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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. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) is highly affected by global climate change, and thus, assessing climate conditions from a tourism perspective is significant for the sustainable development of tourism. In this study, a reanalysis dataset from ERA-5 was employed to assess the climate comfort level for tourism and analyse its spatio-temporal patterns from the year 1979 to 2020. A tourism climate index (TCI) comprising three component indices was calculated using climatic variables, and increasing trends in both annual and monthly TCI were found. Spatially, higher TCI values and more significant increasing trends were found in the eastern, southern and peripheral regions of the TP, as well as in the Qaidam Basin with lower altitude. Increasing TCI values indicate more comfortable tourism conditions and may bring in more opportunities for tourism development. All these results provide valuable guidance for local constructions and development for tourism in TP corresponding to the climate change. [ABSTRACT FROM AUTHOR]

Herein, electrochemical reductive carboxylation of para‐quinone methides with CO2 was developed. A variety of value‐added carboxylic acids can be accessed in moderate to excellent yields. Preliminary studies showed that para‐quinone methides were reduced to benzyl carbanion during the reaction. This protocol features high atom economy, operational simplicity, and environmental friendliness, enabling the synthesis of various carboxylic compounds. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A straightforward protocol for the visible‐light‐promoted decarboxylative 1,6‐conjugate addition of N‐aryl glycines to para‐quinone methides under transition‐metal‐free conditions is disclosed. This method provides scalable and efficient access to 2,2‐diarylanilines with biological and pharmacologic potential in good to excellent yields (39 examples, up to 97% yield). The synthetic utility of this work has been illustrated in the efficient synthesis of 3‐substituted indoline. [ABSTRACT FROM AUTHOR]

Multisource geoscience data can provide significant information for mineral exploration in a variety of ways. For example, remote-sensing images record the spectral characteristics of objects, and geochemical data represent the enrichment or depletion of geochemical elements, which reflect the physical and chemical attributes of geological features. In this study, a hybrid model comprising data fusion and machine learning was applied for lithological mapping. This process is illustrated through a case study of mapping several lithological units in the Cuonadong Dome, in the northeastern part of the Himalayas, China. In this process, multisource data fusion technology is first used to provide more abundant information by integrating geochemical data and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote-sensing images, retaining both the geochemical patterns and the textural structure of the remote-sensing images. Then, a random forest metric learning (RFML) approach is employed to achieve a high classification performance based on the fused data. RFML adopts metric learning in the classification process of each decision tree calculation, making full use of the advantages of random forest and metric learning. Seven target lithological units were discriminated with 93.0% overall accuracy. This excellent performance demonstrates the effectiveness of the hybrid method in the geological exploration of areas in poor environments that have undergone limited geological research. [ABSTRACT FROM AUTHOR]

A novel electrocatalyzed method for the preparation of dibenzosiloles was developed through intramolecular C−H/Si−H dehydrogenative coupling strategy starting from biarylhydrosilanes. Both electro‐donating and electro‐withdrawing substitution groups were tolerated for this transformation, and the desired dibenzosilole products could be obtained in moderate to excellent yields. A sila‐Friedel‐Crafts reaction mechanism was proposed on the basis of previous literature and our controlled experiments. [ABSTRACT FROM AUTHOR]

A photocatalytic E to Z isomerization of alkenes using an iridium photosensitizer under mild reaction conditions is disclosed. This method provides scalable and efficient access to Z‐cinnamyl ether and allylic alcohol derivatives in high yields with excellent stereoselectivity. Importantly, this method also provides a powerful strategy for the selective synthesis of Z‐magnolol and honokiol derivatives possessing potential biological activity. [ABSTRACT FROM AUTHOR]

