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Journal ArticleDOI

Synergistic evaluation of Sentinel 1 and 2 for biomass estimation in a tropical forest of India

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).
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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.

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Citations
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Assessing the niche of Rhododendron arboreum using entropy and machine learning algorithms: role of atmospheric, ecological, and hydrological variables

TL;DR: In this article , four machine learning and regression-based algorithms, namely, generalized linear model, maximum entropy, boosted regression tree, and random forest (RF) are used to model the geographical distribution of Rhododendron arboreum, which is economically and medicinally important species found in the fragile ecosystem of Himalayas.

AVIRIS-NG hyperspectral data for biomass modeling: from ground plot selection to forest species recognition

TL;DR: In this paper , an airborne hyperspectral data of airborne visible infrared imaging spectrometer-next generation data was demonstrated to estimate above ground biomass (AGB) of a tropical dry deciduous forest.
Journal ArticleDOI

Spectral Mixture Analysis Of Aviris-Ng Data For Grouping Plant Functional Types

TL;DR: In this article , the spectral mixture analysis was applied to identify and map plant functional types (PFTs) in the AVIRIS-NG campaign site, namely Shoolpaneshwar Wildlife Sanctuary (site id 67), using AVIRis-NG data combined with spectral mixture analyses that accounts for endmember variability.
References
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Book

Pattern Recognition and Machine Learning

TL;DR: Probability Distributions, linear models for Regression, Linear Models for Classification, Neural Networks, Graphical Models, Mixture Models and EM, Sampling Methods, Continuous Latent Variables, Sequential Data are studied.
Book

C4.5: Programs for Machine Learning

TL;DR: A complete guide to the C4.5 system as implemented in C for the UNIX environment, which starts from simple core learning methods and shows how they can be elaborated and extended to deal with typical problems such as missing data and over hitting.
Book

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

TL;DR: In this paper, the authors describe the important ideas in these areas in a common conceptual framework, and the emphasis is on concepts rather than mathematics, with a liberal use of color graphics.
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