Individual mapping of large polymorphic shrubs in high mountains using satellite images and deep learning

• URL: http://arxiv.org/abs/2401.17985v2
• Date: Tue, 01 Oct 2024 08:25:14 GMT
• Title: Individual mapping of large polymorphic shrubs in high mountains using satellite images and deep learning
• Authors: Rohaifa Khaldi, Siham Tabik, Sergio Puertas-Ruiz, Julio Peñas de Giles, José Antonio Hódar Correa, Regino Zamora, Domingo Alcaraz Segura,
• Abstract summary: We release a large dataset of individual shrub delineations on freely available satellite imagery. We use an instance segmentation model to map all junipers over the treeline for an entire biosphere reserve. Our model achieved an F1-score in shrub delineation of 87.87% on the PI data and 76.86% on the FW data.
• Score: 1.6889377382676625
• License:
• Abstract: Monitoring the distribution and size of long-living large shrubs, such as junipers, is crucial for assessing the long-term impacts of global change on high-mountain ecosystems. While deep learning models have shown remarkable success in object segmentation, adapting these models to detect shrub species with polymorphic nature remains challenging. In this research, we release a large dataset of individual shrub delineations on freely available satellite imagery and use an instance segmentation model to map all junipers over the treeline for an entire biosphere reserve (Sierra Nevada, Spain). To optimize performance, we introduced a novel dual data construction approach: using photo-interpreted (PI) data for model development and fieldwork (FW) data for validation. To account for the polymorphic nature of junipers during model evaluation, we developed a soft version of the Intersection over Union metric. Finally, we assessed the uncertainty of the resulting map in terms of canopy cover and density of shrubs per size class. Our model achieved an F1-score in shrub delineation of 87.87% on the PI data and 76.86% on the FW data. The R2 and RMSE of the observed versus predicted relationship were 0.63 and 6.67% for canopy cover, and 0.90 and 20.62 for shrub density. The greater density of larger shrubs in lower altitudes and smaller shrubs in higher altitudes observed in the model outputs was also present in the PI and FW data, suggesting an altitudinal uplift in the optimal performance of the species. This study demonstrates that deep learning applied on freely available high-resolution satellite imagery is useful to detect medium to large shrubs of high ecological value at the regional scale, which could be expanded to other high-mountains worldwide and to historical and forthcoming imagery.

Related papers

• Tree Species Classification using Machine Learning and 3D Tomographic SAR -- a case study in Northern Europe [0.0]
  Tree species classification plays an important role in nature conservation, forest inventories, forest management, and the protection of endangered species. In this study, we employed TomoSense, a 3D tomographic dataset, which utilizes a stack of single-look complex (SLC) images.
  arXiv  Detail & Related papers  (2024-11-19T22:25:26Z)
• Unified Deep Learning Model for Global Prediction of Aboveground Biomass, Canopy Height and Cover from High-Resolution, Multi-Sensor Satellite Imagery [0.196629787330046]
  We present a new methodology which uses multi-sensor, multi-spectral imagery of 10 meters and a deep learning based model which unifies the prediction of above ground biomass density (AGBD), canopy height (CH), canopy cover (CC) The model is trained on millions of globally sampled GEDI-L2/L4 measurements. We validate the capability of our model by deploying it over the entire globe for the year 2023 as well as annually from 2016 to 2023 over selected areas.
  arXiv  Detail & Related papers  (2024-08-20T23:15:41Z)
• 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)
• HarvestNet: A Dataset for Detecting Smallholder Farming Activity Using Harvest Piles and Remote Sensing [50.4506590177605]
  HarvestNet is a dataset for mapping the presence of farms in the Ethiopian regions of Tigray and Amhara during 2020-2023. We introduce a new approach based on the detection of harvest piles characteristic of many smallholder systems. We conclude that remote sensing of harvest piles can contribute to more timely and accurate cropland assessments in food insecure regions.
  arXiv  Detail & Related papers  (2023-08-23T11:03:28Z)
• 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)
• Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery [101.18253437732933]
  We present an Agave tequilana Weber azul crop segmentation and maturity classification using very high resolution satellite imagery. We solve real-world deep learning problems in the very specific context of agave crop segmentation. With the resulting accurate models, agave production forecasting can be made available for large regions.
  arXiv  Detail & Related papers  (2023-03-21T03:15:29Z)
• 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)
• Global canopy height estimation with GEDI LIDAR waveforms and Bayesian deep learning [20.692092680921274]
  NASA's Global Ecosystem Dynamics Investigation (GEDI) is a key climate mission whose goal is to advance our understanding of the role of forests in the global carbon cycle. We present a novel supervised machine learning approach to interpret GEDI waveforms and regress canopy top height globally.
  arXiv  Detail & Related papers  (2021-03-05T23:08:27Z)
• 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.