BG9.2 | Remote Sensing for forest applications

This session explores the potentials and limitations of various remote sensing applications in forestry, with the focus on the identification and integration of different methodologies and techniques from different sensors and in-situ data for providing qualitative and quantities forest information.
In general, remote sensing allows examining and gathering information about an object or a place from a distance, using a wide range of sensors and platforms. A key development in remote sensing has been the increased availability of data with very high temporal, spatial and spectral resolution. In the last decades, several types of remote sensing data, including optical, multispectral, radar, LiDAR from different platforms (i.e. terrestrial, mobile, UAV, aerial and satellite platforms), have been used to detect, classify, evaluate and measure the earth surface, including different vegetation cover and forest structure. For the forest sector, such information allows efficient quantification of the state and monitoring of changes over time and space, in support of sustainable forest management, forest and carbon inventory or for monitoring forest health and their disturbances. Remote sensing data can provide both qualitative and quantitative information about forest ecosystems. In a qualitative analysis, forest cover types and species composition can be classified, whereas the quantitative analysis can measure and estimate different forest structure parameters related to single trees (e.g. DBH, height, basal area, timber volume, etc.) and to the whole stand (e.g. number of trees per unite area, spatial distribution, etc.). However, to meet the various information requirements, different data sources should be adopted according to the application, the level of detail required and the extension of the area under study. The integration of in-situ measurements with satellite/airborne/UAV imagery, Structure from Motion, LiDAR and geo-information systems offers new possibilities, especially for interpretation, mapping and measuring of forest parameters and will be a challenge for future research and application.

Convener: Markus Hollaus | Co-conveners: Christian Ginzler, Eva Lindberg, Xinlian Liang, Emanuele Lingua
Orals
| Fri, 28 Apr, 08:30–12:30 (CEST)
 
Room 2.95
Posters on site
| Attendance Wed, 26 Apr, 14:00–15:45 (CEST)
 
Hall A
Orals |
Fri, 08:30
Wed, 14:00

Orals: Fri, 28 Apr | Room 2.95

Chairpersons: Christian Ginzler, Eva Lindberg
08:30–08:35
08:35–08:45
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EGU23-9996
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BG9.2
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ECS
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On-site presentation
Shuhong Qin, José Bermúdez, Cheryl A. Rogers, Kangyu So, Alemu Gonsamo, and Hong Wang

This study aims at mapping wall-to-wall forest aboveground biomass (AGB) of Canada by directly upscaling the national forest inventory (NFI) plot measurements with machine learning method and satellite observations. We used the geolocated ground plots provided by NFI project from 10 provinces over the period 1992 to 2018. This dataset contained ground plots with measurements that were performed up to three times since 1992. We cleaned the data based on age and historical disturbance information to retain as many plots as possible for model training, while ensuring that the AGB in the used plots did not vary greatly or affected by disturbance from the date of measurement up to 2020. Finally, if there were repeat measurements in the remaining plots, we only kept the latest measurement records. The input features for estimation model were extracted from seasonal composited Sentinel 1 spectral images, Sentinel 2 L band SAR images and ALOS PALSAR yearly mosaic data. The Machine learning method - Random Forest Regression was used for AGB estimation. We trained the RF model locally and uploaded the model to the GEE platform to predict a wall-to-wall AGB map for Canada. To train and select the best performing model, we employed three categories of training and validation methods including random split (RS, repeated 100 times), simple 10-fold cross-validation (S10C, repeated 10 times) and stratified 10-fold cross-validation (ST10C, repeated 10 times). The prediction uncertainty of the model was determined by the Quantile Regression (QR at 5%,50% and 95%) equations between the mean bias and the mean prediction of 100 model. The bias of the model showed a characteristic V-shape pattern when compared to the predicted AGB values, which showed the range of bias value widened as the predicted AGB values increased. This distribution of bias can be described by the 5%, 50% and 95% QR line equation response to the lower, median and upper bounds of model prediction bias. With those equations, we can generate bias variation range for all predicted pixels.

How to cite: Qin, S., Bermúdez, J., A. Rogers, C., So, K., Gonsamo, A., and Wang, H.: Direct upscaling of national forest inventory aboveground biomass of Canada with Sentinel and ALOS PALSAR observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9996, https://doi.org/10.5194/egusphere-egu23-9996, 2023.

08:45–08:55
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EGU23-10091
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BG9.2
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ECS
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On-site presentation
Jose David Bermudez Castro, Shuhong Qin, Camile Sothe, and Alemu Gonsamo

Accurate estimates of forest aboveground biomass (AGB) are essential for assessing forest carbon stocks and their change over time to support policies for climate change mitigation, resource management, and biodiversity conservation. Among the methodologies to estimate AGB, those that include accurate forest canopy height (CH) information present better estimates due to the direct relationship between AGB and CH. Therefore, to cover large areas, Light Detection and Ranging (LiDAR) remote sensing technology is preferred because it can provide highly accurate and precise measurements of the distance from the ground to the top of the canopy. However, developing continuous acquisition campaigns using LiDAR technology at continental scales at high-spatial resolution is too expensive.  

The Global Ecosystem Dynamics Investigation (GEDI) offers a unique opportunity to overcome this challenge. The GEDI mission uses a laser instrument mounted on the International Space Station (ISS) to measure the distance from the ISS to the Earth’s surface with high accuracy and spatial resolution. However, GEDI does not provide a spatially continuous CH map. Instead, it captures 25 m spatial resolution footprint samples over the Earth’s surface following a sparse-grid-based sampling pattern between 51.6° N and 51.6° S. In this acquisition setup, the samples are spaced every 60 m in the along-track direction and 600 m in the across-track direction.  

To estimate CH for areas not covered by the sparse GEDI mission, we propose a non-linear mapping function using Convolutional Neural Networks with Uncertainty estimates (UCNNs) with input data from other satellites and output a continuous estimate of CH with a measure of uncertainty. Specifically, we use coregistered multitemporal data from Sentinel-1, Sentinel-2, and ALOS PALSAR. From Sentinel imagery, we use bimonthly composites each year from April-May, June-July, and August-September to capture the dynamics of the spectral and structural tree information in Canada. From ALOS PALSAR, we use the one-year composite, and from GEDI data, we use strong-beam samples from June to July from the corresponding year, while excluding all low-quality samples. Experiments were conducted for 2020 in the Province of Ontario, Canada, whose climate is considered continental, with temperatures ranging from humid in the south, with cold winters and warm summers, to sub-Arctic in the north. To avoid overfitting, we apply spatial cross-validation splitting the study region into five non-overlapping areas. The cumulative uncertainty histogram shows that 90% of samples present an uncertainty of CH less than 5 meters. These results are the first step towards spatially continuous mapping of canopy height using multitemporal and multisource satellite data, with implications for improving assessment of forest biomass estimation and carbon monitoring from space. 

How to cite: Bermudez Castro, J. D., Qin, S., Sothe, C., and Gonsamo, A.: Convolutional Neural Networks Regression Model with Uncertainty Estimates to predict GEDI Canopy height at 30m resolution using multisource SAR and optical observations , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10091, https://doi.org/10.5194/egusphere-egu23-10091, 2023.

08:55–09:05
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EGU23-9562
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BG9.2
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Highlight
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On-site presentation
Maurizio Santoro, Oliver Cartus, Jukka Miettinen, Oleg Antropov, Arnan Araza, and Martin Herold

The role of remote sensing observations in quantifying the biomass of forests is frequently debated because of both their strengths and limitations. Satellite remote sensing is nowadays standard in research activities thanks to missions designed to last over decades. Nonetheless, satellites cannot measure the organic mass stored in trees. As such, indirect approaches are developed that combine multiple observations and mathematical models together with ground-based observations to provide a set of estimates presented in the form of a map.

While small-scale studies profit from a strategy that collects observations best suited to estimate biomass, continental and global mapping efforts need to restrict to datasets that have been collected following observation plans and are free of charge. In turn, this increases the demand on the performance of the models selected to link the predictor metrics derived from remote sensing and the response variable biomass. A map of biomass is eventually the result of an interplay between sensitivity of the remote sensing data to response forest variables, the spatial resolution of the sensors, the number of remote sensing observations and the capability of the models to reproduce the relationship between predictors and response variables. A consequence of such interplay is the level of accuracy affecting the biomass estimate, which ultimately is a key parameter to inform user communities on the reliability and efficiency of biomass maps. A comparison of biomass estimates obtained with different predictors and models for the same region provides additional measures to increase our understanding of the uncertainty affecting current biomass maps derived from satellite data.

In this presentation, we explore such uncertainties by comparing four maps of forest aboveground biomass (AGB) based on satellite images acquired in 2020 and covering Europe. The maps were based on different predictors (Sentinel-1 and ALOS-2 PALSAR-2, ASCAT, SMOS as well as spaceborne LiDAR metrics) but share the same modelling framework for biomass retrieval. Depending on the spatial resolution of the satellite data, spatial scales ranging between 100 m and 25 km were covered.

Validation of each of the datasets indicates that the overall spatial distribution of AGB is well captured even in regions with dense mature forests. However, the maps show substantial discrepancies at the level of individual pixels, regardless of the set of predictors. In addition, the precision of individual AGB estimates is rather low, between 30 and 50% of the estimated value. AGB biases were identified in specific regions and were mostly explained as imperfect modelling of the relationship between predictors and forest variables. The maps’ precision increases with spatial averaging; nonetheless, the spatial correlation of errors implies that the resulting estimates can still be affected by non-negligible uncertainty. These results in turn explain why AGB values from the different maps are highly correlated although the magnitudes can be substantially different. In conclusion, the reliability of biomass maps from satellite data is questionable at the scale of the spatial resolution; their use is instead advised at the landscape scale and for understanding broad spatial patterns.

How to cite: Santoro, M., Cartus, O., Miettinen, J., Antropov, O., Araza, A., and Herold, M.: Understanding the uncertainty of forest aboveground biomass maps derived from satellite observations, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9562, https://doi.org/10.5194/egusphere-egu23-9562, 2023.

