Preliminary study on integrated wireless smart terminals for leaf area index measurement

Qu, Yonghua; Meng, Jun; Wan, Huawei; Li, Yetao

doi:10.1016/j.compag.2016.09.011

Cited by 23 publications

(18 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, underestimation was more severe in PocketLAI than in LAISmart, mostly because canopy gap fraction was overestimated. However, Qu et al [19] found that the underestimation of LAI caused by gap fraction overestimation was inversely proportional to the real gap fraction. Hence, for sparse discrete forests with a large gap fraction, overestimation of the canopy gap fraction caused by the classification method would not lead to an obvious loss of LAI estimat45ane et al [45] also reached a similar conclusion that "none of the more complicated classification methods yielded results that greatly differed from a simple global binary threshold classification" after they carefully investigated four complicated algorithms and a simple global optimum threshold method.…”

Section: Discussionmentioning

confidence: 97%

“…With the impact of the camera FOV, the real zenith angle in PocketLAI was greater than 68°, therefore the corresponding G-value should be less than 0.49. Because the FOV of the camera in a mobile phone is basically around 70 • [19], the mean of the G-value of the first three angles in LAI-2000 was taken to be the G-value corresponding to the LAISmart viewing zenith angle, and the mean of the last two angles was taken to be the G-value corresponding to the PocketLAI viewing zenith angle of 57.5 • . The calculated G-values of the two methods are shown as the solid triangle in Figure 5.…”

Section: Discussionmentioning

confidence: 99%

“…As imaging methods, LAISmart [19] and PocketLAI [15] capture canopy photos using a smartphone camera sensor, then an automatic classification algorithm is used to separate image pixels into leaf and background to calculate canopy gap fraction. While calculating leaf area index from gap fraction, both methods share a common canopy light attenuation theory that describes how light transits from the top of the canopy to the sensors placed underneath.…”

Section: Estimation Of Lai Using a Smartphone Camera Sensormentioning

confidence: 99%

See 2 more Smart Citations

Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone-Based Method

Qu¹,

Wang²,

Song³

et al. 2017

Forests

Self Cite

View full text Add to dashboard Cite

Forest leaf area index (LAI) is a key characteristic affecting a field canopy microclimate. In addition to traditional professional measuring instruments, smartphone-based methods have been used to measure forest LAI. However, when smartphone methods were used to measure conifer forest LAI, very different performances were obtained depending on whether the smartphone was held at the zenith angle or at a 57.5 • angle. To further validate the potential of smartphone sensors for measuring conifer LAI and to find the limits of this method, this paper reports the results of a comparison of two smartphone methods with an LAI-2000 instrument. It is shown that the method with the smartphone oriented vertically upwards always produced better consistency in magnitude with LAI-2000. The bias of the LAI between the smartphone method and the LAI-2000 instrument was explained with regards to four aspects that can affect LAI: gap fraction; leaf projection ratio; sensor field of view (FOV); and viewing zenith angle (VZA). It was concluded that large FOV and large VZA cause the 57.5 • method to overestimate the gap fraction and hence underestimate conifer LAI. For the vertically upward method, the bias caused by the overestimated gap fraction is compensated for by an underestimated leaf projection ratio.

show abstract

Section: Discussionmentioning

confidence: 97%

Section: Discussionmentioning

confidence: 99%

Section: Estimation Of Lai Using a Smartphone Camera Sensormentioning

confidence: 99%

See 1 more Smart Citation

Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone-Based Method

Qu¹,

Wang²,

Song³

et al. 2017

Forests

Self Cite

View full text Add to dashboard Cite

show abstract

“…Two hundred and seventy quadrats across 90 fields were established during the sequential field campaigns and 259 observed LAI were used in this study. LAI-2000 [24,25] and the LAISmart system [26] were used to obtain the LAI field data and Method 1 was used to establish sample points in each quadrat. For LAI-2000, a one-up-seven-down pattern was used to gather the LAI.…”

Section: Introductionmentioning

confidence: 99%

Preliminary Study of Soil Available Nutrient Simulation Using a Modified WOFOST Model and Time-Series Remote Sensing Observations

et al. 2018

Self Cite

View full text Add to dashboard Cite

Abstract:The approach of using multispectral remote sensing (RS) to estimate soil available nutrients (SANs) has been recently developed and shows promising results. This method overcomes the limitations of commonly used methods by building a statistical model that connects RS-based crop growth and nutrient content. However, the stability and accuracy of this model require improvement. In this article, we replaced the statistical model by integrating the World Food Studies (WOFOST) model and time series of remote sensing (T-RS) observations to ensure stability and accuracy. Time series of HJ-1 A/B data was assimilated into the WOFOST model to extrapolate crop growth simulations from a single point to a large area using a specific assimilation method. Because nutrient-limited growth within the growing season is required and the SAN parameters can only be used at the end of the growing season in the original model, the WOFOST model was modified. Notably, the calculation order was changed, and new soil nutrient uptake algorithms were implemented in the model for nutrient-limited growth estimation. Finally, experiments were conducted in the spring maize plots of Hongxing Farm to analyze the effects of nutrient stress on crop growth and the SAN simulation accuracy. The results confirm the differences in crop growth status caused by a lack of soil nutrients. The new approach can take advantage of these differences to provide better SAN estimates. In general, the new approach can overcome the limitations of existing methods and simulate the SAN status with reliable accuracy.

show abstract

“…Due to greater availability of cameras for capturing images, imaging sensor-based methods are becoming increasingly popular. Among imaging sensor-based methods, a new development that uses a smartphone camera sensor rather than a general-purpose digital camera has emerged recently [4][5][6][7][8][9]. Building on the high performance-price ratio and multi-sensor integration of the smartphone, this approach has attracted much attention to measuring LAI using smartphone camera sensors.…”

Section: Introductionmentioning

confidence: 99%

Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone Camera Sensors

Wang

Song

2017

Preprint

Self Cite

View full text Add to dashboard Cite

Plant leaf area index (LAI) is a key characteristic affecting field canopy microclimate. In addition to traditional professional measuring instruments, smartphone camera sensors have been used to measure plant LAI. However, when smartphone methods were used to measure conifer forest LAI, very different performances were obtained depending on whether the smartphone was held at the zenith angle or at a 57.5° angle. To validate further the potential of smartphone sensors for measuring conifer LAI and to find the limits of this method, this paper reports the results of a comparison of two smartphone methods with an LAI-2000 instrument. It is shown that both methods can be used to reveal the conifer leaf-growing trajectory. However, the method with the phone oriented vertically upwards always produced better consistency in magnitude with LAI-2000. The bias of the LAI between the smartphone method and the LAI-2000 instrument was explained with regard to four aspects that can affect LAI: gap fraction, leaf projection ratio, sensor field of view (FOV), and viewing zenith angle (VZA). It was concluded that large FOV and large VZA cause the 57.5° method to overestimate the gap fraction and hence underestimate conifer LAI, especially when tree height is greater than 2.0 m. For the vertically upward method, the bias caused by the overestimated gap fraction is compensated for by an underestimated leaf projection ratio.

show abstract

Preliminary study on integrated wireless smart terminals for leaf area index measurement

Cited by 23 publications

References 22 publications

Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone-Based Method

Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone-Based Method

Preliminary Study of Soil Available Nutrient Simulation Using a Modified WOFOST Model and Time-Series Remote Sensing Observations

Potential and Limits of Retrieving Conifer Leaf Area Index Using Smartphone Camera Sensors

Contact Info

Product

Resources

About