Remote sensing based forest cover classification using machine learning

1 Vol.:(0123456789) Scientific Reports | (2024) 14:69 | https://doi.org/10.1038/s41598-023-50863-1 www.nature.com/scientificreports Remote sensing based forest cover classification using machine learning Gouhar Aziz 1, Nasru Minallah 3, Aamir Saeed 1, Jaroslav Frnda 2,4* & Waleed Khan 3 Pakistan falls significantly below the recommended forest coverage level of 20 to 30 percent of total area, with less than 6 percent of its land under forest cover. This deficiency is primarily attributed to illicit deforestation for wood and charcoal, coupled with a failure to embrace advanced techniques for forest estimation, monitoring, and supervision. Remote sensing techniques leveraging Sentinel-2 satellite images were employed. Both single-layer stacked images and temporal layer stacked images from various dates were utilized for forest classification. The application of an artificial neural network (ANN) supervised classification algorithm yielded notable results. Using a single-layer stacked image from Sentinel-2, an impressive 91.37% training overall accuracy and 0.865 kappa coefficient were achieved, along with 93.77% testing overall accuracy and a 0.902 kappa coefficient. Furthermore, the temporal layer stacked image approach demonstrated even better results. This method yielded 98.07% overall training accuracy, 97.75% overall testing accuracy, and kappa coefficients of 0.970 and 0.965, respectively. The random forest (RF) algorithm, when applied, achieved 99.12% overall training accuracy, 92.90% testing accuracy, and kappa coefficients of 0.986 and 0.882. Notably, with the temporal layer stacked image of the Sentinel-2 satellite, the RF algorithm reached exceptional performance with 99.79% training accuracy, 96.98% validation accuracy, and kappa coefficients of 0.996 and 0.954. In terms of forest cover estimation, the ANN algorithm identified 31.07% total forest coverage in the District Abbottabad region. In comparison, the RF algorithm recorded a slightly higher 31.17% of the total forested area. This research highlights the potential of advanced remote sensing techniques and machine learning algorithms in improving forest cover assessment and monitoring strategies. Background The forest ecosystem plays a vital role in preserving environmental equilibrium through pollution mitigation, flood regulation, and soil erosion prevention. The Food and Agricultural Organization recommends a forest cover of 20–30% for a country1. Pakistan has a limited forest cover, comprising 5.1 percent of the total land area, equivalent to 4.478 million hectares2. This translates to just 0.021 hectares per person, significantly below the global average of 1 hectare per person. Over the past thirty years, over 60 percent of the Himalayan Forest has undergone destruction3. The scarcity of forests in Pakistan can be attributed to the rapid growth of population and poverty, coupled with a lack of awareness among the people. The primary drivers of deforestation in the country are the extraction of wood, fuel, and charcoal by the local population2. However, Certain regions in Pakistan, including Mansehra, Abbottabad and Swat, boast rich biodiversity with over 430 tree species. The conventional and manual approaches to supervising forests present challenges in terms of being time-consuming, expensive, and labour-intensive. The task of physically visiting forests to document information about each tree is both challenging and costly. Monitoring becomes particularly challenging in hilly areas, especially during harsh and cold weather conditions when these areas are covered in snow. In this age of technological progress, it is essential for the government to prioritize the integration of advanced and scientific technologies, such as Remote Sensing4, to effectively manage deforestation. OPEN 1Department of Computer Science and Information Technology, University of Engineering and Technology, Peshawar, Pakistan. 2Department of Quantitative Methods and Economic Informatics, Faculty of Operation and Economics of Transport and Communication, University of Zilina, Zilina, Slovakia. 3National Centre for Big Data and Cloud Computing, University of Engineering and Technology, Peshawar, Pakistan. 4Department of Telecommunications, Faculty of Electrical Engineering and Computer Science, VSB Technical University of Ostrava, 70800Ostrava,CzechRepublic. *email:jaroslav[email protected] 2 Vol:.