Search Results (39)

In distributed radar, a coherent system often gains attention for its higher detection potential in contrast to its non-coherent counterpart. However, even for a distributed coherent radar, it is difficult to coherently accumulate local observations in the searching mode if target returns in local channels are decorrelated. In order to obtain the superiority of coherent processing while overcoming the real implementation difficulties of a coherent framework, this paper studies a distributed coherent detection algorithm for fusion detection. It is utilized in detecting a target during tracking while a target is searched for in a non-coherent manner. From historic observations on target tracking, relative phase delays in different channels are predicted by a phase lock loop and then used to compensate phases for observations in the current frame. Moreover, to enhance the detection performance of distributed radar during tracking, a switching rule between phase prediction-based coherent and non-coherent processing is proposed based on their detection performance. Numerical results indicate that the switching operation can improve the detection probability during tracking, and the non-coherent operation can still provide a moderate detection performance if the phase prediction is unreliable. Full article

The objective of hyperspectral pansharpening is to fuse low-resolution hyperspectral images (LR-HSI) with corresponding panchromatic (PAN) images to generate high-resolution hyperspectral images (HR-HSI). Despite advancements in hyperspectral (HS) pansharpening using deep learning, the rich spectral details and large data volume of HS images place higher demands on models for effective spectral extraction and processing. In this paper, we present HyperGAN, a hyperspectral image fusion approach based on Generative Adversarial Networks. Unlike previous methods that deepen the network to capture spectral information, HyperGAN widens the structure with a Wide Block for multi-scale learning, effectively capturing global and local details from upsampled HSI and PAN images. While LR-HSI provides rich spectral data, PAN images offer spatial information. We introduce the Efficient Spatial and Channel Attention Module (ESCA) to integrate these features and add an energy-based discriminator to enhance model performance by learning directly from the Ground Truth (GT), improving fused image quality. We validated our method on various scenes, including the Pavia Center, Eastern Tianshan, and Chikusei. Results show that HyperGAN outperforms state-of-the-art methods in visual and quantitative evaluations. Full article

The circumpolar Taiga–Tundra Ecotone significantly influences the feedback mechanism of global climate change. Achieving large-scale individual tree crown (ITC) extraction in the transition zone is crucial for estimating vegetation biomass in the transition zone and studying plants’ response to climate change. This study employed aerial images and airborne LiDAR data covering several typical transitional zone regions in northern Finland to explore the ITC delineation method based on deep learning. First, this study developed an improved multi-scale ITC delineation method to enable the semi-automatic assembly of the ITC sample collection. This approach led to the creation of an individual tree dataset containing over 20,000 trees in the transitional zone. Then, this study explored the ITC delineation method using the Mask R-CNN model. The accuracies of the Mask R-CNN model were compared with two traditional ITC delineation methods: the improved multi-scale ITC delineation method and the local maxima clustering method based on point cloud distribution. For trees with a height greater than 1.3 m, the Mask R-CNN model achieved an overall recall rate ($A_r$) of 96.60%. Compared to the two conventional ITC delineation methods, the $A_r$ of Mask R-CNN showed an increase of 1.99 and 5.52 points in percentage, respectively, indicating that the Mask R-CNN model can significantly improve the accuracy of ITC delineation. These results highlight the potential of Mask R-CNN in extracting low trees with relatively small crowns in transitional zones using high-resolution aerial imagery and low-density airborne point cloud data for the first time. Full article

Accurate classification of forest stand is crucial for protection and management needs. However, forest stand classification remains a great challenge because of the high spectral and textural similarity of different tree species. Although existing studies have used multiple remote sensing data for forest identification, the effects of different spatial resolutions and combining multi-source remote sensing data for automatic complex forest stand identification using deep learning methods still require further exploration. Therefore, this study proposed an object-oriented convolutional neural network (OCNN) classification method, leveraging data from Sentinel-2, RapidEye, and LiDAR to explore classification accuracy of using OCNN to identify complex forest stands. The two red edge bands of Sentinel-2 were fused with RapidEye, and canopy height information provided by LiDAR point cloud was added. The results showed that increasing the red edge bands and canopy height information were effective in improving forest stand classification accuracy, and OCNN performed better in feature extraction than traditional object-oriented classification methods, including SVM, DTC, MLC, and KNN. The evaluation indicators show that ResNet_18 convolutional neural network model in the OCNN performed the best, with a forest stand classification accuracy of up to 85.68%. Full article

