Evaluating the Efficacy of Sentinel-2 versus Aerial Imagery in Serrated Tussock Classification

• URL: http://arxiv.org/abs/2512.11267v1
• Date: Fri, 12 Dec 2025 04:10:44 GMT
• Title: Evaluating the Efficacy of Sentinel-2 versus Aerial Imagery in Serrated Tussock Classification
• Authors: Rezwana Sultana, Manzur Murshed, Kathryn Sheffield, Singarayer Florentine, Tsz-Kwan Lee, Shyh Wei Teng,
• Abstract summary: Serrated tussock (textitNassella trichotoma) is a highly competitive invasive grass species that disrupts native grasslands, reduces pasture productivity, and increases land management costs.<n>Current ground surveys and subsequent management practices are effective at small scales, but they are not feasible for landscape-scale monitoring.<n>Satellite-based remote sensing provides a more cost-effective and scalable alternative, though often with lower spatial resolution.
• Score: 1.7975159705384043
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Invasive species pose major global threats to ecosystems and agriculture. Serrated tussock (\textit{Nassella trichotoma}) is a highly competitive invasive grass species that disrupts native grasslands, reduces pasture productivity, and increases land management costs. In Victoria, Australia, it presents a major challenge due to its aggressive spread and ecological impact. While current ground surveys and subsequent management practices are effective at small scales, they are not feasible for landscape-scale monitoring. Although aerial imagery offers high spatial resolution suitable for detailed classification, its high cost limits scalability. Satellite-based remote sensing provides a more cost-effective and scalable alternative, though often with lower spatial resolution. This study evaluates whether multi-temporal Sentinel-2 imagery, despite its lower spatial resolution, can provide a comparable and cost-effective alternative for landscape-scale monitoring of serrated tussock by leveraging its higher spectral resolution and seasonal phenological information. A total of eleven models have been developed using various combinations of spectral bands, texture features, vegetation indices, and seasonal data. Using a random forest classifier, the best-performing Sentinel-2 model (M76*) has achieved an Overall Accuracy (OA) of 68\% and an Overall Kappa (OK) of 0.55, slightly outperforming the best-performing aerial imaging model's OA of 67\% and OK of 0.52 on the same dataset. These findings highlight the potential of multi-seasonal feature-enhanced satellite-based models for scalable invasive species classification.

Related papers

• Habitat Classification from Ground-Level Imagery Using Deep Neural Networks [1.3408365072149797]
  This study applies state-of-the-art deep neural network architectures to ground-level habitat imagery.<n>We evaluate two families of models -- convolutional neural networks (CNNs) and vision transformers (ViTs)<n>Our results demonstrate that ViTs consistently outperform state-of-the-art CNN baselines on key classification metrics.
  arXiv  Detail & Related papers  (2025-07-05T12:07:13Z)
• 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)
• Multi-label classification for multi-temporal, multi-spatial coral reef condition monitoring using vision foundation model with adapter learning [20.74182654369854]
  This study introduces an approach integrating the DINOv2 vision foundation model with the LoRA fine-tuning method.<n>The experimental results demonstrate that the DINOv2-LoRA model achieved superior accuracy, with a match ratio of 64.77%, compared to 60.34% achieved by the best conventional model.
  arXiv  Detail & Related papers  (2025-03-29T08:32:44Z)
• 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)
• Individual mapping of large polymorphic shrubs in high mountains using satellite images and deep learning [1.6889377382676625]
  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.
  arXiv  Detail & Related papers  (2024-01-31T16:44:20Z)
• 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)
• Evaluation of the potential of Near Infrared Hyperspectral Imaging for monitoring the invasive brown marmorated stink bug [53.682955739083056]
  The brown marmorated stink bug (BMSB), Halyomorpha halys, is an invasive insect pest of global importance that damages several crops. The present study consists in a preliminary evaluation at the laboratory level of Near Infrared Hyperspectral Imaging (NIR-HSI) as a possible technology to detect BMSB specimens.
  arXiv  Detail & Related papers  (2023-01-19T11:37:20Z)
• Potato Crop Stress Identification in Aerial Images using Deep Learning-based Object Detection [60.83360138070649]
  The paper presents an approach for analyzing aerial images of a potato crop using deep neural networks. The main objective is to demonstrate automated spatial recognition of a healthy versus stressed crop at a plant level. Experimental validation demonstrated the ability for distinguishing healthy and stressed plants in field images, achieving an average Dice coefficient of 0.74.
  arXiv  Detail & Related papers  (2021-06-14T21:57:40Z)
• 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.