Carbon fluxes related to land use and land cover change in Baden-Württemberg

Geo-spatial Information Science

OpenStreetMap (OSM) is a dataset in constant change and this dynamic needs to be better understood. Based on 12-year time series of seven OSM data contribution activities extracted from 20 large cities worldwide, we investigate the temporal dynamic of OSM data production, more specifically, the auto- and cross-correlation, temporal trend, and annual seasonality of these activities. Furthermore, we evaluate and compare nine different temporal regression methods for forecasting such activities in horizons of 1–4 weeks. Several insights could be obtained from our analyses, including that the contribution activities tend to grown linearly in a moderate intra-annual cycle. Also, the performance of the temporal forecasting methods shows that they yield in general more accurate estimations of future contribution activities than a baseline metric, i.e. the arithmetic average of recent previous observations. In particular, the …

arXiv preprint arXiv:2401.04218

We present a benchmark for assessing the capability of Large Language Models (LLMs) to discern intercardinal directions between geographic locations and apply it to three prominent LLMs: GPT-3.5, GPT-4, and Llama-2. This benchmark specifically evaluates whether LLMs exhibit a hierarchical spatial bias similar to humans, where judgments about individual locations' spatial relationships are influenced by the perceived relationships of the larger groups that contain them. To investigate this, we formulated 14 questions focusing on well-known American cities. Seven questions were designed to challenge the LLMs with scenarios potentially influenced by the orientation of larger geographical units, such as states or countries, while the remaining seven targeted locations less susceptible to such hierarchical categorization. Among the tested models, GPT-4 exhibited superior performance with 55.3% accuracy, followed by GPT-3.5 at 47.3%, and Llama-2 at 44.7%. The models showed significantly reduced accuracy on tasks with suspected hierarchical bias. For example, GPT-4's accuracy dropped to 32.9% on these tasks, compared to 85.7% on others. Despite these inaccuracies, the models identified the nearest cardinal direction in most cases, suggesting associative learning, embodying human-like misconceptions. We discuss the potential of text-based data representing geographic relationships directly to improve the spatial reasoning capabilities of LLMs.

HEUREKA'24-Optimierung in Verkehr und Transport, Stuttgart, 13rd-14th March 2024

Zur Beschreibung der Attraktivität von Gebieten im Rahmen der Zielwahlmodellierung werden oftmals Informationen über Points-of-Interest (POI) aus OpenStreetMap (OSM) genutzt. Wir haben die Vollständigkeit der OSM-POI-Datenbank für 129 Untersuchungsgebiete mithilfe von Vollerhebungen geprüft. Die Vollständigkeit der OSM-Datenbank unterscheidet sich zwischen einzelnen Kategorien erheblich. OSM ist in den Kategorien Gastronomie und Einzelhandel in weiten Teilen vollständig und nach stichprobenartiger Prüfung im Anwendungsfall für die Modellierung nutzbar. In den Kategorien Dienstleistung mit Kundenverkehr und Medizinische Versorgung fehlen in OSM zumeist eine Vielzahl an POI. Strukturelle Einflüsse räumlicher oder intrinsischer Indikatoren konnten nicht nachgewiesen werden.

ERDKUNDE

Heat poses a significant risk to human health, particularly for vulnerable populations, such as pregnant women, older individuals, young children and people with pre-existing medical conditions. In view of this, we formulated a heat stress-avoidant routing approach in Heidelberg, Germany, to ensure mobility and support day-to-day activities in urban areas during heat events. Although the primary focus is on pedestrians, it is also applicable to cyclists. To obtain a nuanced understanding of the needs and demands of the wider population, especially vulnerable groups, and to address the challenge of reducing urban heat stress, we used an inter-and transdisciplinary approach. The needs of vulnerable groups, the public, and the city administration were identified through participatory methods and various tools, including interactive city walks. Solution approaches and adaptation measures to prevent heat stress were evaluated and integrated into the development of a heat-avoiding route service through a co-design process. The findings comprise the identification of perceived hotspots for heat (such as large public spaces in the city centre with low shading levels), the determination of commonly reported symptoms resulting from severe heat (eg, fatigue or lack of concentration), and the assessment of heat adaptation measures that were rated positively, including remaining in the shade and delaying errands. Additionally, we analysed and distinguished between individual and community adaptation strategies. Overall, many respondents did not accurately perceive the risk of heat stress in hot weather, despite severe limitations. As a result, the heat …

