Search

This research was done through spatial analysis to verify whether the use of public policies in the sugarcane sector contributes to the expansion of the functional connectivity of the landscape. In this context, our paper aimed to analyze the role of the Agro-environmental Protocol for the Sugar-Alcohol Sector in landscape connectivity based on two main questions: (1) Did the actions taken by the sugar-energy sector signatory towards the protocol cause an increase in forest patches in riparian Permanent Preservation Areas (PPA)? (2) After signing the protocol, was there an improvement in functional connectivity, and if so, what was the improvement in functional connectivity within all restored permanent preservation areas? As such, we compared the evolution of riparian PPA based on functional connectivity indices for the years 2006, 2020 and analyzed these indices in a hypothetical scenario with all PPA restored. Connectivity analyses have shown significant differences between the analyzed scenarios. However, the indices have shown extremely low values in all scenarios. We conclude that even though the actions proposed by the public policy may have resulted in an increase in the total forested area and have positively affected connectivity, they may be insufficient to affect the resilience of the natural landscape. [ABSTRACT FROM AUTHOR]

Lualaba Province, located in the southeastern Democratic Republic of the Congo (DRC), consists of five territories with varied dominant land uses: agriculture (Dilolo, Kapanga, and Musumba in the west) and mining (Mutshatsha and Lubudi in the east). The province also includes protected areas with significant governance challenges. The tropical dry forests that cover the unique Miombo woodland of Lualaba are threatened by deforestation, which poses risks to biodiversity and local livelihoods that depend on these forests for agriculture and forestry. To quantify the spatio-temporal dynamics of Lualaba's landscape, we utilized Landsat images from 1990 to 2024, supported by a Random Forest Classifier. Landscape metrics were calculated at multiple hierarchical levels: province, territory, and protected areas. A key contribution of this work is its identification of pronounced deforestation trends in the unique Miombo woodlands, where the overall woodland cover has declined dramatically from 62.9% to less than 25%. This is coupled with a marked increase in landscape fragmentation, isolation of remaining woodland patches, and a shift toward more heterogeneous land use patterns, as evidenced by the Shannon diversity index. Unlike previous research, our study distinguishes between the dynamics in agricultural territories—which are particularly vulnerable to deforestation—and those in mining areas, where Miombo forest cover remains more intact but is still under threat. This nuanced distinction between land use types offers critical insights into the differential impacts of economic activities on the landscape. Our study also uncovers significant deforestation within protected areas, underscoring the failure of current governance structures to safeguard these critical ecosystems. This comprehensive analysis offers a novel contribution to the literature by linking the spatial patterns of deforestation to both agricultural and mining pressures while simultaneously highlighting the governance challenges that exacerbate landscape transformation. Lualaba's Miombo woodlands are at a critical juncture, and without urgent, coordinated intervention from local and international stakeholders, the ecological and socio-economic foundations of the region will be irreversibly compromised. Urgent action is needed to implement land conservation policies, promote sustainable agricultural practices, strengthen Miombo woodland regulation enforcement, and actively support protected areas. [ABSTRACT FROM AUTHOR]

Introduction: Multiple sclerosis (MS) is an immune-mediated central nervous system disorder and a growing global health challenge affecting nearly 3 million people worldwide. Incidence and prevalence continue to increase with no known cause or cure. Globally governments and non-profit organizations fund research toward better understanding of and treatments for multiple sclerosis. Methods: This study identified MS research projects funded between 2021 and 2023 by government and non-profit organization sources. Projects were described by type of scientific approach, Pathways to Cure research category (i.e. Stop, Restore, End), and other key characteristics. Results: Over 2,300 MS research projects were identified through 16 non-profit MS organizations and 18 government databases. The overall global portfolio of these projects is valued at nearly one and a half billion Euros. The majority of projects were classified in the Stop category (60%). Research collaboration occurs in many forms among the research community; around 272 projects were reported to be co-funded. Conclusion: Global MS research collaboration will accelerate progress toward increased knowledge, effective treatments, improved health outcomes, and ultimately cures for MS. This landscape analysis highlights the current distribution of MS research investment between topics and begins to suggest where the MS community should focus to increase potential impact for current and future endeavors. [ABSTRACT FROM AUTHOR]

Context: Typical landscape-scale studies comprise the delimitation of landscapes followed by the calculation of one or more landscape metrics. Performing an analysis at multiple spatial scales is often required, occasionally followed by the selection of a particular scale according to the response variable of interest. More complex research goals might require a thorough inspection of landscapes, plus a selection of landscapes that would fulfill certain conditions regarding their landscape metrics. These tasks can usually be programmatically challenging, especially if multiple spatial scales are being analyzed. Objectives: The R package multilandr builds on several spatial-oriented R packages to provide a toolbox to develop and inspect multi-scale landscapes based on simple spatial inputs. Methods and results: The package delivers functions to calculate metrics within a multi-scale framework. Also, it provides several utility functions to visualize correlations between metrics, filter landscapes that fulfill certain predefined conditions or select a wide-range gradient of landscapes for a given metric, among other useful tasks. This paper introduces the functionality of multilandr through a step-by-step instruction guide and case studies. Conclusions: The R package multilandr provides a set of functions to facilitate typical methodological workflow of landscape-scale studies in the R environment, for both beginner and expert R users. It provides the functionality to perform a systematic filtering and selection of landscapes according to a given experimental design. The package is especially programmed to develop multi-scale designs but is also useful for the calculation of metrics of a set of landscapes from any GIS-related project. [ABSTRACT FROM AUTHOR]

