Fusion-Based Approaches and Machine Learning Algorithms for Forest Monitoring: A Systematic Review

Simple Summary

This study explores new methods for monitoring forests and estimating tree biomass by combining multiple types of satellite data. Traditionally, researchers have used data from single sources but combining different satellite images can capture more detailed information about forests, improving our understanding of forest health and changes over time. However, forests are complex, and it is challenging to identify different types of trees because they often have similar spectral signatures in satellite images. The fusion of different satellite images with advanced machine learning can help analyze large amounts of combined data accurately specifically for monitoring. This study investigates the most effective fusion methods, key factors, accuracy assessment techniques, and challenges involved in applying fusion approaches to monitor forest ecosystems. Our findings will help scientists make better choices about which data and analysis methods to use, improving forest monitoring and contributing to environmental conservation efforts.

Abstract

Multi-source remote sensing fusion and machine learning are effective tools for forest monitoring. This study aimed to analyze various fusion techniques, their application with machine learning algorithms, and their assessment in estimating forest type and aboveground biomass (AGB). A keyword search across Web of Science, Science Direct, and Google Scholar yielded 920 articles. After rigorous screening, 72 relevant articles were analyzed. Results showed a growing trend in optical and radar fusion, with notable use of hyperspectral images, LiDAR, and field measurements in fusion-based forest monitoring. Machine learning algorithms, particularly Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), leverage features from fused sources, with proper variable selection enhancing accuracy. Standard evaluation metrics include Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Overall Accuracy (OA), User’s Accuracy (UA), Producer’s Accuracy (PA), confusion matrix, and Kappa coefficient. This review provides a comprehensive overview of prevalent techniques, data sources, and evaluation metrics by synthesizing current research and highlighting data fusion’s potential to improve forest monitoring accuracy. The study underscores the importance of spectral, topographic, textural, and environmental variables, sensor frequency, and key research gaps for standardized evaluation protocols and exploration of multi-temporal fusion for dynamic forest change monitoring.

1. Introduction

Advancements in satellite imagery, aerial data, and machine learning have enhanced forest monitoring capabilities [1]. However, challenges remain in capitalizing on diverse data sources to improve forest classification and biomass estimation [2]. Forest has an influence on global climate change as it is a significant source of carbon storage, actively influencing global climate patterns and working as a direct or indirect buffer for climate change worldwide [3,4,5]. Effective forest management involves various planning stages and activities, primarily based on achieving specific goals. This can be challenging as a forest ecosystem includes various elements like composition, function, and structure [6]. Composition involves species distribution or different levels of biodiversity indices, while function consists of the rate and capacity of carbon sequestrations and related processes. On the other hand, forest structure is a broader framework, including different physical attributes and the abovementioned characteristics [7]. Forest biomass is another aspect of forests that gets significant attention. In simple terms, forest biomass is the dry weight of organic matter generated per unit of forest area within a specific timeline [8]. Approximately 70% to 90% of the total forest biomass is constituted by Aboveground biomass (AGB), representing a crucial carbon reservoir within forest ecosystems [9]. AGB provides valuable information about forest growth, health, and dynamics. Accurate and rapid information about forest AGB data holds the utmost importance in forest management, determining its impact on carbon cycles, dynamics, and ecosystems [10,11]. However, obtaining accurate and timely AGB estimates requires advanced techniques due to forests’ complex structure and spatial heterogeneity.

Forest monitoring via remote sensing has evolved significantly since 1987 and is now widely utilized across various disciplines, offering global forest cover and change products at different spatial resolutions [12]. These products support national forest inventories and management programs, providing detailed estimates of forest attributes [13,14]. The adoption of remote sensing technologies in forest management varies based on nation-specific considerations, data quality, and resolution [13,15,16,17]. Remote sensing offers advantages, including low cost, high temporal resolutions, and broad coverage, making it valuable for forest monitoring [11,18,19]. Forest monitoring by using vegetation indices from optical bands like red, near-infrared (NIR), or red edge bands correlates strongly with forest health and AGB [20,21,22]. However, they may underestimate biomass in dense forest canopies due to saturation [23]. Hyperspectral data, with higher spectral resolution than optical data, offers an alternative but may introduce data redundancy and complexity [19,24,25]. Furthermore, spatial scale differences between remotely sensed data and management operations pose challenges, especially for localized and diversified forests [13]. Using the capability of the different spectral bands from optical and hyperspectral images is a key practice in modern forest management. However, relying solely on optical imagery can be limited by cloud cover and atmospheric conditions. Thus, combining the strengths of multiple data sources can provide a comprehensive and accurate assessment [26,27].

In this context, Synthetic Aperture Radar (SAR) data complements optical and hyperspectral images for forest investigations. SAR-equipped satellites provide medium-resolution data (<20 m) with frequent revisit times, ideal for areas with dense cloud cover [8,28,29]. SAR backscatter, influenced by vegetation structure and ground conditions, aids in forest mapping, AGB estimation, and distinguishing between forest types [30]. A more recent and advanced application of SAR in forest research is to extract forest height and stand structure using tomographic methods. SAR tomography creates a 3D image of the forest scene by generating a synthetic aperture in elevation through the coherent combination of images acquired from parallel flight tracks [25]. However, SAR data also have limitations, including saturation issues, particularly in L-band and C-band sensors utilized for AGB estimation [8]. LiDAR sensors, alongside optical and radar, are another option for forest management, especially in monitoring and estimating AGB. Previous studies demonstrate LiDAR’s utility in assessing forest structures and stand age [31,32,33,34]. They effectively estimate canopy complexity and forest conditions across various ages under different conditions [35,36,37,38]. These advanced LiDAR technologies are increasingly used in forest management, supporting new ecological applications [39]. Although airborne LiDAR’s coverage can be limited and costs are higher [34], recent advances in satellite LiDAR systems can address these issues. Global Ecosystem Dynamics Investigation (GEDI), a recently launched sensor, offers near-global AGB density estimates using full-waveform LiDAR data. Advancements in AGB estimation from various satellite data types are an area of interest in the research community [40].

