Aboveground biomass estimation in intervened and non-intervened Nothofagus pumilio forests using remotely sensed data-Reference-Cited by-同舟云学术

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Aboveground biomass estimation in intervened and non-intervened Nothofagus pumilio forests using remotely sensed data

Published:2011-12-08 Issue:12 Volume:33 Page:3816-3833
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Poulain Marcela¹,Peña Marco²,Schmidt Andreas¹,Schmidt Harald³,Schulte Andreas¹

Affiliation:

1. Wald-Zentrum, Institute of Landscape Ecology, Westfälische Wilhelms- Universität Münster, 48149 Münster, Germany

2. Centro de Estudio de Recursos Naturales Oterra, Universidad Mayor, Santiago, Chile

3. Faculty of Forest Science, Universidad de Chile, Santiago, Chile

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2011.635716

Reference53 articles.

1. Aronoff, S., 2005, Remote Sensing for GIS Managers, p. 487 (New York: ESRI Press).

2. Per-Pixel Analysis of Forest Structure

3. Measuring the Color of Growing Turf with a Reflectance Spectrophotometer 1

4. A Spectral Unmixing Approach to Leaf Area Index (LAI) Estimation at the Alpine Treeline Ecotone

5. Measuring carbon in forests: current status and future challenges

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1. Estimating tree aboveground biomass using multispectral satellite-based data in Mediterranean agroforestry system using random forest algorithm;Remote Sensing Applications: Society and Environment;2021-08

2. Application of Convolutional Neural Network on Lei Bamboo Above-Ground-Biomass (AGB) Estimation Using Worldview-2;Remote Sensing;2020-03-16

3. Estimation of aboveground biomass in Mediterranean forests by statistical modelling of ASTER fraction images;International Journal of Applied Earth Observation and Geoinformation;2014-09

4. Intra-and-Inter Species Biomass Prediction in a Plantation Forest: Testing the Utility of High Spatial Resolution Spaceborne Multispectral RapidEye Sensor and Advanced Machine Learning Algorithms;Sensors;2014-08-20

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