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There is an implementation gap between government plans and land-use changes at the local level in the Vietnamese Mekong Delta. This stands in the way of the sustainable development of the delta, especially in the face of environmental degradation, climate change, and water-use conflicts.To narrow the gap between plans and practice, the government needs a better understanding of what drives land-use decisions at the farm level. Our research developed and applied a multi-scale framework to identify the principal drivers of land-use changes at the farm level in the Vietnamese Mekong Delta over the past 40 years. We conducted semi-structured interviews with 31 farmers in the highly flooded zone, then used transcript analysis to quantify the influence of the drivers mentioned by farmers. We found drastic shifts in land uses, predominantly towards rice intensification. Among the 43 change drivers the farmers mentioned, those operating at the regional scale were particularly influential, including the activities of local authorities, neighborhood effects, and the development of water management infrastructure. Market factors have become more prominent in the last two decades, motivating farmers to shift from double to triple rice or to gradually diversify into vegetables.However, agricultural diversification remains limited by the agro-hydrological context, which favors triple rice cropping, as well as household scale factors such as natural and physical assets of the farm,household capital, and labor capacity. The local community also played a key role in land-use change,though with a double-edged effect, both delaying implementation of central government policy and forcing farmers to follow the majority’s decision.

The development of a European Spatial Data Infrastructure (SDI) officially started with the entry into force of the INSPIRE Directive in 2007. INSPIRE’s implementation phase should be completed by the European Union (EU) and its member states at the end of 2021: a pivotal point to evaluate INSPIRE’s current governance and explore future scenarios. First, INSPIRE’s governing system is evaluated through an online survey by its involved stakeholders. Second, these results are applied in an agent-based model to explore potential governance scenarios and strategies. The results show that strong aspects of INSPIRE’s governing system are the supported vision and its formal structures, such as standards, technology and roles and responsibilities. Weak aspects are the access to resources, especially budget and time resources, and data use. The agent-based simulations show that INSPIRE is probably more constrained by its budget resources than its current dominant hierarchical interaction mix, although a combination of adaptive governance and continuous budget proved the most sustainable governance scenario.

In order to facilitate and coordinate spatial data sharing and exchange, many organisations have developed spatial data infrastructures (SDIs). SDI governance plays a pivotal role in the development and evolution of an SDI, but as SDIs are complex adaptive systems, governing is a challenge. This research therefore proposes a complexity perspective to SDI governance by exploring the use of agent-based modelling to simulate and examine SDI governance interactions. In this agent-based simulation, we examine interactions between SDI stakeholders, data availability and the effects of different governance styles (hierarchical, network and laissez-faire governance) and budget policies. The simulation shows that it is possible to mimic SDI governance dynamics through agent-based modelling. By running different scenarios, it appears that a network approach is more successful compared to a hierarchical or laissez-faire approach. Expert validation shows that overall the results of the simulation are credible and insightful, although improvements can be made to make the model more realistic. With agent-based modelling, SDI governance becomes more tangible and visible, which facilitates discussion and understanding. Agent-based modelling therefore appears to be a helpful new approach in a better understanding of the complexities and dynamics of SDI governance

Reference data for large-scale land cover map are commonly acquired by visual interpretation of remotely sensed data. To assure consistency, multiple images are used, interpreters are trained, sites are interpreted by several individuals, or the procedure includes a review. But little is known about important factors influencing the quality of visually interpreted data. We assessed the effect of multiple variables on land cover class agreement between interpreters and reviewers. Our analyses concerned data collected for validation of a global land cover map within the Copernicus Global Land Service project. Four cycles of visual interpretation were conducted, each was followed by review and feedback. Each interpreted site element was labelled according to dominant land cover type. We assessed relationships between the number of interpretation updates following feedback and the variables grouped in personal, training, and environmental categories. Variable importance was assessed using random forest regression. Personal variable interpreter identifier and training variable timestamp were found the strongest predictors of update counts, while the environmental variables complexity and image availability had least impact. Feedback loops reduced updating and hence improved consistency of the interpretations. Implementing feedback loops into the visually interpreted data collection increases the consistency of acquired land cover reference data.