09:05–09:15
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EGU23-2769
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BG9.2
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ECS
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On-site presentation
Alba Viana Soto and Cornelius Senf

Forest canopy disturbances such as caused by bark beetle, fire, windthrow or harvest have increased in the past three decades and are expected to increase further in response to climate and land use change. Consistent information on forest canopy disturbances is therefore essential to understanding changes in forest dynamics, structure and demography over time and space. As part of the ForestPaths Horizon project we aim to create the next generation forest disturbance maps, extending both the time frame and context of existing pan-European forest disturbance assessments. Disturbances are mapped using the Landsat archive at 30 meters resolution for 1984-2021. A new machine-learning based approach trained on manually labelled reference pixels is applied to the time series, estimating forest disturbances annually and accounting for stand-replacing and non-stand replacing disturbances, as well as different causal agents (i.e., bark beetle, fire, windthrow or harvest). Summarising annual disturbance maps over time ultimately allows to detect multiple disturbance events and recovery signals per pixel and thus for the characterization of complex disturbance trajectories (e.g., multiple fires, thinnings before final harvest). We test our approach at national levels for three countries accounting for three forest biomes: boreal (Finland), temperate (Germany) and Mediterranean (Spain); covering a total land area of 1,194,526 km2 and a total of 65,623 Landsat images. The results from those initial tests will provide information on the accuracy and precision of the annual, wall-to-wall maps of forest disturbances and pave the road for a consistent disturbance monitoring system of all of Europe’s forests.

How to cite: Viana Soto, A. and Senf, C.: Next generation of European forest disturbance maps based on the Landsat archive, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2769, https://doi.org/10.5194/egusphere-egu23-2769, 2023.

09:15–09:25
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EGU23-15039
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BG9.2
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ECS
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On-site presentation
Sietse van der Woude, Johannes Reiche, Martin Herold, Frank Sterck, and Gert-Jan Nabuurs

Forest disturbance detection studies in European temperate forests are currently largely based on optical imagery, often using fixed thresholds on vegetation indices (Francini et al. 2020; Thonfeld et al. 2022) to distinguish between disturbed and non-disturbed forest. Such approaches are limited by data availability, (especially in winter due to persistent cloud cover), and do not take natural seasonal variability as a result of forest phenology into account in the signal. Radar-based disturbance monitoring has been successfully applied over wet tropical forests (Reiche et al. 2021), but implementation in Europe is challenging due to seasonal signal variability and heterogenous forest composition. In addition, the detection of low-intensity disturbances has not been widely studied. This study will explore the capability of dense Sentinel-1 C-band time series to track disturbances of varying intensities in temperate European forests, using a set of 14 experimental sites in the Netherlands as a case study. These sites contain homogeneous forest cover (Beech, Douglas Fir, and Scots Pine) and four disturbance intensities per site which were carried out at a known date. They simulated clearcut, shelterwood, high-thinning and control management regimes, with 100%, 80%, 20%, and 0% basal area removed in each regime respectively, see figure. High-resolution Lidar and drone data were used to derive the canopy cover fraction at a 10m resolution pixel level, which were then compared with Sentinel 1 backscatter timeseries. The results indicate that at a canopy cover loss of 30-40% (of total pixel area), 75% (+-15%) of pixels are detectable as ‘disturbed’ on average. In addition, geometric effects related to radar viewing geometry such as layover and shadow affect the detection potential. Shadow effects ‘pull’ backscatter values down, while layover effects ‘push’ backscatter values up, resulting in lower detection potential at equal canopy cover loss values. Finally, it was found that using the information contained in opposing orbit directions can increase detection potential at all canopy cover loss values by mitigating inaccuracies introduced by geometric effects. Overall, these results could be of great importance in the development of a radar-based system for large scale (near-real time) disturbance detection in European temperate forest.

References

Francini, Saverio et al. 2020. “Near-Real Time Forest Change Detection Using PlanetScope Imagery.” European Journal of Remote Sensing 53(1): 233–44.

Reiche, Johannes et al. 2021. “Forest Disturbance Alerts for the Congo Basin Using Sentinel-1.” Environmental Research Letters 16(2).

Thonfeld, Frank et al. 2022. “A First Assessment of Canopy Cover Loss in Germany’s Forests after the 2018–2020 Drought Years.” Remote Sensing 14(3): 562.

How to cite: van der Woude, S., Reiche, J., Herold, M., Sterck, F., and Nabuurs, G.-J.: Towards radar-based disturbance detection in temperate forest: Testing the limits of Sentinel-1 C-band backscatter in the detection of canopy cover loss using experimental sites, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15039, https://doi.org/10.5194/egusphere-egu23-15039, 2023.

09:25–09:35
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EGU23-17409
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BG9.2
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On-site presentation
Elham Shafeian, Fabian Fassnacht, and Hooman Latifi

According to recent studies, many semi-arid forests are rapidly declining, which necessitates a profound understanding of the processes and causes of degradation. The Zagros Forest in Iran has been degraded during the past few decades. The analysis of forest degradation in this region is still in its initial phases, with no detailed investigation of the underlying causes. Understanding forest degradation is crucial for more effective forest management, particularly in arid and semi-arid regions.
Since one core principle of remote sensing is to identify changes in signals from multiple acquisitions that relate to the status of vegetation in a specific area, they offer efficient techniques for assessing forest degradation across large and rarely accessible forests. Frequently used remote sensing metrics to assess vegetation health are measures of vegetation greenness. For example, vegetation indices may be computed from optical remote sensing data and used to quantify forest degradation over time. Time series of vegetation indices can track forest degradation across large areas by identifying the decrease in photosynthetic activity caused by leaf loss, defoliation, and structural changes in trees.
However, numerous studies examining forest degradation either focus on dense forests or use very high-resolution remote sensing data, which is often expensive and generally difficult to obtain for large regions. Furthermore, most forest monitoring studies using remote sensing have focused on deforestation rather than forest degradation. Detecting forest degradation is challenging compared with detecting tree mortality induced by abrupt disturbances because degradation processes last longer and have a more subtle signal. 
Because of the free accessibility, relatively high spatial resolution, and long and consistent acquisition record, Landsat time series are a viable source of data for monitoring and assessing forest degradation and disturbances, as well as providing continuous reporting on forest changes. There are several methods to monitor forest disturbances, but most of these are better suited to monitoring large-scale deforestation than subtle changes in forest status. These include Landsat-based detection of trends in disturbance and recovery (LandTrendr) and breaks for additive season and trend (BFAST).
The aim of the study is to compare the mentioned algorithms with other methods, such as random forest classification, anomaly analysis, and Sen's slope. We applied the aforementioned methodologies to Landsat time series data from 1986 to 2021 to separate healthy from declining forest patches in a representative portion of the Zagros.
The highest random forest accuracy result returned an overall accuracy and kappa value of 0.77 and 0.54, respectively. The most accurate results of the anomaly analysis were an overall accuracy and kappa value of 0.58 and 0.005, respectively. Sen's slope had the lowest accuracy among the applied methods, with the highest overall accuracy and kappa values of 0.53 and 0.0039, respectively. These results indicate that the detection of degraded forest regions using Landsat data is challenging and may only be possible if additional information is added to the analysis. We hypothesize that a particularly weak vegetation signal of sparse canopy cover before the bright soil background hampers the detectability of subtle degradation processes.

How to cite: Shafeian, E., Fassnacht, F., and Latifi, H.: Using Landsat Time Series to detect forest degradation in semi-arid areas, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17409, https://doi.org/10.5194/egusphere-egu23-17409, 2023.

09:35–09:45
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EGU23-11391
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BG9.2
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ECS
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On-site presentation
Bart Slagter, Kurt Fesenmyer, and Johannes Reiche

The construction of logging roads has a major ecological impact on tropical forests and leads to large carbon emissions (Kleinschroth & Healy, 2017, Umunay et al. 2019). Negative impacts and emissions of logging roads could potentially be drastically lowered with the adoption of reduced-impact logging practices (Umunay et al. 2019). Accurate, timely, and dynamic logging road maps would help quantify and prioritize opportunities for improved road management and forest conservation across the globe. However, to-date, the limited mapping of logging roads has required time-consuming field data collection or manual digitization from satellite images. The open availability of Sentinel-1 radar and Sentinel-2 optical satellite imagery at high spatiotemporal resolutions now offers a unique opportunity for better automated logging road monitoring in the tropics.

In this study, we employ Sentinel-1 and Sentinel-2 data for near real-time mapping of logging roads in the Congo Basin tropical forests. We monitor newly constructed roads based on Sentinel-1 change ratio composites and cloud-masked Sentinel-2 composites. We acquired an extensive reference dataset of manually digitized logging roads to train and test a convolutional neural network for road/non-road classifications.

First results indicate promising capacities of Sentinel-1 and -2 data to monitor logging roads especially in forest types in the Republic of Congo and the Democratic Republic of Congo. Forest landscapes in Gabon, Equatorial Guinea and Cameroon appeared to be more challenging for logging road monitoring due to effects of cloud-cover and elevation. Near-future work includes model refinements, the acquisition of more reference data, and a Google Earth Engine-based wall-to-wall application of our model to produce a dynamic Congo Basin logging road dataset.

 

References:

Kleinschroth, Fritz, Healy, John R. (2017), Impacts of logging roads on tropical forests, Biotropica 49(5): 620–635 2017

Umunay, Peter M., Gregoire, Timothy G., Gopalakrishna, Trisha, Ellis, Peter W., Putz, Francis E. (2019) Selective logging emissions and potential emission reductions from reduced-impact logging in the Congo Basin, Forest Ecology and Management 437 360-371

How to cite: Slagter, B., Fesenmyer, K., and Reiche, J.: Rapid monitoring of Congo Basin logging roads with Sentinel-1 and Sentinel-2 data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11391, https://doi.org/10.5194/egusphere-egu23-11391, 2023.

