Erik Næsset

Orcid: 0000-0002-2460-5843

Affiliations:
  • Norwegian University of Life Sciences, Ås, Norway


According to our database1, Erik Næsset authored at least 54 papers between 1996 and 2024.

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Bibliography

2024
Effect of Albedo Footprint Size on Relationships between Measured Albedo and Forest Attributes for Small Forest Plots.
Remote. Sens., August, 2024

Biomass Change Estimated by TanDEM-X Interferometry and GEDI in a Tanzanian Forest.
Remote. Sens., March, 2024

2023
Mapping and Estimating Aboveground Biomass in an Alpine Treeline Ecotone under Model-Based Inference.
Remote. Sens., July, 2023

Comparison of Different Remotely Sensed Data Sources for Detection of Presence of Standing Dead Trees Using a Tree-Based Approach.
Remote. Sens., May, 2023

Monitoring tree occupancy and height in the Norwegian alpine treeline using a time series of airborne laser scanner data.
Int. J. Appl. Earth Obs. Geoinformation, March, 2023

Forest Parameter Prediction by Multiobjective Deep Learning of Regression Models Trained with Pseudo-Target Imputation.
CoRR, 2023

2022
On the Potential of Sequential and Nonsequential Regression Models for Sentinel-1-Based Biomass Prediction in Tanzanian Miombo Forests.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2022

Fine-Spatial Boreal-Alpine Single-Tree Albedo Measured by UAV: Experiences and Challenges.
Remote. Sens., 2022

Wood Decay Detection in Norway Spruce Forests Based on Airborne Hyperspectral and ALS Data.
Remote. Sens., 2022

UAV-Based Hyperspectral Imagery for Detection of Root, Butt, and Stem Rot in Norway Spruce.
Remote. Sens., 2022

Detection of heartwood rot in Norway spruce trees with lidar and multi-temporal satellite data.
Int. J. Appl. Earth Obs. Geoinformation, 2022

2021
Comparing 3D Point Cloud Data from Laser Scanning and Digital Aerial Photogrammetry for Height Estimation of Small Trees and Other Vegetation in a Boreal-Alpine Ecotone.
Remote. Sens., 2021

Constructing Forest Biomass Prediction Maps from Radar Backscatter by Sequential Regression with a Conditional Generative Adversarial Network.
CoRR, 2021

2020
Use of Remotely Sensed Data to Enhance Estimation of Aboveground Biomass for the Dry Afromontane Forest in South-Central Ethiopia.
Remote. Sens., 2020

Remote Sensing Support for the Gain-Loss Approach for Greenhouse Gas Inventories.
Remote. Sens., 2020

Use of local and global maps of forest canopy height and aboveground biomass to enhance local estimates of biomass in miombo woodlands in Tanzania.
Int. J. Appl. Earth Obs. Geoinformation, 2020

Generation of Lidar-Predicted Forest Biomass Maps from Radar Backscatter with Conditional Generative Adversarial Networks.
Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, 2020

2019
Classifications of Forest Change by Using Bitemporal Airborne Laser Scanner Data.
Remote. Sens., 2019

A Model-Dependent Method for Monitoring Subtle Changes in Vegetation Height in the Boreal-Alpine Ecotone Using Bi-Temporal, Three Dimensional Point Data from Airborne Laser Scanning.
Remote. Sens., 2019

Optimizing Field Data Collection for Individual Tree Attribute Predictions Using Active Learning Methods.
Remote. Sens., 2019

Estimating Forest Volume and Biomass and Their Changes Using Random Forests and Remotely Sensed Data.
Remote. Sens., 2019

Effects of UAV Image Resolution, Camera Type, and Image Overlap on Accuracy of Biomass Predictions in a Tropical Woodland.
Remote. Sens., 2019

Modelling Site Index in Forest Stands Using Airborne Hyperspectral Imagery and Bi-Temporal Laser Scanner Data.
Remote. Sens., 2019

Local validation of global biomass maps.
Int. J. Appl. Earth Obs. Geoinformation, 2019

2018
Generalized Hierarchical Model-Based Estimation for Aboveground Biomass Assessment Using GEDI and Landsat Data.
Remote. Sens., 2018

Predicting Selected Forest Stand Characteristics with Multispectral ALS Data.
Remote. Sens., 2018

