Search Results (16)

Accurate land cover classification is vital for informed water resource management, especially in irrigation-dependent regions facing increased climate variability. Using fused multi-sensor remote sensing imagery from Landsat 8 and Sentinel-2, this study assesses the effectiveness of three machine learning classifiers: Random Forest (RF), Gradient Tree Boosting (GTB), and Naive Bayes (NB) in creating land cover maps for the Tisza-Körös Valley Irrigation System (TIKEVIR) in Hungary. Water bodies, built-up areas, forests, grasslands, and major crops were among the important land cover categories that were classified for the two agricultural seasons (2018 and 2022). RF performed consistently in 2022 and reached its best accuracy in 2018 (OA = 0.87, KC = 0.83, PI = 0.94). While NB’s performance in 2022 remained less consistent, GTB’s performance increased. The findings show that RF works effectively for generating accurate land cover data, providing useful information for regional monitoring, and assisting in water and environmental management decision-making. Full article

Effective monitoring and management of potato late blight (PLB) is essential for sustainable agriculture. This study describes a methodology to improve PLB identification on a large scale. The method combines unsupervised and supervised machine learning algorithms. To improve the monitoring accuracy of the PLB regression model, the study used the K-Means algorithm in conjunction with morphological operations to identify potato growth areas. Input data consisted of monthly NDVI from Sentinel-2 and VH bands from Sentinel-1 (covering the year 2021). The identification results were validated on 221 field survey samples with an F1 score of 0.95. To monitor disease severity, we compared seven machine learning models: CART decision trees (CART), Gradient Tree Boosting (GTB), Random Forest (RF), single optical data Random Forest Time series model (TS–RF), single radar data Random Forest Time series model (STS–RF), multi-source data Gradient Tree Boosting Time series model (MSTS–GTB), and multi-source data Random Forest Time series model (MSTS–RF). The MSTS–RF model was the best performer, with a validation RMSE of 20.50 and an R² of 0.71. The input data for the MSTS–RF model consisted of spectral indices (NDVI, NDWI, NDBI, etc.), radar features (VH-band and VV-band), texture features, and Sentinel-2 bands synthesized as a monthly time series from May to September 2021. The feature importance analysis highlights key features for disease identification: the NIR band (B8) for Sentinel-2, DVI, SAVI, and the VH band for Sentinel-1. Notably, the blue band data (458–523 nm) were critical during the month of May. These features are related to vegetation health and soil moisture are critical for early detection. This study presents for the first time a large-scale map of PLB distribution in China with an accuracy of 10 m and an RMSE of 26.52. The map provides valuable decision support for agricultural disease management, demonstrating the effectiveness and practical potential of the proposed method for large-scale monitoring. Full article

Climate change mitigation relies heavily on understanding carbon storage dynamics in terrestrial ecosystems. This study examines the relationship between carbon storage (kg/m²) and various climatic variables, including precipitation, temperature, humidity, and radiation. Machine learning models such as Random Forest (RF), Gradient Tree Boost (GTB), Artificial Neural Networks (ANN), Support Vector Machines (SVM), and Multiple Regression (MR) were applied. Among these, Random Forest exhibited the highest explanatory power (R² = 0.95, Adj. R² = 0.75, F-score = 4.721, Accuracy = 0.67), while ANN showed the highest predictive accuracy (Accuracy = 0.80). The results underline the significant role of climatic factors in shaping carbon dynamics, emphasizing the integration of machine learning-based models in carbon capture and sequestration (CCS) strategies. Furthermore, carbon storage dynamics in Utah from 1991 to 2020 were analyzed using remote sensing data and multiple regression models. Carbon storage was found to be highest in forested areas, wetlands, and natural grasslands, while agricultural and wildfire-affected zones exhibited lower carbon stocks. Climatic factors, particularly precipitation, temperature, and humidity, were identified as significant drivers of carbon sequestration, with moderate precipitation and favorable temperatures enhancing carbon retention. The study highlights the importance of region-specific CCS strategies, which rely on accurate climate-driven carbon storage assessments, for ensuring sustainable resource management and mitigating anthropogenic climate impacts. Full article

