National-scale mapping of tree species composition in Switzerland from high-resolution aerial imagery and multimodal data

Yixin Zhou; Luca Ferrari; Verena C. Griess; Nataliia Rehush; Konrad Schindler; Mirela Beloiu

cs.CVNov 5, 2026

National-scale mapping of tree species composition in Switzerland from high-resolution aerial imagery and multimodal data

Authors: Yixin Zhou, Luca Ferrari, Verena C. Griess, Nataliia Rehush, Konrad Schindler, Mirela Beloiu

Organizations: Photogrammetry and Remote Sensing Group, ETH Zurich, Zurich, 8093, Switzerland · Forest Resources Management Group, ETH Zurich, Zurich, 8093, Switzerland · Ecosystem Ecology Group, Swiss Federal Research Institute WSL, Zürcherstrasse 111, Birmensdorf, 8902, Switzerland · ETH Swiss Geo Lab, D4 Business Village, Platz 1a, Root, 6039, Switzerland ·

Abstract

Tree-species composition shapes forest productivity and how forests respond to climate disturbances such as drought, windthrow and insect outbreaks. Yet most national forest maps assign each pixel a single dominant species and cannot show the share of accompanying species. We mapped the basal-area shares of ten main tree species and a residual Other class across Switzerland at 30 m resolution. A deep-learning model combining 0.1 m aerial RGB imagery, airborne laser scanning (ALS), annual AlphaEarth satellite embeddings, and climate variables was trained on basal-area observations from 3,992 Swiss National Forest Inventory (NFI) sites. In spatial cross-validation, the model reached a macro- $R^2$ of 0.485 across the 11 classes. On an independent forest-reserve dataset, the map reached a macro- $R^2$ of 0.552 for individual 30 m pixels and 0.733 for plots, and identified the dominant class in 73.2% of pixels and 81.1% of plots. The map shows maple and ash as widespread minor components that dominant-class maps omit. It also reproduces the elevational sequence from broadleaved lowland stands through spruce to larch and Arolla pine near the treeline. By resolving the species mixture within each 30 m pixel, the map complements field inventory statistics with spatially continuous information that can support forest monitoring, the management of mixed stands, and climate-adaptation planning across Switzerland.

Figures & tables

Study/product	Prediction target	Predictor modalities	Scale
Categorical
Francini et al. (2024)	7 species groups	S-2	Netherlands
Mu et al. (2025a)	8 species/species groups	S-2	Germany
Waser et al. (2017)	2 leaf types; broadleaf probability	Aerial multispectral + terrain	Switzerland
Blickensdörfer et al. (2024)	11 species/species groups	S-1 + S-2 + environment	Germany
Koch et al. (2024)	15 species	S-2 + terrain	Switzerland

Table 1: Large-area studies and products mapping tree species or leaf type. S-1/S-2 = Sentinel-1/2; ALS = airborne laser scanning; HSI = hyperspectral imagery; AEF = AlphaEarth Foundations satellite embeddings.

Panel of Figure 1 — Figure 1: Overview of the Swiss National Forest Inventory (NFI) sites used in our study. (a) Number of retained NFI sites in each of Switzerland’s 26 cantons, in five classes (3,992 sites in total); cantons are labeled with their official two-letter abbreviations. (b) Number of retained sites by NFI survey year (2017–2022). (c) Number of retained tree-level observations assigned to each of the 10 Swiss NFI main tree species and the residual Other class, colored by leaf type. Here and in all other maps of this article and its Supplementary Material, map lines delineate study areas and do not necessarily depict accepted national boundaries.

Modality	Dataset	Spatial resolution	Acquisition time
Aerial RGB	SWISSIMAGE	0.1 m	Leaf-on acquisition nearest the NFI survey year
ALS	swissSURFACE3D	15–20 points m -2	Single available acquisition
Satellite embeddings	AlphaEarth Foundations	10 m	NFI survey year
Climate	CHELSA BIOCLIM+	${\sim}$ 1 km	1981–2010 long-term mean

Table 2: Input data modalities. ALS = airborne laser scanning.

