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.
Traditional soil salinity studies are time-consuming and expensive, especially over large areas. This study proposed an innovative deep learning convolutional neural network (DL-CNN) data-driven ...approach for SSD mapping. Multi-spectral remote sensing data encompassing Landsat series images provide the possibility for frequent assessment of SSD in various regions of the world. Therefore, Landsat 7 ETM+ and 8 OLI images were acquired for years 2005, 2010, 2015 and 2019. Totally, 704 sample points collected from the top 20 cm of the soil surface, which 70% was used to train the network and the remains (30%) were utilized to validate the network. Accordingly, DL-CNN model trained using remote sensing (RS)-derived variables (land surface temperature (LST), Soil moisture (SM) and evapotranspiration) and geospatial data such as NDVI and landuse. To train the CNN, ReLu, Cross-entropy and ADAM were employed respectively as activation, loss/cost functions and optimizer. The results indicated the high confidence of OA 0.94.02, 0.93.99, 0.94.87 and 0.95.0 respectively for years 2005, 2010, 2015 and 2019. These accuracies demonstrated the best performance of automated DL-CNN for SSD mapping compared to RS soil salinity indexes. Furthermore, the FR and WOE models applied in order to generate a geospatial assessment of the DL-CNN classification results. According to the FR model, landuse, LST, LST and NDVI with the frequency ratio of 0.98.25, 0.94.03, 0.97.23 and 0.96.36 selected respectively as more effective factors for SSD in the study area for years 2005, 2010, 2015 and 2019. Also based on the WOE model, landuse, LST, landuse and NDVI with the WOE of 0.88.25, 0.91.88, 0.87.43 and 0.89.02 were ranked respectively for years 2005, 2010, 2015 and 2019 as efficient variables for SSD. In sum, our introduced method can be recommended for SDD spatial modelling in other favored areas with similar environmental conditions.
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•We proposed and developed an automated deep learning convolutional neural network (DL-CNN) data-driven approach for soil salinity distribution modelling and mapping.•A novel approach is a satisfactory model for soil salinity distribution mapping.•Results established DL-CNN data-driven approach can help relevant researchers in soil science to simulate the foundation of the soil salinization scenario in semi-arid and arid regions.
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.
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.
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•An exclusively soil-based assessment of the soil crusting susceptibility overestimates the expression of the observable soil crusting phenomenon.•The CORINE Land Cover and the MODIS ...NDVI support soil crusting susceptibility assessment.•Adaptation of the physicochemical and textural crusting pedotransfer rules was based on local pedological knowledge and national scale soil mapping data.•Transparency in problem consideration is particularly relevant for map-based assessment and complex problem analysis in an interdisciplinary context.
Map-based assessment of land degradation processes supports national monitoring and protection of the soil resources, in line with the Land Degradation Neutrality (LDN) and the Sustainable Development Goals (SDGs) initiatives. Soil crusting susceptibility, a complex expression of land degradation, with fundamental impact on soil functions, has not yet been estimated in Greek territory. To address this knowledge gap, current research uses well-known pedotransfer rules (PRLeB), partially adjusted to suit the local soil database format. The PRLeB, already applied at European scale, examine the soil crusting problem as a function of fundamental soil characteristics. From the first analysis a paradox emerged. Moderately and highly crusting prone soils were encountered in forested areas. This paradox indicated the need for a second formalization of the soil crusting problem (ePRLeB). The CORINE Land Cover (CLC) 2018 and a seasonal vegetation cover index (Normalized Difference Vegetation Index -NDVI) were used to divide the study area into categories and to capture the temporal variability of the problem, accordingly. Three land use categories emerged. The water bodies and the artificial areas were characterized as Non-evaluated areas (NE). Areas with considerable vegetation coverage (NDVI greater than 0.5) were characterized as Non-prone to crusting (NC), and the rest of the areas were characterized as Potentially prone to crusting (PC). The PRLeB approach was applied only to the PC areas. In both formalizations, the physicochemical and textural aspects of soil crusting adopted the same PRLeB. With the first formalization, 15% and 39% of the soils in Greece were characterized as very low and low susceptible to soil crusting respectively. With the second formalization 58 – 63% of the soils were characterized by no and 5 – 7% by very low crusting susceptibility. Current work explored two fundamentally different facets of the soil crusting problem. The first formalization, which can be expressed as the soil susceptibility to crusting, considers crusting as an inherent soil process. The second formalization, expressed as soil crusting susceptibility encounters crusting as a complex phenomenon involving natural but not anthropogenic factors. The study indicated that vegetation and seasonality are important factors in the map-based assessment of the observable soil crusting phenomenon. Also, it was highlighted that the unambiguous problem specification is a critical aspect for complex problem assessment and communication, particularly within an interdisciplinary context.
•We developed a semi-automated object-based rule set for landform detecting and mapping.•A novel object based algorithms has developed and examined for landform mapping.•The Fuzzy Synthetic ...Evaluation technique was applied for accuracy assessment.•Results presents new and efficient approach for Geo/GISciences development.
Landform mapping has increasingly become part of the digital domain. While the majority of approaches evaluates Digital Elevation Models (DEM) on a per-pixel basis, some examples exist were object-based image analysis (OBIA) has been applied to terrain data to identify a variety of landforms, including glacial landforms. The main objective of this study is to develop a semi-automated object-based rule set for detecting and delineating volcanic and glacier landforms in the area of the Sahand Mountain, Northern Iran. First, we applied a multi-resolution segmentation algorithm on a freely available Sentinel-2 optical satellite image and then selected the relevant features to define appropriate segmentation scales for each landform category. Object-based rule sets were then developed using spatial (DEM and its derivatives, e.g. slope, aspect, curvature and flow accumulation) and spectral information. Volcanic and glacial landforms were detected and classified into eight classes: caldera, volcanic cone, tuff formation, andesite lava, dacite lava, glacier valley, suspension valley, glacier cirque. An accuracy assessment was applied based on the fuzzy synthetic evaluation technique, together with the error matrix and kappa coefficient, using field data and geomorphological units derived from geological maps and very high resolution aerial photographs. The resulting overall accuracies for each class were 96.2%, 93.3%, 92.4%, 94.2%, 93.01, 95.1, 90.1 and 90.5, respectively. Our research demonstrated that spatial (e.g. density, shape index, length/width) and spectral (e.g. mean, brightness and standard deviation) algorithms together with a grey-level co-occurrence matrix (GLCMs) were efficient features for detecting and mapping volcanic and glacial landforms. We conclude that the OBIA-based algorithms and features provide a high potential for detecting and classifying landforms. Results of this study are of great importance for geomorphology and geology as well as geo-tourism societies and the semi-automated landform mapping contributes to the framework of GIScience.
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.
In 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.
Based 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.
Compliant 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 L
. 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.
The 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 (L
) variability (R
= 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).
This 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.
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.
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.
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