| 1. |
- Aksoy, Samet, et al.
(författare)
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Forest Biophysical Parameter Estimation via Machine Learning and Neural Network Approaches
- 2023
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Ingår i: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. - : IEEE. - 9798350320107 - 9798350320091 - 9798350331745 ; , s. 2661-2664
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Konferensbidrag (refereegranskat)abstract
- This paper presents the first results of the ongoing development of new forest mapping methods for the Swedish national forest mapping case using Airborne Laser Scanning (ALS) data, utilizing the recent findings in machine learning (ML) and Artificial Intelligence (AI) techniques. We used Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) as ML models. In addition, Neural networks (NN) based approaches were utilized in this study. ALS derived features were used to estimate the stem volume (V), above-ground biomass (AGB), basal area (B), tree height (H), stem diameter (D), and forest stand age (A). XGBoost ML algorithm outperformed RF 1 % to 3 % in the R² metric. NN model performed similar to ML model, however it is superior in the estimation of V, AGB, and B parameters.
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| 2. |
- Doeweler, Fabian, et al.
(författare)
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Linking High-Resolution UAV-Based Remote Sensing Data to Long-Term Vegetation Sampling : A Novel Workflow to Study Slow Ecotone Dynamics
- 2024
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Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 16:5
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Tidskriftsartikel (refereegranskat)abstract
- Unravelling slow ecosystem migration patterns requires a fundamental understanding of the broad-scale climatic drivers, which are further modulated by fine-scale heterogeneities just outside established ecosystem boundaries. While modern Unoccupied Aerial Vehicle (UAV) remote sensing approaches enable us to monitor local scale ecotone dynamics in unprecedented detail, they are often underutilised as a temporal snapshot of the conditions on site. In this study in the Southern Alps of New Zealand, we demonstrate how the combination of multispectral and thermal data, as well as LiDAR data (2019), supplemented by three decades (1991-2021) of treeline transect data can add great value to field monitoring campaigns by putting seedling regeneration patterns at treeline into a spatially explicit context. Orthorectification and mosaicking of RGB and multispectral imagery produced spatially extensive maps of the subalpine area (similar to 4 ha) with low spatial offset (Craigieburn: 6.14 +/- 4.03 cm; Mt Faust: 5.11 +/- 2.88 cm, mean +/- standard error). The seven multispectral bands enabled a highly detailed delineation of six ground cover classes at treeline. Subalpine shrubs were detected with high accuracy (up to 90%), and a clear identification of the closed forest canopy (Fuscospora cliffortioides, >95%) was achieved. Two thermal imaging flights revealed the effect of existing vegetation classes on ground-level thermal conditions. UAV LiDAR data acquisition at the Craigieburn site allowed us to model vegetation height profiles for similar to 6000 previously classified objects and calculate annual fine-scale variation in the local solar radiation budget (20 cm resolution). At the heart of the proposed framework, an easy-to-use extrapolation procedure was used for the vegetation monitoring datasets with minimal georeferencing effort. The proposed method can satisfy the rapidly increasing demand for high spatiotemporal resolution mapping and shed further light on current treeline recruitment bottlenecks. This low-budget framework can readily be expanded to other ecotones, allowing us to gain further insights into slow ecotone dynamics in a drastically changing climate.
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| 3. |
- Fjeld, Dag, et al.
(författare)
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Modelling forest road trafficability with satellite-based soil moisture variables
- 2024
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Ingår i: International Journal of Forest Engineering. - : Taylor & Francis Group. - 1494-2119 .- 1913-2220. ; 35:1, s. 93-104
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Tidskriftsartikel (refereegranskat)abstract
- Recent decades have seen increased temperatures and precipitation in the Nordic countries with long-term projections for reduced frost duration and depth. The consequence of these trends has been a gradual shift of delivery volumes to the frost-free season, requiring more agile management to exploit suitable weather conditions. Bearing capacity and trafficability are dependent on soil moisture state and in this context two satellite missions offer potenially useful information on soil moisture levels; NASA's SMAP (Soil Moisture Active Passive) and ESA's Sentinel-1. The goal of this pilot study was to quantify the performance of such satellite-based soil moisture variables for modeling forest road bearing capacity (e-module) during the frost-free season. The study was based on post-transport registrations of 103 forest road segments on the coastal and interior side of the Scandinavian mountain range. The analysis focused on roads of three types of surface deposits. Weekly SMAP soil moisture values better explained the variation in road e-module than soil water index (SWI) derived from Sentinel-1. Soil Water Index (SWI), however, reflected the weather conditions typical for operations on the respective surface deposit types. Regression analysis using (i) SMAP-based soil dryness index and (ii) its interaction with surface deposit types, together with (iii) the ratio between a combined SMAP_SWI dryness index and segment-specific depth to water (DTW) explained over 70% of the variation in road e-module. The results indicate a future potential to monitor road trafficability over large supply areas on a weekly level, given further refinement of study methods and variables for improved prediction.