(1234567890) Scientific Reports | (2024) 14:69 | https://doi.org/10.1038/s41598-023-50863-1 www.nature.com/scientificreports/ Remote sensing technologies Remote Sensing employs satellites and sensors to study the Earth’s surface, providing valuable information from a distance. Commercial satellites like Sentinel-2, Modis, and Landsat offer enhanced spatial, spectral, and temporal resolution, providing open data for remote sensing. Sentinel-2, with its 13 multispectral bands, including the vegetation red edge bands, offers 5days of temporal data for regular analysis. Sentinel-2’s capabilities make it well-suited for detailed forest analysis, change detection, and comprehensive feature analysis. Various techniques simplify the estimation process in remote sensing, yielding notably accurate results in detecting and estimating different forest types, offering insights into their health and maturity. In our research, we utilized Sentinel-2’s temporal data for forest analysis, benefiting from its features as an open data satellite. Sentinel-2 proves to be a valuable resource, contributing to an enhanced understanding of forests, encompassing their health and maturity status. Our designated study area encompasses District Abbottabad, located within the Hazara Division of the Khyber Pakhtunkhwa province in Pakistan. This district falls under the Wet Mountains Agri Ecozone, featuring verdant hills and is widely recognized as a popular summer resort destination. Through the utilization of temporal data from Sentinel-2, our research yielded noteworthy results. Equation (1) provides the formula for calculating the area of the Sentinel-2 image. Li etal. employed multispectral Sentinel-2 satellite imagery5 to evaluate the effectiveness of forest-type mapping in Shangri-La, the administrative region of Yunnan Province, China. They applied the Random Forest algorithm within the Google Earth Engine (GEE)5, with a primary focus on identifying and detecting various forest types. The study aimed to assess the Random Forest algorithm’s efficacy within the GEE platform and distinguish variations in the main forest types across an extensive area. Furthermore, the research aimed to estimate critical features for forest classification. The analysis successfully identified eight distinct forest cover types, achieving a 95.76% accuracy in distinguishing between forest and non-forest areas, along with a Kappa coefficient of 91.34%. The utilization of the Google Earth Engine platform played a pivotal role in effectively monitoring the dynamic changes in forest cover. Conventional approaches for monitoring, classifying, and estimating tobacco crop yield are expensive and time-consuming. The absence of an advanced system utilizing state-of-the-art remote sensing technologies for monitoring, classification, and yield estimation of tobacco crops was evident in Pakistan. To bridge this gap, Khan etal. in collaboration4 with the Pakistan Tobacco Board (PTB), conducted research to establish an innovative machine learning mechanism. They employed temporally layer-stacked Sentinel-2 satellite data to estimate tobacco crops in Pakistan. For the detection of tobacco crops, the researchers devised a model based on an Artificial Neural Network. Implementing the Artificial Neural Network classifier with a single image4 achieved an Overall accuracy of 88.49%, which was further improved to 90.45% through the application of NDVI stacking. Notably, through experiments with temporally stacked imagery, they attained an Overall accuracy of 95.81%, marking a significant 7.32% improvement over the benchmark scheme. Like many other countries, China experiences the effects of Land Use Land Cover (LULC) changes. In tackling this challenge in the Ganan Prefecture from 2000 to 2018, Liu etal.6 utilized the dense time stacking of multitemporal Landsat images and implemented the random forest algorithm on the Google Earth Engine (GEE) platform for LULC mapping. The classification accuracy for the entire dataset fell within the range of 89.14% to 91.41% and Kappa Coefficient 0.86. The primary land use and land cover (LULC) categories in the study area were grassland, making up 50% of the total area, and forest, encompassing 25%. Forest dynamics result from various factors, with seasonal influences playing a significant role. In response, Jiang etal.7 introduced Forest-CD, a model that utilizes high-resolution images (VHR). This model employs an encoder–decoder architecture, integrating background information. The encoder, driven by the Swin Transformer, systematically extracts change features, effectively mimicking global information. Conversely, the Forest Change Detection decoder employs the feature pyramid network to recover fused information and feature scales at different levels. Analysis of an extensive forest dataset indicates that the Forest-CD network, utilizing VHR images, attains a higher F1 score. Additionally, the outcomes from Forest-CD demonstrate a decrease in pseudo changes. The Random Forest machine learning algorithms find frequent applications in data classification8,9, object recognition10,11, and image segmentation12. Feng etal.13 introduced a novel training sample selection method specifically designed for Random Forest Modeling in greenhouse identification using Super View-1 imagery. This innovative approach enhances classification accuracy and generalization capabilities. The new Random Forest Modeling allows for the automatic selection of