Pith. sign in

REVIEW 3 major objections 5 minor 66 references

Foundation-Model Earth Representations Enable Regional-Scale Forest Aboveground Biomass Monitoring Across the Northeastern United States

T0 review · 3 major / 5 minor · reviewed 2026-08-02 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A globally available satellite-embedding product can estimate forest aboveground biomass across the northeastern US, reaching R² = 0.82 without airborne LiDAR.

desk verdict Useful application-level extension of GSE-based biomass mapping, but the headline R²=0.82 rests on a validation split that leaks plot identity; Scenario I is the defensible result. read the letter →

arxiv 2607.27217 v1 pith:4BUS4J74 submitted 2026-06-25 stat.AP cs.LG

Shashika Lamahewage , Chandi Witharana This is my paper · ORCID
classification stat.APcs.LG
keywords abovegroundbiomasssatelliteembeddingsfoundationmodelLiDARforestcarbonmonitoringmachinelearningspatialautocorrelationtemporalgrowthadjustment
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

The paper tries to establish that learned satellite-embedding features can substitute for airborne LiDAR in regional forest carbon monitoring. Using a pretrained Earth-observation foundation model's annual embeddings, continuous inventory plots, and a per-plot growth adjustment, the authors expand a 589-plot dataset to about 6,800 plot-year observations and report R² = 0.82 for GSE-only aboveground biomass prediction. Combining LiDAR with the same embeddings gives R² = 0.79 at the smaller sample size, with model bias reduced by over 70% relative to default models. If this holds, annual biomass maps at 10–15 m resolution become feasible without new airborne surveys and without revealing sensitive plot locations. That would matter because most regional AGB models with national inventory data level off at R² between 0.2 and 0.65.

What carries the argument

The load-bearing mechanism is the annual GSE time series: 64 learned embedding values per 10 m pixel, summarized per plot as maximum, mean, minimum, and standard deviation to form 256 predictors, then reduced by principal-component analysis to about 54 components at 95% variance. The second mechanism is a per-plot annual growth adjustment: plot-level aboveground biomass between consecutive remeasurements is converted to a linear annual rate and propagated to each GSE or LiDAR acquisition year. That single step is what turns 589 LiDAR-compatible plots into 6,801 plot-year observations, so the headline accuracy gain and bias reduction depend on it. Around these sit tree-ensemble regressors, pe

What would settle it

Re-run Scenario II with leave-one-plot-out or leave-region-out cross-validation. If R² drops markedly below the 0.82 headline—say, below the LiDAR-only scenario I models—the annual-GSE advantage is mostly temporal interpolation of the same plots, not spatial or temporal generalization.

Watch

Extended reading notes

Core claim

On its own terms, the paper claims that the 64-dimensional GSE representations produced by a multimodal Earth-observation foundation model encode enough ecological structure to predict forest aboveground biomass across seven northeastern states. When combined with airborne LiDAR and continuous forest inventory data, the best tuned model reaches R² = 0.79 on 589 plots. When the annual GSE time series is used to grow the training set to 6,801 plot-year observations through linear per-plot growth adjustment, a GSE-only gradient-boosted tree model reaches R² = 0.82 and reduces model bias by more than 70% relative to the default configuration. The paper also reports that adding geographic coordin

Load-bearing premise

The paper's own Discussion admits that allometric-model uncertainty was not evaluated, and although it cites the standard warning on spatial validation, it never applies spatial or plot-block cross-validation: Scenario II derives 6,801 annual observations from roughly 589 plots by linear growth adjustment, and the 80/20 random split does not group by plot, so the R² can measure plot-level memorization rather than prediction for new forest locations or years.

Editorial extensions

If this is right

  • Regional agencies could generate annual biomass and carbon maps from existing inventory plots plus freely available satellite embeddings, without waiting for new airborne LiDAR campaigns.
  • In areas with no LiDAR, GSE-only models offer a credible regional baseline at R² = 0.82, though with larger absolute errors at high-biomass extremes.
  • Sparse field networks become far more informative: the growth-adjustment trick multiplies effective sample size tenfold, which could transfer to other sparsely measured ecological variables.
  • The reduced residual spatial autocorrelation in combined models implies fewer spatially clustered artifacts, strengthening the case for using these maps in carbon accounting.
  • A publicly updatable biomass product can be produced without sharing protected plot coordinates, making monitoring more transparent and accessible.

