and other frameworks such as the City Prosperity Index (CPI). Like the SDGs, many of the 77 UMF indicators lack data, and reporting is largely conducted at a national rather than a local level. Citizen science represents one important data source to address these data gaps at a more local level. Hence, the aim of this paper is to undertake a systematic review of where citizen science could potentially provide data for the UMF indicators using secondary data from citizen science projects. The results showed that citizen science data are already contributing and could contribute to 52 UMF indicators (~68%). Integrating citizen science into urban decision-making is essential so that local communities are at the heart of creating safe, inclusive, resilient, and sustainable cities
Policy white paper: upscaling citizen engagement for climate resilience
No full textThe deliverable D4.5 presents a strategic roadmap, structured around
four interconnected pillars and sixteen recommendations, which addresses the institutional, financial, cultural and practical challenges that hinder the scaling of stakeholder and citizen engagement for climate adaptation and resilienc
Material Demand and Energy Saving Potential of Renovation of Norwegian Residential Buildings: A Bottom-up Approach
No full textAs the stock of aging buildings increases, renovation is an alternative to demolition and new construction, reducing environmental impacts and material waste. Effective retrofitting should enhance thermal comfort, minimize energy demand, particularly for heating, cooling, and ventilation, and optimize material use. Building on our previous study of Norwegian residential archetypes, we evaluate three retrofit strategies using a bottom-up, physics-based approach. Our findings show that even minimal interventions, such as window replacement, significantly reduce heating demand. However, a more comprehensive retrofit—including external wall and roof insulation, window and door replacement, and balanced ventilation—achieves greater energy savings while maintaining indoor comfort
Network properties of the global waste trade
Get PDFThe network properties of the global waste trade were assessed by using time series data of material and monetary flows between 2000 and 2022 from the online experimental database of Chatham House. More specifically, indicators from ecological network analysis and ascendency analysis were used to identity patterns which may not otherwise be directly identifiable, and to compare the network properties of the global waste trade to those of natural ecosystems. Focus was given on the distribution of monetary and material flows, on policy recommendations, and on future research avenues which we think are relevant for obtaining a more comprehensive understanding of socio-economic systems such as trade networks. This work provides a solid example of the application of network-based methods as an eco-mimicry approach for assessing the sustainability and fragility of socio-economic systems which can be of relevance to researchers and policy makers interested on transitions towards regenerative circular economies
A principle-based framework to determine countries’ fair warming contributions to the Paris Agreement
Get PDFEquity is a cornerstone of global climate policy, yet differing perspectives mean that international agreement on how to allocate mitigation efforts remains elusive. A rich literature informs this question, but a gap remains in approaches that appropriately consider non-CO2 emissions and their warming contributions. In this study, we address this gap and define a global warming budget applicable to all anthropogenic greenhouse gases that is allocated to countries based on principles drawn from international treaties and environmental law. We find that by 2021 a range of 84 to 90 countries, including but not limited to all major developed countries, exhausted their budget share compatible with keeping warming to 1.5 °C (with 50% likelihood) under all allocation approaches considered in this study. A similar picture emerges for limiting warming to 2 °C (with 67% likelihood). A large group of countries will hence exceed their fair shares even if their pledges under the Paris Agreement represent their deepest possible emission reductions. Considerations of fairness should therefore start exploring aspects beyond domestic emissions reductions
New Directions in Mapping the Earth’s Surface with Citizen Science and Generative AI
Get PDFAs more satellite imagery has become openly available, efforts in mapping the Earth’s surface have accelerated. Yet the accuracy of these maps is still limited by the lack of in-situ data needed to train machine learning algorithms. Citizen science has proven to be a valuable approach for collecting in-situ data through applications like Geo-Wiki and Picture Pile, but better approaches for optimizing volunteer time are still required. Although machine learning is being used in some citizen science projects, advances in generative Artificial Intelligence (AI) are yet to be fully exploited. This paper discusses how generative AI could be harnessed for land cover/land use mapping by enhancing citizen science approaches with multi-modal large language models (MLLMs), including improvements to the spatial awareness of AI
White Noise and Its Misapplications: Impacts on Time Series Model Adequacy and Forecasting
Get PDFThis paper contributes significantly to time series analysis by discussing the empirical properties of white noise and their implications for model selection. This paper illustrates the ways in which the standard assumptions about white noise typically fail in practice, with a special emphasis on striking differences in sample ACF and PACF. Such findings prove particularly important when assessing model adequacy and discerning between residuals of different models, especially ARMA processes. This study addresses issues involving testing procedures, for instance, the Ljung–Box test, to select the correct time series model determined in the review. With the improvement in understanding the features of white noise, this work enhances the accuracy of modeling diagnostics toward real forecasting practice, which gives it applied value in time series analysis and signal processing