Hyperspectral anomaly detection (HAD) aims to identify pixels that significantly differ from the background without prior knowledge. While deep learning-based reconstruction methods have shown promise, they often suffer from limited feature representation, inefficient training cycles, and sensitivity to imbalanced data distributions. To address these challenges, this paper proposes a novel contrastive–transfer-synergized dual-stream transformer for hyperspectral anomaly detection (CTDST-HAD). The framework integrates contrastive learning and transfer learning within a dual-stream architecture, comprising a spatial stream and a spectral stream, which are pre-trained separately and synergistically fine-tuned. Specifically, the spatial stream leverages general visual and hyperspectral-view datasets with adaptive elastic weight consolidation (EWC) to mitigate catastrophic forgetting. The spectral stream employs a variational autoencoder (VAE) enhanced with the RossThick–LiSparseR (R-L) physical-kernel-driven model for spectrally realistic data augmentation. During fine-tuning, spatial and spectral features are fused for pixel-level anomaly detection, with focal loss addressing class imbalance. Extensive experiments on nine real hyperspectral datasets demonstrate that CTDST-HAD outperforms state-of-the-art methods in detection accuracy and efficiency, particularly in complex backgrounds, while maintaining competitive inference speed.

Land-based hyperspectral imaging provides high spatial and spectral resolution for detecting camouflaged targets, but practical deployment remains limited by strong target background spectral similarity, scarce annotated hyperspectral samples, and the computational cost of full-band processing. To address these issues, this paper proposes HCTDNet (Hyperspectral Camouflaged Target Detection Network), a land-based hyperspectral image analysis framework. The method first employs band extraction for data dimensionality reduction, compressing multi-channel hyperspectral images into 3-channel virtual RGB representations, which reduces spectral redundancy while preliminarily enhancing camouflaged target saliency. A pre-trained RGB camouflaged target detector is then adopted as the backbone model, with its parameters frozen to maintain stability, while trainable modality-specific prompts are learned to improve training efficiency. Finally, model fine-tuning is performed using a self-constructed camouflaged target dataset to enhance robustness in detecting camouflaged targets within virtual RGB images. During inference, preprocessed hyperspectral images are fed into the model to generate detection results for camouflaged target regions. The experiments performed on our self-collected land-based hyperspectral dataset with camouflaged targets reveal that HCTDNet achieves superior detection performance compared with seven classical hyperspectral target detection methods while maintaining an average inference speed of approximately 16 FPS. The proposed framework provides an efficient and near-real-time applicable solution for land-based hyperspectral camouflaged target detection, showing significant practical potential.

Gap probability (P) is a key indicator of vegetation canopy structure and can be effectively estimated using intensity data from airborne laser scanning (ALS) point clouds. However, point cloud intensity is highly susceptible to radiometric effects. Even for a specific natural target, its intensity can vary across three-dimensional space, which may reduce the accuracy of P estimation. To address this issue, we propose a novel method for estimating P that corrects the influence of radiometric effects on point cloud intensity (PRE_COR). The method consists of the following main steps: first, laser pulses are classified into vegetation-ground, pure-vegetation, and pure-ground pulses. Then, the intensity of vegetation-ground pulses is corrected using the inverse distance square law and the cosine law of incidence angle. Finally, the corrected intensity values are used to estimate the vegetation-ground reflectance ratio based on a linear relationship between their return energies. This ratio is then used to calculate the canopy P. The proposed method was evaluated using both simulated ALS point cloud data and (National Ecological Observatory Network) NEON ALS point cloud data. The results show that for the simulated data, under varying canopy cover, flight altitudes, and mean scan angles, the proposed method achieved relative root mean square errors (rRMSE) below 5.37%, 5.94%, and 21.51%, and mean absolute errors (MAE) below 0.025, 0.011, and 0.067, respectively. Compared with the traditional PFitted method, rRMSE was reduced by up to 1.90%, 1.94%, and 21.20%, and MAE decreased by up to 0.010, 0.003, and 0.096, respectively. For the NEON ALS data, when the scan angle exceeded 20°, the proposed method may improv accuracy by more than 5.39%, with possible MAE improvements exceeding 0.019. Overall, these results demonstrate that correcting radiometric effects on point cloud intensity can substantially enhance both the accuracy and stability of canopy P estimation, with particularly notable benefits under large scan angle conditions.