ERDKUNDE

Heat poses a significant risk to human health, particularly for vulnerable populations, such as pregnant women, older individuals, young children and people with pre-existing medical conditions. In view of this, we formulated a heat stress-avoidant routing approach in Heidelberg, Germany, to ensure mobility and support day-to-day activities in urban areas during heat events. Although the primary focus is on pedestrians, it is also applicable to cyclists. To obtain a nuanced understanding of the needs and demands of the wider population, especially vulnerable groups, and to address the challenge of reducing urban heat stress, we used an inter-and transdisciplinary approach. The needs of vulnerable groups, the public, and the city administration were identified through participatory methods and various tools, including interactive city walks. Solution approaches and adaptation measures to prevent heat stress were evaluated and integrated into the development of a heat-avoiding route service through a co-design process. The findings comprise the identification of perceived hotspots for heat (such as large public spaces in the city centre with low shading levels), the determination of commonly reported symptoms resulting from severe heat (eg, fatigue or lack of concentration), and the assessment of heat adaptation measures that were rated positively, including remaining in the shade and delaying errands. Additionally, we analysed and distinguished between individual and community adaptation strategies. Overall, many respondents did not accurately perceive the risk of heat stress in hot weather, despite severe limitations. As a result, the heat …

Nature Communications

OpenStreetMap (OSM) has evolved as a popular dataset for global urban analyses, such as assessing progress towards the Sustainable Development Goals. However, many analyses do not account for the uneven spatial coverage of existing data. We employ a machine-learning model to infer the completeness of OSM building stock data for 13,189 urban agglomerations worldwide. For 1,848 urban centres (16% of the urban population), OSM building footprint data exceeds 80% completeness, but completeness remains lower than 20% for 9,163 cities (48% of the urban population). Although OSM data inequalities have recently receded, partially as a result of humanitarian mapping efforts, a complex unequal pattern of spatial biases remains, which vary across various human development index groups, population sizes and geographic regions. Based on these results, we provide recommendations for data …

Proceedings of the OSM Science

Advances in Artificial Intelligence (AI) and, specifically, in Deep Learning (DL) have fostered geospatial analysis and remote sensing, culminating in the establishment of GeoAI [1, 2] and the solidification of research on methodologies and techniques for AI-assisted mapping [3-7]. Nevertheless, a particular challenge lies in the substantial demand for training examples in DL. Manual labelling of these examples is labour-intensive, consuming a considerable amount of time and financial resources. Alternatively, semi or automated labelling of data emerges as a prominent solution, as exemplified by the tool ohsome2label [8], which harnesses data from the OpenStreetMap [9] to label satellite images. However, moving from characterising object types (road, river, building) based on geometry to categorising them by attributes might result in an imbalanced class distribution in the utilised Machine Learning (ML) dataset.Such imbalances are common in numerous practical applications. Learning from skewed datasets can be particularly challenging and often requires non-conventional ML techniques. A comprehensive awareness of the issues associated with class imbalance, as well as strategies for mitigating them, is essential [10]. In the context of spatial data, the distribution of classes can vary from country to country and region to region, adding a new layer of complexity and exacerbating this issue. In this context, an analysis was conducted on the distribution of road types, defined by the values of the OSM" highway" tag, in diverse-profile nations. The aim was to evaluate the extent of class imbalance and to identify any consistent patterns in the …

ISPRS International Journal of Geo-Information

Natural hazards threaten millions of people all over the world. To address this risk, exposure and vulnerability models with high resolution data are essential. However, in many areas of the world, exposure models are rather coarse and are aggregated over large areas. Although OpenStreetMap (OSM) offers great potential to assess risk at a detailed building-by-building level, the completeness of OSM building footprints is still heterogeneous. We present an approach to close this gap by means of crowd-sourcing based on the mobile app MapSwipe, where volunteers swipe through satellite images of a region collecting user feedback on classification tasks. For our application, MapSwipe was extended by a completeness feature that allows to classify a tile as “no building”, “complete” or “incomplete”. To assess the quality of the produced data, the completeness feature was applied to four regions. The MapSwipe-based assessment was compared with an intrinsic approach to quantify completeness and with the prediction of an existing model. Our results show that the crowd-sourced approach yields a reasonable classification performance of the completeness of OSM building footprints. Results showed that the MapSwipe-based assessment produced consistent estimates for the case study regions while the other two approaches showed a higher variability. Our study also revealed that volunteers tend to classify nearly completely mapped tiles as “complete”, especially in areas with a high OSM building density. Another factor that influenced the classification performance was the level of alignment of the OSM layer with the satellite imagery.