Reading between the lines

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

  • The decisive untested question is whether R² = 0.82 survives plot-block or spatial cross-validation; if it collapses, the reported gain is interpolation of known plots rather than generalization to unseen forests.
  • The same temporal-stacking recipe—annual embeddings plus a linear growth model—could be applied to other sparse long-term field networks (soil carbon, wetland biomass), but only where a reliable change model exists.
  • A direct comparison against simpler spectral indices would reveal whether GSE adds real biological signal or simply benefits from the enlarged training sample and spatial coordinates.
  • Because PCA keeps maximum-variance components rather than biomass-relevant ones, a supervised or target-informed component selection might raise GSE-only performance further.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper evaluates Google Satellite Embeddings (GSE), an Earth observation foundation-model product, combined with airborne LiDAR and continuous forest inventory data from NEFIN, to estimate aboveground biomass (AGB) across the northeastern United States. Three machine-learning algorithms (RF, ExtraTrees, XGBoost) are tested under two scenarios: Scenario I uses 589 plots with both LiDAR and GSE coverage; Scenario II expands the sample to 6,801 observations by using annual GSE data and linearly growth-adjusted AGB labels from the same plots. The authors report R² = 0.79 for combined LiDAR–GSE models in Scenario I and R² = 0.82 for the GSE-only Scenario II, claiming that annual GSE observations enable scalable, regional carbon monitoring. The paper also reports Moran's I spatial autocorrelation analyses, permutation feature importance, and Monte Carlo stability simulations.

Significance. If the results held, the paper would make a meaningful contribution by demonstrating that globally available, annually updated satellite embeddings can complement or substitute for airborne LiDAR in regional AGB monitoring. The use of a real, unfuzzed regional inventory network and the direct comparison of LiDAR, GSE, and combined predictors are useful strengths. The paper also provides transparency about residual spatial autocorrelation and model stability through Moran's I and Monte Carlo analyses. However, the central empirical claim—that annual GSE expansion yields R² = 0.82—is not supported by the validation design. The reported performance is likely inflated by leakage between training and test observations that share the same plots and by the use of interpolated, non-independent annual labels. These issues undermine the paper's main conclusion that GSE enables reliable annual biomass monitoring.

major comments (3)
  1. [Machine Learning Model Training and Validation (Table 2, Scenario II)] The headline R² = 0.82 for Scenario II is not a valid estimate of out-of-sample generalization. The 6,801 annual observations are generated from only ~589 unique plots via the linear growth adjustment of Eq. (2). The 80/20 random split is performed on observations, not on plots, and no plot-grouped cross-validation (e.g., GroupKFold by plot or leave-location-out) is applied. Consequently, the same plot contributes to both training and test sets, allowing the model to memorize plot-level intercepts and interpolate within a plot's deterministic annual series. This leakage path alone invalidates the R² = 0.82 claim as a measure of generalization to new forest locations or years. The paper cites Ploton et al. (2020) and Roberts et al. (2017) on spatial validation but does not implement their recommended blocking schemes.
  2. [Annual Aboveground Biomass Adjustment (Eq. 2)] The response variable for Scenario II is not independently measured annual AGB. Equation (2) linearly interpolates (or extrapolates) between two plot inventories, so each 'annual observation' is a deterministic function of the plot's endpoint AGB values and the time difference. Even if the train/test split were grouped by plot, held-out annual samples of the same plot are not independent tests of the model's ability to predict real annual change; they are tests of the model's ability to reproduce a linear interpolation. The paper presents Scenario II as evidence for annual biomass monitoring, but the validation cannot support that conclusion. Independent validation against actual remeasurements or a strict leave-plot-and-year-out design is required.
  3. [Scenario I validation (Table 5)] Scenario I also lacks spatial blocking. The 589 plots are split randomly at the observation level, but many plots are geographically clustered (Figure 1), and residual Moran's I values remain significant for some models even after spatial correction (e.g., RF G1 after: I = 0.0698, p = 0.033). Random splitting of spatially autocorrelated data inflates R² relative to spatial cross-validation, as demonstrated in the very papers cited (Ploton et al. 2020; Roberts et al. 2017). The R² = 0.79 for combined LiDAR–GSE models should therefore be interpreted cautiously and should be recomputed with spatial or blocked cross-validation before being presented as a robust performance estimate.
minor comments (5)
  1. [Abstract and Introduction] Typos: 'AplphaEarth' should be 'AlphaEarth'; 'fundings' should be 'findings'; 'Sentinal-1' should be 'Sentinel-1'. In the Introduction, the sentence 'However, inconsistencies in spatial and temporal resolution among traditional RS sources have prolonged the reproducibility...' is awkward; consider revising.
  2. [Table 2] The column header says 'No. of plots' but Scenario II reports 6,801 observations, not unique plots. Clarify that this is the number of annual observations generated from 589 plots.
  3. [Equations (3)–(7)] The equations are presented without standalone numbering in the running text; ensure they are numbered and referenced clearly. Also check the formula for R²: the denominator uses \(\bar{y}_i\), which appears to be a typo for \(\bar{y}\).
  4. [Results, Monte Carlo section] The Monte Carlo stability analysis is performed on G3 (combined LiDAR–GSE) models, but the main claim about GSE's standalone value comes from Scenario II. Clarify why the stability analysis does not cover the GSE-only annual expansion model that produces R² = 0.82.
  5. [Discussion] The statement 'these unlikely high AGB values could result from allometric uncertainty... catalyzed annual growth adjustments due to disturbance' is speculative; the paper does not provide evidence linking extreme residuals to either cause. Consider softening or providing diagnostics.