Each sensor has unique strengths and limitations, making multisource data fusion an alternative method for improving accuracy and filling data gaps [41,42,43]. Fusion identifies consistent areas, enhances mapping accuracy, and fills data gaps [29,44,45]. For example, using the Gram-Schmidt fusion algorithm improved overall accuracy in marsh vegetation identification from 78% to 86.65% [46]. Fusion also enhances crop classification and cultivar separation [47], overcoming AGB saturation, and improving spectral information [40]. Combining high-resolution panchromatic with medium-resolution multispectral images enhances image clarity and spatial details but may lack sufficient spectral information for accurate AGB estimation [48]. Thus, exploring different fusion techniques from various sensors with appropriate variables is essential for effective long-term forest monitoring and AGB estimation [8,11,19,40,49].

Advanced satellite technology and vast data require sophisticated statistical methods for forest monitoring and management [47,50,51]. Machine learning algorithms can handle these computational requirements and are commonly used for various land classifications [31,47,52,53,54,55]. While machine learning models are suitable for many cases, deep neural networks are increasingly applied to handle large volumes of data and optimize classification and prediction performance [56,57,58,59]. Deep learning minimizes subjectivity and bias in feature extraction, enhancing classification and prediction [60]. However, complex computation algorithms do not always guarantee better accuracy; performance varies among studies and algorithms [19,34,61].

Remote sensing has proven to be a valuable tool for effective decision-making and planning. The increasing availability of diverse remote sensing data and advancements in computational capacity have provided opportunities for developing various fusion and machine learning algorithms in this field. While numerous studies have explored multisource fusion for land cover classification, a comprehensive analysis specifically focusing on forest ecosystems using a combination of fusion and machine learning techniques and evaluating their performance is lacking. This review addresses this gap through a systematic review of the application of fusion and machine learning techniques for forest classification and AGB estimation using different remote sensing data. This is particularly relevant given the increasing complexity of available datasets and the need for robust and accurate forest monitoring methods. The following questions need to be answered: What are the prevailing satellite and aerial imageries integrated into fusion techniques for forest monitoring? Which variables are commonly incorporated into fusion techniques, and how do these variables enhance the machine-learning models for forest classification and biomass estimation? What contemporary machine learning algorithms are employed to analyze fused datasets derived from diverse sources? How is the accuracy of fusion-based machine learning models systematically evaluated in the context of forest monitoring? The paper focuses primarily on the recent advancements in fusion and machine learning techniques and their practical aspects of remote sensing in the field of forestry.

2. Methodology

The methodology of this study involves a thorough and systematic process of identifying, selecting, and synthesizing relevant literature to address specific research questions or objectives to ensure that the review is comprehensive, reliable, and informative for readers. The methodology involves conducting a comprehensive literature search on three established databases: Web of Science, Science Direct, and Google Scholar. These databases offer various scholarly articles on various topics, making them valuable resources for retrieving relevant literature [62,63]. Furthermore, these databases have comprehensive coverage, relevance, and high-quality indexing of peer-reviewed articles [64,65]. Also, these databases are widely recognized for their rigorous indexing criteria and multidisciplinary content, making them ideal for accessing scientific publications related to remote sensing, machine learning, and forest monitoring. Here, Science Direct is chosen as it provides full-text access to high-quality, peer-reviewed articles, making it easier to review methodologies and findings in detail. While Scopus covers more sources, many articles are behind paywalls, limiting direct access [66].

An advanced search has been done based on predefined keywords and concepts to find relevant papers on the study objectives (

Table 1. Search Criteria used to find relevant articles from the selected databases.

Search Criteria	Database	Number
TS = (Forest) OR TS = (Biomass) OR TS = (Above ground biomass) OR TS = (Type)) AND TS = (Machine learning) AND TS = (Fusion) AND (TS = (Remote Sensing) OR TS = (Satellite) OR TS = (SAR) OR TS = (Lidar) OR TS = (Optical)) NOT TS = (Height) NOT TS = (Crop *) NOT TS = (Farm *) NOT TS = (Salinity) NOT TS = (Plantation) NOT TS = (Sea) NOT TS = (Earthquake) NOT TS = (Urban) NOT TS = (Snow) NOT TS = (Habitat) NOT TS = (Geological) NOT TS = (Fire)	Web of Science	195
(Forest OR Biomass) AND remote sensing AND fusion AND Machine Learning NOT (habitat OR fire OR Crop OR Land)	Science Direct	679
forest biomass satellite data fusion machine learning fusion, OR image OR fusion, OR multisource “remote sensing machine learning”	Google Scholar	46

Note: The asterisk (*) is a wildcard operator that retrieves multiple word variations (e.g., “Crop*” includes “crop,” “crops,” “cropping”).

). The keywords were carefully chosen based on commonly used terms in forest monitoring, remote sensing, and machine learning literature. To refine the scope of the review, exclusion keywords were used to filter out studies related to other land cover types, prioritizing those on forest ecosystems. This strategy enabled the creation of a more focused and relevant literature base, enhancing the effectiveness of addressing the research questions.

Researchers use Boolean operators (e.g., AND, NOT, OR) and wildcards to refine the search and generate more targeted results. These Boolean operations combine keywords and logical operators to create more specific search strings. For example, using the “AND” operator ensures that articles must contain each concept’s keywords and help narrow down the search results. On the other hand, the “OR” operator broadens the search by capturing articles containing any keywords within the topic of interest. Wildcards allow variations in spelling or word forms without specifying each variation separately, ensuring flexibility, enhancing the search process, and increasing the likelihood of capturing relevant articles.

