scholarly journals Comparison of Unsupervised Algorithms for Vineyard Canopy Segmentation from UAV Multispectral Images

2019 ◽  
Vol 11 (9) ◽  
pp. 1023 ◽  
Author(s):  
Paolo Cinat ◽  
Salvatore Filippo Di Gennaro ◽  
Andrea Berton ◽  
Alessandro Matese
Keyword(s):  
Precision Agriculture ◽  
Near Infrared ◽  
Crop Management ◽  
Computation Time ◽  
Technical Aspect ◽  
Targeted Intervention ◽  
Stable Algorithm ◽  
Management Procedure ◽  
Digital Elevation ◽  
Elevation Model

Technical resources are currently supporting and enhancing the ability of precision agriculture techniques in crop management. The accuracy of prescription maps is a key aspect to ensure a fast and targeted intervention. In this context, remote sensing acquisition by unmanned aerial vehicles (UAV) is one of the most advanced platforms to collect imagery of the field. Besides the imagery acquisition, canopy segmentation among soil, plants and shadows is another practical and technical aspect that must be fast and precise to ensure a targeted intervention. In this paper, algorithms to be applied to UAV imagery are proposed according to the sensor used that could either be visible spectral or multispectral. These algorithms, called HSV-based (Hue, Saturation, Value), DEM (Digital Elevation Model) and K-means, are unsupervised, i.e., they perform canopy segmentation without human support. They were tested and compared in three different scenarios obtained from two vineyards over two years, 2017 and 2018 for RGB (Red-Green-Blue) and NRG (Near Infrared-Red-Green) imagery. Particular attention is given to the unsupervised ability of these algorithms to identify vines in these different acquisition conditions. This ability is quantified by the introduction of over- and under- estimation indexes, which are the algorithm’s ability to over-estimate or under-estimate vine canopies. For RGB imagery, the HSV-based algorithms consistently over-estimate vines, and never under-estimate them. The k-means and DEM method have a similar trend of under-estimation. While for NRG imagery, the HSV is the more stable algorithm and the DEM model slightly over-estimates the vines. HSV-based algorithms and the DEM algorithm have comparable computation time. The k-means algorithm increases computational demand as the quality of the DEM decreases. The algorithms developed can isolate canopy vegetation data, which is useful information about the current vineyard state, and can be used as a tool to be efficiently applied in the crop management procedure within precision viticulture applications.

scholarly journals Vegetation Extraction Using Visible-Bands from Openly Licensed Unmanned Aerial Vehicle Imagery

Drones ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 27 ◽  
Author(s):  
Athos Agapiou
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Precision Agriculture ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Small Scale ◽  
Area Of Interest ◽  
Sensing Applications ◽  
Digital Elevation ◽  
Aerial Vehicle ◽  
Elevation Model

Red–green–blue (RGB) cameras which are attached in commercial unmanned aerial vehicles (UAVs) can support remote-observation small-scale campaigns, by mapping, within a few centimeter’s accuracy, an area of interest. Vegetated areas need to be identified either for masking purposes (e.g., to exclude vegetated areas for the production of a digital elevation model (DEM) or for monitoring vegetation anomalies, especially for precision agriculture applications. However, while detection of vegetated areas is of great importance for several UAV remote sensing applications, this type of processing can be quite challenging. Usually, healthy vegetation can be extracted at the near-infrared part of the spectrum (approximately between 760–900 nm), which is not captured by the visible (RGB) cameras. In this study, we explore several visible (RGB) vegetation indices in different environments using various UAV sensors and cameras to validate their performance. For this purposes, openly licensed unmanned aerial vehicle (UAV) imagery has been downloaded “as is” and analyzed. The overall results are presented in the study. As it was found, the green leaf index (GLI) was able to provide the optimum results for all case studies.

scholarly journals Visible and near-infrared digital images for determination of ice velocities and surface elevation during a surge on Osbornebreen, a tidewater glacier in Svalbard

