Investigating relationships between Landsat ETM+ sensor data and leaf area index in a boreal conifer forest

Abstract Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.Methods: To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.Result: The results show that: (1) the soil background reduced the accuracy of the LAI prediction, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data improved LAI prediction accuracy and achieved the best accuracy (R2 = 0.815 and RMSE = 1.023). (3) When compared to other variables, 23 CHM, NRCT, NDRE, and BLUE are crucial for LAI estimation. Even the simple Multiple Linear Regression model could achieve high prediction accuracy (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction.Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.

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Leaf area index retrieval using IRS LISS-III sensor data and validation of the MODIS LAI product over central India

IEEE Transactions on Geoscience and Remote Sensing ◽

10.1109/tgrs.2006.876028 ◽

2006 ◽

Vol 44 (7) ◽

pp. 1858-1865 ◽

Cited By ~ 17

Author(s):

M.R. Pandya ◽

R.P. Singh ◽

K.N. Chaudhari ◽

G.D. Bairagi ◽

R. Sharma ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Central India ◽

Sensor Data ◽

Area Index ◽

Liss Iii ◽

Modis Lai

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Improving Leaf Area Index Retrieval Using Multi-Sensor Images and Stacking Learning in Subtropical Forests of China

Remote Sensing ◽

10.3390/rs14010148 ◽

2021 ◽

Vol 14 (1) ◽

pp. 148

Author(s):

Yang Chen ◽

Lixia Ma ◽

Dongsheng Yu ◽

Kaiyue Feng ◽

Xin Wang ◽

...

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Complex Terrain ◽

Absolute Error ◽

Sensor Data ◽

Area Index ◽

Subtropical Forests ◽

Optical Images ◽

The Status ◽

Elevation Model

The leaf area index (LAI) is a key indicator of the status of forest ecosystems that is important for understanding global carbon and water cycles as well as terrestrial surface energy balances and the impacts of climate change. Machine learning (ML) methods offer promising ways of generating spatially explicit LAI data covering large regions based on optical images. However, there have been few efforts to analyze the LAI in heterogeneous subtropical forests with complex terrain by fusing high-resolution multi-sensor data from the Sentinel-1 Synthetic Aperture Radar (SAR), Sentinel-2 Multi Spectral Instrument (MSI), and Advanced Land Observing Satellite-1 digital elevation model (DEM). Here, forest LAI mapping was performed by integrating the MSI, SAR, and DEM data using a stacking learning (SL) approach that incorporates distinct predictions from a set of optimized individual ML algorithms. The method’s performance was evaluated by comparison to field forest LAI measurements acquired in Xingguo and Gandong of subtropical China. The results showed that the addition of the SAR and DEM images using the SL model compared to the inputs of only optical images reduced the mean absolute error (MAE) and root mean square error (RMSE) by 26% and 18%, respectively, in Xingguo, and by 12% and 8%, respectively, in Gandong. Furthermore, the combination of all images had the best prediction performance. SL was found to be more robust and accurate than conventional individual ML models, while the MAE and RMSE were decreased by 71% and 64%, respectively, in Xingguo, and by 68% and 59%, respectively, in Gandong. Therefore, the SL model using the three-source data combination produced satisfied prediction accuracy with the coefficients of determination (R2), MAE, and RMSE of 0.96, 0.17, and 0.28, respectively, in Xingguo and 0.94, 0.30, and 0.47, respectively, in Gandong. This study revealed the potential of the SL algorithm for retrieving the forest LAI using multi-sensor data in areas with complex terrain.

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

10.20944/preprints201701.0077.v1 ◽

2017 ◽

Author(s):

Yonghua Qu ◽

Jian Wang ◽

Jinling Song ◽

Jindi Wang

Keyword(s):

Leaf Area Index ◽

Leaf Area ◽

Zenith Angle ◽

Tree Height ◽

Measuring Instruments ◽

Conifer Forest ◽

Area Index ◽

Gap Fraction ◽

Smartphone Sensors ◽

Camera Sensors

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.

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