LAINet – A wireless sensor network for coniferous forest leaf area index measurement: Design, algorithm and validation

2014 ◽  
Vol 108 ◽  
pp. 200-208 ◽  
Author(s):  
Yonghua Qu ◽  
Wenchao Han ◽  
Lizhe Fu ◽  
Congrong Li ◽  
Jinling Song ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Sensor Network ◽  
Coniferous Forest ◽  
Wireless Sensor ◽  
Area Index ◽  
Design Algorithm ◽  
Index Measurement ◽  
Measurement Design

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.

scholarly journals Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion

2019 ◽  
Vol 11 (3) ◽  
pp. 244 ◽  
Author(s):  
Gaofei Yin ◽  
Aleixandre Verger ◽  
Yonghua Qu ◽  
Wei Zhao ◽  
Baodong Xu ◽  
...  
Keyword(s):  
Wireless Sensor Network ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Sensor Network ◽  
Growth And Yield ◽  
Wireless Sensor ◽  
Observation System ◽  
Spatiotemporal Resolution ◽  
Area Index ◽  
High Spatiotemporal Resolution

Many applications, including crop growth and yield monitoring, require accurate long-term time series of leaf area index (LAI) at high spatiotemporal resolution with a quantification of the associated uncertainties. We propose an LAI retrieval approach based on a combination of the LAINet observation system, the Consistent Adjustment of the Climatology to Actual Observations (CACAO) method, and Gaussian process regression (GPR). First, the LAINet wireless sensor network provides temporally continuous field measurements of LAI. Then, the CACAO approach generates synchronous reflectance data at high spatiotemporal resolution (30-m and 8-day) from the fusion of multitemporal MODIS and high spatial resolution Landsat satellite imagery. Finally, the GPR machine learning regression algorithm retrieves the LAI maps and their associated uncertainties. A case study in a cropland site in China showed that the accuracy of LAI retrievals is 0.36 (12.7%) in terms of root mean square error and R2 = 0.88 correlation with ground measurements as evaluated over the entire growing season. This paper demonstrates the potential of the joint use of newly developed software and hardware technologies in deriving concomitant LAI and uncertainty maps with high spatiotemporal resolution. It will contribute to precision agriculture, as well as to the retrieval and validation of LAI products.

scholarly journals Quantifying the Effects of Normalisation of Airborne LiDAR Intensity on Coniferous Forest Leaf Area Index Estimations

2017 ◽  
Vol 9 (2) ◽  
pp. 163 ◽  
Author(s):  
Haotian You ◽  
Tiejun Wang ◽  
Andrew Skidmore ◽  
Yanqiu Xing
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Coniferous Forest ◽  
Airborne Lidar ◽  
Area Index

Retrieving leaf area index for coniferous forest in Xingguo County, China with Landsat ETM+ images

2007 ◽  
Vol 85 (3) ◽  
pp. 624-627 ◽  
Author(s):  
Q. Tian ◽  
Z. Luo ◽  
J.M. Chen ◽  
M. Chen ◽  
F. Hui
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Coniferous Forest ◽  
Area Index

An assessment of the MODIS collection 5 leaf area index product for a region of mixed coniferous forest

2011 ◽  
Vol 115 (2) ◽  
pp. 767-780 ◽  
Author(s):  
M.G. De Kauwe ◽  
M.I. Disney ◽  
T. Quaife ◽  
P. Lewis ◽  
M. Williams
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Coniferous Forest ◽  
Area Index

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
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