Figure 2: Forest reserve validation data. Left: locations of the 132 plots of the Swiss Forest Reserves Monitoring network used for independent validation. Right: an example plot polygon (red outline), with circles marking the retained trees colored by mapped class and sized by DBH.

Figure 3: Workflow of this study. (a) Construction of site-level basal-area compositions from NFI tree records. (b) Extraction and screening of the four input modalities. (c) Model training and five-fold spatial block cross-validation. (d) National mapping with the refitted model and independent evaluation.

Figure 4: Overview of the multimodal architecture. Modality-specific encoders project each input into a token set in a shared embedding space: a frozen DINOv3 ViT-H+/16 for the SWISSIMAGE crop, a sparse 3D U-Net with learned queries for the ALS point cloud, and multilayer perceptrons for the AlphaEarth patch and the CHELSA variables (Section 3.1.1 ). A learned query pools the aerial features into the anchor $\mathbf{h}_{0}$ ; the other modalities enter as residual corrections $\boldsymbol{\Delta}_{m}$ , each scaled by a learned gate $g_{m}$ (Section 3.1.2 ). Three heads predict stand type, leaf type, and species composition, with the loss terms of Section 3.1.3 .

Figure 5: Spatial cross-validation design and example reference sites. (a) The 3,992 retained NFI sites (gray dots) were grouped into 10 km spatial blocks, and each block was assigned to one of the five cross-validation folds; the bar chart gives the mean site-level basal-area share of each mapped class in the five folds. Numbered markers (1–10) locate the example sites in (b). (b) One pure 30 $\times$ 30 m site for each of the ten main tree species, with white circles marking the recorded NFI trees and the frame color indicating leaf type.

Model	Backbone	Leaf level		Species level
Model	Backbone	MAE	$R^{2}$	MAE	$R^{2}$	$F_{1}$
ResNet-50	ResNet-50, ImageNet	0.109 $\pm$ 0.004	0.757 $\pm$ 0.020	0.058 $\pm$ 0.002	0.380 $\pm$ 0.043	0.515 $\pm$ 0.027
Random Forest	–	0.167 $\pm$ 0.004	0.669 $\pm$ 0.017	0.083 $\pm$ 0.001	0.313 $\pm$ 0.012	0.342 $\pm$ 0.031
AlphaEarth MLP	–	0.149 $\pm$ 0.008	0.667 $\pm$ 0.027	0.084 $\pm$ 0.002	0.157 $\pm$ 0.014	0.194 $\pm$ 0.012
Ours	ViT-L/16, SAT-493M	0.103 $\pm$ 0.006	0.781 $\pm$ 0.020	0.052 $\pm$ 0.002	0.446 $\pm$ 0.062	0.570 $\pm$ 0.043
	ViT-L/16, LVD-1689M	0.097 $\pm$ 0.004	0.794 $\pm$ 0.013	0.051 $\pm$ 0.002	0.444 $\pm$ 0.063	0.566 $\pm$ 0.051

Table 3: Comparison with reference baselines and across frozen DINOv3 backbones. Best value in bold, second best underlined.

Modality				Leaf level		Species level
RGB	ALS	AEF	CHELSA	MAE	$R^{2}$	MAE	$R^{2}$	$F_{1}$
✓	✓	✓	✓	0.093 $\pm$ 0.003	0.803 $\pm$ 0.017	0.048 $\pm$ 0.001	0.485 $\pm$ 0.038	0.585 $\pm$ 0.014
✓	✓	✓		0.094 $\pm$ 0.002	0.808 $\pm$ 0.013	0.049 $\pm$ 0.002	0.465 $\pm$ 0.056	0.581 $\pm$ 0.031
✓		✓	✓	0.093 $\pm$ 0.002	0.814 $\pm$ 0.013	0.048 $\pm$ 0.001	0.483 $\pm$ 0.052	0.593 $\pm$ 0.026
✓	✓		✓	0.099 $\pm$ 0.004	0.791 $\pm$ 0.017	0.050 $\pm$ 0.002	0.464 $\pm$ 0.070	0.580 $\pm$ 0.029
✓	✓			0.099 $\pm$ 0.003	0.793 $\pm$ 0.018	0.051 $\pm$ 0.001	0.461 $\pm$ 0.044	0.581 $\pm$ 0.027

Table 4: Leave-one-modality-out and reduced-modality ablations. AEF = AlphaEarth annual embeddings. Best value in bold, second best underlined.

