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REVIEW 4 major objections 6 minor 2 cited by

High Resolution Tree Height Mapping of the Amazon Forest using Planet NICFI Images and LiDAR-Informed U-Net Model

T0 review · 4 major / 6 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read A U-Net regression model trained on Planet NICFI imagery with LiDAR references maps Amazon forest canopy height at ~4.78 m resolution with a 3.68 m mean absolute error, beating global height products.

desk verdict A useful but not yet fully validated Amazon canopy height product; the claimed 3.68 m accuracy is in-distribution, not independent Amazon-wide. read the letter →

arxiv 2501.10600 v1 pith:5DWLTET3 submitted 2025-01-17 cs.CV

Fabien H Wagner , Ricardo Dalagnol , Griffin Carter , Mayumi CM Hirye , Shivraj Gill , Le Bienfaiteur Sagang Takougoum , Samuel Favrichon , Michael Keller
show 14 more authors
Jean PHB Ometto Lorena Alves Cynthia Creze Stephanie P George-Chacon Shuang Li Zhihua Liu Adugna Mullissa Yan Yang Erone G Santos Sarah R Worden Martin Brandt Philippe Ciais Stephen C Hagen Sassan Saatchi
This is my paper · ORCID
classification cs.CV
keywords canopyheightU-NetPlanetNICFILiDARAmazonforestdeeplearningregressionmonitoringregenerationdetection
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper tries to establish that the height of the Amazon forest can be mapped wall-to-wall from freely available Planet NICFI satellite mosaics at a resolution fine enough to see individual tree crowns. A U-Net regression model translates 4.78 m RGB-NIR images into canopy height, with airborne LiDAR canopy height models as training reference. On a validation sample spread across the Amazon, the model's mean absolute error is 3.68 m, and bias stays low across most of the height range, with little saturation up to 40-50 m. If correct, this yields an Amazon-wide 4.78 m height map for 2020-2024 with an average canopy height near 22 m, and makes logging, deforestation, and regrowth visible as height changes in time series.

What carries the argument

The load-bearing object is the U-Net architecture adapted for regression: an encoder-decoder convolutional network with skip connections and roughly 35 million parameters that takes 256 × 256 × 4 RGBNIR tiles at 4.78 m and outputs a single-band per-pixel height map rescaled from 0-1 to 0-100 m. It is trained with mean squared error weighted by a binary presence/absence mask of the LiDAR canopy height model, so training focuses on pixels where reference data exist. The paired reference was built by computing canopy height models from airborne LiDAR at 1 m and resampling them to 4.78 m with the median, and the same-date plus neighboring NICFI mosaics were used as inputs under a no-change assumption. The skip connections are what allow crown texture and local context to survive through the decoder, which is why the model can reproduce crown-level detail instead of only smooth height averages.

What would settle it

Collect fresh airborne LiDAR transects in the western Amazon and Guiana Shield within two weeks of a NICFI basemap and compare predictions; if the model's error against this independent LiDAR is systematically larger than 3.68 m, the claimed Amazon-wide low bias is falsified.

Watch

Extended reading notes

Core claim

The central discovery is that a locally trained regression U-Net can recover LiDAR-grade canopy height from 4.78 m Planet NICFI imagery across the whole Amazon, where global canopy height products saturate or blur. The paper reports a mean absolute error of 3.68 m on 3,436 validation tiles, close alignment of median predicted height with LiDAR medians, and reliable estimates up to 45-50 m, while a 0.5 m RGB-based global product, a 10 m Sentinel-2 product, and a 10 m Sentinel-1/2 product show stronger saturation and larger errors in this region. From these predictions, the paper derives a weighted mean Amazon canopy height of 22.09 m, a median of 22.25 m, and a 97.5th percentile of 32.10 m, and maps a ring of the tallest forests around the central Amazon and individual giant trees with crowns wider than 50 m.

Load-bearing premise

The load-bearing premise is that forest canopy height stayed unchanged between the LiDAR acquisition and the paired NICFI image, including images taken up to a year earlier or later.