In this paper, a keyword search using Boolean operators was conducted on the topic so that it could capture information based on title, keywords, and abstract. Topic search gives an idea about the relevance of the articles to the specified research objectives and ensures the exclusion of irrelevant articles [67]. As the topic covers keywords, titles, and abstracts, it can capture more relevant articles instead of searching on titles or abstracts individually. From the search results, the selection process is conducted to retrieve a comprehensive set of articles that are directly related to the research questions of the study. This process involves assessing the methodology, the reliability of the findings, and the applicability of the results to the research question or objective.

This structural review process provides transparency and completeness in reporting and helps authors present their methodology and findings in a structured and transparent manner [68]. This process supports facilitating the replication of the review process, enhancing the credibility of the study based on synthesized evidence. Furthermore, following specified guidelines promotes good reporting practices among early-career scientists, securing endorsement and support from journal editors and regulators. There are inherent limitations despite having a comprehensive search and rigorous selection process. Database coverage, search terms, publication types, and search terms can limit the search results and exclude articles that do not explicitly mention the relevancy of the articles in their topics. Refining search strategies, utilizing wildcards, including multiple databases, and conducting thorough searches of existing literature can minimize these limitations [67,68,69].

This review employed a rigorous process to identify and select relevant and high-quality articles for evaluation and data extraction. The initial step involved including peer-reviewed articles written in English from 2000 onwards. The selection is limited to English-language publications to ensure accessibility and consistency in interpretation, as automated translation tools may introduce inaccuracies. The study included only peer-reviewed articles from reputable databases relevant to the study, matched the keyword alignment, and had methodological rigor to reduce selection bias. To search, three databases—Web of Science, Science Direct, and Google Scholar—were chosen as reliable sources of scholarly papers. The search across these databases yielded many articles based on the keyword combination and search criteria. Specifically, the Web of Science database returned 195 articles, Science Direct provided 679 articles, and Google Scholar presented 46 articles in response to the selected search criteria conducted in early June 2023 (Table 1). All 920 retrieved records were initially screened based on their titles and abstracts to assess their relevance to the research questions. The search and screening process was manually performed within the selected databases. Articles were excluded if they focused solely on land cover monitoring or classification without data fusion and machine learning. Articles were further excluded if the study employed single-source data with machine learning or multisource data without machine learning. Articles that passed the title and abstract screening were retrieved for full-text review. This process involved a detailed assessment of the results’ methodology, findings, and applicability to forest monitoring using multisource fused data and machine learning. At this stage, articles were excluded if they did not explicitly address forest monitoring using fusion and machine learning. While deep learning algorithms were not the central focus of this study, studies that incorporated both deep learning and machine learning were included in the analysis.

The review process was focused on evaluating the methodological rigor and reporting quality of the selected studies related to the research questions. This involved assessing the clarity and completeness of methodology descriptions, including data sources, fusion techniques, machine learning algorithms, the justification for chosen methods, and the appropriateness of evaluation metrics. The data were extracted and organized into a standardized data extraction table and then synthesized and analyzed to address the research questions. After a comprehensive screening, a total of 37 articles from the Web of Science, 18 articles from Science Direct, and 18 articles from Google Scholar were selected for further analysis. A diligent check for duplicate articles was conducted using an Excel file to remove duplicates. As a result, one article was removed as it was found in both the Science Direct and Google Scholar databases (Figure 1).

It is important to note that only peer-reviewed articles written in English were included in this review, excluding non-peer-reviewed articles, book chapters, and articles published in languages other than English. However, by adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, a well-established guideline for systematic reviews, the researchers ensured that the selected sample represents the study’s objectives and goals.

3. Results

The selected review papers are used to extract both qualitative and quantitative information for further analysis and to answer the research questions. Based on the publishing year of the reviewed paper, we can see an increasing trend in using fusion and machine learning algorithms in forest monitoring. In the last five years, the numbers have risen significantly (Figure 2). This can be attributed to freely available data sources, advanced computation capabilities, and improvement in machine learning algorithms. Furthermore, these methods produce better accuracy and can handle high volumes of temporal data, making them a practical approach for long monitoring and data generation.

The geographic distribution of selected published papers varies significantly, and there is no homogeneity. The majority of the papers are focused on the United States and China, but this fusion-based approach is also being applied in different countries in Europe. Some applications are observed in Asia, South America, the southern part of Africa, and Australia (Figure 3). A possible explanation is the lack of available high-resolution satellite and ground truth data. Also, the forest ecosystem varies significantly across the globe, so no universal fusion-based approach will capture the dynamics of the global forest. However, with the release of open-source satellite data and cloud computing platforms, these methods can be tested under different conditions to test their suitability and applicability for different environments, revealing new information for conservation efforts.

Data fusion techniques are commonly utilized by integrating optical and radar data, as this enhances classification accuracy by incorporating spectral and textural properties. Combining optical and hyperspectral data provides detailed spectral information for distinguishing tree cover types while merging optical data with LiDAR enhances forest biomass estimation accuracy (

Table 2. Fusion combination used in the selected articles. Here, Field data represents aerial photography from UAV; only radar and optical represent combining different radar and optical satellite data, respectively. There are 6 review papers that have been excluded from the table below.

Combination	Number
Optical & Radar	16
Optical & LiDAR	6
Hyperspectral & LiDAR	5
Optical & Hyperspectral	9
Optical & Field Data	9
LiDAR & Field Data	6
Hyperspectral & Field Data	10
Only Radar	2
Only Optical	1
All type	2

). Validation of remote sensing measurements often involves aerial photographs, ground-based LiDAR data, or direct field measurements as reference data. The selection of data sources depends on factors like availability, resource constraints, and study objectives.

In terms of optical satellite data, Sentinel 2 and Landsat data are mostly used due to their availability. Also, these data have a fine spatial and temporal resolution, making them a good option for long-term monitoring. On the other hand, high spatial resolution optical satellite data like RapidEye, WorldView-2, EO Hyperion, PlanetScope, and Gaofen (GF) are also being used when there is a need for high spatial resolution and precise mapping. Aerial imagery from AVIRIS is another popular option for monitoring changes in forest dynamics. Radar satellite data like Sentinel 1, PALSAR, and TANDEM X are the most popular options for getting radar images. Like optical satellites, these radar data are chosen based on spatial and temporal resolution, availability, and application. Recently launched, LiDAR-based GEDI is also getting much attention as these data are freely available and have global coverage (Figure 4).

