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Monitoring and understanding urban development requires up-to-date information on multiple urban land-use classes. Manual classification and deep learning approaches based on very-high resolution imagery have been applied successfully, but the required resources limits their capacity to map urban land use at larger scales. Here, we use a combination of open-source satellite imagery, constituting of data from Sentinel-1 and Sentinel-2, and socioeconomic data, constituting of points-of-interest and spatial metrics from road networks to classify urban land-use at a national scale, using a deep learning approach. A related challenge for large-scale mapping is the availability of ground truth data. Therefore, we focus our analysis on the transferability of our classification approach, using ground truth labels from a nationwide land-use dataset for the Netherlands. By dividing the country into four regions, we tested whether a combination of satellite data and socioeconomic data increases the transferability of the classification approach, compared to using satellite data only. The results indicate that socioeconomic data increases the overall accuracy of the classification for the Netherlands by 3 percentage points. In a transfer learning approach we find that adding socioeconomic data increases the accuracy between 3 and 5 percentage points when trained on three regions and tested on the independent fourth one. In the case of training and testing on one region and testing on another, the increase in overall accuracy increased up to 9 percentage points. In addition, we find that our deep learning approach consistently outperforms a random forest model, used here as benchmark, in all of the abovementioned experiments. Overall, we find that socioeconomic data increases the accuracy of urban land use classification, but variations between experiments are large.

Starting from 2021 International Maritime Organization (IMO) introduced more demanding NOx emission restrictions for ships operating in waters of the North and Baltic Seas. All methods currently used for ship compliance monitoring are financially and time-demanding. Thus, it is important to prioritize the inspection of ships that have a high chance of being non-compliant. TROPOMI/S5P instrument for the first time allows a distinction of NO2 plumes from individual ships. Here, we present a method for the selection of potentially non-compliant ships using automated machine learning (AutoML) on TROPOMI/S5P satellite data. The study is based on the analysis of 20 months of data in the Mediterranean Sea region. To each ship, we assign a Region of Interest (RoI), where we expect the ship plume to be located. We then train a regression model to predict the amount of NO2 that is expected to be produced by a ship with specific properties operating in the given atmospheric conditions. We use a genetic algorithm-based AutoML for the automatic selection and configuration of a machine-learning pipeline that maximizes prediction accuracy. The difference between the predicted and actual amount of produced NO2 is a measure of inspection worthiness. We rank the analyzed ships accordingly. We cross-check the obtained ranks using a previously developed method for supervised ship plume segmentation. We quantify the amount of NO2 produced by a given ship by summing up concentrations within the pixels identified as a “plume”. We rank the ships based on the difference between the obtained concentrations and the ship emission proxy. Ships that are also ranked as highly deviating by the segmentation method need further attention. For example, by checking their data for other explanations. If no other explanations are found, these ships are advised to be the candidates for fuel inspection.

Scattering of light in the atmosphere and low sea surface albedo decrease the sensitivity of satellites to air pollution close to the sea surface. To reliably retrieve tropospheric nitrogen dioxide (NO2) columns using the TROPOspheric Monitoring Instrument (TROPOMI), it is therefore necessary to have good a priori knowledge of the vertical distribution of NO2. In this study, we used an aircraft of the Royal Belgian Institute of Natural Sciences, part of the Belgian coastguard structure, which was already equipped with a sniffer sensor system, measuring CO2, NOx and SO2. This instrumentation enables us (1) to capture pollution plumes originating from ships sailing within an Emission Control Area, and (2) to validate TROPOMI tropospheric NO2 columns over the polluted North Sea in summer 2021 and (3) to evaluate vertical profile shapes from several chemical models. We observe multiple clear signatures of ship plumes from seconds after emission to multiple kilometers downwind. Besides that, our results show that the chemical transport model TM5, which is used in the retrieval of the operational TROPOMI data, tends to underestimate surface level pollution while overestimating NO2 at higher levels over the polluted North Sea. The higher horizontal resolutions in the regional CAMS ensemble mean and LOTOS EUROS improve the surface level pollution estimates, but the models still systematically overestimate NO2 levels at higher altitudes, indicating exaggerated vertical mixing in the models. When replacing the TM5 a priori NO2 profiles with the aircraft-measured NO2 profiles in the air mass factor (AMFs) calculation, we find that recalculated AMFs reduce, and the retrieved NO2 columns increase by 20%. This indicates a significant low bias in TROPOMI tropospheric NO2 measurements over the North Sea. This low bias has important implications for estimating emissions over the sea. While TROPOMI NO2 low biases caused by the TM5 a priori profiles have previously also been reported over land, the reduced vertical mixing and smaller surface albedo over sea makes this issue especially relevant over sea and coastal regions.