Circularity Check

2 steps flagged · score 6.0 of 10

Scenario II's annual AGB 'prediction' is evaluated against interpolated labels from Eq. 2, and the random observation-level split lets the same plot appear in both training and test; the headline R²=0.82 therefore partially reduces to memorizing plot-level growth trends.

  1. fitted input called prediction [Methods - Response Variable Calculations, Eq. 2; Results - Scenario II Results; Table 2]
    "ΔAGB= AGBt2- AGBt1 / t2-t1 ... By only using GSE data and AGB growth adjustment, we expanded the sample size to 6,802 observations without masking the data out due to limited aerial LiDAR availability."

    The annual AGB labels in Scenario II are not independent measurements: they are per-plot linear interpolations/extrapolations between two inventory AGB values, with the slope ΔAGB computed from the same plot's own earlier and later inventories via Eq. 2. An 'annual' label for a plot in year t is AGB_t1 + ΔAGB·(t−t1), a deterministic function of inputs already in the dataset. Thus 'predicting annual AGB' means predicting an interpolated target, not an independently measured annual biomass; the tenfold expansion is an expansion of synthetic labels, and the reported R²=0.82 is partly accuracy against this constructed response rather than against new ground truth.

  2. other [Methods - Machine Learning Model Training and Validation; Table 2; Results - Scenario II Results]
    "Each model was fitted to a randomly selected 80% of the dataset leaving the remaining 20% for independent testing, with 10-fold CV applied for validation [11]."

    The 'independent' test set is not independent at the plot level. The 6,802 annual observations are derived from roughly 589 unique plots propagated across 2017–2025, and the random 80/20 split is applied to observations, not plots, with no plot-grouped cross-validation. Consequently, the same plot can contribute both training and test rows, and all its annual labels share the same Eq. 2 intercept and slope. The test R² then partly measures the model's memory of plot-level identity and growth trajectory through GSE/spatial features, rather than generalization to new forest locations or years. The paper cites Ploton et al. [44] and Roberts et al. [46] on spatial validation but does not implement plot-blocked or spatial CV.

full rationale

The central Scenario I result (LiDAR and LiDAR+GSE models with R² up to 0.79 on 589 plot-level samples) is not circular: the response is field-inventory AGB adjusted to the LiDAR acquisition year, the predictors are external LiDAR/GSE features, and the claim is an empirical comparison. The circularity burden is concentrated in Scenario II, where the paper's headline improvement to R²=0.82 depends on an expanded dataset whose annual labels are generated by the paper's own Eq. 2 from each plot's two inventory measurements. Because the validation split is at the observation level, not the plot level, the same plot's synthetic annual series can appear on both sides of the split, so the test metric does not establish that GSE embeddings predict independently measured annual AGB at new locations or years. The self-citations to the authors' prior papers (Lamahewage et al. 2025) provide methodological context and filtering criteria but are not the load-bearing source of the headline claim, so they do not independently raise the score. Overall, Scenario I is self-contained and externally grounded, while Scenario II's headline prediction is partially circular due to constructed labels and plot-level leakage; hence a moderate score of 6 rather than a higher one.

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

The central claim depends on treating the linearly interpolated annual AGB as ground truth, assuming GSE features align with small field plots despite geolocation errors, and assuming that random splitting of repeated plot observations yields independent test data. The first two are domain assumptions; the last is an unsupported modeling assumption that likely inflates the headline accuracy.

