Pradesh Jena, Francis Dutta, Bijoy Krishna Handique
et al.
Abstract Precision farming (PF) has emerged as a game-changer in agriculture, offering technological solutions to address the critical challenges of food security and climate change. However, the widespread adoption of PF faces hurdles due to the complexities of diverse cropping systems and the high costs associated with advanced ground-based instruments. To overcome this, an innovative approach was introduced in Bandia village of Assam, India by using UAV multispectral and Sentinel 2 data synergistically. The UAV imagery acquired on 17th March 2021, with ten multispectral bands (444–842 nm) was used for classifying different land use types using Object Based Image Analysis (OBIA) technique. The classification resulted into a moderately diverse cropping system with maize and rice cultivated as dominant crops occupying 45.56% and 40.87% of the total cultivated area. The diversity of the cropping pattern was further validated by ecological indices, with Shannon's Diversity Index (DI) at 1.09, Simpson's DI at 0.62, and Evenness Index at 0.78. Successively, crop above ground biomass, leaf area index and height were monitored based on the optimized Partial Least Square Regression (PLSR) model using vegetation indices from both the platforms. Cost analysis of this approach revealed a remarkable 99% cost reduction compared to traditional PF techniques. Our findings strongly suggest that the synergistic use of UAV and satellite data offers a more comprehensive view of agriculture lands, enabling high-precision monitoring of crop growth and development throughout the growth cycle and facilitating improved field level management.
Wildfires are an integral component of Mediterranean ecosystems. The forest management practices implemented following such forest fires can significantly influence soil chemistry and metal dynamics. This study investigates the effects of different forest management strategies, including natural regeneration, grading (e.g., gradoni terrace making), and subsoiling with ripper on soil levels of major, trace, and heavy metals in a fire-affected forest in the southwestern part of Türkiye. Soil samples were collected 2.5 years after the containment of the wildfire and analyzed for selected metals (Fe, Ca, Al, Mn, Cr, Ni, Zn, Cu, Pb, Co, As, and Hg) concentrations. The findings indicated that subsoiling with a ripper resulted in elevated levels of multiple potentially toxic metals, including Cr (223.22 ± 60.47 mg/kg), Ni (150.54 ± 27.33 mg/kg), Zn (156.18 ± 66.14 mg/kg), and As (6.72 ± 1.30 mg/kg), compared to other treatments. These findings demonstrate that management interventions such as subsoiling with a ripper can significantly alter the distribution and concentration of trace metals. Future research integrating topographic variation and earlier sampling would further strengthen our understanding of post-fire metal dynamics.
J. M. Wolstenholme, F. Cooper, R. E. Thomas
et al.
Abstract Hedgerows are a key component of the UK landscape that form boundaries, borders and limits of land whilst providing vital landscape‐scale ecological connectivity for a range of organisms. They are diverse habitats in the agricultural landscape providing a range of ecosystem services. Poorly managed hedgerows often present with gaps, reducing their ecological connectivity, resulting in fragmented habitats. However, hedgerow gap frequency and spatial distributions are often unquantified at the landscape‐scale. Here we present a novel methodology based on deep learning (DL) that is coupled with high‐resolution aerial imagery. We demonstrate how this provides a route towards a rapid, adaptable, accurate assessment of hedgerow and gap abundance at such scales, with minimal training data. We present the training and development of a DL model using the U‐Net architecture to automatically identify hedgerows across the East Riding of Yorkshire (ERY) in the UK and demonstrate the ability of the model to estimate hedgerow gap types, lengths and their locations. Our method was both time efficient and accurate, processing an area of 2479 km2 in 32 h with an overall accuracy of 92.4%. The substantive results allow us to estimate that in the ERY alone, there were 3982 ± 302 km of hedgerows and 2865 ± 217 km of hedgerow gaps (with 339 km classified as for access). Our approach and study show that hedgerows and gaps can be extracted from true colour aerial imagery without the requirement of elevation data and can produce meaningful results that lead to the identification of prioritisation areas for hedgerow gap infilling, replanting and restoration. Such replanting could significantly contribute towards national tree planting goals and meeting net zero targets in a changing climate.
Abel Centella-Artola, Arnoldo Bezanilla-Morlot, Roberto Serrano-Notivoli
et al.
The paper presents a high-resolution (-3km) gridded dataset for daily precipitation across Cuba for 1961-2008, called CubaPrec1. The dataset was built using the information from the data series of 630 stations from the network operated by the National Institute of Water Resources. The original station data series were quality controlled using a spatial coherence process of the data, and the missing values were estimated on each day and location independently. Using the filled data series, a grid of 3 × 3 km spatial resolution was constructed by estimating daily precipitation and their corresponding uncertainties at each grid box. This new product represents a precise spatiotemporal distribution of precipitation in Cuba and provides a useful baseline for future studies in hydrology, climatology, and meteorology. The data collection described is available on zenodo: https://doi.org/10.5281/zenodo.7847844
Computer applications to medicine. Medical informatics, Science (General)
Formalized knowledge representation is the foundation of Big Data computing, mining and visualization. Current knowledge representations regard information as items linked to relevant objects or concepts by tree or graph structures. However, geographic knowledge differs from general knowledge, which is more focused on temporal, spatial, and changing knowledge. Thus, discrete knowledge items are difficult to represent geographic states, evolutions, and mechanisms, e.g., the processes of a storm “{9:30-60 mm-precipitation}-{12:00-80 mm-precipitation}-…”. The underlying problem is the constructors of the logic foundation (ALC description language) of current geographic knowledge representations, which cannot provide these descriptions. To address this issue, this study designed a formalized geographic knowledge representation called GeoKG and supplemented the constructors of the ALC description language. Then, an evolution case of administrative divisions of Nanjing was represented with the GeoKG. In order to evaluate the capabilities of our formalized model, two knowledge graphs were constructed by using the GeoKG and the YAGO by using the administrative division case. Then, a set of geographic questions were defined and translated into queries. The query results have shown that GeoKG results are more accurate and complete than the YAGO’s with the enhancing state information. Additionally, the user evaluation verified these improvements, which indicates it is a promising powerful model for geographic knowledge representation.