Hyperspectral and LiDAR data for the prediction via machine learning of tree species, volume and biomass: a possible contribution for updating forest management plans

• URL: http://arxiv.org/abs/2209.15248v1
• Date: Fri, 30 Sep 2022 06:06:25 GMT
• Title: Hyperspectral and LiDAR data for the prediction via machine learning of tree species, volume and biomass: a possible contribution for updating forest management plans
• Authors: Daniele Michelini, Michele Dalponte, Angelo Carriero, Erico Kutchart, Salvatore Eugenio Pappalardo, Massimo De Marchi, Francesco Pirotti
• Abstract summary: This work intends to lay the foundations for identifying the prevailing forest types and the delineation of forest units in the Autonomous Province of Trento (PAT) Data from LiDAR and hyperspectral surveys of 2014 made available by PAT were acquired and processed.
• Score: 0.3848364262836075
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work intends to lay the foundations for identifying the prevailing forest types and the delineation of forest units within private forest inventories in the Autonomous Province of Trento (PAT), using currently available remote sensing solutions. In particular, data from LiDAR and hyperspectral surveys of 2014 made available by PAT were acquired and processed. Such studies are very important in the context of forest management scenarios. The method includes defining tree species ground-truth by outlining single tree crowns with polygons and labeling them. Successively two supervised machine learning classifiers, K-Nearest Neighborhood and Support Vector Machine (SVM) were used. The results show that, by setting specific hyperparameters, the SVM methodology gave the best results in classification of tree species. Biomass was estimated using canopy parameters and the Jucker equation for the above ground biomass (AGB) and that of Scrinzi for the tariff volume. Predicted values were compared with 11 field plots of fixed radius where volume and biomass were field-estimated in 2017. Results show significant coefficients of correlation: 0.94 for stem volume and 0.90 for total aboveground tree biomass.

Related papers

• Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning [16.071397465972893]
  ForAINet is able to perform a segmentation across diverse forest types and geographic regions. System has been tested on FOR-Instance, a dataset of point clouds that have been acquired in five different countries using surveying drones.
  arXiv  Detail & Related papers  (2023-12-22T21:54:35Z)
• 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)
• 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)
• 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)
• Information fusion approach for biomass estimation in a plateau mountainous forest using a synergistic system comprising UAS-based digital camera and LiDAR [9.944631732226657]
  The objective of this study was to quantify the aboveground biomass (AGB) of a plateau mountainous forest reserve. We utilized digital aerial photogrammetry (DAP), which has the unique advantages of speed, high spatial resolution, and low cost. Based on the CHM and spectral attributes obtained from multispectral images, we estimated and mapped the AGB of the region of interest with considerable cost efficiency.
  arXiv  Detail & Related papers  (2022-04-14T04:04:59Z)
• Deep Learning Based 3D Point Cloud Regression for Estimating Forest Biomass [15.956463815168034]
  Knowledge of forest biomass stocks and their development is important for implementing effective climate change mitigation measures. Remote sensing using airborne LiDAR can be used to measure vegetation biomass at large scale. We present deep learning systems for predicting wood volume, above-ground biomass (AGB), and subsequently carbon directly from 3D LiDAR point cloud data.
  arXiv  Detail & Related papers  (2021-12-21T16:26:13Z)
• 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)
• Visualizing hierarchies in scRNA-seq data using a density tree-biased autoencoder [50.591267188664666]
  We propose an approach for identifying a meaningful tree structure from high-dimensional scRNA-seq data. We then introduce DTAE, a tree-biased autoencoder that emphasizes the tree structure of the data in low dimensional space.
  arXiv  Detail & Related papers  (2021-02-11T08:48:48Z)
• A CNN Approach to Simultaneously Count Plants and Detect Plantation-Rows from UAV Imagery [56.10033255997329]
  We propose a novel deep learning method based on a Convolutional Neural Network (CNN) It simultaneously detects and geolocates plantation-rows while counting its plants considering highly-dense plantation configurations. The proposed method achieved state-of-the-art performance for counting and geolocating plants and plant-rows in UAV images from different types of crops.
  arXiv  Detail & Related papers  (2020-12-31T18:51:17Z)
• 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)
• Two-View Fine-grained Classification of Plant Species [66.75915278733197]
  We propose a novel method based on a two-view leaf image representation and a hierarchical classification strategy for fine-grained recognition of plant species. A deep metric based on Siamese convolutional neural networks is used to reduce the dependence on a large number of training samples and make the method scalable to new plant species.
  arXiv  Detail & Related papers  (2020-05-18T21:57:47Z)

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.