09:45–09:55
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EGU23-12493
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BG9.2
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ECS
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On-site presentation
Da Guo, Ronghai Hu, and Xiaoning Song

Canopy spatial structure plays an essential role in ecosystem function and the carbon cycle. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) provided continuous three-dimensional sampling observation that can be used to derive canopy structure parameters. Although ICESat-2 data is delivering global estimates of forest structure, analysis of the performance of ICESat-2 data across a range of forest conditions remains limited. Therefore, the overall goal of this study was to evaluate the structural estimates of plant area index (PAI) from ICESat-2 data over temperate deciduous forest structural types. The PAI was derived using the geolocated photon data (ATL03) and the segment-based path length distribution method based on 100-m ICESat-2 vegetation product data (ATL08) segments. The ground-measured data used to evaluate the accuracy of PAI inversion at 100-m ATL08 segments was collected in the Saihanba forest reservation, northern China, which was covered by temperate deciduous needle-leaved forest. The results showed that the ICESat-2 PAI was in good agreement with ground-measured data, which indicated that the method had a better performance in retrieving PAI with ICESat-2 data. Moreover, we compared the effects of the characteristic of signal photons in the segments on the accuracy of PAI inversion and found that the accuracy of PAI inversion was limited by the quality of signal photons. Findings from this study highlight the method for estimating PAI with ICESat-2 data that may be suitable for a range of cover types.

How to cite: Guo, D., Hu, R., and Song, X.: Performance evaluation of ICESat-2 laser altimeter data for retrieving plant area index, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12493, https://doi.org/10.5194/egusphere-egu23-12493, 2023.

09:55–10:05
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EGU23-13836
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BG9.2
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ECS
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On-site presentation
Xiaoyi Wang, Tao Wang, Jinfeng Xu, Zehao Shen, Yongping Yang, Anping Chen, Shaopeng Wang, Eryuan Liang, and Shilong Piao

High-elevation trees cannot always reach the thermal treeline, the potential upper range limit set by growing-season temperature. But delineation of the realized upper range limit of trees and quantification of the drivers, which lead to trees being absent from the treeline, is lacking. Here, we used 30 m resolution satellite tree-cover data, validated by more than 0.7 million visual interpretations from Google Earth images, to map the realized range limit of trees along the Himalaya which harbours one of the world’s richest alpine endemic flora. The realized range limit of trees is ~800 m higher in the eastern Himalaya than in the western and central Himalaya. Trees had reached their thermal treeline positions in more than 80% of the cases over eastern Himalaya but are absent from the treeline position in western and central Himalaya, due to anthropogenic disturbance and/or pre-monsoon drought. By combining projections of the deviation of trees from the treeline position due to regional environmental stresses with warming-induced treeline shift, we predict that trees will migrate upslope by ~140 m by the end of the twenty-first century in the eastern Himalaya. This shift will cause the endemic flora to lose at least ~20% of its current habitats, highlighting the necessity to reassess the effectiveness of current conservation networks and policies over the Himalaya.

How to cite: Wang, X., Wang, T., Xu, J., Shen, Z., Yang, Y., Chen, A., Wang, S., Liang, E., and Piao, S.: Enhanced habitat loss of the Himalayan endemic flora driven by warming-forced upslope tree expansion, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13836, https://doi.org/10.5194/egusphere-egu23-13836, 2023.

10:05–10:15
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EGU23-16242
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BG9.2
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ECS
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Virtual presentation
Femke van Geffen, Ronny Hänsch, Begüm Demir, Stefan Kruse, Ulrike Herzschuh, and Birgit Heim

Circumboreal forests represent close to 30% of all forested areas and are changing in response to climate, with potentially important feedback mechanisms to regional and global climate through altered carbon cycles and albedo dynamics (e.g., Loranty et al., 2018). A large portion of these boreal forests are located in Siberia, Russia. Here the forests are made up of mainly two types: evergreen (coniferous i.e., Pine, Picea) and summergreen (deciduous i.e., Larix) needle-leaf.  The evergreen–summergreen forest zone stretching across Western Central Yakutia is a dynamic vegetation transition zone with high disturbance due to forest fire and potential invasion of evergreen forest taxa to the east into the summergreen dominated forest zone that needs mapping and monitoring.

Sentinel-2 based Remote Sensing offers the opportunity to obtain forest type maps on a 10-20 m spatial scale. We provide in the SiDroForest (Siberian drone-mapped forest inventory) data collection (https://doi.org/10.1594/PANGAEA.933268), a Sentinel-2 data set containing Level-2 Bottom of Atmosphere labelled image patches for the early (April-May), peak (June-July) and late (August-September) summer seasons (van Geffen et al., 2022). This dataset contains 63 30 by 30-meter labelled patches with vegetation labels assigned derived from fieldwork measurements taken by the Alfred Wegener Institute in Siberia, Russia in 2018.

Building on the SiDroForest dataset, we used K-means clustering to perform an unsupervised classification of Sentinel-2 for five  locations from the SiDroForest set.  We then assigned two broad forest classes in the Sentinel-2 images, summergreen and evergreen. We used the SiDroForest Sentinel-2 patches as validation data for the K-means generated classes in addition to the fieldwork and expert knowledge.

The new dataset contains 100,000 labelled pixels, distributed over  the two classes. We created the dataset for three time stamps to include different forest phenophases in the classification. The phenophases make it easier to distinguish between the two types of forests as summergreen’s spectral signal changes significantly over the seasons.

We trained a Gaussian Naïve Bayes (GNB), a Random Forest (RF) and a Decision Tree (DT) classifier on three-time stamps separately. A combination of Sentinel-2 bands and the NDVI were evaluated with the different classifiers. The highest average accuracy score was achieved with a DT classifier and a balanced set for the two classes and the early summer time stamp and the NDVI band (82%). The peak summer also performed decently with 74%, but the accuracy dropped to 60% for the late summer time stamp. 

We used the trained DT to classify Sentinel-2 data at two locations in Siberia ; Lake Khamra and Nyurba. We masked out all non-forest data and created a forest map to measure the distribution of evergreen and summergreen over the larger areas. With our analyses we will improve the understanding of satellite data for monitoring remote places. The insights from the Siberian boreal forests are valuable in analyses of the boreal forests located in other parts of the world as well in these times or rapidly changing climate.

How to cite: van Geffen, F., Hänsch, R., Demir, B., Kruse, S., Herzschuh, U., and Heim, B.: Evergreen and summergreen classification with Sentinel-2 data, K-means clustering derived labels and Machine learning methods, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16242, https://doi.org/10.5194/egusphere-egu23-16242, 2023.

Coffee break
Chairpersons: Markus Hollaus, Xinlian Liang
10:45–10:50
10:50–11:00
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EGU23-8798
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BG9.2
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ECS
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On-site presentation
Thiên-Anh Nguyen, Marc Rußwurm, Benjamin Kellenberger, and Devis Tuia

The availability of high-resolution remote sensing imagery has enabled precise mapping of the forest cover at large scale. Since forest cover evolves due to land use change, climate and extreme events, understanding its past dynamics becomes crucial in a changing climate context. In this work we analyze historical aerial imagery acquired in Switzerland since 1946 [2] for high-resolution forest mapping. We focus on the 1500–2500m a.s.l. altitude range in the Valais and Vaud Alps, where agricultural land abandonment and climate change have caused forest cover changes.

The times series are composed of single-band, panchromatic images until year 1998, then RGB images up to the year 2020, the last acquisition date over our study area. As a reference for the forest cover in 2020, we use the Topographic Landscape Model SwissTLM3D [1]. For previous years, we plan to manually generate labels to evaluate our results.

We frame forest mapping as a multi-temporal semantic segmentation task: given a time series of images, we predict a map for each image
attributing every pixel to the class "forest" or "non-forest". To solve this task, we develop a deep learning model composed of:

  • a segmentation module, trained with the images and labels from the year 2020;
  • a temporal module, which takes consecutive features generated by the segmentation module and outputs a multi-temporal segmentation map. This module is trained using a Mean Squared Error (MSE) loss enforcing temporal consistency.

We analyze predictions obtained with three models, each one containing one or two of the modules described above. We observe that using the full spectral information of the input images leads to a better delineation of forest borders for both old and recent images (Table 1, Figure 1). By adding the temporal module, the accuracy on the last image is practically unchanged (Table 1), while temporal consistency along the time series is improved (Figure 2).

 

Table 1: Segmentation scores for the year 2020 on the validation set, for all pixels and for pixels under 10m distance of forest borders
Model # inputs Temporal module Mean F-1 score (all) Mean F-1 score (forest borders)
Mono-temporal grayscale 1 no 0.86 0.63
Mono-temporal RGB 3 no 0.89 0.72
Multi-temporal RGB 3 yes 0.88 0.72

 

 

 

Our method is currently not suited for abrupt forest loss, and is prone to error spreading from previous predictions. Future work will consist in designing a temporal consistency loss that better reflects known dynamics of the forest cover, in order to obtain a more accurate segmentation for the oldest images and encourage physical consistency across time.

References
[1] Swisstopo. SwissTLM3D. https://www.swisstopo.admin.ch/en/geodata/landscape/tlm3d.html [Online; accessed 06.01.2023].
[2] Swisstopo. Orthoimages. https://www.swisstopo.admin.ch/en/geodata/images/ortho.html [Online; accessed 06.01.2023].

How to cite: Nguyen, T.-A., Rußwurm, M., Kellenberger, B., and Tuia, D.: Mapping forest cover dynamics in the Swiss Alps using 70 years of aerial imagery, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8798, https://doi.org/10.5194/egusphere-egu23-8798, 2023.

11:00–11:10
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EGU23-17260
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BG9.2
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On-site presentation
Janis Ivanovs and Mait Lang

Airborne laser scanning (ALS) data has been widely used for the assessment of various forest inventory parameters, such as forest stand height, biomass, etc. However, the spatial distribution of the ALS point cloud can be affected by various factors related to the survey methodology and forest stand characteristics. This study uses national coverage high-resolution ALS data with minimum point density of 4 points per square meter in combination with National forest inventory (NFI) field data to construct forest stand height models for forest stands dominated by 6 most common tree species in Latvia in mixed forest stand conditions- Pinus sylvestris L., Betula pendula Roth, Picea abies (L.) Karst, Populus tremula L., Alnus incana (L.) Moench and Alnus glutinosa (L.) Gaertn. We also take into account the ALS technology used and variations in the growing season. The ALS point cloud data was cut along the borders of the NFI plots and a statistical analysis of the spatial distribution of points within the borders of the NFI plots was performed. The results show that the RMSE value of the linear model using all NFI plot data is 1.91m, while the data sets divided by different tree species and seasonality reach the RMSE value in the range of 1.4m to 3.8m for Scots pine and Birch respectively.

