Tree Species Classification Based on Sentinel-2 Imagery and Random Forest Classifier in the Eastern Regions of the Qilian Mountains

Ma, Minfei; Liu, Jianhong; Liu, Mingxing; Zeng, Jingchao; Li, Yuanhui

doi:10.3390/f12121736

Cited by 31 publications

(17 citation statements)

References 45 publications

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“…The results showed that topographic variables played a significant role in tree species classification, and the introduction of topographic variables increased the classification accuracy from 75.60% to 81.70%. Ma et al [10] classified forest tree species in the eastern part of the Qilian Mountains based on Sentinel-2 spectral features, texture features, and topographic features. The results showed that the combination of elevation, slope, slope aspect, and texture features can increase the separation of tree species, with an overall accuracy of 86.49% and a kappa coefficient of 0.83.…”

Section: Introductionmentioning

confidence: 99%

Forest Tree Species Classification Based on Sentinel-2 Images and Auxiliary Data

You

Huang

Qin

et al. 2022

Forests

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Most research on forest tree species classification based on optical image data uses information such as spectral reflectance, vegetation index, texture, and phenology data. However, owing to the limited spectral resolution of multispectral images and the high cost of hyperspectral data, there is room for improvement in the classification of tree species in large areas based on optical images. The combined application of multispectral images and other auxiliary data can provide a new method for improving tree species classification accuracy. Hence, Sentinel-2 images were used to extract spectral reflectance, spectral index, texture, and phenological information. Data for topography, precipitation, air temperature, ultraviolet aerosol index, NO2 concentration, and other variables were included as auxiliary data. Models for forest tree species classification were constructed through feature combination and feature optimization using the random forest (RF), gradient tree boost (GTB), support vector machine (SVM), and classification and regression tree (CART) algorithms. The classification results of 16 feature combinations with the 4 classification methods were compared, and the contributions of different features to the classification models of forest tree species were evaluated. Finally, the optimal classification model was selected to identify the spatial distribution of forest tree species in the study area. The model based on feature optimization gave the best results among the 16 feature combination models. The overall accuracy and kappa coefficient were increased by 18% and 0.21, respectively, compared with the spectral classification model, and by 17% and 0.20, respectively, compared with the spectral and spectral index classification model. By analyzing the feature optimization model, it was found that terrain, ultraviolet aerosol index, and phenological information ranked as the top three features in terms of importance. Although the importance of spectral reflectance and spectral index features was lower, the number of feature variables accounted for a large proportion of the total. The importance of commonly used texture features was limited, and these features were not present in the feature optimization model. The RF algorithm had the highest classification accuracy, with an overall accuracy of 82.69% and a kappa coefficient of 0.80, among the four classification algorithms. The results of GTB were close to those of RF, and the difference in overall classification accuracy was only 0.14%. However, the results of the SVM and CART algorithms were relatively weaker, with overall classification accuracies of about 70%. It can be concluded that the combined application of Sentinel-2 images and auxiliary data can improve forest tree species classification accuracy. The model based on feature optimization achieved the highest classification accuracy among the 16 feature combination models. The spectral reflectance and spectral index data extracted from optical images are useful for tree species classification, but the effect of texture features was very limited. Auxiliary data, such as topographic features, ultraviolet aerosol index, phenological features, NO2 concentration features, topographic diversity features, precipitation features, temperature features, and multi-scale topographic location index data, can effectively improve forest tree species classification accuracy. The RF algorithm had the highest accuracy, and it can be used for tree species classification space distribution identification. The combined application of Sentinel-2 images and auxiliary data can improve classification accuracy, but the highest accuracy of the model was only 82.69%, which leaves room for improvement. Thus, more effective auxiliary data and the vertical structural parameters extracted from satellite LiDAR can be combined with multispectral images to improve forest tree species classification accuracy in future research.

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Section: Introductionmentioning

confidence: 99%

Forest Tree Species Classification Based on Sentinel-2 Images and Auxiliary Data

You

Huang

Qin

et al. 2022

Forests

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“…In the Grand-Est, the determination of species was rst determined by photointerpretation as spruce or r or as mixed conifers (performed by IGN) and then re ned using Sentinel-2 (S2) spectral time series. Phenology courses are highly suitable for forest tree species discrimination (Grabska et al, 2019;Lisein et al, 2015;Ma et al, 2021). In order to identify and remove every non-spruce pixel, all S2 spectral bands of 10 and 20 m were rst summarized for each of the four quarters of the year, by averaging all cloud-free observations occurring during the quarter.…”