Overview of Approaches and Machine Learning Algorithms for Forest Monitoring

The selected papers include diverse machine-learning algorithms for forest monitoring. RF is the most utilized due to its ability to handle complex datasets and provide accurate predictions. SVM, K-Nearest Neighbor (KNN), and Multivariate Linear Regression (MLR) are also prominent. Sometimes, MLR is used to identify relevant relationships among variables and serves as a benchmark for assessing machine learning models’ performance. On the other hand, deep learning algorithms like Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) handle vast data and excel in image fusion and classification tasks. Here is to note that there are also other algorithms, like Extreme Gradient Boosting (XGBoost), Bayesian classifier, and Light Gradient Boosting (LightGBM) are also used in this field, and the choice of algorithms primarily relies on the data quality, quantity, and research goals (

Table 3. Algorithms used in the selected papers.

Name of Algorithm	Reference
Least Absolute Shrinkage and Selection Operator (Lasso)	[70]
Ridge Regression (Ridge)	[70]
Extreme Gradient Boosting (Xgboost)	[47,48,70,71,72,73]
Random Forest (RF)	[8,11,19,29,31,32,33,34,41,42,43,44,47,48,49,50,52,53,54,55,56,57,61,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86]
Multivariate Adaptive Regression Splines	[50]
Multivariate Linear Regression	[49,50,51,75,79,86,87,88]
Gram Schmidt (GS)	[11]
Nearest Neighbor Diffusion Pan Sharpening (NND)	[11]
Wavelet Resolution Merge (WRM)	[11]
Brovey Transform (BT)	[89]
Support Vector Machine (SVM)	[19,33,34,51,52,53,54,55,61,71,76,84,85,86,89,90,91,92,93,94]
Artificial Neural Network (ANN)	[3,19,34,41,54,84,89]
Bayesian Classifier	[87,89,95]
Light Gradient Boosting (LightGBM)	[41,47,72,96]
Deep Neural Network	[53]
Dynamic Global Vegetation Models (Dgvms)	[97]
K-Nearest Neighbor (KNN)	[41,56,79,87,88,93,98]
U-NET	[56]
Decision Tree (DT)	[8,54]
Maximum Likelihood Classification	[89]
Dynamic Global Vegetation Models (Dgvms)	[97]
Convolution Neural Network (CNN)	[57,58,86,93,99,100]
Robust Regression	[8]
Decision Stump	[8]
Gradient Boosting	[40,48]
Linear Discriminant Analysis And Sparse Regularisation (LDASR)	[90]
Random Subspace (RS)	[90]
Deeplabv3+	[59]
Hrnet Deep Learning Algorithms	[59]
Multi-Layer Perceptron (MLP)	[45,96]
Gaussian Process Regression (GPR)	[51,93]
Breaking Ties (BT) Methods	[101]
Multinomial Logistic Regression (MLR)	[101]
Pointwise Direction Embedding Deep Network (PDE-Net)	[102]
Classification and Regression Tree (CART)	[19]
PROSAIL-PRO Model	[53]
Monte Carlo Simulations	[103]
Plot-Scale Methodology (AGB & Biomass Horizontal Distribution Model (HDM))	[104]
K-DBN Algorithm	[49]
Weighted CW-Knn And G-Knn	[88]

). These various machine learning algorithms, ranging from RF, LightGBM, and SVM to Multivariate Linear Regression and deep learning algorithms like ANN and CNN, showcase the extent, capacity, and diversity of methodologies employed in handling multisource data for forest monitoring research.

4. Discussions

4.1. Data Sources

A wide range of satellite and aerial data sources are applied to classify forest cover and estimate biomass under different climatic and habitat conditions. The reviews highlight that satellite images have been widely applied to monitor forest dynamics in different biomes. The study demonstrates that satellite images are widely used to monitor forest dynamics across various biomes, from the Tropical Rainforests of the Amazon to the Boreal Forests of Canada and Russia, including Subtropical, Mediterranean forests, and Mangroves [8,73,79,91,103]. This global application highlights the versatility of satellite data for diverse forest ecosystems.

Landsat 8, Sentinel-1, Sentinel-2, Gaofen-2 (GF-2), GaoFen-1 WFV, and WorldView-2 are the most commonly used for forest monitoring as they offer moderate resolution images ranging from 10 to 30 m along with coarse resolution, the Moderate Resolution Imaging Spectroradiometer (MODIS) [52,53,77,90]. As noted by these studies, choosing between resolution and coverage is a key consideration when selecting satellite data. The study also confirms this common observation that high-resolution images provide rich detail, and moderate-resolution sensors offer valuable broader spatial context. Recently, the use of high-resolution satellite constellations like PlanetScope satellites and Pléiades satellites is gaining popularity as they can capture imagery at a spatial resolution of 0.5 to 5 m. As Kluczek et al., 2023 also observed, the increasing use of high-resolution data likely reflects the growing demand for detailed local information despite the added cost and processing challenges [61]. The combined use of these different resolution satellites allows researchers to assess forest extent and dynamics across large areas. The study found that SAR data, particularly from Sentinel-1, RADARSAT, and TANDEM-X, offer unique insights into forest structure and moisture content. Similar findings have been observed by Bjerreskov et al., 2021 and Zhang et al., 2022 [29,86].

