Spatially-explicit modelling of grassland classes – an improved method of integrating a climate-based classification model with interpolated climate surfaces

2014 ◽  
Vol 36 (2) ◽  
pp. 175 ◽  
Author(s):  
Xiaoni Liu ◽  
Hongxia Wang ◽  
Jing Guo ◽  
Jingqiong Wei ◽  
Zhengchao Ren ◽  
...  
Keyword(s):  
Environmental Management ◽  
Classification Model ◽  
Spatially Explicit ◽  
Forest Steppe ◽  
Climate Variables ◽  
Climate Data ◽  
Humid Forest ◽  
Cool Temperate ◽  
Total Study Area

Spatially-explicit modelling of grassland classes is important to site-specific planning for improving grassland and environmental management over large areas. In this study, a climate-based grassland classification model, the Comprehensive and Sequential Classification System (CSCS) was integrated with spatially interpolated climate data to classify grassland in Gansu province, China. The study area is characterised by complex topographic features imposed by plateaus, high mountains, basins and deserts. To improve the quality of the interpolated climate data and the quality of the spatial classification over this complex topography, three linear regression methods, namely an analytic method based on multiple regression and residues (AMMRR), a modification of the AMMRR method through adding the effect of slope and aspect to the interpolation analysis (M-AMMRR) and a method which replaces the inverse distance-weighted approach for residue interpolation in M-AMMRR with an ordinary kriging approach (I-AMMRR), for interpolating climate variables were evaluated. The interpolation outcomes from the best interpolation method were then used in the CSCS model to classify the grassland in the study area. Climate variables interpolated included the annual cumulative temperature and annual total precipitation. The results indicated that the AMMRR and M-AMMRR methods generated acceptable climate surfaces but the best model fit and cross validation result were achieved by the I-AMMRR method. Twenty-six grassland classes were classified for the study area. The four grassland vegetation classes that covered more than half of the total study area were ‘cool temperate-arid temperate zonal semi-desert’, ‘cool temperate-humid forest steppe and deciduous broad-leaved forest’, ‘temperate-extra-arid temperate zonal desert’, and ‘frigid per-humid rain tundra and alpine meadow’. The vegetation classification map generated in this study provides spatial information on the locations and extents of the different grassland classes. This information can be used to facilitate government agencies’ decision-making in land-use planning and environmental management, and for vegetation and biodiversity conservation. The information can also be used to assist land managers in the estimation of safe carrying capacities, which will help to prevent overgrazing and land degradation.

scholarly journals The implications of a changing climate on agricultural land classification in England and Wales

2012 ◽  
Vol 152 (1) ◽  
pp. 23-37 ◽  
Author(s):  
C. A. KEAY ◽  
R. J. A. JONES ◽  
J. A. HANNAM ◽  
I. A. BARRIE
Keyword(s):  
Agricultural Land ◽  
Formal Method ◽  
Climate Variables ◽  
Climate Data ◽  
Land Classification ◽  
England And Wales ◽  
Chemical Soil ◽  
Soil Site ◽  
Land Grading

SUMMARYThe agricultural land classification (ALC) of England and Wales is a formal method of assessing the quality of agricultural land and guiding future land use. It assesses several soil, site and climate criteria and classifies land according to whichever is the most limiting. A common approach is required for calculating the necessary agroclimatic parameters over time in order to determine the effects of changes in the climate on land grading. In the present paper, climatic parameters required by the ALC classification have been re-calculated from a range of primary climate data, available from the Meteorological Office's UKCP09 historical dataset, provided as 5 km rasters for every month from 1914 to 2000. Thirty-year averages of the various agroclimatic properties were created for 1921–50, 1931–60, 1941–70, 1951–80, 1961–90 and 1971–2000. Soil records from the National Soil Inventory on a 5 km grid across England and Wales were used to determine the required soil and site parameters for determining ALC grade. Over the 80-year period it was shown that the overall climate was coolest during 1951–80. However, the area of land estimated in retrospect as ‘best and most versatile (BMV) land’ (Grades 1, 2 and 3a) probably peaked in the 1951–80 period as the cooler climate resulted in fewer droughty soils, more than offsetting the land which was downgraded by the climate being too cold. Overall there has been little change in the proportions of ALC grades among the six periods once all 10 factors (climate, gradient, flooding, texture, depth, stoniness, chemical, soil wetness, droughtiness and erosion) are taken into account. This is because it is rare for changes in climate variables all to point in the same direction in terms of ALC. Thus, a reduction in rainfall could result in higher grades in wetter areas but lead to lower classification in drier areas.

