scholarly journals Estimating Above-Ground Biomass of Potato Using Random Forest and Optimized Hyperspectral Indices

2021 ◽  
Vol 13 (12) ◽  
pp. 2339
Author(s):  
Haibo Yang ◽  
Fei Li ◽  
Wei Wang ◽  
Kang Yu
Keyword(s):  
Random Forest ◽  
Vegetation Index ◽  
Tuber Formation ◽  
Growth Stages ◽  
Spectral Indices ◽  
Above Ground Biomass ◽  
Full Spectrum ◽  
Ground Biomass ◽  
Tuber Bulking ◽  
Input Variables

Spectral indices rarely show consistency in estimating crop traits across growth stages; thus, it is critical to simultaneously evaluate a group of spectral variables and select the most informative spectral indices for retrieving crop traits. The objective of this study was to explore the optimal spectral predictors for above-ground biomass (AGB) by applying Random Forest (RF) on three types of spectral predictors: the full spectrum, published spectral indices (Pub-SIs), and optimized spectral indices (Opt-SIs). Canopy hyperspectral reflectance of potato plants, treated with seven nitrogen (N) rates, was obtained during the tuber formation and tuber bulking from 2015 to 2016. Twelve Pub-SIs were selected, and their spectral bands were optimized using band optimization algorithms. Results showed that the Opt-SIs were the best input variables of RF models. Compared to the best empirical model based on Opt-SIs, the Opt-SIs based RF model improved the prediction of AGB, with R2 increased by 6%, 10%, and 16% at the tuber formation, tuber bulking, and for across the two growth stages, respectively. The Opt-SIs can significantly reduce the number of input variables. The optimized Blue nitrogen index (Opt-BNI) and Modified red-edge normalized difference vegetation index (Opt-mND705) combined with an RF model showed the best performance in estimating potato AGB at the tuber formation stage (R2 = 0.88). In the tuber bulking stage, only using optimized Nitrogen planar domain index (Opt-NPDI) as the input variable of the RF model produced satisfactory accuracy in training and testing datasets, with the R2, RMSE, and RE being 0.92, 208.6 kg/ha, and 10.3%, respectively. The Opt-BNI and Double-peak nitrogen index (Opt-NDDA) coupling with an RF model explained 86% of the variations in potato AGB, with the lowest RMSE (262.9 kg/ha) and RE (14.8%) across two growth stages. This study shows that combining the Opt-SIs and RF can greatly enhance the prediction accuracy for crop AGB while significantly reduces collinearity and redundancies of spectral data.

scholarly journals Novel Feature-Extraction Methods for the Estimation of Above-Ground Biomass in Rice Crops

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4369
Author(s):  
David Alejandro Jimenez-Sierra ◽  
Edgar Steven Correa ◽  
Hernán Darío Benítez-Restrepo ◽  
Francisco Carlos Calderon ◽  
Ivan Fernando Mondragon ◽  
...  
Keyword(s):  
Feature Extraction ◽  
Vegetation Index ◽  
Ground Truth ◽  
Gaussian Mixture ◽  
Extraction Methods ◽  
Temporal Variations ◽  
Biomass Estimation ◽  
Growth Stages ◽  
Above Ground Biomass ◽  
Ground Biomass

Traditional methods to measure spatio-temporal variations in above-ground biomass dynamics (AGBD) predominantly rely on the extraction of several vegetation-index features highly associated with AGBD variations through the phenological crop cycle. This work presents a comprehensive comparison between two different approaches for feature extraction for non-destructive biomass estimation using aerial multispectral imagery. The first method is called GFKuts, an approach that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo-based K-means, and a guided image filtering for the extraction of canopy vegetation indices associated with biomass yield. The second method is based on a Graph-Based Data Fusion (GBF) approach that does not depend on calculating vegetation-index image reflectances. Both methods are experimentally tested and compared through rice growth stages: vegetative, reproductive, and ripening. Biomass estimation correlations are calculated and compared against an assembled ground-truth biomass measurements taken by destructive sampling. The proposed GBF-Sm-Bs approach outperformed competing methods by obtaining biomass estimation correlation of 0.995 with R2=0.991 and RMSE=45.358 g. This result increases the precision in the biomass estimation by around 62.43% compared to previous works.

