Search Results (29)

Accurate and timely extraction and evaluation of sandy land are essential for ecological environmental protection; it is urgent to do the research to support the sustainable development goals (SDGs) of Land Degradation Neutrality. This study used Sentinel-1 Synthetic Aperture Radar (SAR) data and Landsat 8 OLI multispectral data as the main data sources. Combining the rich spectral information from optical data and the penetrating advantages of radar data, a feature-level fusion method was employed to unveil the intrinsic nature of vegetative cover and accurately identify sandy land. Simultaneously, leveraging the results obtained from training with measured data, a comprehensive desertification assessment model was proposed, which combines multiple indicators to achieve a thorough evaluation of sandy land. The results showed that the method based on feature-level fusion achieved an overall accuracy of 86.31% in sandy land detection in Gansu Province, China. The integrated multi-indicator model C22_C/FVC is the ratio of correlation texture features of VH to vegetation cover based on which sandy land can be classified into three categories. When C22_C/FVC is less than 2.2, the pixel is classified as fixed sandy land. Pixels of semi-fixed sandy land have an indicator value between 2.2 and 5.2. Shifting sandy land has values greater than 5.2. Results showed that shifting sandy land and semi-fixed sandy land are the predominant types in Gansu Province, with 85,100 square kilometers and 87,100 square kilometers, respectively. The acreage of fixed sandy land was the least, 51,800 square kilometers. The method presented in this paper is robust for the detection and evaluation of sandy land from satellite imageries, which can potentially be applied for conducting high-resolution and large-scale detection and evaluation of sandy land. Full article

With the development of remote sensing technology, the estimation of the chlorophyll content (CHLC) of vegetation via satellite data has become an important means of monitoring vegetation health, and high-precision estimation has been the focus of research in this field. In this study, we used larch affected by Yarl’s larch looper (Erannis jacobsoni Djak) in the boundary region of Mongolia as the research object, simulated the multispectral reflectance, downscaled Sentinel-2A satellite data, performed mixed-pixel decomposition, analyzed the potential of Sentinel-2A satellite data for estimating the chlorophyll content by calculating the spectral indices (SIs) and spectral derivatives (SDFs) of images, and then extracted sensitive spectral features as the model training set. Spectral features sensitive to the chlorophyll content were extracted to establish the training set, and, finally, the chlorophyll content estimation model for larch was constructed on the basis of the partial least squares algorithm (PLSR). The results revealed that SI and SDF based on simulated remote sensing data were highly sensitive to the chlorophyll content under the influence of pests, with the SAVI and EVI2 spectral indices as well as the D_B2 and D_B5 spectral derivatives being the most sensitive to the chlorophyll content. The estimation models based on simulated data performed significantly better than models without simulated data in terms of accuracy, especially those based on SDF-PLSR. The simulated spectral reflectance well reflected the spectral characteristics of the larch canopy and was sensitive to damaged larch, especially in the green light, red edge, and near-infrared bands. The proposed approach improves the accuracy of chlorophyll content estimation via Sentinel-2A data and enhances the ability to monitor changes in the chlorophyll content under complex forest conditions through simulations, providing new technical means and a theoretical basis for forestry pest monitoring and vegetation health management. Full article

In order to improve the accuracy of multispectral image inversion of soil and plant analytical development (SPAD) of the cotton canopy, image segmentation methods were utilized to remove the background interference, such as soil and shadow in UAV multispectral images. UAV multispectral images of cotton bud stage canopies at three different heights (30 m, 50 m, and 80 m) were acquired. Four methods, namely vegetation index thresholding (VIT), supervised classification by support vector machine (SVM), spectral mixture analysis (SMA), and multiple endmember spectral mixture analysis (MESMA), were used to segment cotton, soil, and shadows in the multispectral images of cotton. The segmented UAV multispectral images were used to extract the spectral information of the cotton canopy, and eight vegetation indices were calculated to construct the dataset. Partial least squares regression (PLSR), Random forest (FR), and support vector regression (SVR) algorithms were used to construct the inversion model of cotton SPAD. This study analyzed the effects of different image segmentation methods on the extraction accuracy of spectral information and the accuracy of SPAD modeling in the cotton canopy. The results showed that (1) The accuracy of spectral information extraction can be improved by removing background interference such as soil and shadows using four image segmentation methods. The correlation between the vegetation indices calculated from MESMA segmented images and the SPAD of the cotton canopy was improved the most; (2) At three different flight altitudes, the vegetation indices calculated by the MESMA segmentation method were used as the input variable, and the SVR model had the best accuracy in the inversion of cotton SPAD, with R² of 0.810, 0.778, and 0.697, respectively; (3) At a flight altitude of 80 m, the R² of the SVR models constructed using vegetation indices calculated from images segmented by VIT, SVM, SMA, and MESMA methods were improved by 2.2%, 5.8%, 13.7%, and 17.9%, respectively, compared to the original images. Therefore, the MESMA mixed pixel decomposition method can effectively remove soil and shadows in multispectral images, especially to provide a reference for improving the inversion accuracy of crop physiological parameters in low-resolution images with more mixed pixels. Full article

