RESDINET (Network for novel remote sensing technologies in forest disturbance ecology) 2023-2025

The project enhances networking activities between the Institute of Forest Ecology, Slovak Academy of Sciences (IFE SAS) and the top-class counterparts Finnish Geospatial Research Institute, The University of Eastern Finland and Swedish University of Agricultural Sciences. The project will enhance IFE SAS staff management capacities, administrative skills and scientific capabilities in the use of novel remote sensing technologies (RST) in forest disturbance ecology (FDE). Rigorous
analyses of severe insect-induced disturbances using novel RST will be carried out in test areas representing different forest and
climate types: mountain forests in Slovakia and boreal forests in Finland and Sweden. We will integrate in situ UAV and drone
acquired remotely sensed data, existing multitemporal geospatial information and field data, particularly data on bark beetle
population density, visible infestation symptoms linked to outbreak phases, and tree physiology parameters measured using
electronic dendrometers or sapflow meters. The combined dataset will be used to develop new tools for landscale-level early bark
beetle attack identification and for designing bark beetle infestation risk assessment model. We will draw on the latest advances in
drone technologies and image analytical tools, including deep Convolutional Neural Networks based machine learning techniques
and Artificial Intelligence algorithms. We expect to obtain important scientific results and contribute new knowledge to this scientific

Role: Co-applicant
Applicants: Rastislav Jakus, Miroslav Blazenec et al. (IFE SAS), Langning Huo, Henrik Persson, Eva Lindberg et al. (SLU), Eija Honkavaara et al. (FGI), Päivi Lyytikäinen-Saarenmaa et al. (UEF).

Mapping of biodiversity indicators in forests using remote sensing 2022

This project will derive maps of biodiversity indicators in forests from
remotely sensed data and data from the National Forest Inventory. The
long-term goal is to derive biodiversity indicators for the whole of
Sweden. This will contribute to the environmental quality objectives
Sustainable Forests and A Rich Diversity of Plant and Animal Life.

Role: Co-applicant
Applicants: Eva Lindberg, Henrik Persson, Langning Huo, Jörgen Wallerman, Mats Nilsson

Planning of a pilot project for remote sensing based forest assortment estimates – a simulation study 2022-2023

The project will investigate the impacting conditions for carrying out regular airborne laser scanning forest inventories to generate volume estimates for a forest company’s timber assortment, using remote sensing. Simulations are used to address cost parameters, accuracy and geographical distribution in relation to clustering of the forest.

Role: Main applicant
Applicants: Henrik Persson, Johan Holmgren, Patrik Ulvdal, Göran Ståhl

A prolonged observation series of bark beetle infestations from the field and remote sensing 2022-2023

The project will continue the spruce bark beetle research at the test site Remningstorp. The specific objectives include spreading patterns and early detection using drone and satellite images.

Role: Co-applicant
Applicants: Langning Huo, Eva Lindberg, Jonas Bohlin, Henrik Persson

Multi-phase inventory of forest using high-resolution laser scanning 2022-2023

This project investigates how the use of high-resolution laser scanning of forest acquired from airplanes and helicopters can be used to estimate tree properties for large areas. The methods are applied to the single-tree level and include the variables diameter breast height, stem volume, tree species and Lorey’s height.

Role: Main applicant
Applicants: Henrik Persson, Johan Holmgren, Kenneth Olofsson

Establishing a forest damage database for advancing the use of remote sensing in monitoring damages 2021-2022

This project investigates how a reference database of forest damages can be established to improve remote sensing based methods for monitoring forest damages. The project is in collaboration with the Swedish Forest Agency.

Role: Main applicant
Applicants: Henrik Persson, Eva Lindberg

Improved detection and prediction of spruce bark beetle infestations 2022-2023

The objective of this project is to develop methods for bark beetle attack detection and prediction, using drones and satellite images. The temporal, spectral, and spatial characteristics of the infestations will be detailed studied from the individual tree scale to the canopy scale (groups of trees). Refined methods for the detection and prediction using high-resolution images from different sensors will be proposed. Damage maps and forecasting maps will be generated for southern Sweden and results will be validated using harvester data.

Role: co-applicant
Applicants: Langning Huo, Eva Lindberg, Henrik Persson

Mapping of state and changes with time series of satellite radar data for future forestry planning 2021-2022

The project aims to investigate time series of X-, C- and L-band synthetic aperture radar (SAR) data, separately and in combination, from the satellites TanDEM-X, Sentinel-1 and ALOS/-2, respectively, for tree height, diameter, basal area, stem volume and above-ground biomass estimation, and their change over time with special focus on diameter distribution. The time series will be used to map forest changes and they will be the base for proposing appropriate silviculture treatments. The change studies will also include detection and mapping of clear-cuts, thinning and growth.