Urbanisation is changing food systems globally, and in particular in sub-Saharan Africa and South Asia. This transformation can affect rural livelihoods in multiple ways. Evidence on what enabling conditions are needed to materialise the opportunities and limit risks is scattered. Here we review scientific literature to elaborate on how urbanisation affects food systems, and on the enabling conditions that subsequently shape opportunities for rural livelihoods. We find that urbanisation leads to a rising and changing food demand, both direct and indirect land use changes, and often to more complex market linkages. Evidence shows that a wide range of enabling conditions can contribute to the materialisation of opportunities for rural livelihoods in this context. Reviewed evidence suggests that the connectivity to urban centres is pivotal, as it provides access to finance, inputs, information, services, and off-farm employment. As a result, physical and communication infrastructure, the spatial pattern of urbanisation, and social networks connecting farmers to markets are identified as important enabling factors for the improvement of rural livelihood outcomes. Our findings suggest that coordinated and inclusive efforts are needed at different scales to make sure rural livelihoods benefit from urbanisation and food system transformation.

There is a scientific consensus on the need for spatially detailed information on urban landscapes at a global scale. These data can support a range of environmental services, since cities are places of intense resource consumption and waste generation and of concentrated infrastructure and human settlement exposed to multiple hazards of natural and anthropogenic origin. In the face of climate change, urban data are also required to explore future urbanization pathways and urban design strategies in order to lock in long-term resilience and sustainability, protecting cities from future decisions that could undermine their adaptability and mitigation role. To serve this purpose, we present a 100 m-resolution global map of local climate zones (LCZs), a universal urban typology that can distinguish urban areas on a holistic basis, accounting for the typical combination of micro-scale land covers and associated physical properties. The global LCZ map, composed of 10 built and 7 natural land cover types, is generated by feeding an unprecedented number of labelled training areas and earth observation images into lightweight random forest models. Its quality is assessed using a bootstrap cross-validation alongside a thematic benchmark for 150 selected functional urban areas using independent global and open-source data on surface cover, surface imperviousness, building height, and anthropogenic heat. As each LCZ type is associated with generic numerical descriptions of key urban canopy parameters that regulate atmospheric responses to urbanization, the availability of this globally consistent and climate-relevant urban description is an important prerequisite for supporting model development and creating evidence-based climate-sensitive urban planning policies. This dataset can be downloaded from https://doi.org/10.5281/zenodo.6364594 (Demuzere et al., 2022a).

The growing demand for food, water, and shelter change the way people use land. These changes have affected or even caused conflict in several locations. However, conflicts do not erupt in isolation; they are the result of multiple interacting causes. There is limited structural understanding of these causes. In this study, we systematically coded case studies that report on conflict related to land-use change, including deforestation in commodity frontiers, agricultural development on common land, and urban development. Based on an analysis of 62 cases, we identified population growth, overlapping land rights, ethnic fragmentation, and economic inequality as the most frequently reported root causes, while rises in land prices was the most often reported proximate cause. Reported institutional causes suggest that the problem is not necessarily the complete absence of governance mechanisms, but rather that governance mechanisms are not fully equipped to deal with the complexities of the observed land-use changes.

Agricultural land use is transforming rapidly in Southeast Asia, often supported by development policies aiming primarily at economic growth. However, the socioeconomic outcomes of these changes for smallholder farmers remain unclear. Here, we systematically review cases of agricultural land use change in Southeast Asia to assess their socioeconomic outcomes and potential trade-off and synergies in these outcomes. Of the 126 reviewed cases, we find mostly positive outcomes for income (SDG 1, 100 cases) and employment (SDG 8, 11 cases), while outcomes on health (SDG 3, 9 cases) were mixed, and outcomes for food security (SDG 2, 44 cases), gender equality (SDG 5, 13 cases), and economic equality (SDG 10, 14 cases) were mostly negative. Studies describing multiple outcomes show indications of synergies between income and food security, and between income and employment, but also potential trade-offs between income and economic equality. In addition, we find that economic land concessions result in multiple negative outcomes more often than other types of land governance regimes. The results provide evidence that economic gains from agricultural land use change often come at a cost of other dimensions of sustainable development.