Key words: Forest inventory, airborne laser scanning, phenology, large scale forest mapping

How to cite: Ivanovs, J. and Lang, M.: Impact of different tree species composition and seasonality on forest stand height predictions using airborne laser scanning and National forest inventory data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17260, https://doi.org/10.5194/egusphere-egu23-17260, 2023.

11:10–11:20
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EGU23-2288
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ECS
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On-site presentation
Moritz Bruggisser, Zuyuan Wang, and Lars T. Waser

Forest edges represent the transition zone between the open country side and the forest interior. Ideally, the edge zone consists of a shrub belt (vegetation height < 4 m) and a shelterbelt with a distinct height gradient towards the forest interior. Forest edges provide several ecological functions. They offer habitats for plant and animal species, regulate fluxes of nutrients and pollutants between surrounding agricultural areas and the forest, or regulate the microclimate. Repeated assessment of forest edge conditions will help forest owners the maintenance of these edge functions considering the increased pressure due to e.g., intensified agriculture.

The aim of the ongoing project is to provide a map of forest edge structure characterization for entire Switzerland (total forest edge length 186’773 km). We used the latest freely available full coverage airborne laser scanning (ALS) data, which provides point densities of 15-20 points/m2 and a forest mask provided by the Swiss National Forest Inventory (NFI). The high point densities allowed to assess both the horizontal and the vertical structure of the forest edges. On the one hand, we extracted information on the edge composition which is closely related to parameters extracted within NFIs. These comprise detailed information on the shelterbelt composition including its slope, presence or absence of the shrub layer and detection of overhanging trees. Furthermore, we computed the vegetation height distribution and the number of vegetation layers within the edge zone. On the other hand, ALS data was used to compute additional features such as the horizontal canopy cover and canopy gaps, the vertical canopy density, and the 3D light availability within the forest edge zone.

We followed a sampling-based approach and characterized the forest edge structure for discrete sampling points at the edge of the forest mask. The forest edge zone covers an area of +/- 25 m along the forest mask from the sample point and +/- 30 m into and outside of the forest, respectively, measured perpendicular to the forest mask edge. Validation of the derived parameters is based on more than 300 terrestrial NFI plots comprising forest edges. This discrete sampling-based forest edge characterization map could potentially be transferred into a quasi-continuous forest edge description by increasing the number of sampling-points on the forest edge.

Repeated six-yearly ALS acquisitions by the Federal Office of Topography swisstopo will enable to produce regular forest edge characterization data sets as a basis for the monitoring of the forest edge development at a countrywide extent. This will help to identify forest edge areas which are at severe threat of degradation and thus require treatment intervention. Thereby, the quality of forest edges can be preserved or improved for important ecosystem services.

How to cite: Bruggisser, M., Wang, Z., and Waser, L. T.: Countrywide characterization of forest edge structure from airborne laser scanning data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2288, https://doi.org/10.5194/egusphere-egu23-2288, 2023.

11:20–11:30
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EGU23-11458
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On-site presentation
Marius Rüetschi, Livia Piermattei, Mauro Marty, and Lars T. Waser

This work aims to develop a highly automated workflow for generating a forest cover map and detecting forest gaps at the countrywide level (i.e. Switzerland) using the alpha shape approach (Edelsbrunner et al. 1983).

Forest provides society with several functions. In Switzerland e.g., more than 50% of the forests have a protection function and mitigate or prevent the impact of a natural hazard. The accurate detection of forest gaps (openings in the forest canopy) is crucial for properly managing and planning protection forests. In addition, knowledge of the distribution of forest gaps is a useful indicator to assess forest structure and biodiversity. Although the required information is collected at the plot level within the framework of the National Forest Inventory (NFI), remote sensing allows us to derive spatially explicit and accurate products at the pixel level for the entire country.

The countrywide available 1 m spatial resolution Vegetation Height Model (VHM) (Ginzler & Hobi, 2015) serves as a basis to extract forest cover and forest gaps. The VHM was generated from image-based point clouds acquired between 2013 and 2021 for the full coverage of Switzerland. In the first step, a forest cover map was derived using the VHM. In a second step, a dense forest cover map was generated and forest gaps were delineated taking into account the Swiss NFI forest definition criteria comprising minimum tree height and width, crown coverage, and land use. In summary, the overall workflow consists of extracting the tree top points from the VHM (FINT software). Erroneous tree tops were removed using the probability forest mask derived from Sentinel-1/-2 data (Rüetschi et al. 2021). We then derived forest area and non-forest area polygons from the filtered tree top points using alpha shape (lasboundary, LAStools from rapidlasso) that computes a boundary polygon that encloses the points.

A dense forest cover map is calculated using a moving window approach and forest areas greater than 60% are extracted. The forest gaps detection within the dense forest cover map follows a similar approach adopted for the forest cover map, but the alpha shape polygons are extracted from the VHM which is converted to the las format. The entire workflow is developed in Python.

Accuracy assessments of forest cover boundary and forest gaps based on terrestrial and stereo image-interpreted NFI plots are promising and reveal an overall agreement of more than 95% over the entire country.

Reference

Edelsbrunner, H., Kirkpatrick, D.G., Seidel, R., 1983. On the shape of a set of points in the plane. IEEE Transactions on Information Theory, 29(4), pp.551-559.

Ginzler, C. and Hobi, M.L., 2015. Countrywide stereo-image matching for updating digital surface models in the framework of the Swiss National Forest Inventory. Remote Sensing, 7(4), pp.4343-4370.

Rüetschi, M., Weber, D., Koch, T.L., Waser, L.T., Small, D. and Ginzler, C., 2021. Countrywide mapping of shrub forest using multi-sensor data and bias correction techniques. International Journal of Applied Earth Observation and Geoinformation, 105, 102613.

How to cite: Rüetschi, M., Piermattei, L., Marty, M., and Waser, L. T.: Automated detection of countrywide forest cover and forest gaps using alpha shape, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11458, https://doi.org/10.5194/egusphere-egu23-11458, 2023.

11:30–11:40
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EGU23-8254
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On-site presentation
Emily Lines, William Flynn, Stuart Grieve, Harry Owen, and Paloma Ruiz-Benito

The recent explosion in availability of high resolution remote sensing technologies and, crucially, the tools to analyse the 3D data they produce is leading to substantial interest in using them for widespread forest structural monitoring. The level of detail contained in the entire 3D shape of trees, fully captured within these data, can generate a wide range of metrics of interest to ecologists, but the potential metrics of interest and their uncertainties have not been fully explored. In particular, the value of different technologies - whether passive or active sensors, and from the ground or the air - for accurately deriving different metrics is not well known.

 

Working across a range of European forest ecosystems, we have constructed a unique 3D dataset of European forest structural properties from passive and active sensors. We segment individual trees from concurrent and co-located Structure from Motion photogrammetry (SfM) (passive sensor), and UAV LiDAR, and terrestrial laser scanning (active sensors) campaigns, and use these to compute tree structural metrics. We compare the ability of these different technologies to accurately measure key tree properties across a diversity gradient in multiple biomes.

How to cite: Lines, E., Flynn, W., Grieve, S., Owen, H., and Ruiz-Benito, P.: Comparison of extracted ecological features of forests from multiple 3D technologies, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8254, https://doi.org/10.5194/egusphere-egu23-8254, 2023.

11:40–11:50
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EGU23-8454
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On-site presentation
Veronica Escobar-Ruiz, Keith Morrison, Sofie Sjögersten, Matthias B. Siewert, and Nigel Fox

This work reports on a novel C-band monostatic UAV-radar system deployed over two forested wetlands in arctic Sweden, near to the Abisko research station. A Videodrone X4S drone acted as the carrying body, allowing programmable and repeatable flight paths. The radar system is multi-polarized (VV, VH, HV, HH), using one transmitter and optionally one or two receivers. The radar operates in a sawtooth FMCW mode, monotonically stepping in frequency across 5.2 GHz to 5.6 GHz. The choice of sweep time (1 to 8 ms) and number of data points (128 to 2048) are programmable and selected before a flight. The radar is triggered in flight manually from the ground using a Wi-Fi link, and which then repeatedly loops over a pre-set number of 10 s scans. Here, the choice of a drone speed of 5 m s-1 meant that each scan covered a 50 m flight line. There is a re-setting time of 0.3 s between scans. The wetlands are covered by a sparse forest, primarily of birch typically 3 to 7 m tall. We used the tomographic profiling (TP) scheme to collect high-resolution maps of the vertical scattering through the forest canopy. Such information is not available from the coarser satellite imagery, which provides no information on the vertical distribution of the backscatter, not even on the relative strengths of the ground and canopy returns. As the TP scheme has the antennas forward facing, only a narrow image transect beneath the flight path is collected. A synthetic aperture technique is used both to sharpen the real beam in the along track direction, and additionally steer it in angle. Thus, post-measurement, a single flight can be processed to capture the incidence angle response of the whole scene at a single incidence angle, selectable over a ~40 degree range. The results show how the forest backscatter response changes from one dominated by a ground return close to nadir viewing, to one dominated by the canopy above 20 degrees incidence angle. Comparisons and comment will also be provided of the differing responses with polarisation. 

How to cite: Escobar-Ruiz, V., Morrison, K., Sjögersten, S., Siewert, M. B., and Fox, N.: A novel C-Band UAV-Radar for 3D characterisation of forest canopy backscatter profiles - Preliminary results, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8454, https://doi.org/10.5194/egusphere-egu23-8454, 2023.

