Search Results (30)

Research on circular development in China’s urban planning remains limited, particularly regarding marginalized groups’ actions. This study addresses the gap by examining circular practices within informal food systems in Chengdu’s urban villages. It highlights residents’ bottom-up initiatives in food production and consumption and their interactions with the broader urban context. Using street interviews and Research through Design, it develops community-based visions to improve these actions and the needed planning tools for implementation. It also explores how circular development could support urban regeneration by recognizing overlooked resources and practices. Semi-structured expert interviews reveal barriers in China’s planning system to accommodate such visions. Findings indicate that local circular actions—driven by local labor and knowledge and efforts to tackle polluted land and idle spaces—offer valuable opportunities for circular development. However, deficiencies in planning tools for spatial planning, waste treatment, land contamination regulation, and vulnerability recognition create barriers to upscaling these initiatives. This study calls for integrating circular development into China’s spatial planning by strengthening top-down tools and fostering grassroots initiatives to promote sustainable resource flows, ecosystem health, and social equity. It also offers broader insights into promoting circular development by recognizing and integrating informal, bottom-up practices in cities undergoing informal settlement regeneration. Full article

Soil organic carbon (SOC) is essential for maintaining ecosystem health, and its depletion is widely recognized as a key indicator of soil degradation. Activities such as mining and wildfire disturbances significantly intensify soil degradation, leading to quantitative and qualitative declines in SOC. Accurate SOC monitoring is critical, yet traditional methods are often costly and time-intensive. Advances in technologies like Unmanned Aerial Vehicles (UAVs) and satellite remote sensing (SRS) now offer efficient and scalable alternatives. Combining UAV and satellite data through machine learning (ML) techniques can improve the accuracy and spatial resolution of SOC monitoring, facilitating better soil management strategies. In this context, this study proposes a methodology that integrates geochemical data (SOC) with UAV-derived information, upscaling the UAV data to satellite platforms (GEOSAT-2 and SENTINEL-2) using ML techniques, specifically random forest (RF) algorithms. The research was conducted in two distinct environments: a reclaimed open-pit coal mine, representing a severely degraded ecosystem, and a high-altitude region prone to recurrent wildfires, both characterized by extreme environmental conditions and diverse soil properties. These scenarios provide valuable opportunities to evaluate the effects of soil degradation on SOC quality and to assess the effectiveness of advanced monitoring approaches. The RF algorithm, optimized with cross-validation (CV) techniques, consistently outperformed other models. The highest performance was achieved during the UAV-to-SENTINEL-2 upscaling, with an R² of 0.761 and an rRMSE of 8.6%. Cross-validation mitigated overfitting and enhanced the robustness and generalizability of the models. UAV data offered high-resolution insights for localized SOC assessments, while SENTINEL-2 imagery enabled broader-scale evaluations, albeit with a smoothing effect. These findings underscore the potential of integrating UAV and satellite data with ML approaches, providing a cost-effective and scalable framework for SOC monitoring, soil management, and climate change mitigation efforts. Full article

In the context of increasing interest in ecosystem-based adaptation (EbA), there remains a paucity of discussion regarding the transition from perception to practice in aiding farmer households to adapt to climate change (CC) while maintaining the provision of essential ecosystem services. Thus, this study aims to explore policymakers’ and local people’s perceptions, from thinking about the implementation of EbA strategies to responding to CC in current and future agricultural production and forestry in upland Thua Thien Hue province, Vietnam. This study has adopted the Model of Private Proactive Adaptation to CC to investigate the perceptions of EbA among various administrative and household levels through in-depth interviews and focus group discussion methods. Our findings indicate a significant relationship between the perceptions and understanding of EbA among policymakers and farmer households, and the adoption of EbA practices. Many EbA practices are already well-established and have demonstrated their ability to enhance ecosystem services provision, adaptation benefits, and livelihood and food security. These benefits are crucial for helping farmer households to adapt to CC. However, current financial, technical, and market constraints hinder the broader adoption of these practices. Therefore, to increase adaptive capacity to CC and upscale EbA practices, EbA interventions must consider technical, financial, and market aspects. Furthermore, it is essential to provide evidence from both scientific and practical perspectives and disseminate information on EbA practices to encourage broader adoption by local farmers. In addition, supportive policies from various departmental and agency levels are necessary for managers in the agricultural and forest sectors as well as households to recognize EbA as a vital strategy for developing agriculture and forestry in a manner that is sustainable and resilient to CC. Full article