Editorial extensions

If this is right

  • The Amazon forest has a weighted mean canopy height of about 22 m, with the tallest forests forming a roughly 1,000 km-wide arc around the central Amazon and a hotspot in the Guiana Shield.
  • Selective logging becomes visible as month-to-month negative height differences in Planet NICFI time series, allowing disturbance to be located even when it does not clear the canopy.
  • Deforestation and regrowth can be followed from height trajectories: cleared pixels fall near zero and stay low, while abandoned pasture shows gradual height increase reaching 15-20 m by 2024.
  • Individual giant trees, including crowns 50-70 m wide, can be identified from the height map, enabling automatic searches for the Amazon's largest trees.
  • On the same validation tiles, the locally calibrated U-Net outperforms three 2020 global canopy height maps, especially for heights above 30-40 m where those products saturate.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • I infer that the same recipe should transfer to other NICFI-covered tropical forests in Africa and Southeast Asia, because the image source is pan-tropical and the model is locally trainable; the main bottleneck will be local LiDAR reference data for calibration.
  • I infer that the monthly NICFI cadence could be used to build dense height time series that turn regeneration curves into carbon-recovery estimates without repeated LiDAR or commercial data, an extension the paper only sketches.
  • I infer that the reported 3.68 m error likely understates true error in recently disturbed areas, because the training assumption of unchanged height over up to one year corrupts labels where logging or regrowth occurred between LiDAR and satellite acquisition.
  • I infer that blending the optical U-Net with radar-based height estimates would improve cloud-dominated zones such as the Andes slopes, where the paper itself notes its product is weakest.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper trains a U-Net regression model on Planet NICFI RGB-NIR imagery (4.78 m) with LiDAR-derived canopy height labels to produce a 2020-2024 Amazon canopy height map. Validation MAE is reported as 3.68 m, and the model is compared against three global canopy height products (Tolan, Pauls, Lang). The paper also demonstrates height-based detection of logging, deforestation, and regeneration, and reports an area-weighted mean Amazon canopy height of 22.09 m. The central claims are the accuracy and superiority of the model in the Amazon and the usability of the resulting map.

Significance. If the accuracy claim held under independent evaluation, this would be a valuable high-resolution forest structure product for a data-poor region, with potential applications to carbon monitoring and conservation. The paper uses publicly available LiDAR and NICFI data and provides a detailed description of the preprocessing and training pipeline, which are strengths. However, the validation is not spatially or temporally independent, and the Amazon-wide mean is given without uncertainty, so the headline numbers should be treated as provisional pending additional validation.

major comments (4)
  1. [Section 2.7] The validation is constructed by selecting one 256x256 patch from each NICFI tile that overlaps the LiDAR CHM, while the remaining ~47,000 training patches come from the same tiles and the same LiDAR programs/sites. Because neighboring patches in a tile share forest structure, illumination, and sensor calibration, the held-out patches are not spatially independent of training. The reported MAE of 3.68 m therefore measures in-distribution fit, not accuracy over the full Amazon, and the same limitation applies to the comparisons with Tolan, Pauls, and Lang in Section 3.1. Please provide a leave-one-site-out or spatially disjoint evaluation, and re-run the product comparisons on those held-out regions.
  2. [Section 2.7] The procedure pairs each LiDAR CHM with the closest NICFI image and also includes images from the previous and subsequent dates, allowing a time window of up to one year (±6 months for biannual NICFI, ±1 month for monthly). Height changes from growth, logging, or natural disturbance within that window corrupt the training and validation labels, and because such events are spatially correlated with forest types and disturbance history, the learned image-to-height mapping can be biased. Please quantify the sensitivity of the reported accuracy to this window, for example by restricting the validation to image-label pairs within one month and by excluding pixels with known disturbance from the analysis.
  3. [Section 3.9 and Table 1] The Amazon mean canopy height of 22.09 m is reported without any uncertainty or correction for known model biases, including underestimation above 50 m (Section 3.1), cloud/shade artifacts (Section 3.7), and the relaxed cloud masking applied in low-observation regions such as the Guiana Shield and western Amazon (Section 3.8). Please provide an error budget, confidence intervals on the mean and percentiles, or a comparison with independent regional height estimates.
  4. [Section 3.1] The claim of outperforming existing global products is established only on validation patches drawn from the training domain; this does not demonstrate superiority over the full Amazon, particularly in underrepresented regions such as the Guiana Shield, the western Amazon, and the Andes. Please report per-region accuracy (for example, the four Feldpausch regions of Figure 1) and, ideally, validation on LiDAR or GEDI data not used in training.
minor comments (6)
  1. [Section 3.1, Figure 3 caption] The caption mentions Tolan's model and Lang's model but the figure has four panels including Pauls's model; please correct the caption.
  2. [Section 3.9, Figure 12] The text contains repeated phrases such as 'The the Guiana Shield' and 'in the Shield'; please edit.
  3. [Table 1] The heading 'T able 1' contains a stray space; please fix.
  4. [Section 2.7] Please state explicitly how many unique LiDAR tiles/sites contribute to training versus validation, and whether any training patch is taken from the same NICFI tile as a validation patch.
  5. [Equation 1] Please define n, i, and the exact condition for a 'valid observation' (height above 5 m and non-cloud observation count above 5) in the text.
  6. [Section 4.2 / Data Availability] The decision to keep the dataset non-open is explained, but the Data Availability section should state whether the model weights, the prediction code, or the derived height map will be released, as this affects reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the central accuracy claim is a supervised-learning validation against held-out LiDAR CHM, not a self-derived prediction.

