A Random Forest Modelling Procedure for a Multi-Sensor Assessment of Tree Species Diversity

Mallinis, Giorgos; Chrysafis, Irene; Korakis, Georgios; Pana, Eleanna; Kyriazopoulos, Apostolos P.

doi:10.3390/rs12071210

Cited by 22 publications

(25 citation statements)

References 66 publications

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“…The medium spatial resolution of Landsat imagery can map the community of tree species; however, it cannot identify individual tree species. The Sentinel 2 was not used in any studies, but most recent studies, including Mallinis et al [106], have shown its robustness for modeling tree species diversity. The authors found that Sentinel 2 performed better than RapidEye, which has a higher spatial resolution.…”

Section: Discussionmentioning

confidence: 99%

Two Decades Progress on the Application of Remote Sensing for Monitoring Tropical and Sub-Tropical Natural Forests: A Review

Gyamfi-Ampadu

Gebreslasie

2021

Forests

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Forest covers about a third of terrestrial land surface, with tropical and subtropical zones being a major part. Remote sensing applications constitute a significant approach to monitoring forests. Thus, this paper reviews the progress made by remote sensing data applications to tropical and sub-tropical natural forest monitoring over the last two decades (2000–2020). The review focuses on the thematic areas of aboveground biomass and carbon estimations, tree species identification, tree species diversity, and forest cover and change mapping. A systematic search of articles was performed on Web of Science, Science Direct, and Google Scholar by applying a Boolean operator and using keywords related to the thematic areas. We identified 50 peer-reviewed articles that studied tropical and subtropical natural forests using remote sensing data. Asian and South American natural forests are the most highly researched natural forests, while African natural forests are the least studied. Medium spatial resolution imagery was extensively utilized for forest cover and change mapping as well as aboveground biomass and carbon estimation. In the latest studies, high spatial resolution imagery and machine learning algorithms, such as Random Forest and Support Vector Machine, were jointly utilized for tree species identification. In this review, we noted the promising potential of the emerging high spatial resolution satellite imagery for the monitoring of natural forests. We recommend more research to identify approaches to overcome the challenges of remote sensing applications to these thematic areas so that further and sustainable progress can be made to effectively monitor and manage sustainable forest benefits.

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Section: Discussionmentioning

confidence: 99%

Two Decades Progress on the Application of Remote Sensing for Monitoring Tropical and Sub-Tropical Natural Forests: A Review

Gyamfi-Ampadu

Gebreslasie

2021

Forests

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“…Multiple linear regression (MLR) models have been widely applied in predicting plant species diversity using environmental and spectral factors. One of the assumptions is that there is no linear relationship among explanatory variables while using MLR models (Mallinis et al 2020). Unfortunately, multicollinearity is a prevalent problem in the linear regression models solved by ordinary least square (OLS) methods.…”

Section: Methodsmentioning

confidence: 99%

“…A huge amount of observation data, generated from these monitoring networks, has improved our ability to recognize and monitor the change of PSD (Boucher et al 2020, Moudry andDevillers 2020). Focusing on site-scale surveys, traditional field-based methods can provide valuable and high-quality data at plot scale (Mallinis et al 2020). However, predicting species diversity over large areas still remains a challenge due to the difficulties of data acquisition (Mallinis et al 2020;Rocchini et al 2015), as well as the bias generated from the sampling process and strategy (Lohmus et al 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Focusing on site-scale surveys, traditional field-based methods can provide valuable and high-quality data at plot scale (Mallinis et al 2020). However, predicting species diversity over large areas still remains a challenge due to the difficulties of data acquisition (Mallinis et al 2020;Rocchini et al 2015), as well as the bias generated from the sampling process and strategy (Lohmus et al 2018). Therefore, the prediction of plant species diversity over large area cannot be addressed by field surveys alone, and other related techniques should be considered (Fauvel et al 2020).…”

Section: Introductionmentioning

confidence: 99%

“…As the typical ensemble learning algorithms, eXtreme gradient boosting (XGBoost), and random forest (RF) can establish a non-linear relationship, but also select the relative important factors. Most importantly, they are also suitable for small datasets besides largescale datasets (Mallinis et al 2020;Wu et al 2020). Therefore, they have become the most representative tree-based ensemble learning models and were selected to predict the spatial distribution of plant species diversity.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A comparative mapping of plant species diversity using ensemble learning algorithms combined with high accuracy surface modeling

