Results 1 to 10 of about 561,134 (206)

Improving Heterogeneous Forest Height Maps by Integrating GEDI-Based Forest Height Information in a Multi-Sensor Mapping Process

Remote Sensing, 2022
Forests are one of the key elements in ecological transition policies in Europe. Sustainable forest management is needed in order to optimise wood harvesting, while preserving carbon storage, biodiversity and other ecological functions.
David Morin, Milena Planells, Nicolas Baghdadi, Alexandre Bouvet, Ibrahim Fayad, Thuy Le Toan, Stéphane Mermoz, Ludovic Villard   +7 more
doaj   +5 more sources

A Maximum Likelihood Based Nonparametric Iterative Adaptive Method of Synthetic Aperture Radar Tomography and Its Application for Estimating Underlying Topography and Forest Height [PDF]

Sensors, 2018
Synthetic aperture radar tomography (TomoSAR) is an important way of obtaining underlying topography and forest height for long-wavelength datasets such as L-band and P-band radar.
Xing Peng, Xinwu Li, Changcheng Wang, Haiqiang Fu, Yanan Du   +4 more
doaj   +2 more sources

Constructing a Finer-Resolution Forest Height in China Using ICESat/GLAS, Landsat and ALOS PALSAR Data and Height Patterns of Natural Forests and Plantations

Remote Sensing, 2019
Monitoring forest height is crucial to determine the structure and biodiversity of forest ecosystems. However, detailed spatial patterns of forest height from 30 m resolution remotely sensed data are currently unavailable. In this study, we present a new
Huabing Huang, Caixia Liu, Xiaoyi Wang
doaj   +3 more sources

Forest Height Retrieval Based on the Dual PolInSAR Images

Remote Sensing, 2022
A new algorithm for forest height estimation based on dual polarimetric interferometric SAR data is presented in this study. The main objective is to consider the efficiency of the dual-polarization data compared to the full polarimetric images with ...
Tayebe Managhebi, Yasser Maghsoudi, Meisam Amani   +2 more
doaj   +2 more sources

Monitoring changes of forest height in California

Frontiers in Remote Sensing
Forests of California are undergoing large-scale disturbances from wildfire and tree mortality, caused by frequent droughts, insect infestations, and human activities.
Samuel Favrichon, Jake Lee, Yan Yang, Yan Yang, Ricardo Dalagnol, Ricardo Dalagnol, Fabien Wagner, Le Bienfaiteur Sagang, Le Bienfaiteur Sagang, Sassan Saatchi, Sassan Saatchi, Sassan Saatchi   +11 more
doaj   +2 more sources

Spaceborne Multifrequency PolInSAR-Based Inversion Modelling for Forest Height Retrieval

Remote Sensing, 2020
Spaceborne and airborne polarimetric synthetic-aperture radar interferometry (PolInSAR) data have been extensively used for forest parameter retrieval.
Shashi Kumar, Himanshu Govil, Prashant K. Srivastava, Praveen K. Thakur, Satya P. S. Kushwaha   +4 more
doaj   +3 more sources

Using GEDI Waveforms for Improved TanDEM-X Forest Height Mapping: A Combined SINC + Legendre Approach

Remote Sensing, 2021
In this paper, we consider a new method for forest canopy height estimation using TanDEM-X single-pass radar interferometry. We exploit available information from sample-based, space-borne LiDAR systems, such as the Global Ecosystem Dynamics ...
Hao Chen, Shane R. Cloude, Joanne C. White   +2 more
doaj   +1 more source

Forest Biomass Mapping Using Continuous InSAR and Discrete Waveform Lidar Measurements: A TanDEM-X/GEDI Test Study

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
This article addresses the implementation of an above ground biomass (AGB) estimation scheme relying on the height-to-biomass allometry at stand level in the context of the synergistic use of continuous TanDEM-X (bistatic) interferometric synthetic ...
Changhyun Choi, Matteo Pardini, John Armston, Konstantinos P. Papathanassiou   +3 more
doaj   +1 more source

Deep Learning Model Transfer in Forest Mapping Using Multi-Source Satellite SAR and Optical Images

Remote Sensing, 2023
Deep learning (DL) models are gaining popularity in forest variable prediction using Earth observation (EO) images. However, in practical forest inventories, reference datasets are often represented by plot- or stand-level measurements, while high ...
Shaojia Ge, Oleg Antropov, Tuomas Häme, Ronald E. McRoberts, Jukka Miettinen   +4 more
doaj   +1 more source

A Deep Learning Framework for the Estimation of Forest Height From Bistatic TanDEM-X Data

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2023
Up-to-date canopy height model (CHM) estimates are of key importance for forest resource monitoring and disturbance analysis. In this article, we present a study on the potential of deep learning (DL) for the regression of forest height from TanDEM-X ...
Daniel Carcereri, Paola Rizzoli, Dino Ienco, Lorenzo Bruzzone   +3 more
doaj   +1 more source