scholarly journals Canopy cover or remotely sensed vegetation index, explanatory variables of above-ground biomass in an arid rangeland, Iran

2018 ◽  
Vol 10 (5) ◽  
pp. 767-780 ◽  
Author(s):  
Fatemeh Pordel ◽  
Ataollah Ebrahimi ◽  
Zahra Azizi
Keyword(s):  
Vegetation Index ◽  
Canopy Cover ◽  
Remotely Sensed ◽  
Above Ground Biomass ◽  
Explanatory Variables ◽  
Ground Biomass ◽  
Arid Rangeland

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.

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 Are Inventory Based and Remotely Sensed Above-Ground Biomass Estimates Consistent?

PLoS ONE ◽  
2013 ◽  
Vol 8 (9) ◽  
pp. e74170 ◽  
Author(s):  
Timothy C. Hill ◽  
Mathew Williams ◽  
A. Anthony Bloom ◽  
Edward T. A. Mitchard ◽  
Casey M. Ryan
Keyword(s):  
Remotely Sensed ◽  
Above Ground Biomass ◽  
Ground Biomass

scholarly journals Structural, physiognomic and above-ground biomass variation in savanna–forest transition zones on three continents – how different are co-occurring savanna and forest formations?

2015 ◽  
Vol 12 (10) ◽  
pp. 2927-2951 ◽  
Author(s):  
E. M. Veenendaal ◽  
M. Torello-Raventos ◽  
T. R. Feldpausch ◽  
T. F. Domingues ◽  
F. Gerard ◽  
...  
Keyword(s):  
South America ◽  
Woody Plant ◽  
Canopy Cover ◽  
Forest Transition ◽  
Vegetation Types ◽  
Above Ground Biomass ◽  
Forest Species ◽  
Stable States ◽  
Transition Zones ◽  
Ground Biomass

Abstract. Through interpretations of remote-sensing data and/or theoretical propositions, the idea that forest and savanna represent "alternative stable states" is gaining increasing acceptance. Filling an observational gap, we present detailed stratified floristic and structural analyses for forest and savanna stands located mostly within zones of transition (where both vegetation types occur in close proximity) in Africa, South America and Australia. Woody plant leaf area index variation was related to tree canopy cover in a similar way for both savanna and forest with substantial overlap between the two vegetation types. As total woody plant canopy cover increased, so did the relative contribution of middle and lower strata of woody vegetation. Herbaceous layer cover declined as woody cover increased. This pattern of understorey grasses and herbs progressively replaced by shrubs as the canopy closes over was found for both savanna and forests and on all continents. Thus, once subordinate woody canopy layers are taken into account, a less marked transition in woody plant cover across the savanna–forest-species discontinuum is observed compared to that inferred when trees of a basal diameter > 0.1 m are considered in isolation. This is especially the case for shrub-dominated savannas and in taller savannas approaching canopy closure. An increased contribution of forest species to the total subordinate cover is also observed as savanna stand canopy closure occurs. Despite similarities in canopy-cover characteristics, woody vegetation in Africa and Australia attained greater heights and stored a greater amount of above-ground biomass than in South America. Up to three times as much above-ground biomass is stored in forests compared to savannas under equivalent climatic conditions. Savanna–forest transition zones were also found to typically occur at higher precipitation regimes for South America than for Africa. Nevertheless, consistent across all three continents coexistence was found to be confined to a well-defined edaphic–climate envelope with soil and climate the key determinants of the relative location of forest and savanna stands. Moreover, when considered in conjunction with the appropriate water availability metrics, it emerges that soil exchangeable cations exert considerable control on woody canopy-cover extent as measured in our pan-continental (forest + savanna) data set. Taken together these observations do not lend support to the notion of alternate stable states mediated through fire feedbacks as the prime force shaping the distribution of the two dominant vegetation types of the tropical lands.

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.

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