A series of metal–organic frameworks (MOFs), namely {[Cd(H2TCPP)]·2H2O}n (1), {[Pb2(TCPP)]}n (2), {[La(H2TCPP)]·3.7H2O}n (3), {[Sr3(HTCPP)2]·6H2O}n (4), {[Ce(HTCPP)]·H2O}n (5) and {[Mn2(TCPP)]·2.13H2O}n (6), are prepared from 2,3,5,6-(4-carboxyl-tetraphenyl)pyrazine (H4TCPP) with various metal salts under hydrothermal conditions and are well characterized. The structural analysis shows that compound 1 presents a 2-fold interpenetrating structure, which exhibits a uninodal 4-c net with a {66}{6·6·6·6(2)·6(2)·6(2)} topology. Compound 2 shows a 3-nodal (5,6,11)-connected 3D framework with a point symbol of {410}{414·6}{434·621}. Compound 3 features a (4,8)-connected 3D network built from a binuclear La cluster with a point symbol of {416·612}{44·62}2. Compound 4 demonstrates a 5-nodal (4,5,6,7,8)-connected network with a {410·65}{412·69}{418·610}{46}{47·63} topology. Compound 5 exhibits a 3D interlocked eight-connected 20-c net with a point symbol of {378·498·514}, while compound 6 forms a 3,4,4,5,17-connected net with a point symbol of {4·52}2{432·56·684·74·810}{45·5}{46}{48·52}. In addition, almost all the MOFs display significant fluorescence quenching behaviors by the addition of various nitro-aromatic compounds (NACs), especially TNP. More importantly, six new MOFs have been further investigated to evaluate their electrocatalytic activities for the hydrogen evolution reaction (HER). The results demonstrate that all the MOFs exhibit improved performance compared to ligand H4TCPP for the HER with the lowest onset overpotential of 244 mV and the smallest Tafel slope of 248 mV dec−1, respectively. [ABSTRACT FROM AUTHOR]

Numerous pansharpening methods were proposed in recent decades for fusing low-spatial-resolution multispectral (MS) images with high-spatial-resolution (HSR) panchromatic (PAN) bands to produce fused HSR MS images, which are widely used in various remote sensing tasks. The effect of misregistration between MS and PAN bands on quality of fused products has gained much attention in recent years. An improved method for misaligned MS and PAN imagery is proposed, through two improvements made on a previously published method named RMI (reduce misalignment impact). The performance of the proposed method was assessed by comparing with some outstanding fusion methods, such as adaptive Gram-Schmidt and generalized Laplacian pyramid. Experimental results show that the improved version can reduce spectral distortions of fused dark pixels and sharpen boundaries between different image objects, as well as obtain similar quality indexes with the original RMI method. In addition, the proposed method was evaluated with respect to its sensitivity to misalignments between MS and PAN bands. It is certified that the proposed method is more robust to misalignments between MS and PAN bands than the other methods. [ABSTRACT FROM AUTHOR]

Pansharpening is an important technique used to generate high-quality high-spatial-resolution multispectral (MS) bands by fusing low-spatial-resolution MS bands and a panchromatic (PAN) band obtained by the same sensor. A PAN-modulation (PM)-based pansharpening method taking account of haze, which is referred as Haze- and Ratio- based (HR) method, has been demonstrated to yield good performances, indicating that the impact of haze should be considered in PM-based methods. It is obvious that the haze values used in the HR fusion influence the spectral vectors of fused pixels, thus affect the spectral distortion of fused images. In order to reach stable and good performances of the HR method, the determination of the optimal haze values is discussed in this study. First, six approaches for haze values determination, which are variations of the histogram minimal approach and the darkest pixel approach employed by the image-based dark-object subtraction method for atmospheric correction of remote-sensed images, are compared. Then, an improved approach for haze values determination is proposed. The proposed approach is proved to be effective for improving the performance of the HR method. This is very important for the employment of the HR method in practical applications and by more researchers. [ABSTRACT FROM PUBLISHER]

A novel classification method based on multiple-point statistics (MPS) is proposed in this article. The method is a modified version of the spatially weighted k-nearest neighbour (k-NN) classifier, which accounts for spatial correlation through weights applied to neighbouring pixels. The MPS characterizes the spatial correlation between multiple points of land-cover classes by learning local patterns in a training image. This rich spatial information is then converted to multiple-point probabilities and incorporated into the k-NN classifier. Experiments were conducted in two study areas, in which the proposed method for classification was tested on a WorldView-2 sub-scene of the Sichuan mountainous area and an IKONOS image of the Beijing urban area. The multiple-point weighted k-NN method (MPk-NN) was compared to several alternatives; including the traditional k-NN and two previously published spatially weighted k-NN schemes; the inverse distance weighted k-NN, and the geostatistically weighted k-NN. The classifiers using the Bayesian and Support Vector Machine (SVM) methods, and these classifiers weighted with spatial context using the Markov random field (MRF) model, were also introduced to provide a benchmark comparison with the MPk-NN method. The proposed approach increased classification accuracy significantly relative to the alternatives, and it is, thus, recommended for the identification of land-cover types with complex and diverse spatial distributions. [ABSTRACT FROM AUTHOR]