full rationale

The paper's derivation chain is an ordinary supervised regression: Planet NICFI images are inputs, LiDAR-derived CHMs are training targets (Sec. 2.2, 2.6, 2.7), and the 3.68 m MAE is computed on 3,436 held-out 256x256 patches against LiDAR reference heights (Sec. 3.1, Fig. 3a). The validation heights are external LiDAR measurements, not model outputs, so the accuracy figure is not circular by construction. The Amazon-wide mean (22.09 m) is a model prediction applied to tiles without LiDAR, which is extrapolation rather than circularity; the paper states this mean is computed from model predictions and acknowledges the Andes and cloud/shade regions are less reliable. Self-citations appear in Sec. 2.5 and Sec. 3.5-3.6, where the authors' own deforestation/logging products (Wagner et al. 2023; Dalagnol et al. 2023) are used to date events when illustrating height-change detection, but these products are not training targets for the height model and the height-change detection is an illustrative application, not the central map-accuracy claim. Therefore no load-bearing step reduces to its own inputs. Independence concerns (spatial autocorrelation of validation patches drawn from the same NICFI tiles as training, and up-to-one-year label temporal mismatch, Sec. 2.7) are validity/robustness issues, not circularity, and are outside this pass's mandate.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim depends on the validity of LiDAR as ground truth, the temporal stability assumption in Section 2.7, and the spatial independence of validation. Several hand-chosen constants (CHM scaling, cloud buffer, height and observation thresholds) affect the reported numbers. No new entities are introduced.

free parameters (4)
  • CHM scaling factor = 2.5
    Used to convert DSM-DTM difference into 8-bit integer height (Section 2.2). Chosen by hand to maximize height resolution in 8 bits; affects loss function and model output scaling.
  • Cloud/shade mask buffer = ~1200 m
    Added around cloud-masked pixels to guarantee cloud-free inputs (Section 4). Chosen by hand; reduces effective sample size and may exclude near-cloud tall trees.
  • Minimum forest height threshold = 5 m
    Pixels with height above 5 m are treated as forest for computing the Amazon mean height (Section 2.8). Chosen by hand; changes the mean height estimate.
  • Minimum valid observations threshold = 5
    A pixel must have at least 5 non-cloud observations to be included in the mean height composite (Section 2.8). Chosen by hand; affects coverage and possibly the height estimate in cloudy regions.
assumptions (5)
  • domain assumption LiDAR-derived canopy height models are accurate ground truth for tree height.
    Used throughout Section 2.2; no uncertainty propagation from LiDAR processing (pit-free algorithm, TIN interpolation, etc.).
  • domain assumption Canopy height is recoverable from Planet NICFI optical reflectance and texture.
    The whole approach assumes a learned mapping exists between RGB-NIR images and height; stated implicitly in Sections 1 and 2.6.
  • domain assumption No significant canopy height change occurs within the time window between LiDAR acquisition and NICFI image date (up to ±6 months for biannual, ±1 month for monthly).
    Stated in Section 2.7; load-bearing for correctness of training labels.
  • domain assumption NICFI radiometric inconsistencies are tolerated by deep learning without calibration.
    Section 2.1 claims deep learning can handle absolute radiometric inaccuracies; no explicit test is provided.
  • domain assumption The validation sample of one image per NICFI tile extent is spatially independent enough to estimate generalization.
    Section 2.7; tiles may contain multiple LiDAR flights, and adjacent tiles may share forest types, leading to possible spatial autocorrelation.

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Cite this review

Pith. "Pith review of High Resolution Tree Height Mapping of the Amazon Forest using Planet NICFI Images and LiDAR-Informed U-Net Model." pith.science (2026). https://pith.science/paper/5DWLTET3

@misc{pith2026250110600,
  author       = {Pith},
  title        = {Pith review of: High Resolution Tree Height Mapping of the Amazon Forest using Planet NICFI Images and LiDAR-Informed U-Net Model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/5DWLTET3}},
  note         = {Machine review of arXiv:2501.10600}
}
read the original abstract

Tree canopy height is one of the most important indicators of forest biomass, productivity, and ecosystem structure, but it is challenging to measure accurately from the ground and from space. Here, we used a U-Net model adapted for regression to map the mean tree canopy height in the Amazon forest from Planet NICFI images at ~4.78 m spatial resolution for the period 2020-2024. The U-Net model was trained using canopy height models computed from aerial LiDAR data as a reference, along with their corresponding Planet NICFI images. Predictions of tree heights on the validation sample exhibited a mean error of 3.68 m and showed relatively low systematic bias across the entire range of tree heights present in the Amazon forest. Our model successfully estimated canopy heights up to 40-50 m without much saturation, outperforming existing canopy height products from global models in this region. We determined that the Amazon forest has an average canopy height of ~22 m. Events such as logging or deforestation could be detected from changes in tree height, and encouraging results were obtained to monitor the height of regenerating forests. These findings demonstrate the potential for large-scale mapping and monitoring of tree height for old and regenerating Amazon forests using Planet NICFI imagery.

