Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

Omer, Galal; Mutanga, Onisimo; Abdel‐Rahman, Elfatih M.; Adam, Elhadi

doi:10.3390/rs8040324

Cited by 46 publications

(29 citation statements)

References 105 publications

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“…For the LUT method, it has been recommended to choose 100,000 reflectance realizations and use the best 50 cases to achieve a most efficient retrieval (Darvishzadeh, et al, ; Richter et al, ; Verrelst et al, ; Weiss et al, ). Other machine learning algorithms, such as the Bayesian network algorithm (V.C.E Laurent et al, ; Qu, Zhang, et al, ; Quan et al, ; Yao et al, ), the support vector machine regression algorithm (Durbha et al, ; Fortin et al, ; Omer et al, ), and the Gaussian process regression method (García‐Haro et al, ; Verrelst, Rivera, et al, ), have also been explored in a number of inversion studies. The choice of a particular retrieval method depends on the mathematical properties of the function to be minimized.…”

Section: Remote Sensing Methodsmentioning

confidence: 99%

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

Fang

Baret

Plummer

et al. 2019

Reviews of Geophysics

450

252

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Leaf area index (LAI) is a critical vegetation structural variable and is essential in the feedback of vegetation to the climate system. The advancement of the global Earth Observation has enabled the development of global LAI products and boosted global Earth system modeling studies. This overview provides a comprehensive analysis of LAI field measurements and remote sensing estimation methods, the product validation methods and product uncertainties, and the application of LAI in global studies. First, the paper clarifies some definitions related to LAI and introduces methods to determine LAI from field measurements and remote sensing observations. After introducing some major global LAI products, progresses made in temporal compositing and prospects for future LAI estimation are analyzed. Subsequently, the overview discusses various LAI product validation schemes, uncertainties in global moderate resolution LAI products, and high resolution reference data. Finally, applications of LAI in global vegetation change, land surface modeling, and agricultural studies are presented. It is recommended that (1) continued efforts are taken to advance LAI estimation algorithms and provide high temporal and spatial resolution products from current and forthcoming missions; (2) further validation studies be conducted to address the inadequacy of current validation studies, especially for underrepresented regions and seasons; and (3) new research frontiers, such as machine learning algorithms, light detection and ranging technology, and unmanned aerial vehicles be pursued to broaden the production and application of LAI.

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Section: Remote Sensing Methodsmentioning

confidence: 99%

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

Fang

Baret

Plummer

et al. 2019

Reviews of Geophysics

450

252

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show abstract

“…These indices have been effectively and successfully used to estimate and map vegetation canopy LAI from moderate and high resolution multispectral remote sensing data [3,34,35]. To identify the optimal VIs for LAI estimation using the simulated datasets, five curve-fitting models were established for each VI at each growth stage.…”

Section: Vegetation Indices and Inversion Algorithmsmentioning

confidence: 99%

Estimation and Mapping of Winter Oilseed Rape LAI from High Spatial Resolution Satellite Data Based on a Hybrid Method

et al. 2017

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Leaf area index (LAI) is a key input in models describing biosphere processes and has widely been used in monitoring crop growth and in yield estimation. In this study, a hybrid inversion method is developed to estimate LAI values of winter oilseed rape during growth using high spatial resolution optical satellite data covering a test site located in southeast China. Based on PROSAIL (coupling of PROSPECT and SAIL) simulation datasets, nine vegetation indices (VIs) were analyzed to identify the optimal independent variables for estimating LAI values. The optimal VIs were selected using curve fitting methods and the random forest algorithm. Hybrid inversion models were then built to determine the relationships between optimal simulated VIs and LAI values (generated by the PROSAIL model) using modeling methods, including curve fitting, k-nearest neighbor (kNN), and random forest regression (RFR). Finally, the mapping and estimation of winter oilseed rape LAI using reflectance obtained from Pleiades-1A, WorldView-3, SPOT-6, and WorldView-2 were implemented using the inversion method and the LAI estimation accuracy was validated using ground-measured datasets acquired during the 2014-2015 growing season. Our study indicates that based on the estimation results derived from different datasets, RFR is the optimal modeling algorithm amidst curve fitting and kNN with R 2 > 0.954 and RMSE <0.218. Using the optimal VIs, the remote sensing-based mapping of winter oilseed rape LAI yielded an accuracy of R 2 = 0.520 and RMSE = 0.923 (RRMSE = 93.7%). These results have demonstrated the potential operational applicability of the hybrid method proposed in this study for the mapping and retrieval of winter oilseed rape LAI values at field scales using multi-source and high spatial resolution optical remote sensing datasets. Details provided by this high resolution mapping cannot be easily discerned at coarser mapping scales and over larger spatial extents that usually employ lower resolution satellite images. Our study therefore has significant implications for field crop monitoring at local scales, providing relevant data for agronomic practices and precision agriculture.

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“…Han et al used random forest (RF) and support vector machine (SVM) methods to invert the canopy LAI of apple trees; the estimation accuracy of the RF model was better than that of the SVM model, and RF was suitable for apple LAI estimation throughout the full fruit growth period [23]. Omer et al used artificial neural network (ANN) and SVM methods to predict the LAI of six endangered tree species and found that the SVM model had higher prediction accuracy compared with the ANN model [24]. Verrelst et al used ANN, SVM, nuclear ridge regression (KRR), and Gaussian process regression (GPR) methods to predict LAI and concluded that GPR was a fast and accurate nonlinear retrieval algorithm [25].…”

Section: Introductionmentioning

confidence: 99%

Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models

et al. 2017

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Leaf area index (LAI) is an important indicator of plant growth and yield that can be monitored by remote sensing. Several models were constructed using datasets derived from SRS and STR sampling methods to determine the optimal model for soybean (multiple strains) LAI inversion for the whole crop growth period and a single growth period. Random forest (RF), artificial neural network (ANN), and support vector machine (SVM) regression models were compared with a partial least-squares regression (PLS) model. The RF model yielded the highest precision, accuracy, and stability with V-R 2 , SD R 2 , V-RMSE, and SD RMSE values of 0.741, 0.031, 0.106, and 0.005, respectively, over the whole growth period based on STR sampling. The ANN model had the highest precision, accuracy, and stability (0.452, 0.132, 0.086, and 0.009, respectively) over a single growth phase based on STR sampling. The precision, accuracy, and stability of the RF, ANN, and SVM models were improved by inclusion of STR sampling. The RF model is suitable for estimating LAI when sample plots and variation are relatively large (i.e., the whole growth period or more than one growth period). The ANN model is more appropriate for estimating LAI when sample plots and variation are relatively low (i.e., a single growth period).

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Empirical Prediction of Leaf Area Index (LAI) of Endangered Tree Species in Intact and Fragmented Indigenous Forests Ecosystems Using WorldView-2 Data and Two Robust Machine Learning Algorithms

Cited by 46 publications

References 105 publications

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

Estimation and Mapping of Winter Oilseed Rape LAI from High Spatial Resolution Satellite Data Based on a Hybrid Method

Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models

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