high-quality training samples, resulting in high-precision classification. Furthermore, the researchers anticipate that this improved and advanced Random Forest model can extend its utility to identify various ground objects such as roads and buildings. To address challenges in advancing Remote Sensing technologies, Benson etal.14 introduced multimodal remote sensing model for forest parameter estimation. This approach utilizes Light Detection and Ranging (LiDAR), polarimetric radar, and near-infrared passive optical sensing platforms, coupled with physics-based models. These models prove beneficial in precisely estimating aboveground biomass and measuring Canopy Height in homogeneous areas. The forest parameter estimation algorithm employs a combination of geometric and electromagnetic sensor model methods. Despite having minimal input information, this integrated method yields accurate results for estimating forest structure, along with minimal root mean square errors. In order to precisely map and identify spatiotemporal changes Erfanifard etal.15 carried out a three-decade study in Iran with a focus on mangrove habitats. The Submerged Mangrove Recognition Index (SMRI), a recently (1) Area = (TotalPixel ×100m 2 ) 1000 ×1000 ×100 = Hectares 3 Vol.:(0123456789) Scientific Reports | (2024) 14:69 | https://doi.org/10.1038/s41598-023-50863-1 www.nature.com/scientificreports/ developed technique, and Landsat data from 1990 to 2020 were used in the study. In the process of Mangrove mapping, the researchers utilized four vegetation indices in conjunction with eight mangrove-specific indices. The study found SMRI to be a particularly effective index. Utilizing long-term Landsat data, the estimated mangrove coverage in Iran was approximately 13,000 ha in the year 2020. Wallner etal.16 addressed the dynamic and impactful changes occurring in the Central European forest ecosystem, driven by climate uncertainties and shifts in weather patterns. In response to this, they employed satellite data from ZiYuan-3 (ZY-3) within a Remote Sensing-guided Forest inventory framework. The objective was to reduce the required field sample size while analysing the standard grid inventory. The utilization of 3D ZY-3 demonstrated its suitability in supporting forest inventory by effectively minimizing sample size and enhancing inventory frequencies. Sundarban a mangrove forest situated at Nijhum National Park17 faces challenges in the degradation of the forest cover. A study was conducted by Islam etal. in NDP to find out the decades’ changes in the mangrove forest by using GIS tools and remote sensing available data. They used maximum likelihood classification techniques by using Landsat images of 3 decades from 1990 to 2020. SAVI and NDVI-based classification is performed for forest cover changes in comparison with supervised classification. With this work, they find out that in the first decade from 1990 to 2000 almost one-third of deforestation occurred. However, in the last decade increase of 310.32ha have recorded in mangrove forest cover. Deforestation changes the forest structure, functionality, and ecosystem process18. Challenges facing deforestation are the estimation of emissions and identifying the area affected and the total amount of biomass lost. Till now no reliable method is established to identify the causes of deforestation to monitor forest fire, cattle grazing, and fuelwood collection. High spatial and temporal images are used to detect small-scale disturbances. However, using high-resolution images is costly too. For forest fires and detecting logging, Gao etal.18 suggested the SMA Remote sensing method for the detection of deforestation. To measure the intensity of deforestation Lidar and radar are suitable because of their capacity to measure the 3D of the forest structure and biomass measurement. In the last decades, deforestation and woodland is greatly affected by natural disasters19. To detect the early smoke and flame various remote sensing technologies systems and algorithms are used by Barmpoutis etal.19. Terrestrial, airborne, and spaceborne-based systems are identified. Large Earth Observation Satellite proved to be successful in wide-range broadcasting in early smoke and flame detection. CubeSats is a low-Earth-Orbiting satellite that has a significant advantage over traditional satellites in smoke detection and fire detection, they are also economical, better in temporal resolution, have good response time, and in better coverage. A Spatio-temporal study has been conducted by Negassa etal.20 on Kotmo forest which is situated in the Guto Gita District of the East Wollega zone of Ethiopia to find the status of the Forest cover by using the GIS and Remote Sensing techniques. By using geospatial techniques, it is recorded that the total area of dense forest in Kotmo forest was 32.73% in 1991. Which decreased to 26.16% in 2002. The forest further decreased to 20.5% in 2019. A decrease in the open forest is