Starting from 2021, new and more demanding NOx emission standards came into force for ships entering the Baltic and North Sea waters. All methods that are currently used for ships compliance monitoring are expensive and require close proximity to the ship. As a result, a continuous and global execution of new regulations cannot be performed. The unprecedentedly high spatial resolution of the Tropospheric Monitoring Instrument onboard the Copernicus Sentinel 5 Precursor satellite (TROPOMI/S5P) allows the visual distinction of NO2 plumes produced by individual ships of substantial size. In order to have a scalable method for the estimation of NOx produced by individual ships, however, the detection of plumes needs to be automated.Here we propose an automated approach for segmentation of NO2 plumes from individual ships using supervised learning on TROPOMI/S5P satellite data. For each ship, based on local wind conditions, as well as speed and direction (heading) of the ship, we automatically determine a Region of Interest (ROI) - an area, where the plume of the ship is expected to be located. We standardize the size and orientation of ROI, creating a standardized model of a plume - a plume cone. We then divide the plume cone into predefined subregions so that each subregion has a different probability to contain a plume of the ship. Using this information, we train a machine learning model that separates a plume produced by the analyzed ship from a background and plumes of different origin.All studied machine learning models significantly outperform a benchmark method based on a globally optimized threshold of NO2 levels. These promising results enable us to make a next step in developing a tool for continuous NOx emission monitoring of individual ships above the open sea waters.This work is funded by the Netherlands Human Environment and Transport Inspectorate, the Dutch Ministry of Infrastructure and Water Management, and the SCIPPER project which receives funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement Nr.814893.

There is a scientific consensus on the need for spatially detailed information on urban landscapes at a global scale to support a range of environmental services, as cities are acknowledged as places of: intense resource consumption and waste generation and foci of population and infrastructure that are exposed to multiple hazards of natural and anthropogenic origin. In the face of climate change, urban data is also required for future urbanisation pathways and urban design strategies, in order to “lock in” long-term resilience and sustainability, protecting cities from future decisions that could undermine their adaptability. Eventually, these global form-based, contextually specific urban planning and urban design strategies are the ultimate guarantors of successful life cycle costs, payback, and liveability. Moreover, these strategies are needed to identify the relevant data for planning and climate on neighbourhood, city and global scales, and to become part of a basic infrastructure to support a host of studies on exposure to environmental hazards, energy demand, climate adaptation and mitigation solutions and human health, as examples. Therefore, a more holistic urban landscape description is required, that goes beyond the urban mask and that enables the assessment of the spatial impact of urban planning decisions that will alter urban canopy parameters (UCPs) and their climate outcome. The global Local Climate Zone (LCZ) map presented here serves this purpose, as the LCZ typology is the only universal classification that can distinguish urban surfaces on a holistic basis, accounting for the typical combination of micro-scale land-covers and associated physical properties, all being the consequence of historic urbanisation patterns that reflect local terrain, culture, economy, etc. The 100m resolution global LCZ map is generated by feeding an unprecedented amount of labelled training areas (partly sourced from the LCZ Generator - https://lcz-generator.rub.de/) and earth observation imagery into lightweight random forest models. Its quality is assessed using the default bootstrap-based cross validation alongside a thematic benchmark for 150 selected functional urban areas using independent global and open- source data on surface cover, surface imperviousness, anthropogenic heat and building height.Complementing the single cities‘ LCZ maps accessible via the LCZ Generator, the global LCZ map for the first time reveals the world‘s intra-urban heterogeneity heterogeinity. In addition, as each LCZ type is associated with generic numerical descriptions of key UCPs, parameters critical to model atmospheric responses to urbanisation, the availability of this globally consistent and climate-relevant urban description is an essential prerequisite for developing fit-for-purpose integrated climate-sensitive urban planning policies.

