Search Results (575)

Plinthite is a major pedogenic feature in the Kamuli catena, posing significant challenges for agricultural land use. This study investigates the morphological expression and mineralogical insights into plinthite within the soil-landscape of Kamuli District. Soil characterization involved detailed field morphological descriptions along the Kamuli catena followed by laboratory characterization of major soil properties. Plinthite mineralogy was determined using X-ray diffraction (XRD) and scanning electron microscopy (SEM). Morphology of plinthic soils varied along the catena with summit pedons exhibiting shallow plinthic horizons and backslope pedons showing comparatively deeper occurrences. The lowlands underlain by alluvium of the Holocene lacked plinthite. Mineralogical analysis of ten plinthite samples identified two distinct assemblages. Group 1 (quartz, kaolinite, hematite, goethite, manganite) represents a highly weathered endmember associated with stable summits. Group 2 (muscovite, kaolinite, hematite, goethite, manganite), with elevated K, Mg, Na, and Ca in SEM-EDS, indicating they are recent compared to Group 1. This elemental composition directly reflects the signature of the parent material preserved within Group 2 samples. Plinthite in the Kamuli catena is a relict feature, whose formation is tied to past drainage regimes. Its multi-stage history is recorded in the two mineralogical groups separated by hundreds of thousands of years of landscape evolution. Group 1 represents plinthite from the deeply weathered African Surface. Group 2 is later formed on the substrate exposed by stripping along the Victoria Nile. Full article

Hydrothermal alteration mapping is a critical component of porphyry copper exploration because alteration assemblages provide important vectors toward mineralization. This study presents a systematic evaluation of supervised machine learning algorithms for delineating hydrothermal alteration zones using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) short-wave infrared (SWIR) surface reflectance data (AST_07XT). The investigation focuses on the Nain region within the central Urumieh–Dokhtar Magmatic Arc (UDMA), Iran, a major metallogenic belt hosting numerous porphyry copper systems. Representative spectral endmembers corresponding to Al–OH-bearing and Mg–OH-bearing hydrothermal alteration minerals were extracted using Minimum Noise Fraction (MNF), Pixel Purity Index (PPI), and n-dimensional visualization techniques. These endmembers were subsequently used to train and evaluate a comprehensive suite of supervised machine learning classifiers, including linear, kernel-based, tree-based, ensemble, probabilistic, boosting, and neural-network algorithms for pixel-wise hydrothermal alteration mapping. Model performance was evaluated using multiple statistical metrics, including overall accuracy (OA), average accuracy (AA), precision, recall, F1-score, Cohen’s kappa coefficient, area under the ROC curve (AUC), spatial cross-validation accuracy, uncertainty analysis, and spatial agreement analysis. Among the evaluated classifiers, SVM_Linear, SVM_RBF, LDA, and MLP achieved the highest classification performance, with overall accuracies exceeding 94% and strong spatial consistency between classified maps. The resulting alteration maps display spatially coherent distributions of Al–OH and Mg–OH minerals that are consistent with established hydrothermal alteration zoning models in porphyry–epithermal systems. The mapped hydrothermal alteration zones show strong spatial correspondence with known mineralized areas and alteration patterns within the Urumieh–Dokhtar Magmatic Arc, confirming the geological reliability of the classification results. Uncertainty analysis further indicates high model confidence across most alteration zones, with higher uncertainty values mainly restricted to transitional and spectrally heterogeneous regions. The results demonstrate that integrating ASTER SWIR imagery with supervised machine learning algorithms provides a robust, scalable, and transferable framework for regional-scale hydrothermal alteration mapping and mineral exploration in porphyry copper provinces. Full article