also recorded i.e., 18.19% and 16.14% in 1991 and 2019 simultaneously. However, a considerable amount of increase in agricultural land is recorded from 24.78% in 1991 to 29.21% and 33.50% in the years 2002 and 2019, respectively. This study suggests policy interventions to protect the Kotmo forest priority area from loss and degradation. Biomass mapping is a vital and practical tool in the realm of forest management, particularly for monitoring forests and evaluating deforestation processes. For this purpose, Sharifi etal.21 conducted a study aimed to employ Multivariate Relevance Vector Regression (MVRVR) as a Bayesian model with a kernel-based framework for predicting above-ground biomass (AGB) in the Hyrcanian forests of Iran. Using field data and multi-temporal PALSAR backscatter values for Training and Testing, the researchers compared the results with alternative methods such as multivariate linear regression (MLR), multilayer perceptron neural network (MLPNN), and support vector regression (SVR). The findings revealed that the SVR model outperformed others, especially at the lowest saturation point. The MVRVR model significantly enhanced AGB estimation precision, showing exceptional performance, particularly insituations involving the maximum saturation point. In the realm of remote sensing, hyperspectral images (HSIs) distinguish themselves as a valuable source of information, owing to their distinctive features applicable in various contexts. But still due to many reasons the hyperspectral images performance are reducing due to many reasons especially to the limited number of samples. In order to improve the HIS accuracy Ghaderizadeh etal.22 proposed the creation of a classification model for hyperspectral images (HSI) named MDBRSSN, an acronym for Multiscale Dual-Branch Residual Spectral–Spatial Network with Attention. The proposed model underwent experiments on four datasets, showcasing its excellence compared to state-of-the-art methods, particularly in scenarios with a restricted number of Training samples. The proposed model achieved Overall accuracies of 99.64%, 98.93%, 98.17%, and 96.57% with only 1%, 1%, 5%, and 5% of labelled data for Training, respectively. These results surpass those of state-of-the-art methods. Various factors, including flooding, contribute to deforestation. The importance of implementing a real-time monitoring system for evaluating flood risks and improving disaster response times cannot be overstated. In a study conducted by A. Shirifi23, the classification of SAR data involved employing thresholding, machine learning algorithms, and an object-based method. The thresholding process played a crucial role in identifying flooded regions. Upon comparing the results, the machine learning algorithm exhibited significant success. These findings highlight the importance of Sentinel-1 images as crucial data for refining methodological guides, indicating their potential as a novel resource for monitoring flood risks. Remote sensing proves beneficial in flood control efforts, as floods can contribute to deforestation. Floods pose a potential threat in numerous locations, with heightened susceptibility observed in forests, the agricultural industry, and infrastructure situated near rivers. This vulnerability is attributed to the widespread impact of floods on forests and agricultural land across diverse areas. Tariq etal.24 implemented an experimental approach to evaluate the vulnerability of flood mapping in the northern areas of Punjab, Pakistan, through the integration 4 Vol:.(1234567890) Scientific Reports | (2024) 14:69 | https://doi.org/10.1038/s41598-023-50863-1 www.nature.com/scientificreports/ of FR and AHP techniques. Eight parameters were deliberately selected to determine the weight of relative significance, employing pairwise matrix correlation. Six parameters are from Remote Sensing imagery including Sentinel-2 satellite. The flood hazard map was generated using ArcGIS algorithms to identify the extremely high, moderate, and low flood zones in the final output. Several research studies have recommended the utilization of remote sensing imagery for environmental monitoring. For this purpose Mohammadi etal.25 employed Sentinel-1 SAR data, along with the utilization of Sentinel-2 imagery, to promptly identify oil spills in the Persian Gulf. They employed VV-polarized images from Sentinel-1 SAR data to illustrate the existence of oil patches. Sentinel-2 data distinguishes itself as a highly effective sensor for detecting oil slicks, thanks to its exceptional spatial, spectral, and temporal resolution. They recommended that If users lack access to field data, it is advisable to employ the OBIA method to assess the accuracy of results derived from SAR data. Zaman etal.26 conducted a study with the goal of determining the ideal zones for saffron cultivation in Miyaneh. The research utilized Landsat 8 satellite images and applied the Weighted Linear Combination (WLC) method. The