Purpose: Mekong Delta's shrimp farming contributes to socio-economic development but tends to reduce the mangrove area. On the one hand, NGOs advocate balancing ecology and economy, while on the other hand, the Vietnamese government supports intensifying shrimp production. The latter strategy increases shrimp diseases and marginalises smallholders. To influence the opinions, knowledge and behaviour of shrimp farmers, we developed and tested a serious board game. Approach: Through several iterations, we designed a realistic board game mimicking local shrimp farming. Then, after conducting three sessions of gameplay with farmers in three sites, we assessed how this gameplay influenced players. We used a semi-experimental set-up by collecting data on the day of the gameplay, and on two further time-points. Findings: After the gameplays, farmers reported to have learned about the risks of monoculture-intensive-shrimp system and the advantages of hybrid systems. Likert ratings showed that players adopted innovations faster and, in case of shrimp diseases, consulted commercial service-providers less, but with other farmers more. They confirmed to have learned that the best strategy for them would be to adopt hybrid systems, i.e. intensify part of the area of mixed-mangrove or improved-extensive shrimp farms. Theoretical implications: Realistic board games can create opportunities for social learning and training of groups which are not always easy to reach. Practical implications: Board games can support transfer of knowledge and skills to farmers, and can develop their critical thinking. Creation: The findings that board games are effective in the context of a top-down extension systems offer value opportunities.

Soil salinity increase is a serious and global threat to agricultural production. The only database that currently provides soil salinity data with global coverage is the Harmonized World Soil Database, but it has several limitations when it comes to soil salinity assessment. Therefore, a new assessment is required. We hypothesized that combining soil properties maps with thermal infrared imagery and a large set of field observations within a machine learning framework will yield a global soil salinity map. The thermal infrared imagery acts as a dynamic variable and allows us to characterize the soil salinity change. For this purpose we used Google Earth Engine computational environment. The random forest classifier was trained using 7 soil properties maps, thermal infrared imagery and the ECe point data from the WoSIS database. In total, six maps were produced for 1986, 2000, 2002, 2005, 2009, 2016. The validation accuracy of the resulting maps was in the range of 67–70%. The total area of salt affected lands by our assessment is around 1 billion hectares, with a clear increasing trend. Comparison with 3 studies investigating local trends of soil salinity change showed that our assessment was in correspondence with 2 of these studies. The global map of soil salinity change between 1986 and 2016 was produced to characterize the spatial distribution of the change. We conclude that combining soil properties maps and thermal infrared imagery allows mapping of soil salinity development in space and time on a global scale.

Dynamics of coupled Social-Ecological Systems (SES) result from the interplay of society and ecology. To assess SES resilience, we constructed an Agent-Based Model (ABM) of a land use system as a stereotypical example of SES and investigated how resilience of the represented system is affected by both external disturbances and internal dynamics. The model explicitly considered different aspects of resilience in a framework derived from literature, which includes “resilience to”, “resilience of”, “resilience at”, “resilience due to”, and “indicators of resilience”. External disturbances were implemented as shocks in crop yields. Internal dynamics comprised of two types of social interaction between agents (learning and cooperation), an ecological feedback of soil depletion and an economic feedback of agglomeration benefits. We systematically varied these mechanisms and measured indicators that reflected spatial, social, and economic resilience. Results showed that (1) internal mechanisms increased the ability of the system to recover from external shocks, (2) feedbacks resulted in different regimes of crop cultivation, each with a distinctive set of functions, and (3) resilience is not a generic system property, but strongly depends on what system function is considered. We recommend future studies to include internal dynamics, especially feedbacks, and to systematically assess them across different aspects of resilience.