Role: co-applicant
Applicants: Johan Fransson, Lars Ulander, Henrik Persson, Ivan Huuva.

Satellites and drones for future remote sensing research 2021-2022

The project will operate at the test site Remningstorp and is divided in two parts. 1. An experiment with controlled bark beetle infestations and collect related reference data for remote sensing research. The aim is to delineate infested single trees. 2. Inventory of forest stands that were recently managed, in order to enable time series analyses of forest changes using ALOS-2 PALSAR-2. The hypothesis that the ratio of the polarization channels HV/VV can be used to correct disturbing weather affects during the acquisition. This has been verified for an airborne setting and this project will verify this also for space borne images.

Role: main applicant and project leader
Applicants: Henrik Persson, Johan Fransson, Langning Huo, Eva Lindberg.

Development of methods for satellite based research and monitoring of carbon and water cycle in boreal forest ecosystems 2021-2024

The project will use a multi-band and multi-polarization radar mounted on a 50 m high tower combined with extensive measurements of the surrounding forest, air, water and carbon flux. It is colocated with ICOS and SITES measurement at the test site Krycklan, located in northern Sweden..

Role: co-applicant
Applicants: Johan Fransson, Mats Nilsson, Hjalmar Laudon, Tomas Lundmark, Matthias Peichl, Henrik Persson, Lars Ulander.

Measuring forest vitality with radar remote sensing 2021-2023

In the past few years, Sweden’s forests have experienced unprecedented levels of degradation due to ecological stress, fires and insect damage. These effects are a consequence of unusually hot summers due to our changing climate. New techniques for monitoring forest health are necessary to proactively manage forests and to sustain the environmental and economic value of Sweden’s forests. Recent advances in radar remote sensing of forests have shown that information about the biological activity of forests can be observed using radars. With world leading radar and forest research infrastructures in Sweden, methods for monitoring forest vitality using radar data can be developed. In the proposed project, high-quality, co-located radar, ecological, atmospheric and hydrological data will be collected. The data will be used to gain a quantitative, systems-based understanding of how ecological processes, forest disturbances and radar observations are coupled at timescales of minutes to years. The work in
this project will lead to more efficient and cost-saving forest management practices based on radar remote sensing.

Role: project participant
Applicants: Lars Ulander, Johan Fransson, Mats Nilsson, Henrik Persson.

Forest inventory using airborne laser scanning from low elevation 2021-2022

The project will develop and evaluate a new method for forest inventory using very dense laser scanning data acquired from low elevation. Current forest inventories typically use a design-based field sample of their inventory, which represents their entire forest. The field inventory normally consists of manual measurements on random or systematically distributed circular plots. Most commonly, diameter, height and tree species are registered. For many of the Swedish companies, this implies manual visits of more than 16 000 field plots. This project will evaluate a strip sampling approach, where a helicopter carrying a laser scanner will fly across the stands at low elevation. The hypothesis is that this may provide more or similar information to a lower price, compared to manual field visits. The project will develop algorithms for detection of single trees, classify the tree species, and estimate forest height, diameter and stem volume.

Role: project leader

Improved understanding of the influence of reference data accuracy when used in remote sensing 2018-2020

The use and quality of remote sensing for forest monitoring improves all the time and currently, remote sensing may sometimes provide as accurate predictions as those obtained from field inventories. Yet, field data are still used as reference when new remote sensing methods are evaluated. This project will develop a framework for quantifying various error sources related to the field inventory, and correct for these when field data are used in remote sensing based predictions of forest. The dominant error sources will be identified and we will quantify their influence. Then, methods for correcting for these errors will be proposed, in order to objectively evaluate various remote sensing estimates, regardless of sensor or study area.