Engineering Proceedings

Urban heat islands, characterized by higher temperatures in cities compared to surrounding areas, have been studied using various techniques. However, during heat waves, existing models often underestimate the intensity of these heat islands compared to empirical measurements. To address this, an hourly time-series-based model for predicting heat island intensity during heat wave conditions is proposed. The model was developed and validated using empirical data from the National Monitoring Network in Temuco, Chile. Results indicate a strong correlation (r > 0.98) between the model’s predictions and actual monitoring data. Additionally, the study emphasizes the importance of considering the unique microclimatic characteristics and built environment of each city when modelling urban heat islands. Factors such as urban morphology, land cover, and anthropogenic heat emissions interact in complex ways, necessitating tailored modelling approaches for the accurate representation of heat island phenomena.

Proceedings of the OSM Science

1 GIScience Research Group, Heidelberg University, Heidelberg, Germany; francis. andorful@ uni-heidelberg. de, nir. fulman@ uni-heidelberg. de 2 HeiGIT-Heidelberg Institute for Geoinformation Technology, 69120 Heidelberg, Germany; sven. lautenbach@ uni-heidelberg. de, christina. ludwing@ uni-heidelberg. de, herfort@ uni-heidelberg. de, zipf@ uni-heidelberg. de

International Journal of Applied Earth Observation and Geoinformation

The disease transmitting mosquito Aedes Aegypti is an increasing global threat. It breeds in small artificial containers such as rainwater tanks and can be characterized by a short flight range. The resulting high spatial variability of abundance is challenging to model. Therefore, we tested an approach to map water tank density as a spatial proxy for urban Aedes Aegypti habitat suitability. Water tank density mapping was performed by a semi-supervised self-training approach based on open accessible satellite imagery for the city of Rio de Janeiro. We ran a negative binomial generalized linear regression model to evaluate the statistical significance of water tank density for modeling inner-urban Aedes Aegypti distribution measured by an entomological surveillance system between January 2019 and December 2021. Our proposed semi-supervised model outperformed a supervised model for water tank detection …

Nature Communications

OpenStreetMap (OSM) has evolved as a popular dataset for global urban analyses, such as assessing progress towards the Sustainable Development Goals. However, many analyses do not account for the uneven spatial coverage of existing data. We employ a machine-learning model to infer the completeness of OSM building stock data for 13,189 urban agglomerations worldwide. For 1,848 urban centres (16% of the urban population), OSM building footprint data exceeds 80% completeness, but completeness remains lower than 20% for 9,163 cities (48% of the urban population). Although OSM data inequalities have recently receded, partially as a result of humanitarian mapping efforts, a complex unequal pattern of spatial biases remains, which vary across various human development index groups, population sizes and geographic regions. Based on these results, we provide recommendations for data …

This discussion paper originated as the concluding publication of one of the pilot projects of the "Climate Action Science" research initiative at Heidelberg Center for the Environment (HCE), focusing on the Rhine-Neckar district and the city of Heidelberg. The aim of the explorative project was to generate a first overview on greenhouse gas emission data in order to initiate climate action of various actors and to provide well-founded support by using accurate infor-mation. The focus during the pilot phase was on the collection, compilation and evaluation of the quality of heterogeneous data sets and methods for a greenhouse gas emission inventory, as well as on the information preparation and evaluation of different inventory and presentation options. These should in turn be adapted to the needs of different users and fields of applica-tion. The study focused on different German approaches to greenhouse gas accounting, espe-cially in Baden-Württemberg compared to other German states, and in detail on the City of Heidelberg compared to the surrounding municipalities in the Rhine-Neckar district. The over-arching goal is to use the results beyond the case study projected here as a stimulus and pre-liminary work for further projects and activities in the overall "Climate Action Science" project. Several difficulties were encountered in processing the emissions inventory and compiling var-ious data sets on emissions in the study area. Three basic situations were identified: 1. De-sired data is not available (measurements required), 2. Desired data is not freely accessible (stakeholder involvement), 3. Data generation via proxy data. In the pilot phase …

