Integrating national forest inventory, airborne lidar, and satellite imagery for wall-to-wall mapping of forest structure with computer vision

• URL: http://arxiv.org/abs/2606.20291v1
• Date: Thu, 18 Jun 2026 14:31:43 GMT
• Title: Integrating national forest inventory, airborne lidar, and satellite imagery for wall-to-wall mapping of forest structure with computer vision
• Abstract summary: We introduce the VibrantForests framework, developed and applied to map forest attributes.<n>We generate estimates of canopy cover, canopy height, aboveground live tree biomass, basal area, and quadratic mean diameter at 10-meter resolution.<n>We demonstrate predictive capability spanning the full spectrum of forest conditions ranging from sparse-canopy/low-biomass to dense-canopy/high-biomass.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Remote sensing is increasingly relied upon to deliver actionable science for forest and wildfire risk management across large landscapes. Wall-to-wall, annually updated maps are a persistent need for effective forest management. Many planning systems and data collections combine disparate data sources with different purposes, vintages, and prediction quality, which leads to confounding behavior in operational planning systems. We introduce the VibrantForests framework, developed and applied to map forest attributes and provide a coherent foundation for effective forest and wildfire planning. VibrantForests includes a satellite-based forest structure model trained on lidar-derived samples and applied across the contiguous United States to concurrently generate estimates of canopy cover, canopy height, aboveground live tree biomass, basal area, and quadratic mean diameter at 10-meter resolution. We demonstrate predictive capability spanning the full spectrum of forest conditions ranging from sparse-canopy/low-biomass to dense-canopy/high-biomass. Results show that our model extends the range at which saturation is commonly encountered in comparable passive-sensor models, and reduces regression-to-mean behavior that commonly produces overestimation of forest attributes in small/sparse conditions and underestimation in large/dense conditions. The VibrantForests framework addresses a key limitation in large-area forest and wildfire planning by delivering coherent wall-to-wall estimates of management-relevant attributes at annual cadence and 10m resolution.

Related papers

• FORMSpoT: A Decade of Tree-Level, Country-Scale Forest Monitoring [40.631100826517375]
  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.
  arXiv  Detail & Related papers  (2025-12-18T19:35:09Z)
• Bringing SAM to new heights: Leveraging elevation data for tree crown segmentation from drone imagery [68.69685477556682]
  Current monitoring methods involve ground measurements, requiring extensive cost, time and labor.<n>Drone remote sensing and computer vision offer great potential for mapping individual trees from aerial imagery at broad-scale.<n>We compare methods leveraging Segment Anything Model (SAM) for the task of automatic tree crown instance segmentation in high resolution drone imagery.<n>We also study the integration of elevation data into models, in the form of Digital Surface Model (DSM) information, which can readily be obtained at no additional cost from RGB drone imagery.
  arXiv  Detail & Related papers  (2025-06-05T12:43:11Z)
• 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)
• Not Every Tree Is a Forest: Benchmarking Forest Types from Satellite Remote Sensing [1.2266381182650026]
  This work introduces ForTy, a benchmark for global-scale FORest TYpes mapping using multi-temporal satellite data.<n>The benchmark comprises 200,000 time series of image patches, each consisting of Sentinel-2, Sentinel-1, climate, and elevation data.<n>We evaluate the forest types dataset using several baseline models, including convolution neural networks and transformer-based models.
  arXiv  Detail & Related papers  (2025-05-03T12:20:50Z)
• NeRF-Accelerated Ecological Monitoring in Mixed-Evergreen Redwood Forest [0.0]
  We present a comparison of MLS and NeRF forest reconstructions for the purpose of trunk diameter estimation in a mixed-evergreen Redwood forest. We propose an improved DBH-estimation method using convex-hull modeling.
  arXiv  Detail & Related papers  (2024-10-09T20:32:15Z)
• FoMo: Multi-Modal, Multi-Scale and Multi-Task Remote Sensing Foundation Models for Forest Monitoring [23.192593517094714]
  We present the first unified Forest Monitoring Benchmark (FoMo-Bench)<n>FoMo-Bench consists of 15 diverse datasets encompassing satellite, aerial, and inventory data.<n>To enhance task and geographic diversity in FoMo-Bench, we introduce TalloS, a global dataset combining satellite imagery with ground-based annotations for tree species classification.
  arXiv  Detail & Related papers  (2023-12-15T09:49:21Z)
• 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)
• Mapping historical forest biomass for stock-change assessments at parcel to landscape scales [0.0]
  Map products can help identify where, when, and how forest carbon stocks are changing as a result of both anthropogenic and natural drivers alike. These products can thus serve as inputs to a wide range of applications including stock-change assessments, monitoring reporting, and verification frameworks.
  arXiv  Detail & Related papers  (2023-04-05T17:55:00Z)
• A Novel Semisupervised Contrastive Regression Framework for Forest Inventory Mapping with Multisensor Satellite Data [5.652290685410878]
  We develop a novel semisupervised regression framework for wall-to-wall mapping of continuous forest variables. The framework is demonstrated over a boreal forest site using Copernicus Sentinel-1 and Sentinel-2 imagery. Achieved prediction accuracies are strongly better compared to using vanilla UNet or traditional regression models.
  arXiv  Detail & Related papers  (2022-12-01T03:26:02Z)
• 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)
• From Static to Dynamic Prediction: Wildfire Risk Assessment Based on Multiple Environmental Factors [69.9674326582747]
  Wildfire is one of the biggest disasters that frequently occurs on the west coast of the United States. We propose static and dynamic prediction models to analyze and assess the areas with high wildfire risks in California.
  arXiv  Detail & Related papers  (2021-03-14T17:56:17Z)

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.