scholarly journals A zone-based approach for processing and interpreting variability in multi-temporal yield data sets

2018 ◽  
Vol 148 ◽  
pp. 299-308 ◽  
Author(s):  
Corentin Leroux ◽  
Hazaël Jones ◽  
James Taylor ◽  
Anthony Clenet ◽  
Bruno Tisseyre
Keyword(s):  
Data Sets ◽  
Yield Data ◽  
Multi Temporal

scholarly journals Multi-temporal yield pattern analysis method for deriving yield zones in crop production systems

2020 ◽  
Vol 21 (6) ◽  
pp. 1263-1290
Author(s):  
Gerald Blasch ◽  
Zhenhai Li ◽  
James A. Taylor
Keyword(s):  
Crop Production ◽  
Pattern Analysis ◽  
Production Systems ◽  
Principal Component ◽  
Temporal Variations ◽  
Data Sets ◽  
Yield Data ◽  
Multi Temporal ◽  
Spatio Temporal ◽  
Cereal Fields

Abstract Easy-to-use tools using modern data analysis techniques are needed to handle spatio-temporal agri-data. This research proposes a novel pattern recognition-based method, Multi-temporal Yield Pattern Analysis (MYPA), to reveal long-term (> 10 years) spatio-temporal variations in multi-temporal yield data. The specific objectives are: i) synthesis of information within multiple yield maps into a single understandable and interpretable layer that is indicative of the variability and stability in yield over a 10 + years period, and ii) evaluation of the hypothesis that the MYPA enhances multi-temporal yield interpretation compared to commonly-used statistical approaches. The MYPA method automatically identifies potential erroneous yield maps; detects yield patterns using principal component analysis; evaluates temporal yield pattern stability using a per-pixel analysis; and generates productivity-stability units based on k-means clustering and zonal statistics. The MYPA method was applied to two commercial cereal fields in Australian dryland systems and two commercial fields in a UK cool-climate system. To evaluate the MYPA, its output was compared to results from a classic, statistical yield analysis on the same data sets. The MYPA explained more of the variance in the yield data and generated larger and more coherent yield zones that are more amenable to site-specific management. Detected yield patterns were associated with varying production conditions, such as soil properties, precipitation patterns and management decisions. The MYPA was demonstrated as a robust approach that can be encoded into an easy-to-use tool to produce information layers from a time-series of yield data to support management.

scholarly journals Evaluating irrigated rice yields in Japan within the Climate Zonation Scheme of the Global Yield Gap Atlas

2021 ◽  
pp. 1-12
Author(s):  
S. Ishikawa ◽  
T. Nakashima ◽  
T. Iizumi ◽  
M. C. Hare
Keyword(s):  
Production Capacity ◽  
Aridity Index ◽  
Data Sets ◽  
Yield Gap ◽  
Yield Data ◽  
Irrigated Rice ◽  
Rice Yields ◽  
The Stability ◽  
Temperature Seasonality ◽  
Government Statistics

Abstract The Global Yield Gap Atlas (GYGA) is an international project that addresses global food production capacity in the form of yield gaps (Yg). The GYGA project is unique in employing its original Climate Zonation Scheme (CZS) composed of three indexed factors, i.e. Growing Degree Days (GDD) related to temperature, Aridity Index (AI) related to available water and Temperature Seasonality (TS) related to annual temperature range, creating 300 Climate Zones (CZs) theoretically across the globe. In the present study, the GYGA CZs were identified for Japan on a municipality basis and analysis of variance (ANOVA) was performed on irrigated rice yield data sets, equating to actual yields (Ya) in the GYGA context, from long-term government statistics. The ANOVA was conducted for the data sets over two decades between 1994 and 2016 by assigning the GDD score of 6 levels and the TS score of 2 levels as fixed factors. Significant interactions with respect to Ya were observed between GDD score and TS score for 13 years out of 21 years implying the existence of favourable combinations of the GDD score and the TS score for rice cultivation. The implication was also supported by the observation with Yg. The lower values of coefficient of variance obtained from the CZs characterized by medium GDD scores indicated the stability over time of rice yields in these areas. These findings suggest a possibility that the GYGA-CZS can be recognized as a tool suitable to identify favourable CZs for growing crops.

scholarly journals Data-collection strategy for challenging native SAD phasing

2016 ◽  
Vol 72 (3) ◽  
pp. 421-429 ◽  
Author(s):  
Vincent Olieric ◽  
Tobias Weinert ◽  
Aaron D. Finke ◽  
Carolin Anders ◽  
Dianfan Li ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Sets ◽  
Yield Data ◽  
Guide Rna ◽  
Multiple Data ◽  
Data Collection Strategy ◽  
Sad Phasing ◽  
Anomalous Signal ◽  
Multiple Data Sets ◽  
Low X

Recent improvements in data-collection strategies have pushed the limits of native SAD (single-wavelength anomalous diffraction) phasing, a method that uses the weak anomalous signal of light elements naturally present in macromolecules. These involve the merging of multiple data sets from either multiple crystals or from a single crystal collected in multiple orientations at a low X-ray dose. Both approaches yield data of high multiplicity while minimizing radiation damage and systematic error, thus ensuring accurate measurements of the anomalous differences. Here, the combined use of these two strategies is described to solve cases of native SAD phasing that were particular challenges: the integral membrane diacylglycerol kinase (DgkA) with a low Bijvoet ratio of 1% and the large 200 kDa complex of the CRISPR-associated endonuclease (Cas9) bound to guide RNA and target DNA crystallized in the low-symmetry space groupC2. The optimal native SAD data-collection strategy based on systematic measurements performed on the 266 kDa multiprotein/multiligand tubulin complex is discussed.