1997 ◽  
Vol 24 ◽  
pp. 255-261 ◽  
Author(s):  
Cecilie Rolstad ◽  
Jostein Amlien ◽  
Jon-Ove Hagen ◽  
Bengt Lundén
Keyword(s):  
Velocity Field ◽  
Near Infrared ◽  
Aerial Photographs ◽  
Correlation Technique ◽  
Digital Elevation ◽  
Compressive Flow ◽  
Tidewater Glacier ◽  
Extension Zone ◽  
Elevation Model ◽  
Cross Correlation Technique

A field of vectors showing the average velocity of the surging glacier Osbornebreen, Svalbard, was determined by comparing sequential SPOT (Système pour l’Observation de la Terre) and Landsat thematic mapper images. Crevasses which developed during the initial phase of the surge in the winter of 1986–87 were tracked using a fast Fourier chip cross-correlation technique. A digital elevation model (DEM) was developed using digital photogrammetry on aerial photographs from 1990. This new DEM was compared with a map drawn in 1966. The velocity field could be almost entirely determined with 1 month separation of the images, but only partly determined with images 1 year apart, due to changes of the crevasse pattern. The velocity field is similar to that found for Kronebreen, a continuously fast-moving tidewater glacier. No distinct zones of compressive flow were present and the data gave no evidence of a compression zone/surge front traveling downstream. The velocity field, the rapid advance of the terminus and the development of transverse crevasses in the upper accumulation area within a 6 month period may indicate that the surge developed as a zone of extension starting near the terminus and propagating quickly upstream. The crevasse pattern in the images is therefore interpreted to be the result of the extension zone traveling upstream, and, as the whole glacier starts to slide, the crevasse pattern alters according to the bedrock topography.

scholarly journals Sensitivity analysis of the DEM resolution and effective parameters of runoff yield in the SWAT model: a case study

2019 ◽  
Vol 69 (1) ◽  
pp. 39-54 ◽  
Author(s):  
Mohammad Nazari-Sharabian ◽  
Masoud Taheriyoun ◽  
Moses Karakouzian
Keyword(s):  
Sensitivity Analysis ◽  
Assessment Tool ◽  
Swat Model ◽  
Computation Time ◽  
Effective Parameters ◽  
Digital Elevation ◽  
Elevation Model ◽  
The Impact ◽  
Reduce Computation Time

Abstract This study investigates the impact of different digital elevation model (DEM) resolutions on the topological attributes and simulated runoff, as well as the sensitivity of runoff parameters in the Mahabad Dam watershed in Iran. The watershed and streamlines were delineated in ArcGIS, and the hydrologic analyses were performed using the Soil and Water Assessment Tool (SWAT). The sensitivity analysis on runoff parameters was performed, using the Sequential Uncertainties FItting Ver. 2 algorithm, in the SWAT Calibration and Uncertainty Procedures (SWAT-CUP) program. The results indicated that the sensitivity of runoff parameters, watershed surface area, and elevations changed under different DEM resolutions. As the distribution of slopes changed using different DEMs, surface parameters were most affected. Furthermore, higher amounts of runoff were generated when DEMs with finer resolutions were implemented. In comparison with the observed value of 8 m3/s at the watershed outlet, the 12.5 m DEM showed more realistic results (6.77 m3/s). Comparatively, the 12.5 m DEM generated 0.74% and 2.73% more runoff compared with the 30 and 90 m DEMs, respectively. The findings of this study indicate that in order to reduce computation time, researchers may use DEMs with coarser resolutions at the expense of minor decreases in accuracy.

scholarly journals A computationally efficient depression-filling algorithm for digital elevation models applied to proglacial lake drainage

2016 ◽  
Author(s):  
Constantijn J. Berends ◽  
Roderik S. W. van de Wal
Keyword(s):  
Geographical Area ◽  
Computation Time ◽  
Equation Model ◽  
Limiting Factor ◽  
Computationally Efficient ◽  
Digital Elevation ◽  
Lake Drainage ◽  
Elevation Model ◽  
Flood Fill