Figure 6: Dominant mapped class of the national product. Top: the class with the largest predicted basal-area share. For display, the 30 m map was aggregated to 210 m by majority class, and cells with less than 50% forest cover are left blank; rings give the composition of three cantons. Minor classes are shown in Fig. 7 . Bottom: three forest reserves of Section 4.3.1 , marked a–c above, at the native 30 m resolution. Red outlines are the reserve plot polygons.

Figure 7: Predicted basal-area composition of each mapped class across Switzerland. Every panel gives the predicted basal-area proportion of one class on a common linear 0–1 scale: a–e the five coniferous groups, f–j the five broadleaved groups, and k the residual Other class. For display, the 30 m predictions were averaged over the forest pixels of 990 m cells, and only cells with at least 25% forest cover are shown. Gray marks forest in which the proportion of the class is below 0.005; areas without forest are white.

Metric	Spatial 5-fold CV	Independent, plot-level	Independent, pixel-level
	NFI ( $n=3{,}992$ )	Forest reserve ( $n=132$ )	Forest reserve ( $n=414$ )
$R^{2}$	0.483	0.733	0.552
MAE	0.048	0.038	0.042
RMSE	0.125	0.088	0.109
Dominant-class accuracy	0.734	0.811	0.732
Dominant-class $F_{1}$	0.585	0.704	0.596

Table 5: Accuracy across evaluation supports.

R^{2}

is averaged over the ten classes other than chestnut, and MAE and RMSE over all 11 classes.

Figure 8: Observed and predicted basal-area proportion of each mapped class. (a) At the 132 forest reserve plots (plot-level support). (b) At 414 forest-reserve pixels, reconstructed on the product’s native 30 m grid (pixel-level support). The dashed line is the 1:1 relationship. Panel annotations give the per-class MAE,

R^{2}

, and the number of plots or pixels in which the class was observed (

n_{+}

). n/a:

R^{2}

not evaluable.

Figure 9: Row-normalized confusion matrix of the dominant class at (a) the 132 forest reserve plots (plot-level support) and (b) the same 414 forest-reserve pixels (pixel-level support). Cell labels give the percentage of the observed row total; $n$ in the row labels is the number of plots or pixels in which each class was the observed dominant one. Chestnut never dominated a plot or pixel and is therefore not evaluable as an observed class.

Figure 10: Agreement between the national product and NFI4 ( Abegg et al., 2023 ) across the five Swiss production regions (Fig. S5 ). (a) Basal-area composition of each region in the product and in NFI4; segments below 5% are left unlabeled. (b) The same 55 region-by-class pairs, with color denoting the mapped class and marker shape the production region. The inset enlarges the range below 10%, in which most pairs lie; the dashed line is the 1:1 relationship and the solid line the ordinary least-squares fit, and MAE and RMSE are taken about the 1:1 line in percentage points (pp).

Figure 11: Predicted tree-species composition along the Swiss elevation gradient. (a) The five coniferous groups. (b) The five broadleaved groups and the residual Other class, sharing the elevation axis with (a). Curves give the mean predicted basal-area share of each class in each elevation band, with elevation from swissALTIRegio. The gray backdrop, read against the right-hand axis in (b), gives the mapped forest area of each band. Only bands containing at least 5,000 forest pixels (4.5 km 2 ) are shown.

Appendix figures & tables18 assets

Supplementary material from the paper’s appendix.