Current super-resolution algorithms exhibit limitations when processing noisy remote sensing images rich in surface information, as they tend to amplify noise during the recovery of high-frequency signals. To mitigate this issue, this paper presents a novel approach that incorporates the concept of compressed sensing and explores the super-resolution problem of remote sensing images for space cameras, particularly for high-speed imaging systems. The proposed algorithm employs K-singular value decomposition (K-SVD) to jointly train high- and low-resolution image blocks, updating them column by column to obtain overcomplete dictionary pairs. This approach compensates for the deficiency of fixed dictionaries in the original algorithm. In the process of dictionary updating, we innovatively integrate the circle chaotic mapping into the solution process of the dictionary sequence, replacing pseudorandom numbers. This integration facilitates balanced traversal and simplifies the search for global optimal solutions. For the optimization problem of sparse coefficients, we utilize the orthogonal matching pursuit method (OMP) instead of the L1 norm convex optimization method used in most reconstruction techniques, thereby complementing the K-SVD dictionary update algorithm. After upscaling and denoising the image using the dictionary pair mapping relationship, we further emphasize image edge details with local gradients as constraints. When compared with various representative super-resolution algorithms, our algorithm effectively filters out noise and stains in low-resolution images. It not only performs well visually but also stands out in objective evaluation indicators such as the peak signal-to-noise ratio and information entropy. The experimental results validate the effectiveness of the proposed method in super-resolution remote sensing images, yielding high-quality remote sensing image data. Full article

For the significant distortion problem caused by the special projection method of equi-rectangular projection (ERP) images, this paper proposes an omnidirectional image super-resolution algorithm model based on position information transformation, taking SwinIR as the base. By introducing a space position transformation module that supports deformable convolution, the image preprocessing process is optimized to reduce the distortion effects in the polar regions of the ERP image. Meanwhile, by introducing deformable convolution in the deep feature extraction process, the model’s adaptability to local deformations of images is enhanced. Experimental results on publicly available datasets have shown that our method outperforms SwinIR, with an average improvement of over 0.2 dB in WS-PSNR and over 0.030 in WS-SSIM for ×4 pixel upscaling. Full article

Deep convolutional neural networks have demonstrated significant performance improvements in face super-resolution tasks. However, many deep learning-based approaches tend to overlook the inherent structural information and feature correlation across different scales in face images, making the accurate recovery of face structure in low-resolution cases challenging. To address this, this paper proposes a method that fuses multi-scale features while preserving the facial structure. It introduces a novel multi-scale residual block (MSRB) to reconstruct key facial parts and structures from spatial and channel dimensions, and utilizes pyramid attention (PA) to exploit non-local self-similarity, improving the details of the reconstructed face. Feature Enhancement Modules (FEM) are employed in the upscale stage to refine and enhance current features using multi-scale features from previous stages. The experimental results on CelebA, Helen and LFW datasets provide evidence that our method achieves superior quantitative metrics compared to the baseline, the Peak Signal-to-Noise Ratio (PSNR) outperforms the baseline by 0.282 dB, 0.343 dB, and 0.336 dB. Furthermore, our method demonstrates improved visual performance on two additional no-reference datasets, Widerface and Webface. Full article

River rehabilitation and ecological engineering are becoming critical issues for improving river status when ecological habitats and connectivity have been altered by human pressures. Amongst the range of existing rehabilitation options, some specifically focus on rebuilding fluvial forms and improving physical processes. The aim of this contribution is to illustrate how geomorphological expertise and process-based thinking contribute to river rehabilitation success. This semantic contribution is intended to feed the rehabilitation debate, particularly concerning the design of actions and the proposed references for monitoring target reaches and evaluating rehabilitation effects empirically. This article is also based on lessons learned from practical cases, mainly in gravel-bed rivers. Geomorphic understanding is needed at a local level to achieve an adequate diagnosis of river functioning, estimate human impacts and potential remnant river responsiveness, and to assess the gains and risks from rehabilitation, as well as to appraise success or failure through several pre- and post-project assessment strategies. Geomorphological studies can also be upscaled in a top-down manner (from high-order controls to small-scale processes, understanding detailed processes in their regional or basin-wide context), providing large-scale information at the regional, national, or even global level, information that can be used to diagnose the health of riverscapes in relation to local site-specific contexts. As such, geomorphological studies support strategic planning and prioritization of rehabilitation works according to specific contexts and river responsiveness, so as to move from opportunistic to objective-driven strategies. Full article