study period extended from November 2019 to May 2020. The results indicated that the prime locations for saffron cultivation in the examined area are concentrated in a strip running from the southwest to the southeast, along with specific northern regions. Sharifi etal.27 in the field of remote sensing advocated for the use of Polarimetric Synthetic Aperture Radar technology (PolSAR) when SAR images face challenges due to speckle noise. They highlighted the effectiveness of PolSAR in capturing images across different polarizations as a practical and alternative solution. The Fast ICA method is strongly endorsed for its proficiency in reducing speckle, preserving details, and demonstrating remarkable speed. Kossari etal.28 introduced a rapid method for dimensioning the Attitude Determination and Control System (ADCS) of Earth observation satellites. They applied a matching diagram technique, well-established in aircraft industries for aircraft design. The study emphasized spatial and temporal resolutions as the key performance requirements (PRs). Yuh etal.29 conducted a comparative analysis of four distinct machine learning algorithms to monitor changes in Land Use and Land Cover (LULC) in northern Cameroon. Their study utilized Landsat 7 ETM and Landsat 8 OLI imagery from November 2000 and November 2020. KNN, SVM, RF, and ANN were among the algorithms that were assessed, all of them showed a commendable level of accuracy. The KNN algorithm produced a Kappa Coefficient of 89% and an Overall Accuracy of 91.1% for the year 2020. Likewise, the ANN algorithm produced a high 94% Kappa Coefficient along with a high 95.8% Overall Accuracy. The RF algorithm demonstrated a 94% Kappa Coefficient and an Overall Accuracy of 90.3%. With a Kappa Coefficient of 87%, the Overall accuracy for SVM was 88.6%. The study’s conclusions showed that there was a notable reduction in the amount of forest cover between 2000 and 2020 as a result of the conversion of these forested regions into agricultural land, mostly for the production of crops. Moradi etal.30 explored changes in forest cover in the Zagros Mountains, Western Iran, utilizing Landsat imagery. They applied a CNN deep learning algorithm to discern alterations in the landscape. The results of their study revealed a substantial decline in forest cover over the past thirty years. The CNN algorithm proved effective in distinguishing oak forest from water and agricultural classes. Their research achieved a high accuracy of 97% and a Kappa coefficient of 94.7% when utilizing Landsat TM imagery. Similarly, with Landsat ETM imagery, they attained a 95% Overall accuracy and a Kappa coefficient of 94.1%. This work is organized as follows. The methods and material are discussed in “Methods and material” while the results of our experimentation are discussed in “Experiments and results”. Discussion on our proposed algorithms and obtained results are entailed in “Discussion”. Lastly, we succinctly conclude in “Conclusion” along with some future propositions. Methods and material To initiate the forest cover detection project, the Abbottabad region has been designated as the pilot area. This geographically diverse area is characterized by rolling hills and enveloped by lush green mountains, making it a renowned summer retreat admired for its forested charm. The process of gathering accurate data and geographical points unfolded in multiple stages. Initially, on-site inspections are conducted to categorize the classes, and the following four classes are chosen. i.Fields ii.Forest iii.Urban area iv.Shrubs. Secondly, to enhance the reliability of the data, the shape file for Abbottabad is acquired from the Pakistan Forest Institute in Peshawar, a well-respected organization in the country. To ensure data accuracy and utilize cutting-edge technology, the "Geosurvey App," an indigenous application developed by the National Center of Big Data and Cloud Computing (NCBC) in Peshawar (https:// www. ncbcp eshaw ar. com), is employed. Utilizing the “GeoSurvey App” for the Fields class, we meticulously choose and outline a total of 900 polygons. The Forest class comprises 901 carefully selected polygons. Likewise, for the Urban class, we identify and pick 900 polygons. The data pertaining to Shrubs polygons is retrieved from the Forestry Planning and Monitoring System in Peshawar. Figure1 displays the shape file for the Abbottabad district. In the third phase, Sentinel-2 satellite images are acquired. The experimentation involves working with both a single downloaded image and a temporally sequenced downloaded image. Specifically, a Sentinel-2 single image from October 27th, 2021, for District Abbottabad is obtained. For the temporal image set, four images from September 2nd, 2021, October 27th, 2021, November 11th, 2021, and December 11th, 2021, are downloaded. 