Aerial and satellite LiDAR, such as GEDI And ICESAT LiDAR data, provide precise information about canopy height models (CHMs), digital terrain models (DTMs), digital surface models (DSMs), and vegetation profiles, allowing for accurate identification and classification of tree species, estimation of AGB, and assessment of forest health [19,40,84,103]. Consistent with the findings of Dassot et al., 2011, Guo et al., 2011, and Hamraz et al., 2019, the review confirms the superior accuracy of LiDAR-based biomass estimates compared to methods using only optical or SAR data [39,58,76]. Furthermore, airborne hyperspectral imagery, unmanned aerial vehicle (UAV) images, and digital orthophoto quarter quads (DOQQs) provide detailed spectral and spatial information for accurate forest monitoring, species classification, and object-based feature extraction [55,80,85,99]. These studies and the review demonstrate the increasing importance of multi-source data, including hyperspectral and UAV imagery, for detailed forest analysis.

Integrating LiDAR data with other satellite and aerial data sources enhances classification accuracy and prediction. This corroborates the findings of Scholl et al., 2021, emphasizing the importance of combining data sources to leverage the complementary information they provide [99]. Even with having vast satellite systems, there are limitations associated with limited coverage or restricted access, computational limitations in processing large volumes of optical, radar, and LiDAR data, and costs associated with acquiring high-resolution satellite and aerial data. These challenges, as also noted by previous studies, highlight the need for continued development of efficient data processing techniques and improved data accessibility [6,105,106]. Despite these limitations related to data availability, accessibility, cost, and processing complexity, satellite and LiDAR data remain invaluable for capturing the spectral, structural, and textural characteristics of forest ecosystems.

4.2. Variable Selection

A well-classified forest cover should capture actual vegetation community composition, pattern, and complexity with adequate precision and accuracy [63]. With the abundance of satellite sensors, selecting the optimal features for classification can be challenging as it contributes to reducing data redundancy and lowering computational costs. The accuracy and performance of the machine and deep learning algorithms depend on the input variables, so careful selection of variables is a crucial step to classifying forests and estimating biomass accurately [43,77,80]. The review also emphasizes the importance of careful selection of variables as it influences the accuracy of forest classification and biomass estimation.

Variables used in forest classification and biomass estimation can be broadly categorized as spectral, topographic, textural, and environmental variables [48,70,88,89]. The spectral feature widely includes different bands from different satellites and indices derived from their combination. The Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), Enhanced Vegetation Index (EVI), Modified Soil-Adjusted Vegetation Index (MSAVI), Optimized Soil-Adjusted Vegetation Index (OSAVI), Red Edge derived Vegetation Index (red-edge), Ratio Vegetation Index (RVI), Leaf Area Index (LAI), Leaf Water Content, Fraction of Vegetation Cover (FVC), Chlorophyll Content in the Leaf (LAICAB), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) are commonly used due their capacity to capture different aspects of vegetation properties, such as chlorophyll content, leaf area index, and moisture content, which are crucial for forest cover classification [8,11,47,53,56,75]. The review supports the widespread use of these vegetation indices, as they provide valuable insights into the biophysical characteristics crucial for forest classification. Topographic variables influence the growth and distribution of vegetation. Elevation, slope, and aspect represent the characteristics of the Earth’s terrain. These variables provide insight into water availability, soil composition, and other environmental factors. Studies showed that integrating topographic variables improves model performance and classification accuracy by providing crucial contextual information [11,71,79,81]. Topographic variables provide additional information, leading to better accuracy and model performance. Integrating these variables can enhance model performance and classification accuracy. On the other hand, textural variables quantify the texture or the arrangement of pixels within an image, providing information about the composition, structure, and complexity of vegetation communities within a given area. SAR data can capture textural components, and combining SAR with optical data can improve forest classification. Previous studies have applied this approach and obtained improved results [49,61,86].

Environmental variables such as temperature and precipitation can also add helpful information to different models leading to detailed characterization, prediction, and classification [70]. Various methods can be applied to find out the most suitable variables. Principal Component Analysis (PCA) or feature selection algorithms can identify the most informative and discriminative variables; Lasso and Ridge models can select relevant variables and mitigate the impact of multicollinearity through regularization techniques; Garson’s Algorithm, Boruta algorithm, or Recursive Feature Elimination (RFE) methods assess the importance of each variable to the classification task, assisting in discovering the relevant variables [42,70,72,77,80,84]. The study highlights the diverse range of available variable selection techniques and underscores their importance in creating robust forest classification and biomass estimation models. Combining satellite-derived variables with advanced selection methods creates a robust framework for forest classification and biomass estimation. The main goal is to include an optimal set of variables while retaining essential information for modeling.

4.3. Fusion Techniques

Fusion techniques combine various satellite, UAV, LiDAR, and Radar sensor images to enhance forest classification and biomass estimation accuracy [62]. These methods combine data to capture different aspects of vegetation structure, biomass, and composition [42]. The most commonly utilized fusion techniques are data stacking, where spatial and temporal dimensions of the data are aligned and concatenated, which can be used as input for classification and biomass estimation algorithms [11]; Feature-Level Fusion, where features are extracted from different data sources into a single feature vector to create a fused feature [85]; Decision-level fusion combines classification results from individual classifiers using majority voting or weighted voting techniques to obtain the final classification output [71]; Ensemble Methods, where multiple classifiers are integrated to create an ensemble of models, and the results of these models are then combined to make the final prediction [41]; Deep Learning-based Fusion, where algorithms such as CNNs and Recurrent Neural Networks (RNNs) are used to capture complex relationships between the data sources and fuse information [57]. The review also found that these fusion techniques are widely used and effective at combining different data types to improve forest monitoring.

Beyond fusion techniques, image transformation methods have also proven valuable in enhancing multi-source data for forest classification and biomass estimation. The Intensity-Hue-Saturation (IHS) Transformation technique separates the intensity (grayscale) information from the hue and saturation (color) information, thus maintaining the color fidelity of the original imagery while enhancing the spatial details from the high-resolution image. These techniques can introduce textural information, which helps estimate forest biomass as it can distinguish between forest and deforestation areas [95]. PCA technique transforms the original image bands into a new set of orthogonal bands, which capture the principal components of the most significant variability in the data. This method helps in enhancing the spatial resolution of the imagery [47]. These transformations, including IHS, PCA, and Brovey, have been successfully applied to address the saturation problem in forest AGB estimation, particularly in challenging environments like subtropical monsoon humid climates. Wavelet transform decomposes images into different frequency bands, combining them to preserve both spectral and spatial information, improving spatial resolution and texture. Liu et al., 2021 and Shahfahad et al., 2022 have shown that these transformation techniques enhance multi-source data for improved forest analysis [59,92].