Springshed management across mountainous states, such as India and Nepal, has paved the way for the groundwater recharge process. In contrast, despite introducing several interventions, the Bangladeshi government has never been officially exposed to such sustainable ideas for a spring revival. Therefore, this study aims to diagnose water security for the Himalayan region by applying an environmental security framework. Community perceptions documented through focus group discussions and key informant interviews, as well as water sample testing, helped highlight the existing issues of water scarcity, accessibility, quality, and governance structure. Exemplifying the condition of Bandarban in Bangladesh, notable gaps were found in spring-related scientific understanding. Specifically, the lack of adequate reservoirs, institutional coordination, water supply, utility maintenance, and accessibility hurdles were identified as areas requiring immediate attention. As a recovery route, a six-step protocol of springshed management shows more promising outcomes. However, Sikkim communities in India raised questions over its efficacy due to the improper execution of said protocols. A limited understanding of hill science, including inventory and inadequate inspections before implementation, were found to result in only partial success. Upgrading remains a challenge as maladaptation might increase landslides. Therefore, development plans demand rigorous science-based investigation, consideration of local community knowledge, and (pilot) monitoring before the upscaling of springshed projects can be successfully conducted. Full article

Brain tumors are among the deadliest forms of cancer, characterized by abnormal proliferation of brain cells. While early identification of brain tumors can greatly aid in their therapy, the process of manual segmentation performed by expert doctors, which is often time-consuming, tedious, and prone to human error, can act as a bottleneck in the diagnostic process. This motivates the development of automated algorithms for brain tumor segmentation. However, accurately segmenting the enhanced and core tumor regions is complicated due to high levels of inter- and intra-tumor heterogeneity in terms of texture, morphology, and shape. This study proposes a fully automatic method called the selective deeply supervised multi-scale attention network (SDS-MSA-Net) for segmenting brain tumor regions using a multi-scale attention network with novel selective deep supervision (SDS) mechanisms for training. The method utilizes a 3D input composed of five consecutive slices, in addition to a 2D slice, to maintain sequential information. The proposed multi-scale architecture includes two encoding units to extract meaningful global and local features from the 3D and 2D inputs, respectively. These coarse features are then passed through attention units to filter out redundant information by assigning lower weights. The refined features are fed into a decoder block, which upscales the features at various levels while learning patterns relevant to all tumor regions. The SDS block is introduced to immediately upscale features from intermediate layers of the decoder, with the aim of producing segmentations of the whole, enhanced, and core tumor regions. The proposed framework was evaluated on the BraTS2020 dataset and showed improved performance in brain tumor region segmentation, particularly in the segmentation of the core and enhancing tumor regions, demonstrating the effectiveness of the proposed approach. Our code is publicly available. Full article

The environmental impact assessment is a process required in many countries. It highlights future activities with a significant impact on the environment. Water, as an environmental factor, needs adequate methods for quantifying cumulative impact of hydrotechnical works. In most cases, for new developments, baseline data is collected before the beginning of the construction, but for waterworks already in place, a different approach is needed. In line with the EU Water Framework Directive (Directive 2000/60/EC), the overall purpose of the research is to develop an approach for the hydromorphological cumulative impact assessment integrating different spatial scales for existing water intakes with transversal barriers on mountain rivers in Romania. Being a research study developed for a specific issue—post-construction impact assessment, some innovative actions were required. Lack of information in the pre-construction phase was an important constraint. Customizing formulas of certain indicators established within the Romanian method for hydromorphological status assessment of rivers proved to be a practical solution to show both local and waterbody hydromorphological impact. Upscaling the impact from the local scale to the river sector and the waterbody allows awareness of the spatial extent of the impact and understanding of the importance of the thresholds of significant impact for a broader audience. In order to better highlight the approach, this paper shows practical examples. The whole chain of the drivers–pressures–state–impacts–responses (DPSIR) framework is applied in the case of two river water bodies with hydropower generation facilities in place. In addition, some recommendations for actions are provided. Full article