This paper evaluates the potential of using artificial intelligence (AI) and machine learning (ML) approaches for classification of Landsat satellite imagery for environmental coastal mapping. The aim is to identify changes in patterns of land cover types in a coastal area around Cheetham Wetlands, Port Phillip Bay, Australia. The scripting approach of the Geographic Resources Analysis Support System (GRASS) geographic information system (GIS) uses AI-based methods of image analysis to accurately discriminate land cover types. Four ML algorithms are applied, tested and compared for supervised classification. Technical approaches are based on using the 'r.learn.train' module, which employs the scikit-learn library of Python. The methodology includes the following algorithms: (1) random forest (RF), (2) support vector machine (SVM), (3) an ANN-based approach using a multi-layer perceptron (MLP) classifier, and (4) a decision tree classifier (DTC). The tested methods using AI demonstrated robust results for image classification, with the highest overall accuracy exceeding 98% and reached by the SVM and RF models. The presented scripting approach for GRASS GIS accurately detected changes in land cover types in southern Victoria over the period of 2013–2024. From our findings, the use of AI and ML algorithms offers effective solutions for coastal monitoring by analysis of change detection using multi-temporal RS data. The demonstrated methods have potential applications in coastal and wetland monitoring, environmental analysis and urban planning based on Earth observation data. [ABSTRACT FROM AUTHOR]

In the context of the Lubumbashi Charcoal Production Basin (LCPB), the socio-economic repercussions of the COVID-19 pandemic have exacerbated pressures on populations dependent on forest resources for their subsistence. This study employs a comprehensive methodological approach, integrating advanced remote sensing techniques, including image classification, mapping, and detailed landscape analysis, to quantify alterations in forest cover within the LCPB during the pandemic period. Our findings reveal a consistent trend of declining forested area, characterized by processes of attrition and dissection observed throughout various study phases, spanning from May 2019 to November 2023. This reduction in forest cover, notably more pronounced in the vicinity of Lubumbashi city and the northern zone of the LCPB, proved to be less pronounced between November 2019 and September 2020, underscoring the influence of COVID-19 pandemic-induced confinement measures on forest management practices in the region. However, subsequent to this period of restriction, deforestation activity intensified, leading to significant landscape transformations within the LCPB, primarily attributable to expanded human activities, consequently resulting in a notable decrease in the proportion of land occupied by these natural ecosystems. Consequently, the size of the largest forest patch declined substantially, decreasing from 14.62% to 8.20% between May 2019 and November 2023, thereby fostering a heightened density of forest edges over time. Our findings provide a significant contribution to understanding the complex interactions between the COVID-19 pandemic and deforestation phenomena, emphasizing the urgent need to adopt adaptive management strategies and appropriate conservation measures in response to current economic challenges. [ABSTRACT FROM AUTHOR]

As a result of doing the Contemporary Projects Design Critique workshop at the Master of Landscape Architecture (UPC), we explore and define a new strategy to analyze and interact with the site of study, which we call Site Vibration Tool. In continuation, we tested the tool through the seaUbarcelona performance at the Port Vell of Barcelona waterfront. We came into a double conclusion. The first one, derivate from the site. Having as a start point, the Barcelona’s 92 strategy of opening the city to the sea, we think over how that strategy has respond those 92 expectations in today’s reality and will transcend in the future of the city’s waterfront, considering the climate change. And in second place, an invitation to enter into our mind. The experience using Site Vibration Tool, we perceive and add tangible and intangible aspects of the place in a landscape proposal, encouraging our colleagues to incorporate it to their practice.

It is important to develop statistical techniques to analyze high-dimensional data in the presence of both complex dependence and possible heavy tails and outliers in real-world applications such as imaging data analyses. We propose a new robust high-dimensional regression with coefficient thresholding, in which an efficient nonconvex estimation procedure is proposed through a thresholding function and the robust Huber loss. The proposed regularization method accounts for complex dependence structures in predictors and is robust against heavy tails and outliers in outcomes. Theoretically, we rigorously analyze the landscape of the population and empirical risk functions for the proposed method. The fine landscape enables us to establish both statistical consistency and computational convergence under the high-dimensional setting. We also present an extension to incorporate spatial information into the proposed method. Finite-sample properties of the proposed methods are examined by extensive simulation studies. An application concerns a scalar-on-image regression analysis for an association of psychiatric disorder measured by the general factor of psychopathology with features extracted from the task functional MRI data in the Adolescent Brain Cognitive Development (ABCD) study. for this article are available online. [ABSTRACT FROM AUTHOR]

Lualaba Province, located in the southeastern Democratic Republic of the Congo (DRC), consists of five territories with varied dominant land uses: agriculture (Dilolo, Kapanga, and Musumba in the west) and mining (Mutshatsha and Lubudi in the east). The province also includes protected areas with significant governance challenges. The tropical dry forests that cover the unique Miombo woodland of Lualaba are threatened by deforestation, which poses risks to biodiversity and local livelihoods that depend on these forests for agriculture and forestry. To quantify the spatio-temporal dynamics of Lualaba’s landscape, we utilized Landsat images from 1990 to 2024, supported by a Random Forest Classifier. Landscape metrics were calculated at multiple hierarchical levels: province, territory, and protected areas. A key contribution of this work is its identification of pronounced deforestation trends in the unique Miombo woodlands, where the overall woodland cover has declined dramatically from 62.9% to less than 25%. This is coupled with a marked increase in landscape fragmentation, isolation of remaining woodland patches, and a shift toward more heterogeneous land use patterns, as evidenced by the Shannon diversity index. Unlike previous research, our study distinguishes between the dynamics in agricultural territories—which are particularly vulnerable to deforestation—and those in mining areas, where Miombo forest cover remains more intact but is still under threat. This nuanced distinction between land use types offers critical insights into the differential impacts of economic activities on the landscape. Our study also uncovers significant deforestation within protected areas, underscoring the failure of current governance structures to safeguard these critical ecosystems. This comprehensive analysis offers a novel contribution to the literature by linking the spatial patterns of deforestation to both agricultural and mining pressures while simultaneously highlighting the governance challenges that exacerbate landscape transformation. Lualaba’s Miombo woodlands are at a critical juncture, and without urgent, coordinated intervention from local and international stakeholders, the ecological and socio-economic foundations of the region will be irreversibly compromised. Urgent action is needed to implement land conservation policies, promote sustainable agricultural practices, strengthen Miombo woodland regulation enforcement, and actively support protected areas.