Tree height is a key descriptor of forest canopy structure, yet a single nadir view captures only part of the directional structural information contained in canopy reflectance. This study investigated whether viewing geometry provides directional information useful for UAV-based optical tree-height retrieval in a Larix principis-rupprechtii plantation. Multi-angular reflectance observations extracted from overlapping UAV multispectral images were used to fit the RossThick–LiSparseR (RTLSR) kernel-driven bidirectional reflectance distribution function (BRDF) model. The fitted model was then used to reconstruct directional reflectance at 27 prescribed viewing geometries along the solar principal plane, while UAV-LiDAR data provided grid-level reference tree heights. Nine vegetation indices were derived and evaluated using linear regression, random forest, and gradient boosting regressor under single- and multi-angle schemes. DVI, EVI, GDVI, TVI, and GOSAVI exhibited consistent angle-dependent sensitivity to tree height, with their strongest correlations and lowest retrieval errors generally occurring within the intermediate forward-viewing sector (30°–45°) in the BRDF-derived dataset. For example, the absolute correlation between GDVI and tree height increased from 0.429 at nadir to 0.507 at 40°. Among the tested index–scheme–model combinations, the lowest validation error was obtained using GDVI under the Forward + 0° scheme with random forest, reducing RMSE from 2.19 m at nadir to 1.79 m (RMSE% = 10.25 %). However, the relative performance of the tested angular schemes varied among models, indicating that angular configuration and retrieval algorithm jointly affected performance, while increasing the number of angular features alone did not consistently reduce retrieval error. Overall, the findings indicate that viewing geometry can provide useful directional information for within-site optical tree-height retrieval and that intermediate forward-viewing information may complement nadir observations.

With the growing global emphasis on forest resource monitoring, evaluating the accuracy of retrieving key individual tree parameters-such as tree position, tree height, and diameter at breast height (DBH)-using Terrestrial Laser Scanning (TLS) has become an important research focus. TLS has been widely applied in forest surveys due to its significant advantages in data acquisition efficiency and measurement precision. However, studies on the accuracy of extracting forest parameters from single-station, single-scan TLS data remain limited, underscoring the need for systematic evaluation and validation. This paper analyzes the accuracy and effectiveness of TLS in extracting structural parameters (tree height and DBH) and its position using Poplar and Styphnolobium as examples by using TLS, Airborne laser Scanning (ALS), and combining with field measurements. Results show that tree height estimates from single-scan TLS is limited in accuracy: the RMSE of 11.61 m in the Populus plot and 2.13 m in the Styphnolobium plot. Within a 50 m radius, single-scan TLS achieves a tree detection rate of 55.96-64.26% and a DBH RMSE of 1.60 cm (RRMSE: 9.03%). In addition, the point root mean square error of individual tree measurements remains at 0.11 m. These findings highlight the potential of TLS as an effective tool for forest inventory and provide a basis for evaluating the reliability of TLS-based plot measurements.

Accurate long-term estimation of fractional vegetation cover (FVC) is crucial for monitoring vegetation dynamics. Satellite-based methods, such as the dimidiate pixel method (DPM), struggle with spatial heterogeneity due to coarse resolution. Existing methods using unmanned aerial vehicles (UAVs) combined with satellite data (UCS) inadequately leverage the high spatial resolution of UAV imagery to address spatial heterogeneity and are seldom applied to long-term FVC monitoring. To overcome spatial challenges, an improved dimidiate pixel method (IDPM) is proposed here, utilizing 2021 Landsat imagery to generate FVCDPM via DPM and upscaled UAV imagery for FVCUAV as ground references. The IDPM uses the pruned exact linear time method to segment the normalized difference vegetation index (NDVI) into intervals, within which DPM performance is evaluated for potential improvements. Specifically, if the difference (D) between FVCDPM and FVCUAV is nonzero, NDVI-derived texture features are incorporated into FVCDPM through multiple linear regression to enhance accuracy. To address temporal challenges and ensure consistency across years, the 2021 NDVI serves as a reference for inter-year NDVI calibration, employing least squares regression (LSR) and histogram matching (HM) to identify the most effective method for extending the IDPM to other years. Results demonstrate that 1) the IDPM, by developing distinct DPM improvement models for different NDVI intervals, considerably improves UAV and satellite data integration, with a 48.51% increase in R2 and a 56.47% reduction in root mean square error (RMSE) compared to the DPM and UCS and 2) HM is found to be more suitable for mining areas, increasing R2 by 25.00% and reducing RMSE by 54.05% compared to LSR. This method provides an efficient, rapid solution for mitigating spatial heterogeneity and advancing long-term FVC estimation.