free parameters (4)
  • PCA cumulative variance threshold = 0.95 (retains 54 PCs for GSE features)
    Chosen by hand to balance dimensionality reduction and variance retention; directly determines the GSE predictor set in all models.
  • Temporal matching window = ±3 years
    Ad hoc window for aligning inventory measurements with LiDAR/GSE acquisition; affects which plots are retained and the growth adjustment magnitude.
  • 2017 GSE-as-2015-2017 assumption = Not a number; assumed representative
    Ad hoc assumption that 2017 embeddings cover 2015–2017, made to retain more CFI plots; not independently tested.
  • ML hyperparameters (grid search) = Selected per model via GridSearchCV (e.g., n_estimators 300/500, max_depth, learning_rate)
    Fitted to the training data by 10-fold CV; standard practice but adds flexibility that can overfit if validation is not independent.
assumptions (5)
  • domain assumption Chojnacky allometric equations (Eq. 1) convert DBH to AGB without bias.
    Tree-level biomass is not measured directly; the paper acknowledges allometric uncertainty but does not propagate it into model targets.
  • ad hoc to paper Linear annual growth adjustment (Eq. 2) accurately models AGB change between inventories.
    Annual labels are constructed by straight-line interpolation, which may misrepresent disturbance, silvicultural treatment, and nonlinear growth.
  • domain assumption GSE features carry ecologically meaningful, plot-relevant signal at 10 m resolution.
    The embeddings are trained on diverse Earth observation data; their direct link to field biomass is the hypothesis under test.
  • domain assumption Field plot coordinates align with 10 m GSE pixels and LiDAR metrics despite geolocation uncertainties exceeding 30 m.
    The paper acknowledges this uncertainty but uses pixel-level features without correction.
  • ad hoc to paper The random 80/20 split produces statistically independent test observations.
    With repeated annual observations per plot, this assumption is false and inflates reported R².

how reviews work

0 comments
Cite this review

Pith. "Pith review of Foundation-Model Earth Representations Enable Regional-Scale Forest Aboveground Biomass Monitoring Across the Northeastern United States." pith.science (2026). https://pith.science/paper/4BUS4J74

@misc{pith2026260727217,
  author       = {Pith},
  title        = {Pith review of: Foundation-Model Earth Representations Enable Regional-Scale Forest Aboveground Biomass Monitoring Across the Northeastern United States},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4BUS4J74}},
  note         = {Machine review of arXiv:2607.27217}
}
read the original abstract

Forest aboveground biomass (AGB) is a critical indicator of ecosystem productivity and terrestrial carbon storage, yet regional carbon monitoring remains constrained by the sparse spatial and temporal availability of field inventories and airborne structural measurements. Recent Earth observation foundation models provide globally consistent geospatial representations derived from diverse multimodal datasets, offering a potential pathway toward scalable biomass monitoring. Here, we evaluate Google Satellite Embeddings (GSE), generated by the AplphaEarth Foundation Model, for regional-scale AGB estimation across diverse temperate forest ecosystems in the northeastern United States. We integrated annual GSE observations, airborne LiDAR, and continuous forest inventory measurements from the Northeastern Forest Inventory Network (NEFIN) within a machine-learning framework. Combined LiDAR-GSE models achieved an R^2 of 0.79 for AGB estimation. Capitalizing on annual GSE observations expanded the training dataset by more than tenfold through temporal growth adjustment, increasing predictive performance to R^2 = 0.82 while reducing model bias by over 70%. Spatial autocorrelation analyses showed that integrating foundation-model representations and structural predictors substantially reduced residual spatial dependence. Monte Carlo simulations demonstrated that hyperparameter optimization reduced model-performance variability by 27.9%. Our findings demonstrate that foundation-model Earth representations capture ecologically meaningful information relevant to forest biomass and provide a scalable framework for annual carbon monitoring in regions with incomplete airborne LiDAR coverage. Our fundings establish a pathway toward next-generation forest carbon assessment based on globally available foundation-model Earth observations.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

66 extracted references · 9 canonical work pages

  1. [1]

    and Ciais, P., 2011

    Pan, Y ., Birdsey, R.A., Fang, J., Houghton, R., Kauppi, P.E., Kurz, W.A., Phillips, O.L., Shvidenko, A., Lewis, S.L., Canadell, J.G. and Ciais, P., 2011. A large and persistent carbon sink in the world’s forests. science, 333(6045), pp.988-993

    2011

  2. [2]

    Hoover, Katie, and Anne A. Riddle. 2022. U.S. Forest Carbon Data: In Brief. Version 10. Congressional Research Service, Report No. R46313. Accessed November 5, 2025. https://crsreports.congress.gov

    2022

  3. [3]

    Carbon sequestration in mangrove forests

    Alongi, D.M., 2012. Carbon sequestration in mangrove forests. Carbon management, 3(3), pp.313-322

    2012

  4. [4]

    and D'Amato, A.W., 2019

    Catanzaro, P. and D'Amato, A.W., 2019. Forest carbon: an essential natural solution for climate change. University of Massachusetts Amherst

    2019

  5. [5]

    Lamahewage, S.H.G., Witharana, C., Riemann, R. et al. Aboveground biomass estimation using multimodal remote sensing observations and machine learning in mixed temperate forest. Sci Rep 15, 31120 (2025). https://doi.org/10.1038/s41598-025-15585-6

    work page doi:10.1038/s41598-025-15585-6 2025

  6. [6]

    A carbon monitoring system for mapping regional, annual aboveground biomass across the northwestern USA