Above-ground biomass estimation of Indian tropical forests using X band Pol-InSAR and Random Forest

2021 ◽  
Vol 21 ◽  
pp. 100462
Author(s):  
Sadhana Yadav ◽  
Hitendra Padalia ◽  
Sanjiv K. Sinha ◽  
Ritika Srinet ◽  
Prakash Chauhan
Keyword(s):  
Random Forest ◽  
Tropical Forests ◽  
Biomass Estimation ◽  
Above Ground Biomass ◽  
Ground Biomass ◽  
X Band

scholarly journals New Textural Indicators for Assessing Above-Ground Cotton Biomass Extracted from Optical Imagery Obtained via Unmanned Aerial Vehicle

2020 ◽  
Vol 12 (24) ◽  
pp. 4170
Author(s):  
Pengfei Chen ◽  
Fangyong Wang
Keyword(s):  
Unmanned Aerial Vehicle ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Biomass Estimation ◽  
Growth Stages ◽  
Spectral Indices ◽  
Green Band ◽  
Validation Data ◽  
Texture Parameters ◽  
Aerial Vehicle

Although textural information can be used to estimate vegetation biomass, its use for estimating crop biomass is rare, and previous methods lacked a mechanistic explanation for the relationship to biomass. The objective of the present study was to develop mechanistic textural indices for estimating cotton biomass and solving saturation problems at medium and high biomass levels. A nitrogen (N) fertilization experiment was established, and unmanned aerial vehicle optical images and field measured biomass data were obtained during critical cotton growth stages. Based on these data, two textural indices, namely the normalized difference texture index combining contrast and the inverse difference moment of the green band (NBTI (CON, IDM)g) and normalized difference texture index combining entropy and the inverse difference moment of the green band (NBTI (ENT, IDM)g), were proposed by analyzing the mechanism of texture parameters for biomass prediction and the law of texture parameters changing with biomass. These indices were compared with spectral indices commonly used for biomass estimation using independent validation data, such as the normalized difference vegetation index (NDVI). The results showed that the proposed textural indices performed better than the spectral indices with no saturation problems occurring. The combination of spectral and textural indices using a stepwise regression method performed better for biomass estimation than using only spectral or textural indices. This method has considerable potential for improving the accuracy of biomass estimations for the subsequent delineation of precise cotton management zones.

Improvement in Estimation of Aboveground Biomass of Albizia lebbeck using Fraction Reflectance of Landsat TM Data

2015 ◽  
Vol 1 (01) ◽  
pp. 99-105
Author(s):  
O. P. Tripathi ◽  
J. Deka ◽  
J. Y. Yumnam ◽  
P. Mahanta
Keyword(s):  
Satellite Data ◽  
Vegetation Index ◽  
Linear Regression Analysis ◽  
Landsat Tm ◽  
Surface Reflectance ◽  
Above Ground Biomass ◽  
Mixed Pixel ◽  
Ground Biomass ◽  
Medium Resolution ◽  
Reflectance Data