Increased soil salinity is a significant agricultural problem that decreases yields for common agricultural crops. Its dynamics require cost and labour effective measurement techniques and widely acknowledged methods are not present yet. We investigated the potential of Unmanned Aerial Vehicle (UAV) remote sensing to measure salt stress in quinoa plants. Three different UAV sensors were used: a WIRIS thermal camera, a Rikola hyperspectral camera and a Riegl VUX-SYS Light Detection and Ranging (LiDAR) scanner. Several vegetation indices, canopy temperature and LiDAR measured plant height were derived from the remote sensing data and their relation with ground measured parameters like salt treatment, stomatal conductance and actual plant height is analysed. The results show that widely used multispectral vegetation indices are not efficient in discriminating between salt affected and control quinoa plants. The hyperspectral Physiological Reflectance Index (PRI) performed best and showed a clear distinction between salt affected and treated plants. This distinction is also visible for LiDAR measured plant height, where salt treated plants were on average 10 cm shorter than control plants. Canopy temperature was significantly affected, though detection of this required an additional step in analysis – Normalised Difference Vegetation Index (NDVI) clustering. This step assured temperature comparison for equally vegetated pixels. Data combination of all three sensors in a Multiple Linear Regression model increased the prediction power and for the whole dataset R2 reached 0.46, with some subgroups reaching an R2 of 0.64. We conclude that UAV borne remote sensing is useful for measuring salt stress in plants and a combination of multiple measurement techniques is advised to increase the accuracy.

Policies supporting rice production and investments in water infrastructure enabled intensification and diversification of farming systems in the Vietnamese Mekong Delta (VMD) over the past 20 years. Yet, demands of food security, economic development, and climate change continue to pose diverging and often conflicting challenges for water resources management in the upper, central, and coastal zones of the delta. The major changes effected in the VMD’s hydrological regime and land-use patterns are acknowledged in the literature, but few studies have examined the interplay between these dynamics at the delta scale. Based on time-series maps and statistical data on land-use, flooding, and salinity intrusion, we investigated the interrelations between land-use dynamics and changes in hydrological regime across the VMD in three representative periods. Land-use was found to be highly variable, changing by 14.94% annually between 2001 and 2012. Rice cropping underwent the greatest change, evolving from single cropping of traditional varieties towards double and triple cropping of high-yielding varieties. Aquaculture remained stable after rapid expansion in the 1990s and early 2000s. Meanwhile, flooding and salinity intrusion were increasingly controlled by hydrological infrastructure erected to supply freshwater for agriculture. Effects of this infrastructure became particularly evident from 2001 to 2012. During this period, spatial and temporal impacts on flooding and salinity intrusion were found, which extended beyond the rice fields to affect adjacent lands and livelihood activities. Unforeseen effects will likely be aggravated by climate change, suggesting a need to rethink the scale of planning towards a more integrated hydrologic approach.

The Climate-Smart Landscape (CSL) approach has recently emerged as an integrated management strategy to address the increasing pressures on agricultural production, ecosystem conservation, rural livelihoods, and climate change mitigation/adaptation. Agribusiness companies play a controversial role in achieving CSL goals. On one hand, their operations cause land and natural resource degradation. On the other hand, they have technical, managerial, and financial resources to invest in more sustainable landscapes. Despite the controversy, empirical evidence on which practices agribusiness companies plan to undertake for sustainable landscape management and their intentions to contribute achieving CSL objectives is scarce and fragmented in the academic literature. To fill in this knowledge gap, this paper describes and reviews agribusiness companies' planned practices for integrated landscape management worldwide. Additionally, this paper evaluates to what extent companies plan to engage in two crucial factors to achieving CSL: stakeholder involvement and activities' monitoring. Results show that despite many agribusiness-led initiatives have potential in contributing to more sustainable practices, major steps should still be taken to involve stakeholders and monitoring their process and outcomes to actually achieve CSL goals.

Data availability is a persistent constraint in social policy analysis. Web 2.0 technologies could provide valuable new data sources, but first, their potentials and limitations need to be investigated. This paper reports on a method using Twitter data for deriving indications of active citizenship, taken as an example of social indicators. Active citizenship is a dimension of social capital, empowering communities and reducing possibilities of social exclusion. However, classical measurements of active citizenship are generally costly and time-consuming. This paper looks at one of such classic indicators, namely, responses to the survey question ‘contacts to politicians’. It compares official survey results in Spain with findings from an analysis of Twitter data. Each method presents its own strengths and weakness, thus best results may be achieved by the combination of both. Official surveys have the clear advantage of being statistically robust and representative of a total population. Instead, Twitter data offer more timely and less costly information, with higher spatial and temporal resolution. This paper presents our full methodological workflow for analysing and comparing these two data sources. The research results advance the debate on how social media data could be mined for policy analysis.