The Sustainable Development Science Satellite 1 (SDGSAT-1) satellite, launched in November 2021, is dedicated to providing data detailing the “traces of human activities” for the implementation of the United Union’s 2030 Agenda for Sustainable Development and global scientific research. The glimmer imager (GI) that is equipped on SDGSAT-1 can provide nighttime light (NL) data with a 10 m panchromatic (PAN) band and red, green, and blue (RGB) bands of 40 m resolution, which can be used for a wide range of applications, such as in urban expansion, population studies of cities, and economics of cities, as well as nighttime aerosol thickness monitoring. The 10 m PAN band can be fused with the 40 m RGB bands to obtain a 10 m RGB NL image, which can be used to identify the intensity and type of night lights and the spatial distribution of road networks and to improve the monitoring accuracy of sustainable development goal (SDG) indicators related to city developments. Existing remote sensing image fusion algorithms are mainly developed for daytime optical remote sensing images. Compared with daytime optical remote sensing images, NL images are characterized by a large amount of dark (low-value) pixels and high background noises. To investigate whether daytime optical image fusion algorithms are suitable for the fusion of GI NL images and which image fusion algorithms are the best choice for GI images, this study conducted a comprehensive evaluation of thirteen state-of-the-art pansharpening algorithms in terms of quantitative indicators and visual inspection using four GI NL datasets. The results showed that PanNet, GLP_HPM, GSA, and HR outperformed the other methods and provided stable performances among the four datasets. Specifically, PanNet offered UIQI values ranging from 0.907 to 0.952 for the four datasets, whereas GSA, HR, and GLP_HPM provided UIQI values ranging from 0.770 to 0.856. The three methods based on convolutional neural networks achieved more robust and better visual effects than the methods using multiresolution analysis at the original scale. According to the experimental results, PanNet shows great potential in the fusion of SDGSAT-1 GI imagery due to its robust performance and relatively short training time. The quality metrics generated at the degraded scale were highly consistent with visual inspection, but those used at the original scale were inconsistent with visual inspection. Full article

With the continuous improvement in people’s living standards and the change in consumption concept, green food is favored by more and more consumers. Consumer repurchase behavior is a necessary condition to activate the market, expand the consumption scale and stabilize the continuous growth of the market. Repurchase intention is the most direct factor affecting consumers’ green food repurchase intention. Therefore, it is necessary to study consumers green food repurchase intentions. This study collects data from 303 consumer surveys on green food consumption to explore the impact of consumer satisfaction with consumption experience on green food repurchase intention and further explore the mechanisms and influence boundaries. The results show that (1) consumer experience satisfaction positively affects green food repurchase intention; (2) consumer experience satisfaction can improve consumers’ green food repurchase intention through consumer perceptions of social value, green self-efficacy and warm glow; (3) the higher the degree of consumer inertia, the stronger the influence of green self-efficacy and warm glow on consumers’ green food repurchase intention; and (4) the higher the degree of consumer subjective norms, the stronger the influence of consumer perceived social value, green self-efficacy and warm glow on the consumer’s green food repurchase intention. This study provides a new perspective and theoretical framework for promoting consumers’ green food repurchase intention, and it may have certain theoretical significance and practical impact on green food market growth, sustainable carrying of the ecological environment and high-quality development of agriculture. Full article

Accurate identification of individual tree species (ITS) is crucial to forest management. However, current ITS identification methods are mainly based on traditional image features or deep learning. Traditional image features are more interpretative, but the generalization and robustness of such methods are inferior. In contrast, deep learning based approaches are more generalizable, but the extracted features are not interpreted; moreover, the methods can hardly be applied to limited sample sets. In this study, to further improve ITS identification, typical spectral and texture image features were weighted to assist deep learning models for ITS identification. To validate the hybrid models, two experiments were conducted; one on the dense forests of the Huangshan Mountains, Anhui Province and one on the Gaofeng forest farm, Guangxi Province, China. The experimental results demonstrated that with the addition of image features, different deep learning ITS identification models, such as DenseNet, AlexNet, U-Net, and LeNet, with different limited sample sizes (480, 420, 360), were all enhanced in both study areas. For example, the accuracy of DenseNet model with a sample size of 480 were improved to 87.67% from 85.41% in Huangshan. This hybrid model can effectively improve ITS identification accuracy, especially for UAV aerial imagery or limited sample sets, providing the possibility to classify ITS accurately in sample-poor areas. Full article

