Object-Based Approach Using Very High Spatial Resolution 16-Band WorldView-3 and LiDAR Data for Tree Species Classification in a Broadleaf Forest in Quebec, Canada

Varin, Mathieu; Chalghaf, Bilel; Joanisse, Gilles

doi:10.3390/rs12183092

Cited by 23 publications

(11 citation statements)

References 124 publications

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“…WorldView-3 could be a useful sensor for identification with a higher spatial resolution and at the same time potentially sufficient spectral resolution. It was already successfully used to with GEOBIA to extract olive tree crowns [73], oil palms [74], or broadleaf trees [75].…”

Section: Remote Sensing Data Useful For Rubus Caesius Identificationmentioning

confidence: 99%

Intra-Annual Variabilities of Rubus caesius L. Discrimination on Hyperspectral and LiDAR Data

et al. 2020

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The study was focused on a plant native to Poland, the European dewberry Rubus caesius L., which is a species with the ability to become excessively abundant within its original range, potentially causing significant changes in ecosystems, including biodiversity loss. Monitoring plant distributions over large areas requires mapping that is fast, reliable, and repeatable. For Rubus, different types of data were successfully used for classification, but most of the studies used data with a very high spectral resolution. The aim of this study was to indicate, using hyperspectral and Light Detection and Ranging (LiDAR) data, the main functional trait crucial for R. caesius differentiation from non-Rubus. This analysis was carried out with consideration of the seasonal variability and different percentages of R. caesius in the vegetation patches. The analysis was based on hyperspectral HySpex images and Airborne Laser Scanning (ALS) products. Data were acquired during three campaigns: early summer, summer, and autumn. Differentiation based on Linear Discriminate Analysis (LDA) and Non-Parametric Multivariate Analysis of Variance (NPMANOVA) analysis was successful for each of the analysed campaigns using optical data, but the ALS data were less useful for identification. The analysis indicated that selected spectral ranges (VIS, red-edge, and parts of the NIR and possibly SWIR ranges) can be useful for differentiating R. caesius from non-Rubus. The most useful indices were ARI1, CRI1, ARVI, GDVI, CAI, NDNI, and MRESR. The obtained results indicate that it is possible to classify R. caesius using images with lower spectral resolution than hyperspectral data.

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Section: Remote Sensing Data Useful For Rubus Caesius Identificationmentioning

confidence: 99%

Intra-Annual Variabilities of Rubus caesius L. Discrimination on Hyperspectral and LiDAR Data

et al. 2020

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“…Wessel [8] successfully classified deciduous trees, oak trees, and others using both pixel-based and object-oriented methods. Mathieu [9] verified the high accuracy of object-oriented classification methods in mapping multiple tree species. However, these methods rely on local computer analysis, which leads to issues such as low efficiency, long processing times, and uncertain identification accuracy.…”

Section: Introductionmentioning

confidence: 89%

Garlic Crops’ Mapping and Change Analysis in the Erhai Lake Basin Based on Google Earth Engine

Li,

Pan,

Peng

et al. 2024

Agronomy

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Garlic (Allium sativum) is an important economic crop in China. In terms of using remote sensing technology to identify it, there is still room for improvement, and the high-precision classification of garlic has become an important issue. However, to the best of our knowledge, few studies have focused on garlic area mapping. Here, we propose a method for identifying garlic crops using samples and a multi-feature dataset under limited conditions. The results indicate the following: (1) In the land-use classification of the Erhai Lake Basin, the importance ranking of the characteristic bands, from high to low, is as follows: spectral features, vegetation features, texture features, and terrain features. (2) The random forest method based on feature selection demonstrates high accuracy in land-use classification within the Erhai Lake Basin in Yunnan Province. The overall classification accuracy reached 95.79%, with a Kappa coefficient of 0.95. (3) From 1999 to 2023, the expansion of garlic cultivation in the Erhai Lake Basin showed a trend of initially strengthening from north to south, which was followed by weakening. The vertical development of garlic cultivation reached saturation, showing a slow trend toward horizontal expansion between 2005 and 2018. The planting distributions in various townships in the Erhai Lake Basin gradually shifted from relatively uniform distributions to upstream development. This study utilized the Google Earth Engine (GEE) cloud computing platform and machine learning algorithms to compensate for the lack of statistical data on garlic cultivation in the Erhai Lake Basin. Moreover, it accurately, rapidly, and efficiently extracted planting information, demonstrating significant potential for practical applications.

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“…A WorldView-3 image of the Huangshan study area obtained on 10 March 2019, was also selected. This image has 1 panchromatic band with a spatial resolution of 0.31 m and 8 multispectral bands with a spatial resolution of 1.24 m [50].…”

Section: Experimental Datamentioning

confidence: 99%

Individual Tree Species Identification Based on a Combination of Deep Learning and Traditional Features

Chen

Jing

et al. 2023

Remote Sensing

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Accurate identification of individual tree species (ITS) is crucial to forest management. However, current ITS identification methods are mainly based on traditional image features or deep learning. Traditional image features are more interpretative, but the generalization and robustness of such methods are inferior. In contrast, deep learning based approaches are more generalizable, but the extracted features are not interpreted; moreover, the methods can hardly be applied to limited sample sets. In this study, to further improve ITS identification, typical spectral and texture image features were weighted to assist deep learning models for ITS identification. To validate the hybrid models, two experiments were conducted; one on the dense forests of the Huangshan Mountains, Anhui Province and one on the Gaofeng forest farm, Guangxi Province, China. The experimental results demonstrated that with the addition of image features, different deep learning ITS identification models, such as DenseNet, AlexNet, U-Net, and LeNet, with different limited sample sizes (480, 420, 360), were all enhanced in both study areas. For example, the accuracy of DenseNet model with a sample size of 480 were improved to 87.67% from 85.41% in Huangshan. This hybrid model can effectively improve ITS identification accuracy, especially for UAV aerial imagery or limited sample sets, providing the possibility to classify ITS accurately in sample-poor areas.

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Object-Based Approach Using Very High Spatial Resolution 16-Band WorldView-3 and LiDAR Data for Tree Species Classification in a Broadleaf Forest in Quebec, Canada

Cited by 23 publications

References 124 publications

Intra-Annual Variabilities of Rubus caesius L. Discrimination on Hyperspectral and LiDAR Data

Intra-Annual Variabilities of Rubus caesius L. Discrimination on Hyperspectral and LiDAR Data

Garlic Crops’ Mapping and Change Analysis in the Erhai Lake Basin Based on Google Earth Engine

Individual Tree Species Identification Based on a Combination of Deep Learning and Traditional Features

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