Faktor-Faktor yang Berpengaruh dalam Mendapatkan Pekerjaan bagi Lulusan Statistika IPB dengan Menggunakan Metode CHAID

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Aulia Dwi Oktavia ◽  
Aam Alamudi ◽  
Budi Susetyo
Keyword(s):  
College Graduates ◽  
Influential Factors ◽  
Classification Model ◽  
Statistical Software ◽  
Waiting Period ◽  
Level Of Education ◽  
Economic Problems ◽  
Factors Influencing

Unemployment is one of the economic problems in Indonesia. Judging from the level of education that was completed there were unemployment from the level of college graduates. This encourages the level of competition in getting jobs to be more stringent, so that college graduates (bachelor of Statistics in IPB) must have the preparation of various factors to maintain the quality of their graduates. The quality of college graduates can be seen from the length of time waiting to get a job. This study aims to determine the influential factors in getting a job for graduates of the IPB Statistics degree, so that the CHAID method can be used in this study. The results of CHAID's analysis in this study in the form of tree diagrams using α = 10% explained that the factors influencing the waiting period variables were sex, internship, and the ability to master statistical software, where the accuracy value generated by the classification model was 79.3 %.

Productivity and quality of soybean seeds with different fertilization on podzolized chernozem

2020 ◽  
Vol 1 (97) ◽  
pp. 136-144
Author(s):  
H.M. Hospodarenko ◽  
◽  
I.V. Prokopchuk ◽  
K. P. Leonova ◽  
V.P. Boyko
Keyword(s):  
Crop Rotation ◽  
Crop Yields ◽  
Spring Barley ◽  
Mineral Fertilizer ◽  
Nitrogen Fertilizers ◽  
Mineral Fertilizers ◽  
Forest Steppe ◽  
Soybean Seeds ◽  
Different Doses

The productivity of agricultural crops is the most variable and integral indicator of their vital activity, which accumulates their genetic potential, soil fertility, weather conditions and components of agricultural technology. Soybean under optimal growing conditions (the reaction of the soil is close to neutral, sufficient phosphorus and potassium nutrition, the use of nitraginization) assimilates from the air about 70 % of the total nitrogen requirement. Therefore, it is believed that it is enough to apply only a starting dose of nitrogen fertilizers (20–40 kg/ha a. s.), to get a high yield with good indicators of grain quality. The results of studies of the influence of long-term (8 years) application of different doses and ratios of fertilizers in field crop rotation on podzolized chernozem in the conditions of the Right -Bank Forest-Steppe of Ukraine on the yield and quality of soybean seeds preceded by spring barley were presented. It was found that crop yields could be increased by 18–77 % owing to different doses, ratios and types of fertilizers. The highest indicators of seed yields for three years of the research (3,02 t/ha) were obtained under the application of mineral fertilizers at a dose of N110P60K80 per 1 ha of crop rotation area, including under soybean – N60P60K60. Exclusion of the nitrogen component from the complete fertilizer (N60P60K60) reduced its yield by 26 %, phosphorus – by 17, and potassium by 11 %. There was no significant decrease in soybean yield in the variant of the experiment with a decrease in the proportion of potassium in the composition of complete mineral fertilizer (N60P60K30) for three years of study. The largest mass of 1000 soybean seeds was formed at doses of N60К60 fertilizers, and their protein content — under the application of complete mineral fertilizer in doses of N60P60K60 and N60P60K30.

INFLUENCE OF ENCAPSULATED UREA ON PRODUCTIVITY AND QUALITY OF EARLY RIPE POTATO TUBERS IN THE NORTHERN FOREST-STEPPE OF TYUMEN REGION

2019 ◽  
Keyword(s):  
Ammonium Nitrate ◽  
Mineral Fertilizer ◽  
Natural Fertility ◽  
Forest Steppe ◽  
Potato Tubers ◽  
Average Yield ◽  
Northern Forest ◽  
Control Option ◽  
Increase In Productivity

Introduction of complex mineral fertilizer of an azofoska in combination with ammonium nitrate and urea to early ripe potatoes of Zhukovsky and Red Scarlett variety on the planned productivity of 40 t/hectare has allowed to achieve a goal. At the same time in control option without fertilizers the productivity was 23,2-24,8 t/hectare. Use of encapsulated urea has led to decrease in productivity and level of profitability by 26,3-30,9%. Early ripe potatoes of Zhukovsky and Red Scarlett variety on natural fertility of the chernozem leached in the northern forest-steppe of the Tyumen region have created average yield of 23,2-24,8 t/hectare for years of researches. Use of complex mineral fertilizer of an azofoska in combination with ammonium nitrate and urea on the planned productivity of 40 t/hectare has led to increase in productivity on the first variety to 39,5 on the second variety up to 41,4 t/hectare. Introduction of the encapsulated urea has led to decrease in productivity of the early ripe potato tubers studied. At the same time, the peel was gentle and when cleaning it was strongly injured. As to the content of starch (11,9-12,6%) at both varieties the big difference between ex-perience options isn't revealed. The similar picture was observed also according to tastes of tubers. It has made 3,2-3,5 points at Zhukovsky variety and 3,4-3,7 points at Red Scarlett's variety. Profitability level in con-trol option at Zhukovsky variety was 157,3%, at Red Scarlett's variety – 140,5%. In options with non-encapsulated ammonium nitrate and urea the first variety got 172,6-184,1%, second variety – 190,4-207,2%. In option with encapsulated urea at varieties under study the profitability level has decreased 26,3-30,9.