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| 4. |
- Fransson, Johan, Professor, 1967-
(författare)
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ForestMap : The next generation of forest maps – adapting a Nordic success story across the globe
- 2022
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Annan publikation (populärvet., debatt m.m.)abstract
- Forests provide countless values to people and society, both in developing as in developed countries. For the developed countries, the main value has been from wood and products from wood. In many developing countries, the forest is still seen as a common right of access, supplying firewood, food and building material. Today, new societal values are provided by the forests, important to human well-being. Presumably, the most important value of the forests is that they have been identified to have a major role in global climate change, where defore station, afforestation and new strategies to actively increase carbon sequestration, are very important processes. Moreover, the forests are critical habitats for biodiversity and there is increasing evidence that biodiversity contributes to forest ecosystem functioning and the provision of ecosystem services. However, there is very little global discussion on how improved management of productive forests could contribute to mitigation of climate change and enrichment of biodiversity. If forest owners could utilize efficient decision tools for improved management and precision forest management, they would benefit with higher yield and net turnover, which in turn motivates them to further improve the management. Higher net turnover is also a motivating factor to reinvest in additional management and afforestation, hence, creating a positive loop that mitigate climate change by increased carbon sequestration and is beneficial to biodiversity. Extrapolated to the global forest estate of 3.9 billion hectares, these data suggest that about 77% of the world’s forest is owned and administered by governments, about 4% is reserved for communities, about 7% is owned by local communities, and about 12% is owned by individuals. In the above context, a fundamental need from forest stakeholders is data about the forest state and change in terms of biomass, tree species composition, and forest cover. However, depending on the stakeholder, the need of data, required accuracy, willingness to pay and need of decision support is very much variating. When also considering that many stakeholders are illiterate and may not have adequate competence to interpret data into management decisions, there is a clear need for a solution that also can strengthen equality (including gender equality)among stakeholders. The ForestMap project will provide new means for forest mapping globally and provide new open data crucial for sustainable forest management and mitigation of climate change. The overall objective is to advance the societal values of forest use by developing and evaluating a new methodology to produce forest maps across the globe. The project will develop easily applicable methods for forest map production using crowd sourced data from smartphones and remote sensing data from space- and airborne systems. Artificial Intelligence (AI) algorithms will be developed in order to produce tailor-made forest maps to stakeholder’s needs, corresponding to their willingness to pay. The societal value of the forest maps used in existing and new business models will also be explored.
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| 5. |
- Fransson, Johan, Professor, 1967-, et al.
(författare)
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ForestMap : Mapping Forest Attributes Across the Globe - First Case Study
- 2023
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Ingår i: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. - : IEEE. - 9798350320107 - 9798350331745 - 9798350320091 ; , s. 3395-3397
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Konferensbidrag (refereegranskat)abstract
- This paper presents the project ForestMap – a project aiming to develop and distribute new methods, which provide the benefits of accurate forest maps to a global audience. Using the recent developments in remote sensing, machine learning, and Artificial Intelligence (AI) the goal is to export the Scandinavian success stories to a wide range of stakeholders in the world.
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| 6. |
- Gundermann, Niels, et al.
(författare)
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Object Identification in Land Parcels Using a Machine Learning Approach
- 2024
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Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 16:7
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Tidskriftsartikel (refereegranskat)abstract
- This paper introduces an AI-based approach to detect human-made objects and changes in these on land parcels. To this end, we used binary image classification performed by a convolutional neural network. Binary classification requires the selection of a decision boundary, and we provided a deterministic method for this selection. Furthermore, we varied different parameters to improve the performance of our approach, leading to a true positive rate of 91.3% and a true negative rate of 63.0%. A specific application of our work supports the administration of agricultural land parcels eligible for subsidiaries. As a result of our findings, authorities could reduce the effort involved in the detection of human made changes by approximately 50%.
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| 7. |
- Huo, Langning, et al.
(författare)
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Comparing spectral differences between healthy and early infested spruce forests caused by bark beetle attacks using satellite images
- 2022
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Ingår i: <em>IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium</em>, Hybrid Symposium, Kuala Lumpur, Malaysia, 17-21 July, 2022. - : IEEE. - 9781665427920 - 9781665427937 ; , s. 7709-7712
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Konferensbidrag (refereegranskat)abstract
- Detecting forest insect damage before the visible discoloration (green attacks) using remote sensing data is challenging, but important for damage control. In recent years, the European spruce bark beetle (Ips typographus, L.) has damaged large amounts of forest in Europe. However, it is still debatable how early the infestations can be detected with remote sensing data. Some studies showed a spectral difference between healthy and green-attacked spruce trees at the plot level, while others showed that spectral differences existed before attacks. Therefore, a hypothesis is proposed that no spectral difference can be identified between green-attacked forests compared to healthy forests if the differences do not exist before the attacks. In this study, we tested this hypothesis using Sentinel-2 and WorldView-3 SWIR images on 24 healthy plots and 24 plots with mild, moderate, and severe attacks. In the results, the severely attacked plots did not show significant spectral differences in the Sentinel-2 images until August, and the sensitivity was found in the blue, red, red-edge, and SWIR band. Only the red band showed a significant difference between the healthy and moderately attacked plots in August, and only the blue, red, and SWIR band showed significant differences in September, October, and November. No significant differences were observed in the WorldView-3 images at the plot or individual tree level. We accepted the hypothesis that green attacks do not show spectral differences with the healthy forests when the differences do not exist before the attacks. We concluded that the SWIR bands were sensitive to attacks in the Sentinel-2 images with 10 m resolution, but not in the WorldView-3 images with 3.7 m resolution. Further studies are needed to explore the methodology of using WorldView-3 SWIR images for the early detection of forest infestation.