The spatial distribution of flammable tree species directly influences forest fuel structure and fire risk patterns. However, mixed pixels limit the ability of conventional classification methods to characterize continuous within-pixel variation in species composition, thereby constraining fine-scale forest mapping. To address this issue, this study developed a subpixel mapping framework for flammable tree species in Yajiang County, Sichuan Province, by integrating Sentinel-2 time-series data with a spectral mixing–unmixing strategy. Using 2019 Sentinel-2 time-series data and National Forest Inventory (NFI) data, temporal mixed samples with known abundance fractions were generated using a linear spectral mixing model. An XGBoost-based collaborative multi-regression framework was then applied to estimate the proportions of different tree-species endmembers within complex forest pixels. Quantitative evaluation using synthetic mixed samples showed that the model achieved stable unmixing performance across different random mixing scenarios. The best performance was obtained under the Mixed 2 scenario with a sample size of 250 K, reaching an R² of 0.821. The resulting maps revealed continuous spatial variation in the abundance and composition of flammable tree species. Mountain pine was the most widespread and dominant species, followed by spruce and mountain oak, whereas birch and fir mainly exhibited localized patchy distributions. An additional NFI-based categorical evaluation assessed the consistency of the final maps with real forest inventory records. The identification accuracies were 93.95% for pure stands and 91.22% for mixed stands, while the species classification accuracies were 87.28% for pure stands and 84.41% for dominant species in mixed stands. The proposed framework provides useful spatial information for regional forest fuel assessment and fire risk management. Full article

To elucidate the solute sources, migration and enrichment mechanisms of water bodies in the endorheic lake region of the Qiangtang Plateau on the Tibetan Plateau and clarify the hydrogeochemical cycling patterns in alpine arid environments, this study focuses on two core scientific objectives: quantitative identification of the multi-source contributions of aquatic solutes, and revelation of the key processes governing the enrichment of strategic elements including lithium (Li) and boron (B). To achieve these goals, we conducted systematic hydrogeological field investigations and collected 28 multi-type water samples, covering springs, rivers, thermal springs, freshwater lakes, salt lake brines, atmospheric precipitation, and glacial meltwater. The physicochemical properties, major ions, and trace elements of all samples were comprehensively analyzed. On this basis, the hydrogeochemical characteristics, evolutionary processes, and solute origins of regional waters were systematically explored. Combined with PHREEQC numerical simulation, principal component analysis (PCA), and Pearson correlation analysis, the dominant controlling factors of water geochemistry were quantified, and a conceptual hydrogeochemical evolution model was established. The results reveal a clear hydrogeochemical evolutionary gradient across the study area: water bodies evolve from low-salinity HCO₃-Ca recharge end-members and transitional HCO₃·SO₄-Ca(Mg) type water to highly mineralized Cl-Na (SO₄·Cl-Na) salt lake brines, accompanied by synchronous enrichment of Li, B, arsenic (As), and other characteristic elements. Solute accumulation in regional waters is governed by the ternary coupling effects of evaporative concentration, rock weathering and leaching, and deep geothermal fluid input, while cation exchange and mineral dissolution–precipitation reactions further modulate ionic composition and ratios. Elements including As, Li, B, and chloride (Cl⁻) exhibit conservative migration behaviors in non-hydrothermal waters, whereas thermal springs possess unique geochemical signatures driven by deep fluid recharge. PCA results indicate that evaporative concentration serves as the primary controlling factor with a contribution rate of 55.39%; rock weathering provides the basic solute load (17.09%); and the coupled processes of deep fluid mixing and carbonate precipitation regulate elemental fractionation (14.21%). These findings systematically clarify the hydrogeochemical evolution laws and multi-source coupling mechanisms of inland lake waters in the Qiangtang Plateau. Furthermore, this study establishes a conceptual framework of “multi-source recharge–water–rock interaction–evaporative concentration”, advances the understanding of alpine hydrological cycling under climate change, and provides a solid scientific foundation for hydrological cycle research and green exploration of strategic mineral resources in endorheic salt lake regions. Full article

We investigated the autumn-to-winter evolution of water-mass structure and nutrient concentrations in Funka Bay, southwestern Hokkaido, Japan, from October to February (2012–2019). Hydrographic and biogeochemical profiles show a recurrent seasonal transition from strongly stratified conditions in October, with low surface nutrients and bottom enrichment, to increasingly homogeneous distributions by mid-winter as vertical mixing intensifies. Depth-averaged nutrient concentrations generally decreased from October to December and increased from December to February, except during December 2015–February 2016. To assess whether February nutrient levels can be explained by Oyashio supply alone, we calculated February nutrient concentrations using a two-endmember mixing model (Oyashio endmember and December Funka Bay water) with an additional regeneration term that assumes nutrients consumed during the October–December autumn bloom were fully regenerated during December–February and redistributed by winter mixing. Under this framework, the expected February concentrations agreed with observations in all winters except 2015, when observed nutrients were lower than expected nutrients, consistent with additional biological drawdown after the early onset of the bloom by late February. These results indicate that the pre-bloom winter nutrient environment in Funka Bay is shaped by variable Oyashio intrusion superimposed on seasonal mixing and internal regeneration processes. Full article

