scholarly journals Leaf Area Index Estimation Algorithm for GF-5 Hyperspectral Data Based on Different Feature Selection and Machine Learning Methods

2020 ◽  
Vol 12 (13) ◽  
pp. 2110
Author(s):  
Zhulin Chen ◽  
Kun Jia ◽  
Chenchao Xiao ◽  
Dandan Wei ◽  
Xiang Zhao ◽  
...  
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Random Forest ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Energy Utilization ◽  
Estimation Accuracy ◽  
Soil Conditions ◽  
Area Index ◽  
Estimation Models

Leaf area index (LAI) is an essential vegetation parameter that represents the light energy utilization and vegetation canopy structure. As the only in-operation hyperspectral satellite launched by China, GF-5 is potentially useful for accurate LAI estimation. However, there is no research focus on evaluating GF-5 data for LAI estimation. Hyperspectral remote sensing data contains abundant information about the reflective characteristics of vegetation canopies, but these abound data also easily result in a dimensionality curse. Therefore, feature selection (FS) is necessary to reduce data redundancy to achieve more reliable estimations. Currently, machine learning (ML) algorithms have been widely used for FS. Moreover, the same ML algorithm is usually conducted for both FS and regression in LAI estimation. However, no evidence suggests that this is the optimal solution. Therefore, this study focuses on evaluating the capacity of GF-5 spectral reflectance for estimating LAI and the performances of different combination of FS and ML algorithms. Firstly, the PROSAIL model, which coupled leaf optical properties model PROSPECT and the scattering by arbitrarily inclined leaves (SAIL) model, was used to generate simulated GF-5 reflectance data under different vegetation and soil conditions, and then three FS methods, including random forest (RF), K-means clustering (K-means) and mean impact value (MIV), and three ML algorithms, including random forest regression (RFR), back propagation neural network (BPNN) and K-nearest neighbor (KNN) were used to develop nine LAI estimation models. The FS process was conducted twice using different strategies: Firstly, three FS methods were conducted to search the lowest dimension number, which maintained the estimation accuracy of all bands. Then, the sequential backward selection (SBS) method was used to eliminate the bands having minimal impact on LAI estimation accuracy. Finally, three best estimation models were selected and evaluated using reference LAI. The results showed that although the RF_RFR model (RF used for feature selection and RFR used for regression) achieved reliable LAI estimates (coefficient of determination (R2) = 0.828, root mean square error (RMSE) = 0.839), the poor performance (R2 = 0.763, RMSE = 0.987) of the MIV_BPNN model (MIV used for feature selection and BPNN used for regression) suggested using feature selection and regression conducted by the same ML algorithm could not always ensure an optimal estimation. Moreover, RF selection preserved the most informative bands for LAI estimation so that each ML regression method could achieve satisfactory estimation results. Finally, the results indicated that the RF_KNN model (RF used as feature selection and KNN used for regression) with seven GF-5 spectral band reflectance achieved the better estimation results than others when validated by simulated data (R2 = 0.834, RMSE = 0.824) and actual reference LAI (R2 = 0.659, RMSE = 0.697).

scholarly journals UAV Based Estimation of Forest Leaf Area Index (LAI) through Oblique Photogrammetry

2021 ◽  
Vol 13 (4) ◽  
pp. 803
Author(s):  
Lingchen Lin ◽  
Kunyong Yu ◽  
Xiong Yao ◽  
Yangbo Deng ◽  
Zhenbang Hao ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Point Cloud ◽  
Forest Canopy ◽  
Estimation Method ◽  
Vertical Direction ◽  
Estimation Accuracy ◽  
3D Point Cloud ◽  
Area Index ◽  
Better Than

As a key canopy structure parameter, the estimation method of the Leaf Area Index (LAI) has always attracted attention. To explore a potential method to estimate forest LAI from 3D point cloud at low cost, we took photos from different angles of the drone and set five schemes (O (0°), T15 (15°), T30 (30°), OT15 (0° and 15°) and OT30 (0° and 30°)), which were used to reconstruct 3D point cloud of forest canopy based on photogrammetry. Subsequently, the LAI values and the leaf area distribution in the vertical direction derived from five schemes were calculated based on the voxelized model. Our results show that the serious lack of leaf area in the middle and lower layers determines that the LAI estimate of O is inaccurate. For oblique photogrammetry, schemes with 30° photos always provided better LAI estimates than schemes with 15° photos (T30 better than T15, OT30 better than OT15), mainly reflected in the lower part of the canopy, which is particularly obvious in low-LAI areas. The overall structure of the single-tilt angle scheme (T15, T30) was relatively complete, but the rough point cloud details could not reflect the actual situation of LAI well. Multi-angle schemes (OT15, OT30) provided excellent leaf area estimation (OT15: R2 = 0.8225, RMSE = 0.3334 m2/m2; OT30: R2 = 0.9119, RMSE = 0.1790 m2/m2). OT30 provided the best LAI estimation accuracy at a sub-voxel size of 0.09 m and the best checkpoint accuracy (OT30: RMSE [H] = 0.2917 m, RMSE [V] = 0.1797 m). The results highlight that coupling oblique photography and nadiral photography can be an effective solution to estimate forest LAI.