11:50–12:00
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EGU23-12859
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ECS
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On-site presentation
Dimitri Gominski, Martin Brandt, and Rasmus Fensholt

Automated detection with deep learning opens the way for large-scale mapping of individual trees from aerial or satellite imagery. Convolutional neural networks offer unprecedented performance, under the condition that numerous and accurate labels are available to train and evaluate networks. Those two conditions are difficult to meet in the context of tree mapping, due to the high variability of tree shapes, species and environments, and to the lack of unambiguous ground truth data. Consequently, models learn on noisy data, do not reach optimality, and the errors seen during training are propagated to the predictions.

Here, we characterize and address the different types of noise in individual tree labels, notably comission/omission errors and positional errors. We propose a new method for tree detection, with an additional degree of freedom to account for annotation errors. We train and evaluate models on two large-scale datasets of aerial images in Denmark and France with manual annotations. Our approach, along with model ensembling, is able to learn from noisy point annotations and generalizes well to new areas, including dense forests.

How to cite: Gominski, D., Brandt, M., and Fensholt, R.: Deep learning for individual tree detection with noisy labels, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12859, https://doi.org/10.5194/egusphere-egu23-12859, 2023.

12:00–12:10
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EGU23-14332
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On-site presentation
Martin Mokros and Gokul Kottilapurath Surendran

Close-range technologies capable of capturing forest ecosystems in three-dimensional space with great detail are revolutionising precision forestry research and practice, mainly by increasing the level of automation for data collection and processing. Furthermore, they provide options to measure some parameters directly, for example, volume or biomass. However, automatic tree species recognition still needs to be properly solved, which is a crucial and challenging task. A couple of approaches by different authors were done to overcome the challenge when data from close-range technologies are used. The authors mainly utilised 3D structures of whole trees or, in some cases, bark structures using point clouds. Or derived 2D blueprints of whole trees from point clouds to distinguish between tree species. In our approach, we are using images of bark. Usually, images are taken during the data acquisition by close-range technologies as a resource for photogrammetry or for colourising the point clouds in the case of terrestrial laser scanning, for example. Carpentier et al. (2018) did an experiment with 23 tree species in Canada and used convolutional neural networks to classify tree species with an accuracy of almost 94%. We focused on benchmarking multiple machine learning and deep learning algorithms in our experiment. Namely: Random forest; Decision tree; Support Vector Machine; Gradient boost; K-nearest Neighbors; Gaussian Naïve Bayes; Multilayer Perceptron; Convolutional neural networks.

In our first experiment, we collected two datasets of bark images using Sony alfa 7 and Canon EOS 4000D. We have collected 1755 images in Slovakia (1369) and Czechia (386); both datasets contain four tree species. The four species from Slovak datasets are European beech, sessile oak, Norway spruce, and European silver fir. Czechia data consists of the species European beech, large-leaved linden, Norway maple, and Scots pine. However, the bark images from Slovakia are from managed forests, and there is a variety of markings on bark; for that, images are cropped to small regions excluding the markings.

The most accurate results were achieved by CNN, which provides 94% accuracy on Slovak exact cropped dataset with a 50% dropout and 91% on an exact cropped dataset with a 50% dropout. When CNN is not considered, the most accurate algorithm was Multilayer perceptron with an accuracy of 92%.

The following research will focus on implementing such tree species classification within the point cloud processing workflow when close-range technologies are used. Secondly, Carpentier et al. (2018) created Barknet 1.0, where they stored 23,000 high-resolution bark images of 23 tree species in Canada. Our next goal is to develop a database of tree species across Europe. To achieve such a challenging task, we will do it within the 3DForEcoTech COST Action, a European collaborative project focusing on close-range technologies and their implementation for precision forestry and forest ecology.

References

Carpentier, M., Giguere, P. and Gaudreault, J., 2018, October. Tree species identification from bark images using convolutional neural networks. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (pp. 1075-1081). IEEE.

How to cite: Mokros, M. and Kottilapurath Surendran, G.: A Comparative Analysis of Machine Learning Algorithms for Tree Species Recognition Using An Image-Based Approach with Implementation Potential for Close-range Technologies, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14332, https://doi.org/10.5194/egusphere-egu23-14332, 2023.

12:10–12:20
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EGU23-14479
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ECS
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On-site presentation
Martí Perpinyà-Vallès, Maria José Escorihuela, Aitor Ameztegui, and Laia Romero

Restoration and conservation efforts in critical regions affecting large populations with adverse climatic conditions, such as the Sahel, in Africa, also provide the grounds for ecosystem services in these areas. Accurate quantification and monitoring of trees in this context are essential for effectively implementing climate mitigation strategies and supporting local communities. Satellite technologies have emerged as powerful tools to obtain carbon stock estimates. However, tree count and coverage are underestimated in these semi-arid and dryland regions, and fine-grained estimates of carbon stocks can unlock tailored management and action and generate a deeper understanding of the distribution of these stocks. We present the first high-resolution, tree-level validated approach to estimate Above Ground Carbon stock leveraging Very High-Resolution imagery (0.5m), field-collected data, and Machine Learning algorithms. Local experts and youth and women communities participating in the Great Green Wall Initiative collected individual tree geolocation in 8 sites within the drylands of the Sahel region (Burkina Faso and Niger). We built a database of tree-level aboveground carbon (AGC) based on field measurements by using allometric equations and carbon conversion factors, and we trained and validated an Artificial Neural Network to predict AGC based on remote sensing imagery variables processed on individual segmented tree crowns. The validation resulted in a R2 of 0.69, a Root Mean Square Error (RMSE) of 355.6 kg and a relative RMSE of 51%. When aggregating results at coarser spatial resolutions (plot and site), the relative RMSE decreased below 20% for all areas. AGC density (AGCd) errors remained under 6 Mg ha-1 on ranges of AGCd up to 26 Mg ha-1, reaching errors of less than a ton of carbon per hectare for half the study sites. A comparison with other methodologies in the recent literature was carried out and showed a competitive performance of this approach in these regions, with R2 of other similar studies being between 0.6 and 0.95, and RMSE ranging from 0.25 to 100 Mg ha-1. Model results confirm the current trend of underestimating the AGC stocks in drylands using coarser resolution data. Most of the available data in the region estimated the total AGC stocks of the 8 study sites to be less than half compared to the validated model results. The only map that predicted an overshot AGC stock compared to our study was a SAR-based approach at 25-meter resolution by Bouvet et al. 2018, in which the authors claimed more significant relative errors in dry regions. Our results confirm that most previous approaches implemented in drylands produce biased estimations of carbon. Our model exploiting VHR imagery offers the possibility to remedy the lack of resolution and then aggregate at the desired level of granularity. This first-of-its-kind validation at the individual tree level demonstrates the capability of very high-resolution models to correctly assess carbon stocks in the now underestimated drylands and semi-arid areas.

How to cite: Perpinyà-Vallès, M., Escorihuela, M. J., Ameztegui, A., and Romero, L.: Accurate quantification of carbon stocks at the individual tree level in semi-arid regions in Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14479, https://doi.org/10.5194/egusphere-egu23-14479, 2023.

12:20–12:30
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EGU23-15779
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ECS
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On-site presentation
Mattia Balestra, Roberto Pierdicca, Alessandro Vitali, Enrico Tonelli, Stefano Chiappini, and Carlo Urbinati

Nowadays data-integration opens up new possibilities for land surveys, involving both remote and proximal sensing devices. The fast advancement of both technology and devices allowed researchers to gather data from afar, making these acquisitions affordable and suitable even in locations with limited accessibility. We surveyed 3 veteran chestnut trees (Castanea sativa) by integration of Mobile Laser Scanner clouds with the top of the canopies reconstructed through photogrammetry, using an Unmanned Aerial Vehicle (UAV) equipped with RGB camera. These 3D models can be used to extract  precise tree metric data, compared with those collected in the field with traditional measurements, such as diameter at breast height (DBH), total height (TH), crown basal area (CBA) and crown volume (CV), providing valuable information on tree assessment and its potential carbon stock. Moreover, the veteran trees have exceptional genetic and cultural values and therefore must be properly inventoried, monitored and protected. We conducted our surveys during summer, when the trees had a crown full of leaves and in winter, when they were almost completely defoliated. We used a GNSS and a total station to collect ground control points, based on available satellites signal. We followed a circular path all around the three veteran chestnut trees with the MLS device, scanning the entire tree from multiple angles and thus obtaining detailed and accurate point clouds of the trees’ skeleton and including at least 3 highly reflective targets. With the UAV, we collected nadiral RGB images to reconstruct the upper part of the canopies and, using the same targets, we merged them with the MLS outputs. We used a Sony Alpha77 single-lens reflex camera to collect detailed, high-quality 3D data of our veteran trunks through the process of close-range photogrammetry. The latter have been merged with the previous 3D models obtained and thus completing the veteran trees reconstruction. Through manual segmentation, we split between trees skeleton and canopy. We extracted the TH and the crown basal area in both seasons using 3DForest software. DBH has been extracted by slicing the RGB trunks at 1.30m and creating a mesh of the sliced portions while the space occupied by the crowns has been computed through the volume obtained by the mesh created with the Alpha Shape algorithm. The volume of the canopies was determined in both the winter and summer seasons to compare the space they occupy when they are in vigor with the space they take up when there are no leaves. Our results, for the 3 individuals, appear to be concordant with the DBH and the TH obtained in the field by traditional measurements while the CBA and CV have not been measured in the field since they are challenging with these ancient trees. The DBH range values are between 150 - 190 cm, the TH is between 18 - 23 m, the CBA and CV range are respectively between 165 - 176 m2 and 255 – 314 m3 in winter while 180 – 258 m2 and 328 – 406 m3 in the summer surveys.

How to cite: Balestra, M., Pierdicca, R., Vitali, A., Tonelli, E., Chiappini, S., and Urbinati, C.: A geomatics data integration approach for veteran chestnut trees 3D modeling, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15779, https://doi.org/10.5194/egusphere-egu23-15779, 2023.