    Hudak, Andrew T., Patrick A. Fekety, Van R. Kane, Robert E. Kennedy, Steven K. Filippelli, Michael J. Falkowski, Wade T. Tinkham et al. "A carbon monitoring system for mapping regional, annual aboveground biomass across the northwestern USA. "Environmental Research Letters15, no. 9 (2020): 095003

    2020

  7. [7]

    and Ku, N.W., 2014

    Sheridan, R.D., Popescu, S.C., Gatziolis, D., Morgan, C.L. and Ku, N.W., 2014. Modeling forest aboveground biomass and volume using airborne LiDAR metrics and forest inventory and analysis data in the Pacific Northwest. Remote Sensing, 7(1), pp.229-255. https://doi.org/10.3390/rs70100229

    work page doi:10.3390/rs70100229 2014

  8. [8]

    and Jenkins, J.C., 2014

    Chojnacky, D.C., Heath, L.S. and Jenkins, J.C., 2014. Updated generalized biomass equations for North American tree species. Forestry, 87(1), pp.129-151

    2014

Show all 66 references
  1. [9]

    and Birdsey, R.A., 2003

    Jenkins, J.C., Chojnacky, D.C., Heath, L.S. and Birdsey, R.A., 2003. National-scale biomass estimators for United States tree species. Forest science, 49(1), pp.12-35

    2003

  2. [10]

    M., Johnson, L., and Phoenix, D

    Tamiminia, H., Salehi, B., Mahdianpari, M., Beier, C. M., Johnson, L., and Phoenix, D. B.: A COMPARISON OF DECISION TREE-BASED MODELS FOR FOREST ABOVE- GROUND BIOMASS ESTIMATION USING A COMBINATION OF AIRBORNE LIDAR AND LANDSAT DATA, ISPRS Ann. Photogramm. Remote Sens. Spatial...

    2021 doi

  3. [11]

    and Beier, C.M.,

    Johnson, L.K., Mahoney, M.J., Bevilacqua, E., Stehman, S.V ., Domke, G.M. and Beier, C.M.,

  4. [12]

    and Kimes, D.S.,

    Boudreau, J., Nelson, R.F., Margolis, H.A., Beaudoin, A., Guindon, L. and Kimes, D.S.,

  5. [13]

    and Astrup, R.,

    Puliti, S., Breidenbach, J., Schumacher, J., Hauglin, M., Klingenberg, T.F. and Astrup, R.,

  6. [14]

    and Lin, H., 2026

    Xu, G., Wu, S., Xu, C., Yang, X., Du, Y ., Wang, G., Long, J. and Lin, H., 2026. Mapping Forest Aboveground Carbon Storage by Integrating Multi-Source Optical and Multi- 36 Temporal Sentinel-1 SAR Data in Mixed Broadleaf–Coniferous Forests. Remote Sensing, 18(4), p.570

    2026

  7. [15]

    and Padalia, H., 2021

    Nandy, S., Srinet, R. and Padalia, H., 2021. Mapping forest height and aboveground biomass by integrating ICESat‐2, Sentinel‐1 and Sentinel‐2 data using Random Forest algorithm in northwest Himalayan foothills of India. Geophysical Research Letters, 48(14), p.e2021GL093799. ht...

    2021 doi

  8. [16]

    F., Kazmierski, M

    Brown, C. F., Kazmierski, M. R., Pasquarella, V J., Rucklidge, W. J., Samsikova, M., Zhang, C., Shelhamer, E., Lahera, E., Wiles, O., Ilyushchenko, S., Gorelick, N., Zhang, L. L., Alj, S., Schechter, E., Askay, S., Guinan, O., Moore, R., Boukouvalas, A., & Kohli, P.(2025). Alp...

    2025 doi arXiv

  9. [17]

    Available at: https://deepmind.google/blog/alphaearth-foundations-helps-map-our- planet-in-unprecedented-detail/?utm source= chatgpt.com (Accessed: 5 May 2026)

    Google DeepMind (2025) AlphaEarth Foundations helps map our planet in unprecedented detail. Available at: https://deepmind.google/blog/alphaearth-foundations-helps-map-our- planet-in-unprecedented-detail/?utm source= chatgpt.com (Accessed: 5 May 2026)

    2025

  10. [18]

    Available athttps://developers.google.com/earth-engine/tutorials/community/satellite-embedding-01- introduction (Accessed: 3 April 2026)

    Google Earth Engine (2025) Introduction to the Satellite Embedding Dataset. Available athttps://developers.google.com/earth-engine/tutorials/community/satellite-embedding-01- introduction (Accessed: 3 April 2026)

    2025

  11. [19]

    and Ahmad, A., 2025

    Khan, H. and Ahmad, A., 2025. Evaluating AlphaEarth Foundation Embeddings for Pixel-and Object-Based Land Cover Classification in Google Earth Engine