In this paper, an intelligent weather conditions fuzzy adjustment based on spatial features (IWeCASF) is developed. It is indispensable for our regional soil moisture estimation approach, complementing a point estimation model of soil moisture from the literature. The point estimation model requires the weather conditions at the point where an estimate is made. Therefore, IWeCASF's aim is to determine these weather conditions. The procedure begins measuring them at only one checkpoint, called the primary checkpoint. The model determines the weather conditions anywhere within a region through image processing algorithms and fuzzy inference systems. The results are compared with the measurement records and with a spatial interpolation method. The performance is similar to or better than interpolation, especially in the rain, where the model developed is more accurate due to the certainty of replication. Additionally, IWeCASF does not require more than one measurement point. Therefore, it is a more appropriate approach to complement the point estimation model for enabling a regional soil moisture estimation. [ABSTRACT FROM AUTHOR]

The development of drone and computer vision technologies has enabled automated landscape image analysis, unlocking new feature extraction capabilities. This paper presents an integrated framework leveraging aerial drone data and machine learning for landscape imaging. A diverse dataset of drone-captured scenery spanning urban and rural areas provides the foundation. Preprocessing techniques ready the images before quantitative color analysis and texture feature extraction reveal topographical patterns. Structural boundaries are identified through edge detection and Hough transforms. Deep convolutional neural networks semantically segment images into classified landscape regions. Weighted color block matching retrieves similar images by prioritizing salient areas. Experiments demonstrate 95% accuracy in landscape feature classification, with precision of 0.93, recall of 0.95, and F1 score of 0.94, outperforming existing methods. Extracted color, texture, spectral, and geometric patterns enable interpretation of terrain, vegetation health, human-made objects, and ecological processes. The proposed approach facilitates monitoring, decision-making, and optimization across agriculture, urban planning, conservation, and other applications. Overall, the results validate that combining drones, computer vision, and deep learning can automate the analysis of complex landscape images to generate actionable insights. This methodology pioneers the next generation of intelligent remote sensing systems for comprehending our living environments. [ABSTRACT FROM AUTHOR]

In the context of the Lubumbashi Charcoal Production Basin (LCPB), the socio-economic repercussions of the COVID-19 pandemic have exacerbated pressures on populations dependent on forest resources for their subsistence. This study employs a comprehensive methodological approach, integrating advanced remote sensing techniques, including image classification, mapping, and detailed landscape analysis, to quantify alterations in forest cover within the LCPB during the pandemic period. Our findings reveal a consistent trend of declining forested area, characterized by processes of attrition and dissection observed throughout various study phases, spanning from May 2019 to November 2023. This reduction in forest cover, notably more pronounced in the vicinity of Lubumbashi city and the northern zone of the LCPB, proved to be less pronounced between November 2019 and September 2020, underscoring the influence of COVID-19 pandemic-induced confinement measures on forest management practices in the region. However, subsequent to this period of restriction, deforestation activity intensified, leading to significant landscape transformations within the LCPB, primarily attributable to expanded human activities, consequently resulting in a notable decrease in the proportion of land occupied by these natural ecosystems. Consequently, the size of the largest forest patch declined substantially, decreasing from 14.62% to 8.20% between May 2019 and November 2023, thereby fostering a heightened density of forest edges over time. Our findings provide a significant contribution to understanding the complex interactions between the COVID-19 pandemic and deforestation phenomena, emphasizing the urgent need to adopt adaptive management strategies and appropriate conservation measures in response to current economic challenges.

With rapid urban development in Beijing, there is a critical need to explore urban natural resources and understand their underlying mechanisms. Urban blue space (UBS) has gained increasing attention due to its potential to drive microcirculation, mitigate heat islands, and enhance residents' well-being. In this study, we used remote sensing data to extract UBS in Beijing and employed exploratory spatial data analysis (ESDA) methods to examine its spatial and temporal development over the past two decades. We adopted a mesoscopic perspective to uncover the full spectrum of landscape patterns and quantitatively simulate the mechanisms influencing the area of UBS and landscape patterns. Our findings are as follows: (1) The UBS area in Beijing exhibited fluctuating growth from 2000 to 2020. (2) Spatial clustering of UBS was stable with subtle changes. (3) The ecological conditions in Beijing improved over the last 21 years, indicated by increased habitat diversity and richness, while notable landscape fragmentation posed significant challenges. (4) Science and technology management-related factors, such as UEM, EDUI, and STI, emerged as the most influential mechanisms for the UBS area. The coefficients for these factors were 0.798, 0.759, and 0.758, respectively. Following closely were vegetation conditions (NDVI) with a coefficient of 0.697 and an annual average temperature (T) with a coefficient of 0.692. (5) Precipitation was identified as the most vital influencing factor for the UBS landscape, with a significant correlation coefficient of 0.732. It was followed by residential population (POP), with a coefficient of 0.692, and economic conditions represented by gross domestic product (GDP), with a coefficient of 0.691. [ABSTRACT FROM AUTHOR]

