FORMSpoT: A Decade of Tree-Level, Country-Scale Forest Monitoring

• URL: http://arxiv.org/abs/2512.17021v1
• Date: Thu, 18 Dec 2025 19:35:09 GMT
• Title: FORMSpoT: A Decade of Tree-Level, Country-Scale Forest Monitoring
• Authors: Martin Schwartz, Fajwel Fogel, Nikola Besic, Damien Robert, Louis Geist, Jean-Pierre Renaud, Jean-Matthieu Monnet, Clemens Mosig, Cédric Vega, Alexandre d'Aspremont, Loic Landrieu, Philippe Ciais,
• Abstract summary: We introduce FORMSpoT (Forest Mapping with SPOT Time series), a decade-long (2014-2024) nationwide mapping of forest canopy height at 1.5 m resolution.<n>To enable robust change detection across heterogeneous acquisitions, we developed a dedicated post-processing pipeline.<n>In mountainous forests, where disturbances are small and spatially fragmented, FORMSpoT-$$ achieves an F1-score of 0.44, representing an order of magnitude higher than existing benchmarks.
• Score: 40.631100826517375
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The recent decline of the European forest carbon sink highlights the need for spatially explicit and frequently updated forest monitoring tools. Yet, existing satellite-based disturbance products remain too coarse to detect changes at the scale of individual trees, typically below 100 m$^{2}$. Here, we introduce FORMSpoT (Forest Mapping with SPOT Time series), a decade-long (2014-2024) nationwide mapping of forest canopy height at 1.5 m resolution, together with annual disturbance polygons (FORMSpoT-$Δ$) covering mainland France. Canopy heights were derived from annual SPOT-6/7 composites using a hierarchical transformer model (PVTv2) trained on high-resolution airborne laser scanning (ALS) data. To enable robust change detection across heterogeneous acquisitions, we developed a dedicated post-processing pipeline combining co-registration and spatio-temporal total variation denoising. Validation against ALS revisits across 19 sites and 5,087 National Forest Inventory plots shows that FORMSpoT-$Δ$ substantially outperforms existing disturbance products. In mountainous forests, where disturbances are small and spatially fragmented, FORMSpoT-$Δ$ achieves an F1-score of 0.44, representing an order of magnitude higher than existing benchmarks. By enabling tree-level monitoring of forest dynamics at national scale, FORMSpoT-$Δ$ provides a unique tool to analyze management practices, detect early signals of forest decline, and better quantify carbon losses from subtle disturbances such as thinning or selective logging. These results underscore the critical importance of sustaining very high-resolution satellite missions like SPOT and open-data initiatives such as DINAMIS for monitoring forests under climate change.