Both remote sensing and geochemical exploration technologies are effective tools for detecting target objects. Although information on anomalous geochemical elemental abundances differs in terms of professional attributes from remote sensing data, both are based on geological bodies or phenomena on the Earth’s surface. Therefore, exploring the use of remote sensing data with high spatial resolution to improve the accuracy of small-scale geochemical data, and fusing them to obtain large-scale geochemical layers could provide new data for geological and mineral exploration through inversion. This study provides a method of fusing remote sensing images with small-scale geochemical data based on a linear regression model that improves the resolution of geochemical elemental layers and provides reference data for mineral exploration in areas lacking large-scale geochemical data. In the Xianshuigou area of Northwest China, a fusion study was conducted using 200,000 geochemical and remote sensing data. The method provides fused large-scale regional chemical data in well-exposed areas where large-scale geochemical data are lacking and could provide potential data sources for regional mineral exploration. Full article

Several large-scale porphyry copper deposits (PCDs) with high economic value have been excavated in the Duolong ore district, Tibet, China. However, the high altitudes and harsh conditions in this area make traditional exploration difficult. Hydrothermal alteration minerals related to PCDs with diagnostic spectral absorption features in the visible–near-infrared–shortwave-infrared ranges can be effectively identified by remote sensing imagery. Mainly based on hyperspectral imagery supplemented by multispectral imagery and geochemical element data, the Duolong ore district was selected to conduct data-driven PCD prospectivity modelling. A total of 11 known deposits and 17 evidential layers of multisource geoscience information related to Cu mineralization constitute the input datasets of the predictive models. A deep learning convolutional neural network (CNN) model was applied to mineral prospectivity mapping, and its applicability was tested by comparison to conventional machine learning models, such as support vector machine and random forest. CNN achieves the greatest classification performance with an accuracy of 0.956. This is the first trial in Duolong to conduct mineral prospectivity mapping combined with remote imagery and geochemistry based on deep learning methods. Four metallogenic prospective sites were delineated and verified through field reconnaissance, indicating that the application of deep learning-based methods in PCD prospecting proposed in this paper is feasible by utilizing geoscience big data such as remote sensing datasets and geochemical elements. Full article

Accurate and efficient individual tree species (ITS) classification is the basis of fine forest resource management. It is a challenge to classify individual tree species in dense forests using remote sensing imagery. In order to solve this problem, a new ITS classification method was proposed in this study, in which a hierarchical convolutional neural network (H-CNN) model and multi-temporal high-resolution Google Earth images were employed. In an experiment conducted in a forest park in Beijing, China, GE images of several significant phenological phases of broad-leaved forests, namely, before and after the mushrooming period, the growth period, and the wilting period, were selected, and ITS classifications based on these images along with several typical CNN models and the H-CNN model were conducted. In the experiment, the classification accuracy of the multitemporal images was higher by 7.08–12.09% than those of the single-temporal images, and the H-CNN model offered an OA accuracy 2.66–3.72% higher than individual CNN models, demonstrating that multitemporal images rich in the phenological features of individual tree species, together with a hierarchical CNN model, can effectively improve ITS classification. Full article

Population migration, accompanied by urbanization, has led to an increase in the urban-settled population. However, quantitative studies on the distribution of urban-settled population, especially at fine scale, are limited. This study explored the relationship between characteristics of human perceived environment and the distribution of settled population, and proposed a quantitative method to predict the distribution trend of settled population. Through the semantic segmentation of street view images and accessibility calculation based on traffic isochrone and points-of-interest, we determined human perception factors. The influence of human perception factors was quantified using the geographic detector method, and the settlement intention index (SII) was constructed combining the analytic hierarchy process to predict the distribution trend of settled population. The results indicated the following. (1) Human perception was one of the important factors influencing the distribution of urban-settled population, and the cycling accessibility to traffic facilities was closely related to the distribution of settled population. (2) The accessibility and visibility of green space with low independent influence portrayed a strong enhancement on the interactive effect of other perception factors. (3) The SII mapping of Beijing showed that the SII was reliable. This study analyzes the role of human perception in shaping the environment, and provides reference for population-related urban planning problems. Full article