Accurate and reliable knowledge about grassland distribution is essential for farmers, stakeholders, and government to effectively manage grassland resources from agro-economical and ecological perspectives. This study developed a novel pixel-based grassland classification approach using three supervised machine learning (ML) algorithms, which were assessed in the province of Manitoba, Canada. The grassland classification process involved three stages: (1) to distinguish between vegetation and non-vegetation covers, (2) to differentiate grassland from non-grassland landscapes, and (3) to identify three specific grassland classes (tame, native, and mixed grasses). Initially, this study investigated different satellite data, such as Sentinel-1 (S1), Sentinel-2 (S2), and Landsat 8 and 9, individually and combined, using the random forest (RF) method, with the best performance at the first two steps achieved using a combination of S1 and S2. The combination was then utilized to conduct the first two steps of classification using support vector machine (SVM) and gradient tree boosting (GTB). In step 3, after filtering out non-grassland pixels, the performance of RF, SVM, and GTB classifiers was evaluated with combined S1 and S2 data to distinguish different grassland types. Eighty-nine multitemporal raster-based variables, including spectral bands, SAR backscatters, and digital elevation models (DEM), were input for ML models. RF had the highest classification accuracy at 69.96% overall accuracy (OA) and a Kappa value of 0.55. After feature selection, the variables were reduced to 61, increasing OA to 72.62% with a Kappa value of 0.58. GTB ranked second, with its OA and Kappa values improving from 67.69% and 0.50 to 72.18% and 0.58 after feature selection. The impact of raster data quality on grassland classification accuracy was assessed through multisensor image fusion. Grassland classification using the Hue, Saturation, and Value (HSV) fused images showed higher OA (59.18%) and Kappa values (0.36) than the Brovey Transform (BT) and non-fused images. Finally, a web map was created to show grassland results within the Soil Landscapes of Canada (SLC) polygons, relating soil landscapes to grassland distribution and providing valuable information for decision-makers and researchers. Future work may include extending the current methodology by considering other influential variables, like meteorological parameters or soil properties, to create a comprehensive grassland inventory across the whole Prairie ecozone of Canada. Full article

Accurate forest tree-species classification not only provides data support for forest resource management but also serves as a crucial parameter for simulating various ecological processes. However, the results of forest tree-species classification have been affected by multiple factors, such as the spectral resolution, spatial resolution, and radiometric resolution of imagery, the classification algorithms used, the sample size, and the timing of image acquisition phases. Although there are many studies on the impact of individual factors on tree-species classification, there is a lack of systematic studies quantifying the magnitude of these factors’ influences, leading to uncertainties about the relative importance of different factors. In this study, Landsat-8, Landsat-9, and Sentinel-2 imagery was used as the foundational data, and random forest (RF), gradient tree boosting (GTB), and support vector machine (SVM) algorithms were employed to classify forest tree species. High-accuracy regional forest tree-species classification was achieved by exploring the impacts of spectral resolution, spatial resolution, radiometric resolution, classification algorithms, sample size, and image time phases. The results show that, for the commonly used Landsat-8, Landsat-9, and Sentinel-2 imagery, the tree-species classification results from Landsat-9 are the best, with an overall accuracy of 74.21% and a kappa of 0.71. Among the various influencing factors, the classification algorithm, image time phases, and sample size have relatively larger impacts on tree-species classification results, each exceeding 10%, while the positive impact of radiometric resolution is the smallest, at only 3.15%. Conversely, spectral and spatial resolutions had negative effects on tree-species classification results, at −4.09% and −1.4%, respectively. Based on the 30-m spring Landsat-9 and Sentinel-2 imagery, with 300 samples for each tree-species category, the classification results using the RF algorithm were the best, with an overall accuracy of 87.07% and a kappa coefficient of 0.85. The results indicate that different factors have different impacts on forest tree-species classification results, with classification algorithms, image time phases, and sample size having the largest impacts. Higher spatial and spectral resolutions do not improve the classification accuracy. Therefore, future studies should focus on selecting appropriate classification algorithms, sample sizes, and images from seasons with greater tree differences to improve tree-species classification results. Full article