The expansion of impervious surface area (ISA) in megacities of China often leads to land surface temperature (LST) aggregation effects, which affect living environments by impacting thermal comfort levels, thus becoming an issue of public concern. However, from an urban–rural synchronous comparison perspective, the study of LST responses to ISA changes is still lacking in the central coastal megalopolises of China. To solve this issue, a collaborative methodology of artificial digitization—fully constrained least squares mixed pixel decomposition—split-window algorithm—PCACA model was established for Qingdao using land use dataset and remote sensing images. The conclusions are below. Long time series of land use monitoring indicated that the expansion ratios of urban and rural areas were 131.29% and 43.42% in the past 50 years (i.e., from 1970 to 2020). Within urban and rural areas, a synchronous ISA increase was observed, with ratios of +9.14% (140.55 km²) and +7.94% (28.04 km²), respectively. Higher ratios and area changes were found in the urban regions, and a similar ISA change pattern in both urban and rural regions was captured by the ISA horizontal epitaxial expansion and vertical density enhancement. Further, the horizontal gradient effect displayed that the mean LSTs were 28.75 °C, 29.77 °C and 31.91 °C in the urban areas and 28.73 °C, 29.66 °C and 31.65 °C in the rural areas in low-, medium-, and high-density ISAs. The vertical density effect showed that the LST change was 1.02 °C and 2.14 °C in the urban areas but 0.93 °C and 1.99 °C in the rural areas during the ISA-density transition from low- to medium- and from medium- to high-density, respectively. Potential surface thermal indicators were assessed, and the urban regions displayed higher sensible heat flux (280.13 W/m²) compared to the rural regions (i.e., 274.76 W/m²). The mechanism effect of the ISA changes on LST in the urban and rural regions was revealed. These findings form a new comparative perspective of the urban–rural synchronous change in the central coastal megalopolis of China and can provide a practical reference for relevant studies. Full article

With its ecological, economic, and social benefits, urban green space (UGS) plays an important role in urban planning. Accordingly, it is also an important indicator in the evaluation of urban liveability. However, the extraction and statistical analysis of UGS are difficult because urban land use involves complex types and UGS exhibits fragmented distribution and common vegetation extraction models such as the NDVI model and pixel bipartite model. In addition, there are few studies that analyze UGS in Hangzhou with a pixel decomposition model. Therefore, applying the mixed pixel decomposition model with GF-1 data, the following three objectives were set in this study: (1) analyzing the temporal changes of UGS in Shangcheng District, Hangzhou from 2018 to 2020; (2) analyzing the spatial distribution characteristics of UGS in the six main urban areas of Hangzhou in 2020; (3) analyzing the rationality and influencing factors of UGS distribution in Hangzhou. In Shangcheng District, the overall UGS area increased from 2018 to 2020 due to the increase in forest area rather than grassland area. As for the main built-up area in Hangzhou, medium and high coverage of UGS were primarily observed, with an overall high level of greening and a relatively uniform vegetation cover. Only a few areas showed very low UGS coverage. Some differences were observed among administrative regions under the influence of topography, but the overall coverage is high. At the same time, the distribution of UGS in Hangzhou is closely related to policy guidance, the needs of urban residents, and the requirements of economic development. This research not only can provide a new way to analyze UGS features in Hangzhou but also provides scientific guidance for governments in urban planning. Full article