Posters on site: Wed, 26 Apr, 14:00–15:45 | Hall A

Chairpersons: Christian Ginzler, Emanuele Lingua
RS for forestry applications - posters
A.291
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EGU23-15600
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ECS
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Veronika Döpper, Robert Jackisch, Josias Gloy, Tabea Rettelbach, Julia Boike, Inge Grünberg, Ingmar Nitze, Alexandra Runge, Cornelia Inauen, Sophia Barth, Veit Helm, Léa Enguehard, Birgit Kleinschmit, Ulrike Herzschuh, Birgit Heim, Guido Grosse, and Stefan Kruse

Remotely sensed point clouds provide detailed structural data of landscapes and ecosystem characteristics. Especially in the analysis of forests and topography, this data type has proven its ability to derive relevant quantitative parameters such as biomass or subsidence rates. Arctic and boreal permafrost ecosystems are severely affected by climate change and resulting vegetation shifts, environmental disturbances, and permafrost thaw which lead to rapid changes in these northern environments that can be detected and characterized with point cloud datasets. In recent decades, the amount of point clouds acquired and generated in high-latitude regions by terrestrial (TLS), mobile (MLS), unmanned aerial system (UAS) based (ULS), up to airborne-based (ALS) LiDAR (Light detection and ranging) and Structure from Motion (SfM) has steadily increased. Multi-temporal datasets are available for a wide range of observation targets.

The characteristics of the point clouds such as the extent of the area covered as well as the point density and thus the level of detail differ depending on the sensor, method, and the acquisition specifications. To use point cloud data for topographic, morphological, and forestry analysis, segmentation and classification of the point cloud into specific components such as individual trees, stems, foliage, or terrain features is essential. This is a time-consuming manual process and not feasible when addressing large datasets. Several previous analyses showed the potential for machine learning-based semantic segmentation of a single point cloud type, e.g., terrestrial LiDAR (TLS) with identical acquisition mode and sensor. We aim at an automated segmentation of different point cloud types generated by i) TLS, MLS, ULS and ALS as well as ii) SfM using (multi)spectral UAS and airborne image data to enable an analysis of Arctic and boreal permafrost ecosystems. Thereby, we will focus on the following questions:

1) How can we reduce the time consuming process of labeling the point clouds?

2) Can we train a model for segmentation using all point clouds or does transfer learning lead to better results?

3) To what level of detail can we accurately segment and classify the different point cloud types?

With this automated segmentation and classification, we aim to open up the possibility of exploiting the information contained in the multitude of point cloud data for a variety of ecological research applications.

How to cite: Döpper, V., Jackisch, R., Gloy, J., Rettelbach, T., Boike, J., Grünberg, I., Nitze, I., Runge, A., Inauen, C., Barth, S., Helm, V., Enguehard, L., Kleinschmit, B., Herzschuh, U., Heim, B., Grosse, G., and Kruse, S.: Towards an automatic segmentation and classification of multi-source point clouds for Arctic to boreal permafrost ecosystem analysis, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15600, https://doi.org/10.5194/egusphere-egu23-15600, 2023.

A.292
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EGU23-2716
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Lisa Mandl, Alba Viana-Soto, Rupert Seidl, and Cornelius Senf

Natural disturbances and post-disturbance recovery are principal drivers of forest ecosystem dynamics and both are sensitive to climate change. While disturbances and their causes and consequences have received considerable attention from the scientific community in recent years, there is – however – a substantial lack of knowledge on post-disturbance recovery. Recovery is considered an essential measure of forest resilience to climate change, especially with regard to ecosystem service provision (e.g., protection from avalanches, water purification). Disturbances remove the top tree canopy, exposing the forest floor composed of different land cover types, such as bare soil, grassland and shrubby vegetation, which will gradually transition to treed vegetation over succession. The assessment of forest recovery by means of medium resolution optical remote sensing data (i.e., ~20 m spatial grain) poses some challenges in analyzing those spatially and temporally heterogenous recovery trajectories. To tackle this problem, we employed a temporally generalized regression-based spectral unmixing approach to dense time series of Landsat and Sentinel-2 data with the aim of characterizing the post-disturbance recovery trajectories across a large study site covering the eastern Alps (~125,000 km²). For training the spectral unmixing approach, we developed a multi-year spectral library for three endmembers: treed vegetation, non-treed vegetation and bare soil. Selection of pure endmembers was based on the LUCAS database, a pan-European disturbance map and Google Earth imageries. Applying the generalized regression-based spectral unmixing approach to a dense time series of Landsat and Sentinel-2 images results in annual fraction maps for the three endmembers, which can be used to characterize recovery trajectories after major disturbance events. Each pixel’s post-disturbance trajectory can thereby be described in a three-dimensional space composed of variable fractions of treed vegetation, noon-treed vegetation and bare soil. To facilitate interpretation of recovery trajectories, we focus on specific disturbance events covering the storms Kyrill (2007), Uschi (2003), and Vaia (2018). This allows for identifying (dis-)similarities between recovery trajectories of the same disturbance event and thus to investigate the full breath of potential recovery patterns after natural disturbances.

How to cite: Mandl, L., Viana-Soto, A., Seidl, R., and Senf, C.: Trends and patterns in post-disturbance forest recovery estimated from Landsat and Sentinel-2 data using regression-based spectral unmixing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2716, https://doi.org/10.5194/egusphere-egu23-2716, 2023.

A.293
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EGU23-3750
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Guanting Lv

Quantifying forest biomass carbon (C) stock is critical for determining the regional carbon balance, but, a lack of both field observations spanning large climatic gradients and proper upscaling methods which take the spatial pattern into account, means there is little knowledge regarding forest C stock at high spatial resolutions. Here, we address this problem by combining a deep convolutional neural network (CNN) algorithm with in situ measurements, Global Ecosystem Dynamics Investigation (GEDI) observations, and Landsat and PALSAR-2 images to develop a new, spatially explicit estimate of forest aboveground carbon density (ACD) circa 2020 at a 30 m spatial resolution for northeast China, home to nearly one-third of China’s forested area. The result yields a high coefficient of determination (R2) of 0.83 and a relatively low root mean squared error (RMSE) of 5.28 MgC ha-1, and is superior to traditional pixel-based and in situ based methods. Through linking in situ measurements with nearly 0.13 million GEDI observations, we obtained important samples across spatially variable environmental conditions, and in remote and rugged regions (when increasing the number of GEDI samples, RMSE decreased by 73.5%). CNN was able to extract important spatial patterns and performed well in capturing the spatial variation of forest carbon density. We also propose a CNN-based perturbation method to rank variable importance, which shows that the distribution of forest C storage is mainly determined by precipitation and forest age. Based on the proposed method, the local forest aboveground biomass C stock is estimated to be 3.52 ± 0.10 PgC, with an age-related forest aboveground biomass C sink of 7.94 TgC year-1 before 2060. Terrestrial ecosystem models generally underestimate the regional C stock, partially because of biases in forest age simulations. The study highlights the importance of using deep learning methods to gain further process understanding of forest carbon dynamics under climate change.

How to cite: Lv, G.: Potential value of combining CNN, GEDI and multi-source remote sensing data to improve the estimate of aboveground forest carbon storage in northeast China, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3750, https://doi.org/10.5194/egusphere-egu23-3750, 2023.

A.294
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EGU23-3058
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Katherine Strattman, Jed O. Kaplan, and Eduardo Eiji Maeda

In Hong Kong’s sub-tropical ecosystems, anthropogenic wildfires burn 5% of the territory’s natural areas every year. With projected climate change including warmer temperatures in the winter dry season, wildfires in Hong Kong may increase in frequency and intensity in the future. Increased wildfire would threaten biodiversity, water resources, reduce carbon storage, and hinder ongoing efforts to restore and rehabilitate forests. To better understand wildfire behavior and project how future climate change could affect wildfire occurrence in Hong Kong it is essential to understand the characteristics of wildfire fuels in local ecosystems. However, no information on wildfire fuels in Hong Kong is currently available 

Here we aim to characterize wildfire fuels in Hong Kong to develop “fuel models” for the typical Hong Kong vegetation communities of grassland, shrubland, and forest. These fuel models describe wildfire fuels in terms of five derived metrics: fuel load, surface area to volume ratio, fuel bed depth, packing ratio, and bulk density. A fuel model describes how fire will behave in an ecosystem and is an important input for wildfire modeling. 

We developed fuel models for Hong Kong using ground-based Simultaneous Location and Mapping Light Detection and Ranging (SLAM LiDAR). During the winter dry season of 2022-2023, we surveyed grassland, shrubland, and forest plots at Kadoorie Farm and Botanical Gardens, New Territories, Hong Kong with an Emesent Hovermap ST SLAM LiDAR scanner. Fuel models were developed using a voxelization approach by dividing the LiDAR point clouds into uniform voxels, in which the different fuel metrics were estimated. We used field-based measurements to assess the accuracy of the LiDAR-derived wildfire fuel characteristics. Our results demonstrate the potential for SLAM LiDAR to make fast, accurate, and non-destructive characterization of wildfire fuels. The fuel models we developed will be essential for wildfire modeling, land management, and potentially for operational firefighting activities including resource allocation.

How to cite: Strattman, K., Kaplan, J. O., and Maeda, E. E.: Wildfire Fuel Characterization in Subtropical Ecosystems Using Ground-Based SLAM LiDAR, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3058, https://doi.org/10.5194/egusphere-egu23-3058, 2023.

A.295
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EGU23-6956
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BG9.2
Hancheng Guo and Yanyu Wang

Understanding terrestrial ecosystem dynamics requires a comprehensive examination of vegetation changes. Remote sensing technology has been established as an effective approach to reconstructing vegetation change history, investigating change properties, and evaluating the ecological effects. However, current remote sensing techniques are primarily focused on break detection but ignore long-term trend analysis. In this study, we proposed a novel framework based on a change detection algorithm and a trend analysis method that could integrate both short-term disturbance detection and long-term trends to comprehensively assess vegetation change. With this framework, we characterized the vegetation changes in Zhejiang Province from 1990 to 2020 using Landsat and landcover data. Benefiting from combining break detection and long-term trend analysis, the framework showcased its capability of capturing a variety of dynamics and trends of vegetation. The results show that the vegetation was browning in the plains while greening in the mountains, and the overall vegetation was gradually greening during the study period. By comparison, detected vegetation disturbances covered 57.71% of the province’s land areas (accounting for 66.92% of the vegetated region) which were mainly distributed around the built-up areas, and most disturbances (94%) occurred in forest and cropland. There were two peak timings in the frequency of vegetation disturbances: around 2003 and around 2014, and the proportions of more than twice disturbances in a single location were low. The results illustrate that this framework is promising for the characterization of regional vegetation growth, including long-term trends and short-term features. The proposed framework enlightens a new direction for the continuous monitoring of vegetation dynamics.