Abstract. We present and evaluate several optimizations to a standard flood-fill algorithm in terms of computational efficiency. As an example, we determine the land/ocean-mask for a 1 km resolution digital elevation model (DEM) of North America and Greenland, a geographical area of roughly 7000 by 5000 km (roughly 35 million elements), about half of which is covered by ocean. Determining the land/ocean-mask with our improved flood-fill algorithm reduces computation time by 90 % relative to using a standard stack-based flood-fill algorithm. In another experiment, we use the bedrock elevation, ice thickness and geoid perturbation fields from the output of a coupled ice-sheet–sea-level equation model at 30,000 years before present and determine the extent of Lake Agassiz, using both the standard and improved versions of the flood-fill algorithm. We show that several optimizations to the flood-fill algorithm used for filling a depression up to a water level, that is not defined at forehand, decrease the computation time by up to 99 %. The resulting reduction in computation time allows determination of the extent and volume of depressions in a DEM over large geographical grids or repeatedly over long periods of time, where computation time might otherwise be a limiting factor.

scholarly journals Potential of Sentinel-2 Images for Estimating of Soil Resistivity over Agricultural Fields

Proceedings ◽  
2019 ◽  
Vol 24 (1) ◽  
pp. 18
Author(s):  
Remy Fieuzal ◽  
Vincent Bustillo ◽  
David Collado ◽  
Gerard Dedieu
Keyword(s):  
Electrical Resistivity ◽  
Near Infrared ◽  
Soil Layers ◽  
Digital Elevation ◽  
Combined Use ◽  
Input Variables ◽  
Elevation Model ◽  
High Level ◽  
Sentinel 2 ◽  
Soil Electrical Resistivity

The aim of this study is to assess the possibilities of the VNIR (Visible and Near InfraRed) and SWIR (Short Wavelength InfraRed) satellite data for estimating intra-plot patterns of soil electrical resistivity consistent with ground measurements. The methodology is based on optical reflectances that constitute the input variables of random forest, alone or in combination with parameters derived from a digital elevation model (DEM). Over a field located in southwestern France, the results show high level of accuracy for the 0–50 and 0–100 cm soil layers (with R² of 0.69 and 0.59, and a relative RMSE of 18% and 16%, respectively), the performances being lower for the 0–170 cm layer (R² of 0.39, relative RMSE of 20%). The combined use of optical reflectances with parameters derived from the DEM slightly improves the performances, whatever the considered layer. The influence of each reflectance on soil electrical resistivity estimates is finally analyzed, showing that the wavelengths acquired in the SWIR have a relative higher importance than VNIR reflectance.

scholarly journals Soil Moisture Mapping Based on Multi-Source Fusion of Optical, Near-Infrared, Thermal Infrared, and Digital Elevation Model Data via the Bayesian Maximum Entropy Framework

2020 ◽  
Vol 12 (23) ◽  
pp. 3916
Author(s):  
Leran Han ◽  
Chunmei Wang ◽  
Qiyue Liu ◽  
Gengke Wang ◽  
Tao Yu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Maximum Entropy ◽  
Near Infrared ◽  
Bayesian Maximum Entropy ◽  
General Knowledge ◽  
Combined Approach ◽  
Soft Data ◽  
Digital Elevation ◽  
Source Data ◽  
Elevation Model

This paper proposes a combined approach wherein the optical, near-infrared, and thermal infrared data from the Landsat 8 satellite and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) global digital elevation model (GDEM) data are fused for soil moisture mapping under sparse sampling conditions, based on the Bayesian maximum entropy (BME) framework. The study was conducted in three stages. First, based on the maximum entropy principle of the information theory, a Lagrange multiplier was introduced to construct general knowledge, representing prior knowledge. Second, a principal component analysis (PCA) was conducted to extract three principal components from the multi-source data mentioned above, and an innovative and operable discrete probability method based on a fuzzy probability matrix was used to approximate the probability relationship. Thereafter, soft data were generated on the basis of the weight coefficients and coordinates of the soft data points. Finally, by combining the general knowledge with the prior information, hard data (HD), and soft data (SD), we completed the soil moisture mapping based on the Bayesian conditioning rule. To verify the feasibility of the combined approach, the ordinary kriging (OK) method was taken as a comparison. The results confirmed the superiority of the soil moisture map obtained using the BME framework. The map revealed more detailed information, and the accuracies of the quantitative indicators were higher compared with that for the OK method (the root mean squared error (RMSE) = 0.0423 cm3/cm3, mean absolute error (MAE) = 0.0399 cm3/cm3, and Pearson correlation coefficient (PCC) = 0.7846), while largely overcoming the overestimation issue in the range of low values and the underestimation issue in the range of high values. The proposed approach effectively fused inexpensive and easily available multi-source data with uncertainties and obtained a satisfactory mapping accuracy, thus demonstrating the potential of the BME framework for soil moisture mapping using multi-source data.

