On the potential of Wireless Sensor Networks for the in-situ assessment of crop leaf area index

2016 ◽  
Vol 128 ◽  
pp. 149-159 ◽  
Author(s):  
Jan Bauer ◽  
Bastian Siegmann ◽  
Thomas Jarmer ◽  
Nils Aschenbruck
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Wireless Sensor ◽  
Area Index ◽  
In Situ Assessment

Processing and filtering of leaf area index time series assessed by in-situ wireless sensor networks

2019 ◽  
Vol 165 ◽  
pp. 104867 ◽  
Author(s):  
Jan Bauer ◽  
Thomas Jarmer ◽  
Siegfried Schittenhelm ◽  
Bastian Siegmann ◽  
Nils Aschenbruck
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Wireless Sensor ◽  
Area Index ◽  
Index Time Series

System Support for the In Situ Testing of Wireless Sensor Networks via Programmable Virtual Onboard Sensors

2016 ◽  
Vol 65 (4) ◽  
pp. 744-753 ◽  
Author(s):  
Manos Koutsoubelias ◽  
Nasos Grigoropoulos ◽  
Spyros Lalis ◽  
Petros Lampsas ◽  
Serafeim Katsikas ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Wireless Sensor ◽  
System Support ◽  
In Situ Testing

scholarly journals Smart Solar Panels: In-situ Monitoring of Photovoltaic Panels based on Wired and Wireless Sensor Networks

2013 ◽  
Vol 36 ◽  
pp. 535-545 ◽  
Author(s):  
P. Papageorgas ◽  
D. Piromalis ◽  
K. Antonakoglou ◽  
G. Vokas ◽  
D. Tseles ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
In Situ Monitoring ◽  
Wireless Sensor ◽  
Solar Panels ◽  
Photovoltaic Panels

scholarly journals INDIGO: An In Situ Distributed Gossip Framework for Sensor Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Paritosh Ramanan ◽  
Goutham Kamath ◽  
Wen-Zhan Song
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Distributed Algorithms ◽  
Sleep Time ◽  
Wireless Sensor ◽  
Consensus Problem ◽  
Design Development ◽  
Distributed Consensus ◽  
Gossip Algorithms

With the onset of Cyber-Physical Systems (CPS), distributed algorithms on Wireless Sensor Networks (WSNs) have been receiving renewed attention. The distributed consensus problem is a well studied problem having a myriad of applications which can be accomplished using asynchronous distributed gossip algorithms on Wireless Sensor Networks (WSNs). However, a practical realization of gossip algorithms for WSNs is found lacking in the current state of the art. In this paper, we propose the design, development, and analysis of a novel in situ distributed gossip framework called INDIGO. A key aspect of INDIGO is its ability to perform on a generic system platform as well as on a hardware oriented testbed platform in a seamless manner allowing easy portability of existing algorithms. We evaluate the performance of INDIGO with respect to the distributed consensus problem as well as the distributed optimization problem. We also present a data driven analysis of the effect certain operating parameters like sleep time and wait time have on the performance of the framework and empirically attempt to determine asweet spot. The results obtained from various experiments on INDIGO validate its efficacy, reliability, and robustness and demonstrate its utility as a framework for the evaluation and implementation of asynchronous distributed algorithms.

scholarly journals Validation of Leaf Area Index Measurement System Based on Wireless Sensor Network

2021 ◽  
Author(s):  
Rongjin Yang ◽  
Lu Liu ◽  
Qiang Liu ◽  
Xiuhong Li ◽  
Lizeyan Yin ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Wireless Sensor Network ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Sensor Network ◽  
Measurement System ◽  
Wireless Sensor ◽  
Area Index ◽  
The Real ◽  
Real Value

Abstract Accurate measurement of leaf area index (LAI) is important for agricultural analysis such as the estimation of crop yield, which makes its measurement work important. There are mainly two ways to obtain LAI: ground station measurement and remote sensing satellite monitoring. Recently, reliable progress has been made in long-term automatic LAI observation using wireless sensor network (WSN) technology under certain conditions. We developed and designed an LAI measurement system (LAIS) based on a wireless sensor network to select and improve the appropriate algorithm according to the image collected by the sensor, to get a more realistic leaf area index. The corn LAI was continuously observed from May 30 to July 16, 2015. Research on hardware has been published, this paper focuses on improved system algorithm and data verification. By improving the finite length average algorithm, the data validation results are as follows: 1. The slope of the fitting line between LAIS measurement data and the real value is 0.944, and the root means square error (RMSE) is 0.264 (absolute error ~ 0-0.6), which has high consistency with the real value. 2. The measurement error of LAIS is less than LAI2000, although the result of our measurement method will be higher than the actual value, it is due to the influence of weeds on the ground. 3. LAIS data can be used to support the retrieval of remote sensing products. We find a suitable application situation of our LAIS system data, and get our application value as ground monitoring data by the verification with remote sensing product data, which supports its application and promotion in similar research in the future.

Evaluation of understory LAI estimation methodologies over forest ecosystem ICOS sites across Europe

2020 ◽  
Author(s):  
Jan-Peter George ◽  
Jan Pisek ◽  
Keyword(s):  
Remote Sensing ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Forest Ecosystem ◽  
Remote Sensing Data ◽  
Ecosystem Functions ◽  
Area Index ◽  
Fractional Cover ◽  
Semi Empirical

<p>Leaf area index (i.e. one-half the total green leaf area per unit of horizontal ground surface area) is a crucial parameter in carbon balancing and modeling. Forest overstory and understory layers differ in carbon and water cycle regimes and phenology, as well as in ecosystem functions. Separate retrievals of leaf area index (LAI) for these two layers would help to improve modeling forest biogeochemical cycles, evaluating forest ecosystem functions and also remote sensing of forest canopies by inversion of canopy reflectance models. The aim of this study is to compare currently available understory LAI assessment methodologies over a diverse set of greenhouse gas measurement sites distributed along a wide latitudinal and elevational gradient across Europe. This will help to quantify  the fraction of the canopy LAI which is represented by understory, since this is still the major source of uncertainty in global LAI products derived from remote sensing data. For this, we took ground photos as well as in-situ reflectance measurements of the understory vegetation at 30 ICOS (Integration Carbon Observation System) sites distributed across 10 countries in Europe. The data were analyzed by means of three conceptually different methods for LAI estimation and comprised purely empirical (fractional cover), semi-empirical (in-situ NDVI linked to the radiative transfer model FLiES), and purely deterministic (Four-scale geometrical optical model) approaches. Finally, our results are compared with global forest understory LAI maps derived from remote sensing data at 1 km resolution (Liu et al. 2017). While we found some agreement among the three methods (e.g. Pearson-correlation between empirical and semi-empirical = 0.63), we also identified sources that are particularly prone to error inclusion such as inaccurate assessment of fractional cover from ground photos. Relationships between understory LAI and long-term climate variables were weak and suggested that understory LAI at the ICOS sites is probably more strongly determined by microclimatic conditions.</p><p><strong>Liu Y. et al. (2017):</strong> Separating overstory and understory leaf area indices for global needleleaf and deciduous broadleaf forests by fusion of MODIS and MISR data. Biogeosciences 14: 1093-1110.</p>

Satellite and in situ derived corn and soybean biomass and leaf area index: Response to controlled tile drainage under varying weather conditions

2015 ◽  
Vol 160 ◽  
pp. 118-131 ◽  
Author(s):  
Angela Kross ◽  
David R. Lapen ◽  
Heather McNairn ◽  
Mark Sunohara ◽  
Catherine Champagne ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Weather Conditions ◽  
Tile Drainage ◽  
Area Index
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