5 Vol.:(0123456789) Scientific Reports | (2024) 14:69 | https://doi.org/10.1038/s41598-023-50863-1 www.nature.com/scientificreports/ The following procedures are executed on these downloaded images: 1. Preprocessing is carried out using SNAP Desktop, with resampling parameters being configured. 2. The resampled data is subsequently employed for further processing. 3. All the images are layer-stacked. 4. A mask is constructed, and this mask is applied to extract the Abbottabad image from the shapefile. 5. A CSV file is generated for the region of interest (ROI). In the fourth phase, following the establishment of the Remote Sensing Dataset, experiments are conducted using Artificial Intelligence Neural Network algorithms with diverse parameters and Random Forest algorithm. Artificial Neural Networks (ANNs) proficiently manage diverse remote sensing data, incorporating both multispectral and hyperspectral imagery. Their versatility allows for seamless adaptation to the diverse spectral bands and resolutions commonly encountered in various remote sensing applications. Artificial Neural Networks (ANNs) have proven effective across diverse applications in remote sensing, such as land cover classification8, object detection11, vegetation and crops monitoring4, and terrain analysis8. Artificial Neural Networks find applications in various domains such as image processing and character recognition31, classification32, forecasting, enhancement33, analysis34, estimation, and prediction35. Their adaptability renders them suitable for a broad spectrum of tasks within the field. Specifically, networks with a significant number of parameters may be prone to overfitting, capturing noise or specific patterns in the Training data that may not generalize effectively to new, unseen data. The whole procedure is depicted in Fig.2. Within the machine learning domain, Random Forest (RF) is widely acknowledged as a frequently employed ensemble learning technique suitable for both classification and regression tasks. In this work, we opted for RF due to its basic ensembled structure and computational feasibility as compared to other bagging and boosting based ensemble learning techniques (i.e., XGBoost, CATBoost etc.). As it can be seen in Fig.3, it operates as an ensemble model, generating multiple decision trees using randomly selected subsets of Training samples and variables. The Random Forest (RF) classifier demonstrates reduced sensitivity36 in comparison to other streamlined machine learning classifiers concerning the quality of Training samples and overfitting concerns. Random Forests may require substantial computational resources, especially when dealing with a substantial number of trees and features. The Training and evaluation of a large ensemble can be computationally demanding. Figure1. District Abbottabad shape file. 6 Vol:.(1234567890) Scientific Reports | (2024) 14:69 | https://doi.org/10.1038/s41598-023-50863-1 www.nature.com/scientificreports/ Artificial neural networks Classification Khan and Minallah4 employed the Artificial Neural Network algorithm in their study. They emphasized that artificial neurons serve as the fundamental components of Artificial Neural Networks37. For the implementation of a neural network, a minimum of three layers is required, namely the Input Layer, the Hidden Layer, and the Output Layer, as depicted in Fig.4. The Input Layer transmits input to the Hidden Layer, also known as the middle layer, which addresses problems by utilizing multiple Processing Elements (PE). The Output Layer, the final layer, generates output based on given input parameters. Initially, every Artificial Neural Network goes through Training to understand and compare its reactions when given new pixels, figuring out which side of a linear separating line they fall on35. After that, the processing part depends on the inputs and weights from the layer before. The Processing Element (PE) handles a set of inputs, like X = × 1, × 2, × 3……xN, where w is the connection weight, θ is a bias, and Z0 is the Output Layer. Feed forward neural network A Feed-Forward Neural Network (FFNN) utilizes a layer of interconnected neurons for the processing and transmission of information. It falls under the category of Artificial Neural Networks that use a supervised classification method to approximate a classifier. During FFNN Training, adjustments are made to the weights at the nodes with the goal of reducing the disparity between the activation of the output nodes and the input. The network must learn the appropriate weights and biases to precisely classify the input data. Certain features of Feed-Forward Networks encompass: • Processing Elements (PEs) are structured in layers, wherein the input layer accepts input data, the output layer produces outputs, and the intermediary layers, known as hidden layers, do not have external connections but exclusively interact with other layers within the model. • Information travels in a single direction, moving from the input layer through the hidden layer and reaching the output layer. • FFNNs are non-cyclic, signifying the absence of feedback connections in the network, which inhibits neurons from exchanging information with each other in a reverse manner. Figure2. Artificial neural network algorithm methodology. 