Managing Soil Variability at Different Spatial Scales as a Basis for Precision Agriculture

2015 ◽  
pp. 52-87
Keyword(s):  
Precision Agriculture ◽  
Spatial Scales ◽  
Soil Variability

Application of Remote Sensing in Agriculture

2014 ◽  
Vol 13 (1) ◽  
Author(s):  
Jan Piekarczyk
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Crop Production ◽  
Satellite Images ◽  
Crop Yields ◽  
Spatial Scales ◽  
Strong Relationship ◽  
Physical Parameters ◽  
Agricultural Crop ◽  
Remote Sensing Methods

AbstractWith increasing intensity of agricultural crop production increases the need to obtain information about environmental conditions in which this production takes place. Remote sensing methods, including satellite images, airborne photographs and ground-based spectral measurements can greatly simplify the monitoring of crop development and decision-making to optimize inputs on agricultural production and reduce its harmful effects on the environment. One of the earliest uses of remote sensing in agriculture is crop identification and their acreage estimation. Satellite data acquired for this purpose are necessary to ensure food security and the proper functioning of agricultural markets at national and global scales. Due to strong relationship between plant bio-physical parameters and the amount of electromagnetic radiation reflected (in certain ranges of the spectrum) from plants and then registered by sensors it is possible to predict crop yields. Other applications of remote sensing are intensively developed in the framework of so-called precision agriculture, in small spatial scales including individual fields. Data from ground-based measurements as well as from airborne or satellite images are used to develop yield and soil maps which can be used to determine the doses of irrigation and fertilization and to take decisions on the use of pesticides.

Soil variability through spatial scales in a permanently disturbed natural spruce-fir-beech forest

2011 ◽  
Vol 130 (6) ◽  
pp. 1075-1091 ◽  
Author(s):  
Pavel Šamonil ◽  
Martin Valtera ◽  
Stanislav Bek ◽  
Barbora Šebková ◽  
Tomáš Vrška ◽  
...  
Keyword(s):  
Spatial Scales ◽  
Beech Forest ◽  
Soil Variability

scholarly journals Estimating Maize Above-Ground Biomass Using 3D Point Clouds of Multi-Source Unmanned Aerial Vehicle Data at Multi-Spatial Scales

2019 ◽  
Vol 11 (22) ◽  
pp. 2678 ◽  
Author(s):  
Zhu ◽  
Sun ◽  
Peng ◽  
Huang ◽  
Li ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Spatial Scales ◽  
Point Clouds ◽  
Support Vector ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
3D Point Clouds ◽  
Vehicle Data ◽  
Aerial Vehicle ◽  
Multispectral Camera

Crop above-ground biomass (AGB) is a key parameter used for monitoring crop growth and predicting yield in precision agriculture. Estimating the crop AGB at a field scale through the use of unmanned aerial vehicles (UAVs) is promising for agronomic application, but the robustness of the methods used for estimation needs to be balanced with practical application. In this study, three UAV remote sensing flight missions (using a multiSPEC-4C multispectral camera, a Micasense RedEdge-M multispectral camera, and an Alpha Series AL3-32 Light Detection and Ranging (LiDAR) sensor onboard three different UAV platforms) were conducted above three long-term experimental plots with different tillage treatments in 2018. We investigated the performances of the multi-source UAV-based 3D point clouds at multi-spatial scales using the traditional multi-variable linear regression model (OLS), random forest (RF), backpropagation neural network (BP), and support vector machine (SVM) methods for accurate AGB estimation. Results showed that crop height (CH) was a robust proxy for AGB estimation, and that high spatial resolution in CH datasets helps to improve maize AGB estimation. Furthermore, the OLS, RF, BP, and SVM methods all maintained an acceptable accuracy for AGB estimation; however, the SVM and RF methods performed slightly more robustly. This study is expected to optimize UAV systems and algorithms for specific agronomic applications.

scholarly journals Assessing the financial and environmental impacts of precision irrigation in a humid climate 

2019 ◽  
Vol 46 (No. 1) ◽  
pp. 43-52 ◽  
Author(s):  
Daniel El Chami ◽  
Jerry W. Knox ◽  
André Daccache ◽  
Edward Keith Weatherhead
Keyword(s):  
Precision Agriculture ◽  
Energy Savings ◽  
Irrigation System ◽  
Cost Benefit ◽  
Added Value ◽  
Soil Variability ◽  
Humid Climate ◽  
Water Savings ◽  
Additional Equipment ◽  
Precision Irrigation