The Laplacian pyramid, another image fusion technique, decomposes images into multiple levels of detail and combines corresponding levels, improving visual quality and classification accuracy by preserving both spatial and spectral characteristics [86,90]. The study indicates that the Laplacian pyramid method is a computationally efficient and effective approach for image fusion in forest applications. Fusion techniques are also extensively used with LiDAR data for accurate forest classification and biomass prediction. Combining LiDAR with hyperspectral, field inventory, satellite, and multispectral data with advanced statistical models enhances classification and modeling efficiency (Scholl et al., 2021 [99]). Multivariable spatial regression models integrate LiDAR, hyperspectral, and field inventory data to improve predictive capabilities [87]. The Individual Tree Crown (ITC) Approach combines LiDAR and tree attribute data for predicting tree parameters like height, diameter, and biomass [78]. Marklund’s biomass models incorporate LiDAR and tree attribute data for individual tree AGB estimation [98]. LiDAR is also fused with multispectral data in a multisource framework to extract features, enhancing forest cover classification accuracy [53]. LiDAR-derived models like DTM, DSM, and CHM are fused with other data to improve forest feature extraction and biomass prediction [103]. These studies highlight the significant contribution of LiDAR fusion techniques to accurate forest assessment.

Radar data fusion is another important area of research for forest classification and biomass estimation. Researchers achieve accurate forest classification and biomass estimation by fusing radar data with relevant sources and algorithms [8,30]. SAR images operating in the microwave spectrum can penetrate clouds, allowing more information for vegetation monitoring [50]. SAR Backscatter coefficients, like gamma naught (γ°) or sigma naught (σ°), quantify the signal intensity and vary with SAR sensor frequencies and polarizations [82]. This scattering mechanism is influenced by different frequencies and polarizations of SAR sensors (e.g., C-band, L-band, quad-pol). SAR covariables, including SAR vegetation indices, capture vital information about forest structure and characteristics that can enhance the prediction models. Also, SAR vegetation indices can capture vegetation density, canopy structure, biomass distribution, and vegetation health information such as vegetation structure, biomass, moisture content, and canopy scattering properties [8,19,54]. Furthermore, textural features, including entropy, correlation, angular second moment, and contrast, provide information about the heterogeneity of vegetation, which is vital for biomass prediction and forest type identification [95]. Recent studies also focus on integrating radar imagery with optical imagery, LiDAR, and other environmental covariables to enhance forest classification and biomass estimation. Fusing topographic data (e.g., elevation, slope, aspect), climate data (e.g., temperature, precipitation), soil characteristics, and other environmental covariables allow for a more comprehensive understanding of the factors influencing forest structure, biomass, and species distribution [50,76,97]. Different radar fusion techniques like statistical methods, machine learning algorithms, and geospatial modeling approaches are applied depending on the study’s goals and the data availability. The review supports the growing trend of integrating radar data with other sources, recognizing the unique advantages of SAR data for forest monitoring, especially in cloud-prone regions.

4.4. Classifier

Researchers select classifiers and algorithms for fusion-based forest monitoring based on a variety of factors, including specific objectives, data characteristics and availability, and algorithm performance in previous studies [63]. Research problems, complexity, interpretability requirements, and computational resources also play a role. This study emphasizes the importance of carefully considering these factors when choosing a classification or regression algorithm for forest monitoring.

SVM is a supervised learning algorithm used for classification and regression tasks where SVM is used to classify different forest types or species by training it on various features derived from LiDAR, hyperspectral imagery, or multispectral imagery. SVM separates different classes by finding an optimal hyperplane [72,91]. RF is an ensemble learning method that utilizes the predictions of multiple trees for optimal prediction. RF is widely adopted because it can capture complex relationships between input variables and forest classes, identify important features, and handle large datasets [79,83,87]. In forest classification, kNN is used to class labels by their k-th nearest neighbors in the feature space. Although relatively simple and intuitive, the performance of kNN is sensitive to the number of K (neighbors), as it directly influences the number of classes and the feature scaling [79,88]. The review highlights these commonly used algorithms and suggests that the optimal choice depends on the goal of the study.

Object-Based Image Analysis (OBIA) is another popular approach that uses spatial and spectral properties and assigns class labels for image segmentation and classification. Tree attributes such as height, shape, and texture are used to classify forest types or species by applying different class objects from various remote sensing data. OBIA improves classification accuracy by capturing the spatial context [48,73]. Identifying relevant features for classification and using them to make a robust model that is computationally efficient is an essential part of applying ML models in forest dynamics analysis. Principal Components Analysis (PCA) transforms a high-dimensional dataset into a lower-dimensional space. PCA is also a popular method as it simplifies the classification problem and improves computational efficiency through dimension reduction and maximizing variability. PCA can identify the most significant variation, improve computational efficiency, and increase accuracy [42,92]. Recursive Feature Elimination (RFE) is a feature selection method used to identify the most relevant features by iteratively removing less important features from the dataset and evaluating the performance of the classification model. In RFE, important features are identified, and a subset of these important features is used to achieve the best classification accuracy. RFE helps in minimizing the overfitting issue and improves computational efficiency [72]. The Genetic Algorithm for the Identification of a Robust Subset (GARS) seeks an optimal feature subset, reducing dimensionality and improving model interpretability [72]. These feature selection techniques highlighted above play a crucial role in building robust and computationally efficient models for forest dynamics analysis.