Nighttime light imagery plays a crucial role in diverse applications such as urban planning, environmental monitoring, and economic analysis. Although NPP-VIIRS provides a long and continuous Nighttime light time series, its relatively low spatial resolution limits detailed spatial analysis. Achieving Nighttime light data with both high spatial and temporal resolution remains a key challenge. This study investigates the effectiveness of several deep learning–based super-resolution (SR) models for enhancing NPP-VIIRS Nighttime light imagery using Luojia1-01 data as high-resolution reference imagery. Five representative models—ESPCN, RDN, SRFBN, SwinIR, and RealESRGAN—were selected to cover a range of network architectures including CNN, RNN, ResNet, DenseNet, Transformer, and GAN. A paired SR dataset was constructed from Luojia1-01 and NPP-VIIRS images, and the selected models were trained and evaluated on this dataset. Model performance was assessed across different urban scales and lighting conditions (e.g., dense urban cores, road networks) using PSNR, SSIM, FSIM, and the 95th percentile metrics. Results indicate that model performance varies substantially across scene types, with RealESRGAN showing superior detail recovery and overall image quality (PSNR = 31.96, SSIM = 0.85, FSIM = 0.85). The 95th percentile distribution of the RealESRGAN-enhanced images closely matches that of high-resolution reference data. These findings demonstrate that deep learning–based SR methods can substantially improve the spatial resolution and visual quality of NPP-VIIRS Nighttime light imagery, enabling finer-scale analysis of urban structures and temporal dynamics. This work provides an effective technical framework for reconstructing historical high-resolution Nighttime light data and expanding their applicability in urban, environmental, and socioeconomic studies.

Forest structure parameters are critical for understanding and managing forest ecosystems, yet sparse forests have received limited attention in previous studies. To address this research gap, this study systematically evaluates and compares the sensitivity of active Synthetic Aperture Radar (SAR) and passive optical remote sensing to key forest structure parameters in sparse forests, including Diameter at Breast Height (DBH), Tree Height (H), Crown Width (CW), and Leaf Area Index (LAI). Using the novel computer-graphics-based radiosity model applicable to porous individual thin objects, named Radiosity Applicable to Porous Individual Objects (RAPID), we simulated 38 distinct sparse forest scenarios to generate both SAR backscatter coefficients and optical reflectance across various wavelengths, polarization modes, and incidence/observation angles. Sensitivity was assessed using the coefficient of variation (CV). The results reveal that C-band SAR in HH polarization mode demonstrates the highest sensitivity to DBH (CV = −6.73%), H (CV = −52.68%), and LAI (CV = −63.39%), while optical data in the red band show the strongest response to CW (CV = 18.83%) variations. The study further identifies optimal acquisition configurations, with SAR data achieving maximum sensitivity at smaller incidence angles and optical reflectance performing best at forward observation angles. This study addresses a critical gap by presenting the first systematic comparison of the sensitivity of multi-band SAR and VIS/NIR data to key forest structural parameters across sparsity gradients, thereby clarifying their applicability for monitoring young and middle-aged sparse forests with high carbon sequestration potential.

To ensure the effective implementation of food waste reduction in college cafeterias, Capital Normal University developed an automatic plate recognition system based on machine vision technology. The system operates by obtaining images of plates (whether clean or not) and the diners’ faces through multi-directional monitoring, then employs several deep learning models for the automatic localization and identification of the plates. Face recognition technology links the identification results of the plates to the diners. Additionally, the system incorporates innovative educational mechanisms such as online feedback and point redemption to encourage student participation and foster thrifty habits. These initiatives also provide more accurate training samples, enhancing the system’s precision and stability. Our findings indicate that machine vision technology is suitable for rapid identification and location of clean plates. Even without optimized network parameters, the U-Net network demonstrates high recognition accuracy (MIOU of 68.64% and MPA of 78.21%) and ideal convergence speed. Pilot data showed a 13% reduction in overall waste in the cafeteria and over 75% user acceptance of the mechanism. The implementation of this system has significantly improved the efficiency and accuracy of plate recognition, offering an effective solution for food waste prevention in college canteens.

