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

Abstract. Leaf Area Index (LAI) is a quantity that characterizes canopy foliage content. As leaf surfaces are the primary sites of energy, mass exchange, and fundamental production of terrestrial ecosystem, many important processes are directly proportional to LAI. With this, LAI can be considered as an important parameter of plant growth. Multispectral optical images have been widely utilized for mangrove-related studies, such as LAI estimation. In Sentinel-2, for example, LAI can be estimated using a biophysical processor in SNAP or using various machine learning algorithms. However, multispectral optical images have disadvantages due to its weather-dependence and limited canopy penetration. In this study, a multi-sensor approach was implemented by using free multi-spectral optical images (Sentinel-2 ) and synthetic aperture radar (SAR) images (Sentinel-1) to perform Leaf Area Index (LAI) estimation. The use of SAR images can compensate for the above-mentioned disadvantages and it then can pave the way for regular mapping and assessment of LAI, despite any weather conditions and cloud cover. In this study, generation of LAI models that explores linear, non-linear and decision trees modelling algorithms to incorporate Sentinel-1 derivatives and Sentinel-2 LAI were executed. The Random Forest model have exhibited the most robust model having the lowest RMSE of 0.2845. This result poses a concrete relationship of a biophysical entity derived from optical parameters to RADAR derivatives to which opens the opportunity of integrating both systems to compensate each disadvantages and produce a more efficient quantification of LAI.

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Exploitation of time series Sentinel-2 data and different machine learning algorithms for detailed tree species classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing ◽

10.1109/jstars.2021.3098817 ◽

2021 ◽

pp. 1-1

Author(s):

Yanbiao Xi ◽

Chunying Ren ◽

Qingjiu Tian ◽

Yongxing Ren ◽

Xinyu Dong ◽

...

Keyword(s):

Machine Learning ◽

Time Series ◽

Tree Species ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Species Classification ◽

Tree Species Classification ◽

Sentinel 2

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A Comparison of Hybrid Machine Learning Algorithms for the Retrieval of Wheat Biophysical Variables from Sentinel-2

Remote Sensing ◽

10.3390/rs11050481 ◽

2019 ◽

Vol 11 (5) ◽

pp. 481 ◽

Cited By ~ 26

Author(s):

Deepak Upreti ◽

Wenjiang Huang ◽

Weiping Kong ◽

Simone Pascucci ◽

Stefano Pignatti ◽

...

Keyword(s):

Machine Learning ◽

Least Squares ◽

Chlorophyll Content ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Area Index ◽

Random Forest Tree ◽

Ground Validation ◽

Study Sites ◽

Sentinel 2

This study focuses on the comparison of hybrid methods of estimation of biophysical variables such as leaf area index (LAI), leaf chlorophyll content (LCC), fraction of absorbed photosynthetically active radiation (FAPAR), fraction of vegetation cover (FVC), and canopy chlorophyll content (CCC) from Sentinel-2 satellite data. Different machine learning algorithms were trained with simulated spectra generated by the physically-based radiative transfer model PROSAIL and subsequently applied to Sentinel-2 reflectance spectra. The algorithms were assessed against a standard operational approach, i.e., the European Space Agency (ESA) Sentinel Application Platform (SNAP) toolbox, based on neural networks. Since kernel-based algorithms have a heavy computational cost when trained with large datasets, an active learning (AL) strategy was explored to try to alleviate this issue. Validation was carried out using ground data from two study sites: one in Shunyi (China) and the other in Maccarese (Italy). In general, the performance of the algorithms was consistent for the two study sites, though a different level of accuracy was found between the two sites, possibly due to slightly different ground sampling protocols and the range and variability of the values of the biophysical variables in the two ground datasets. For LAI estimation, the best ground validation results were obtained for both sites using least squares linear regression (LSLR) and partial least squares regression, with the best performances values of R2 of 0.78, rott mean squared error (RMSE) of 0.68 m2 m−2 and a relative RMSE (RRMSE) of 19.48% obtained in the Maccarese site with LSLR. The best results for LCC were obtained using Random Forest Tree Bagger (RFTB) and Bagging Trees (BagT) with the best performances obtained in Maccarese using RFTB (R2 = 0.26, RMSE = 8.88 μg cm−2, RRMSE = 17.43%). Gaussian Process Regression (GPR) was the best algorithm for all variables only in the cross-validation phase, but not in the ground validation, where it ranked as the best only for FVC in Maccarese (R2 = 0.90, RMSE = 0.08, RRMSE = 9.86%). It was found that the AL strategy was more efficient than the random selection of samples for training the GPR algorithm.

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A COMPARISON OF MACHINE-LEARNING REGRESSION ALGORITHMS FOR THE ESTIMATION OF LAI USING LANDSAT - 8 SATELLITE DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w16-679-2019 ◽

2019 ◽

Vol XLII-4/W16 ◽

pp. 679-683

Author(s):

V. P. Yadav ◽

R. Prasad ◽

R. Bala ◽

A. K. Vishwakarma ◽

S. A. Yadav ◽

...