A change of canopy temperature can indicate stress in vegetation. Use of canopy temperature to assess salt stress in specific plant species has been well studied in laboratory and greenhouse experiments, but its potential for use in landscape-level studies using remote sensing techniques has not yet been explored. Our study investigated the application of satellite thermography to assess soil salinity of cropped areas at the landscape level. The study region was Syrdarya Province, a salt-affected, irrigated semi-arid province of Uzbekistan planted mainly to cotton and wheat. We used moderate-resolution imaging spectroradiometer satellite images as an indicator for canopy temperature and the provincial soil salinity map as a ground truth dataset. Using analysis of variance, we examined relations among the soil salinity map and canopy temperature, normalized difference vegetation index, enhanced vegetation index, and digital elevation model. The results showed significant correlations between soil salinity and canopy temperature, but the strength of the relation varied over the year. The strongest relation was observed for cotton in September. The calculated F values were higher for canopy temperature than for the other indicators investigated. Our results suggest that satellite thermography is a valuable landscape-level approach for detecting soil salinity in areas under agricultural crops.

Citizen science is increasingly being used in diverse research domains. With the emergence and rapid development of sensor technologies, citizens potentially have more powerful tools to collect data and generate information to understand their living environment. Although sensor technologies are developing fast, citizen sensing has not been widely implemented yet and published studies are only a few. In this paper, we analyse the practical experiences from an implementation of citizen sensing for urban environment monitoring. A bottom-up model in which citizens develop and use sensors for environmental monitoring is described and assessed. The paper focuses on a case study of Amsterdam Smart Citizens Lab using NO2 sensors for air quality monitoring. We found that the bottom-up citizen sensing is still challenging but can be successful with open cooperation and effective use of online and offline facilities. Based on the assessment, suggestions are proposed for further implementations and research.

Land cover identification and area quantification are key aspects of implementing the European Common Agriculture Policy. Legitimacy of support provided to farmers is monitored using the Land Parcel Identification System (LPIS), with land cover identification performed by visual image interpretation. While the geometric orthoimage quality required for correct interpretation is well understood, little is known about the photometric quality needed for LPIS applications. This paper analyzes the orthoimage quality characteristics chosen by authors as being most suitable for visual identification of agricultural fields. We designed a survey to assess users' preferred brightness and contrast ranges for orthoimages used for LPIS purposes. Survey questions also tested the influence of a background color on the preferred orthoimage brightness and contrast, the preferred orthoimage format and color composite, assessments of orthoimages with shadowed areas, appreciation of image enhancements and, finally, consistency of individuals' preferred brightness and contrast settings across multiple sample images. We find that image appreciation is stable at the individual level, but preferences vary across respondents. We therefore recommend that LPIS operators be enabled to personalize photometric settings, such as brightness and contrast values, and to choose the displayed band combination from at least four spectral bands.

The linear spectral mixing model is a widely used technique in remote sensing to estimate the fractions of several individual surface components present in an image pixel and the pure reflectance spectrum of a component, called endmember, is the model’s necessary parameter. Different methods can be used to extract endmembers, finding out the pure pixel from hyperspectral data is the most common way, but at the regional scale, the selected single pixel can not present the typical component accurately. The objective of this paper is to estimate the feasibility of up-scaling from high spatial resolution data to medium spatial resolution hyperspectral data by linear spectral unmixing based technique for extracting the required endmembers. In this case, an inverted Li-Strahler geometric-optical model is applied to retrieve one of the forest canopy variables (crown closure) in a broadleaved forest natural reserve, located in the Three Gorges region of China. This model needs three important scene components (sunlit canopy, sunlit background and shadow). The three components’ classes are firstly estimated using QuickBird fusional image with 0.6 m spatial resolution by eCognition, an object-based classification method. Then, a 50 by 50 pixels moving window calculated each component’s proportion is matched to individual pixels with 30 m spatial resolution of EO-1 Hyperion data. The umixing model is finally used for deriving the three endmembers based on the Hyperion data with surface reflectance and the per-pixel three fractions computing from QuickBird classification. From the spectral profile comparison, the scaling-based endmembers indicate the mean spectra of the components unlike the pure pixels’ spectra. For validating the results, we use 32 independent sample sites collected in the study area to assess the accuracy of inverted model’s outputs, and the results of scaling-based endmember extraction method (R²=0.60) seem better than the pixel-based method (R²=0.51).