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| 8. |
- Huuva, Ivan, et al.
(författare)
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Detectability of Silvicultural Treatments in Time Series of Penetration Depth Corrected Tandem-X Phase Heights
- 2022
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Ingår i: International Geoscience and Remote Sensing Symposium (IGARSS), Volume 2022-July. - : IEEE (Institute of Electrical and Electronics Engineers). - 2153-6996 .- 2153-7003. - 9781665427920 ; , s. 5909-5912
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Konferensbidrag (refereegranskat)abstract
- This study investigated the potential of utilizing time series of TanDEM-X phase heights, corrected for penetration depth, to detect silvicultural treatments in hemi-boreal forest. In total, 34 field plots with 40 m radius were used in conjunction with detailed forest management records to construct a reliable data set of treatments. The study area is situated in Remningstorp, a forest test site in southern Sweden. In the analysis, the temporal mean corrected phase heights were compared before and after a silvicultural treatment in order to quantify the effects of thinnings and clear-cuts on the phase height. As expected clear-cuts were highly distinguishable, but thinnings, while exhibiting a negative change in phase height on average, were not individually distinct from all untreated plots. Moreover, the results regarding the utility of applying penetration depth correction for the task were inconclusive. Overall, the results look very promising for using time series of phase height from TanDEM-X to map thinnings and clear-cuts, especially when several observations are available before and after the silvicultural treatment.
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| 9. |
- Huuva, Ivan, et al.
(författare)
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Prediction of Hemi-Boreal Forest Biomass Change Using Alos-2 Palsar-2 L-Band SAR Backscatter
- 2023
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Ingår i: IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. - : IEEE. - 9798350320107 - 9798350331745 - 9798350320091 ; , s. 3326-3329
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Konferensbidrag (refereegranskat)abstract
- Pairs of fully polarimetric ALOS-2 PALSAR-2 L-band SAR images were used to model biomass on backscatter change over seven growth seasons in a hemi-boreal forest. The biomass change was related to backscatter change via consecutive field surveys of 263 field plots with a 10 m radius. To correct for differences in backscatter not related to biomass abundance, a HV-VV polarization ratio based correction, previously used on airborne L-band data, was applied to the data. The uncertainty of obtained predictions (lowest model mean RMSE 65.1 t/ha, lowest model mean bias 7.1 t/ha) was almost identical whether model fitting and prediction used data from the same scene pair, or different scene pairs. This could possibly attest to the feasibility of the backscatter correction for PALSAR-2 data, but no large backscatter offsets were observed for uncorrected data, and significant variance in predictions, due to the inherent noise in the data and the comparatively small area of evaluation plots, inhibit the analysis.
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| 10. |
- Huuva, Ivan, et al.
(författare)
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Prediction of Site Index and Age Using Time Series of TanDEM-X Phase Heights
- 2023
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Ingår i: Remote Sensing. - : MDPI. - 2072-4292. ; 15:17
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Tidskriftsartikel (refereegranskat)abstract
- Site index and stand age are important variables in forestry. Site index describes the growing potential at a given location, expressed as the height that trees can attain at a given age under favorable growing conditions. It is traditionally used to classify forests in terms of future timber yield potential. Stand age is used for the planning of management activities such as thinning and harvest. SI has previously been predicted using remote sensing, but usually relying on either very short time series or repeated ALS acquisitions. In this study, site index and forest stand age were predicted from time series of interferometric TanDEM-X data spanning seven growth seasons in a hemi-boreal forest in Remningstorp, a test site located in southern Sweden. The goal of the study was to see how satellite-based radar time series could be used to estimate site index and stand age. Compared to previous studies, we used a longer time series and applied a penetration depth correction to the phase heights, thereby avoiding the need for calibration using ancillary field or ALS data. The time series consisted of 30 TanDEM-X strip map scenes acquired between 2011 and 2018. Established height development curves were fitted to the time series of TanDEM-X-based top heights. This enabled simultaneous estimation of both age and site index on 91 field plots with a 10 m radius. The RMSE of predicted SI and age were 6.9 m and 38 years for untreated plots when both SI and age were predicted. When predicting SI and the age was known, the RMSE of the predicted SI was 4.0 m. No significant prediction bias was observed for untreated plots, while underestimation of SI and overestimation of age increased with the intensity of treatment.
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