Zhao

Yin

et al. 2021

Environ Sci Pollut Res

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Plant species diversity (PSD) has always been an essential component of biodiversity and plays an important role in ecosystem functions and services. However, it is still a huge challenge to simulate the spatial distribution of PSD due to the difficulties of data acquisition and unsatisfactory performance of predicting algorithms over large areas. A surge in the number of remote sensing imagery, along with the great success of machine learning, opens new opportunities for the mapping of PSD. Therefore, different machine learning algorithms combined with high-accuracy surface modeling (HASM) were firstly proposed to predict the PSD in the Xinghai, northeastern Qinghai-Tibetan Plateau, China. Spectral reflectance and vegetation indices, generated from Landsat 8 images, and environmental variables were taken as the potential explanatory factors of machine learning models including least absolute shrinkage and selection operator (Lasso), ridge regression (Ridge), eXtreme Gradient Boosting (XGBoost), and Random Forest (RF). The prediction generated from these machine learning methods and in situ observation data were integrated by using HASM for the high-accuracy mapping of PSD including three species diversity indices. The results showed that PSD was closely associated with vegetation indices, followed by spectral reflectance and environmental factors. XGBoost combined with HASM (HASM-XGBoost) showed the best performance with the lowest MAE and RMSE. Our results suggested that the fusion of heterogeneous data and the ensemble of heterogeneous models may revolutionize our ability to predict the PSD over large areas, especially in some places limited by sparse field samples.

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Tree species diversity mapping from spaceborne optical images: The effects of spectral and spatial resolution

Liu,

Frey,

Munteanu

et al. 2024

Remote Sens Ecol Conserv

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Increasingly available spaceborne sensors provide unprecedented opportunities for large‐scale, timely and continuous tree species diversity (TSD) monitoring. However, given differences in spectral and spatial resolutions, the choice of sensor is not always straightforward. In this work, we investigated the effects of spatial and spectral resolutions for four spaceborne sensors (RapidEye, Landsat‐8, Sentinel‐2 and PlanetScope) on TSD mapping in an area of approximately 4000 km2 within the Black Forest, Germany. We employed a random forest (RF) regression model to predict Shannon–Wiener diversity based on seven types of spectral heterogeneity metrics (texture, coefficient of variation, Rao's Q, convex hull volume, spectral angle mapper, convex hull area and spectral species diversity) and a full survey dataset from 135 one‐ha sample plots. We compared the RF model's performance across sensors and spatial resolutions. Our results demonstrated that the Sentinel‐2‐based TSD model achieved the highest accuracy (mean R2: 0.477, mean root‐mean‐square error (RMSE): 0.274). The RapidEye‐based TSD model produced lower accuracy (mean R2: 0.346, mean RMSE: 0.303), but it was better than the PlanetScope‐ and Landsat‐based TSD models. The 10 m (for Sentinel‐2 and RapidEye) and 15 m (for PlanetScope) were the best spatial resolutions for predicting TSD. The NIR band was the most favourable spectral band for predicting TSD. Texture metrics and Rao's Q outperformed the other spectral heterogeneity metrics. Our results highlighted that spaceborne optical imagery (especially Sentinel‐2) can be successfully used for large‐scale TSD mapping but that the choice of sensors can significantly affect the resulting mapping accuracy in temperate montane forests.

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A Random Forest Modelling Procedure for a Multi-Sensor Assessment of Tree Species Diversity

Cited by 22 publications

References 66 publications

Two Decades Progress on the Application of Remote Sensing for Monitoring Tropical and Sub-Tropical Natural Forests: A Review

Two Decades Progress on the Application of Remote Sensing for Monitoring Tropical and Sub-Tropical Natural Forests: A Review

A comparative mapping of plant species diversity using ensemble learning algorithms combined with high accuracy surface modeling

Tree species diversity mapping from spaceborne optical images: The effects of spectral and spatial resolution

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