Cultivation of Rapeseed in the Southern Urals and Its Rational Use

2020 ◽  
Vol 67 (1) ◽  
pp. 78-86
Author(s):  
Nikolay S. Sergeev ◽  
Mikhail V. Zapevalov ◽  
Alexander V. Gritsenko
Keyword(s):  
Southern Urals ◽  
Research Purpose ◽  
Partial Replacement ◽  
Biodiesel Fuel ◽  
Forest Steppe ◽  
The Southern Urals ◽  
Rational Use ◽  
Chelyabinsk Region ◽  
Spring Rape

In the continental climate of the southern Urals, rapeseed compares favorably with many forage and traditional silage crops with a high protein content and adaptive properties. The cultivation of rapeseed guarantees the production of its own seeds, up to 40 percent of oil, 60 percent of cake and 98 percent of rapeseed flour. (The research purpose) The research purpose is in improving the efficiency of rapeseed cultivation and rational use of rapeseed seeds, rapeseed flour and oil in the agricultural production in the Chelyabinsk region. (Materials and methods) The influence of various forecrops on the productivity and quality of spring rape seeds in the links of grain-pair crop rotations in the Northern forest-steppe of the Chelyabinsk region were studied. Authors have analyzed the chemical composition of the soil and seeds of spring rape after various forecrops. (Results and discussion) The article proposes to reduce energy costs during pressing and reduce residual oil in the cake after pre-grinding of rapeseed by cutting method using a centrifugal-rotary shredder. The article shows that rapeseed flour has a good flowability and is easily mixed with other feeds. It was found that partial replacement of concentrates with rapeseed flour in the amount of 8-12 percent of the total weight in the diet of lactating cows contributes to an increase in milk productivity by 1.1-1.8 kilograms in terms of milk of 4 percent fat content. (Conclusions) It has been revealed that in order to increase the yield and quality of spring rape seeds, it is necessary to place them on the best forecrops. It was found that when 75 percent of rapeseed oil is mixed with 25 percent of diesel fuel, the obtained biodiesel is not inferior to diesel in terms of energy indicators. The article proves that when cultivating rapeseed for seeds on an area of 100 hectares, it is possible to produce 94.5 tons of biodiesel fuel, 106.0 tons of cake with an oil content of 5 percent and 8.4 tons of oil sludge, the estimated economic effect after sale is of 3,813,325 rubles.

scholarly journals AI-based localization and classification of skin disease with erythema

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ha Min Son ◽  
Wooho Jeon ◽  
Jinhyun Kim ◽  
Chan Yeong Heo ◽  
Hye Jin Yoon ◽  
...  
Keyword(s):  
Neural Network ◽  
Skin Diseases ◽  
Classification Model ◽  
Screening Tests ◽  
Sensitivity Score ◽  
Common Skin ◽  
Novel Method ◽  
Improved Performance ◽  
High Level

AbstractAlthough computer-aided diagnosis (CAD) is used to improve the quality of diagnosis in various medical fields such as mammography and colonography, it is not used in dermatology, where noninvasive screening tests are performed only with the naked eye, and avoidable inaccuracies may exist. This study shows that CAD may also be a viable option in dermatology by presenting a novel method to sequentially combine accurate segmentation and classification models. Given an image of the skin, we decompose the image to normalize and extract high-level features. Using a neural network-based segmentation model to create a segmented map of the image, we then cluster sections of abnormal skin and pass this information to a classification model. We classify each cluster into different common skin diseases using another neural network model. Our segmentation model achieves better performance compared to previous studies, and also achieves a near-perfect sensitivity score in unfavorable conditions. Our classification model is more accurate than a baseline model trained without segmentation, while also being able to classify multiple diseases within a single image. This improved performance may be sufficient to use CAD in the field of dermatology.

scholarly journals Toward an Automatic Quality Assessment of Voice-Based Telemedicine Consultations: A Deep Learning Approach