Appendix

Step	Screening criterion	Description	Tree points	Sites
1	Raw NFI tree observations	All available tree records	63,969	5,998
2	Recent inventory observations	Recorded in 2017 or later	42,831	4,052
3	Canopy-layer screening	Exclude the lowest stratum	39,863	4,043
4	Quality control and completeness	Exclude disturbed sites and sites with degraded imagery; require complete multimodal records	39,380	3,992

Appendix

Table S1: Attrition of tree-level observations and sites through the eligibility filtering pipeline.

Figure S1: Conceptual examples of the three principal site-level basal-area distribution forms and their complementary targets. The examples represent a pure stand, a mixed stand within one leaf type, and a mixed stand spanning both leaf types. Left: aerial imagery and NFI sample circles; center: schematic class composition and the 0.80 stand-type threshold; right: the corresponding broadleaved–coniferous composition.

Figure S2: GPS positional extent of the retained NFI tree records at 3,992 eligible sites. Left: cumulative distribution of the minimum enclosing-circle radius of retained tree positions; vertical dashed lines mark 5 m and 10 m. Right: containment within centered square pixels of increasing side length; the solid curve gives sites fully contained, the dashed curve individual tree points contained, and the vertical dashed line marks the 30 m national-product grid.

Pixel side length	10 m	14 m	18 m	20 m	24 m	30 m (used here)
Sites fully contained (%)	10.9	24.0	49.2	65.5	90.6	99.0
Tree points contained (%)	34.6	61.2	82.7	90.2	98.1	99.9

Appendix

Table S2: Containment of retained NFI tree positions within a square output pixel of the given side length, centered on the site coordinate, for the 3,992 eligible sites.

Figure S3: Reference year of the national-scale inference. Each forest pixel was assigned the year of the most recent leaf-on SWISSIMAGE acquisition available for its area, and that year also determined which annual AlphaEarth embedding was used. Percentages give the share of the mapped forest area falling in each year.

No.	English	Latin	Included species
1	Spruce	Picea spp.	Picea abies ; Picea spec.
2	Fir	Abies alba	Abies alba
3	Pine	Pinus spp.	Pinus sylvestris ; Pinus nigra ; Pinus strobus ; Pinus mugo arborea
4	Larch	Larix spp.	Larix decidua ; Larix kaempferi
5	Arolla pine	Pinus cembra	Pinus cembra
6	Beech	Fagus sylvatica	Fagus sylvatica

Appendix

Table S3: Definition of the 11 mapped classes. The ten main tree species follow the Swiss NFI grouping; Other contains all retained taxa outside those ten. English and Latin give the common and scientific name of each class, respectively.

Taxon	Leaf type	Taxon	Leaf type
Abies alba	Coniferous	Pinus sylvestris	Coniferous
Acer campestre	Broadleaved	Platanus spec.	Broadleaved
Acer opalus	Broadleaved	Populus alba	Broadleaved
Acer platanoides	Broadleaved	Populus nigra s.l.	Broadleaved
Acer pseudoplatanus	Broadleaved	Populus spec.	Broadleaved
Aesculus hippocastanum	Broadleaved	Populus tremula	Broadleaved

Appendix

Table S4: Assignment of the 71 retained NFI taxa to broadleaved or coniferous leaf type.

Figure S4: Seasonal NDVI trajectories by mapped class, derived from the quality-screened Sentinel-2 time series. Lines show the class median across sites; shaded bands show the bootstrap confidence interval of the median.

Variable	Interpretation	Unit
bio01	Annual mean temperature	∘ C
bio02	Mean diurnal temperature range	∘ C
bio03	Isothermality (bio02/bio07 $\times$ 100)	%
bio04	Temperature seasonality (standard deviation $\times$ 100)	∘ C $\times$ 100
bio05	Maximum temperature of the warmest month	∘ C
bio06	Minimum temperature of the coldest month	∘ C

Appendix

Table S5: Definitions of the 19 CHELSA BIOCLIM+ variables used by the model.