A major reason for the spectral distortions of fused images generated by current image-fusion methods is that the fused versions of mixed multispectral (MS) sub-pixels (MSPs) corresponding to panchromatic (PAN) pure pixels remain mixed. The MSPs can be un-mixed spectrally to pure pixels having the same land cover classes in a fine classification map during the fusion process. Since it is difficult to produce such a land cover classification map using only MS and PAN images, a Digital Surface Model (DSM) derived from airborne Light Detection And Ranging data were employed in this study to facilitate the classification. In a novel fusion method proposed in this paper, MSPs near and across boundaries between vegetation and non-vegetation are identified using MS, PAN, and normalized Digital Surface Model (nDSM). The identified MSPs then are fused to pure pixels with respect to the corresponding land cover class in the classification map. In a test on WorldView-2 images over an urban area and the corresponding nDSM, the fused image generated by the proposed method was visually and quantitatively compared with fused images obtained using common image-fusion methods. The fused images generated by the proposed method yielded minimal spectral distortions and sharpened boundaries between vegetation and non-vegetation. [ABSTRACT FROM AUTHOR]

The Neoproterozoic successions in the Aksu region, NW China, which lies unconformably on the Precambrian Aksu Group basement, comprises the Qiaoenbrak, Yuermeinak, Sugetbrak, and Chigebrak formations (from bottom to top). The two lowermost units include two distinct glacial diamictites, which indicate distinct episodes of glaciations. We report the major and trace element (including rare earth element) data for the Qiaoenbrak, Yuermeinak, and Sugetbrak formations to identify the paleoclimatic perturbations. The chemical index of alteration (CIA) values show variations from Qiaoenbrak to Yuermeinak, then Sugetbrak formations. The diamictites have relatively lower chemical index of alteration values (45.23-59.64) than inter-, post- and non-glacial sediments (48.28-66.96). This result supported the condition that the diamictites underwent relatively weak chemical weathering from a dry-cold sedimentary environment, which is associated with the sedimentary facies description. The lower Neoproterozoic successions recoded at least two glaciations, one is Qiaoenbrak glaciation and the other is Yuermeinak glaciation. [ABSTRACT FROM AUTHOR]

The Neoproterozoic succession in the Aksu region of northwestern China forms an unconformable boundary with the lower Precambrian Aksu basement group and consists of the Qiaoenbrak, Yuermeinak, Sugetbrak, and Chigebrak Formations. The two lowermost units include distinct glaciogenic diamictites that indicate distinct episodes of glaciation. In this study, we report the LA-ICP-MS U–Pb ages of detrital zircons and geochemical data from the lower Neoproterozoic strata. The age of the detrital zircon constrains the maximum depositional age to between 769 ± 10 and 727 ± 8 Ma for the Qiaoenbrak diamictites, which are associated with the Kaigas glaciation that occurred during the early Cryogenian period. The youngest detrital zircon age of 719 ± 9 Ma corresponds to the maximum depositional age of the Yuermeinak diamictites, which are associated with the Sturtian glaciation. The detrital zircons from the lower Neoproterozoic strata in the Aksu area indicated four peak ages of 2484, 1948, 861, and 647–581 Ma, which are consistent with the major tectonic episodes in the Tarim Block. The peak age of 2484 Ma represents an Archaean basement, which participated in the worldwide continental nuclei growth event from the late Neoarchaean to the early Palaeoproterozoic. The peak age of 1948 Ma may be associated with the assembly of the Columbia supercontinent, and the 861 and 647–581 Ma are likely associated with the break-up of the Rodinia supercontinent. The combination of geological and geochemical characteristics between the Qiaoenbrak Formation and Aksu Group indicates that the Qiaoenbrak Formation may be penecontemporaneous with the Aksu Group in an active continental margin tectonic setting. Following the break-up of the Rodinia supercontinent, the margin of the Aksu evolved into a passive margin and the Yuermeinak and Sugetbrak Formations were deposited. [ABSTRACT FROM AUTHOR]