The low spatial resolution of hyperspectral images means that existing mixed pixels rely heavily on spectral information, making it difficult to differentiate between the target of interest and the background. The endmember extraction method is powerful in enhancing spatial structure information for hyperspectral anomaly detection, whereas most approaches are based on matrix representation, which inevitably destroys the spatial or spectral information. In this paper, we treated the hyperspectral image as a third-order tensor and proposed a novel anomaly detection method based on a low-rank linear mixing model of the scene background. The obtained abundance maps possessed more distinctive features than the raw data, which was beneficial for identifying anomalies in the background. Specifically, the low-rank tensor background was approximated as the mode-3 product of an abundance tensor and endmember matrix. Due to the distinctive features of the background’s abundance maps, we characterized them by tensor regularization and imposed low rankness through CP decomposition, smoothness, and sparsity. In addition, we utilized the ${\ell }_{1,1,2}$-norm to characterize the tube-wise sparsity of the anomaly, since it accounted for a small portion of the scene. The experimental results obtained using five real datasets demonstrated the outstanding performance of our proposed method. Full article

FVC (fractional vegetation cover) is highly correlated with wheat plant density in the reviving period, which is an important indicator for conducting variable-rate nitrogenous topdressing. In this study, with the objective of improving inversion accuracy of wheat plant density, an innovative approach of retrieval of FVC values from remote sensing images of a UAV (unmanned aerial vehicle) was proposed based on the mixed pixel decomposition method. Firstly, remote sensing images of an experimental wheat field were acquired by using a DJI Mini UAV and endmembers in the image were identified. Subsequently, a linear unmixing model was used to subdivide mixed pixels into components of vegetation and soil, and an abundance map of vegetation was acquired. Based on the abundance map of vegetation, FVC was calculated. Consequently, a linear regression model between the ground truth data of wheat plant density and FVC was established. The coefficient of determination (R²), RMSE (root mean square error), and RRMSE (Relative-RMSE) of the inversion model were calculated as 0.97, 1.86 plants/m², and 0.677%, which indicates strong correlation between the FVC of mixed pixel decomposition method and wheat plant density. Therefore, we can conclude that the mixed pixel decomposition model of the remote sensing image of a UAV significantly improved the inversion accuracy of wheat plant density from FVC values, which provides method support and basic data for variable-rate nitrogenous fertilization in the wheat reviving period in the manner of precision agriculture. Full article

Terrain classification is an important research direction in the field of remote sensing. Hyperspectral remote sensing image data contain a large amount of rich ground object information. However, such data have the characteristics of high spatial dimensions of features, strong data correlation, high data redundancy, and long operation time, which lead to difficulty in image data classification. A data dimensionality reduction algorithm can transform the data into low-dimensional data with strong features and then classify the dimensionally reduced data. However, most classification methods cannot effectively extract dimensionality-reduced data features. In this paper, different dimensionality reduction and machine learning supervised classification algorithms are explored to determine a suitable combination method of dimensionality reduction and classification for hyperspectral images. Soft and hard classification methods are adopted to achieve the classification of pixels according to diversity. The results show that the data after dimensionality reduction retain the data features with high overall feature correlation, and the data dimension is drastically reduced. The dimensionality reduction method of unified manifold approximation and projection and the classification method of support vector machine achieve the best terrain classification with 99.57% classification accuracy. High-precision fitting of neural networks for soft classification of hyperspectral images with a model fitting correlation coefficient (R²) of up to 0.979 solves the problem of mixed pixel decomposition. Full article