Role: project leader

New forest tree species maps from time series of satellite images

The aim of this project is to develop and evaluate a methodology to derive tree species maps from freely available Sentinel-1 (S1) and S2 satellite data using dense multispectral airborne laser scanning (ALS) as training data in a middle step and tree species information from field sample plots as the final training data. The power of this approach is the individual tree analysis of ALS data. Only a small field dataset is needed, but since tree species is identified for individual trees detected in ALS data, a large number of tree species combinations can be automatically estimated for satellite pixels within transects and then used for training of wall-to-wall satellite data

Role: co-applicant

Tree species classification from remote sensing – next generation forest maps for ecology and management

Sustainable forest management requires accurate geospatial information about forest state. Estimates of size related forest variables such as stem volume and tree height can be derived with high accuracy from Airborne
Laser Scanning (ALS) data and surface models from matching of aerial images. However, tree species has not yet been estimated with sufficient accuracy for user needs. Thus, there is a need for a system that can map tree species at nationwide scale and at regular time intervals.
New technologies offer the potential for this. The new Sentinel-2 satellites provide multi-spectral data with short intervals, enabling utilization of phenological differences between tree species. Very High Resolution (VHR) images from satellite and aerial images make analysis of tree crowns structure possible. Multispectral ALS data provide both geometric and spectral information, and single-photon-counting (SPC ) laser data enable dense 3D points clouds over large areas with the possibility to analyze the 3D structure of tree crowns. SAR-data are available for large areas and have the potential to contribute to tree species classification.
This project will develop methods to derive tree species information from remotely sensed (RS) data. The intention is that the methods should be feasible for nationwide use. The final methodology will combine RS data that are identified as most suitable for tree species maps. The design of the maps will be determined with the help of a user group.

Role: co-applicant

Detection of spruce bark beetle attacks using remote sensing

This project will evaluate various methods for using remote sensing sensors to detect spruce bark beetle attacks at the test site Remningstorp. The focus is development of methods based on satellite images, especially Sentinel-1 and Sentinel-2, to more efficiently monitor and react to related damages.

Role: co-applicant

New Space Digital Economy Innovation Center (KvarkenSpaceEco)

The “Kvarken Space Eco” project will implement a long-lasting regional economic development structure for space-based business and innovation, i.e. a Kvarken Space Center. The planned center’s primary objective is to support regional businesses to develop opportunities within the “new space economy” and commercialise existing space-based data. Factors have developed in the space industry that allow much less expensive processes to be implemented in space relative to the past. To aid the region in taking a stake in these new economic activities as well as available data and services, the center will assure that an understanding of the latest technologies is available, along with the capacity to implement them. The center will share knowledge and implement demonstration projects to bring the regional businesses to the level needed to independently manage their own space business activities. The center will work closely with regional education systems and development companies and will promote the necessary themes to build and sustain a workforce capable of advancing regional participation and value-creation in new space.

Role: co-applicant

Can spruce bark beetle attacks be detected in radar tower time series?

This project will investigate if spruce bark beetle attacks can be detected with radar. This project is possible due to time series of C- L- and P-band radar measurements acquired from a tower within the project BorealScat Within the time frame the project has been active (since 2016), the observed forest has also been infested by bark beetles, which has created an interesting setup to monitor changes in the radar signal during the entire process before, during and past attacks.

Role: co-applicant

Mapping above-ground biomass in natural forest and shrub lands in central Chile

The project goal is mapping and monitoring carbon stocks in terms of above-ground biomass in natural forest and shrub lands in central Chile. Partners: Pontificia Universidad Católica de Chile.

Role: collaborator


This project is enables continuous monitoring of boreal forest series using C- L- and P-band radar measurements acquired from a 50 m high tower. This project enables a separation of reasons causing changes in the observed radar signal, especially due to precipitation, snow, wind and temperature. The project is lead by Chalmers Technical University, with Prof. Lars Ulander and Albert Monteith. SLU is a collaborator responsible for ground surveys and forest inventories.

Role: collaborator

Some finished projects

(Since the publication procedure in the field of forest remote sensing may be months or years, some finished projects may still generate new publications, due to the write and review process continuing beyond the project end. The following list of projects is not complete.)

  • Technical Assistance in the Implementation of a C-band Convoy Mission Demonstration Campaign. Financed by ESA. Partners: NIBIO (Norway), SLU, Chalmers (Sweden).
  • GlobBiomass, Mapping global biomass and biomass change. 2015-2017.
    The main purpose is to better characterize and to reduce uncertainties of AGB estimates by developing an innovative synergistic mapping approach in five regional sites for the epochs 2005, 2010 and 2015 and for one global map for the year 2010.
  • Forest biomass and biomass change with spaceborne SAR. 2015-2018. Partners: SLU, Chalmers, Gamma RS.
  • Retrieval of forest biomass and biomass change with spaceborne SAR. 2013-2016. Partners: SLU, Chalmers, Gamma RS.
  • AdvancedSAR – Advanced Techniques for Forest Biomass and Biomass Change Mapping Using Novel Combination of Active Remote Sensing Sensors. 2013-2017.