Hyperspectral unmixing is an important technique for mineral mapping because natural geological scenes commonly contain mixed pixels composed of multiple spectrally overlapping materials. In mineral environments, these mixtures are often intimate rather than purely areal, and nonlinear scattering effects may weaken the validity of linear mixing assumptions. Although autoencoder-based hyperspectral unmixing methods can jointly estimate endmembers and abundances in an unsupervised manner, they often suffer from insufficient physical constraints, unstable endmember learning, and limited geological interpretability. To address these issues, this study proposes a physically constrained and library-guided convolutional autoencoder for mineral hyperspectral unmixing. The method retains an interpretable linear reconstruction backbone while introducing a Hapke-consistency regularization term to incorporate physically motivated nonlinear scattering behavior during endmember optimization. In addition, a library-aware endmember anchor module is designed to improve initialization quality, reduce endmember drift, and guide optimization toward spectrally meaningful solutions. The proposed method was evaluated on both simulated hyperspectral datasets and real airborne SASI data. On the simulated datasets, the method achieved improved endmember spectral fidelity and lower abundance estimation error than several representative autoencoder-based baselines, with the advantage being more evident under nonlinear mixing conditions. Ablation experiments further showed that the Hapke-consistency term mainly improved physical plausibility, whereas the anchor module enhanced optimization stability and spectral consistency. On the real airborne dataset, the proposed method produced endmember spectra that were more consistent with field and laboratory mineral references and generated spatially more coherent abundance maps. These results indicate that incorporating physically motivated constraints and mineral-library priors into deep autoencoder frameworks can improve the robustness and interpretability of mineral hyperspectral unmixing. The proposed framework provides a practical direction for hyperspectral mineral mapping in mixed and spectrally complex geological environments. Full article

Multiple biomarker datasets and compound-specific sulfur isotopic compositions (δ³⁴S) of dibenzothiophenes (DBTs) were analyzed for crude oil from Well B6 on the Maigaiti Slope, Tarim Basin. The very low concentrations of DBTs (124.9 μg/g oil), diamondoids (92.7 μg/g oil), and thiadiamondoids (0.20 μg/g oil), together with the absence of 25-norhopane, indicate that the B6 oil has not undergone significant secondary alteration, including thermochemical sulfate reduction (TSR), extensive thermal cracking, or biodegradation. No clear evidence of oil mixing was observed either. Aliphatic and aromatic biomarker distributions suggest that the parent source rocks contain type I–II₁ kerogen, with dominant algal and bacterial organic inputs deposited under low-salinity, weakly reducing conditions, broadly comparable to those of the Upper Ordovician Lianglitag Formation source rocks (UOLS). Oil–source correlation using compound-specific δ³⁴S values of DBTs indicates that B6 oil is derived from UOLS (or similar undiscovered source rocks), not from Cambrian source rocks. This is consistent with biomarker evidence. As the first identified Ordovician-derived oil showing relatively light DBT δ³⁴S values (average ~6.41‰), close to those of Ordovician kerogen (average ~5.62‰), and with minimal secondary overprinting, B6 oil has strong potential to serve as a UOLS end-member oil. This will likely open new exploration opportunities for deep hydrocarbon from previously untapped strata in the southwestern Tarim Basin. Full article

The formation of a large complex crater is accompanied by the simultaneous formation of coeval sub-geological units that have diverged physical properties, such as a central melt pool and an ejecta blanket. Crater populations formed on different geological units of a given young complex craters usually exhibit different size–frequency distributions (SFDs), but the difference disappears for relatively old craters, e.g., the Copernicus crater with an age of about 800 million years ago (Ma). However, there is a lack of temporal and theoretical constraints on the evolutionary pathway connecting these two SFD end-member states. Here, by observing crater SFDs of complex craters with ages between about 75 Ma and 871 Ma, we find a decrease in the crater SFD difference between coeval geological units with increasing age. The time-dependent crater SFD difference is consistent with modeled production functions with consideration of time-dependent target physical properties. The time dependence of target properties potentially arises from impact-induced damage, which efficiently converts coherent melt into ejecta-like fragments. Our results also imply that the proportion of self-secondary craters to the diameter ≥120 m crater population superposing on the facies of lunar complex craters with age older than crater Tycho is possibly less than 50% and decreases with time. Full article