We examine the impact of changes in ozone (O₃), particulate matter (PM_2.5), temperature, and humidity on the health of vegetation in dense urban environments, using a very high-resolution, ground-based Visible and Near-Infrared (VNIR, 0.4–1.0 μm with a spectral resolution of 0.75 nm) hyperspectral camera deployed by the Urban Observatory (UO) in New York City. Images were captured at 15 min intervals from 08h00 to 18h00 for 30 days between 3 May and 6 June 2016 with each image containing a mix of dense built structures, sky, and vegetation. Vegetation pixels were identified using unsupervised k-means clustering of the pixel spectra and the time dependence of the reflection spectrum of a patch of vegetation at roughly 1 km from the sensor that was measured across the study period. To avoid illumination and atmospheric variability, we introduce a method that measures the ratio of vegetation pixel spectra to the spectrum of a nearby building surface at each time step relative to that ratio at a fixed time. This “Compound Ratio” exploits the (assumed) static nature of the building reflectance to isolate the variability of vegetation reflectance. Two approaches are used to quantify the health of vegetation at each time step: (a) a solar-induced fluorescence indicator ($SIFi$) calculated as the simple ratio of the amplitude of the Compound Ratio at 0.75 μm and 0.9 μm, and (b) Principal Component Analysis (PCA) decomposition designed to capture more global spectral features. The time dependence of these vegetation health indicators is compared to that of O₃, PM_2.5, temperature, and humidity values from a distributed and publicly available in situ air quality sensor network. Assuming a linear relationship between vegetation health indicators and air quality indicators, we find that changes in both SIF indicator values and PC amplitudes show a strong correlation ($r^2$ value of 40% and 47%, respectively) with changes in air quality, especially in comparison with nearby buildings used as controls ($r^2$ value of 1% and 4%, respectively, and with all molecular correlations consistent with zero to within 3$\sigma$ uncertainty). Using the SIF indicator, O₃ and temperature exhibit a positive correlation with changes in photosynthetic rate in vegetation, while PM_2.5 and humidity exhibit a negative correlation. We estimate full covariant uncertainties on the coefficients using a Markov Chain Monte Carlo (MCMC) approach and demonstrate that these correlations remain statistically significant even when controlling for the effects of diurnal sun-sensor geometry and temperature variability. This work highlights the importance of quantifying the effects of various air quality parameters on vegetation health in urban environments in order to uncover the complexity, covariance, and interdependence of the numerous factors involved. Full article

The application of remote sensing technology in grassland monitoring and management has been ongoing for decades. Compared with traditional ground measurements, remote sensing technology has the overall advantage of convenience, efficiency, and cost effectiveness, especially over large areas. This paper provides a comprehensive review of the latest remote sensing estimation methods for some critical grassland parameters, including above-ground biomass, primary productivity, fractional vegetation cover, and leaf area index. Then, the applications of remote sensing monitoring are also reviewed from the perspective of their use of these parameters and other remote sensing data. In detail, grassland degradation and grassland use monitoring are evaluated. In addition, disaster monitoring and carbon cycle monitoring are also included. Overall, most studies have used empirical models and statistical regression models, while the number of machine learning approaches has an increasing trend. In addition, some specialized methods, such as the light use efficiency approaches for primary productivity and the mixed pixel decomposition methods for vegetation coverage, have been widely used and improved. However, all the above methods have certain limitations. For future work, it is recommended that most applications should adopt the advanced estimation methods rather than simple statistical regression models. In particular, the potential of deep learning in processing high-dimensional data and fitting non-linear relationships should be further explored. Meanwhile, it is also important to explore the potential of some new vegetation indices based on the spectral characteristics of the specific grassland under study. Finally, the fusion of multi-source images should also be considered to address the deficiencies in information and resolution of remote sensing images acquired by a single sensor or satellite. Full article

Previous studies have shown that scattering mechanism ambiguity and negative power issues still exist in model-based polarization target decomposition algorithms, even though deorientation processing is implemented. One possible reason for this is that the dynamic range of the model itself is limited and cannot fully satisfy the mixed scenario. To address these problems, we propose a hybrid polarimetric target decomposition algorithm (GRH) with a generalized volume scattering model (GVSM) and a random particle cloud volume scattering model (RPCM). The adaptive volume scattering model used in GRH incorporates GVSM and RPCM to model the volume scattering model of the regions dominated by double-bounce scattering and the surface scattering, respectively, to expand the dynamic range of the model. In addition, GRH selects the volume scattering component between GVSM and RPCM adaptively according to the target dominant scattering mechanism of fully polarimetric synthetic aperture radar (PolSAR) data. The effectiveness of the proposed method was demonstrated using AirSAR dataset over San Francisco. Comparison studies were carried out to test the performance of GRH over several target decomposition algorithms. Experimental results show that the GRH outperforms the algorithms we tested in this study in decomposition accuracy and reduces the number of pixels with negative powers, demonstrating that the GRH can significantly avoid mechanism ambiguity and negative power issues. Full article