Hyperspectral anomaly detection (HAD) is widely used in various fields including military, agriculture, mining, and food safety inspection. However, the absence of prior information on targets poses significant challenges to feature extraction and anomaly identification. To address this issue, this paper proposes a novel dual-window transformer framework integrated with a pyramid structure and constrained self-attention mechanism, which effectively leverages both local and global spectral information for anomaly detection. The dual-window transformer is designed to extract deep spectral features by capturing discriminative patterns between central pixels and their surrounding background. Simultaneously, the constrained self-attention module incorporates global contextual information into the feature representation. Furthermore, a stepwise downsampling pyramid architecture is introduced to reduce the sensitivity of the model to dual-window size selection while facilitating the propagation of global information from higher to lower layers. Extensive hyperparameter analysis and comparative experiments demonstrate the robustness and superiority of the proposed framework. The source code is publicly available at: https://github.com/aosilu/DWT-P-CSA-HAD.

Hyperspectral images contain rich spatial distribution and spectral information of land features, but they also introduce high information redundancy and computational complexity. This paper proposes dimensionality reduction methods that integrate spatial-spectral preservation and minimum noise fraction (MNF) to better analyze and utilize the spatial and spectral information in hyperspectral images. While performing the minimum noise separation transformation, the proposed method aims to preserve the spatial structure of the image as much as possible, maximizing both the signal-to-noise ratio and the spatial structure similarity of the image. The component selection strategy involves grouping components and calculating the average change in the relative position of all pixels in the feature space. The component group that most closely matches the spectral relative position before transformation is selected as the final dimensionality reduction result. Experimental results demonstrate that the proposed method is highly sensitive to noise estimation and requires a relatively accurate noise covariance matrix. The method effectively preserves spatial information, with negligible impact on the accuracy of object detection methods, and outperforms other comparative approaches. It ensures the effectiveness of downstream object detection tasks while significantly reducing computational time. The code of the proposed method is available at https://github.com/aosilu/spatial-spectral-preservation-MNF.

Target detection (TD) is a research hotspot in the field of hyperspectral imaging (HSI). Traditional TD methods often mine targets from HSIs under a single imaging condition, without considering the influence of imaging conditions. In fact, the spectra of ground objects in HSIs are uncertain and affected by the imaging conditions (weather, atmospheric, light, time, and other angle conditions including zenith angle). Hyperspectral data changes under different imaging conditions. Therefore, the detection result for a single imaging condition cannot accurately reflect the effectiveness of the detection method used. It is necessary to analyze the performance of various detection methods under different imaging conditions, to find a more applicable detection method. In this paper, we study the performance of TD methods under various land-based imaging conditions. We first summarize classical TD methods and evaluation methods. Then, the detection effects under various imaging conditions are analyzed. Finally, the concepts of the stability coefficient (SC) and effective area under the curve (EAUC) are proposed to comprehensively evaluate the applicability of detection methods under land-based imaging conditions, in terms of both detection accuracy and stability. This is conducive to our selection of detection methods with better applicability in land-based contexts, to improve detection accuracy and stability.

Precise estimation of forest above ground biomass (AGB) is essential for assessing its ecological functions and determining forest carbon stocks. It is difficult to directly obtain diameter at breast height (DBH) based on remote sensing imagery. Therefore, it is crucial to accurately estimate the AGB with features extracted directly from RS. This paper demonstrates the feasibility of estimating AGB from crown radius (R) and tree height (H) features extracted from multi-source RS data. Accurate information on tree height (H), crown radius (R), and diameter at breast height (DBH) can be obtained through point clouds generated by airborne laser scanning (ALS) and terrestrial laser scanning (TLS), respectively. Nine allometric growth equations were used to fit coniferous forests (Larix principis-rupprechtii) and broadleaf forests (Fraxinus chinensis and Sophora japonica). The fitting performance of models constructed using only "H" or "R" was compared with that of models constructed using both combined. The results showed that the quadratic polynomial model constructed with "H+R" fitted the AGB estimation better in each vegetation type, especially in the scenario of mixed tall and short coniferous forests, in which the R2 and RMSE were 0.9282 and 25.30 kg (rRMSE 17.31%), respectively. Therefore, using high-resolution data to extract crown radius and tree height can achieve high-precision, global-scale estimation of forest above ground biomass.