Focusing on the trend of continuously seeking high-precision tree species classification results in small areas from the perspectives of sensors and classification algorithms. This study aimed to explore the effects of data sources, classifiers, and seasons on classification accuracy in regions with significant environmental variation, examining patterns of tree species classification to enhance the transferability of classification. Considering two typical forest distribution regions in the north and south of China, this study utilized the revisitation cycle and open-source advantages of Sentinel-2 and Landsat-8. Leveraging the Google Earth Engine (GEE) platform, this study captured spectral features, vegetation indices, and texture features for single seasonal and seasonal combination images. With the assistance of Sentinel-1A and SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model), backscattering coefficient features and topographical features were extracted and input with features captured from Sentinel-2 and Landsat-8 into three types of classifiers: random forest (RF), support vector machine (SVM), and gradient tree boosting (GTB) for major tree species classification. In this research, we discovered that the best classification for single season in the northern study area was spring, whereas, for the southern study area, it was winter. Seasonal combination images effectively improved the classification accuracy of single seasonal images, with Sentinel-2 imagery displaying better classification performance compared to Landsat-8, and the optimal classifier differing between the north and the south. The inclusion of topographical or backscattering coefficient features in the four-season combination imagery contributed to improvements in classification accuracy, with topographical features significantly enhancing the classification performance in the topographically varied southern study area. The evaluation of feature importance indicated that elevation was the most critical feature for classification, while spectral features and vegetation indices were also significant. In the southern study area with large topographical discrepancies, subdividing into different terrain units led to improved tree species classification accuracy in medium-altitude, gentle slope areas. These findings provide insights into the regularity of enhancing tree species classification accuracy in environmentally diverse areas through the use of multi-source remote sensing data and multi-seasonal imagery. Consequently, the results offer a reference for the identification of tree species across large areas and the creation of spatial distribution maps. Full article

The escalating evolution of aquaculture has wielded a profound and far-reaching impact on regional sustainable development, ecological equilibrium, and food security. Currently, most aquaculture mapping efforts mainly focus on coastal aquaculture ponds rather than diverse inland aquaculture areas. Recognizing all types of aquaculture areas and accurately classifying different types of aquaculture areas remains a challenge. Here, on the basis of the Google Earth Engine (GEE) and the time-series Sentinel-1 and -2 data, we developed a novel hierarchical framework extraction method for mapping fine inland aquaculture areas (aquaculture ponds + rice-crawfish fields) by employing distinct phenological disparities within two temporal windows (T1 and T2) in Qianjiang, so-called “Home of Chinese Crawfish”. Simultaneously, we evaluated the classification performance of four distinct machine learning classifiers, namely Random Forest (RF), Support Vector Machine (SVM), Classification and Regression Trees (CART), and Gradient Boosting (GTB), as well as 11 feature combinations. Following an exhaustive comparative analysis, we selected the optimal machine learning classifier (i.e., the RF classifier) and the optimal feature combination (i.e., feature combination after an automated feature selection method) to classify the aquaculture areas with high accuracy. The results underscore the robustness of the proposed methodology, achieving an outstanding overall accuracy of 93.8%, with an F1 score of 0.94 for aquaculture. The result indicates that an area of 214.6 ± 10.5 km² of rice-crawfish fields, constituting approximately 83% of the entire aquaculture area in Qianjiang, followed by aquaculture ponds (44.3 ± 10.7 km², 17%). The proposed hierarchical framework, based on significant phenological characteristics of varied aquaculture types, provides a new approach to monitoring inland freshwater aquaculture in China and other regions of the world. Full article

The Fez region in Morocco has experienced changes in agricultural land use as a result of climate change. These changes include erratic rainfall, rising temperatures, and evapotranspiration. The objective of this research is to investigate the impact of these changes on agricultural land use between 2018 and 2022 using remote sensing data (sentinel-2 and MODIS), climate data, drought index (Vegetation Condition Index (VCI)) and two machine learning algorithms (Random Forest (RF) and Gradient Tree Boost (GTB). The RF and GTB algorithms were trained and tested, and their performance was analyzed, revealing that the GTB algorithm is more efficient than the RF, with a Kaffa coefficient of 91% and overall accuracy of 93%. The analysis of climate change on land use and land cover (LULC) variations revealed a significant (54%) reduction in rainfall. Furthermore, agricultural land use and water were reduced by 41% and 17%, respectively. Conversely, barren land and built-up areas increased by 58% and 4%, respectively, and the annual mean VCI decreased from 39.72 in 2018 to 19.9 in 2022. The study concluded that climate change had a significant impact on the region’s agricultural land cover, and decreases in rainfall directly affect agricultural land use. Full article