Because of high visible and near-infrared (VNIR) reflectance, and deep shortwave infrared (SWIR) absorption, snow and ice are unique among terrestrial land cover. As such, both are well-suited to mapping and monitoring using optical remote sensing. However, to date, almost all studies of snow and ice spectroscopy have been limited to single or small numbers of specific cryospheric environments. These studies serve a diversity of objectives, but together also suggest the importance of the global continuum of snow and ice composition and spectroscopy. The continuum of snow and ice composition gives rise to the characteristics that allow different types of snow and ice to be distinguished optically. Particularly with imaging spectrometers. Characterization of this continuum of reflectance can facilitate development of physical models to quantify snow and ice composition and abundance, particularly in the presence of other types of land cover. In this study, a collection of ~140,000,000 visible through SWIR (VSWIR) reflectance spectra, collected by NASA’s EMIT imaging spectrometer from 56 diverse cryospheric environments, is used to characterize the continuum of snow and ice reflectance. This continuum is characterized using linear dimensionality reduction to quantify the dimensionality and topology of the spectral feature space of snow and ice. The resulting spectral feature space is effectively two-dimensional with a planar spectral feature continuum bounded by dry and wet snow, ice and dark targets (e.g., shadow, water). Because of the near collinearity of snow and ice endmember reflectances, linear spectral mixture models based only on these endmembers are ill-posed and unstable to inversion. However, in landscapes where sufficiently homogeneous seasonal snow is present with other land cover types, the standardized spectroscopic mixture model based on the Substrate, Vegetation and Dark (SVD) continuum can be extended with an instance-specific snow endmember (SVD + snow) to yield plausible areal fraction estimates with small misfits to observed spectra. More generally, the snow–ice-dark continuum can also be represented accurately with an optimal normalized difference index exploiting compositionally distinct differential absorptions at ~650 and ~1230 nm to distinguish dry from wet snow from white and blue ice. This optimized index, referred to as the Continuous Cryosphere Index (CCI), minimizes BRDF effects of topographic slope and aspect relative to illumination, while avoiding the saturation that causes the Normalized Difference Snow Index (NDSI) to conflate wet snow with white and blue ice reflectance. In addition to imaging spectrometers like EMIT, operational sensors like MODIS, VIIRS and WorldView-3 have spectral bands near 650 nm and 1230 nm, so they could also be used for CCI mapping. Full article

Hyperspectral remote sensing is increasingly utilized due to its high spectral resolution and broad observational capabilities, and hyperspectral unmixing aims to decompose mixed pixels into their constituent endmembers with corresponding classes. The core research directions in this area include how to construct a proprietary spectral library and how to optimize the corresponding abundance maps. However, due to the influence of complex terrain and variable illumination conditions, hyperspectral images (HSI) exhibit significant spectral variability (SV), which undermines the performance of traditional unmixing methods. In the paper, we propose an SV and class-constrained diffusion model (SVCDM) for unsupervised hyperspectral unmixing that integrates endmember extraction and abundance optimization. Specifically, a Dirichlet-based variational autoencoder is employed to construct a spectral library from the original HSI with a class constraint and prior distribution, which subsequently guide a conditional diffusion model to learn the distribution. During the reverse process, the endmembers are iteratively updated at each time step, enhancing diversity while ensuring class consistency. Subsequently, the endmember matrix is synthesized with the original HSI to optimize the abundance maps under the linear mixing assumption. The proposed SVCDM effectively mitigates the impact of SV induced by imaging characteristics. Experimental results demonstrate that the SVCDM achieves a root mean square error (RMSE) of 0.0371 for abundance maps on a synthetic dataset and a spectral angle mapper (SAM) for endmembers of 0.0309 on the Samson dataset, outperforming existing state-of-the-art hyperspectral unmixing methods on both synthetic and real datasets. Full article