scholarly journals Visible and near-infrared digital images for determination of ice velocities and surface elevation during a surge on Osbornebreen, a tidewater glacier in Svalbard

1997 ◽  
Vol 24 ◽  
pp. 255-261 ◽  
Author(s):  
Cecilie Rolstad ◽  
Jostein Amlien ◽  
Jon-Ove Hagen ◽  
Bengt Lundén
Keyword(s):  
Velocity Field ◽  
Near Infrared ◽  
Aerial Photographs ◽  
Correlation Technique ◽  
Digital Elevation ◽  
Compressive Flow ◽  
Tidewater Glacier ◽  
Extension Zone ◽  
Elevation Model ◽  
Cross Correlation Technique

A field of vectors showing the average velocity of the surging glacier Osbornebreen, Svalbard, was determined by comparing sequential SPOT (Système pour l’Observation de la Terre) and Landsat thematic mapper images. Crevasses which developed during the initial phase of the surge in the winter of 1986–87 were tracked using a fast Fourier chip cross-correlation technique. A digital elevation model (DEM) was developed using digital photogrammetry on aerial photographs from 1990. This new DEM was compared with a map drawn in 1966. The velocity field could be almost entirely determined with 1 month separation of the images, but only partly determined with images 1 year apart, due to changes of the crevasse pattern. The velocity field is similar to that found for Kronebreen, a continuously fast-moving tidewater glacier. No distinct zones of compressive flow were present and the data gave no evidence of a compression zone/surge front traveling downstream. The velocity field, the rapid advance of the terminus and the development of transverse crevasses in the upper accumulation area within a 6 month period may indicate that the surge developed as a zone of extension starting near the terminus and propagating quickly upstream. The crevasse pattern in the images is therefore interpreted to be the result of the extension zone traveling upstream, and, as the whole glacier starts to slide, the crevasse pattern alters according to the bedrock topography.

Demarcation of Levees in Irrigated Rice Fields: Laser Technology vs. GNSS-RTK

2019 ◽  
Vol 35 (3) ◽  
pp. 431-437 ◽  
Author(s):  
Marcos Valle Bueno ◽  
Alexssandra Dayane Soares de Campos ◽  
Jaqueline Trombetta da Silva ◽  
Lessandro Coll Faria ◽  
Fabrício da Silva Terra ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Production Systems ◽  
Water Layer ◽  
Satellite System ◽  
Rice Fields ◽  
Laser Technology ◽  
Technological Advances ◽  
Digital Elevation ◽  
Elevation Model ◽  
Global Navigation Satellite

Abstract. Levees are small land dikes made every rice-cultivation season that allow for flood irrigation in rice fields. Currently, levees are demarcated by utilizing a laser technology (LT) system. However, with current technological advances, the demarcation of levees with the Global Navigation Satellite System (GNSS) and real-time kinematics (RTK) correction has been highlighted in rice production systems in southern Brazil. The objective of this study was to compare the performance between LT and GNSS-RTK systems applied in the demarcation of levees that are used in flooded rice fields. To this end, an experimental area of approximately 27 ha located in Jaguarão, Rio Grande do Sul, Brazil, was used. From a digital elevation model, the area was subdivided into three subareas according to the mean slope: flat (0.16%), intermediate (0.36%), and gently undulated (1.3%). The total length of the levees for the three subareas was 8 km. The relative performances of both demarcation systems were evaluated by analyzing the vertical and horizontal behavior of the levees and the water layer spatial distribution. The results indicated that the demarcation of levees by GNSS-RTK systems is more accurate than that by the LT system, especially in flat areas. In these areas, the GNSS-RTK demarcation system permits a reduction in the total number of levees, as well as an increase in the vertical equidistance between levees and/or an increase in the height of the levee itself. The length of the levee is shorter than in a demarcation using the GNSS-RTK system. Keywords: Contour line, Irrigation, Lowlands, Precision agriculture.