How to cite: Guo, H. and Wang, Y.: A novel framework for vegetation change characterization from time series Landsat images, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6956, https://doi.org/10.5194/egusphere-egu23-6956, 2023.

A.296
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EGU23-7624
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BG9.2
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Léa Enguehard, Birgit Heim, Stefan Kruse, Begüm Demir, Robert Jackisch, Josias Gloy, Sarah Haupt, Laura Schild, Femke Van Geffen, Veronika Döpper, Ronny Hänsch, Nicola Falco, and Ulrike Herzschuh

Boreal forests, which represent roughly one-third of the world’s total forested area, provide critical ecosystem services including carbon stocks, climate feedback, permafrost stability, biodiversity, and economic benefits. Located in the northern latitude, they are mainly dominated by evergreen needle-leaf tree taxa (Pinus, Picea, Abies) in North America, Northern Europe, and Western Siberia, and by deciduous needle-leaf tree taxa (Larix) in Eastern Siberia. Remote sensing applications in high latitudes are possible but remain challenging for optical satellite sensors due to frequent cloud coverage, forest fires, and low illumination. Additionally, there is little data available prepared as multi-label datasets for remote sensing applications focusing on the structure of boreal forests, specifically on Larix deciduous trees. Furthermore, labeled datasets of summer green and evergreen forest types for specific satellite sensors would enable remote sensing and deep learning applications such as classification, and ultimately improve our understanding of evergreen and summer green tree dynamics. An example of such a dataset is the TreeSatAI multi-sensor Artificial Intelligence Benchmark Archive (doi.org/10.5281/zenodo.6780578), which provides labels on species and forest composition in Europe. Another one is the SiDroForest data collection, consisting of a synthetic Unmanned Aerial Vehicle (UAV) Siberian Larch Dataset (doi.org/10.1594/PANGAEA.932795) and Sentinel-2 image patches (doi.org/10.1594/PANGAEA.933268) of 54 forest plots in Eastern Siberia. 

Here we are building up an extensive multi-labeled training dataset based on optical Sentinel-2 image patches (60 x 60 m image patch of the 10 m and 20 m S2-bands), including meta-data information on summer green and evergreen tree species and forest structure from vegetation plots. Over 250 vegetation plots were collected since 2011 from nine field expeditions of the Alfred Wegener Institute in Eastern Siberia (doi.org/10.5194/essd-14-5695-2022) and Western Canada, where vegetation was sampled and described, and UAV images were taken (UAV solely in 2021 and 2022). In addition to in-situ plots, we gathered all cloud-free Sentinel-2 data from late spring to early fall (May to October) that geographically coincides with the vegetation plots. Therefore, the dataset contains different phenophases of evergreen and summer green forests and provides detailed label information on forest structure – such as tree species and density. The multi-labeling will include broader and more detailed forest-type classes. Some examples of higher-level labels are “Sparse larch forest” or “Dense evergreen forest’’. The poster will demonstrate how we defined forest labels from in-situ data, UAV, Sentinel-2, and their corresponding spectral signatures.

We anticipate our dataset to be a starting point for a significantly more extensive one with the addition of radar satellite sensors such as Sentinel-1 and TanDEM-X, and other ground vegetation plots (new expedition expected in Alaska and Canada in summer 2023), data search in literature and repositories– e.g. NASA Arctic Boreal Vulnerability Experiment. Our dataset will be publicly available and can be used as a training dataset for deep learning algorithms to identify and characterize evergreen and summer green needle-leaf trees in boreal forest regions.

How to cite: Enguehard, L., Heim, B., Kruse, S., Demir, B., Jackisch, R., Gloy, J., Haupt, S., Schild, L., Van Geffen, F., Döpper, V., Hänsch, R., Falco, N., and Herzschuh, U.: AI-vergreen: a multi-label Sentinel-2 training dataset of summer green (Larix) and evergreen needle leaf forest types in boreal forest biomes for remote sensing applications, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7624, https://doi.org/10.5194/egusphere-egu23-7624, 2023.

A.297
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EGU23-9111
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BG9.2
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ECS
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Marianne Böhm, Markus Zehner, Konstantin Schellenberg, José-Luis Bueso-Bello, Paola Rizzoli, Christiane Schmullius, and Clémence Dubois

Describing forest structure is fundamental to understanding forest ecology and calculating biomass estimations. To enable its characterization with large spatial coverage, we investigate data recorded by airborne LiDAR and three different radar frequencies over a deciduous broadleaf forest at the Hainich National Park in central Germany. This study aims at distilling the microwave frequencies and polarisations that most closely relate to structural metrics extracted from the LiDAR point clouds, and are therefore most promising for extending spatial or temporal coverage.

The LiDAR point clouds, which are provided openly by the Thuringian State Office for Land Management and Geoinformation, were processed to five structural metrics at 25 m x 25 m pixel size. These metrics comprise an estimation of fractional cover based on vegetation return numbers,  an intensity-based fractional cover approach (Hopkinson & Chasmer 2009), the skewness and standard deviation of the height distribution, as well as the the vertical complexity index as defined by van Ewijk (2011). These metrics were compared to terrain-corrected backscatter of phenologically matching scenes from three different sensor frequencies: an X Band scene from DLR TerraSAR-X, C Band from Copernicus Sentinel-1, and L Band from JAXA ALOS-2. 

The scenes represent leaf-off conditions. To reduce misleading factors, the analysis was limited to areas with moderate slope angles below 10 degrees. Subsequently, regression models between the lidar metrics and backscatter intensities were built.
First results from bivariate correlations indicate the best match between ALOS-2 HV and fractional cover (r²=0.41) as well as standard deviation (r²= 0.43). Among the metrics, fractional cover is associated most closely with backscatter in all frequencies: the highest correlation coefficients amount to 0.37 for X Band (VV), 0.22 for C Band (VH), and 0.41 for L Band (HV), respectively. In general, C Band exhibits the lowest pairwise correlations with most density metrics, compared to L- and X Band. 
The poster will show the results of multivariate regression models and discuss which combination of frequencies and polarizations is best suited for the derivation of specific forest structure parameters at larger scales.

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Hopkinson, C., & Chasmer, L. (2009). Testing LiDAR models of fractional cover across multiple forest ecozones. Remote Sensing of Environment, 113(1), 275–288. DOI:10.1016/j.rse.2008.09.012

van Ewijk, K. Y., Treitz, P. M., & Scott, N. A. (2011). Characterizing Forest Succession in Central Ontario using Lidar-derived Indices. Photogrammetric Engineering & Remote Sensing, 77(3), 261–269. DOI: 10.14358/PERS.77.3.261

How to cite: Böhm, M., Zehner, M., Schellenberg, K., Bueso-Bello, J.-L., Rizzoli, P., Schmullius, C., and Dubois, C.: Characterizing forest structure using LiDAR and multi-frequency SAR remote sensing, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9111, https://doi.org/10.5194/egusphere-egu23-9111, 2023.

A.298
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EGU23-11612
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BG9.2
Shiori Takamura, Yuichi Onda, Yupan Zhang, Asahi Hashimoto, Hiroaki Kato, Takashi Gomi, and Chenwei Chiu

Many Japanese cypress plantation forests have been degraded due to inadequate forest management, resulting in low solar radiation on the forest floor. In recent years to increase forest floor radiation, however, assessment methods for the impact of thinning have not been developed. The light environment in forests has been estimated by Hemispheric photography with a fisheye lens camera and using image analysis software Hemisfer to determine the amount of solar radiation in the forest and canopy openness situation. To precisely recreate the actual canopy structure, it is challenging due to the limitations of fisheye lenses, which cause distortions as the distance from the center of the captured area grows and the projected area decreases. In addition, taking Hemispherical photos in the forest is labor and time intensive work.

In this study, to explore the forest light environment in cypress plantations, we not only performed image analysis of hemispheric photography, but also estimated forest solar radiation using drone LiDAR data. The study site was a cypress plantation forest located in Mt.Karasawa, Sano City, Tochigi Prefecture. The site is a south-facing slope with a slope angle of approximately 30 degrees. 25 pyranometers were set up in the forest in a grid pattern with 1-meter intervals to measure the spatial distribution of solar radiation in the forest. Total solar radiation was measured by a radiometer installed outside the forest. For Hemispherical image analysis, the software Hemisfer was used to calculate direct and diffused solar radiation in the forest. The drone generated high-density point cloud data with a point cloud density of 2000 pts/m2 was converted to 1cm3 voxel data first, then canopy openness was calculated by clipping the area directly above each pyranometer into a cylindrical buffer and calculating the percentage of the total number to the canopy points number. Considering the significant effect of direct sunlight in forest solar radiation, we varied the solar height in 5° intervals to simulate the actual angle of sunlight penetrating the tree canopy and recalculated the openness.

While the Hemispheric photography did not capture the detailed solar radiation changes in the measured data, the UAV LIDAR data succeeded in reproducing solar radiation changes closer to the measured data by considering the canopy openness. Therefore, there is a possibility of more accurate estimation by using LiDAR data together.

How to cite: Takamura, S., Onda, Y., Zhang, Y., Hashimoto, A., Kato, H., Gomi, T., and Chiu, C.: Estimation of Solar Radiation in Forests Using Drone LiDAR Data in Japanese Artificial Forests, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11612, https://doi.org/10.5194/egusphere-egu23-11612, 2023.