Beijing Satellite 3 is a high-performance optical remote sensing satellite with a spatial resolution of 0.3–0.5 m. It can provide timely and independent ultra-high-resolution spatial big data and comprehensive spatial information application services. At present, there is no relevant research on the fusion method of BJ-3A satellite images. In many applications, high-resolution panchromatic images alone are insufficient. Therefore, it is necessary to fuse them with multispectral images that contain spectral color information. Currently, there is a lack of research on the fusion method of BJ-3A satellite images. This article explores six traditional pixel-level fusion methods (HPF, HCS, wavelet, modified-IHS, PC, and Brovey) for fusing the panchromatic image and multispectral image of the BJ-3A satellite. The fusion results were analyzed qualitatively from two aspects: spatial detail enhancement capability and spectral fidelity. Five indicators, namely mean, standard deviation, entropy, correlation coefficient, and average gradient, were used for quantitative analysis. Finally, the fusion results were comprehensively evaluated from three aspects: spectral curves of ground objects, absolute error figure, and object-oriented classification effects. The findings of the research suggest that the fusion method known as HPF is the optimum and appropriate technique for fusing panchromatic and multispectral images obtained from BJ-3A. These results can be utilized as a guide for the implementation of BJ-3A panchromatic and multispectral data fusion in real-world scenarios.

Air target recognition in real-world scenarios has become an important part of the military offensive and defensive systems of various countries. By identifying aerial targets captured by image acquisition equipment and utilizing the obtained information to achieve effective identification of friend or foe, identifying enemy sources, combat capabilities, and intentions, important references are provided for tactical decision-making. With the continuous development of military technology, traditional manual based recognition methods are no longer capable of identifying aerial targets. The role of deep learning related algorithms, which gradually replace traditional image processing algorithms, in the field of target recognition is becoming increasingly prominent. This article will use deep learning methods to study aerial target recognition. Provide a detailed description of the design and implementation process of the Faster R-CNN training model, including model structure, network layers, and feature selection. This model mainly includes a Region Proposal Network (RPN) and a set of convolutional neural networks for extracting target features and predicting target bounding boxes. Build a basic environment for training models, and establish a dataset of air targets related to military equipment. The dataset samples are annotated according to the VOC2007 dataset format. Train the model using a dataset, conduct testing and detection after training, and finally analyze the calculation results of Eval evaluation indicators to further optimize the algorithm model and improve recognition rate. The experiment verified the effectiveness and feasibility of the Faster R-CNN training model, and applied it to aerial target recognition tasks. The experimental results show that the model can quickly and accurately identify aerial targets.

Accurate diameter at breast height (DBH) and tree height (H) information can be acquired through terrestrial laser scanning (TLS) and airborne LiDAR scanner (ALS) point cloud, respectively. To utilize these two features simultaneously but avoid the difficulties of point cloud fusion, such as technical complexity and time-consuming and laborious efforts, a feature-level point cloud fusion method (FFATTe) is proposed in this paper. Firstly, the TLS and ALS point cloud data in a plot are georeferenced by differential global navigation and positioning system (DGNSS) technology. Secondly, point cloud processing and feature extraction are performed for the georeferenced TLS and ALS to form feature datasets, respectively. Thirdly, the feature-level fusion of LiDAR data from different data sources is realized through spatial join according to the tree trunk location obtained from TLS and ALS, that is, the tally can be implemented at a plot. Finally, the individual tree parameters are optimized based on the tally results and fed into the binary volume model to estimate the total volume (TVS) in a large area (whole study area). The results show that the georeferenced ALS and TLS point cloud data using DGNSS RTK/PPK technology can achieve coarse registration (mean distance ≈ 40 cm), which meets the accuracy requirements for feature-level point cloud fusion. By feature-level fusion of the two point cloud data, the tally can be achieved quickly and accurately in the plot. The proposed FFATTe method achieves high accuracy (with error of 3.09%) due to its advantages of combining different LiDAR data from different sources in a simple way, and it has strong operability when acquiring TVS over large areas.