Context: Naturally recovering secondary forests are frequently re-cleared before they can recover to pre-disturbance conditions. Identifying landscape factors associated with persistence success will help planning cost-efficient and effective forest restoration. Objectives: The ability of secondary forest to persist is an often undervalued requisite for long-term ecosystem restoration. Here we identify the landscape context for naturally regenerated forests to persist through time within central Panama. Methods: We developed a random forest classification (RFC) calibration method to identify areas with high (≥ 90%) and low (< 90%) likelihood of forest persistence success based on their spatial relation with nine landscape explanatory variables. Results: The RFC model discriminated between secondary forests areas that persisted and did not persisted with an error rate of 2%. By tuning, we obtained a precision of 0.94 (94%) in the validation test. The two most important explanatory variables involved in the persistence dynamic were elevation and distance to the nearest rural area. Naturally regenerated forests lasted longer in patches that were closer to both Gatun and Alajuela Lakes as to protected areas, but further from rural communities, roads, urban areas and in patches with higher elevation and steeper slopes. Conclusion: By tracking remote sensed, landscape context metrics of easy collection, we developed a prediction map of central Panama areas with high (≥ 90%) and low (> 90%) probability of natural forest regeneration and persistence success within the next 30 years. This map represents a basis for management decisions and future investigations for effective, long-term forest-landscape restoration. [ABSTRACT FROM AUTHOR]

The past two decades have witnessed beef and dairy genetic consortiums implementing data-driven germplasm propagation programs in global cattle systems. These programs optimize for multi-dimensional economic, environmental, and animal welfare related sustainability goals and are driven by consumer demands and shifts in supply chains. To summarize these programs and gain insights into the sustainability journey of industry, we did a comprehensive landscape analysis of the strategic initiatives of key global genetics companies, their germplasm offerings, and data platforms offered in three major cattle producing geographies, namely the USA, Brazil, and Europe. Within these locales, they followed selection index-based genomic and reproductive management strategies for both beef, dairy, and beef-on-dairy production, tailored to relevant market and climatic conditions (Figure 1). While individual traits in selection indices ranged between 40 to 90 traits, all traits could be broadly grouped into three: namely production, longevity, health and reproduction. In the USA, companies like ABS Global, Select Sires, Genex Cooperative, ST Genetics, Zoetis, Vytelle offer genomic selection programs encompassing germplasm products, and genetic and production data tracking platforms across the beef, dairy, and beefon- dairy value chains. These companies have implemented reproductive management programs such as in-vitro fertilized embryo transfer, sexed semen, along with genomic selection with a focus on future replacements based on multi-trait sustainability indices. These initiatives are customized for each of the cow-calf, backgrounding, and feedlot sector of beef production. In dairy, these companies have diversified data-driven production programs customizable to conventional and organic production systems. In Europe, companies like CRV, Viking Genetics, and Alta Genetics prioritize traits related to resource efficiency, animal welfare and environmental impact. Semen companies also offer data management tools tailored to European market requirements, alongside services for on-farm semen production and storage to reduce transportation related emissions. In Brazil, companies such as ABS Pecplan, CRV Brazil, and Semex Brazil, developed herd management solutions focusing on traits like heat tolerance, tropical environmental adaptability, and disease resistance. The landscape analysis revealed a dynamic industry, evolving to meet sustainability goals and market demands. These enterprises collaborate with stakeholders across the value chain, including producers, policymakers, and industry associations, to scale data-driven production systems that balance profitability and environmental footprint of cattle production. As such, they have positioned themselves to capitalize on the growing interest in sustainable livestock production practices worldwide by aligning scientific and technical strategies with evolving regulatory requirements and consumer demands. The global cattle industry is positioned to venture forward by leveraging niche carbon markets and new marketing channels propelled by emerging principles of environmental, social, and corporate governance. Strategic germplasm and data-tracking platform initiatives by these groups thus become an integral part of the livestock sustainability solution palette and will contribute its share to a climate-neutral Earth by 2050. [ABSTRACT FROM AUTHOR]

Background: This paper presents a new software tool, Inglämnlagare, developed to be open-source, that restructures information about ancient remains in Sweden for analysis. The background is a new version of the ancient sites database, the Historic Environment Record, curated by the Swedish National Heritage Board, that was launched in 2018 with a new database model that structures the information differently compared to previous versions. Methods: The program, written in Python programming language, has multicore support in order to improve performance for large files and uses regular expressions to extract information about individual features of composite sites. Such features, together with their summed amount, are written as new individual fields to a comma-separated value file. The program is delivered as a source script file that can be executed in any Python environment. Use cases: As an example of use, a case study of exploring graves of rectangular shape found within Sweden is provided. The use case also describes the different steps involved in preparing the data in QGIS to run the program, as well as some methods to efficiently analyse and visualize the output. Conclusions: Inglämnlagare will make more information from the Swedish record of ancient sites accessible for research and can be used to explore different content of the record more efficiently than previously possible. While the tool is written specifically for this dataset it also provides an example of how open-source tools can be used for data wrangling making information designed for a specific purpose, such as online dissemination, appropriate for analysis.

Background: This paper presents a new software tool, Inglämnlagare, developed to be open-source, that restructures information about ancient remains in Sweden for analysis. The background is a new version of the ancient sites database, the Historic Environment Record, curated by the Swedish National Heritage Board, that was launched in 2018 with a new database model that structures the information differently compared to previous versions. Methods: The program, written in Python programming language, has multicore support in order to improve performance for large files and uses regular expressions to extract information about individual features of composite sites. Such features, together with their summed amount, are written as new individual fields to a comma-separated value file. The program is delivered as a source script file that can be executed in any Python environment. Use cases: As an example of use, a case study of exploring graves of rectangular shape found within Sweden is provided. The use case also describes the different steps involved in preparing the data in QGIS to run the program, as well as some methods to efficiently analyse and visualize the output. Conclusions: Inglämnlagare will make more information from the Swedish record of ancient sites accessible for research and can be used to explore different content of the record more efficiently than previously possible. While the tool is written specifically for this dataset it also provides an example of how open-source tools can be used for data wrangling making information designed for a specific purpose, such as online dissemination, appropriate for analysis.