A.299
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EGU23-857
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BG9.2
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ECS
|
Xiao Liu and Matthias Forkel

The distribution of leaves, branches and trunks in the canopy is critical for water, carbon and energy cycles in forests. Light ranging and detection (lidar) and synthetic aperture radar (SAR) are two active remote sensing methods which show the potential to detect forest structure dynamics at different height. Lidar can penetrate the canopy gap and record a reflectivity profile. The vertical distribution of structure metrics, for example, canopy cover, can then be estimated based on this profile. For example, in Amazonian tropical forests, the leaf area index derived using ground- and space-borne lidar at canopy layer and understory layer shows converse behaviour over seasons. For SAR systems, wavelength determines the penetration depth of microwave signal and polarisation reflects the scattering mechanisms between signal and objects. SAR backscatter has been used for monitoring forest phenology and forest classification. However, the potential of using lidar and SAR to monitor canopy structure changes in temperate forests has not been analysed. The relationships between SAR backscatter with the lidar-derived structure metrics at height levels are also not clear.

In this study, we attempt to investigate the influence of forest structure at height levels on SAR backscatter in two study sites in Germany: one deciduous forest in Hainich and one coniferous forest in Tharandt. Level 2B product of the Global Ecosystem Dynamics Investigation (GEDI) and Sentinel-1 ground range detected (GRD) backscatter are used in this study. There are dense multi-temporal GEDI observations around 51ºN, which makes it possible to continuously monitor the forest structure at same area. Auxiliary data including digital terrain model, slope, forest type and forest phenology data are used to extract GEDI footprints which cover similar forests. We only focus on GEDI footprints with forest height between 25 m and 30 m since most trees in study sites are distributed in this range and they have distinguishable canopies. Sentinel-1 data with fixed relative orbit is used for mitigating the influence of incidence angle. SAR backscatter at filtered GEDI locations are extracted for comparison.

We analyse the correlation between the time series of GEDI-derived total structure metrics (e.g., cover), structure metric profiles and Sentinel-1 backscatter metrics (e.g., VH, VV, VV/VH ratio). For deciduous forest, the 15-20 m layer and 20-25 m layer have stronger correlation to Sentinel-1 VH backscatter and VV/VH ratio than other layers as well as the total structure metric. No significant correlation is found between structure metrics and Sentinel-1 backscatter in coniferous forest. We propose to further develop approaches to investigate the joint potential of space-borne lidar and SAR observations to monitor changes in forest canopy structure.

How to cite: Liu, X. and Forkel, M.: Monitoring forest canopy structure dynamics from space using GEDI and Sentinel-1, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-857, https://doi.org/10.5194/egusphere-egu23-857, 2023.

A.300
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EGU23-1020
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BG9.2
|
Ramesh K. Ningthoujam, Sandy P. Harrison, and Iain Colin Prentice

Spatio-temporal quantification of vegetation diversity and structure is important for accurate monitoring of terrestrial ecosystems from space. Landsat data have been employed to provide estimates of vegetation biophysical properties due to their medium (30 m) spatial resolution, sufficient (16 days) temporal resolution, and spectral sensitivity to biophysical properties. This study has explored the use of Landsat-derived vegetation indices (VIs) related to greenness, moisture content and fire severity, recorded during the peak and late growing seasons, to estimate in situ observed species number, basal area and aboveground biomass (AGB) in tropical biomes that are affected by fire, and were surveyed at various stages of post-fire recovery. Linear and logarithmic regressions and coefficients of determination (R2) were computed to assess the relationships of species number, basal area and AGB with ten broadband VIs, with goodness of fit measured by root mean squared error (RMSE). Best fits were obtained using peak-season Green Chlorophyll Index (CI Green), Normalized Difference Moisture Index (NDMI) and Normalized Burn Ratio (NBR2) for species number (R2 = 0.50–0.68, RMSE = 3–4), basal area (R2 = 0.23–0.37, RMSE = 1.0–1.1 m2 ha–1) and AGB (R2 = 0.66–0.74, RMSE = 1.1–1.2 Mg ha-1) in open savanna and savanna forest. Late-season Normalized Difference Vegetation Index (NDVI), NDMI and NBR showed stronger relationships for species number (R2 = 0.88, RMSE = 5.72), basal area (R2 = 0.24–0.68, RMSE = 0.03–9.7 m2 ha–1) and AGB (R2 = 0.20–0.73, RMSE = 1.4–19.2 Mg ha–1) in most of the more complex forest biomes. These results are promising for the wider application of Landsat data especially from Landsat-8 Operational Land Imager (OLI) multispectral sensor to infer post-fire vegetation recovery in tropical ecosystems.

How to cite: Ningthoujam, R. K., Harrison, S. P., and Prentice, I. C.: Improved inference of tropical vegetation properties using seasonal Landsat Vegetation Indices, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1020, https://doi.org/10.5194/egusphere-egu23-1020, 2023.

A.301
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EGU23-13212
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BG9.2
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ECS
Yongho Song and Woo-Kyun Lee

As global environmental problems arise due to climate change, there is an increasing demand for accurate calculation of carbon storage at the regional level for national greenhouse gas management. However, there is no standard for calculating the amount of storage in local forests, so a highly accurate estimation method that can replace tree excavation is needed. Technological development of Unmanned Aerial Vehicles(UAVs) has made it possible to secure high-quality, precise data of various data required for remote sensing, and it was attempted to estimate carbon storage.

First, to identify the tree species distribution of the site, land cover classification and tree species were classified using optical and spectroscopic images obtained by time-series UAV imaging. Next, the data acquired through UAV LiDAR imaging are High Vegetation Pulse (HVP) obtained at the top of vegetation, Medium Vegetation Pulse (MVG) corresponding to intermediate vegetation, Low Vegetation Pulse (LVP) corresponding to lower vegetation and classified as Ground Pulse (GP). Finally, the carbon storage of forest biomass in the region was calculated using the derived tree species distribution map and UAV LiDAR. The data derived from this study are expected to be used as basic data for calculating regional forest carbon stocks through remote sensing in the future.

How to cite: Song, Y. and Lee, W.-K.: Calculation of Biomass Carbon Storage by Individual Trees Using UAV LiDAR, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13212, https://doi.org/10.5194/egusphere-egu23-13212, 2023.

A.302
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EGU23-16542
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BG9.2
|
ECS
Qin Ma, Yanjun Su, Qin Ma, Chunyue Niu, Xiangzhong Luo, Lingli Liu, Maggi Kelly, and Qinghua Guo

The increasingly frequent and severe droughts caused by global warming is threating the forest ecosystem health with pervasive tree mortality. Canopy Structure is one of the important factors that regulating drought-induced tree mortality. However, how tree structural influences the spatial and temporal patterns of tree mortality during droughts remains controversial. Through an analysis of nearly 1.5 million trees during the 2012-2016 drought in California, USA, we found tree mortality first decreased with height for small trees, then increased with tree height in the middle sized trees, and decreased again with tree height for matured big trees. We also found relative tree canopy size compared to neighboring trees demonstrates a consistent negative relationship with tree mortality across species. This new finding may be explained by the fact that trees in a structurally complex forest with tall neighboring trees may have higher crown shadow ratio and less water loss to evapotranspiration during the drought. Therefore, the relatively smaller trees in a structurally complex forest have higher survival rate even during an extreme drought. Our findings suggest that a new forest management strategy that re-establishes heterogeneity in tree species and forest structure could improve forest resiliency to severe and extended droughts.

How to cite: Ma, Q., Su, Y., Ma, Q., Niu, C., Luo, X., Liu, L., Kelly, M., and Guo, Q.: Structurally complex forests are more resilient to extreme droughts, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16542, https://doi.org/10.5194/egusphere-egu23-16542, 2023.

A.303
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EGU23-17256
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BG9.2
Raitis Melniks, Martins Vanags Duka, and Andis Lazdins

Most of the long-term operational infrastructure, including the drainage ditch network, has been developed before compliance with climate change was included in the planning process. Therefore, it is essential to obtain accurate data on the location and condition of the ditch network in order to be able to assess its suitability for foreseeable conditions and the need for improvement measures. The aim of this study is to develop a mapping method for identification and classification of the drainage ditch network, which can be used for surface runoff modeling and to increase accuracy of estimation of greenhouse gas (GHG) and carbon emissions. The study area consists of 20 objects throughout Latvia with a total area of 175 km2. Digital elevation models (DEMs) in two resolutions, which were created using three different interpolation methods, were used for the analysis. Several multi-level data filtering methods were applied to identify and classify ditch network, including flow patterns, which can be used in surface runoff process. The method we developed correctly identified 85–89 % of ditches, depending on the DEM used, in comparison to the reference data. Mapped ditches are located within 3 m range of the reference data in 89–93% of cases. Ditch properties were identified within DEM resolution accuracy. The elaborated model is robust and uses openly available source data and can be used for large scale ditch mapping with sufficient accuracy necessary for hydrological modelling and GHG accounting in the national inventories.

How to cite: Melniks, R., Duka, M. V., and Lazdins, A.: Mapping and classifying drainage ditches in forested landscapes usingLiDAR data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17256, https://doi.org/10.5194/egusphere-egu23-17256, 2023.

A.304
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EGU23-12930
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BG9.2
|
ECS
Shen Tan and Yao Zhang

Forest three-dimensional structure is important information for forest projection, and has shown promising applicability in simulating canopy radiative transfer. Light Detection And Ranging (LiDAR) technology provides a cost-effective way to retrieve forest structure. Compared with other methods, the pseudo-waveform (PWV) is a simpler but more robust way to obtain vertical structure information, but has only been tested in limited regions for two main limitations. First, given the cost of collecting in situ LiDAR observations, considering a comprehensive evaluation of the response of PWVs to canopy architecture remains impossible. Second, while radiative transfer models (RTMs) generate reasonable LiDAR signals, representing tree objects in a cost-effective way is still a bottleneck for large-scale simulation and analysis. As a necessary evaluation for retrieving forest structure from LiDAR, a light RTM was employed to simulate PWVs in this study. Based on the analysis, we aim to answer the following questions: 1) Can the tree objects be represented in a simpler way and with limited metrics, 2) Are the PWV responses reasonably to the variation in canopy architectures under uniform scenes, and 3) What is the PWV response to tree height uncertainties.

How to cite: Tan, S. and Zhang, Y.: Can pseudo waveforms from discrete point clouds represent the vertical structure of divergent canopies?, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12930, https://doi.org/10.5194/egusphere-egu23-12930, 2023.