European Neuropsychopharmacology

The COVID-19 pandemic strongly impacted people's daily lives. However, it remains unknown how the pandemic situation affects daily-life experiences of individuals with preexisting severe mental illnesses (SMI). In this real-life longitudinal study, the acute onset of the COVID-19 pandemic in Germany did not cause the already low everyday well-being of patients with schizophrenia (SZ) or major depression (MDD) to decrease further. On the contrary, healthy participants’ well-being, anxiety, social isolation, and mobility worsened, especially in healthy individuals at risk for mental disorder, but remained above the levels seen in patients. Despite being stressful for healthy individuals at risk for mental disorder, the COVID-19 pandemic had little additional influence on daily-life well-being in psychiatric patients with SMI. This highlights the need for preventive action and targeted support of this vulnerable population.

Transportation Research Record

OpenStreetMap (OSM) data are geographical data that are easy and open to access and therefore used for a large set of applications including travel demand modeling. However, often there is a limited awareness about the shortcomings of volunteered geographic information data, such as OSM. One important issue for the application in travel demand modeling is the completeness of OSM elements, particularly points of interest (POI), since it directly influences the predictions of trip distributions. This might cause unreliable model sensitivities and end up in wrong predictions leading to expensive misinterpretations of the effects of policy measures. Because of a lack of large-scale real-world data, a detailed assessment of the quality of POI from OSM has not been done yet. Therefore, in this work, we assess the quality of POI from OSM for use within travel demand models using surveyed real-world data from 49 areas …

ISPRS International Journal of Geo-Information

As one of the major greenhouse gas (GHG) emitters that has not seen significant emission reductions in the previous decades, the transportation sector requires special attention from policymakers. Policy decisions, thereby need to be supported by traffic emission assessments. Estimations of traffic emissions often rely on huge amounts of actual traffic data whose availability is limited, hampering the transferability of the estimation approaches in time and space. Here, we propose a high-resolution estimation of traffic emissions, which is based entirely on open data, such as the road network and points of interest derived from OpenStreetMap (OSM). We estimated the annual average daily GHG emissions from individual motor traffic for the OSM road network in Berlin by combining the estimated Annual Average Daily Traffic Volume (AADTV) with respective emission factors. The AADTV was calculated by simulating car trips with the open routing engine Openrouteservice, weighted by activity functions based on statistics of the German Mobility Panel. Our estimated total annual GHG emissions were 7.3 million t CO2 equivalent. The highest emissions were estimated for the motorways and major roads connecting the city center with the outskirts. The application of the approach to Berlin showed that the method could reflect the traffic pattern. As the input data is freely available, the approach can be applied to other study areas within Germany with little additional effort.

arXiv preprint arXiv:2307.02574

Accurate building height estimation is key to the automatic derivation of 3D city models from emerging big geospatial data, including Volunteered Geographical Information (VGI). However, an automatic solution for large-scale building height estimation based on low-cost VGI data is currently missing. The fast development of VGI data platforms, especially OpenStreetMap (OSM) and crowdsourced street-view images (SVI), offers a stimulating opportunity to fill this research gap. In this work, we propose a semi-supervised learning (SSL) method of automatically estimating building height from Mapillary SVI and OSM data to generate low-cost and open-source 3D city modeling in LoD1. The proposed method consists of three parts: first, we propose an SSL schema with the option of setting a different ratio of "pseudo label" during the supervised regression; second, we extract multi-level morphometric features from OSM data (i.e., buildings and streets) for the purposed of inferring building height; last, we design a building floor estimation workflow with a pre-trained facade object detection network to generate "pseudo label" from SVI and assign it to the corresponding OSM building footprint. In a case study, we validate the proposed SSL method in the city of Heidelberg, Germany and evaluate the model performance against the reference data of building heights. Based on three different regression models, namely Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN), the SSL method leads to a clear performance boosting in estimating building heights with a Mean Absolute Error (MAE) around 2.1 meters, which …