Conventional processing methods for remote sensing data are inefficient and time -- consuming. Using the object-oriented classification method this study extracted the distribution of mangrove forests of 2000, 2010, and 2020 in the Shankou Mangrove Nature Reserve in Guangxi based on the GEE cloud platform and Landsat TM/OLI remote sensing data. Then, this study monitored the spatio - temporal variations in mangrove forests in the study area in combination with the landscape analysis method and revealed their driving factors. The results are as follows: (1) During 2000-2020, the mangrove forests in the study area increased by about 63 hm², including a significant increase of about 40 hm² during 2010-2020; (2) Compared with other land use types, the mangrove forests showed the most intense conversion with spartina alterniflora areas and mudflats, with 152 hm² of spartina alterniflora areas and mudflats being converted to mangrove forests and 122 hm² of mangrove forests being converted to spartina alterniflora areas over the 20 years; (3) During 2000-2020, the mangrove landscape in the study area showed decreased fragmentation, increased patch aggregation, continuously expanded landscape dominance, and landward migration of the mangrove forest centroid; (4) Among the factors affecting the area of mangrove forests in the nature reserve, the control of invasive vegetation and moderate aquaculture can increase the area of mangrove forests, while climate changes and invasive vegetation had adverse effects on the growth of mangrove forests. The results of this study will provide a method reference and data basis for the conservation and management of mangrove wetlands in Shankou, Guangxi. [ABSTRACT FROM AUTHOR]

Free-ranging cats are invasive species threatening biodiversity worldwide. They may also impose an environmental risk to humans and livestock through the transmission of zoonotic diseases. We investigated antibody levels against Toxoplasma gondii in free-ranging cats and black rats (definitive and representative intermediate hosts) by ELISA and determined their relationships with landscape environmental factors on Tokunoshima Island, Japan, the Natural World Heritage site. We found a higher seroprevalence (>70%) in both cats and black rats in landscapes where the cattle barn density was high. This was consistent with higher density of rats revealed in our trapping survey. The spatial scale of landscape factors affecting infection was broader in cats (1 km buffer radius) than in black rats (100 m buffer radius). Both cats and rats showed an increasing trend in optical density (OD) values with increasing body weight and landscape cattle barn density, suggesting that the antibody concentration increases as the chance of exposure to T. gondii in the environment increases. Thus, management actions to stop humans from feeding cats and to control rat populations without using cats are both necessary to reduce the human health risk as well as to conserve endangered species on the island.

Marble has been extracted from the Carrara quarries (Apuan Alps, Italy) for over two millennia. This cultural landscape is characterised by singular landforms called ravaneti, causing environmental problems due to debris flow, thus imperilling human settlements and natural heritage. Geomorphological and vegetation analyses have highlighted that in a decade, vegetation cover greatly increased, indicating potential for passive restoration of dump deposits. The assessment of plant assemblages indicates a decrease in plant richness and endemics, and an increase in wood and alien species. The conservation of historical ravaneti landscape requires weighted restoration actions to reduce geomorphological hazards and preserve native biodiversity. [ABSTRACT FROM AUTHOR]

In recent years waterfronts have progressively become the focus of local administrations, consultancy agencies, and private developers concerned with public health, city branding, and real estate development. Subsequently, they turned into central stages in which cities and societies can be represented, contested, and inverted. However, many questions remain unanswered concerning their capability to function as counter-spaces in the fast-changing dynamics of citizens' encounters and recreation in global cities. This paper employs mixed methods to examine the context-dependent association between space and behaviours. The comparative analysis of four waterfront parks in Venice, Shenzhen, Hong Kong, and New York sheds light on heterotopic sites' production and use. Two models emerged: transient spaces of compensation and time-accumulating spaces of illusion. Beyond the novel research design, the significance of this study lies in validating Foucauldian-Lefebvrian heterotopology as an authoritative analytical paradigm for a critical interpretation of the urban. [ABSTRACT FROM AUTHOR]

In this article, we describe a large-scale data collection effort about the environmental learning field in Maine. This approach utilized a stakeholder engagement process which included over 600 project managers, educators, and school administrators from both formal and informal education settings. Our focus on collecting data about specific environmental learning projects allowed us to analyze alignment with a selection of best practices in environmental education drawn from NAAEE's K-12 Guidelines for Excellence. We highlight the characteristics of "high-mastery" projects – those most aligned with the targeted best practices in environmental education – and describe implications for supporting growth of the environmental learning field in Maine moving forward and equitable access to quality programming for youth. [ABSTRACT FROM AUTHOR]

Aim: Biodiversity hotspots often span international borders, thus conservation efforts must as well. China is one of the most biodiverse countries and the length of its international land borders is the longest in the world; thus, there is a strong need for transboundary conservation. We identify China's transboundary conservation hotspots and analyse the potential effects of the Belt and Road Initiative (BRI) on them to provide recommendations for conservation actions. Location: China, Asia. Methods: We compiled a species list of terrestrial vertebrates that span China's borders. Using their distribution, we extracted the top 30% of the area with the highest richness value weighted by Red List category and considered these transboundary hotspots for conservation priority. Then we analysed protected area (PA) coverage and connectivity to identify conservation gaps. To measure potential impact of the BRI, we counted the species whose distribution range is traversed by the BRI, and calculated the aggregation index, proportion of natural land and night light index along its routes. Results: We identified 1964 terrestrial vertebrate species living in the border region. We identified four transboundary hotspots and found insufficient PA coverage and low connectivity in three of them. The BRI routes intersected all four hotspots and traversed 82.4% (1619/1964) of the transboundary species, half of which (918) are sensitive to the potential risks brought by the BRI. Night light index increased generally along the BRI. However, the proportion of natural land and the aggregation index near the BRI showed different trends in hotspots. Main Conclusions: There is an urgent need for conservation action in China's transboundary region. The BRI should put biodiversity conservation at the core of its development strategy. Furthermore, we suggest using the planned BRI as a platform for dialogue and consultation, knowledge and data sharing, and joint planning to promote transboundary conservation. [ABSTRACT FROM AUTHOR]