Understanding the hydrochemical characteristics and formation mechanisms of rivers in the Huxi Catchment is essential for water resource conservation, as these rivers serve as the primary water source for Taihu Lake. A total of 14 surface water samples were collected from the rivers in Huxi catchment, and the concentrations of seven major ions—namely, ${\mathrm{N}\mathrm{a}}^{+}$, ${\mathrm{K}}^{+}$, ${\mathrm{C}\mathrm{a}}^{2+}$, ${\mathrm{M}\mathrm{g}}^{2+}$, ${\mathrm{C}\mathrm{l}}^{-}$, ${\mathrm{S}\mathrm{O}}_4^{2-}$, and ${\mathrm{H}\mathrm{C}\mathrm{O}}_3^{-}$—were determined. Positive Matrix Factorization (PMF), Absolute Principal Component Score–Multiple Linear Regression (APCS-MLR), and the Principal Component Analysis-based Endmember Mixing Model (PCA-EMM) were employed to quantify the contributions of anthropogenic activities. While APCS-MLR can only identify the impacts of human activities, PMF and PCA-EMM can further distinguish between agricultural activities and wastewater discharge. Significant positive correlations were observed between the PMF and PCA-EMM results, but PMF overestimated the contribution of anthropogenic impacts. PCA-EMM showed that the natural background accounted for 63%, while human activities contributed 37% (domestic sewage 23%, agricultural activities 14%). By integrating ion composition data from representative sources, PCA-EMM overcomes the limitations of traditional methods that lack source verification and provides robust methodological support for the source apportionment of water chemistry. Full article

To elucidate the sources and spatial variations in organic matter in surface sediments from Lingdingyang of the Pearl River Estuary, 18 surface sediment samples were collected and analyzed for obtaining total organic carbon (TOC), total nitrogen (TN), atomic TOC/TN ratio (C/N_atom), stable carbon and nitrogen isotopes (δ¹³C, δ¹⁵N), and glycerol dialkyl glycerol tetraethers (GDGTs). A three-endmember framework was constructed using the BIT and δ¹³C to constrain the sources of the organic matter. The results showed a significant positive correlation between TOC and TN, with relatively higher values in Jiaoyi Bay and western Lingdingyang, lower values in eastern Lingdingyang, and intermediate values in Shenzhen Bay. The C/N_atom, δ¹³C, and δ¹⁵N results revealed that the sedimentary organic matter in the study area exhibits mixed-source characteristics, influenced by soil, C₃ plants, and marine autochthonous organic matter. Among the subregions, Jiaoyi Bay is more strongly influenced by terrestrial inputs, while Shenzhen Bay receives relatively higher contributions from marine autochthonous organic matter. The GDGTs results showed that Jiaoyi Bay is characterized by elevated abundances of both brGDGTs and isoGDGTs, whereas isoGDGTs were also relatively enriched in Shenzhen Bay. brGDGTs exhibited a significant negative correlation with δ¹³C, whereas BIT showed no significant correlation with either brGDGTs or δ¹³C, indicating that BIT cannot be simply regarded as a unique proxy for soil input, but rather reflects the combined effects of in situ production, changes in archaeal lipids, and sedimentary preservation. The three-endmember model further revealed significant spatial variations in the sources of organic matter in surface sediments from Lingdingyang. Overall, the combined use of multiple proxies is more effective than any single proxy in revealing the sources and spatial differentiation of sedimentary organic matter in this subtropical, complex estuarine environment. Full article

In porous ignimbrites, porosity defines the first-order control on elastic trend, but rocks with similar porosity can still behave differently because their pore networks are arranged differently. We analyzed 50 specimens from seven ignimbrite units in Mexico using density and porosity measurements, permeability tests, mercury intrusion porosimetry, image-based pore descriptors, and ultrasonic P- and S-wave velocities. At the unit scale averages, total porosity ranges from 31.4% in Tl to 42.9%, but elastic properties and permeability vary widely, showing that porosity alone does not define a unique physical state. Two end-member pore-network tendencies can be recognized: crack-linked, throat-restricted systems and more equant or intergranular systems. At similar porosity, crack-dominated networks are generally less stiff and less permeable, whereas more equant networks show higher permeability and stiffer behavior under dry conditions. Effective-medium models indicate that most samples are consistent with KT aspect ratios of 0.15–0.20 and a critical-porosity range of 40–60%. Overall, porosity defines the first-order elastic trend, whereas pore-network architecture explains much of the remaining hydraulic variability and part of the residual elastic spread. Full article