In this paper, we propose an advanced scripting approach using Python and R for satellite image processing and modelling terrain in Côte d’Ivoire, West Africa. Data include Landsat 9 OLI/TIRS C2 L1 and the SRTM digital elevation model (DEM). The EarthPy library of Python and ‘raster’ and ‘terra’ packages of R are used as tools for data processing. The methodology includes computing vegetation indices to derive information on vegetation coverage and terrain modelling. Four vegetation indices were computed and visualised using R: the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index 2 (EVI2), Soil-Adjusted Vegetation Index (SAVI) and Atmospherically Resistant Vegetation Index 2 (ARVI2). The SAVI index is demonstrated to be more suitable and better adjusted to the vegetation analysis, which is beneficial for agricultural monitoring in Côte d’Ivoire. The terrain analysis is performed using Python and includes slope, aspect, hillshade and relief modelling with changed parameters for the sun azimuth and angle. The vegetation pattern in Côte d’Ivoire is heterogeneous, which reflects the complexity of the terrain structure. Therefore, the terrain and vegetation data modelling is aimed at the analysis of the relationship between the regional topography and environmental setting in the study area. The upscaled mapping is performed as regional environmental analysis of the Yamoussoukro surroundings and local topographic modelling of the Kossou Lake. The algorithms of the data processing include image resampling, band composition, statistical analysis and map algebra used for calculation of the vegetation indices in Côte d’Ivoire. This study demonstrates the effective application of the advanced programming algorithms in Python and R for satellite image processing. Full article

In Europe, willow (Salix spp.) trees have been used commercially since the 1980s at a large scale to produce renewable energy. While reducing fossil fuel needs, growing short rotation coppices (SRCs), such as poplar or willow, may have a high impact on local air quality as these species are known to produce high amounts of isoprene, which can lead to the production of tropospheric ozone (O₃). Here, we present a long-term leaf-scale study of biogenic volatile organic compound (BVOC) emissions from a Swedish managed willow site with the aim of providing information on the seasonal variability in BVOC emissions during two growing seasons, 2015–2016. Total BVOC emissions during these two seasons were dominated by isoprene (>96% by mass) and the monoterpene (MT) ocimene. The average standardized (STD, temperature of 30 °C and photosynthetically active radiation of 1000 µmol m⁻² s⁻¹) emission rate for isoprene was 45.2 (±42.9, standard deviation (SD)) μg g_dw⁻¹ h⁻¹. Isoprene varied through the season, mainly depending on the prevailing temperature and light, where the measured emissions peaked in July 2015 and August 2016. The average STD emission for MTs was 0.301 (±0.201) μg g_dw⁻¹ h⁻¹ and the MT emissions decreased from spring to autumn. The average STD emission for sesquiterpenes (SQTs) was 0.103 (±0.249) μg g_dw⁻¹ h⁻¹, where caryophyllene was the most abundant SQT. The measured emissions of SQTs peaked in August both in 2015 and 2016. Non-terpenoid compounds were grouped as other VOCs (0.751 ± 0.159 μg g_dw⁻¹ h⁻¹), containing alkanes, aldehydes, ketones, and other compounds. Emissions from all the BVOC groups decreased towards the end of the growing season. The more sun-adapted leaves in the upper part of the plantation canopy emitted higher rates of isoprene, MTs, and SQTs compared with more shade-adapted leaves in the lower canopy. On the other hand, emissions of other VOCs were lower from the upper part of the canopy compared with the lower part. Light response curves showed that ocimene and α-farnesene increased with light but only for the sun-adapted leaves, since the shade-adapted leaves did not emit ocimene and α-farnesene. An infestation with Melampsora spp. likely induced high emissions of, e.g., hexanal and nonanal in August 2015. The results from this study imply that upscaling BVOC emissions with model approaches should account for seasonality and also include the canopy position of leaves as a parameter to allow for better estimates for the regional and global budgets of ecosystem emissions. Full article