Determining which algorithm provides the best accuracy is challenging, as performance is highly dependent on the specific application, data source, study area, variables, and sampling information. Statistical validation and comparisons between machine learning techniques are essential for drawing robust conclusions [107,108]. While comprehensive comparisons among these techniques are sparse, some studies have indicated that SVR performs better than RF, and kNN regression shows the lowest root mean square error (RMSE) for biomass prediction [53]. The review underscores the need for more comparative studies across different algorithms and datasets to provide more precise guidance on algorithm selection for specific forest monitoring applications. Statistical validation of results and comparisons among different families of regression techniques are necessary for generalizing conclusions. While some studies have shown the improved performance of regression trees and ensembles like RF [51], further research is needed to compare these techniques with other methods under diverse conditions and dataset combinations.

4.5. Accuracy Assessment

Evaluating the accuracy metrics of different methods in forest classification and biomass estimation is essential as this provides a comprehensive evaluation of model performance. Using a range of metrics allows researchers to compare methods, identify error sources, and improve models through hyperparameter tuning [72,73]. Different accuracy metrics have been used in previous studies. In machine learning models, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) are commonly used to assess the prediction of forest attributes like AGB and species diversity [48,70]. Predictive models are considered accurate and better performing when the MAE and RMSE values are low [51,104]. Another commonly used metric is Accuracy metrics, where the values are compared between observed and predicted classifications in forest mapping tasks to evaluate the model performance. The study underlines the importance of these metrics in assessing the accuracy of predictive models for forest attributes.

Accuracy metrics include Overall Accuracy (OA), which measures the proportion of correctly classified pixels or samples over the total number of pixels or samples [101]; User’s Accuracy (UA), which measures the probability of predicting a class accurately [34,78]; Producer’s Accuracy (PA), which measures the likelihood of a correctly predicted class [57,84]; Kappa coefficient takes both correct and incorrect classifications into account and calculates the difference between observed and predicted classifications beyond what would be expected by chance. It provides a normalized measure of accuracy to compare between different methods [42,47]. The review highlights the widespread use of OA, UA, PA, and Kappa as key accuracy indicators and underscores their value in comparing different classification approaches.

Confusion matrices are essential for forest-type classification, presenting classification results in a tabular format and showing the number of correctly and incorrectly classified pixels or samples for each class [54,56,82]. These matrices provide the basis for calculating OA, UA, PA, and the F1-score [34]. The error matrix provides crucial insights into the context and reliability of accurate results, making it another essential central tool for accuracy assessment analysis. It offers valuable information, and by examining the error matrix of a map, readers can understand whether a specific class has disproportionately influenced the overall accuracy. For instance, it is possible to enhance overall accuracy by increasing sampling within specific spectrally homogeneous classes [109,110]. The study also underlines the importance of confusion and error matrices in providing a detailed understanding of classification performance beyond simple overall accuracy.

For biomass prediction models, R-squared (R²) and RMSE are commonly used. Higher R² values and lower RMSE values indicate better model fit and accuracy [74]. Often independent validation is carried out to evaluate model performance. In independent validation, a new dataset that was not used in the training process is used. This ensures the robustness and reliability of the model and provides more confidence in performance for unseen data [52,81]. Quantile loss assesses the difference between predicted and observed biomass values at different quantiles for evaluating the predictive performance. The Quantile loss provides a comprehensive assessment of model performance by assessing the accuracy at different quantiles across the biomass distribution. This helps identify whether the model has reached a saturation point or not [40].

Feature importance analysis, using techniques like heat maps and variable ranking methods, helps determine the most relevant variables for accurate forest classification and biomass estimation [47,76]. This allows researchers to focus on the most influential factors. K-fold and leave-one-out cross-validation are techniques used to evaluate the performance of machine learning models [50,88]. The Pearson correlation coefficient assesses the linear relationship between predicted and actual biomass [45]. The review emphasizes the importance of feature importance analysis and cross-validation techniques. The metrics used to build and evaluate robust machine learning models will depend on the scope and goal of the research. A detailed explanation of the calculation method will help readers properly interpret the chosen statistic and the study’s accuracy.

4.6. Limitation of Fusion and Machine Learning Algorithms for Forest Monitoring

Even though fusion and machine learning approaches aid forest classification and biomass estimation, these techniques have limitations. Fusion approaches have helped achieve greater accuracy and prediction, but there needs to be more information about the critical steps in effectively integrating different data sources. Evaluating the fused images’ quality is essential to assess the fused data’s robustness and accuracy for forest monitoring. The review underscores the need for standardized evaluation protocols for fusion techniques to facilitate comparisons and identify best practices for data integration that align with the findings of [85].

Rigorous comparisons are also necessary to fully understand the relative advantages and limitations of proposed methods. The study found that many studies lack a thorough discussion of the computational complexity and runtime of different machine learning algorithms, which are critical factors for practical implementation. Also, Shahfahad et al., 2022 found that analyzing large volumes of remote sensing data requires careful consideration of optimal, feasible, and scalable forest monitoring methods [92]. The research articles do not thoroughly discuss the computational complexity and runtime of different proposed machine learning algorithms [100]. Furthermore, the regional scale of many studies raises concerns about the generalizability of proposed methods, as forest ecosystems vary significantly across the globe. The review found that assessing the generalizability of methods across diverse contexts is essential. Previous studies also found that the forest ecosystem varies in terms of vegetation types, topography, and climatic conditions, making it harder to generalize [8,74].

The study emphasizes the importance of robust validation strategies and the need for high-quality, representative ground truth data for reliable model development and evaluation. Previous studies also pointed out that outdated, insufficient, or imprecise ground truth data can introduce errors and biases, affecting model accuracy and reliability [71,84]. The sampling strategies are another issue in Forest monitoring, as forests have complex spatial patterns and variations. Also, using the classic accuracy assessment metrics may not be suitable for advanced methodologies, as this can compromise the reliability of the results. Moreover, the spatial and temporal resolution of the images can affect the model performance. High spatial resolution images are private and are associated with a high cost to acquire them, making them difficult for large-scale applications. Chusnah et al., 2023 found that temporal resolution can also affect the model performance as the multisource data must be collected in the same time frame [74].

