•Monthly IPAR is estimated for cereals in Castilla León using Beer Lambert’s law.•Modeled PAR, satellite LAI, and bibliographic k data are used for calculations.•The methodology is applicable at ...different spatial and temporal scales.•The fIPAR estimate for wheat, barley and maize for the whole territory.•Average annual growth and yield patterns for large crop areas is determined.
Agriculture is by far the most important economic activity in the Spanish autonomous region of Castilla y León. Numerous factors influence crop development but one of the most related variables to the photosynthetic process is Photosynthetically Active Radiation (PAR). Estimating Intercepted Photosynthetically Active Radiation (IPAR) in different crops through the Beer-Lambert law could be a relevant factor in crop season planning by enabling photosynthesis monitoring. The Beer-Lambert Law is applied in this study to the data for almost 2 million hectares of wheat, barley, and maize cultivated in Castilla y León in 2021. The fourteen-year data set of Global Horizontal Irradiance (GHI) used to calculate the monthly PAR data in the region was collected at 93 meteorological stations (46 in Castilla y León and 47 in neighboring Spanish and Portuguese regions). Two previously published global calibrated models were employed to calculate the PAR, with a relative Root Mean Square Error (rRMSE) below 6%, for the measured daily mean values of PAR in Burgos. Processing the various NASA Terra and Aqua satellite images yielded the monthly Leaf Area Index (LAI) and the literature review provided the light extinction coefficient (k). The Geographic Information System (GIS) facilitated visualization of IPAR estimates for the three cereal crops in all months of its growing season. Wheat and barley reach their IPAR peaks in June and July, while maize peaks in July and August. In addition, the fraction of Intercepted Photosynthetically Active Radiation (fIPAR) was calculated in different provinces to assess PAR interception for each cereal at different growing stages. In June, almost 50% of the wheat area in Burgos, Palencia and Soria displayed fIPAR values exceeding 45% while in the case of barley only the province of Burgos reached these percentages of area and.fIPAR.
Although organic farming and agroecology are normally not associated with the use of new technologies, it’s rapid growth, new technologies are being adopted to mitigate environmental impacts of ...intensive production implemented with external material and energy inputs. GPS, satellite images, GIS, drones, help conventional farming in precision supply of water, pesticides, fertilizers. Prescription maps define the right place and moment for interventions of machinery fleets. Yield goal remains the key objective, integrating a more efficient use or resources toward an economic-environmental sustainability. Technological smart farming allows extractive agriculture entering the sustainability era. Societies that practice agroecology through the development of human-environmental co-evolutionary systems represent a solid model of sustainability. These systems are characterized by high-quality agroecosystems and landscapes, social inclusion, and viable economies. This book explores the challenges posed by the new geographic information technologies in agroecology and organic farming. It discusses the differences among technology-laden conventional farming systems and the role of technologies in strengthening the potential of agroecology. The first part reviews the new tools offered by geographic information technologies to farmers and people. The second part provides case studies of most promising application of technologies in organic farming and agroecology: the diffusion of hyperspectral imagery, the role of positioning systems, the integration of drones with satellite imagery. The third part of the book, explores the role of agroecology using a multiscale approach from the farm to the landscape level. This section explores the potential of Geodesign in promoting alliances between farmers and people, and strengthening food networks, whether through proximity urban farming or asserting land rights in remote areas in the spirit of agroecological transition. The Open Access version of this book, available at www.taylorfrancis.com, has been made available under a Creative Commons 4.0 license.
Water and vegetation are the two most important land cover features of any natural setting. The Jhenaidah District of Bangladesh is known for its remarkable physical geography, featuring diversified ...vegetation cover and numerous oxbow lakes. Due to several anthropogenic causes, this majestic land cover is degrading rapidly. This study examines the study area's spatiotemporal water and vegetation cover change from 1990 to 2020. Freeware Satellite imageries from the USGS data archive were used as the main secondary data source, ensuring consistency by collecting dry season images. In addition, open discussions with the residents provided valuable insights into the situation. Remote sensing (RS) based Soil Adjusted Vegetation Index (SAVI) was used to detect the water and vegetation cover from the preprocessed satellite imageries. Furthermore, the water and vegetation cover were classified based on a scheme developed by field observation and discussion with the residents. The analysis reveals an overall 84.47% decline in dense vegetation, 63.01% decline in deep water cover, 185.69% increase in shallow water cover, and 16.08% increase in agricultural lands within the mentioned time frame. Almost all the upazila of Jhenauidah district experience the criticality of the land cover change. Among the upazila, Shailkupa faced an unprecedented decline in deep water (95.29%), and Kaliganj faced a heavy decrease in forested vegetation (92.40%). In contrast, shallow water expanded significantly in Sadar Upazila (251.37%), and agricultural land experienced the most increasing trend (32.70%) in Shailakupa Upazila.
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•The performance of DLNN was assessed for flood susceptibility mapping.•DLNN was compared with the MLP-NN and SVM in terms of their performance.•DLNN with ADAM optimization is robust ...and outperformed other models.•DLNN is a new promising tool for predicting flash flood in prone areas.