International Journal of Applied Earth Observation and Geoinformation

The disease transmitting mosquito Aedes Aegypti is an increasing global threat. It breeds in small artificial containers such as rainwater tanks and can be characterized by a short flight range. The resulting high spatial variability of abundance is challenging to model. Therefore, we tested an approach to map water tank density as a spatial proxy for urban Aedes Aegypti habitat suitability. Water tank density mapping was performed by a semi-supervised self-training approach based on open accessible satellite imagery for the city of Rio de Janeiro. We ran a negative binomial generalized linear regression model to evaluate the statistical significance of water tank density for modeling inner-urban Aedes Aegypti distribution measured by an entomological surveillance system between January 2019 and December 2021. Our proposed semi-supervised model outperformed a supervised model for water tank detection …

Environmental Monitoring and Assessment

Climate-induced extreme events with fluctuations in climatic indicators like temperature and precipitation highly influence crop productivity. This study deals with quantitative analysis of climatic variability and crop production (1990–2018) using panel data regression analysis. The focus is on variability of three crops, i.e., paddy, maize, and wheat in the Rangit river basin of Sikkim Himalaya, India. Meterological data were acquired from the Indian Meteorological Department, agricultural data from the state agricultural department and a field survey were also conducted with the farmers, using a structured questionnaire, focused group discussion, and key informant observations. The acquired data was analyzed with the help of correlation and multiple linear regression analysis to analyze the relationship between climatic variability and crop production. The result of the study shows that all three crops are dependent on …

Environmental Monitoring and Assessment

This study aims to investigate and understand the temporal and spatial movement of seawater intrusion into the coastal aquifers. Groundwater salinity increase has affected the entire eastern part of the study area and is primarily influenced by direct and reverse ion exchange reactions associated with intrusion and freshwater influx phases, which alternate over monsoons. To gain insights into the spatiotemporal dynamics of the seawater intrusion process, hydrochemical facies analysis utilizing the HFE-Diagram was employed. Additionally, the study considered the major ionic changes during both the monsoons. The HFE-Diagram analysis of hydrochemical facies revealed distinctions in the behaviour of each coastal aquifer concerning seawater intrusion-induced salinization. In PRM 2020, the data shows that approximately 65% of the samples fall under the freshening phase, while the remaining 35% were …

Environmental Monitoring and Assessment

This study is to understand the fate and ecological consequences of pyroxasulfone in aridisols of Punjab, a detailed dissipation study in soil, its influence on soil enzymes, microbial count and succeeding crops was evaluated. Half-lives (DT 50) increased with an increase in the application rate of pyroxasulfone. Dissipation of pyroxasulfone decreased with increase in organic matter content of soil and was slower in clay loam soil (DT 50 12.50 to 24.89) followed by sandy loam (DT 50 8.91 to 17.78) and loamy sand soil (DT 50 6.45 to 14.89). Faster dissipation was observed under submerged conditions (DT 50 2.9 to 20.99 days) than under field capacity conditions (DT 50 6.45 to 24.89 days). Dissipation increased with increase in temperature with DT 50 varying from 6.46 to 24.88, 4.87 to 22.89 and 2.97 to 20.99 days at 25±2, 35±2 and 45±2 C, respectively. Dissipation was slower under sterile conditions and about …

Environmental Monitoring and Assessment

Frequent floods are a severe threat to the well-being of people the world over. This is particularly severe in developing countries like India where tropical monsoon climate prevails. Recently, flood hazard susceptibility mapping has become a popular tool to mitigate the effects of this threat. Therefore, the present study utilized four distinctive Machine Learning algorithms i.e., K-Nearest Neighbor, Decision Tree, Naive Bayes, and Random Forest to estimate flood susceptibility zones in the Agartala Urban Watershed of Tripura, India. The latter experiences debilitating floods during the monsoon season. A multicollinearity test was conducted to examine the collinearity of the chosen flood conditioning factors, and it was seen that none of the factors were compromised by multicollinearity. Results showed that around three-fourths of the AUW area was classified as moderate to very high flood-prone zones, while over 20 …