scholarly journals Expedited generation of terrain digital classes in flat areas from UAV images for precision agriculture purposes

2017 ◽  
Vol 8 (2) ◽  
pp. 828-832
Author(s):  
M. C. Pineda ◽  
C. Perdomo ◽  
R. Caballero ◽  
A. Valera ◽  
J. A. Martínez-Casasnovas ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Topographic Wetness Index ◽  
Site Specific ◽  
Soil Variables ◽  
Digital Terrain ◽  
Digital Elevation ◽  
Elevation Model ◽  
Uav Images ◽  
Specific Management

Precision agriculture (PA) requires reasonably homogeneous areas for site-specific management. This work explores the applicability of digital terrain classes obtained from a digital elevation model derived from UAV-acquired images, to define management units in in a relative flat area of about 6 ha. Elevation, together with other terrain variables such as: slope degree, profile curvature, plan curvature, topographic wetness index, sediment transport index, were clustered using the Fuzzy Kohonen Clustering Network (FKCN). Four terrain classes were obtained. The result was compared with a map produced by a classification of soil properties previously interpolated by ordinary kriging. The results suggest that areas for site-specific management can be defined from terrain classes based on environmental covariates, saving time and cost in comparison with interpolation of soil variables.

scholarly journals Impact of Resolution and Source of Digital Elevation Model on Topological Attributes and Simulated Runoff by SWAT Model

2021 ◽  
Author(s):  
Farinaz Gholami ◽  
Alireza Nemati ◽  
Yue Li ◽  
Yang Hong ◽  
Junlong Zhang
Keyword(s):  
Digital Elevation Model ◽  
Assessment Tool ◽  
Swat Model ◽  
Computation Time ◽  
Low Resolution ◽  
Dem Resolution ◽  
Digital Elevation ◽  
Hydrologic Response Units ◽  
Elevation Model ◽  
The Impact

The Digital Elevation Model (DEM) of a watershed is one of the most important inputs in most hydrological analyses and plays a key role in the accurate prediction of various hydrological processes. Comprehensive knowledge of the impact of different DEM sources on the performance of a model is essential before utilizing the model. In this study, we evaluated the influence of TOPO1:25000, ASTER, and SRTM DEMs, as input, on the performance of the Soil and Water Assessment Tool (SWAT) model for the prediction of surface runoff. We also investigated the effect of the resolution of the studied DEM sources on the accuracy of the SWAT model in the estimation of runoff. The second objective of this study was to identify the most influential and the least impactful input parameters on the performance of the SWAT model. We studied the Zarrineh River watershed in Iran as a case study to compare the effect of the aforementioned DEM types and DEM resolution on the output of the SWAT model. The outcomes of the study demonstrated that influential parameters on predicted runoff as well as a few watershed parameters, such as reach lengths, reach slopes, number of sub-basins, and the number of hydrologic response units (HRU), differs noticeably when the DEM source and resolution changes. It was also observed that simulated results over-predict the runoff during low precipitation periods and under-predict the runoff during high precipitation months, and the accuracy of the simulated results decreases by reducing the DEM resolution. The results showed that the SWAT model had the best performance when the TOPO1:25000 DEM was used as the input source. Low-resolution DEMs are available to a wider range of researchers. The outcomes of the current study can be employed to estimate the impact of low-resolution input data on the simulated result as well as substantially reduce the computation time by decreasing the input DEMresolution with only a minor reduction of accuracy.

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