Sensors ◽  
2021 ◽  
Vol 21 (9) ◽  
pp. 3279
Author(s):  
Maria Habib ◽  
Mohammad Faris ◽  
Raneem Qaddoura ◽  
Manal Alomari ◽  
Alaa Alomari ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Quality Assessment ◽  
Transcript Level ◽  
Assessment Process ◽  
Classification Model ◽  
Systematic Evaluation ◽  
Transcript Levels ◽  
Perceptual Evaluation

Maintaining a high quality of conversation between doctors and patients is essential in telehealth services, where efficient and competent communication is important to promote patient health. Assessing the quality of medical conversations is often handled based on a human auditory-perceptual evaluation. Typically, trained experts are needed for such tasks, as they follow systematic evaluation criteria. However, the daily rapid increase of consultations makes the evaluation process inefficient and impractical. This paper investigates the automation of the quality assessment process of patient–doctor voice-based conversations in a telehealth service using a deep-learning-based classification model. For this, the data consist of audio recordings obtained from Altibbi. Altibbi is a digital health platform that provides telemedicine and telehealth services in the Middle East and North Africa (MENA). The objective is to assist Altibbi’s operations team in the evaluation of the provided consultations in an automated manner. The proposed model is developed using three sets of features: features extracted from the signal level, the transcript level, and the signal and transcript levels. At the signal level, various statistical and spectral information is calculated to characterize the spectral envelope of the speech recordings. At the transcript level, a pre-trained embedding model is utilized to encompass the semantic and contextual features of the textual information. Additionally, the hybrid of the signal and transcript levels is explored and analyzed. The designed classification model relies on stacked layers of deep neural networks and convolutional neural networks. Evaluation results show that the model achieved a higher level of precision when compared with the manual evaluation approach followed by Altibbi’s operations team.

scholarly journals The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

2021 ◽  
Vol 13 (9) ◽  
pp. 1701
Author(s):  
Leonardo Bagaglini ◽  
Paolo Sanò ◽  
Daniele Casella ◽  
Elsa Cattani ◽  
Giulia Panegrossi
Keyword(s):  
Neural Network ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
High Quality ◽  
Data Record ◽  
Global Precipitation ◽  
Climate Data Record

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

scholarly journals Onboard Radio Frequency Interference as the Origin of Inter-Satellite Biases for Microwave Humidity Sounders

2019 ◽  
Vol 11 (7) ◽  
pp. 866 ◽  
Author(s):  
Imke Hans ◽  
Martin Burgdorf ◽  
Stefan A. Buehler
Keyword(s):  
Time Series ◽  
Radio Frequency ◽  
Time Dependent ◽  
Radio Frequency Interference ◽  
Climate Variables ◽  
Compelling Evidence ◽  
Climate Data ◽  
The Earth ◽  
Correction Scheme

Understanding the causes of inter-satellite biases in climate data records from observations of the Earth is crucial for constructing a consistent time series of the essential climate variables. In this article, we analyse the strong scan- and time-dependent biases observed for the microwave humidity sounders on board the NOAA-16 and NOAA-19 satellites. We find compelling evidence that radio frequency interference (RFI) is the cause of the biases. We also devise a correction scheme for the raw count signals for the instruments to mitigate the effect of RFI. Our results show that the RFI-corrected, recalibrated data exhibit distinctly reduced biases and provide consistent time series.

Reviewer Acknowledgements for Environmental Management and Sustainable Development, Vol. 10 No. 3

2021 ◽  
Vol 10 (3) ◽  
pp. 112
Author(s):  
Jenny Young
Keyword(s):  
Environmental Management ◽  
Sustainable Development

Environmental Management and Sustainable Development (EMSD) would like to thank the following reviewers for reviewing manuscripts from May 1, 2021, to August 1, 2021. Their comments and suggestions were of great help to the authors in improving the quality of their papers. Many authors, regardless of whether EMSD publishes their work, appreciate the helpful feedback provided by the reviewers. Macrothink Institute appreciates the following reviewers’ rigorous and conscientious efforts for this journal. Each of the reviewers listed below returned at least one review during this period. Adriano Magliocco, University of Genoa, ItalyAristotulus Ernst Tungka, University of Sam Ratulangi Manado, IndonesiaChristiane do Nascimento Monte, Universidade Federal Fluminense, BrazilChuck Chuan Ng, Xiamen University Malaysia, MalaysiaDastun Gabriel Msuya, Sokoine University Of Agriculture, TanzaniaGiacomo Chiesa, Politecnico di Torino, ItalyJephias Mapuva, Bindura University, ZimbabweJoão Fernando Pereira Gomes, Instituto Superior de Engenharia de Lisboa, PortugueseMd. Nuralam Hossain, Chongqing University, ChinaOylum Gokkurt Baki, Sinop University, TurkeyPankaj Maheshwari, University of Nevada, USATateda Masafumi, Toyama Prefectural University, Japan

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