The study emphasizes on the ability of spectral mixture analysis (SMA) to improve the estimate of above ground biomass from spectral reflectance of satellite data. Digital number (DN) values of satellite data were converted to surface reflectance and fraction reflectance data. Linear regression analysis was performed between above ground biomass data with both total surface reflectance and SMA produced fraction reflectance values separately. The analysis revealed that the best positive correlation was observed between AGB and the fraction reflectance values of FR_TM 5/3 with R2 = 0.865 whereas with the total reflectance data, atmospherically resistant vegetation index (ARVI) and TM5/3 resulted moderate correlation R2= 0.452 and - 0.248, respectively. Pixel level AGB ranges between 0.001t to 13.53t. The total AGB of the study area (275.85 ha) was estimated to be 14249t. Thus separation of endmembers from the mixed pixel from a course to medium resolution satellite data can play an important role in improving AGB estimation performance, especially for those sites with multiple cover features. The study demonstrates the potentiality of Landsat TM data in concatenate with SMA in improving the estimation of AGB which can be further improved by identifying all possible endmembers and highly configured methodology.

scholarly journals Identification of Cotton Root Rot by Multifeature Selection from Sentinel-2 Images Using Random Forest

2020 ◽  
Vol 12 (21) ◽  
pp. 3504
Author(s):  
Xingrong Li ◽  
Chenghai Yang ◽  
Wenjiang Huang ◽  
Jia Tang ◽  
Yanqin Tian ◽  
...  
Keyword(s):  
Random Forest ◽  
Root Rot ◽  
Vegetation Index ◽  
Texture Features ◽  
Spectral Model ◽  
Spectral Indices ◽  
Texture Model ◽  
Cotton Root ◽  
Cotton Fields ◽  
Sentinel 2

Cotton root rot is a destructive cotton disease and significantly affects cotton quality and yield, and accurate identification of its distribution within fields is critical for cotton growers to control the disease effectively. In this study, Sentinel-2 images were used to explore the feasibility of creating classification maps and prescription maps for site-specific fungicide application. Eight cotton fields with different levels of root rot were selected and random forest (RF) was used to identify the optimal spectral indices and texture features of the Sentinel-2 images. Five optimal spectral indices (plant senescence reflectance index (PSRI), normalized difference vegetation index (NDVI), normalized difference water index (NDWI1), moisture stressed index (MSI), and renormalized difference vegetation index (RDVI)) and seven optimal texture features (Contrast 1, Dissimilarity 1, Entory 2, Mean 1, Variance 1, Homogeneity 1, and Second moment 2) were identified. Three binary logistic regression (BLR) models, including a spectral model, a texture model, and a spectral-texture model, were constructed for cotton root rot classification and prescription map creation. The results were compared with classification maps and prescription maps based on airborne imagery. Accuracy assessment showed that the accuracies of the classification maps for the spectral, texture, and spectral-texture models were 92.95%, 84.81%, and 91.87%, respectively, and the accuracies of the prescription maps for the three respective models were 90.83%, 87.14%, and 91.40%. These results confirmed that it was feasible to identify cotton root rot and create prescription maps using different features of Sentinel-2 imagery. The addition of texture features had little effect on the overall accuracy, but it could improve the ability to identify root rot areas. The producer’s accuracy (PA) for infested cotton in the classification maps for the texture model and the spectral-texture model was 2.82% and 1.07% higher, respectively, than that of the spectral model, and the PA for treatment zones in the prescription maps for the two respective models was 8.6% and 8.22% higher than that of the spectral model. Results based on the eight cotton fields showed that the spectral model was appropriate for the cotton fields with relatively severe infestation and the spectral-texture model was more appropriate for the cotton fields with low or moderate infestation.

scholarly journals MODELLING ABOVE GROUND BIOMASS OF MANGROVE FOREST USING SENTINEL-1 IMAGERY

2018 ◽  
Vol IV-3 ◽  
pp. 13-20 ◽  
Author(s):  
Reginald Jay Labadisos Argamosa ◽  
Ariel Conferido Blanco ◽  
Alvin Balidoy Baloloy ◽  
Christian Gumbao Candido ◽  
John Bart Lovern Caboboy Dumalag ◽  
...  
Keyword(s):  
Random Forest ◽  
Principal Components ◽  
Mangrove Forest ◽  
Above Ground Biomass ◽  
Alos Palsar ◽  
Forest Mapping ◽  
Ground Biomass ◽  
Sar Data ◽  
Volumetric Objects ◽  
Occurrence Matrix