    2025

  12. [20]

    and Jiao, J., 2026

    Jin, C., Jiang, X., Wen, L., Wu, C., Xu, X. and Jiao, J., 2026. Assessing the Utility of Satellite Embedding Features for Biomass Prediction in Subtropical Forests with Machine Learning. Remote Sensing, 18(3), p.436

    2026

  13. [21]

    and Phoenix, D.B.,

    Tamiminia, H., Salehi, B., Mahdianpari, M., Beier, C.M., Johnson, L. and Phoenix, D.B.,

  14. [22]

    and Maruthaveeran, S., 2026

    Min, X., Yusof, M.J.M., Fan, L. and Maruthaveeran, S., 2026. Vegetation Carbon Stock Estimation Using Remote Sensing: A Bibliometric and Critical Review. Forests, 17(4), p.503

    2026

  15. [23]

    Lamahewage, S. H. G., Witharana, C., Riemann, R., Fahey, R., & Worthley, T. (2025). Comparing Machine Learning and Statistical Models for Remote Sensing-Based Forest Aboveground Biomass Estimations. Forests, 16(9), 1430. https://doi.org/10.3390/f16091430

    2025 doi

  16. [24]

    J., O'Neil-Dunne, J., Lu, J., Lamb, R., Dubayah, R

    Tang, H., Ma, L., Lister, A. J., O'Neil-Dunne, J., Lu, J., Lamb, R., Dubayah, R. O., & Hurtt, G. C. (2021). LiDAR Derived Biomass, Canopy Height, and Cover for New England Region, USA, 2015 (V ersion 1). ORNL Distributed Active Archive Center. https://doi.org/10.3334/ORNLDAAC/...

    2021 doi

  17. [25]

    ISPRS annals of the photogrammetry, remote sensing and spatial information sciences, 3, pp.235-241

    A comparison of decision tree-based models for forest above-ground biomass estimation using a combination of airborne lidar and landsat data. ISPRS annals of the photogrammetry, remote sensing and spatial information sciences, 3, pp.235-241

  18. [26]

    Forest Aboveground Biomass and Carbon Sequestration Potential, Northeastern USA

    Ma, L., G. C. Hurtt, H. Tang, R. Lamb, E. Campbell, RO Dubayah, M. Guy et al. "Forest Aboveground Biomass and Carbon Sequestration Potential, Northeastern USA." ORNL DAAC (2022)

    2022

  19. [27]

    and Ruefenacht, B., 2008

    Blackard, J.A., Finco, M.V ., Helmer, E.H., Holden, G.R., Hoppus, M.L., Jacobs, D.M., Lister, A.J., Moisen, G.G., Nelson, M.D., Riemann, R. and Ruefenacht, B., 2008. Mapping US forest 37 biomass using nationwide forest inventory data and moderate resolution information. Remote...

    2008

  20. [28]

    and Healey, S., 2021

    Menlove, J. and Healey, S., 2021. CMS: Forest Aboveground Biomass from FIA Plots across the Conterminous USA, 2009-2019. ORNL DAAC

    2021

  21. [29]

    and Pant, T., 2023, July

    Srivastava, H. and Pant, T., 2023, July. Evaluation of ESA CCI Above Ground Biomass Using Multi-Source, Multi-Spectral Data And Machine Learning. In IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium (pp. 3082-3085). IEEE

    2023

  22. [30]

    Available at: https://prism.oregonstate.edu (Accessed: 7 May 2026)

    PRISM Group, Oregon State University (2026) PRISM Climate Data. Available at: https://prism.oregonstate.edu (Accessed: 7 May 2026)

    2026

  23. [31]

    Forests in Flux: The Effects of Demographic Change on Forest Cover in New England and New York

    Ducey, M., Johnson, K., Belair, E., & Mockrin, M., 2016. Forests in Flux: The Effects of Demographic Change on Forest Cover in New England and New York. . https://doi.org/10.34051/p/2020.261

    2016 doi

  24. [32]

    Potential future dynamics of carbon fluxes and pools in New England forests and their climatic sensitivities: A model‐based study

    Tang, G., Beckage, B., & Smith, B., 2014. Potential future dynamics of carbon fluxes and pools in New England forests and their climatic sensitivities: A model‐based study. Global Biogeochemical Cycles, 28, pp. 286 - 299. https://doi.org/10.1002/2013gb004656

    2014 doi

  25. [33]

    Census Bureau

    U.S. Census Bureau. (2021). State area measurements and internal point coordinates. Accessed June 12, 2025, from https://www.census.gov/geographies/reference- files/2010/geo/state-area.html

    2021

  26. [34]

    C., and L

    Chojnacky, D. C., and L. S. Heath. 2002. Estimating down dead woody material in the United States. General Technical Report NE-298. U.S. Department of Agriculture, Forest Service

