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In many parts of the world, lake drying is caused by water management failures, while the phenomenon is exacerbated by climate change. Lake Urmia in Northern Iran is drying up at such an alarming rate that it is considered to be a dying lake, which has dire consequences for the whole region. While salinization caused by a dying lake is well understood and known to influence the local and regional food production, other potential impacts by dying lakes are as yet unknown. The food production in the Urmia region is predominantly regional and relies on local water sources. To explore the current and projected impacts of the dying lake on food production, we investigated changes in the climatic conditions, land use, and land degradation for the period 1990–2020. We examined the environmental impacts of lake drought on food production using an integrated scenario-based geoinformation framework. The results show that the lake drought has significantly affected and reduced food production over the past three decades. Based on a combination of cellular automaton and Markov modeling, we project the food production for the next 30 years and predict it will reduce further. The results of this study emphasize the critical environmental impacts of the Urmia Lake drought on food production in the region. We hope that the results will encourage authorities and environmental planners to counteract these issues and take steps to support food production. As our proposed integrated geoinformation approach considers both the extensive impacts of global climate change and the factors associated with dying lakes, we consider it to be suitable to investigate the relationships between environmental degradation and scenario-based food production in other regions with dying lakes around the world.

Classification is a very common image processing task. The accuracy of the classified map is typically assessed through a comparison with real-world situations or with available reference data to estimate the reliability of the classification results. Common accuracy assessment approaches are based on an error matrix and provide a measure for the overall accuracy. A frequently used index is the Kappa index. As the Kappa index has increasingly been criticized, various alternative measures have been investigated with minimal success in practice. In this article, we introduce a novel index that overcomes the limitations. Unlike Kappa, it is not sensitive to asymmetric distributions. The quantity and allocation disagreement index (QADI) index computes the degree of disagreement between the classification results and reference maps by counting wrongly labeled pixels as A and quantifying the difference in the pixel count for each class between the classified map and reference data as Q. These values are then used to determine a quantitative QADI index value, which indicates the value of disagreement and difference between a classification result and training data. It can also be used to generate a graph that indicates the degree to which each factor contributes to the disagreement. The efficiency of Kappa and QADI were compared in six use cases. The results indicate that the QADI index generates more reliable classification accuracy assessments than the traditional Kappa can do. We also developed a toolbox in a GIS software environment.

Climate change and its respective environmental impacts, such as dying lakes, is widely acknowledged. Studies on the impact of shrinking hyper-saline lakes suggest severe negative consequences for the health of the affected population. The primary aim was to investigate the relationship between changes in the water level of the hyper-saline Lake Urmia, along with the associated salt release, and the prevalence of hypertension and the general state of health of the local population in Shabestar County north of the lake. Moreover, we sought to map the vulnerability of the local population to the health risks associated with salt-dust scatter using multiple environmental and demographic characteristics. We applied a spatiotemporal analysis of the environmental parameters of Lake Urmia and the health of the local population. We analyzed health survey data from local health care centers and a national STEPS study in Shabestar County, Iran. We used a time-series of remote sensing images to monitor the trend of occurrence and extent of salt-dust storms between 2012 and 2020. To evaluate the impacts of lake drought on the health of the residences, we investigated the spatiotemporal correlation of the lake drought and the state of health of local residents. We applied a GIScience multiple decision analysis to identify areas affected by salt-dust particles and related these to the health status of the residents. According to our results, the lake drought has significantly contributed to the increasing cases of hypertension in local patients. The number of hypertensive patients has increased from 2.09% in 2012 to 19.5% in 2019 before decreasing slightly to 16.05% in 2020. Detailed results showed that adults, and particularly females, were affected most by the effects of the salt-dust scatter in the residential areas close to the lake. The results of this study provide critical insights into the environmental impacts of the Lake Urmia drought on the human health of the residents. Based on the results we suggest that detailed socioeconomic studies might be required for a comprehensive analysis of the human health issues in this area. Nonetheless, the proposed methods can be applied to monitor the environmental impacts of climate change on human health.