As part of an innovative Tripartite-EU collaboration Project that supports seven South American countries, a Landscape Analysis Tool (LAT) was developed and implemented to collect data to complement the Tripartite AMR Country Self-Assessment Survey (TrACSS) process. The LAT enables collection of broader and deeper information to guide development of priority One Health activities, and strengthen national action plans to combat antimicrobial resistance. The Project developed the tool, trained a consultant pool in its use, and implemented it in conjunction with multi-sectoral country teams. The main results were seven priority-informed country workplans that proposed specific activities in line with the Strategic Objectives of each country's national action plan. LAT implementation clearly showed that the tool is a strong complement to the TrACSS process and that there can be considerable benefit to the process of collecting additional data layers, especially to strengthen country ownership of AMR-related information and solidifying multisectoral engagement. Countries elsewhere might consider implementing this complementary tool – either once to establish a baseline – or periodically to gain a better ongoing understanding of the situation on the ground.

In this paper, an intelligent weather conditions fuzzy adjustment based on spatial features (IWeCASF) is developed. It is indispensable for our regional soil moisture estimation approach, complementing a point estimation model of soil moisture from the literature. The point estimation model requires the weather conditions at the point where an estimate is made. Therefore, IWeCASF’s aim is to determine these weather conditions. The procedure begins measuring them at only one checkpoint, called the primary checkpoint. The model determines the weather conditions anywhere within a region through image processing algorithms and fuzzy inference systems. The results are compared with the measurement records and with a spatial interpolation method. The performance is similar to or better than interpolation, especially in the rain, where the model developed is more accurate due to the certainty of replication. Additionally, IWeCASF does not require more than one measurement point. Therefore, it is a more appropriate approach to complement the point estimation model for enabling a regional soil moisture estimation.

This work aims to analyze the connectivity of landscape fragments present in the Tietê-Jacaré Watershed - SP, through the application of metric indices of Connectivity and Fragmentation. Considering the 10-year interval, there was an expansion of agricultural activities with an increase of 24,507.53ha of cultivated areas and loss of vegetation of 32,149ha. A decrease in the number of fragments was also evidenced, as well as an increase in the average size of the fragments, due to the growth of the agricultural matrix with consequent reduction in fragment diversification. [ABSTRACT FROM AUTHOR]

The visual impact of crop shelters in the rural landscape is of paramount importance for the sustainability of landscape planning. It is of utmost importance to protect a beautiful landscape characterized by archaeological sites and natural ecosystems but it is also fundamental to preserve a key economy based on agriculture. To preserve the landscape there is the need to understand its landscape units and to assess the feasibility of the application of landscape character assessment (LCA) at a local scale and adaptation to higher scales in the land-use planning process. In this study, a methodology based on viewshed analysis was integrated into LCA in order to define landscape character areas and types. An application of the method was done on greenhouse systems. Landscape evaluation was carried out by using both ECOVAST guidelines for landscape identification and indicators, which were defined and computed in order to describe the visual impact of greenhouses. The method was applied to a study area located in South Italy, in the Province of Ragusa, in the eastern part of Sicily, where the greenhouse system highly characterizes agricultural activities and made it possible to define landscape guidelines. Finally, a visibility map was drawn up to highlight the areas subject to the greatest visibility. In addition to objectifying the visual impact of greenhouses, the research verified that the proposed LCA-based methodology combined with viewshed analysis can be feasible for supporting the analysis phases of landscape and urban planning under the new territorial governance laws issued for the Region of Sicily in recent years. These analyses were useful to provide information that objectively takes into account the importance of the visual component in the context of landscape planning. Due to the limited surface of the sample area considered in this study, this research work should be considered a pilot or preliminary study. The extension of this methodology to the adjacent municipalities could represent the practical application performed by local authorities to define common regulations suitable to preserve landscape components. [ABSTRACT FROM AUTHOR]

Free-ranging cats are invasive species threatening biodiversity worldwide. They may also impose an environmental risk to humans and livestock through the transmission of zoonotic diseases. We investigated antibody levels against Toxoplasma gondii in free-ranging cats and black rats (definitive and representative intermediate hosts) by ELISA and determined their relationships with landscape environmental factors on Tokunoshima Island, Japan, the Natural World Heritage site. We found a higher seroprevalence (>70%) in both cats and black rats in landscapes where the cattle barn density was high. This was consistent with higher density of rats revealed in our trapping survey. The spatial scale of landscape factors affecting infection was broader in cats (1 km buffer radius) than in black rats (100 m buffer radius). Both cats and rats showed an increasing trend in optical density (OD) values with increasing body weight and landscape cattle barn density, suggesting that the antibody concentration increases as the chance of exposure to T. gondii in the environment increases. Thus, management actions to stop humans from feeding cats and to control rat populations without using cats are both necessary to reduce the human health risk as well as to conserve endangered species on the island. [Display omitted] • High seroprevalence of T. gondii was found in cats and black rats on an island. • Seroprevalence was high (>70%) in landscapes where there were many cattle barns. • Antibody level in cats was lower in forests and higher around residential areas. • Spatial scale of environmental factors affecting infection was broader in cats. [ABSTRACT FROM AUTHOR]