    2002

  27. [35]

    Integrating forest inventory and analysis data into a LIDAR-based carbon monitoring system

    Johnson, Kristofer D., Richard Birdsey, Andrew O. Finley, Anu Swantaran, Ralph Dubayah, Craig Wayson, and Rachel Riemann. "Integrating forest inventory and analysis data into a LIDAR-based carbon monitoring system." Carbon Balance and Management 9 (2014): 1-11

    2014

  28. [36]

    and Herold, M.,

    Chen, N., Tsendbazar, N.E., Suarez, D.R., Silva-Junior, C.H., Verbesselt, J. and Herold, M.,

  29. [37]

    Northeastern Forest Inventory Network (NEFIN)

    University of Vermont, 2024. Northeastern Forest Inventory Network (NEFIN). [online] Available at: https://www.uvm.edu/femc/nefin/ [Accessed 7 Jul. 2025]

    2024

  30. [38]

    Geological Survey

    U.S. Geological Survey. (2023, July 27). National Land Cover Database (NLCD) 2021: Conterminous U.S. land cover [Image]. U.S. Department of the Interior. https://www.usgs.gov/media/images/national-land-cover-database-nlcd-2021-conterminous- us-land-cover

    2023

  31. [39]

    and Aly, M.H., 2025

    Saim, A.A. and Aly, M.H., 2025. Machine Learning and Multisensor Data Fusion for Forest above Ground Biomass Estimation in Arkansas. Earth Systems and Environment, pp.1-18

    2025

  32. [40]

    Geological Survey (2026) USGS Lidar Explorer

    U.S. Geological Survey (2026) USGS Lidar Explorer. Available at: https://apps.nationalmap.gov/lidar-explorer/#/ (Accessed: 2 May 2026)

    2026

  33. [41]

    lidar: A Python package for delineating nested surface depressions from digital elevation data

    Wu, Q., 2021. lidar: A Python package for delineating nested surface depressions from digital elevation data. Journal of Open-Source Software, 6(59), p.2965

    2021

  34. [42]

    National Oceanic and Atmospheric Administration (NOAA), Office for Coastal Management. (2025). Canopy – High Resolution Land Cover, Phase 1 Initial Layers [Data set]. Available from https://coastalimagery.blob.core.windows.net/ccap- landcover/CCAP_bulk_download/High_Resolution...

    2025

  35. [43]

    and Zhao, K.L., 2014

    Fu, W.J., Jiang, P.K., Zhou, G.M. and Zhao, K.L., 2014. Using Moran's I and GIS to study the spatial pattern of forest litter carbon density in a subtropical region of southeastern China. Biogeosciences, 11(8), pp.2401-2409

    2014

  36. [44]

    and Lyapustin, A., 2020

    Ploton, P., Mortier, F., Réjou-Méchain, M., Barbier, N., Picard, N., Rossi, V ., Dormann, C., Cornu, G., Viennois, G., Bayol, N. and Lyapustin, A., 2020. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nature communications, 11(...

    2020

  37. [45]

    and Gräler, B., 2018

    Hengl, T., Nussbaum, M., Wright, M.N., Heuvelink, G.B. and Gräler, B., 2018. Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables. PeerJ, 6, p.e5518

    2018

  38. [46]

    and Warton, D.I., 2017

    Roberts, D.R., Bahn, V ., Ciuti, S., Boyce, M.S., Elith, J., Guillera‐Arroita, G., Hauenstein, S., Lahoz‐Monfort, J.J., Schröder, B., Thuiller, W. and Warton, D.I., 2017. Cross‐validation strategies for data with temporal, spatial, hierarchical, or phylogenetic structure. Ecog...

    2017

  39. [47]

    and Luo, W., 2025

    Fang, J., Wu, M., Zhang, Z. and Luo, W., 2025. Leveraging AlphaEarth Foundations Embeddings for High-Accuracy County-Scale Corn and Soybean Yield Estimation. Authorea Preprints. 38

    2025

  40. [48]

    & Goulden, T

    Tamiminia, H., Salehi, B., Mahdianpari, M. & Goulden, T. State-wide forest canopy height and aboveground biomass map for new York with 10 m resolution, integrating GEDI, Sentinel-1, and Sentinel-2 data. Ecol. Inf. 79, 102404 (2024)

    2024

  41. [49]

    and Albijanic, B., 2024

    Devasahayam, S. and Albijanic, B., 2024. Predicting hydrogen production from co- gasification of biomass and plastics using tree based machine learning algorithms. Renewable Energy, 222, p.119883

    2024

  42. [50]

    and Bai, Y ., 2025

    Su, Y ., Wu, Z., Zheng, X., Qiu, Y ., Ma, Z., Ren, Y . and Bai, Y ., 2025. Harmonizing remote sensing and ground data for forest aboveground biomass estimation. Ecological Informatics, 86, p.103002