Urbanisation patterns in Europe since the 1950s have resulted in a swath of low-density discontinuous development, commonly called peri-urban areas. These areas are characterised by a mixed rural-urban character, are highly dynamic in nature, and are expected to continue growing rapidly in the next few decades. This paper presents a systematic review of the literature on changes in peri-urban areas in Europe. We analysed 142 cases from 121 studies that are spread across Europe, representing a wide range of peri-urban processes. Land cover changes were the most reported changes, followed by socioeconomic changes, land use changes, planning process changes, land management changes, and environmental changes. Over half of the cases reported co-occurring land-cover and socioeconomic processes of change. In addition, we analysed sequential and causal relations between these processes. In this analysis we found that peri-urbanization cannot be conceptualized as driver – land change – impact, because often relations between processes pointed in different directions. Therefore, we characterize peri-urbanization as a multifaceted process that can manifest itself differently in different case study areas. In addition, we found that planning precedes land change processes about as often as it follows these processes, illustrating the specific challenge for planners and policy makers in managing peri-urban areas.

Rural and urban areas are often conceptualized as two separate entities and studied accordingly. However, in reality, they are related in multiple ways. Here we explore this relation between rural and urban areas from a land use perspective. We argue that land should be characterized along a gradient from rural to urban. Further, we argue that land use along this gradient typically combines both rural and urban functions. Finally, we point at the complex patterns of migration and mobility between different types of settlements, which is a multidirectional process that further blurs the distinction between rural and urban areas. These propositions are supported by examples from recent research and suggest the need for a more inclusive approach towards the analysis of rural and urban land use systems, as well as plans and policies that target these systems. Rural and urban areas are often conceptualized as two separate entities and studied accordingly. However, in reality, they are related in multiple ways. Here we explore this relation between rural and urban areas from a land use perspective. We argue that land should be characterized along a gradient from rural to urban. Further, we argue that land use along this gradient typically combines both rural and urban functions. Finally, we point at the complex patterns of migration and mobility between different types of settlements, which is a multidirectional process that further blurs the distinction between rural and urban areas. These propositions are supported by examples from recent research and suggest the need for a more inclusive approach towards the analysis of rural and urban land use systems, as well as plans and policies that target these systems.

Place-based research faces multiple threats, including both natural and global health hazards and political conflicts, which may disrupt fieldwork. The current COVID-19 pandemic shows how these threats can drastically affect social-ecological research activities given its engagement with different local stakeholders, disciplines, and knowledge systems. The crisis reveals the need for adaptive research designs while also providing an opportunity for a structural shift towards a more sustainable and inclusive research landscape.

Globally, urban areas are growing at a faster rate than their population, potentially reducing environmental sustainability due to undesirable land take in (semi)natural and agricultural lands. However, it is unclear to what extent this trend varies locally, which may hamper the formulation and implementation of local-scale policies in the context of the global competition for land. Here, we attribute built-up land change to population dynamics and changes in land take per person, for >75,000 administrative regions worldwide, typically representing municipalities or counties. Results show that changes in land take per person, expressed as the area of built-up land per capita, relate to 38.3%, 49.6%, and 37.5% of the total increase in built-up land during the periods 1975–1990, 1990–2000, and 2000–2015, respectively, but with large local variations. Interestingly, we find that centres of large cities densify in all three periods, while their rural areas show an opposite development, suggesting an urban polarization effect. We also find densification in many regions in the Global South that already have a high population density, leading to potential trade-offs in terms of human wellbeing. Therefore, our work provides novel insights into the debate on sustainable urban development at a global scale.

TROPOMI measurements of tropospheric NO2 columns provide powerful information on emissions of air pollution by ships on open sea. This information is potentially useful for authorities to help determine the (non-)compliance of ships with increasingly stringent NOx emission regulations. We find that the information quality is improved further by recent upgrades in the TROPOMI cloud retrieval and an optimal data selection. We show that the superior spatial resolution of TROPOMI allows the detection of several lanes of NO2 pollution ranging from the Aegean Sea near Greece to the Skagerrak in Scandinavia, which have not been detected with other satellite instruments before. Additionally, we demonstrate that under conditions of sun glint TROPOMI's vertical sensitivity to NO2 in the marine boundary layer increases by up to 60 %. The benefits of sun glint are most prominent under clear-sky situations when sea surface winds are low, but slightly above zero (±2 m/s). Beyond spatial resolution and sun glint, we examine for the first time the impact of the recently improved cloud algorithm on the TROPOMI NO2 retrieval quality, both over sea and over land. We find that the new FRESCO+wide algorithm leads to 50 hPa lower cloud pressures, correcting a known high bias, and produces 1-4·1015 molec/cm2 higher retrieved NO2 columns, thereby at least partially correcting for the previously reported low bias in the TROPOMI NO2 product. By training an artificial neural network on the 4 available periods with standard and FRESCO+wide test-retrievals, we develop a historic, consistent TROPOMI NO2 data set spanning the years 2019 and 2020. This improved data set shows stronger (35-75 %) and sharper (10-35 %) shipping NO2 signals compared to co-sampled measurements from OMI. We apply our improved data set to investigate the impact of the COVID-19 pandemic on ship NO2 pollution over European seas and find indications that NOx emissions from ships reduced by 20-25 % during the pandemic. The reductions in ship NO2 pollution start in March-April 2020, in line with changes in shipping activity inferred from AIS data. [ FROM AUTHOR] Copyright of Atmospheric Measurement Techniques Discussions is the property of Copernicus Gesellschaft mbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all Abstracts.)