This research has identified the landscapes in the state of Chiapas (Mexico) with the best natural conditions by using the theoretical-methodological principles of complex physical geography. To achieve this goal, the biodiversity was assessed by calculating the number of vascular plant species for each landscape unit and correlated with the results of previous research regarding landscape diversity and the anthropization of vegetation cover. The results indicate that the highest levels of biodiversity correspond to the predominant and recurring landscapes. These geographical relationships indicate the influence of the geomorphological component on the distribution of biodiversity, which was quantitatively validated via two statistical correlations. It is found that the complexes of summits, hillslopes, and gullies in mountain landscapes of tectonic-karstic, tectonic-accumulative, and tectonic-intrusive origin, in temperate, semiwarm humid to subhumid, warm humid, and warm subhumid climates located in the physicogeographical regions of the Lacandona Mountains, the Southern Mountains of Chiapas, and the Western Mountains of Chiapas, all have the best preserved geographical landscapes. In recent years, however, geocomplexes have been impacted by society, which is particularly evident in the Northern Mountains of the Chiapas region. The results of this research are an important contribution to land-use planning processes, because they make it possible to objectively establish environmental policies in Chiapas. For instance, this research provides the geographic basis for modifying or promoting new natural protected areas. [ABSTRACT FROM AUTHOR]

The global scenario turned towards converging their focus on sustainability owing to its adverse response in the climatic conditions, else unlike other planets earth may go unbefitting to live. The major contributor in for this worsening scenario of the climatic condition is polluting the atmosphere by various ways. In addition to this, the major polluting factor around the globe is vehicle and industrial waste which is contaminating the environment on hourly basis. The derived outcome of this issue is to cut the pollutants of the fuel mixing with atmospheric air and when this is either stopped or reduced, it saves the mother earth from pollution. The biofuel derived from wastes based catalysts is considered to be a better alternative and such biofuels have lesser environmental hazards when compared with conventional fossil fuels. So, the review in this article narrows down to waste catalyst derived biofuel along with their performance which contributes in the self-sustenance of biodiesel production. The article flow through the path of progress of biofuel and about the destination of its progress in near future which is discussed along with the patent landscape analysis. Scope of the article can be clearly understood from the facts revealed from the patent analysis. So, the first segment discusses about the ways to derive the biofuel from the agriculture waste, biomasses, gasification as well as from the microalgae. Focus of this review has also been given for the description of various natural sources used to derive catalysts, performance of the catalysts used for biodiesel production in terms of its efficiency and the challenges associated with the use of natural sources as catalysts for biodiesel production. [ABSTRACT FROM AUTHOR]

The implementation of the European Landscape Convention (ELC) at national and regional scales has brought with it the need to rethink landscape governance policies. In view of the disparity of possible methods for approaching landscape knowledge derived from the ELC, this article proposes the study of the three most ambitious landscape assessment methods developed in Spain at present. Specifically, those of Valencia, Galicia and Catalonia. Their study and comparative reading allow us to conclude that they have a similar structure imposed in part by the requirements of the Convention. Despite this, all three demonstrate a wide operational flexibility depending on the purpose of each one. As a derivative of the study, it is argued that a method seeking to fulfill both a sustainability and a public participation agenda would have to combine quantitative forms of socio-ecological assessment with a qualitative measurement of cultural appreciation of landscapes.

With rapid urban development in Beijing, there is a critical need to explore urban natural resources and understand their underlying mechanisms. Urban blue space (UBS) has gained increasing attention due to its potential to drive microcirculation, mitigate heat islands, and enhance residents’ well-being. In this study, we used remote sensing data to extract UBS in Beijing and employed exploratory spatial data analysis (ESDA) methods to examine its spatial and temporal development over the past two decades. We adopted a mesoscopic perspective to uncover the full spectrum of landscape patterns and quantitatively simulate the mechanisms influencing the area of UBS and landscape patterns. Our findings are as follows: (1) The UBS area in Beijing exhibited fluctuating growth from 2000 to 2020. (2) Spatial clustering of UBS was stable with subtle changes. (3) The ecological conditions in Beijing improved over the last 21 years, indicated by increased habitat diversity and richness, while notable landscape fragmentation posed significant challenges. (4) Science and technology management-related factors, such as UEM, EDUI, and STI, emerged as the most influential mechanisms for the UBS area. The coefficients for these factors were 0.798, 0.759, and 0.758, respectively. Following closely were vegetation conditions (NDVI) with a coefficient of 0.697 and an annual average temperature (T) with a coefficient of 0.692. (5) Precipitation was identified as the most vital influencing factor for the UBS landscape, with a significant correlation coefficient of 0.732. It was followed by residential population (POP), with a coefficient of 0.692, and economic conditions represented by gross domestic product (GDP), with a coefficient of 0.691.

Sustainable development of Afghanistan is prevented by many factors, both purely anthropogenic genesis, and natural. In this study, author tries to make an initial assessment of the possibility of indicating geoecological problems in Afghanistan by indicators of the sustainable development goals based on landscape analysis. This will allow to identify priorities in the practical application of these indicators, and to conduct field assessments with maximum efficiency. Author makes a point assessment of the priorities of the indication of the geoecological problems of different landscape units of Afghanistan, and the related assessment of tension of the geoecological situation.

