Estimating leaf area index in coniferous and deciduous forests in Sweden using Landsat optical sensor data

2003 ◽  
Author(s):  
Lars Eklundh
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Optical Sensor ◽  
Deciduous Forests ◽  
Sensor Data ◽  
Area Index

scholarly journals Wheat Leaf Area Index Prediction Using Data Fusion Based on High-Resolution Unmanned Aerial Vehicle Imagery

2021 ◽  
Author(s):  
Shuang Wu ◽  
Lei Deng ◽  
Lijie Guo ◽  
Yanjie Wu
Keyword(s):  
High Resolution ◽  
Data Fusion ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Unmanned Aerial Vehicle ◽  
Prediction Accuracy ◽  
Sensor Data ◽  
Support Vector ◽  
Area Index ◽  
Aerial Vehicle

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.

Estimating seasonal variations of leaf area index using litterfall collection and optical methods in four mixed evergreen–deciduous forests

2015 ◽  
Vol 209-210 ◽  
pp. 36-48 ◽  
Author(s):  
Zhili Liu ◽  
Jing M. Chen ◽  
Guangze Jin ◽  
Yujiao Qi
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Seasonal Variations ◽  
Deciduous Forests ◽  
Optical Methods ◽  
Area Index

Estimation of leaf area index with the Li-Cor LAI 2000 in deciduous forests

1998 ◽  
Vol 105 (1-3) ◽  
pp. 55-65 ◽  
Author(s):  
Andrea Cutini ◽  
Giorgio Matteucci ◽  
Giuseppe Scarascia Mugnozza
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Deciduous Forests ◽  
Area Index

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

2006 ◽  
Vol 44 (7) ◽  
pp. 1858-1865 ◽  
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

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

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.

scholarly journals A comparison of optical and direct methods for monitoring the seasonal dynamics of leaf area index in deciduous forests

Silva Fennica ◽  
2001 ◽  
Vol 35 (4) ◽  
Author(s):  
Sylvie Mussche ◽  
Roeland Samson ◽  
Lieven Nachtergale ◽  
An De Schrijver ◽  
Raoul Lemeur ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Seasonal Dynamics ◽  
Direct Methods ◽  
Deciduous Forests ◽  
Area Index

Estimation of Leaf Area Index in dry deciduous forests from IRS‐WiFS in central India

2005 ◽  
Vol 26 (21) ◽  
pp. 4855-4867 ◽  
Author(s):  
M. Kale ◽  
S. Singh ◽  
P. S. Roy
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Central India ◽  
Deciduous Forests ◽  
Area Index

Towards a standardized protocol for measuring leaf area index in deciduous forests with litterfall collection

2019 ◽  
Vol 447 ◽  
pp. 87-94 ◽  
Author(s):  
Xingchang Wang ◽  
Fan Liu ◽  
Chuankuan Wang
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Deciduous Forests ◽  
Area Index ◽  
Standardized Protocol

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

2001 ◽  
Vol 78 (3) ◽  
pp. 239-251 ◽  
Author(s):  
Lars Eklundh ◽  
Lars Harrie ◽  
Andres Kuusk
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Sensor Data ◽  
Conifer Forest ◽  
Area Index

scholarly journals Comparative Analysis of Chinese HJ-1 CCD, GF-1 WFV and ZY-3 MUX Sensor Data for Leaf Area Index Estimations for Maize

2018 ◽  
Vol 10 (2) ◽  
pp. 68 ◽  
Author(s):  
Jing Zhao ◽  
Jing Li ◽  
Qinhuo Liu ◽  
Hongyan Wang ◽  
Chen Chen ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Sensor Data ◽  
Area Index
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