Understanding urban expansion at the watershed scale is important because watersheds are important carriers of ecological and environmental impacts. However, current analyses are mainly restricted to administrative units only. Here, we used a long‐term multitemporal data set of urban land to quantify the spatiotemporal trends in the extent and form of urban expansion from 1992 to 2016 in endorheic and exoreic watersheds, globally. Overall, urban expansion in 70% of watersheds (154/220) showed a decelerating trend. The average urban expansion speed of these watersheds in the last 6 years was approximately half of that in the last 24 years. Urban expansion speed in endorheic watersheds lagged behind the counterparts in exoreic watersheds, with the former approximately 1/4 of the latter. More importantly, the pattern of urban expansion in endorheic watersheds was following the low‐density and sprawling trend in exoreic watersheds, which could exert far‐reaching impacts on the sustainability of endorheic watersheds located in arid lands. These findings suggest the need to look beyond administrative city boundaries for land use planning and policies in the context of watershed management.

Trade-offs between prosperity and urban land per capita in major world cities Globally, urban land expands at a faster rate than the corresponding urban population, which comes at a cost of agricultural and natural land. Wealth has been identified as an underlying driver of this trend, but it is unclear whether more prosperous cities inevitably have a greater urban land consumption. Here, we map urban prosperity indicators to their relevant Sustainable Development Goals for 64 major world cities and relate these to the corresponding urban land consumption (defined here as built-up land per capita). Results indicate a moderately-weak but significant correlation between overall prosperity and urban land consumption (Spearman's correlation, ρ = 0.47, p < 0.001), suggesting a trade-off between both. In addition, we find a regional clustering, with for example cities with relatively low prosperity and low urban land consumption in Africa, and cities with high prosperity and low-to-medium urban land consumption in Europe. The moderately-weak correlation in combination with these regional differences suggests that the observed trade-off is avertable and that other drivers moderate this relation. Consequently, cities can increase their prosperity without additional environmental consequences entailing land take and the conversion of natural and agricultural land.

Global losses of natural area are primarily attributed to cropland expansion, whereas the role of urban expansion is considered minor. However, urban expansion can induce cropland displacement, potentially leading to a loss of forest elsewhere. The extent of this effect is unknown. This study shows that indirect forest losses, through cropland displacement, far exceed direct losses from urban expansion. On a global scale, urban land increased from 33.2 to 71.3 million hectares (Mha) between 1992 and 2015, leading to a direct loss of 3.3 Mha of forest and an indirect loss of 17.8 to 32.4 Mha. In addition, this urban expansion led to a direct loss of 4.6 Mha of shrubland and an indirect loss of 7.0 to 17.4 Mha. Guiding urban development towards more sustainable trajectories can thus help preserve forest and other natural area at a global scale. Urban expansion displaces agricultural land elsewhere, indirectly causing loss of natural land cover. A global study finds that this impact is more substantial than previously thought.

This study investigates the role of the different institutional actors involved in the development and implementation of land use policies in the Ethiopian Rift Valley. The work is based on interviews with key informants from different administrative levels and these results are compared to the relevant policy documents. While the constitution prescribes a participatory policy development process, our results show that in reality policies are made at the highest level and implemented in a top-down approach from the higher to the lower administrative levels. Moreover, the institutional network mainly consists of institutions that are hierarchically linked, while horizontal and diagonal relations are less common and less important. Consistently, higher level institutions are mostly involved in the development of land use policies, while the roles of lower level institutions are predominantly in the implementation thereof. This lack of participation by lower level institutions, in addition to a lack of capacity and absence of clear institutional mandates, hampers the effectiveness of land use policies. Our results also provide suggestions to improve the development, communication, and eventually the acceptability of land use policies towards sustainable land management.