Accurate identification of sandy land plays an important role in sandy land prevention and control. It is difficult to identify the nature of sandy land due to vegetation covering the soil in the sandy area. Therefore, HJ-2A hyperspectral data and GF-3 Synthetic Aperture Radar (SAR) data were used as the main data sources in this article. The advantages of the spectral characteristics of a hyperspectral image and the penetration characteristics of SAR data were used synthetically to carry out mixed-pixel decomposition in the “horizontal” direction and polarization decomposition in the “vertical” direction. The results showed that in the study area of the Otingdag Sandy Land, in China, the accuracy of sandy land detection based on feature-level fusion and single GF-3 data was verified to be 92% in both cases by field data; the accuracy of sandy land detection based on feature-level fusion was verified to be 88.74% by the data collected from Google high-resolution imagery, which was higher than that based on single HJ-2A (74.17%) and single GF-3 data (88.08%). To further verify the universality of the feature-level fusion method for sandy land detection, Alxa sandy land was also used as a verification area and the accuracy of sandy land detection was verified to be as high as 88.74%. The method proposed in this paper made full use of the horizontal and vertical structural information of remote sensing data. The problem of mixed pixels in sparse-vegetation scenes in the horizontal direction and the problem of vegetation covering sandy soil in the vertical direction were both well solved. Accurate identification of sandy land can be realized effectively, which can provide technical support for sandy land prevention and control. Full article

To eliminate the mixed noise in hyperspectral images (HSIs), three-dimensional total variation (3DTV) regularization has been proven as an efficient tool. However, 3DTV regularization is prone to losing image details in restoration. To resolve this issue, we proposed a novel TV, named spatial domain spectral residual total variation (SSRTV). Considering that there is much residual texture information in spectral variation image, SSRTV first calculates the difference between the pixel values of adjacent bands and then calculates a 2DTV for the residual image. Experimental results demonstrated that the SSRTV regularization term is powerful at changing the structures of noises in an original HSI, thus allowing low-rank techniques to get rid of mixed noises more efficiently without treating them as low-rank features. The global low-rankness and spatial–spectral correlation of HSI is exploited by low-rank Tucker decomposition (LRTD). Moreover, it was demonstrated that the l_2,1 norm is more effective to deal with sparse noise, especially the sample-specific noise such as stripes or deadlines. The augmented Lagrange multiplier (ALM) algorithm was adopted to solve the proposed model. Finally, experimental results with simulated and real data illustrated the validity of the proposed method. The proposed method outperformed state-of-the-art TV-regularized low-rank matrix/tensor decomposition methods in terms of quantitative metrics and visual inspection. Full article

Recently, unmixing methods based on nonnegative tensor factorization have played an important role in the decomposition of hyperspectral mixed pixels. According to the spatial prior knowledge, there are many regularizations designed to improve the performance of unmixing algorithms, such as the total variation (TV) regularization. However, these methods mostly ignore the similar characteristics among different spectral bands. To solve this problem, this paper proposes a group sparse regularization that uses the weighted constraint of the $L_{2,1}$ norm, which can not only explore the similar characteristics of the hyperspectral image in the spectral dimension, but also keep the data smooth characteristics in the spatial dimension. In summary, a non-negative tensor factorization framework based on weighted group sparsity constraint is proposed for hyperspectral images. In addition, an effective alternating direction method of multipliers (ADMM) algorithm is used to solve the algorithm proposed in this paper. Compared with the existing popular methods, experiments conducted on three real datasets fully demonstrate the effectiveness and advancement of the proposed method. Full article

The main goal of this research was to propose a new method of polarimetric SAR data decomposition that will extract additional polarimetric information from the Synthetic Aperture Radar (SAR) images compared to other existing decomposition methods. Most of the current decomposition methods are based on scattering, covariance or coherence matrices describing the radar wave-scattering phenomenon represented in a single pixel of an SAR image. A lot of different decomposition methods have been proposed up to now, but the problem is still open since it has no unique solution. In this research, a new polarimetric decomposition method is proposed that is based on polarimetric signature matrices. Such matrices may be used to reveal hidden information about the image target. Since polarimetric signatures (size 18 × 9) are much larger than scattering (size 2 × 2), covariance (size 3 × 3 or 4 × 4) or coherence (size 3 × 3 or 4 × 4) matrices, it was essential to use appropriate computational tools to calculate the results of the proposed decomposition method within an acceptable time frame. In order to estimate the effectiveness of the presented method, the obtained results were compared with the outcomes of another method of decomposition (Arii decomposition). The conducted research showed that the proposed solution, compared with Arii decomposition, does not overestimate the volume-scattering component in built-up areas and clearly separates objects within the mixed-up areas, where both building, vegetation and surfaces occur. Full article