BackgroundIn modern societies, noise is ubiquitous. It is an annoyance and can have a negative impact on human health as well as on the environment. Despite increasing evidence of its negative impacts, spatial knowledge about noise distribution remains limited. Up to now, noise mapping is frequently inhibited by the necessary resources and therefore limited to selected areas.ObjectiveBased on the assumption, that prevalent noise is determined by the arrangement of sources and the surrounding environment in which the sound propagates, we build a geostatistical model representing these parameters. Aiming for a large-scale noise mapping approach, we utilize publicly available data, context-aware feature engineering and a linear land-use regression (LUR) model.MethodsCompliant to the European Noise Directive 2002/49/EG, we work at a high spatial granularity of 10 × 10-m resolution. As reference, we use the day–evening–night noise level indicator Lden. Therewith, we carry out 2000 virtual field campaigns simulating different sampling schemes and introduce spatial cross-validation concepts to test the transferability to new areas.ResultsThe experimental results suggest the necessity for more than 500 samples stratified over the different noise levels to produce a representative model. Eventually, using 21 selected variables, our model was able to explain large proportions of the yearly averaged road noise (Lden) variability (R2 = 0.702) with a mean absolute error of 4.24 dB(A), 3.84 dB(A) for build-up areas, respectively. In applying this best performing model for an area-wide prediction, we spatially close the blank spots in existing noise maps with continuous noise levels for the entire range from 24 to 106 dB(A).SignificanceThis data is new, particular for small communities that have not been mapped sufficiently in Europe so far. In conjunction, our findings also supplement conventionally sampled studies using physical microphones and spatially blocked cross-validations.

Higher crop diversity can enhance biodiversity and ecosystem services; however, it remains unclear to what extent and where crop diversity can be increased. We use spatially explicit multiscale optimization to determine potential and attainable crop diversity with field-level land use data for case studies in Brandenburg, Germany. Our model maximizes crop diversity at the landscape scale while reassigning crop types over multiple years to existing arable fields. The model implements field-level crop sequence rules and maintains the crop composition of each farm and for each year. We found that a 10% higher crop diversity can be attained on average compared to currently observed diversity; minor changes in crop composition would close this gap. Improved crop allocation can contribute to closing the gap between observed and attainable crop diversity, which in turn can increase biodiversity, improve pollination services, and support pest control.

Extreme heat has tremendous adverse effects on human health. Heat stress is expected to further increase due to urbanization, an aging population, and global warming. Previous research has identified correlations between extreme heat and mortality. However, the underlying physical, behavioral, environmental, and social risk factors remain largely unknown and comprehensive quantitative investigation on an individual level is lacking. We conducted a new cross-sectional household questionnaire survey to analyze individual heat impairment (self-assessed and reported symptoms) and a large set of potential risk factors in the city of Berlin, Germany. This unique dataset (n = 474) allows for the investigation of new relationships, especially between health/fitness and urban heat stress. Our analysis found previously undocumented associations, leading us to generate new hypotheses for future research: various health/fitness variables returned the strongest associations with individual heat stress. Our primary hypothesis is that age, the most commonly used risk factor, is outperformed by health/fitness as a dominant risk factor. Related variables seem to more accurately represent humans' cardiovascular capacity to handle elevated temperature. Among them, active travel was associated with reduced heat stress. We observed statistical associations for heat exposure regarding the individual living space but not for the neighborhood environment. Heat stress research should further investigate individual risk factors of heat stress using quantitative methodologies. It should focus more on health and fitness and systematically explore their role in adaptation strategies. The potential of health and fitness to reduce urban heat stress risk means that encouraging active travel could be an effective adaptation strategy. Through reduced CO2 emissions from urban transport, societies could reap double rewards by addressing two root causes of urban heat stress: population health and global warming.

Natural and man-made disasters caused by global climate change affect inhabitant health and well-being in the urban environments. One of the main issues in environmental planning in any form of residential area is monitoring the resilience of such disasters. This challenge has become more complicated when considering socio-ecological aspects of human-environmental systems and different types of land use and land cover in the resilience assessment process. In the present study, the resilience of Varamin, Iran, concerning climate change has been investigated through Analytic Network Process (ANP). The prominent components and indicators of climate resilience in the city were extracted using various datasets, field surveys, interviewing 44 urban planning experts through a questionnaire, and the Delphi method. Four components and 33 indicators were obtained based on the climatic resilience factor and integrated using the ANP model. A total of 393 citizens of Varamin city were invited to complete a questionnaire. The questionnaire was designed based on four environmental, socio-economical, infrastructural, and institutional components. According to the survey, the highest priority in climate resilience was for people who live in primary settlements. The city officials must pay special attention to these areas to make the city resilient and consider it a priority in their planning. Also, the climate change risks factor in resilience from the citizens’ point of view was 2.15, which is less than the desired average and indicates habitat vulnerability against climate change. In addition, socio-economical and infrastructural components had higher resilience than environmental and institutional components. According to the suggestions of the citizens, the cooperation of government, local institutions, and educational organizations is effective in reducing and adapting to the effects of climate change and improving urban resilience.