The TROPOMI and OMI satellite sensors provide an exciting perspective on the sources, dispersion, and fate of air pollution emitted by the international shipping industry. Recently it proved possible to detect plumes of NO2 from individual ships with high-resolution measurements from TROPOMI, especially when observed under sun-glint conditions. In principle, this allows the quantification of NOx emissions from ocean-going ships, but an outstanding scientific question is under which atmospheric conditions ship plumes are best detected. The effects of viewing geometries, local wind speed, partial cloud cover, emission strength as well as chemical boundary conditions on NO2 plume detectability are still a challenge to understand.Here we investigate TROPOMI’s ability to detect NO2 pollution from the international shipping sector under different measurement conditions, and we compare it to that of its predecessor OMI. Uncertainties in cloud properties – and thereby in the resulting Air Mass Factors – are one of the leading sources of uncertainty in the TROPOMI NO2 retrieval. These become increasingly important when investigating small NO2 enhancements close to the Earth’s surface in partly cloudy scenes, i.e. thos from shipping.We examine for the first time the new TROPOMI-FRESCO+DDS algorithm which uses a wider spectral window for the O2-A band than the original FRESCO+, increasing its sensitivity to low clouds. We cross-evaluate the resulting cloud properties against the operational TROPOMI-FRESCO+, VIIRS and OMCLDO2 algorithms on a pixel-by-pixel basis. This comparison reveals it is likely that FRESCO+ cloud heights are biased high by around 100hPa, leading to an overestimated AMF and thus low biased NO2 columns for (partially) cloudy scenes. We explore the AMF correction based on FRESCO+DDS to improve the operational TROPOMI NO2 retrieval for ship plume detection and discuss implications for the detection of COVID-19 associated reductions in shipping, and hence pollution levels over European seas.This work is funded by the Netherlands Human Environment and Transport Inspectorate, the Dutch ministry of Infrastructure and Water Management, and the SCIPPER project which receives funding from the European Union’s Horizon 2020 research and innovation program under grant agreement Nr.814893.

Starting from January 2020, new IMO regulations limiting the Sulphur content of the fuel used by seagoing vessels came into force. As of 2021, new and stricter NOx emission standards are applied for newly built ships entering the North and Baltic Sea. There are various methods that are used to measure the pollution produced by ships in ports or off the coastal areas. Due to practical limitations, however, the conduction of such monitoring above the open sea has not been possible up to now.The TROPOspheric Monitoring Instrument onboard the Copernicus Sentinel 5 Precursor satellite (TROPOMI/S5P) provides the atmosphere monitoring data with an unprecedented spatial resolution. With this instrument plumes produced by individual ships of substantial size can be detected. In our study we focus on application of the TROPOMI NO2 tropospheric column for tracking back the emission produced by individual ships at open sea.On a global scale, individual ships are considered to be low-source pollution emitters. As a result, it is difficult to separate an emission plume from the background pollution, especially, in case of comparable background concentration. In order to improve the distinction between the plume and the background, we propose the use of the local spatial autocorrelation measure Moran’s I. This measure amplifies regular shaped high-concentration structures and suppresses random co-occurring concentration peaks. By means of the Automated Identification Signal (AIS) data that records historical ship locations, the detected structures can be associated with individual ships. We further propose heuristic algorithms using local weather conditions (wind speed/direction) for an efficient ship-plume matching and NO2 concentration estimation.We evaluate the quality of a ship-plume assignment by comparing the estimated NO2 concentration with model-based emission estimations determined from speed and length of the ship. Notable linear correlation between our estimations and the model-based values supports the proposed method.This work contributes to realising global scale verification/estimation of emission plumes with satellites by providing automated and enhanced processing of satellite retrievals for identifying and quantifying of NOx plumes produced by individual seagoing vessels.This work is funded by the Netherlands Human Environment and Transport Inspectorate, the Dutch Ministry of Infrastructure and Water Management, and the SCIPPER project which receives funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement Nr.814893.