Many studies have been conducted in the estimation of forest above ground biomass (AGB) using features from synthetic aperture radar (SAR). Specifically, L-band ALOS/PALSAR (wavelength ~23&amp;thinsp;cm) data is often used. However, few studies have been made on the use of shorter wavelengths (e.g., C-band, 3.75&amp;thinsp;cm to 7.5&amp;thinsp;cm) for forest mapping especially in tropical forests since higher attenuation is observed for volumetric objects where energy propagated is absorbed. This study aims to model AGB estimates of mangrove forest using information derived from Sentinel-1 C-band SAR data. Combinations of polarisations (VV, VH), its derivatives, grey level co-occurrence matrix (GLCM), and its principal components were used as features for modelling AGB. Five models were tested with varying combinations of features; a) sigma nought polarisations and its derivatives; b) GLCM textures; c) the first five principal components; d) combination of models a&amp;minus;c; and e) the identified important features by Random Forest variable importance algorithm. Random Forest was used as regressor to compute for the AGB estimates to avoid over fitting caused by the introduction of too many features in the model. Model e obtained the highest r<sup>2</sup> of 0.79 and an RMSE of 0.44&amp;thinsp;Mg using only four features, namely, &amp;sigma;<sup>&amp;deg;</sup><sub><i>VH</i></sub> GLCM variance, &amp;sigma;<sup>&amp;deg;</sup><sub><i>VH</i></sub> GLCM contrast, PC1, and PC2. This study shows that Sentinel-1 C-band SAR data could be used to produce acceptable AGB estimates in mangrove forest to compensate for the unavailability of longer wavelength SAR.

scholarly journals Chemical Composition of the Above-ground Biomass of Amaranthus cruentus and A. hypochondriacus

2006 ◽  
Vol 75 (1) ◽  
pp. 133-138 ◽  
Author(s):  
B. Písaříková ◽  
J. Peterka ◽  
M. Trčková ◽  
J. Moudrý ◽  
Z. Zralý ◽  
...  
Keyword(s):  
Crude Protein ◽  
Nutritional Value ◽  
Experimental Period ◽  
Growth Stages ◽  
Above Ground Biomass ◽  
Crude Fibre ◽  
Amaranthus Cruentus ◽  
Ground Biomass ◽  
Extract Content ◽  
Gross Energy

Forty samples of dry above-ground biomass of two species and four varieties of Amaranthus cruentus (varieties Olpir, Amar 2 RR-R 150, and A 200 D) and A. hypochondriacus (variety No. 1008) were analyzed to determine their nutritional value during the experimental period covering five growth stages since inflorescence emergence till full ripening of grain from day 80 to day 120 of cultivation. The content of crude protein in the investigated amaranth varieties significantly decreased (from 158.2 ± 1.20 - 185.4 ± 2.33 to 103.8 ± 1.20 - 113.1 ± 0.01 g/kg) as well as did the crude ash content (from 169.9 ± 0.14 - 192.2 ± 0.42 to 129.7 ± 0.14 - 138.4 ± 0.21 g/kg). In contrast, the ether extract content significantly increased (from 12.2 ± 0.14 - 15.9 ± 0.28 to 28.0 ± 0.28 - 36.4 ± 0.14 g/kg) as well as crude fibre (from 144.9 ± 2.12 - 170.0 ± 3.68 to 183.6 ± 7.00 - 276.0 ± 1.20 g/kg), and gross-energy (from 16.6 ± 0.03 - 17.2 ± 0.07 to 18.1 ± 0.14 - 18.4 ± 0.01 MJ/kg) between days 80 and 120 of cultivation. The relatively high content of crude protein in the aboveground biomass in the period between days 80 and 90 of cultivation suggests that the plants could be used as a nutrient substitute for conventional forages.

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