Self-compacting concrete (SCC) is a special form of high-performance concrete that is highly efficient in its filling, flowing, and passing abilities. In this study, an attempt has been made to model the compressive strength (CS) of SCC mixes using machine-learning approaches. The SCC mixes were designed considering lightweight expandable clay aggregate (LECA) as a partial replacement for coarse aggregate; ground granulated blast-furnace slag (GGBS) as a partial replacement for binding material (cement); and incinerated bio-medical waste ash (IBMWA) as a partial replacement for fine aggregate. LECA, GGBS, and IBMWA were replaced with coarse aggregate, cement, and fine aggregate, respectively at different substitution levels of 10%, 20%, and 30%. M30-grade SCC mixes were designed for two different water/binder ratios—0.40 and 0.45—and the CS of the SCC mixes was experimentally determined along with the fresh state properties assessed by slump-flow, L-box, J-ring, and V-funnel tests. The CS of the SCC mixes obtained from the experimental analysis was considered for machine learning (ML)-based modeling using paradigms such as artificial neural networks (ANN), gradient tree boosting (GTB), and CatBoost Regressor (CBR). The ML models were developed considering the compressive strength of SCC as the target parameter. The quantities of materials (in terms of %), water-to-binder ratio, and density of the SCC specimens were used as input variables to simulate the ML models. The results from the experimental analysis show that the optimum replacement percentages for cement, coarse, and fine aggregates were 30%, 10%, and 20%, respectively. The ML models were successful in modeling the compressive strength of SCC mixes with higher accuracy and the least errors. The CBR model performed relatively better than the other two ML models, with relatively higher efficiency (KGE = 0.9671) and the least error (mean absolute error = 0.52 MPa) during the testing phase. Full article

High-resolution multispectral remote sensing images offer valuable information about various land features, providing essential details and spatially accurate representations. In the complex urban environment, classification accuracy is not often adequate using the complete original multispectral bands for practical applications. To improve the classification accuracy of multispectral images, band reduction techniques are used, which can be categorized into feature extraction and feature selection techniques. The present study examined the use of multispectral satellite bands, spectral indices (including Normalized Difference Built-up Index, Normalized Difference Vegetation Index, and Normalized Difference Water Index) for feature extraction, and the principal component analysis technique for feature selection. These methods were analyzed both independently and in combination for the classification of multiple land use and land cover features. The classification was performed for Landsat 9 and Sentinel-2 satellite images in Delhi, India, using six machine learning techniques: Classification and Regression Tree, Minimum Distance, Naive Bayes, Random Forest, Gradient Tree Boosting, and Support Vector Machine on Google Earth Engine platform. The performance of the classifiers was evaluated quantitatively and qualitatively to analyze the classification results with whole image (comprehensive feature) and small subset (targeted feature). The RF and GTB classifiers were found to outperform all others in the quantitative analysis of all input combinations for both Landsat 9 and Sentinel-2 datasets. RF achieved a classification total accuracy of 96.19% for Landsat and 96.95% for Sentinel-2, whereas GTB achieved 91.62% for Landsat and 92.89% for Sentinel-2 in all band combinations. Furthermore, the RF classifier achieved the highest F1 score of 0.97 in both the Landsat and Sentinel datasets. The qualitative analysis revealed that the PCA bands were particularly useful to classifiers in distinguishing even the slightest differences among the feature class. The findings contribute to the understanding of feature extraction and selection techniques for land use and land cover classification, offering insights into their effectiveness in different scenarios. Full article

Wetlands are highly productive ecosystems with the capability of carbon sequestration, providing an effective solution for climate change. Recent advancements in remote sensing have improved the accuracy in the mapping of wetland types, but there remain challenges in accurate and automatic wetland mapping, with additional requirements for complex input data for a number of wetland types in natural habitats. Here, we propose a remote sensing approach using the Google Earth Engine (GEE) to automate the extraction of water bodies and mapping of growing lotus, a wetland type with high economic and cultural values in central Vietnam. Sentinel-1 was used for water extraction with the K-Means clustering, whilst Sentinel-2 was combined with the machine learning smile Random Forest (sRF) and smile Gradient Tree Boosting (sGTB) models to map areas with growing lotus. The water map was derived from S-1 images with high confidence (F₁ = 0.97 and Kappa coefficient = 0.94). sGTB outperformed the sRF model to deliver a growth map with a high accuracy (overall accuracy = 0.95, Kappa coefficient = 0.92, Precision = 0.93, and F₁ = 0.93). The total lotus area was estimated at 145 ha and was distributed in the low land of the study site. Our proposed framework is a simple and reliable mapping technique, has a scalable potential with the GEE, and is capable of extension to other wetland types for large-scale mapping worldwide. Full article

