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The unprecedented growth of metropolitan areas creates challenges for maintaining liveable and biodiverse cities. Urban areas face multiple demands on sparse space whilst various stakeholders similarly aim to promote greening efforts. In this context, authorities need to balance various policy objectives with demands from diverse urban stakeholders. Part of this challenge is the question how generalized data-driven green space planning approaches can be connected to local, contextualized understandings, practices and values related to green space.In this paper, we present a participative application of GIS that contributes to bridging the gap between data-driven and citizen-centred approaches in urban greening. Through an empirical study in Amsterdam, we show how this can link local priorities with larger-scale policy frameworks through deliberative and data-driven co-creation. In our case study, local stakeholders and researchers jointly identified criteria for greening, translated these into indicators and eventually identified potential locations for small-scale greening. Site visits by local experts helped to validate the model results and translate this into concrete plans for greening several locations.Our approach promoted a dialogue between stakeholders, linking spatial data with practical experiences and aligning local priorities with policy programmes. Combining various knowledges from involved stakeholders also contributed to the quality of analysis and validation of modelling results. By increasing transparency and inclusiveness of planning, it also contributed to acceptance of process outcomes and empowering local stakeholders. With increasing urgencies for environmental measures in many cities, we emphasize the potential of transdisciplinary GIS-approaches to navigate different interests and integrate various types of valuable knowledge. We suggest that similar approaches may be applied to other environmental challenges that have a strong spatial character.

The scope and complexity of international forest-related governance have expanded tremendously over the last decades. As many as 41 ‘institutional elements’ were counted by scholars (from UNFF to UNFCCC to SDGs). The questions of how these governance arrangements ‘perform’, for what purpose and for whom are widely contested between scholars and practitioners. This paper compares three different analytical frames, which have been employed by some of the authors. These are 1) the consequences of a fragmented regime complex, 2) the global-local nexus and 3) the critical global political economy. The frames map out their contributions and key differences in analytical perspective and help focus and advance debates. Each perspective is based on different theories, epistemologies and methodological approaches and hence yields different key results. The first frame emphasises institutional and policy fragmentation, the symbolic nature of the agreements and the ineffectiveness of the policy measures; the second shows progress in discourses, institutional design, and on-the-ground performance, while the third finds global governance has reinforced inequalities in power and access to land and natural resources. All authors agree, however, that a shift in the balance of power and novel actor coalitions are necessary to change the current global forest governance trajectory significantly. They also acknowledge the need for much greater diversity in voice and representation in both the research and practice of global forest governance.

Sustainable forest management in South-East Asia is challenged by smallholder livestock husbandry as farmers supplement insufficient on-farm forage with resources extracted from forests. This study assesses which determinants affect forest resource extraction by investigating teak forest usage by Indonesian cattle farmers. Based on a survey of 600 smallholders, we provide an overview of which resources are extracted, assess the factors influencing the likelihood of becoming a forest user group member and analyse characteristics that differ between members and non-members as well as the effect of membership on extraction frequency. Almost half of the farmers collect animal forage and heating material from teak forests or farm forestland. Two thirds of farmers who extract resources are not institutionally organized. Increasing distance to forests as well as diversity of extraction are found to be related with increased odds for highest extraction frequency. Group members differ from non-members mainly in the number of resources extracted as well as the usage of forest grass as a main forage source. Farmers are more likely to be group members with increasing farm size and poverty levels. Socio-economic benefits obtained from group membership should be redesigned so that more smallholders are motivated to join this governance scheme. If sustainable forest management schemes are scaled up to agroforestry-based agricultural intensification, a broad set of national and international benefits could be realized.

African cities are growing rapidly and more than half of their populations live in deprived areas. Local stakeholders urgently need accurate, granular, and routine maps to plan, upgrade, and monitor dynamic neighborhood-level changes. Satellite imagery provides a promising solution for consistent, accurate high-resolution maps globally. However, most studies use very high spatial resolution images, which often cover only small areas and are cost prohibitive. Additionally, model transferability to new cities remains uncertain. This study proposes a scalable and transferable approach to routinely map deprived areas using free, Sentinel-2 images. The models were trained and tested on three cities: Lagos (Nigeria), Accra (Ghana), and Nairobi (Kenya). Contextual features were extracted at 10 m spatial resolution and aggregated to a 100 m grid. Four machine learning algorithms were evaluated, including multi-layer perceptron (MLP), Random Forest, Logistic Regression, and Extreme Gradient Boosting (XGBoost). The scalability of model performance was examined using patches of the different deprived types identified through visual image interpretation. The study also tested the ability of models to map deprived areas of different types across cities. Results indicate that deprived areas have heterogeneous local characteristics that affect large area mapping. The top 25 features for each city show that models are sensitive to the spatial structures of deprived area types. While models performed well on individual cities with XGBoost and MLP achieving an F1 scores of over 80%, the generalized model proves to be more beneficial for modeling multiple cities. This approach offers a promising solution for scaling routine, accurate maps of deprived areas to hundreds of cities that currently lack any such map, supporting local stakeholders to plan, implement, and monitor geotargeted interventions.