Environmental Monitoring and Assessment

The high patronage of herbal medicinal products in Ghana for the treatment of diverse disease conditions raises concerns about patient safety, given that much of the raw materials for production are obtained from the wild or farmlands potentially exposed to varied agrochemical residues. Therefore, the work sought to investigate the contamination of herbal medicinal products with pesticide residues and assess the potential risk posed to patients. As a result, validated gas chromatography with mass spectrometry as a detector was used to determine forty-two pesticides in thirty herbal medicinal products. The performance parameters of the method such as linearity, accuracy, and precision were found as acceptable. Pesticide residues such as chlorpyrifos and/or bifenthrin were found in 4/30 herbal medicinal products. Specifically, 3/30 herbal medicinal products contained only one pesticide, while 1/30 was …

Environmental monitoring and assessment

Presently, microplastic pollution has emerged as a growing environmental risk around the world. Nevertheless, knowledge of the occurrence and characteristics of microplastics in tropical agricultural soil is limited. This study investigated the pollution of surface soil microplastics in two agricultural farms located at Klang Valley, Malaysia. An extraction method based on density separation by using saturated extraction solution (sodium sulfate, ρ = 2 g cm−3 and sucrose, ρ = 1.59 g cm−3 with a ratio 1:1, v/v) was carried out. The study revealed the mean particle size of soil microplastics with 3260.76 ± 880.38 μm in farm A and 2822.31 ± 408.48 μm in farm B. The dominant types of soil microplastics were fragments and films with major colors of white (59%) and transparent (28%) in farm A, while black (52%) and white (37.6%) in farm B. Representatives of soil microplastics detected polymers of polyvinyl chloride (PVC …

Environmental Monitoring and Assessment

In November 2015, the Fundão Dam break released millions of tons of metal-rich tailings into the Doce River Basin (DRB), causing catastrophic damage and potential ecological effects that reached the Atlantic Ocean. This study aimed to evaluate the geochemistry and toxicity of water and sediments collected in the DRB from 2015 to 2019 and to determine the spatial and temporal trends. Water and sediment samples were analyzed for metals and As by inductively coupled plasma optical emission spectrometry (ICP-OES), and acute toxicity for Daphnia similis or D. magna. Results were explored using geochemical indices and correlation analyzes. Overall, higher concentrations of metals and As in water and sediments were observed immediately after dam breakage, but the levels exhibited a decreasing trend over time, although the levels of some elements such as As and Mn remained high in the upper DRB. The …

Environmental Monitoring and Assessment

Air pollution poses a significant challenge in numerous urban regions, negatively affecting human well-being. Nitrogen dioxide (NO2) is a prevalent atmospheric pollutant that can potentially exacerbate respiratory ailments and cardiovascular disorders and contribute to cancer development. The present study introduces a novel approach for monitoring and predicting Delhi’s nitrogen dioxide concentrations by leveraging satellite data and ground data from the Sentinel 5P satellite and monitoring stations. The research gathers satellite and monitoring data over 3 years for evaluation. Exploratory data analysis (EDA) methods are employed to comprehensively understand the data and discern any discernible patterns and trends in nitrogen dioxide levels. The data subsequently undergoes pre-processing and scaling utilizing appropriate techniques, such as MinMaxScaler, to optimize the model’s performance. The …

Environmental Monitoring and Assessment

To effectively delineate the spatial distribution of oil contaminant plumes, geophysical methods indirectly measure the physical properties of the subsurface and can provide spatial information and images on a large scale, as opposed to traditional direct methods such as borehole drilling, sampling, and chemical analysis, which are time-consuming and costly. However, interpreting geophysical responses over non-aqueous phase liquid (NAPL)–contaminated sites is not straightforward due to inconsistent responses from biodegraded oil contaminants. In this study, we performed multi-geophysical surveys including seismic refraction, ground-penetrating radar, electrical resistivity tomography (ERT), and induced polarization (IP) surveys, to locate NAPL-contaminated zones in a clay-rich site. To reduce ambiguity in discriminating between oil contaminants and clay layers, we first figure out the geological structure of …

Environmental Monitoring and Assessment

Comprehensive precipitation data is essential for hydrological, agricultural, and climatological studies. Yet, gaps and sparse rain gauge distribution pose challenges, requiring imputation algorithms to fill data gaps. The aim of this research is to evaluate the performance of several approaches for estimating incomplete precipitation data in the Upper Indus Basin (UIB). Eight various imputation approaches were used on sparsely gauged mountainous UIB on a monthly time series of twenty-four meteorological observatories. Following that, the estimation approaches were evaluated using a rank-based approach comprising four different statistical indicators. The results indicate that multiple linear regression is the best-performing strategy for most of the stations regardless of season or orography, followed by the arithmetic average method and inverse distance weighing method.