    2025

  43. [51]

    Improving Tropical Forest Biomass Predictions with Multi-Output Deep Learning Models for Above-and Belowground Estimates

    Chen, J., 2025. Improving Tropical Forest Biomass Predictions with Multi-Output Deep Learning Models for Above-and Belowground Estimates. Informatica, 49(37)

    2025

  44. [52]

    and Zurita-Milla, R., 2022

    Liu, X., Kounadi, O. and Zurita-Milla, R., 2022. Incorporating spatial autocorrelation in machine learning models using spatial lag and eigenvector spatial filtering features. ISPRS International Journal of Geo-Information, 11(4), p.242

    2022

  45. [53]

    and Huang, H., 2025

    Wang, X., Gai, Y . and Huang, H., 2025. Design based Global Sensitivity Analysis for Quantity-Permutation Models. Technometrics, pp.1-12

    2025

  46. [54]

    and Friedman, J., 2009

    Hastie, T., Tibshirani, R. and Friedman, J., 2009. The elements of statistical learning

    2009

  47. [55]

    and Balzter, H., 2021

    Santoro, M., Cartus, O., Carvalhais, N., Rozendaal, D., Avitabile, V ., Araza, A., De Bruin, S., Herold, M., Quegan, S., Rodríguez-Veiga, P. and Balzter, H., 2021. The global forest above- ground biomass pool for 2010 estimated from high-resolution satellite observations. Eart...

    2021

  48. [56]

    and Ex, S., 2020

    V orster, A.G., Evangelista, P.H., Stovall, A.E. and Ex, S., 2020. Variability and uncertainty in forest biomass estimates from the tree to landscape scale: the role of allometric equations. Carbon Balance and Management, 15(1), p.8

    2020

  49. [57]

    and Hacherl, A., 2023

    Holman, J.O. and Hacherl, A., 2023. Teaching Monte Carlo Simulation with Python. Journal of Statistics and Data Science Education, 31(1), pp.33-44

    2023

  50. [58]

    I., Niemann, K

    Duncanson, L. I., Niemann, K. O., & Wulder, M. A. (2010). Integration of GLAS and Landsat TM data for aboveground biomass estimation. Canadian Journal of Remote Sensing, 36(2), 129–141. https://doi.org/10.5589/m10-037

    2010 doi

  51. [59]

    and Moran, E., 2016

    Lu, D., Chen, Q., Wang, G., Liu, L., Li, G. and Moran, E., 2016. A survey of remote sensing- based aboveground biomass estimation methods in forest ecosystems. International Journal of Digital Earth, 9(1), pp.63-105

    2016

  52. [60]

    and Grana, M., 2022

    Torre-Tojal, L., Bastarrika, A., Boyano, A., Lopez-Guede, J.M. and Grana, M., 2022. Above- ground biomass estimation from LiDAR data using random forest algorithms. Journal of Computational Science, 58, p.101517

    2022

  53. [61]

    V ., Battles, J

    Chen, Q., Laurin, G. V ., Battles, J. J. & Saah, D. Integration of airborne lidar and vegetation types derived from aerial photography for mapping aboveground live biomass. Remote Sens. Environ. 121, 108–117 (2012)

    2012

  54. [62]

    and Jin, Y ., 2024

    Winsemius, S., Babcock, C., Kane, V .R., Bormann, K.J., Safford, H.D. and Jin, Y ., 2024. Improved aboveground biomass estimation and regional assessment with aerial lidar in California’s subalpine forests. Carbon Balance and Management, 19(1), p.41. 39

    2024

  55. [2008]

    Remote Sensing of Environment, 112(10), pp.3876-3890

    Regional aboveground forest biomass using airborne and spaceborne LiDAR in Québec. Remote Sensing of Environment, 112(10), pp.3876-3890

  56. [2021]

    Remote sensing of environment, 265, p.112644

    Above-ground biomass change estimation using national forest inventory data with Sentinel-2 and Landsat. Remote sensing of environment, 265, p.112644

  57. [2022]

    International Journal of Applied Earth Observation and Geoinformation, 114, p.103059

    Fine-resolution landscape-scale biomass mapping using a spatiotemporal patchwork of LiDAR coverages. International Journal of Applied Earth Observation and Geoinformation, 114, p.103059

  58. [2024]

    ISPRS Journal of Photogrammetry and Remote Sensing, 208, pp.233-244

    Revealing the spatial variation in biomass uptake rates of Brazil’s secondary forests. ISPRS Journal of Photogrammetry and Remote Sensing, 208, pp.233-244

Pith tools

Reviewed August 2, 2026 · model on record in the stance chip above.