Heavy metal concentrations of Cd, As, Pb, Cu, Cr, and Hg were investigated for 86 soil samples in Jinzhou near the Bohai Sea in China, in order to identify what anthropological activities influenced their distribution levels. Ordinary cokriging (OCK) was utilized to map six heavy-metal distributions by incorporating their main environmental influencers. The resultant p values for the six OCK mapping models of 0–2.78% indicated good statistical significance of the models, and the relative mean absolute errors of 4.82–12.53% and relative root mean square errors of 6.23–18.21% indicated allowable predication precision for their concentrations. The contamination distributions by OCK mapping were then graded based on the standards of the China National Environmental Monitoring Center and the Chinese Environmental Protection Administration, which showed that Cu and As contaminations in parts of this area were over the natural level but not polluted, Cr contamination was omnipresent over the natural level in this area and even reached the polluted level in parts of this area. The graded contamination maps that were overlapped with land-use maps and Google satellite maps, as well as the verifications reported in literatures, enabled correlations of the different contamination levels of As, Cu, and Cr with human activities. Resultantly, it indicated that As and Cu contamination over the natural level may be related to agricultural planting and aquacultural activities along the coast of Bohai Bay, with the contaminants transported via watercourses; Cr contamination over the natural level may have been due to vehicle emissions; and, Cr pollution may have been from steel manufacturing and geochemical factories Full article

The classification of individual tree species (ITS) is beneficial to forest management and protection. Previous studies in ITS classification that are primarily based on airborne LiDAR and aerial photographs have achieved the highest classification accuracies. However, because of the complex and high cost of data acquisition, it is difficult to apply ITS classification in the classification of large-area forests. High-resolution, satellite remote sensing data have abundant sources and significant application potential in ITS classification. Based on Worldview-3 and Google Earth images, convolutional neural network (CNN) models were employed to improve the classification accuracy of ITS by fully utilizing the feature information contained in different seasonal images. Among the three CNN models, DenseNet yielded better performances than ResNet and GoogLeNet. It offered an OA of 75.1% for seven tree species using only the WorldView-3 image and an OA of 78.1% using the combinations of WorldView-3 and autumn Google Earth images. The results indicated that Google Earth images with suitable temporal detail could be employed as auxiliary data to improve the classification accuracy. Full article

Climate change and human activities have caused dramatic impacts on biodiversity. Although a number of international agreements or initiatives have been launched to mitigate the biodiversity loss, the erosion of terrestrial biome habitats is inevitable. Consequently, the identification of potential suitable habitats under climate change and human disturbance has become an urgent task of biodiversity conservation. In this study, we used the maximum entropy model (MaxEnt) to identify the current and potential future habitats of Asian elephants in South and Southeast Asia. We performed analyses for future projections with 17 scenarios using the present results as baseline. To optimize the modelling results, we delineated the core habitats by using the Core Mapper Tool and compared them with existing protected areas (PAs) through gap analysis. The results showed that the current total area of core habitats is 491,455 km² in size and will be reduced to 332,544 km² by 2090 under SSP585 (the shared socioeconomic pathway). The projection analysis under differential scenarios suggested that most of the core habitats in the current protected areas would remain stable and suitable for elephants in the future. However, the remaining 75.17% of the core habitats lay outside the current PAs, and finally we mapped approximately 219,545 km² of suitable habitats as priority protected areas in the future. Although our model did not perform well in some regions, our analyses and findings still could provide useful references to the planning of protected areas and conservation of Asian elephant. Full article

Insufficient data and imperfect methods are the main obstacles to realize Target 11.4 of the Sustainable Development Goals (SDGs). Very high-resolution (VHR) remote sensing provides a useful tool to elaborate monitor land-cover changes in cultural landscapes so as to evaluate the authenticity and integrity of the cultural heritage sites (CHS). In this study, we developed a semi-automatic two-level workflow to efficiently extract delicate land-cover changes from bi-temporal VHR images (with spatial resolution ≤ 1 m), where most current studies can only manually interpret changes at this scale. Based on the monitoring result, we proposed an indicator named interference degree that can quantify the changes in cultural landscapes of the CHS as a complementary indicator to achieve Target 11.4 for SDGs. Three representative types of CHS with different landscapes were studied in 2015 and 2020 based on the VHR Google Earth images, including cave temples, ancient architectural buildings, and ancient sites. The proposed workflow was demonstrated to be effective in extracting delicate changes efficiently with the accuracy around 85%. The interference degree well reflects the preservation status of these CHS and can be periodically observed in a long term as an evaluation indicator. This study shows the potential to produce the first-hand global-monitoring data of CHS to support Target 11.4, thus serving for the sustainable development of the world’s cultural heritage. Full article