Airborne LiDAR, while valuable, may not be feasible or cover the entire area for every forest ecosystem [34]. Likewise, the effects of different LiDAR acquisition parameters, such as pulse density and scan angle, can influence the density and quality of LiDAR point clouds, which can affect the accuracy of the estimation. A comprehensive analysis of different factors, preprocessing steps, and variable selection methods is needed to understand the proposed methods’ effects and relationships among variables. Most of the studies are mostly focused on the specific tree species or attributes, like coniferous versus deciduous forest, or concentrated on the leaf chlorophyll content. These studies provided valuable insights into particular aspects but may not address the full range of tree species and attributes present in diverse forest ecosystems. This review highlights the need for more comprehensive studies that consider the influence of LiDAR acquisition parameters and evaluate models across diverse forest types.

The accuracy of fusion is much dependent on the vegetation structure and roughness. It is possible to have less accuracy in fusion approaches than in a single optical sensor image. Studies have found that the fusion of optical and SAR images has not performed satisfactorily than single sensor-based vegetation indices in terms of accuracy [111,112]. This can be explained through spatial and temporal variabilities in natural environments. The frequency of SAR data can affect distinguishing different classes. For example, high-frequency SAR is sensitive to subtle variations in vegetation phenology. At the same time, low-frequency SAR has limited canopy penetration and may struggle to differentiate between target species and surrounding vegetation, eventually leading to low accuracy [113]. Low-frequency SAR data is also affected by the incident angle, and soil moisture and roughness of the surface can impact the backscatters of SAR images, introducing confusion to different target classes when combined with optical images [114,115]. On the other hand, Near Infrared (NIR), Shortwave Infrared (SWIR), and red edge bands of optical images correlate more with vegetation parameters such as canopy LAI and chlorophyll density making them suitable for distinguishing vegetation classes. Vegetation indices are also calculated using these bands, and integrating these with other optical bands can increase the accuracy of different vegetation classes [70,116]. These previous studies showed that different vegetation indices capture different aspects, so careful consideration is needed to account for vegetation structure, sensor frequency, incident angle, and environmental variables to optimize fusion techniques for diverse forest conditions.

4.7. Implications of Multi-Source Data Fusion for Forest Monitoring

The study highlights the increasing trend of multisource data fusion with machine learning, which provides significant insights for researchers, policymakers, and practitioners for enhanced forest monitoring. The fusion approach has demonstrated improvement in classification accuracies compared to single-sensor approaches, which benefits practitioners involved in forest inventory and management. The study provides information on data sources and tools for more precise and reliable forest assessments for informed decision-making regarding resource allocation, conservation strategies, and sustainable management practices. The complementary nature of fused data provides researchers with new avenues for developing more robust and reliable monitoring applications. The increasing availability of open-access data provides opportunities for large-scale and continuous forest monitoring for policymakers to develop effective environmental regulations and conservation initiatives. Furthermore, multi-temporal data fusion can detect dynamic forest processes like deforestation, fire impacts, and changes in forest structure, offering researchers and practitioners valuable insights into forest ecology and ecosystems for practical conservation efforts. The study has also highlighted the importance of selecting appropriate fusion techniques and variables based on data availability, specific objectives, and environmental conditions, providing a valuable framework for researchers and practitioners to guide their methodological choices. Finally, rigorous accuracy assessment of the chosen machine learning models, using appropriate metrics, is crucial for ensuring the reliability and scalability of results for real-world applications. This impacts the confidence that practitioners and policymakers can use these tools in decision-making.

5. Conclusions

In summary, the study explored fusion techniques and machine-learning algorithms for enhanced forest monitoring. Specifically, it investigates the satellite and aerial imagery fusion techniques, the variables commonly incorporated into fusion techniques and their role in enhancing machine learning models for forest classification and biomass estimation, the widely applied machine learning algorithms to analyze fused datasets, and the accuracy metrics of the machine learning models in the context of forest monitoring. The review found that a growing number of studies integrate optical and radar imagery around the globe. Satellite data fusion incorporates hyperspectral data, LiDAR, and even field measurements. Landsat, Sentinel, Gaofen, and WorldView satellites, along with aerial imagery and LiDAR platforms like GEDI and ICESat, are frequently employed and often selected based on goal, scope, and availability. These diverse data sources provide spectral, spatial, and textural information, capturing the different aspects like forest extent, canopy height, terrain models, vegetation profiles, dynamics, and structural characteristics of forest ecosystems. The review also identified key variables commonly used in fusion techniques, including different spectral indices, topographic variables, textural features, and environmental variables that enhance machine learning models. However, careful selection of these variables is crucial for optimizing model performance, and they need to be chosen based on forest type and monitoring objective. Furthermore, the review showed that RF, SVM, and KNN were popular choices and had their strengths and limitations. The review suggests that proper variable selection is important in maximizing the potential of these algorithms. In evaluating model performance accuracy, metrics like MAE, RMSE, OA, UA, PA, confusion matrices, and the Kappa coefficient were used as standard. These metrics provide a comprehensive assessment of model performance, allowing researchers to compare different approaches and identify areas for improvement. This review provides a valuable framework for researchers and practitioners in selecting appropriate fusion approaches and machine learning models for forest monitoring. Researchers can generate detailed forest inventories across diverse climatic and geographic regions by carefully considering the data sources, variables, algorithms, and evaluation metrics. Our findings also offer guidance for policymakers, conservationists, and forest managers seeking to adopt these methodologies for sustainable forest management and climate resilience. Future research should prioritize multi-temporal fusion for real-time forest monitoring, leveraging continuous satellite data streams. Developing dynamic models with standardized methods for predicting forest attributes on a global scale, incorporating traditional ecological knowledge of local and indigenous forest management, is another avenue for research. In conclusion, fusion techniques and machine learning can provide a robust foundation and practical solutions for long-term sustainable forest management and accurate biomass estimation.