Farming in Sub-Saharan Africa (SSA) is historically dominated by small-scale farms (SSFs), but evidence suggests that medium-scale farms (MSFs) are becoming increasingly prominent. These MSFs are often portrayed as entrepreneurial innovators, bringing dynamism and commercialization to SSA agriculture without displaying the negative features of land grabbing processes. However, there is little empirical evidence supporting these claims. We deployed a survey of 319 farmers covering a wide range of sizes in the Kenyan Rift Valley. Results show that MSFs are not a new phenomenon in the area, and are mostly farms that incrementally increased in size by buying or renting additional land. Furthermore, we find no differences in yields for various crop types between SSFs and MSFs. On average, MSFs use a higher share of their land for grazing, and have more dairy cattle per farm but less per hectare. The average MSF has a higher propensity to grow cash crops and serve non-local markets than the average SSF, and they employ significantly fewer people per hectare. However, within-category heterogeneity is high for all investigated dimensions, while past decision-making and future aspirations reveal entrepreneurship to occur in all farm size categories. We conclude that only a subset of all MSFs can be characterized as entrepreneurial, while these qualities can also be attached to many SSFs. Hence, we find that farm scale is an imperfect proxy to gauge the characteristics of a farm system, and presenting MSFs as a developmental panacea for SSA's rural areas is therefore unwarranted.

Changes in the amount and location of cropland areas may affect the potential crop production at different spatial scales. However, most studies ignore the impacts of cropland displacement on potential crop production. In many countries, cropland protection policies mainly aim for no loss in cropland area, while there is no restriction on change of cropland location. Taking China as the study area, we analyze the impacts of cropland displacement on potential crop production at four administrative levels during the period 2000 and 2018. At the national level, we find a net decrease in cropland area of 0.81 Mha, while another 19.63 Mha was displaced. The former led to a decrease of 4.20 Mton in potential crop production, while the latter resulted in a decrease of 43.26 Mton as a result of lower quality of the newly cultivated lands. In other words, cropland displacement explains 91% of the total loss in potential crop production at the national scale. However, the contribution of cropland displacement to total change in potential crop production is increasingly smaller at provincial level, municipal level, and county levels. These findings highlight the importance of geographic location on crop production and suggest that cropland policies should consider geographic location in addition to cropland area.

Urban land use is often characterized based on the presence of built-up land, while the land use intensity of different locations is ignored. This narrow focus is at least partially due to a lack of data on the vertical dimension of urban land. The potential of Earth observation data to fill this gap has already been shown, but this has not yet been applied at large spatial scales. This study aims to map urban 3D building structure, i.e. building footprint, height, and volume, for Europe, the US, and China using random forest models. Our models perform well, as indicated by R2 values of 0.90 for building footprint, 0.81 for building height, and 0.88 for building volume, for all three case regions combined. In our multidimensional input variables, we find that built-up density derived from the Global Urban Footprint (GUF) is the most important variable for estimating building footprint, while backscatter intensity of Synthetic Aperture Radar (SAR) is the most important variable for estimating building height. A combination of the two is essential to estimate building volume. Our analysis further highlights the heterogeneity of 3D building structure across space. Specifically, buildings in China tend to be taller on average (10.35 m) compared to Europe (7.37 m) and the US (6.69 m). At the same time, the building volume per capita in China is lowest, with 302.3 m3 per capita, while Europe and the US show estimates of 404.6 m3 and 565.4 m3, respectively. The results of this study (3D building structure data for Europe, the US, and China) are publicly available, and can be used for further analysis of urban environment, spatial planning, and land use projections.

By law, non-governmental organizations (NGOs) in Ethiopia are severely restricted in their activities towards policy development. In this study we explore to what extent these restrictions have affected NGOs in Natural Resource Management in the Oromia regional state of Ethiopia. We quantitatively analyzed 106 semi-structured questionnaires, in order to assess 1) the general characteristics of these NGO, 2) the role of NGOs in natural resource management, 3) the factors that constrain their activities, especially in relation to the proclamation, and 4) the cooperation between NGOs as well as other stakeholders. Results indicate that NGOs are mainly involved in policy implementation, including afforestation, forest management, and soil and water conservation. We find that a more active role in agenda setting and policy formulation is hampered by the 2009 proclamation, which explicitly restricts the role of Ethiopian Residence Charities/Societies (ERCS) and Foreign Charities (FC). Consistently, NGOs, as well as their donors, often avoid involvement in policy development, in fear of potential collusion with the government. In addition, NGOs listed legal and administrative barriers, poor networking and cooperation among NGOs, lack of capacity, lack of information, and a lack of clear role on policy issues as constraints for influencing policies for natural resource management. The extent to which these factors affect NGOs is dependent on their type and the source of their funding.