Section: Focus On Spruce Standsmentioning

confidence: 99%

Spatial and remote sensing monitoring shows the end of the bark beetle outbreak on Belgian and north-eastern France Norway spruce (Picea abies) stands

Arthur,

Jonathan,

Juliette

et al. 2024

Environ Monit Assess

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In 2022, Europe emerged from eight of the hottest years on record, leading to signi cant spruce mortality across Europe. The particularly dry weather conditions of 2018 triggered an outbreak of bark beetles (Ips typographus), causing the loss of thousands of hectares of Norway spruce stands, including in Wallonia and North-eastern France.A methodology for detecting the health status of spruce was developed based on dense time series of satellite imagery (Sentinel-2). The time series of satellite images allowed the modelling of the spectral response of healthy spruce forests over the seasons: a decrease in photosynthetic activity of the forest canopy causes deviations from this normal seasonal vegetation index trajectory. These anomalies are caused by a bark beetle attack and are detected automatically. The method leads in the production of annual spruce health map of Wallonia and Grand-Est.The resulting map are used to assess the damage caused by bark beetle and to study the relation between the dieback and environmental conditions.Lasted six years (2017-2022), bark beetle has destroyed 12.2% (23,674 ha) of the spruce area in Wallonia and Grand-Est of France. This study area is composed of three bioclimatic areas: Plains, Ardennes and Vosges, which have not been equally affected by bark beetle attacks. The plains were the most affected, with 50% of spruce forests destroyed, followed by the Ardennes, which lost 11.3% of its spruce stands. The Vosges was the least affected bioclimatic area, with 5.6% of spruce stands lost.

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“…Various plant classification techniques are currently available; thus, selecting the most appropriate method depends on the site-specific characteristics. Traditional spatial-based classification techniques such as Random Forest (RF) [11,12] and Support Vector Machine (SVM) [13] have been broadly exploited in numerous studies where comparisons with other classifications have been conducted [14][15][16]. However, recent advancements in machine learning (ML) have led to the development of deep learning classification techniques for plant species classification, such as Convolutional Neural Networks (CNN) [17,18], Long Short-Term Memory Networks (LSTMs) [19,20], Recurrent Neural Networks (RNNs) [21,22] and Multilayer Perceptrons (MLPs) [23].…”

Section: Introductionmentioning

confidence: 99%

Forest Habitat Mapping in Natura2000 Regions in Cyprus Using Sentinel-1, Sentinel-2 and Topographical Features

Prodromou,

Theocharidis,

Gitas

et al. 2024

Remote Sensing

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Accurate mapping of forest habitats, especially in NATURA sites, is essential information for forest monitoring and sustainable management but also for habitat characterisation and ecosystem functioning. Remote sensing data and spatial modelling allow accurate mapping of the presence and distribution of tree species and habitats and are valuable tools for the long-term assessment of habitat status required by the European Commission. In order to serve the above, the present study aims to propose a methodology to accurately map the spatial distribution of forest habitats in three NATURA2000 sites of Cyprus by employing Sentinel-1 and Sentinel-2 data as well as topographic features using the Google Earth Engine (GEE). A pivotal aspect of the methodology identified was that the best band combination of the Random Forest (RF) classifier achieves the highest performance for mapping the dominant habitats in the three case studies. Specifically, in the Akamas region, eight habitat types have been mapped, in Paphos nine and six in Troodos. These habitat types are included in three of the nine habitat groups based on the EU’s Habitat Directive: the sclerophyllous scrub, rocky habitats and caves and forests. The results show that using the RF algorithm achieves the highest performance, especially using Dataset 6, which is based on S2 bands, spectral indices and topographical features, and Dataset 13, which includes S2, S1, spectral indices and topographical features. These datasets achieve an overall accuracy (OA) of approximately 91–94%. In contrast, Dataset 7, which includes only S1 bands and Dataset 9, which combines S1 bands and spectral indices, achieve the lowest performance with an OA of approximately 25–43%.

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Tree Species Classification Based on Sentinel-2 Imagery and Random Forest Classifier in the Eastern Regions of the Qilian Mountains

Cited by 31 publications

References 45 publications

Forest Tree Species Classification Based on Sentinel-2 Images and Auxiliary Data

Forest Tree Species Classification Based on Sentinel-2 Images and Auxiliary Data

Spatial and remote sensing monitoring shows the end of the bark beetle outbreak on Belgian and north-eastern France Norway spruce (Picea abies) stands

Forest Habitat Mapping in Natura2000 Regions in Cyprus Using Sentinel-1, Sentinel-2 and Topographical Features

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