Environmental Monitoring and Assessment

Frequent floods are a severe threat to the well-being of people the world over. This is particularly severe in developing countries like India where tropical monsoon climate prevails. Recently, flood hazard susceptibility mapping has become a popular tool to mitigate the effects of this threat. Therefore, the present study utilized four distinctive Machine Learning algorithms i.e., K-Nearest Neighbor, Decision Tree, Naive Bayes, and Random Forest to estimate flood susceptibility zones in the Agartala Urban Watershed of Tripura, India. The latter experiences debilitating floods during the monsoon season. A multicollinearity test was conducted to examine the collinearity of the chosen flood conditioning factors, and it was seen that none of the factors were compromised by multicollinearity. Results showed that around three-fourths of the AUW area was classified as moderate to very high flood-prone zones, while over 20 …

Environmental Monitoring and Assessment

The current research is conducted to model the effect of climate change and land use change (LUC) on the geographical distribution of Quercus brantii Lindl. (QB) forests across their historical range. Forecasting was done based on six general circulation models under RCP 2.6 and RCP 8.5 future climate change scenarios for the future years 2050 and 2070. In order to model the species distribution, different modeling methods were used. The results indicated that, in general, climatic variables had a higher influence on the distribution of QB than land use–related attributes. The mean diurnal range (bio2), the precipitation seasonality (bio15), and the mean temperature of the driest quarter (bio9) were the main predictors in the distribution of QB forests, while land use variables were less important in oak species distribution. The GBM, MaxEnt, and RF had higher accuracy and performance in modeling species …

Environmental Monitoring and Assessment

A large percentage of native grassland ecosystems have been severely degraded as a result of urbanization and intensive commercial agriculture. Extensive nitrogen-based fertilization regimes are widely used to rehabilitate and boost productivity in these grasslands. As a result, modern management frameworks rely heavily on detailed and accurate information on vegetation condition to monitor the success of these interventions. However, in high-density environments, biomass signal saturation has hampered detailed monitoring of rangeland condition. This issue stems from traditional broad-band vegetation indices (such as NDVI) responding to high levels of photosynthetically active radiation (PAR) absorption by leaf chlorophyll, which affects leaf area index (LAI) sensitivity within densely vegetative regions. Whilst alternate hyperspectral solutions may alleviate the problem to a certain degree, they are often too …

Environmental Monitoring and Assessment

In the vicinity of the coast, predominantly groundwater is the sole reliable resource for potable purposes as the surface water sources are highly saline and unfit for human consumption. However, the groundwater in Sagar Island is highly vulnerable to saltwater intrusion. The majority of drinking water comes from government-owned hand pump-equipped tube wells. But during the summer season, many of these tube wells yield significantly less water. Hence, in the current scenario, water quality assessment has become important to the quantity available. Total of 31 samples of deep tube wells (groundwater) are collected at variegated locations during pre-monsoon season throughout Sagar, and then, the physical and chemical quality parameters of these water samples are analysed. Furthermore, a multivariate statistical technique is executed with the aid of the SPSS program. The hydro-chemical parameters that …

Environmental Monitoring and Assessment

Climate-induced extreme events with fluctuations in climatic indicators like temperature and precipitation highly influence crop productivity. This study deals with quantitative analysis of climatic variability and crop production (1990–2018) using panel data regression analysis. The focus is on variability of three crops, i.e., paddy, maize, and wheat in the Rangit river basin of Sikkim Himalaya, India. Meterological data were acquired from the Indian Meteorological Department, agricultural data from the state agricultural department and a field survey were also conducted with the farmers, using a structured questionnaire, focused group discussion, and key informant observations. The acquired data was analyzed with the help of correlation and multiple linear regression analysis to analyze the relationship between climatic variability and crop production. The result of the study shows that all three crops are dependent on …