Hyperspectral unmixing aims to estimate endmember spectra and their corresponding abundance fractions at the subpixel scale, which is a critical preprocessing step for quantitative analysis of hyperspectral remote sensing imagery. While deep learning-based methods have achieved remarkable progress, three fundamental challenges remain: (i) reliance on a single shared spatial prior that cannot decouple the heterogeneous spatial patterns of different land covers; (ii) the lack of synergy in jointly optimizing endmember extraction and abundance estimation; (iii) the poor robustness of unsupervised training to complex mixtures, noise, and class imbalance. To address these issues, we propose a novel unsupervised unmixing framework that integrates adaptive orthogonal multi-faceted graph representation with curriculum learning. Specifically, we design an Adaptive Orthogonal Multi-Faceted Graph Generator (AOMFG) to learn a set of independent orthogonal graph structures, achieving spatially informed decoupling of land cover patterns. Then, a dual-branch collaborative optimization network is constructed: a Graph Convolutional Network (GCN) branch that incorporates the learned spatial topological priors for abundance estimation, and a 1D Convolutional Neural Network (1DCNN) branch that employs a query-attention mechanism to adaptively aggregate pure spectral features for endmember extraction. Finally, we introduce a three-stage curriculum learning strategy that progressively fine-tunes the model, which significantly enhances its performance. Extensive experiments on three widely used real-world benchmark datasets demonstrate that our proposed framework consistently outperforms state-of-the-art methods in both endmember extraction and abundance estimation accuracy. Comprehensive ablation studies, parameter sensitivity analysis, and noise robustness tests further validate the effectiveness of each core component. Full article

The performance of remote sensing-based mineral alteration extraction is significantly restricted in the vegetation area. Spectral unmixing is one of the effective methods to address the vegetation problem during mineral alteration extraction. However, the spectral curves of different tree species vary a lot; if multiple tree species are regarded as a whole during the spectral unmixing stage, the proportions of vegetation would be estimated with more errors. The purpose of this study was to verify the effects of dominant tree species classification on spectral unmixing and reconstruction, and to apply the proposed method to the mineral alteration extraction practice. To accomplish this, the Shabaosi gold deposit region in Mohe City, China, with an area of 650 km², was selected as the study area. Firstly, reference spectral curves, GaoFen-1/6 (GF-1/6) satellite imageries, ZiYuan-1F (ZY-1F) satellite imageries, Sentinel-1B satellite synthetic aperture radar (SAR) data, the ALOS digital elevation model (DEM), and sub-compartment dominant tree species data were collected; subsequently, simulated mixed-pixel reflectance images of ZY-1F, reflectance images of GF-1/6, ZY-1F, backscattering data of Sentinel-1B, slope, aspect, and 5484 tree species samples were derived from the collected data. Secondly, to verify the effect of dominant tree species classification on mineral alteration extraction, the reference spectra of pine, oak, goethite, and kaolinite were used to construct a simulated ZY-1F mixed-pixel image, and spectral unmixing and reconstruction experiments were conducted. Thirdly, fourteen independent variables were selected from the derived data, five dominant tree species classification models were trained and tested using tree species samples via the ResNet50 algorithm, and the pine- and birch-dominated parts were segmented from the ZY-1F images. Fourthly, minimum noise fraction (MNF), pixel purity index (PPI), n-dimensional visualizer auto-clustering, and spectral angle mapper (SAM) methods were separately applied to the pine- and birch-dominated parts of ZY-1F images to extract and identify endmembers; subsequently, the fully constrained least squares (FCLS) and linear spectral unmixing (LSU) methods were separately applied to the pine- and birch-dominated parts to estimate endmember proportions and generate spectrally reconstructed ZY-1F images. Fifthly, the pine- and birch-dominated parts of spectrally reconstructed ZY-1F images were mosaiced, and the SAM was utilized to extract mineral alteration in the study area. The result showed that in the spectral unmixing and reconstruction experiment, the spectral reconstruction error declined from 0.0594 (simulated ZY-1F image without segmentation) to 0.0292 and 0.0388 (simulated ZY-1F image that was segmented by pine- and oak-dominated parts), suggesting that dominant tree species classification could improve the accuracy of spectral unmixing and reconstruction and help obtain a more reliable mineral alteration extraction result. In the study area, the tested overall accuracies (OA) and Kappa coefficients of the five dominant tree species classification models were 0.75 ± 0.03 and 0.50 ± 0.05, respectively, suggesting that conducting dominant tree species classification was feasible in dense vegetation areas and could facilitate mineral alteration extraction. After segmenting the ZY-1F image by pine- and birch-dominated parts and spectral reconstruction, eight main types of alteration, including kaolinite, vesuvianite, montmorillonite, rutile, limonite, mica, sphalerite, and quartz, were identified, and nine mineral alteration areas (MA) were delineated accordingly. Full article