This paper proposes advanced methods based on multiple-point geostatistical simulations (MPSs) for data fusion in digital elevation models (DEMs). MPS-based methods can capture similar patterns using the spatial correlation residing in the training images and then reproduce the area of interest in terms of spatial patterning through conditional simulation. For this, the FILTERSIM algorithm, which is based on MPS, can be applied to augment the prediction results from traditional geostatistical approaches to derive fused datasets from complementary sources. However, the FILTERSIM algorithm discerns similar patterns based on a filter-based prototyping process and does not account for nonstationarity of spatial structures. Thus, an enhanced data fusion method based on a modified FILTERSIM algorithm was proposed, in which new strategies for forming prototypes were incorporated. An experiment was carried out in a study area in Northwest China to test the proposed methods. The aim is to produce fused DEM datasets at fine spatial resolution by integrating fine-resolution but sparsely sampled SRTM data and regularly gridded coarse-resolution GMTED2010 data, and then to test three data fusion methods: 1) the traditional geostatistical interpolation, which provides baseline results for comparison; 2) the conventional FILTERSIM algorithm; and 3) a modified FILTERSIM algorithm. For both the first and second methods, three different kriging approaches were used; for the third method, three new schemes for building prototypes were tested for an improvement in performance. The results show that the improved FILTERSIM method can achieve greater accuracy and retain more spatial structure than the other two methods. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM AUTHOR]

In the evaluation of image fusion methods, spatially degraded multispectral (MS) and panchromatic (PAN) images are frequently employed as test data sets. The degradation is implemented using either averaging or a combination of low-pass filtering and decimation. However, the decimation operation causes the degraded MS and PAN images to be slightly misaligned with each other and with the original MS image, which acts as the reference image in the fusion evaluation. In this study, two image fusion methods based on decimated and undecimated multiresolution analysis techniques were evaluated on three popular types of test data sets consisting of spatially degraded IKONOS MS and PAN images. In the experiment, image misalignments caused by decimation significantly influenced the quality of fused images and resulted in untrustworthy performances of the image fusion methods being evaluated. It was demonstrated that unlike aligned MS and PAN images in actual image fusion, misaligned MS and PAN images in test data sets are inappropriate for image fusion evaluation. [ABSTRACT FROM AUTHOR]

In image fusion of different spatial resolution multispectral (MS) and panchromatic (PAN) images, a spectrally mixed MS pixel superimposes multiple mixed PAN pixels and multiple pure PAN pixels. This verifies that with increased spatial resolution in imaging, a low spatial resolution spectrally mixed subpixel may be unmixed to be a pure pixel. However, spectral unmixing of mixed MS subpixels is rarely considered in current remote-sensing image fusion methods, resulting in blurred fused images. In the image fusion method proposed in this article, such spectral unmixing is realized. In this method, the MS and PAN images are jointly segmented into image objects, image objects are classified to obtain a classification map of the PAN image and each MS subpixel is fused to be a pixel matching the class of the corresponding PAN pixel. Tested on spatially degraded IKONOS MS and PAN images with a significant spatial resolution ratio of 8:1, the fusion method offered fused images with high spectral quality and deblurred visualization. [ABSTRACT FROM PUBLISHER]

In the phenology of pixels, as the end-member proportions of a pixel vary with the progression of seasons, the pixel changes through different versions. In the image fusion of a low spatial resolution multiresolution (MS) pixel, multiple high spatial resolution fused pixels are generated. The original MS pixel and each fused pixel superimpose over different panchromatic (PAN) pixels and have different end-member proportions. Since analogies exist between pixel phenology and image fusion, the spectral change directions of pixels in phenology can be used as a reference to obtain optimal spectral change directions for MS sub-pixels in image fusion. Regarding pixel phenology, it was found that the optimal spectral change direction for an MS sub-pixel in image fusion is along the sub-pixel vector minus a haze vector. Based on this direction and a multivariate regression between the MS and PAN images, we propose a new method for image fusion. In an evaluation using spatially degraded IKONOS MS and PAN images, this method outperforms some selected current image fusion methods. [ABSTRACT FROM AUTHOR]

