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

• URL: http://arxiv.org/abs/2501.10600v1
• Date: Fri, 17 Jan 2025 23:26:28 GMT
• Title: High Resolution Tree Height Mapping of the Amazon Forest using Planet NICFI Images and LiDAR-Informed U-Net Model
• Authors: Fabien H Wagner, Ricardo Dalagnol, Griffin Carter, Mayumi CM Hirye, Shivraj Gill, Le Bienfaiteur Sagang Takougoum, Samuel Favrichon, Michael Keller, 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,
• Abstract summary: Tree canopy height is one of the most important indicators of forest biomass, productivity, and ecosystem structure. 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. 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.
• Score: 6.643812210806946
• License:
• 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.

Related papers

• Forecasting with Hyper-Trees [50.72190208487953]
  Hyper-Trees are designed to learn the parameters of time series models. By relating the parameters of a target time series model to features, Hyper-Trees also address the issue of parameter non-stationarity. In this novel approach, the trees first generate informative representations from the input features, which a shallow network then maps to the target model parameters.
  arXiv  Detail & Related papers  (2024-05-13T15:22:15Z)
• First Mapping the Canopy Height of Primeval Forests in the Tallest Tree Area of Asia [6.826460268652235]
  We have developed the world's first canopy height map of the distribution area of world-level giant trees. This mapping is crucial for discovering more individual and community world-level giant trees.
  arXiv  Detail & Related papers  (2024-04-23T01:45:55Z)
• Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France [0.0]
  This study uses a machine learning approach that was previously developed to produce local maps of forest parameters. We used the GEDI Lidar mission as reference height data, and the satellite images from Sentinel-1, Sentinel-2 and ALOS-2 PALSA-2 to estimate forest height. The height map is then derived into volume and aboveground biomass (AGB) using allometric equations.
  arXiv  Detail & Related papers  (2023-10-23T07:58:49Z)
• 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)
• Sub-Meter Tree Height Mapping of California using Aerial Images and LiDAR-Informed U-Net Model [0.0]
  Tree canopy height is one of the most important indicators of forest biomass, productivity, and species diversity. Here, we used a U-Net model adapted for regression to map the canopy height of all trees in the state of California with very high-resolution aerial imagery. Our model successfully estimated canopy heights up to 50 m without saturation, outperforming existing canopy height products from global models.
  arXiv  Detail & Related papers  (2023-06-02T22:29:58Z)
• 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)
• High-resolution canopy height map in the Landes forest (France) based on GEDI, Sentinel-1, and Sentinel-2 data with a deep learning approach [0.044381279572631216]
  We develop a deep learning model based on multi-stream remote sensing measurements to create a high-resolution canopy height map. The model outputs allow us to generate a 10 m resolution canopy height map of the whole "Landes de Gascogne" forest area for 2020. For all validation datasets in coniferous forests, our model showed better metrics than previous canopy height models available in the same region.
  arXiv  Detail & Related papers  (2022-12-20T14:14:37Z)
• 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)
• Growing Deep Forests Efficiently with Soft Routing and Learned Connectivity [79.83903179393164]
  This paper further extends the deep forest idea in several important aspects. We employ a probabilistic tree whose nodes make probabilistic routing decisions, a.k.a., soft routing, rather than hard binary decisions. Experiments on the MNIST dataset demonstrate that our empowered deep forests can achieve better or comparable performance than [1],[3].
  arXiv  Detail & Related papers  (2020-12-29T18:05:05Z)

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.