Modality	Model input	Encoder	Trainable
Aerial imagery	280 $\times$ 280 $\times$ 3 crop, resized to 256 $\times$ 256 (28 m, 0.1 m GSD)	DINOv3 ViT-H+/16, blocks {7, 15, 23, 31}, 8 $\times$ 8 patch pooling	No
ALS	Points within 14 m radius, 0.1 m voxels	Sparse 3D U-Net (channel widths 32–160) + 64 learned queries	Yes
Satellite embedding	3 $\times$ 3 $\times$ 64 AlphaEarth patch (10 m pixels, 30 m footprint)	Shared 2-layer MLP + positional embedding	Yes
Climate	19 CHELSA BIOCLIM+ variables	2-layer MLP	Yes

Appendix

Table S6: Modality encoders. GSD = ground sampling distance; MLP = multilayer perceptron.

Setting	Value
Optimizer	AdamW
Initial learning rate	$1\times 10^{-4}$
Weight decay	0.05
Adam betas	(0.9, 0.999)
Scheduler	Cosine decay
Minimum learning rate	$1\times 10^{-6}$

Appendix

Table S7: Optimization settings.

Figure S5: The five Swiss NFI production regions used for the comparison with NFI4 regional statistics (Section 4.3.2 ).

Variant	Leaf level		Species level
Variant	MAE	$R^{2}$	MAE	$R^{2}$	$F_{1}$
AlphaEarth embeddings †	0.093 $\pm$ 0.003	0.803 $\pm$ 0.017	0.048 $\pm$ 0.001	0.485 $\pm$ 0.038	0.585 $\pm$ 0.014
Sentinel-2, July composite	0.098 $\pm$ 0.004	0.791 $\pm$ 0.015	0.050 $\pm$ 0.001	0.464 $\pm$ 0.044	0.588 $\pm$ 0.029
Sentinel-2, growing season	0.099 $\pm$ 0.003	0.786 $\pm$ 0.018	0.050 $\pm$ 0.001	0.458 $\pm$ 0.052	0.579 $\pm$ 0.041
Sentinel-2, all year	0.098 $\pm$ 0.003	0.790 $\pm$ 0.011	0.050 $\pm$ 0.001	0.480 $\pm$ 0.042	0.593 $\pm$ 0.020

Appendix

Table S8: Satellite representation. Best in bold, second best underlined. The dagger marks the configuration retained in the reference model.

Variant	Leaf level		Species level
Variant	MAE	$R^{2}$	MAE	$R^{2}$	$F_{1}$
CHELSA BIOCLIM+ †	0.093 $\pm$ 0.003	0.803 $\pm$ 0.017	0.048 $\pm$ 0.001	0.485 $\pm$ 0.038	0.585 $\pm$ 0.014
Climplicit embedding	0.092 $\pm$ 0.003	0.813 $\pm$ 0.015	0.049 $\pm$ 0.001	0.487 $\pm$ 0.042	0.593 $\pm$ 0.027
Location MLP	0.093 $\pm$ 0.003	0.815 $\pm$ 0.014	0.049 $\pm$ 0.002	0.473 $\pm$ 0.044	0.585 $\pm$ 0.023

Appendix

Table S9: Climate representation. Best in bold, second best underlined. The dagger marks the configuration retained in the reference model.

Figure S6: Sensitivity of the pixel-level validation to the minimum-tree-count threshold, with pixel coverage fixed at 80%. The dashed line marks the primary specification (10 trees) used in Section 4.3.1 .

Class set	Spatial 5-fold CV	Independent, plot	Independent, pixel
	NFI ( $n=3{,}992$ )	Forest reserve ( $n=132$ )	Forest reserve ( $n=414$ )
All 11 classes (including chestnut)	0.485	$-3.560$ a	n.e. b
Ten evaluable classes (excluding chestnut)	0.483	0.733	0.552

Appendix

Table S10: Sensitivity of macro-averaged

R^{2}

to the inclusion of chestnut across evaluation supports. Cross-validation values are means of the five fold-level macro-

R^{2}

values. a At plot support, the value including chestnut is reported only as a diagnostic because chestnut has a single non-zero reference observation. b At pixel support, chestnut has zero reference variance and

R^{2}

is therefore not evaluable (n.e.). The second row uses the same ten evaluable classes at all three supports.