This research proposes and evaluates a new approach for flash flood susceptibility mapping based on Deep Learning Neural Network (DLNN)) algorithm, with a case study at a high-frequency tropical storm area in the northwest mountainous region of Vietnam. Accordingly, a DLNN structure with 192 neurons in 3 hidden layers was proposed to construct an inference model that predicts different levels of susceptibility to flash flood. The Rectified Linear Unit (ReLU) and the sigmoid were selected as the activate function and the transfer function, respectively, whereas the Adaptive moment estimation (Adam) was used to update and optimize the weights of the DLNN. A database for the study area, which includes factors of elevation, slope, curvature, aspect, stream density, NDVI, soil type, lithology, and rainfall, was established to train and validate the proposed model. Feature selection was carried out for these factors using the Information gain ratio. The results show that the DLNN attains a good prediction accuracy with Classification Accuracy Rate = 92.05%, Positive Predictive Value = 94.55% and Negative Predictive Value = 89.55%. Compared to benchmarks, Multilayer Perceptron Neural Network and Support Vector Machine, the DLNN performs better; therefore, it could be concluded that the proposed hybridization of GIS and deep learning can be a promising tool to assist the government authorities and involving parties in flash flood mitigation and land-use planning.
Early defibrillation is essential for increasing the chance of survival in out-of-hospital-cardiac-arrest (OHCA). Automated external defibrillator (AED)-equipped drones have a substantial potential ...to shorten times to defibrillation in OHCA patients. However, optimal locations for drone deployment are unknown. Our aims were to find areas of high incidence of OHCA on a national level for placement of AED-drones, and to quantify the number of drones needed to reach 50, 80, 90 and 100% of the target population within eight minutes.
This is a retrospective observational study of OHCAs reported to the Swedish Registry for Cardiopulmonary Resuscitation between 2010–2018. Spatial analyses of optimal drone placement were performed using geographical information system (GIS)-analyses covering high-incidence areas (>100 OHCAs in 2010–2018) and response times.
39,246 OHCAs were included. To reach all OHCAs in high-incidence areas with AEDs delivered by drone or ambulance within eight minutes, 61 drone systems would be needed, resulting in overall OHCA coverage of 58.2%, and median timesaving of 05:01 (min:sec) IQR 03:22–06:19. To reach 50% of the historically reported OHCAs in <8 min, 21 drone systems would be needed; for 80%, 366; for 90%, 784, and for 100%, 2408.
At a national level, GIS-analyses can identify high incidence areas of OHCA and serve as tools to quantify the need of AED-equipped drones. Use of only a small number of drone systems can increase national coverage of OHCA substantially. Prospective real-life studies are needed to evaluate theoretically optimized suggestions for drone placement.
This article presents considerations made to show how important it is to use current information to help make a decision. we are currently faced with major social and technological challenges. The ...main purpose of this discussion is to gain knowledge about the level of use of information technology to create geographic information systems or visual maps of fishery potential in East Java and advantages of GIS in fishing industry. Studies are based on the idea of the need to implement measures aimed at the development and enhancement of fishery potential. The methodological basis for the discussion is an analysis of the literature and case studies.
Today, various methods are applied to analyze the data collected through participatory mapping, including public participation GIS (PPGIS), participatory GIS (PGIS), and collecting volunteered ...geographic information (VGI). However, these methods lack an organized framework to describe and guide their systematic applications. Majority of the published articles on participatory mapping apply a specific subset of analyses that fails to situate the methods within a broader, more holistic context of research and practice. Based on the expert workshops and a literature review, we synthesized the existing analysis methods applied to the data collected through participatory mapping approaches. In this article, we present a framework of methods categorized into three phases: Explore, Explain, and Predict/Model. Identified analysis methods have been highlighted with empirical examples. The article particularly focuses on the increasing applications of online PPGIS and web-based mapping surveys for data collection. We aim to guide both novice and experienced practitioners in the field of participatory mapping. In addition to providing a holistic framework for understanding data analysis possibilities, we also discuss potential directions for future developments in analysis of participatory mapping data.
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Dostopno za:
BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
Potentially toxic elements such as heavy metals are ubiquitous in the environment. Risk-based environmental management relies upon identifying pollution sources, pathways, and the exposed population. ...In a Chinese urban setting, many residents live in high-rise buildings without private gardens. Therefore, the main residential risk of exposure to contaminated soils and dusts may be associated with public open spaces. As children are the most vulnerable receptor, playgrounds represent an important yet often overlooked exposure point. The present study assessed plausible sources of heavy metals at children's playgrounds in a representative metropolitan environment. Soil and equipment dust samples were collected from 71 playgrounds across Beijing, which were analyzed for 11 different heavy metals. Principal component analysis (PCA) was used to identify the latent constructs which control heavy metal variability and reflect potential sources. Cluster analysis (CA) was conducted to group sampled locations, which provided further insights on plausible sources. The main factors extracted from the PCA were then subject to geostatistical analysis. The systematic combination of GIS with multivariate statistical analysis proved valuable for elucidating anthropogenic and natural sources. Elevated Be, V, Cr, Mn, Co, Ni, As in playground soils were found to derive mainly from the natural background (spatial autocorrelation = 2 km), while elevated Cu and Pb was attributed to traffic activities (spatial autocorrelation = 17 km), especially along the routes of Beijing's inner ring-roads, the major roads toward the northwest and northeast, and the international airport. These results suggest that heavy metals in playground equipment dust may derive mainly from atmospheric deposition of air pollution of both natural and anthropogenic origin (spatial autocorrelation = 11–13 km). Among them, Be, V, Mn, Co, Cu, As, Pb were attributed to atmospheric pollution deriving from the north of Beijing, brought by the prevailing northern wind in the winter season; whereas, Cr and Ni may possibly be brought from the southeast by the summer season winds. Knowledge of anthropogenic vs. natural origins of heavy metals in playgrounds is critical in assessing health impact and designing policy instruments for metropolitan areas.
•Knowledge of anthropogenic vs. natural origins of heavy metals is critical.•Systematic combination of GIS with multivariate statistical analysis proved valuable.•Playgrounds represent an important yet often overlooked exposure point.•Cu and Pb in playground soils were attributed to traffic activities.•Most heavy metals in playground dusts may naturally derive from the north of Beijing.
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