Keyword(s):

Machine Learning ◽

Satellite Data ◽

Vegetation Index ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Accurate Estimation ◽

Support Vector ◽

Landsat 8 ◽

Area Index ◽

Global Circulation Models

Abstract. The leaf area index (LAI) is one of key variable of crops which plays important role in agriculture, ecology and climate change for global circulation models to compute energy and water fluxes. In the recent research era, the machine-learning algorithms have provided accurate computational approaches for the estimation of crops biophysical parameters using remotely sensed data. The three machine-learning algorithms, random forest regression (RFR), support vector regression (SVR) and artificial neural network regression (ANNR) were used to estimate the LAI for crops in the present study. The three different dates of Landsat-8 satellite images were used during January 2017 – March 2017 at different crops growth conditions in Varanasi district, India. The sampling regions were fully covered by major Rabi season crops like wheat, barley and mustard etc. In total pooled data, 60% samples were taken for the training of the algorithms and rest 40% samples were taken as testing and validation of the machinelearning regressions algorithms. The highest sensitivity of normalized difference vegetation index (NDVI) with LAI was found using RFR algorithms (R2 = 0.884, RMSE = 0.404) as compared to SVR (R2 = 0.847, RMSE = 0.478) and ANNR (R2 = 0.829, RMSE = 0.404). Therefore, RFR algorithms can be used for accurate estimation of LAI for crops using satellite data.

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FEASIBILITY OF MACHINE LEARNING METHODS FOR SEPARATING WOOD AND LEAF POINTS FROM TERRESTRIAL LASER SCANNING DATA

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-2-w4-157-2017 ◽

2017 ◽

Vol IV-2/W4 ◽

pp. 157-164 ◽

Cited By ~ 6

Author(s):

D. Wang ◽

M. Hollaus ◽

N. Pfeifer

Keyword(s):

Machine Learning ◽

Laser Scanning ◽

Learning Algorithms ◽

Terrestrial Laser Scanning ◽

Gaussian Mixture ◽

Machine Learning Algorithms ◽

Support Vector ◽

Area Index ◽

Training Samples

Classification of wood and leaf components of trees is an essential prerequisite for deriving vital tree attributes, such as wood mass, leaf area index (LAI) and woody-to-total area. Laser scanning emerges to be a promising solution for such a request. Intensity based approaches are widely proposed, as different components of a tree can feature discriminatory optical properties at the operating wavelengths of a sensor system. For geometry based methods, machine learning algorithms are often used to separate wood and leaf points, by providing proper training samples. However, it remains unclear how the chosen machine learning classifier and features used would influence classification results. To this purpose, we compare four popular machine learning classifiers, namely Support Vector Machine (SVM), Na¨ıve Bayes (NB), Random Forest (RF), and Gaussian Mixture Model (GMM), for separating wood and leaf points from terrestrial laser scanning (TLS) data. Two trees, an <i>Erytrophleum fordii</i> and a <i>Betula pendula</i> (silver birch) are used to test the impacts from classifier, feature set, and training samples. Our results showed that RF is the best model in terms of accuracy, and local density related features are important. Experimental results confirmed the feasibility of machine learning algorithms for the reliable classification of wood and leaf points. It is also noted that our studies are based on isolated trees. Further tests should be performed on more tree species and data from more complex environments.

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Tree Species Classification in Mixed Deciduous Forests Using Very High Spatial Resolution Satellite Imagery and Machine Learning Methods

Remote Sensing ◽

10.3390/rs12233926 ◽

2020 ◽

Vol 12 (23) ◽

pp. 3926

Author(s):

Martina Deur ◽

Mateo Gašparović ◽

Ivan Balenović

Keyword(s):

Machine Learning ◽

Satellite Imagery ◽

Tree Species ◽

Learning Algorithms ◽

Spectral Characteristics ◽

Machine Learning Algorithms ◽

Support Vector ◽

Species Classification ◽

Tree Species Classification ◽

Very High

Spatially explicit information on tree species composition is important for both the forest management and conservation sectors. In combination with machine learning algorithms, very high-resolution satellite imagery may provide an effective solution to reduce the need for labor-intensive and time-consuming field-based surveys. In this study, we evaluated the possibility of using multispectral WorldView-3 (WV-3) satellite imagery for the classification of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) in a lowland, mixed deciduous forest in central Croatia. The pixel-based supervised classification was performed using two machine learning algorithms: random forest (RF) and support vector machine (SVM). Additionally, the contribution of gray level cooccurrence matrix (GLCM) texture features from WV-3 imagery in tree species classification was evaluated. Principal component analysis confirmed GLCM variance to be the most significant texture feature. Of the 373 visually interpreted reference polygons, 237 were used as training polygons and 136 were used as validation polygons. The validation results show relatively high overall accuracy (85%) for tree species classification based solely on WV-3 spectral characteristics and the RF classification approach. As expected, an improvement in classification accuracy was achieved by a combination of spectral and textural features. With the additional use of GLCM variance, the overall accuracy improved by 10% and 7% for RF and SVM classification approaches, respectively.

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