7 Vol.:(0123456789) Scientific Reports | (2024) 14:69 | https://doi.org/10.1038/s41598-023-50863-1 www.nature.com/scientificreports/ Connections are established, with a Processing Element (PE) such as H1 connected to inputs x1, x2, and x3, and H2 linked to inputs x1, x2, and x3, as depicted in Figure4. Equation (2) accounts for all the weights in play. PE computes the matrix product of the hidden layer with these weights, includes its own bias, and subsequently applies the activation function. The matrix computation is presented as: =     W11 W12 W13 W21 W22 W23     ∗      x 1 x2 x3      (2) =     W11 ∗x1+W12 ∗x2+W13 ∗x3 W21 ∗x1+W22 ∗x2+W23 ∗x3     (3) H 1=  WIJ ∗li+Bi  Figure3. Artificial neural network algorithm methodology. Figure4. Artificial neural network algorithm. 8 Vol:.(1234567890) Scientific Reports | (2024) 14:69 | https://doi.org/10.1038/s41598-023-50863-1 www.nature.com/scientificreports/ The Hidden layers’ value is computed by adding up the products of the input values and their corresponding weights, as described in Eq.(3). The key purpose of this computation is to ascertain how the system should be adjusted to match the output with the desired target. Even slight modifications in weights can result in substantial changes in output30. This attribute facilitates the learning process. Parameters for neural network Before establishing the parameters, certain decisions must be made, including determining the number of layers to be employed. Generally, three layers are deemed satisfactory, with the first designated as the Input Layer, the subsequent one as the Hidden layer, and the last one as the Output layer. The input layer typically receives nodes corresponding to the number of components (features) in the pixel vectors. The following parameters in Table1 have been set for the ANN algorithm. Random forest algorithm The Random Forest is a supervised classification machine learning algorithm that constructs and grows multiple decision trees to form a "forest." It is employed for both classification and regression problems shown in Fig.5. In classification, it builds decision trees on various samples and takes a majority vote, while in regression, it calculates the average for different samples. A notable feature of the Random Forest Algorithm is its ability to handle datasets with categorical variables for classification, leading to improved results. How random forest algorithm work The Random Forest algorithm employs Bagging or Bootstrap Aggregation Techniques. Bagging entails generating multiple Training subsets from the sample Training data with replacement, and the ultimate output is decided by the majority of votes. Bootstrap randomly selects rows and features from the dataset to create sample datasets for each model. Aggregation consolidates these sample datasets through majority voting to generate the final output. Bootstrap Aggregation is effective in mitigating the variance of high-variance algorithms, like decision trees. Steps involved in random forest algorithm (4) O = (WIJ ∗ H + B) Table 1. Parameters for ANN algorithm. S. no. Parameters Value/type 1 Learning rate 0.01 2 Training momentum 0.900 3 Training RMS exit criteria 0.100 4 Number of hidden layer 2 5 Number of training iterations/epochs 50, 100, 200, 300 6 Activation function Relu 7 No of neurons in hidden layer 64 8 Batch size 32 Figure5. Random forest algorithm. 9 Vol.:(0123456789) Scientific Reports | (2024) 14:69 | https://doi.org/10.1038/s41598-023-50863-1 www.nature.com/scientificreports/ 1. Random Forests operate on a given dataset with N records and K outputs, where N represents the number of samples, and K denotes the number of classes. 2. A decision tree is created for each set of samples to produce the output. 3. In classification, the final output is determined by assigning greater importance to the majority of votes. Safira Desdhanty and Rustam38 implemented the Random Forest algorithm in their research. In their methodology, they define S = {(xi, yi)}, where xi represents the numerical feature, and yi corresponds to the respective labels. Assuming the Random Forest has T features, and P denotes the number of trees in the forest, N trees are randomly selected in the Random Forest, and each is employed to construct a decision tree. This process is repeated P times, and at each node, a small subset of features is created. The best feature for each subset is then determined. The outcome of this procedure is a selected feature A that achieves the highest score38: the algorithm is presented in the table below38. Initialization: A training set S: = {(xi,yi)}, T features, and number of trees in forest P 1. Select M trees from the dataset, in order to to construct a decision tree 2. Redo the previous step P times 3. At each node: 4. Construct