Accurate forest parameters are crucial for ecological protection, forest resource management and sustainable development. The rapid development of remote sensing can retrieve parameters such as the leaf area index, cluster index, diameter at breast height (DBH) and tree height at different scales (e.g., plots and stands). Although some LiDAR satellites such as GEDI and ICESAT-2 can measure the average tree height in a certain area, there is still a lack of effective means for obtaining individual tree parameters using high-resolution satellite data, especially DBH. The objective of this study is to explore the capability of 2D image-based features (texture and spectrum) in estimating the DBH of individual tree. Firstly, we acquired unmanned aerial vehicle (UAV) LiDAR point cloud data and UAV RGB imagery, from which digital aerial photography (DAP) point cloud data were generated using the structure-from-motion (SfM) method. Next, we performed individual tree segmentation and extracted the individual tree crown boundaries using the DAP and LiDAR point cloud data, respectively. Subsequently, the eight 2D image-based textural and spectral metrics and 3D point-cloud-based metrics (tree height and crown diameters) were extracted from the tree crown boundaries of each tree. Then, the correlation coefficients between each metric and the reference DBH were calculated. Finally, the capabilities of these metrics and different models, including multiple linear regression (MLR), random forest (RF) and support vector machine (SVM), in the DBH estimation were quantitatively evaluated and compared. The results showed that: (1) The 2D image-based textural metrics had the strongest correlation with the DBH. Among them, the highest correlation coefficient of −0.582 was observed between dissimilarity, variance and DBH. When using textural metrics alone, the estimated DBH accuracy was the highest, with a RMSE of only 0.032 and RMSE% of 16.879% using the MLR model; (2) Simply feeding multi-features, such as textural, spectral and structural metrics, into the machine learning models could not have led to optimal results in individual tree DBH estimations; on the contrary, it could even reduce the accuracy. In general, this study indicated that the 2D image-based textural metrics have great potential in individual tree DBH estimations, which could help improve the capability to efficiently and meticulously monitor and manage forests on a large scale.

The accurate forest volume is crucial for forest management, but rapid, large-scale, and high-accuracy estimation is still challenging. We proposed a method of coupling allometric growth model and multisource data for forest volume estimation (CAMFVe). First, the diameter at breast height (DBH) estimation model is constructed by terrestrial laser scanning (TLS) and airborne laser scanning (ALS) to obtain more accurate measured volume. Second, the spectral attributes of Landsat and structural attributes of ALS are extracted and upscaled onto the 30-m plot scale, and the optimal attributes for volume estimation are selected. Third, the model of CAMFVe is constructed and applied to obtain the volume of study area. Finally, the applicability of CAMFVe is evaluated under four forest growth environments (different canopy closure and slope categories), and the accuracy is compared with multiple linear regression (MLR), random forest (RF), and support vector machine (SVM). The results show the following. First, the DBH estimation model by TLS and ALS improves the DBH calculation accuracy of ALS with a 2.058 cm reduction in RMSE. Second, the mean of canopy height (H-mean) and enhanced vegetation index (EVI) are identified as the optimal structural and spectral attributes, respectively. Third, the model constructed by H-mean and EVI consistently achieves higher accuracy for most forest growth environments, and the addition of spectral attribute improves volume estimation accuracy with a 10.152% reduction in RMSE compared with the H-mean-based model. Fourth, compared with MLR, RF, and SVM, CAMFVe offers higher accuracy, requires fewer parameters, and is simpler and more efficient. Our proposed method, based on allometric growth model and utilizing vegetation index instead of DBH, provides a solution for large-scale and high-accuracy volume estimation by combining spaceborne light detection and ranging and optical satellite images.

Fractional vegetation cover (FVC) is a vital indicator for monitoring regional vegetation and ecology. Although satellite remote sensing is used to monitor long-term changes in regional FVC, its applications are limited by the spatial resolution. Moreover, for unmanned aerial systems (UASs), obtaining long-term and large-scale images is difficult, and the efficiency of the synergy between UAS and satellite data for long-term FVC monitoring is limited. This article considered a mining area with extreme changes in vegetation as an example and proposed an efficient approach called multiple spatiotemporal-scale FVC prediction (MSFP) for long-term FVC monitoring in the region, which is based on the synergy of high spatial-resolution UAS data with high temporal-resolution Landsat data. First, we used the UAS imagery of several typical mining areas in Qianxi County of China collected in 2021, from which the vegetation information was extracted. Second, the 2-D Gaussian sampling was applied to aggregate, that is, to join/connect them into Landsat pixels. The vegetation index (VI) calculated from contemporary Landsat imagery was further used with the aggregated FVC of each satellite pixel. Finally, the VIs from the satellite imagery for different years were calibrated. The analysis demonstrated that: first, the proposed MSFP yielded improved the coefficient of determination (by 0.437) and decreased root-mean-square error (by 0.200) than the traditional dimidiate pixel method based on satellite imagery; second, the UAS imagery for few typical areas was used to predict the FVC of the large-scale area, thereby providing fine-scale vegetation information; third, the MSFP achieved high accuracy and long-term FVC monitoring by interyear calibration of VI calculated from Landsat data. This article paves the way toward accurate long-term monitoring of regional FVC. The demonstrated methodological framework is simple and operable, thereby opening the prospects for its applications in other environments.