Environmental Monitoring and Assessment

Sacred groves are remnants of primary forests with rich biological diversity, protected by indigenous communities. Their role in carbon sequestration and provision of other ecosystem services is being recognized. We investigated four sacred groves (Idanre Hills, Igbo-Olodumare, Ogun-Onire, and Osun-Osogbo) in southwestern Nigeria for biodiversity conservation, biomass production, and carbon storage. A total of 32 temporary sample plots of 800 m2 each were laid across all the sacred groves. Within each plot, all trees with dbh greater or equal to 10 cm were identified, and their diameters and heights measured. Saplings and seedlings were assessed within 100 m2 and 25 m2 sub-plots, respectively. Non-destructive methods were employed in estimating volume, biomass production, and carbon storage. Shannon–Wiener diversity index, Margalef index, and tree species richness in the four groves ranged from 2 …

Environmental Monitoring and Assessment

Assessing the co-occurrence of multiple health risk factors in coastal ecosystems is challenging due to the complexity of multi-factor interactions and limited availability of simultaneously collected data. Understanding co-occurrence is particularly important for risk factors that may be associated with, or occur in similar environmental conditions. In marine ecosystems, the co-occurrence of harmful algal bloom toxins and bacterial pathogens within the genus Vibrio may impact both ecosystem and human health. This study examined the co-occurrence of Vibrio spp. and domoic acid (DA) produced by the harmful algae Pseudo-nitzschia by (1) analyzing existing California Department of Public Health monitoring data for V. parahaemolyticus and DA in oysters; and (2) conducting a 1-year seasonal monitoring of these risk factors across two Southern California embayments. Existing public health monitoring efforts in the …

Environmental Monitoring and Assessment

In the Arabian Gulf (called also Persian Gulf; hereafter 'the Gulf'), Jana and Karan Islands are recognized as one of the most Important Bird Areas in the region. Many migratory breeding seabirds, like the Greater Crested Tern Thalasseus bergii, White-cheeked Tern Sterna repressa and Bridled Tern Onychoprion anaethetus, depend on these islands during the breeding season. However, these aquatic wildlife species are suffering from intensified urban and industrial coastal development and various contamination events including wars and related oil spills. In this study, we used these three piscivorous top predator birds to analyse the levels of 19 trace elements (TEs; i.e. Al, As, Ba, Ca, Cd, Co, Cr, Cu, Fe, Hg, K, Mg, Mn, Na, Ni, Pb, Sr, V and Zn) in 15 muscular tissue samples from Jana and Karan Islands. PERMANOVA analysis showed no difference in contamination profile between sites nor between species …

Environmental Monitoring and Assessment

The increase in heavy metal concentrations in the air, especially after the Industrial Revolution, is notable for the scientific world because of the adverse effects that threaten environmental and human health. Among the trace elements, nickel (Ni) is carcinogenic, and all barium (Ba) compounds are toxic. Trace elements are critical for human and environmental health. Their threat further increases, especially in the urban areas and surroundings with a high population. In urban areas, the trace element contamination in the airborne can be reduced using plants. However, which plant and plant organs absorb trace elements could not be determined. In the present study, Ni and Ba concentrations in the branch, wood, and leaf samples of 14 species collected from the city center of Mersin province were determined. As a result, broad-leaved species' Ni and Ba concentrations in their leaf sample were generally higher than …

Environmental Monitoring and Assessment

Small island ecosystems and their inhabitants face a significant threat from global warming, jeopardizing their sustainability. These communities are particularly vulnerable to the impact of climate change, as they heavily rely on natural resources for their livelihoods and are more vulnerable than mainland regions. Therefore, it is essential to take urgent action to address the challenges small island states face and promote their resilience in the face of climate change. To preserve the coastal ecosystems in Karimunjawa Islands, Indonesia, this study proposes an alternative spatial plan through micro-zonation analysis. The study conducted literature reviews and field surveys to collect data and develop recommendations for the current spatial plans through spatial, descriptive statistics, and comparative analysis. The findings show that the sea surface temperatures of Karimunjawa and Kemujan Island have increased by …