In an image fusion process, the spatial detail in a panchromatic pixel is injected into the corresponding n-band multispectral (MS) subpixel to yield a synthetic pixel. The synthetic pixel can be regarded as the sum of three terms: the MS subpixel, a shift term, and a product of the spatial detail and an n-dimensional (n-D) spectral change vector. In this paper, the spectral change vector directions in some current image fusion methods are characterized and classified. As the spectral development of subpixels with the improvements in spatial resolution in imaging is analogous to the spectral change of MS subpixels with the improvements in spatial resolution in image fusion, the former is used as a reference to examine the spectral change vector directions in the current image fusion methods. Moreover, image haze and unmixing of mixed MS subpixels are highlighted as two aspects needing attention for further improvement in fusion quality. [ABSTRACT FROM AUTHOR]

Misalignment between multispectral (MS) and panchromatic (PAN) images blurs fused images and reduces the spectral quality of the latter. When a PAN pixel is misaligned with the corresponding MS pixel that is previously upsampled to match the pixel size of the PAN image, it is approximately aligned with a neighbouring MS pixel. Although the two MS pixels have different pixel vector magnitudes, their pixel vector directions are typically close to each other. Therefore, a fused MS pixel aligned with the PAN pixel can be generated by setting its pixel vector direction to be the same as the MS pixel misaligned with the PAN pixel and calculating its pixel vector magnitude regarding the PAN pixel. Using this idea, we propose an image fusion method to effectively eliminate the impact of the misalignment between PAN and MS images on fused images. An experiment on misaligned IKONOS PAN and MS images shows that this method yields fused images with sharp visualization and higher spectral quality than several current fusion approaches. [ABSTRACT FROM AUTHOR]

A thermal infrared (TIR) image is a measure of the Earth's surface temperature and TIR emittance; however, its low spatial resolution severely limits its potential applications. Image fusion techniques can be used to fuse a TIR image with higher spatial resolution reflective bands to generate a synthetic TIR image. Because of the weak correlation between TIR and reflective data, such a synthetic image typically contains significant spectral distortion. In this paper, a multivariate analysis technique is used to derive a variable as a linear function of multiple reflective bands and their non-linearly transformed versions, to produce the maximum correlation with the TIR image. The spatial details of the variable are then injected into the TIR image to yield a synthetic image with reduced spectral distortion. In an experiment on Landsat Thematic Mapper (TM) TIR and reflective data, the fusion method proposed in this paper outperforms several existing methods in preserving the spectral characteristics of TIR data. [ABSTRACT FROM AUTHOR]

Fusion of panchromatic (PAN) and multispectral (MS) images is one of the most promising issues in remote sensing. PAN modulation fusion methods are usually based on an assumption that a ratio of two different-resolution versions of an MS band is equal to a ratio of two different-resolution versions of a PAN image. In such fusion methods, image haze is rarely taken into account, and it may produce serious spectral distortion in synthetic images. In this paper, assuming that the previous ratio relationship only holds for haze-free images, two relevant improvement schemes are proposed to better express the ratio relationship of haze-included images. In a test on a spatially degraded IKONOS dataset, the first scheme synthesizes an image with minimum spectral distortion, and the second modifies several current PAN modulation fusion methods and generates high-quality synthetic products. The experiment results confirm that image haze can seriously impact the quality of fused images obtained by using PAN modulation fusion methods, and it should be taken into account in relevant image fusion. [ABSTRACT FROM AUTHOR]

Deep learning (DL) has shown promising performances in various remote sensing applications as a powerful tool. To explore the great potential of DL in improving the accuracy of individual tree species (ITS) classification, four convolutional neural network models (ResNet-18, ResNet-34, ResNet-50, and DenseNet-40) were employed to classify four tree species using the combined high-resolution satellite imagery and airborne LiDAR data. A total of 1503 samples of four tree species, including maple, pine, locust, and spruce, were used in the experiments. When both WorldView-2 and airborne LiDAR data were used, the overall accuracies (OA) obtained by ResNet-18, ResNet-34, ResNet-50, and DenseNet-40 were 90.9%, 89.1%, 89.1%, and 86.9%, respectively. The OA of ResNet-18 was increased by 4.0% and 1.8% compared with random forest (86.7%) and support vector machine (89.1%), respectively. The experimental results demonstrated that the size of input images impacted on the classification accuracy of ResNet-18. It is suggested that the input size of ResNet models can be determined according to the maximum size of all tree crown sample images. The use of LiDAR intensity image was helpful in improving the accuracies of ITS classification and atmospheric correction is unnecessary when both pansharpened WorldView-2 images and airborne LiDAR data were used. [ABSTRACT FROM AUTHOR]