The Forest Law and Forest Regulations in Turkey have been amended frequently over the past 20 years to meet the increasing demands for non-forestry use of forest land. This led to a significant increase in the non-forestry use of forest areas and the share of these utilizations in all forest areas reached 3,5%. Since the General Directorate of Forest does not release detailed information about these allocations to the public, it is not known how this kind of uses affects or will affect deforestation in the country. For this reason, Environmental Impact Assessment (EIA) reports were used for the first time in this study to obtain detailed information on these uses, and 1.311 investment project were analysed. Thus, results that could not be obtained through the limited data released by the General Directorate of Forestry (GDoF) were reached. Among these results, the distribution of the allocation of forests for non-forestry uses to sub-sectors in each sector and the distribution of allocations by region and province are important for shaping the policies to be followed in the future. Energy and mining sectors are prominent in terms of forest area utilisation. According to the findings of the study, it was determined that 72,7% of the energy projects and 64,6% of the mining projects that received EIA positive decision between 2014 and 2022 have forest area allocations. When the areal distribution of these projects is analysed, it is seen that 33,54% of the total area of energy projects and 31,3% of mining projects are located in forest areas. In other words, if these projects are realised, one third of the project areas will be in forests and these forest areas will be destroyed. At this point, managers have two choices in front of them. Either they will abandon the current and future projects that will destroy nature and create new projects that protect nature, or they will destroy nature in the name of economic growth and further accelerate the deforestation in the country. There is an urgent need for the country's administration to realise that such an economic growth approach is not sustainable and to abandon this approach and to start taking measures against deforestation and the dangers arising from it as soon as possible.

Optimizing the built environment via simulations of building models hinges on standardizing data acquisition. In this research, we put forward distinct levels of detail for geometry and material inputs, specifically tailored for indoor daylight applications. We primarily focus on understanding the uncertainties arising from imprecise estimations of material optical properties and incomplete geometrical inputs in climate-based indoor daylight simulations. Employing a Monte Carlo approach, we analyzed six office and teaching spaces, creating 20 variations for each by altering geometrical completeness and material accuracy. The technique of excluding non-permanent objects below certain sizes in four graduated steps was used to derive and test the impact of various geometrical levels of detail. Our findings reveal that different levels of geometrical completeness lead to errors ranging from 1.08% to 18.05%. Additionally, a twofold increase in simulation time was noted when geometrical detail was enhanced relative to the most basic model. Errors stemming from imprecise definitions of material optical properties showed a normal distribution. The uncertainty in simulation outcomes showed a linear rise with increasing input material uncertainty, lying between 10% to 30%, depending on space configurations. We observed heightened uncertainty near openings, attributed to window transmittance effects. The research underscores that daylight predictions are markedly more sensitive to transmittance uncertainties than to those in reflectance, regardless of the window-to-floor ratio. These insights may help to guide a more efficient data acquisition process of indoor spaces for daylight simulations. Environmental & Climate Design Urban Data Science

Digital twins are a core industry 4.0 technology enabling the virtual replication of real-world objects, mimicking behaviours and states throughout their lifespan. While digital twins have shown significant benefits in industries such as manufacturing, transportation, and healthcare, their application in agriculture is still within its infancy. Their realisation also poses significant challenges, such as the creation of dynamic agricultural objects (e.g., plants). Existing literature on digital twins in agriculture identifies their limited ability to monitor physical objects without predictive capabilities and that there is a significant lack of 3D representations of plants with functional attributes. Yet, incorporating 3D representations of plants with underlying functionality in a digital twin can greatly improve growth, yield, and disease prediction accuracy. This enhancement enables various applications, such as assessing and developing pruning strategies, providing education to growers, guiding pruning robots, and optimizing spraying techniques. To that end, Functional Structural Plant Modelling presents a potential solution by representing the 3D architecture of plants and incorporating the functionality of different plant parts. By conducting a domain analysis of 3D plant phenotyping and FSPM, this study addresses the specific needs of digital twins in agriculture regarding FSPM. The investigation bridges the existing knowledge gap by identifying crucial concepts, including 3D plant modelling with underlying functionality and 3D plant phenotyping for digital twins. Specifically, a framework for 3D FSPM integration into agricultural digital twins is proposed. The framework not only acknowledges the associated requirements and challenges identified in existing literature but also lays foundation for the advancement of digital twins in the agricultural domain.

The rapid growth of heatwaves' severity have increasingly endangered citizens’ health in the last decade. Evidence points to the environmental injustice of heatwaves: inequal heatwave exposure among socioeconomic groups. Failing to use an adequate indicator of thermal comfort at a large scale, the previous studies have not adequately scrutinized the environmental justice of heatwaves and their variations across a large-scale territory. This study is novel in an unprecedented analysis of psychological equivalent temperature (PET), a comprehensive measure of thermal comfort, across socioeconomic groups and the urban-rural gradient of the Netherlands, as a proxy for factors affecting heatwave vulnerability. The results show that heatwave inequality (measured by the Gini coefficient) is higher in less urbanized areas. It shows that the population aged 25–44, immigrants, tenants, and females are the most heat-exposed groups across all levels of urbanization. However, the population aged 25–44 is more likely to be overexposed in urbanized areas, and immigrants are more likely to be overexposed in rural areas. The results open discussion on the necessity of location-specific policies protecting the most heat-exposed groups in different areas. It also paves the way for future studies using broader PET simulations and expanding their scope to include citizens' daily movements.

Imaging spectroscopy integrates traditional computer vision and spectroscopy into a single system and has gained widespread acceptance as a non-destructive scientific instrument for a wide range of applications. The current state of imaging spectroscopy spans diverse applications including but not limited to air-borne and ground-based computer vision systems. This paper presents the current state of research and industrial applications including precision agriculture, material classification, medical science, forensic science, face recognition and document image analysis, environment monitoring, and remote sensing, which can be aided through imaging spectroscopy. In this regard, we further discuss a comprehensive list of applications of imaging spectroscopy, pre-processing techniques, and spectral image acquisition systems. Likewise, publicly available databases and current software tools for spectral data analysis are also documented in this review. This review paper, therefore, could potentially serve as a reference and roadmap for people looking for literature, databases, applications, and tools to undertake additional research in imaging spectroscopy.