scholarly journals RANDOM FOREST REGRESSION FOR THE ESTIMATION OF LEAF AREA INDEX OF OKRA CROP USING GROUND BASED BISTATIC SCATTEROMETER

2018 ◽  
Vol XLII-5 ◽  
pp. 719-725
Author(s):  
S. A. Yadav ◽  
R. Prasad ◽  
A. K. Vishwakarma ◽  
V. P. Yadav
Keyword(s):  
Regression Analysis ◽  
Random Forest ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Growth Stages ◽  
Data Sets ◽  
Angle Of Incidence ◽  
Random Forest Regression ◽  
Area Index ◽  
Scattering Coefficients

<p><strong>Abstract.</strong> The specular bistatic scattering mechanism of Okra's crop was analyzed using dual polarized ground based bistatic scatterometer system at X, C, and L bands in the specular direction with the azimuthal angle(&amp;theta;<span class="thinspace"></span>=<span class="thinspace"></span>0&amp;deg;). An outdoor Okra crop bed of area 10<span class="thinspace"></span>&amp;times;<span class="thinspace"></span>10<span class="thinspace"></span>m<sup>2</sup> was specially prepared for the estimation of leaf area index (LAI) at HH and VV polarizations over the angular range of incidence angle 20&amp;deg; to 60&amp;deg; at steps of 10&amp;deg;. The regression analysis was done between bistatic specular scattering coefficients and crop biophysical parameter at X, C, and L bands for HH and VV polarization at different angle of incidence to determine the optimum parameters of bistatic scatterometer system. The linear regression analysis showed the high correlation at 40&amp;deg; angle of incidence for all bands and polarizations for the Okra crop. The computed scattering coefficients and measured LAI of Okra crop for the seven growth stages at 40&amp;deg; angle of incidence were interpolated into 61 data sets. The data sets were divided into input, validation and testing for the training and testing of the developed random forest regression (RF) model for the estimation of LAI for Okra crop. The estimated values of LAI of Okra crop, by the developed RF regression model, were found more closer to the observed values at X band for VV polarization with coefficient of determination (R<sup>2</sup><span class="thinspace"></span>=<span class="thinspace"></span>0.928) and low root mean square error (RMSE<span class="thinspace"></span>=<span class="thinspace"></span>0.260<span class="thinspace"></span>m<sup>2</sup>/m<sup>2</sup>) in comparison to C and L bands.</p>

scholarly journals A Random Forest Algorithm for Retrieving Canopy Chlorophyll Content of Wheat and Soybean Trained with PROSAIL Simulations Using Adjusted Average Leaf Angle

2021 ◽  
Vol 14 (1) ◽  
pp. 98
Author(s):  
Quanjun Jiao ◽  
Qi Sun ◽  
Bing Zhang ◽  
Wenjiang Huang ◽  
Huichun Ye ◽  
...  
Keyword(s):  
Random Forest ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Chlorophyll Content ◽  
Leaf Angle ◽  
Transfer Model ◽  
Growth Monitoring ◽  
Random Forest Algorithm ◽  
Area Index ◽  
Important Indicator

Canopy chlorophyll content (CCC) is an important indicator for crop-growth monitoring and crop productivity estimation. The hybrid method, involving the PROSAIL radiative transfer model and machine learning algorithms, has been widely applied for crop CCC retrieval. However, PROSAIL’s homogeneous canopy hypothesis limits the ability to use the PROSAIL-based CCC estimation across different crops with a row structure. In addition to leaf area index (LAI), average leaf angle (ALA) is the most important canopy structure factor in the PROSAIL model. Under the same LAI, adjustment of the ALA can make a PROSAIL simulation obtain the same canopy gap as the heterogeneous canopy at a specific observation angle. Therefore, parameterization of an adjusted ALA (ALAadj) is an optimal choice to make the PROSAIL model suitable for specific row-planted crops. This paper attempted to improve PROSAIL-based CCC retrieval for different crops, using a random forest algorithm, by introducing the prior knowledge of crop-specific ALAadj. Based on the field reflectance spectrum at nadir, leaf area index, and leaf chlorophyll content, parameterization of the ALAadj in the PROSAIL model for wheat and soybean was carried out. An algorithm integrating the random forest and PROSAIL simulations with prior ALAadj information was developed for wheat and soybean CCC retrieval. Ground-measured CCC measurements were used to validate the CCC retrieved from canopy spectra. The results showed that the ALAadj values (62 degrees for wheat; 45 degrees for soybean) that were parameterized for the PROSAIL model demonstrated good discrimination between the two crops. The proposed algorithm improved the CCC retrieval accuracy for wheat and soybean, regardless of whether continuous visible to near-infrared spectra with 50 bands (RMSE from 39.9 to 32.9 μg cm−2; R2 from 0.67 to 0.76) or discrete spectra with 13 bands (RMSE from 43.9 to 33.7 μg cm−2; R2 from 0.63 to 0.74) and nine bands (RMSE from 45.1 to 37.0 μg cm−2; R2 from 0.61 to 0.71) were used. The proposed hybrid algorithm, based on PROSAIL simulations with ALAadj, has the potential for satellite-based CCC estimation across different crop types, and it also has a good reference value for the retrieval of other crop parameters.