Quantifying Urban Sprawl With Spatial Autocorrelation Techniques Using Multi-Temporal Satellite Data

2019 ◽  
pp. 1624-1644
Author(s):  
Gabriele Nolè ◽  
Rosa Lasaponara ◽  
Antonio Lanorte ◽  
Beniamino Murgante
Keyword(s):  
Remote Sensing ◽  
Spatial Autocorrelation ◽  
Urban Areas ◽  
Urban Expansion ◽  
Data Sets ◽  
Temporal Data ◽  
Landsat Images ◽  
Urban Settlements ◽  
Multi Temporal ◽  
Soil Consumption

This study deals with the use of satellite TM multi-temporal data coupled with statistical analyses to quantitatively estimate urban expansion and soil consumption for small towns in southern Italy. The investigated area is close to Bari and was selected because highly representative for Italian urban areas. To cope with the fact that small changes have to be captured and extracted from TM multi-temporal data sets, we adopted the use of spectral indices to emphasize occurring changes, and geospatial data analysis to reveal spatial patterns. Analyses have been carried out using global and local spatial autocorrelation, applied to multi-date NASA Landsat images acquired in 1999 and 2009 and available free of charge. Moreover, in this paper each step of data processing has been carried out using free or open source software tools, such as, operating system (Linux Ubuntu), GIS software (GRASS GIS and Quantum GIS) and software for statistical analysis of data (R). This aspect is very important, since it puts no limits and allows everybody to carry out spatial analyses on remote sensing data. This approach can be very useful to assess and map land cover change and soil degradation, even for small urbanized areas, as in the case of Italy, where recently an increasing number of devastating flash floods have been recorded. These events have been mainly linked to urban expansion and soil consumption and have caused loss of human lives along with enormous damages to urban settlements, bridges, roads, agricultural activities, etc. In these cases, remote sensing can provide reliable operational low cost tools to assess, quantify and map risk areas.

Hybrid Neural Models For Rice Yields Times Forecasting

2012 ◽  
Author(s):  
Ruhaidah Samsudin ◽  
Puteh Saad ◽  
Ani Shabri
Keyword(s):  
Time Series ◽  
Hybrid Model ◽  
Moving Average ◽  
Time Series Prediction ◽  
Arima Model ◽  
Data Sets ◽  
Ann Model ◽  
Yield Data ◽  
Rice Yields ◽  
Artificial Neural Network Ann

In this paper, time series prediction is considered as a problem of missing value. A model for the determination of the missing time series value is presented. The hybrid model integrating autoregressive intergrated moving average (ARIMA) and artificial neural network (ANN) model is developed to solve this problem. The developed models attempts to incorporate the linear characteristics of an ARIMA model and nonlinear patterns of ANN to create a hybrid model. In this study, time series modeling of rice yield data in Muda Irrigation area. Malaysia from 1995 to 2003 are considered. Experimental results with rice yields data sets indicate that the hybrid model improve the forecasting performance by either of the models used separately. Key words: ARIMA; Box and Jenkins; neural networks; rice yields; hybrid ANN model

scholarly journals Extraction of detailed level flood hazard zones using multi-temporal historical satellite data-sets – a case study of Kopili River Basin, Assam, India

2016 ◽  
Vol 8 (2) ◽  
pp. 792-802 ◽  
Author(s):  
Shivaprasad Sharma SV ◽  
Parth Sarathi Roy ◽  
Chakravarthi V ◽  
Srinivasarao G ◽  
Bhanumurthy V
Keyword(s):  
River Basin ◽  
Satellite Data ◽  
Flood Hazard ◽  
Data Sets ◽  
Multi Temporal ◽  
Detailed Level

scholarly journals THE COMBINATION OF THE IMAGE AND RANGE-BASED 3D ACQUISITION IN ARCHAEOLOGICAL AND ARCHITECTURAL RESEARCH IN THE ROYAL CASTLE IN WARSAW

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 177-184
Author(s):  
A. Bocheńska ◽  
J. Markiewicz ◽  
S. Łapiński
Keyword(s):  
Laser Scanning ◽  
Retaining Wall ◽  
Weather Conditions ◽  
Point Clouds ◽  
Sensor Data ◽  
Data Sets ◽  
Multi Temporal ◽  
Close Range ◽  
3D Acquisition ◽  
Architectural Research

<p><strong>Abstract.</strong> The paper presents archaeological and architectural research in the Royal Castle in Warsaw where a combination of image- and range-based 3D acquisition was applied. The area examined included excavations situated inside the Tower and near its outer western wall. The work was carried out at various periods and in different weather conditions. As part of the measurements, laser scanning was performed (with a Z+F 5006h scanner) and a series of close-range images were taken. It was important to integrate the data acquired to create a comprehensive documentation of archaeological excavations. When data was acquired from TLS together with photogrammetric data (in different measurement periods), the points' displacements were controlled and analysed. The process of orienting and processing the terrestrial images included photographs taken during the inventory of the tower (Canon 5D Mark II) and photographs provided by the Castle's employees (Canon PowerShot G5 X). Agisoft PhotoScan software was used to orient and process the terrestrial images, and LupoScan for the TLS data. In order to integrate the TLS data and the clouds of points from the photographs from the various stages, they were processed into a raster form; our own software (based on the OpenCV library and the Structure-from-Motion method) and LupoScan software were used to interconnect the multi-temporal and multi-sensor data sets. As a result of processing photographs and TLS data, point clouds in an external reference system were obtained. This data was then used to study the thickness of the walls of the Justice Court Tower, to analyse the course of the retaining wall, and to generate the orthoimages necessary for chronological analysis.</p>

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