Precision agriculture is increasingly used where in-field spatial variability exists; however, the benefits of its use in humid climates are less apparent. This paper reports on a cost-benefit assessment of precision irrigation with variable rate technique (VRI) versus conventional irrigation, both compared to rainfed production, using a travelling hose-reel irrigator fitted with a boom on onions in eastern England. Selected environmental outcomes including water savings and CO<sub>2</sub>e emissions are evaluated. The modelled precision irrigation system, which responds to soil variability, generates better environmental outcomes than the conventional system in terms of water savings and reduced CO<sub>2</sub>e emissions (22.6% and 23.0% lower, respectively). There is also an increase in the ‘added value’ of the irrigation water used (£3.02/m<sup>3</sup> versus £2.36/m<sup>3</sup>). Although precision irrigation leads to significant financial benefits from water and energy savings, these alone do not justify the additional equipment investment costs. However, any changes in yield or quality benefits, equipment costs or greater soil variability than on this site would make investment in precision irrigation more viable. 

scholarly journals Clustering Tools for Integration of Satellite Remote Sensing Imagery and Proximal Soil Sensing Data

2019 ◽  
Vol 11 (9) ◽  
pp. 1036 ◽  
Author(s):  
Md Saifuzzaman ◽  
Viacheslav Adamchuk ◽  
Roberto Buelvas ◽  
Asim Biswas ◽  
Shiv Prasher ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Precision Agriculture ◽  
Data Clustering ◽  
Bare Soil ◽  
Neighborhood Search ◽  
Soil Variability ◽  
Agricultural Fields ◽  
Essential Information ◽  
Spatial Continuity ◽  
Proximal Soil Sensing

Remote sensing (RS) and proximal soil sensing (PSS) technologies offer an advanced array of methods for obtaining soil property information and determining soil variability for precision agriculture. A large amount of data collected by these sensors may provide essential information for precision or site-specific management in a production field. Data clustering techniques are crucial for data mining, and high-density data analysis is important for field management. A new clustering technique was introduced and compared with existing clustering tools to determine the relatively homogeneous parts of agricultural fields. A DUALEM-21S sensor, along with high-accuracy topography data, was used to characterize soil variability in three agricultural fields situated in Ontario, Canada. Sentinel-2 data assisted in quantifying bare soil and vegetation indices (VIs). The custom Neighborhood Search Analyst (NSA) data clustering tool was implemented using Python scripts. In this algorithm, part of the variance of each data layer is accounted for by subdividing the field into smaller, relatively homogeneous, areas. The algorithm’s attributes were illustrated using field elevation, shallow and deep apparent electrical conductivity (ECa), and several VIs. The unique feature of this proposed protocol was the successful development of user-friendly and open source options for defining the spatial continuity of each group and for use in the zone delineation process.

scholarly journals REVISITING THE CURRENT UAV REGULATIONS IN NEPAL: A STEP TOWARDS LEGAL DIMENSION FOR UAVS EFFICIENT APPLICATION

2019 ◽  
Vol XLII-5/W3 ◽  
pp. 107-114 ◽  
Author(s):  
R. Shrestha ◽  
J. Zevenbergen ◽  
U. S. Panday ◽  
B. Awasthi ◽  
S. Karki
Keyword(s):  
Spatial Data ◽  
Precision Agriculture ◽  
Spatial Scales ◽  
Methodological Approach ◽  
Legal Framework ◽  
Geospatial Technology ◽  
Technical Requirements ◽  
Legal Provisions ◽  
Resource Requirements ◽  
Legal Frameworks

Abstract. UAVs-Unmanned Aerial Vehicles- also known as drones, are an emerging geospatial technology that can facilitate data acquisition at various temporal and spatial scales. Notwithstanding, the wide application of UAVs globally, its wider application is found to be growing in Nepal as well. For instance, precision agriculture, forestry, topographical surveying, etc. It seems that there is a correlation between efficient use of UAVs in these sectors and the legal frameworks that regulate the use of UAVs. Therefore, it seems necessary to obtain holistic national view of UAVs regulations. Aligning with this necessity, this paper provides insight on existing legal provisions for UAVs in Nepal by highlighting the importance, impact, and limitations of UAV regulations. The criteria used in the framework to capture the present holistic legal dimension from literature in the web of science database are a) applicability b) technical requirements c) operational requirements/ limitations d) administration procedure e) human resource requirements and f) implementation of ethical constraints. The adopted methodological approach consists of exploratory case studies, systematic reviews of the concerned literature on UAVs regulations and the workshop on “Flight 4 Purpose” in which various UAVs application were discussed. The results show that the existing legal framework has both strengths and weaknesses for its use to capture the spatial data. The way forward is to harmonize the soft and hard regulations so that such geospatial technology can be applied for overall development and ultimately for the societal benefits.

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