A wide variety of settlement systems exist, ranging from small villages to large metropolises. However, spatial analyses are typically confined to the mere presence or absence of built-up land and the changes therein, while more subtle differences between various settlement systems are ignored. In this paper we study the spatial distribution of Chinese settlements in terms of their built-up land, cluster density and cluster size, as well as their changes between 1990 and 2010. Subsequently, we use these three properties to delineate settlement systems, and analyze the observed change trajectories between 1990 and 2010. We find that roughly 70% of all built-up land and more than 50% of all new built-up land added between 1990 and 2010 is included in village landscapes, which challenges the current focus on studying mega-cities. We also find that settlement changes mostly follow small and incremental steps towards more dense urban systems, following multiple different development trajectories. Specifically, rural villages seldom convert into urban systems directly, but typically increase gradually to towns and sub-urban landscapes. Settlement systems provide a first step towards a comprehensive understanding of human settlements and their change trajectories, which can inform targeted land use planning and the development of policies that more explicitly accounts for diversity in settlement types.

Urbanization has been responsible for the loss of cropland worldwide, especially in China. To guarantee national food security, China has implemented a series of policies to protect cropland. One of these policies requires that one-hectare cropland should be reclaimed when urban expansion occupies one-hectare cropland. Since most cropland reclamation leads to a conversion of natural habitat, such as wetland and grassland, urban expansion may lead to (indirect) natural habitat loss in addition to direct loss from conversion of into urban area. While several studies assessed the direct habitat loss resulted from built-up area expansion, few studies investigated the indirect losses caused by cropland displacement. In this paper, a model-based approach is applied to explore both direct and indirect impacts of built-up area expansion on natural habitat loss for the city of Wuhan, China, between 2010 and 2020 using different scenarios. Our scenarios differ in the implementation of strict cropland protection policies and ecosystem conservation strategies. Results show that the indirect loss of natural habitat due to cropland displacement under strict cropland protection policies far outweighs the direct loss due to built-up area expansion alone. Moreover, we found that ecosystem conservation strategies mainly influence the type of natural habitat that is affected, while the total amount of natural habitat loss remains relatively constant.

Understanding of cropland dynamics in a large geographical extent is mostly based on observations of area change, while the changes in landscape pattern are hardly assessed. The total amount of cropland in China has remained relatively stable in recent years, which might suggest there was little change. In this analysis, we combine the number of cropland patches (NP) with the total cropland area (TA) for a more comprehensive characterization of cropland change in China. We use GlobeLand30–a global land cover dataset with a 30 m resolution for the years 2000 and 2010–and characterize changes in TA and NP for each county as increase, stable, or decrease. This characterization shows that 703 out of 2420 counties experienced both cropland loss and increased fragmentation. The predominant cropland loss in these areas, especially in the North China Plain, is converted to artificial land. Another 212 are characterized by the opposite developments: an increase in cropland and decreased fragmentation. These counties, are mainly characterized by a conversion of forest areas and grassland areas. It suggests that the cropland conservation policy in China effectively protected the total cropland area in overall, but the consequences in terms of fragmentation might be underestimated. Counties with no obvious change in both indicators, measuring 279 counties, are mainly located in the Southeast. Our results are further compared with local level case studies: the fair consistency indicates alternatives of applying GlobeLand30 for analyzing landscape changes across scales and for cross-site comparisons.

Land systems are described based on various characteristics, including land cover composition and agricultural production. However, it is uncertain to what extent livestock, particularly monogastric livestock, determines land systems. We included monogastrics in a land system classification, and statistically analyzed the land cover composition and agricultural production of otherwise similar land systems with and without monogastric livestock. The results indicate that land systems with monogastrics are statistically different from their counterparts in the classification without monogastrics in terms of grassland area and crop yields, but are less different in terms of tree area, crop area, and ruminant livestock production. We then used a land systems map that includes monogastrics in the classification and a similar map that does not include monogastrics to project future changes in a novel manner that integrates livestock as a determinant of land systems. The results show that including monogastrics in otherwise similar projections yields less cropland intensification and more cropland expansion in several world regions, including Northern Africa and the Middle East. Other regions, such as Europe and Australia, were characterized by less decrease or more increase in tree area in the application with monogastrics, mainly due to the occurrence of open forests with monogastrics. This study prompts a call for improved characterization of land systems for land use and cover change (LUCC) assessments in order to better represent LUCC driven by monogastric livestock.