Prediction of Forest Attributes with Multispectral Lidar Data.
Proceedings of the 2018 IEEE International Geoscience and Remote Sensing Symposium, 2018

2017
Modelling above Ground Biomass in Tanzanian Miombo Woodlands Using TanDEM-X WorldDEM and Field Data.
Remote. Sens., 2017

Automatic Estimation of Tree Position and Stem Diameter Using a Moving Terrestrial Laser Scanner.
Remote. Sens., 2017

Prediction of Species-Specific Volume Using Different Inventory Approaches by Fusing Airborne Laser Scanning and Hyperspectral Data.
Remote. Sens., 2017

Influence of Plot Size on Efficiency of Biomass Estimates in Inventories of Dry Tropical Forests Assisted by Photogrammetric Data from an Unmanned Aircraft System.
Remote. Sens., 2017

Modeling biophysical properties of broad-leaved stands in the hyrcanian forests of Iran using fused airborne laser scanner data and ultraCam-D images.
Int. J. Appl. Earth Obs. Geoinformation, 2017

Individual tree crown approach for predicting site index in boreal forests using airborne laser scanning and hyperspectral data.
Int. J. Appl. Earth Obs. Geoinformation, 2017

2016
Discrimination between Ground Vegetation and Small Pioneer Trees in the Boreal-Alpine Ecotone Using Intensity Metrics Derived from Airborne Laser Scanner Data.
Remote. Sens., 2016

Detection and Segmentation of Small Trees in the Forest-Tundra Ecotone Using Airborne Laser Scanning.
Remote. Sens., 2016

2015
Inventory of Small Forest Areas Using an Unmanned Aerial System.
Remote. Sens., 2015

Vertical Height Errors in Digital Terrain Models Derived from Airborne Laser Scanner Data in a Boreal-Alpine Ecotone in Norway.
Remote. Sens., 2015

Relative Efficiency of ALS and InSAR for Biomass Estimation in a Tanzanian Rainforest.
Remote. Sens., 2015

Effects of Pulse Density on Digital Terrain Models and Canopy Metrics Using Airborne Laser Scanning in a Tropical Rainforest.
Remote. Sens., 2015

Modeling Aboveground Biomass in Dense Tropical Submontane Rainforest Using Airborne Laser Scanner Data.
Remote. Sens., 2015

2014
Cost-Sensitive Active Learning With Lookahead: Optimizing Field Surveys for Remote Sensing Data Classification.
IEEE Trans. Geosci. Remote. Sens., 2014

Unsupervised Selection of Training Samples for Tree Species Classification Using Hyperspectral Data.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2014

Improving Classification of Airborne Laser Scanning Echoes in the Forest-Tundra Ecotone Using Geostatistical and Statistical Measures.
Remote. Sens., 2014

Automatic Detection of Small Single Trees in the Forest-Tundra Ecotone Using Airborne Laser Scanning.
Remote. Sens., 2014

2013
Tree Species Classification in Boreal Forests With Hyperspectral Data.
IEEE Trans. Geosci. Remote. Sens., 2013

Accuracy and Precision for Remote Sensing Applications of Nonlinear Model-Based Inference.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2013

Detection of biomass change in a Norwegian mountain forest area using small footprint airborne laser scanner data.
Stat. Methods Appl., 2013

Optimizing the ground sample collection with cost-sensitive active learning for tree species classification using hyperspectral images.
Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium, 2013

Unsupervised selection of training plots and trees for tree species classification.
Proceedings of the 2013 IEEE International Geoscience and Remote Sensing Symposium, 2013

2012
An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning.
Remote. Sens., 2012

2011
Stability of Sample-Based Scanning-LiDAR-Derived Vegetation Metrics for Forest Monitoring.
IEEE Trans. Geosci. Remote. Sens., 2011

2010
Simulating X-Band Interferometric Height in a Spruce Forest From Airborne Laser Scanning.
IEEE Trans. Geosci. Remote. Sens., 2010

Using airborne & space lidars for large-area inventory.
Proceedings of the IEEE International Geoscience & Remote Sensing Symposium, 2010

1996
Use of the Weighted Kappa Coefficient in Classification Error Assessment of Thematic Maps.
Int. J. Geogr. Inf. Sci., 1996


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