Related papers

• Estimation of aboveground biomass in a tropical dry forest: An intercomparison of airborne, unmanned, and space laser scanning [0.3499870393443268]
  Article 5 of the Paris Agreement emphasizes the need for high-quality forest data.<n>Tropical dry forests are considered one of the least understood tropical forest environments.<n>AGB values estimated from ten permanent tropical dry forest plots in Costa Rica Guanacaste province ranged from 26.02 Mg/ha to 175.43 Mg/ha.
  arXiv  Detail & Related papers  (2025-10-31T11:53:12Z)
• Data Augmentation and Resolution Enhancement using GANs and Diffusion Models for Tree Segmentation [49.13393683126712]
  Urban forests play a key role in enhancing environmental quality and supporting biodiversity in cities.<n> accurately detecting trees is challenging due to complex landscapes and the variability in image resolution caused by different satellite sensors or UAV flight altitudes.<n>We propose a novel pipeline that integrates domain adaptation with GANs and Diffusion models to enhance the quality of low-resolution aerial images.
  arXiv  Detail & Related papers  (2025-05-21T03:57:10Z)
• Dual-Task Learning for Dead Tree Detection and Segmentation with Hybrid Self-Attention U-Nets in Aerial Imagery [1.693687279684153]
  This study introduces a hybrid postprocessing framework that refines deep learning-based tree segmentation.<n>Tested on high-resolution aerial imagery from boreal forests, the framework improved instance-level segmentation accuracy by 41.5%.<n>The framework's computational efficiency supports scalable applications, such as wall-to-wall tree mortality mapping.
  arXiv  Detail & Related papers  (2025-03-27T12:25:20Z)
• MambaDS: Near-Surface Meteorological Field Downscaling with Topography Constrained Selective State Space Modeling [68.69647625472464]
  Downscaling, a crucial task in meteorological forecasting, enables the reconstruction of high-resolution meteorological states for target regions. Previous downscaling methods lacked tailored designs for meteorology and encountered structural limitations. We propose a novel model called MambaDS, which enhances the utilization of multivariable correlations and topography information.
  arXiv  Detail & Related papers  (2024-08-20T13:45:49Z)
• Fine-tuning of Geospatial Foundation Models for Aboveground Biomass Estimation [2.3429628556845405]
  Fine-tuning of a geospatial foundation model to estimate above-ground biomass has comparable performance to a U-Net trained from scratch. We also explore the transfer-learning capabilities of the models by fine-tuning on satellite imagery with sparse labels from different eco-regions in Brazil.
  arXiv  Detail & Related papers  (2024-06-28T12:54:10Z)
• Combining recurrent and residual learning for deforestation monitoring using multitemporal SAR images [4.296985074708585]
  The Amazon rainforest is the largest forest of the Earth, holding immense importance in global climate regulation. Deforestation detection from remote sensing data in this region poses a critical challenge. This paper proposes three deep-learning models tailored for deforestation monitoring.
  arXiv  Detail & Related papers  (2023-10-09T13:16:20Z)
• Automating global landslide detection with heterogeneous ensemble deep-learning classification [44.99833362998488]
  Landslides threaten infrastructure, including roads, railways, buildings, and human life. Hazard-based spatial planning and early warning systems are cost-effective strategies to reduce the risk to society from landslides. Deep learning models have recently been applied for landside mapping using medium- to high-resolution satellite images as input.
  arXiv  Detail & Related papers  (2023-09-12T10:56:16Z)
• Vision Transformers, a new approach for high-resolution and large-scale mapping of canopy heights [50.52704854147297]
  We present a new vision transformer (ViT) model optimized with a classification (discrete) and a continuous loss function. This model achieves better accuracy than previously used convolutional based approaches (ConvNets) optimized with only a continuous loss function.
  arXiv  Detail & Related papers  (2023-04-22T22:39:03Z)
• Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on Aerial Lidar [14.07306593230776]
  This paper presents the first high-resolution canopy height map concurrently produced for multiple sub-national jurisdictions. The maps are generated by the extraction of features from a self-supervised model trained on Maxar imagery from 2017 to 2020. We also introduce a post-processing step using a convolutional network trained on GEDI observations.
  arXiv  Detail & Related papers  (2023-04-14T15:52:57Z)
• Neuroevolution-based Classifiers for Deforestation Detection in Tropical Forests [62.997667081978825]
  Millions of hectares of tropical forests are lost every year due to deforestation or degradation. Monitoring and deforestation detection programs are in use, in addition to public policies for the prevention and punishment of criminals. This paper proposes the use of pattern classifiers based on neuroevolution technique (NEAT) in tropical forest deforestation detection tasks.
  arXiv  Detail & Related papers  (2022-08-23T16:04:12Z)
• Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Bayesian Deep Learning [74.94436509364554]
  We propose a Bayesian deep learning approach to densely estimate forest structure variables at country-scale with 10-meter resolution. Our method jointly transforms Sentinel-2 optical images and Sentinel-1 synthetic aperture radar images into maps of five different forest structure variables. We train and test our model on reference data from 41 airborne laser scanning missions across Norway.
  arXiv  Detail & Related papers  (2021-11-25T16:21:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.