Class	MAE		RMSE		$R^{2}$		$n_{+}$
Class	Plot	Pixel	Plot	Pixel	Plot	Pixel	Plot	Pixel
Spruce	0.085	0.115	0.172	0.237	0.668	0.677	89	264
Fir	0.034	0.038	0.071	0.092	0.829	0.643	48	100
Pine	0.044	0.063	0.135	0.194	0.718	0.421	50	102
Larch	0.020	0.029	0.058	0.085	0.869	0.728	23	61
Arolla pine	0.031	0.039	0.118	0.138	0.700	0.610	18	56

Appendix

Table S11: Per-class performance at the plot-level (132 forest reserve plots) and pixel-level (414 forest-reserve pixels) independent validations.

n_{+}

is the number of plots or pixels in which the class was observed with a non-zero share. Chestnut has a single non-zero plot-level observation and no positive pixel-level observation, so its

R^{2}

is not evaluable (n.e.) at either support and it is excluded from the macro-

R^{2}

; the macro-MAE and macro-RMSE retain all 11 classes. Dominant-class support at each observation unit is given in the row labels of Fig. 9 .

Class	MAE		$R^{2}$
Class	CV	Pixel-level	CV	Pixel-level
Spruce	0.111	0.115	0.746	0.677
Fir	0.061	0.038	0.668	0.643
Pine	0.024	0.063	0.599	0.421
Larch	0.039	0.029	0.742	0.728
Arolla pine	0.013	0.039	0.339	0.610

Appendix

Table S12: Per-class MAE and

R^{2}

in the spatial five-fold cross-validation and the pixel-level independent validation. Cross-validation values are the mean over the five folds, restricted to the reference configuration. Chestnut is omitted from this side-by-side table because its pixel-level

R^{2}

is not evaluable.

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Tree species mapping in Denmark: A comparison of spectral-temporal features with geospatial foundation model embeddings

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EO Centre of Excellence, DHI, Agern Alle 5, Hørsholm, 2970, Denmark · Department of Geoscience and Natural Resource Management, University of Copenhagen, Øster Voldgade 10, Copenhagen, 1350, Denmark · ceometrics, Strandvejen 273C, Charlottenlund, 2920, Denmark

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We map tree species across Denmark using National Forest Inventory plots and EO data, while evaluating the potential of foundation models for large-scale forest characterization. We compare two alternative input representations for tree species classification: (i) manually engineered spectral-temporal features (STF) derived from multi-temporal Sentinel-1 and Sentinel-2 observations, and (ii) embeddings generated by the EO FMs TESSERA and AlphaEarth. Both representations are complemented with canopy height information. Random forest, XGBoost, and Multi-Layer Perceptron (MLP) classifiers are evaluated for all input representations, with separate assessments for pure and mixed forest stands. The STF-based MLP achieves the highest classification performance, yielding macro F1 scores of 0.843 and 0.653 for pure and mixed stands, respectively. The MLP trained on TESSERA embeddings delivers competitive performance for pure stands, achieving results within 1.1 percentage points of the best-performing model. TESSERA consistently outperforms STF-based models when fewer than approximately 25% of training plots are available, demonstrating a substantial advantage under limited training data. Multi-year observations systematically improve classification accuracy relative to single-year inputs, while ablation experiments reveal the complementary contributions of Sentinel-1 backscatter, spectral indices, and canopy height data. The best-performing model is subsequently applied at the national scale to generate a 10 m tree species map of Denmark. Area-adjusted validation indicates an overall map accuracy of 79.9%. The resulting map, released as an open-access product, is the first high-resolution national tree species map of Denmark and provides a valuable resource for forest monitoring, ecological research, and land management applications.

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