Investigating the rate and the process of land use change and its economic effects during the last years is among the main bases of land management in the future. East Meshgin region in Ardabil province is one of the agricultural production centers, where the construction of the Sabalan dam developed the agricultural activities. In this research, the six land uses, including rangeland, orchard, agriculture, bare land, outcrop, and water bodies during the 1998–2019 period, were classified using the Landsat imagery. Also, assessing the rate and process of land use change has been quantifies using single and comprehensive dynamic degree. The land use change matrix is developed in three time periods (1998–2003, 2003–2008, and 2013–2019). The economic evaluation of land use has been conducted during this period. The results showed that the highest rate of land use change was converting the rangeland to bare lands. While in the period of 2008–2013, the highest rate of land use change was rangeland conversion to agriculture lands that has caused increased net income by 4,027,258 $. The analysis of satellite images related to assess the overall land use change between 1998 and 2019 also confirms the change of rangeland use to bare lands. Also, the results of the single dynamic land use change degree show that in each period, the most change was related to water body area. Also, a considerable rate land use change was occurred in agricultural land use during the 2013–2019 period, which corresponds with the initial operation years of Sabalan Dam. This study is performed to facilitate policymakers, planners, and other development stakeholders in this sector to adopt the best option for land use management and sustainable land development. [ABSTRACT FROM AUTHOR]

In this paper, we analyze the landscape of the true loss of neural networks with one hidden layer and ReLU, leaky ReLU, or quadratic activation. In all three cases, we provide a complete classification of the critical points in the case where the target function is affine and one-dimensional. In particular, we show that there exist no local maxima and clarify the structure of saddle points. Moreover, we prove that non-global local minima can only be caused by ‘dead’ ReLU neurons. In particular, they do not appear in the case of leaky ReLU or quadratic activation. Our approach is of a combinatorial nature and builds on a careful analysis of the different types of hidden neurons that can occur. [ABSTRACT FROM AUTHOR]

Koala (Phascolarctos cinereus ) road kills occur frequently along the Peak Downs highway through the Clarke-Connors ranges. Highway upgrades allowed mitigation of koala-vehicle collision frequency while maintaining koala population connectivity. This project aimed to understand road kill distribution to inform protective infrastructure investment. Koala road kills were associated with: (1) streams and associated alluvia where the dominant vegetation included Eucalyptus tereticornis and E. platyphylla ; (2) ridges supporting E. drepanophylla open forest/woodland abutting streams or alluvia; and (3) mid-lower slopes, dominated by E. drepanophylla that were dissected by minor streams fringed by E. tereticornis ± E. platyphylla . Road kills did not occur in E. drepanophylla open forest/woodland on ridge upper slopes, crests or on hills, although koalas occur in this landscape. Explaining why koala road kills are linked to landscape features requires investigation. It is likely that: (1) landscape elements associated with drainage lines, alluvia and E. tereticornis support a relatively high koala abundance, and hence the road kill risk is correspondingly higher; and (2) the engineered road architecture and road verge characteristics in these landscape elements are conducive to koalas crossing the road. [ABSTRACT FROM AUTHOR]

• FLA has attracted attention of researchers for its research significance and application. • This paper attempts to give an overview of fitness landscape analysis for optimization. • Future research works are discussed and given in four aspects. Over past few decades, as a powerful analytical tool to characterize the fitness landscape of a problem, fitness landscape analysis (FLA) has been widely concerned and utilized for all kinds of optimization areas. Since its introduction by Sewell Wright in 1932, FLA has attracted more and more attention of researchers for its research significance and application value such as an intuitive understanding features of complex optimization problems, explaining evolutionary algorithm behavior, assessing performances of algorithms, and guiding selections and/or configures of algorithms. This paper attempts to give an overview of fitness landscape analysis and its typical application for optimization so far. We hope that this survey can help to understand features of complex optimization problems in depth and thus to improve the certain algorithm performance of for a given optimization problem. [ABSTRACT FROM AUTHOR]

The current landscape around persistent identifiers (PIDs) keeps quickly evolving. Some PIDs like Digital Object Identifiers (DOIs) for publications and datasets or ORCIDs (Open Researcher and Contributor ID) for persistent author identification are already well-established, but there is also a whole additional range of emerging identifiers in the research area, often being implemented under competing approaches. These include among others identifiers for organisations (OrgIDs), for research grants (grantIDs), and projects (RAIDs), for research equipment and facilities (PIDINSTs) and for physical samples (IGSNs). This is then a timely moment to explore the risks and trust-related issues associated with an ever wider implementation of PIDs. Following an earlier work on 'risks and trust in pursuit of a well-functioning Persistent Identifier infrastructure for research' conducted by the Knowledge Exchange (KE) Task & Finish Group on PIDs, the KE commissioned a study in July 2021 to look deeper into these issues. This work, undertaken by the signatories of this paper, will result in the publication of a report and a series of case studies on specific areas of current PID development. At the time the CRIS2022 Conference takes place the work is still underway, but already advanced enough to describe its methodology, early findings, landscape analysis and early recommendations. The full project results are expected to be published by the KE by the end of 2022. [ABSTRACT FROM AUTHOR]

Background: This paper presents a new software tool, Inglämnlagare, developed to be open-source, that restructures information about ancient remains in Sweden for analysis. The background is a new version of the ancient sites database, the Historic Environment Record, curated by the Swedish National Heritage Board, that was launched in 2018 with a new database model that structures the information differently compared to previous versions. Methods: The program, written in Python programming language, has multicore support in order to improve performance for large files and uses regular expressions to extract information about individual features of composite sites. Such features, together with their summed amount, are written as new individual fields to a comma-separated value file. The program is delivered as a source script file that can be executed in any Python environment. Use cases: As an example of use, a case study of exploring graves of rectangular shape found within Sweden is provided. The use case also describes the different steps involved in preparing the data in QGIS to run the program, as well as some methods to efficiently analyse and visualize the output. Conclusions: Inglämnlagare will make more information from the Swedish record of ancient sites accessible for research and can be used to explore different content of the record more efficiently than previously possible. While the tool is written specifically for this dataset it also provides an example of how open-source tools can be used for data wrangling making information designed for a specific purpose, such as online dissemination, appropriate for analysis.