It is important to protect forest and grassland ecosystems because they are ecologically rich and provide numerous ecosystem services. Upscaling monitoring from local to global scale is imperative in reaching this goal. The SDG Agenda does not include indicators that directly quantify ecosystem health. Remote sensing and Geographic Information Systems (GIS) can bridge the gap for large-scale ecosystem health assessment. We systematically reviewed field-based and remote-based measures of ecosystem health for forests and grasslands, identified the most important ones and provided an overview on remote sensing and GIS-based measures. We included 163 English language studies within terrestrial non-tropical biomes and used a pre-defined classification system to extract ecological stressors and attributes, collected corresponding indicators, measures, and proxy values. We found that the main ecological attributes of each ecosystem contribute differently in the literature, and that almost half of the examined studies used remote sensing to estimate indicators. The major stressor for forests was “climate change”, followed by “insect infestation”; for grasslands it was “grazing”, followed by “climate change”. “Biotic interactions, composition, and structure” was the most important ecological attribute for both ecosystems. “Fire disturbance” was the second most important for forests, while for grasslands it was “soil chemistry and structure”. Less than a fifth of studies used vegetation indices; NDVI was the most common. There are monitoring inconsistencies from the broad range of indicators and measures. Therefore, we recommend a standardized field, GIS, and remote sensing-based approach to monitor ecosystem health and integrity and facilitate land managers and policy-makers. Full article

A super-resolution (SR) reconstruction of remote sensing images is becoming a highly active area of research. With increasing upscaling factors, richer and more abundant details can progressively be obtained. However, in comparison with natural images, the complex spatial distribution of remote sensing data increases the difficulty in its reconstruction. Furthermore, most SR reconstruction methods suffer from low feature information utilization and equal processing of all spatial regions of an image. To improve the performance of SR reconstruction of remote sensing images, this paper proposes a deep convolutional neural network (DCNN)-based approach, named the deep residual dual-attention network (DRDAN), which achieves the fusion of global and local information. Specifically, we have developed a residual dual-attention block (RDAB) as a building block in DRDAN. In the RDAB, we firstly use the local multi-level fusion module to fully extract and deeply fuse the features of the different convolution layers. This module can facilitate the flow of information in the network. After this, a dual-attention mechanism (DAM), which includes both a channel attention mechanism and a spatial attention mechanism, enables the network to adaptively allocate more attention to regions carrying high-frequency information. Extensive experiments indicate that the DRDAN outperforms other comparable DCNN-based approaches in both objective evaluation indexes and subjective visual quality. Full article

Understanding the detailed timing of crop phenology and their variability enhances grain yield and quality by providing precise scheduling of irrigation, fertilization, and crop protection mechanisms. Advances in information and communication technology (ICT) provide a unique opportunity to develop agriculture-related tools that enhance wall-to-wall upscaling of data outputs from point-location data to wide-area spatial scales. Because of the heterogeneity of the worldwide agro-ecological zones where crops are cultivated, it is unproductive to perform plant phenology research without providing means to upscale results to landscape-level while safeguarding field-scale relevance. This paper presents an advanced, reproducible, and open-source software for plant phenology prediction and mapping (PPMaP) that inputs data obtained from multi-location field experiments to derive models for any crop variety. This information can then be applied consecutively at a localized grid within a spatial framework to produce plant phenology predictions at the landscape level. This software runs on the ‘Windows’ platform and supports the development of process-oriented and temperature-driven plant phenology models by intuitively and interactively leading the user through a step-by-step progression to the production of spatial maps for any region of interest in sub-Saharan Africa. Maize (Zea mays L.) was used to demonstrate the robustness, versatility, and high computing efficiency of the resulting modeling outputs of the PPMaP. The framework was implemented in R, providing a flexible and easy-to-use GUI interface. Since this allows for appropriate scaling to the larger spatial domain, the software can effectively be used to determine the spatially explicit length of growing period (LGP) of any variety. Full article