For the design or assessment of framed concrete structures under high eccentric loadings, the accurate prediction of the torsional capacity of reinforced concrete (RC) beams can be critical. Unfortunately, traditional semi-empirical equations still fail to accurately estimate the torsional capacity of RC beams, namely for over-reinforced and high-strength RC beams. This drawback can be solved by developing accurate Machine Learning (ML) based models as an alternative to other more complex and computationally demanding models. This goal has been herein addressed by employing several ML techniques and by validating their predictions. The novelty of the present article lies in the successful implementation of ML methods based on Ensembles of Trees (ET) for the prediction of the torsional capacity of RC beams. A dataset incorporating 202 reference RC beams with varying design attributes was divided into testing and training sets. Only three input features were considered, namely the concrete area (area enclosed within the outer perimeter of the cross-section), the concrete compressive strength and the reinforcement factor (which accounts for the ratio between the yielding forces of both the longitudinal and transverse reinforcements). The predictions from the used models were statistically compared to the experimental data to evaluate their performances. The results showed that ET reach higher accuracies than a simple Decision Tree (DT). In particular, The Bagging Meta-Estimator (BME), the Forests of Randomized Trees (FRT), the AdaBoost (AB) and the Gradient Tree Boosting (GTB) reached good performances. For instance, they reached values of R² (coefficient of determination) in the range between 0.982 and 0.990, and values of cvRMSE (coefficient of variation of the root mean squared error) in the range between 10.04% and 13.92%. From the obtained results, it is shown that these ML techniques provide a high capability for the prediction of the torsional capacity of RC beams, at the same level of other more complicated ML techniques and with much fewer input features. Full article

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

Codling moth (CM) (Cydia pomonella L.), a devastating pest, creates a serious issue for apple production and marketing in apple-producing countries. Therefore, effective nondestructive early detection of external and internal defects in CM-infested apples could remarkably prevent postharvest losses and improve the quality of the final product. In this study, near-infrared (NIR) hyperspectral reflectance imaging in the wavelength range of 900–1700 nm was applied to detect CM infestation at the pixel level for three organic apple cultivars, namely Gala, Fuji and Granny Smith. An effective region of interest (ROI) acquisition procedure along with different machine learning and data processing methods were used to build robust and high accuracy classification models. Optimal wavelength selection was implemented using sequential stepwise selection methods to build multispectral imaging models for fast and effective classification purposes. The results showed that the infested and healthy samples were classified at pixel level with up to 97.4% total accuracy for validation dataset using a gradient tree boosting (GTB) ensemble classifier, among others. The feature selection algorithm obtained a maximum accuracy of 91.6% with only 22 selected wavelengths. These findings indicate the high potential of NIR hyperspectral imaging (HSI) in detecting and classifying latent CM infestation in apples of different cultivars. Full article

The identification of underground formation lithology can serve as a basis for petroleum exploration and development. This study integrates Extreme Gradient Boosting (XGBoost) with Bayesian Optimization (BO) for formation lithology identification and comprehensively evaluated the performance of the proposed classifier based on the metrics of the confusion matrix, precision, recall, F1-score and the area under the receiver operating characteristic curve (AUC). The data of this study are derived from Daniudui gas field and the Hangjinqi gas field, which includes 2153 samples with known lithology facies class with each sample having seven measured properties (well log curves), and corresponding depth. The results show that BO significantly improves parameter optimization efficiency. The AUC values of the test sets of the two gas fields are 0.968 and 0.987, respectively, indicating that the proposed method has very high generalization performance. Additionally, we compare the proposed algorithm with Gradient Tree Boosting-Differential Evolution (GTB-DE) using the same dataset. The results demonstrated that the average of precision, recall and F1 score of the proposed method are respectively 4.85%, 5.7%, 3.25% greater than GTB-ED. The proposed XGBoost-BO ensemble model can automate the procedure of lithology identification, and it may also be used in the prediction of other reservoir properties. Full article