Journal ArticleDOI
Synergistic evaluation of Sentinel 1 and 2 for biomass estimation in a tropical forest of India
Ramandeep Kaur M. Malhi,Sultanova Umida Rustamovna,Akash Anand,Prashant K. Srivastava,Sumit Kumar Chaudhary,Manish Kumar Pandey,Mukund Dev Behera,Amit Kumar,Prachi Singh,G. Sandhya Kiran +9 more
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TLDR
In this paper, two nonparametric machine learning algorithms viz Support Vector Machines (SVMs) with different kernel functions were employed for the prediction of above ground biomass using different combinations of VV, VH, Normalized Difference Vegetation Index (NDVI) and Incidence Angle (IA).About:
This article is published in Advances in Space Research.The article was published on 2021-04-08. It has received 18 citations till now. The article focuses on the topics: Normalized Difference Vegetation Index & Random forest.read more
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Effect of vegetation structure on above ground biomass in tropical deciduous forests of Central India
Preet Lal,Amit Kumar,Purabi Saikia,Anup Das,C. Patnaik,Gajendra Kumar,Arvind Chandra Pandey,Parul Srivastava,Chandra Shekhar Dwivedi,Mohammed Latif Khan +9 more
TL;DR: In this article, the above ground biomass (AGB) of tropical deciduous forests in Central India using field-based techniques and spaceborne quad-pol ALOS PALSAR-2 L-band and dual-pol Sen...
Journal ArticleDOI
Monitoring landscape fragmentation and aboveground biomass estimation in Can Gio Mangrove Biosphere Reserve over the past 20 years
TL;DR: In this article , the temporal and spatial changes of landscape pattern of land use/land cover (LULC) over the past 20 years in Can Gio Mangrove Biosphere Reserve (MBR), southern Vietnam were analyzed based on remote sensing data.
Journal ArticleDOI
Remote sensing-based biomass estimation of dry deciduous tropical forest using machine learning and ensemble analysis.
TL;DR: In this article , the authors proposed a framework to monitor above-ground biomass (AGB) at finer scales using open-source satellite data, which integrated four machine learning (ML) techniques with field surveys and satellite data to provide continuous spatial estimates of AGB at finer resolution.
Journal ArticleDOI
Optimal band characterization in reformation of hyperspectral indices for species diversity estimation
Akash Anand,Sultanova Umida Rustamovna,Ramandeep Kaur M. Malhi,Prashant K. Srivastava,Prachi Singh,Ashwini N. Mudaliar,George P. Petropoulos,G. Sandhya Kiran +7 more
TL;DR: In this article, the authors provided modified hyperspectral indices through detection of optimum bands for estimating species diversity within Shoolpaneshwar Wildlife Sanctuary (SWS) in India.
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Estimating Individual Tree Above-Ground Biomass of Chinese Fir Plantation: Exploring the Combination of Multi-Dimensional Features from UAV Oblique Photos
TL;DR: Wang et al. as discussed by the authors proposed an approach to estimate IT-AGB by introducing the color space intensity information into a regression-based model that incorporates three-dimensional point cloud and two-dimensional spectrum feature variables, and the accuracy was evaluated using a leave-one-out cross-validation approach.
References
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Journal ArticleDOI
Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa
Timothy Dube,Onisimo Mutanga +1 more
TL;DR: In this paper, the utility of the newly-launched medium-resolution multispectral Landsat 8 Operational Land Imager (OLI) dataset with a large swath width, in quantifying aboveground biomass (AGB) in a forest plantation was assessed.
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Improving forest aboveground biomass estimation using seasonal Landsat NDVI time-series
Xiaolin Zhu,Desheng Liu +1 more
TL;DR: In this paper, the authors explored the use of NDVI seasonal time-series derived from Landsat images across different seasons to estimate aboveground biomass (AGB) in southeast Ohio by six empirical modeling approaches.
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Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data
Gaia Vaglio Laurin,Gaia Vaglio Laurin,Qi Chen,Jeremy A. Lindsell,David A. Coomes,Fabio Del Frate,Leila Guerriero,Francesco Pirotti,Riccardo Valentini +8 more
TL;DR: In this paper, a Partial Least Square Regression (PLSR) was adopted to cope with multiple inputs and multicollinearity issues; the Variable of Importance in the Projection was calculated to evaluate importance of individual predictors for biomass.
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Aboveground Forest Biomass Estimation with Landsat and LiDAR Data and Uncertainty Analysis of the Estimates
Dengsheng Lu,Qi Chen,Guangxing Wang,Emilio F. Moran,Mateus Batistella,Maozhen Zhang,Gaia Vaglio Laurin,David Saah +7 more
TL;DR: Li et al. as mentioned in this paper provided a brief overview of current forest biomass estimation methods using both Landsat Thematic mapper (TM) image and LiDAR data, and demonstrated that Landsat TM data can provide adequate biomass estimates for secondary succession but are not suitable for mature forest biomass estimates due to data saturation problems.
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Non-parametric prediction and mapping of standing timber volume and biomass in a temperate forest: application of multiple optical/LiDAR-derived predictors
TL;DR: In this article, a mixed temperate forest landscape in southwestern Germany, multiple remote sensing variables from aerial orthoimages, Thematic Mapper data and small footprint light detection and ranging (LiDAR) were used for plot-level nonparametric predictions of the total volume and biomass using three distance measures of Euclidean, Mahalanobis and Most Similar Neighbors as well as a regression tree-based classifier (Random Forest).