Figures

Figures reproduced from arXiv: 2501.10600 by the authors.

Figure 1
Figure 1. Location of the 3,060 LiDAR data points in the Amazon domain used in the validation of the Amazon forest tree canopy height model (shown in gold). As at least one image per flight was used in validation, the validation locations approximately represent the locations of all the LiDAR flights in the Amazon used in the study. The Amazon domain is partitioned into four regions according to Feldpausch et al. (2011). 2.2.… view at source ↗
Figure 2
Figure 2. U-Net model architecture used for canopy height estimation from Planet NICFI images, adapted from Ronneberger et al. (2015). The number of channels is indicated above the cuboids, and the vertical numbers indicate the row and column sizes in pixels. The operations (convolutions, skip connections, max pooling, and upsampling) performed in each layer and their sizes are indicated by the colored arrows. created a spati… view at source ↗
Figure 3
Figure 3. Comparison of predicted versus observed height (m) for the 3,436 validation areas represented as density scatterplots for our canopy height model (a), Tolan’s model (b), and Lang’s model (c). Tolan’s model and Lang’s model, with native spatial resolutions of 0.5 m and 10 m respectively, were warped to our 4.78 m spatial resolution using the median and nearest neighbor algorithms, respectively. Each plot contains app… view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Distribution of the observed height in the validation sample (a); distribution of the predicted height in the validation sample for our model, Tolan’s model, and Lang’s model (b); and distribution of the differences in predicted height in the validation sample between …
Figure 5
Figure 5. Figure 5: Example of canopy height models observed in the validation dataset (column 1), predicted from our canopy height model (column 2), predicted from Tolan’s model (column 3), and predicted from Lang’s model (column 4). Tolan’s model, Pauls’s model and Lang’s model, whose n…
Figure 6
Figure 6. Figure 6: Example of canopy height models observed in the validation dataset (column 1), predicted from our canopy height model (column 2), predicted from Tolan’s model (column 3), and predicted from Lang’s model (column 4). Tolan’s model, Pauls’s model and Lang’s model, whose n…
Figure 7
Figure 7. Figure 7: Percentile 0.05 (a), median (b), and percentile 0.95 (c) of heights computed from canopy heights observed and predicted by our model in the 3,436 validation areas. Median of the points and 95% confidence interval computed by intervals of 5 m is given in blue. Each poin…
Figure 9
Figure 9. Figure 9: Example of height time series in case of deforestation (points 1, 2, and 3), deforestation and regrowth (points 4, 5) and only regrowth (points 6). For each location, the value of the point and its 24 closest neighbors are presented. The locations of these points are d…
Figure 11
Figure 11. Figure 11: Mean number of cloud-free observations for the ∼ 20 km × 20 km pixels corresponding to the Planet NICFI tiles. Only pixels with vegetation height greater than zero were considered in the mean computation. 3.9. Amazon forest canopy height We found that the mean canopy …
Figure 12
Figure 12. Figure 12: Map of the mean and percentile 97.5th of the Amazon forest tree canopy height on the period 2020-2024 (a-b). Each statistics is given for ∼ 20 km × 20 km pixels which correspond to a planet tile. Inside a tile, mean and percentile 97.5th are computed only with pixels …
Figure 13
Figure 13. Figure 13: Tree canopy height differences in meters between Tolan’s product (a), Pauls’s product (b), Lang’s product (c-d), and our canopy height product. For Lang’s product, we compared it to the mean of our product (c) and the 97.5th percentile (d), as Lang models the GEDI 98%…
Figure 14
Figure 14. Figure 14: Canopy height of the Amazon forest (m). To facilitate visualization at very high resolution, the colors represent the estimates from our model, aggregated to an 80 m spatial resolution using the median. For most of the Amazon forest, the model enables the detection of…
Figure 15
Figure 15. Figure 15: Examples of giant trees identified by our Amazon canopy height model. For each individual tree (row), a Planet NICFI image is shown during its leafless period (first column), during its leaf-covered period (second column), and the third column presents our canopy heig…

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Forward citations

Cited by 2 Pith papers

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.