a small subset of F, call it f 5. Separate the most appropriate features in f 6. The category that gains the majority votes will be given a new record The Output will be the selected features that have the highest accuracy score Algorithm Random forest. The following Parameters have been set in Table2 for the Random Forest algorithm. These outcomes play a crucial role in determining the Overall Accuracy and Kappa Coefficient for both the Training and Testing Data sets. Overall accuracy stands out as a frequently used evaluation metric. It signifies the ratio of accurately classified instances, or data points, to the total number of instances in a dataset. This metric serves as a fundamental benchmark for assessing the model’s performance in terms of correct classifications across the entire dataset. The formula for the Kappa coefficient is as follows in Eq. (5): The Kappa coefficient, also known as Cohen’s Kappa, is a statistic that measures the agreement between observed and expected classification results while considering the possibility of agreement occurring by chance. The formula for the Kappa coefficient is as follows in Eq.(6): Overall Agreement is the proportion of observed agreement between the classified results and the reference (ground truth) data. Chance Agreement is the expected agreement due to chance. It is calculated based on the marginal probabilities of agreement for each class. Concerning the Training Data, as delineated in Table3, we have selected 17,101 pixels for the Fields class, 33,045 pixels for the Forest class, 3678 pixels for the Shrubs class, and 9542 pixels for the Urban class. Regarding the Testing Data, as indicated in Table1, 7377 pixels are chosen for the Fields class, 14,166 pixels for the Forest class, 2058 pixels for the Shrubs class, and 4191 pixels for the Urban class. Through the utilization of the Random Forest Supervised machine learning Classification Algorithm, we attained a Training Overall accuracy of 99.79% and Testing Overall accuracy of 97%. Furthermore, the application (5) Overall Accuracy = Sum of Correctly Classified Pixels Total Number of Pixels × 100% (6) Kappa Coefficient = Overall Agrement − Chance Agreement 1 − Chance Agreement Table 2. Parameter for random forest algorithm. S. no. Parameter Value 1 Maximum depth 5, 10, 20 Table 3. Total number of training and testing pixels. Class Training pixel Testing pixels Fields 17,101 7377 Forest 33,045 14,166 Shrubs 3678 2058 Urban 9542 4191 16 Vol:.(1234567890) Scientific Reports | (2024) 14:69 | https://doi.org/10.1038/s41598-023-50863-1 www.nature.com/scientificreports/ utilizing an Artificial Neural Network (ANN) algorithm and Sentinel-2 data surpassed this performance with an Overall accuracy of 97.75% and 0.965 Kappa Coefficient. Conclusion Pakistan faces challenges as a forest-poor country, with less than 6% of its total area covered by forests. To address this issue, advanced techniques employing Machine Learning and Deep Learning algorithms were applied for forest cover classification in District Abbottabad. Notably, the Artificial Neural Network (ANN) and Random Forest algorithms were employed, yielding state-of-the-art results in terms of Overall Accuracy and Kappa Coefficient. The ANN algorithm demonstrated remarkable performance, achieving a best Overall Accuracy of 97.75% and a Kappa Coefficient of 0.965. However, to tackle the overfitting problem inherent in ANN, the Random Forest algorithm was introduced. This approach resulted in a commendable Overall Accuracy of 96.98% and a Kappa Coefficient of 0.954, particularly when using a maximum depth of 20 for the Temporal Layer stacked image. Applying the ANN algorithm to the entire 166,103 hectares area of Abbottabad revealed a forest cover of 51,613 hectares, constituting 31.07% of the total district area. Meanwhile, utilizing the Random Forest algorithm identified a total forest cover of 51,774 hectares, equivalent to 31.17% of the Abbottabad district region. To elevate the precision of forest cover classification, incorporating hyperspectral satellite imagery is recommended. 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Author contributions Conceptualization, G.A. and N.M.; methodology, J.F., A.S., and W.K.; validation, J.F., N.M., and W.K.; formal analysis, G.A., N.M. and A.S.; resources, G.A. and N.M.; data curation, A.S. and J.F.; writing original draft preparation, G.A.; writing review and editing, G.A., N.M. and J.F.; visualization, A.S. and W.K.; supervision, N.M.; project administration, J.F., funding acquisition, J.F. All authors have read and agreed to the published version of the manuscript. Competing interests The authors declare no competing interests. Additional information Correspondence and requests for materials should be addressed to J.F. Reprints and permissions information is available at www.nature.com/reprints. Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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