Leaf area index determination of wheat indicating heterogeneous soil conditions

2006 ◽  
Author(s):  
K. Huber ◽  
J. Eitzinger ◽  
P. Rischbeck ◽  
W. Schneider ◽  
F. Suppan ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Soil Conditions ◽  
Area Index ◽  
Heterogeneous Soil

scholarly journals Effects of Leaf Area Index of Tomato Seedling Populations on Energy Utilization Efficiencies in a Closed Transplant Production System

2003 ◽  
Vol 15 (4) ◽  
pp. 231-238 ◽  
Author(s):  
Shingo YOKOI ◽  
Toyoki KOZAI ◽  
Katsumi OHYAMA ◽  
Tomoyuki HASEGAWA ◽  
Changhoo CHUN ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Production System ◽  
Energy Utilization ◽  
Tomato Seedling ◽  
Area Index

Effects of date of planting on plant emergence, leaf growth, and yield in contrasting potato varieties

1983 ◽  
Vol 101 (1) ◽  
pp. 81-95 ◽  
Author(s):  
J. L. Jones ◽  
E. J. Allen
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Leaf Growth ◽  
Growth And Yield ◽  
Soil Conditions ◽  
Area Index ◽  
Potato Varieties ◽  
Wide Range ◽  
Emerging Treatments ◽  
Soil Temperatures

SUMMARYFive experiments which studied the effects of a wide range of dates of planting on contrasting potato varieties in Pembrokeshire are reported. In three experiments (1976–7) four early varieties (Home Guard, Arran Comet, Irish Peace and Ulster Sceptre) were sprouted from the end of dormancy and compared at four dates of planting, which began as soon as soil conditions allowed (February in 1975 and 1976 and March in 1977). In these experiments all early-emerging treatments were damaged by frost and in 1975 and 1976 date of planting had little effect on leaf area index or yield. In 1977 planting in late April delayed and increased peak leaf area index but reduced yields throughout harvesting. In all experiments the emergence of varieties was affected by date of planting. The varieties with the longest sprouts emerged first only from the earliest plantings; at late plantings all varieties emerged together, which suggests that rate of post-planting sprout elongation decreased in this old seed as planting was delayed despite increasing soil temperatures. The implications for testing of early varieties are discussed.In two further experiments two early varieties (Home Guard in both years and Red Craigs Royal and Arran Comet in 1 year) were compared with three maincrop varieties (Désirée, Maris Piper, Stormont Enterprise) using seed which did not begin to sprout until January at dates of planting beginning in March. Sprout length was again poorly related to earliness of emergence. Delaying planting delayed and increased peak leaf area index in all varieties but only increased yields in the early varieties which had the smallest leaf areas. In maincrop varieties date of planting had little effect on final yields. In these years there were long periods without rain and in 1976 yields were limited by the amount of water available from the soil, for as each treatment exhausted this supply bulking ceased.

scholarly journals A Transformed Triangular Vegetation Index for Estimating Winter Wheat Leaf Area Index

2019 ◽  
Vol 12 (1) ◽  
pp. 16 ◽  
Author(s):  
Naichen Xing ◽  
Wenjiang Huang ◽  
Qiaoyun Xie ◽  
Yue Shi ◽  
Huichun Ye ◽  
...  
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Vegetation Index ◽  
Vegetation Indices ◽  
Saturation Effect ◽  
Estimation Accuracy ◽  
Growth Monitoring ◽  
Area Index ◽  
Multispectral Data ◽  
Red Edge

Leaf area index (LAI) is a key parameter in plant growth monitoring. For several decades, vegetation indices-based empirical method has been widely-accepted in LAI retrieval. A growing number of spectral indices have been proposed to tailor LAI estimations, however, saturation effect has long been an obstacle. In this paper, we classify the selected 14 vegetation indices into five groups according to their characteristics. In this study, we proposed a new index for LAI retrieval-transformed triangular vegetation index (TTVI), which replaces NIR and red bands of triangular vegetation index (TVI) into NIR and red-edge bands. All fifteen indices were calculated and analyzed with both hyperspectral and multispectral data. Best-fit models and k-fold cross-validation were conducted. The results showed that TTVI performed the best predictive power of LAI for both hyperspectral and multispectral data, and mitigated the saturation effect. The R2 and RMSE values were 0.60, 1.12; 0.59, 1.15, respectively. Besides, TTVI showed high estimation accuracy for sparse (LAI < 4) and dense canopies (LAI > 4). Our study provided the value of the Red-edge bands of the Sentinel-2 satellite sensors in crop LAI retrieval, and demonstrated that the new index TTVI is applicable to inverse LAI for both low-to-moderate and moderate-to-high vegetation cover.

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