Background: Urban health is of global concern because the majority of the world's population lives in urban areas. Although mental health problems (e.g. depression) in developing countries are highly prevalent, such issues are not yet adequately addressed in the rapidly urbanising megacities of these countries, where a growing number of residents live in slums. Little is known about the spectrum of mental well-being in urban slums and only poor knowledge exists on health promotive socio-physical environments in these areas. Using a geo-epidemiological approach, the present study identified factors that contribute to the mental well-being in the slums of Dhaka, which currently accommodates an estimated population of more than 14 million, including 3.4 million slum dwellers. Methods: The baseline data of a cohort study conducted in early 2009 in nine slums of Dhaka were used. Data were collected from 1,938 adults (≥ 15 years). All respondents were geographically marked based on their households using global positioning systems (GPS). Very high-resolution land cover information was processed in a Geographic Information System (GIS) to obtain additional exposure information. We used a factor analysis to reduce the socio-physical explanatory variables to a fewer set of uncorrelated linear combinations of variables. We then regressed these factors on the WHO-5 Well-being Index that was used as a proxy for self-rated mental well-being. Results: Mental well-being was significantly associated with various factors such as selected features of the natural environment, flood risk, sanitation, housing quality, sufficiency and durability. We further identified associations with population density, job satisfaction, and income generation while controlling for individual factors such as age, gender, and diseases. Conclusions: Factors determining mental well-being were related to the socio-physical environment and individual level characteristics. Given that mental well-being is associated with physiological well-being, our study may provide crucial information for developing better health care and disease prevention programmes in slums of Dhaka and other comparable settings.

Background Urban forests reduce greenhouse gas emissions by storing and sequestering considerable amounts of carbon. However, few studies have considered the local scale of urban forests to effectively evaluate their potential long-term carbon offset. The lack of precise, consistent and up-to-date forest details is challenging for long-term prognoses. Therefore, this review aims to identify uncertainties in urban forest carbon offset assessment and discuss the extent to which such uncertainties can be reduced by recent progress in high resolution remote sensing. We do this by performing an extensive literature review and a case study combining remote sensing and life cycle assessment of urban forest carbon offset in Berlin, Germany. Main text Recent progress in high resolution remote sensing and methods is adequate for delivering more precise details on the urban tree canopy, individual tree metrics, species, and age structures compared to conventional land use/cover class approaches. These area-wide consistent details can update life cycle inventories for more precise future prognoses. Additional improvements in classification accuracy can be achieved by a higher number of features derived from remote sensing data of increasing resolution, but first studies on this subject indicated that a smart selection of features already provides sufficient data that avoids redundancies and enables more efficient data processing. Our case study from Berlin could use remotely sensed individual tree species as consistent inventory of a life cycle assessment. However, a lack of growth, mortality and planting data forced us to make assumptions, therefore creating uncertainty in the long-term prognoses. Regarding temporal changes and reliable long-term estimates, more attention is required to detect changes of gradual growth, pruning and abrupt changes in tree planting and mortality. As such, precise long-term urban ecological monitoring using high resolution remote sensing should be intensified, especially due to increasing climate change effects. This is important for calibrating and validating recent prognoses of urban forest carbon offset, which have so far scarcely addressed longer timeframes. Additionally, higher resolution remote sensing of urban forest carbon estimates can improve upscaling approaches, which should be extended to reach a more precise global estimate for the first time. Conclusions Urban forest carbon offset can be made more relevant by making more standardized assessments available for science and professional practitioners, and the increasing availability of high resolution remote sensing data and the progress in data processing allows for precisely that.

Highly dynamic peri-urban areas, particularly in the Global South, face many challenges including a lack of infrastructure, ownership conflicts, land degradation, and sustainable food production. This study aims to assess spatial land use characteristics and processes in peri-urban areas using the case of Dar es Salaam, Tanzania. A mixed-method approach was applied, consisting of expert interviews and spatial data analysis, on a local scale along an urban–rural gradient. Expert interviews were conducted during a field study and analyzed regarding the characteristics and processes of peri-urban land development. A GIS-based analysis of land use patterns was applied using satellite imagery and Open Street Map data to identify a number of variables, such as building density and proximity to environmental features. Results show specific patterns of land use indicators, which can be decreasing (e.g., house density), increasing (e.g., tree coverage), static (e.g., house size), or randomly distributed (e.g., distance to river), along a peri-urban gradient. Key findings identify lack of service structures and access to public transport as major challenges for the population of peri-urban areas. The combination of qualitative expert interviews and metrics-based quantitative spatial pattern analysis contributes to improved understanding of the patterns and processes in peri-urban land use changes.