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Land cover change affects the carbon emissions of ecosystems in some way. The qualitative and quantitative understanding of carbon emissions from human activities (e.g., land cover change, industrial production, etc.) is highly significant for realizing the objective of carbon neutrality. Therefore, this paper used GlobeLand30 land cover maps, annual average normalised difference vegetation index (NDVI) data, annual average net ecosystem productivity (NEP) data and statistical yearbook data from 2000 to 2020 to explore the relationship between land cover change and carbon emissions. Specifically, it included land cover change, carbon storage changes influenced by land cover change, spatial and temporal analysis of carbon sources and sinks, land use intensity change and anthropogenic carbon emissions. The results of the study show that the main land cover changes in Shandong province during 2000–2020 was cultivated land conversion to artificial surfaces. Among them, the area of cultivated land converted to artificial surfaces from 2000 to 2010 was 4930.62 km2, and the proportion of cultivated land converted to artificial surfaces from 2010 to 2020 was as high as 78.35%. The total carbon stock of vegetation affected by land cover change decreased by 463.96 × 104 t and 193.50 × 104 t in 2000–2010 and 2010–2020 respectively. The spatial and temporal distribution of carbon sources and sinks differed more markedly from 2000 to 2020, and land use intensity changes in Shandong Province showed an upward trend. Of the total energy production, industry has the largest energy consumption, followed closely by total energy consumption in transportation, storage and postal services. [ABSTRACT FROM AUTHOR]

Benchmark datasets play an important role in evaluating remote sensing image retrieval (RSIR) methods. The current datasets cover many scene categories, but omit an important scene of color steel sheds, which are widely distributed with a large number on the earth's surface. Therefore, we propose a new benchmark dataset of color steel sheds (CSS) from Google map imagery for RSIR and share it open access in our V-RSIR system. The new dataset has rich intra-class and inter-class diversity, and is composed of blue, red and white color steel sheds with the total number of 2407 remote sensing images. We conduct evaluation experiments on the new dataset by using ten low/mid feature-based and ten deep learning feature-based methods. Experimental results indicate that the dataset is effective for evaluating RSIR methods and using the dataset can construct an effective retrieval model for color steel sheds. Besides, we have experimentally demonstrated that color constancy does affect retrieval performance on our CSS dataset. Furthermore, some experiments of merging the CSS dataset with the PatternNet, VGoogle and NWPU45 datasets are also conducted. Experimental results demonstrate that our dataset can be used as a complement to other retrieval datasets. Furthermore, these experimental results can be used as baseline for future applications on RSIR. [ABSTRACT FROM AUTHOR]

Land cover classification is essential for environmental monitoring, sustainable development goals assessment and other fields. Traditionally, it often takes much time and labor costs to copy and pre-process remote sensing images for performing land cover classification with desktop software, which must be installed and licensed. Google Earth Engine (GEE), as a cloud computing platform, provides a large number of remote sensing images, geoprocessing algorithms and massive computational capabilities via web interfaces. However, GEE requires users to possess programming skills of JavaScript or Python language, which can hinder the enthusiasm of many academics without programming skills. This paper developed a web-based and open-access toolkit O-LCMapping for supporting online land cover classification. This toolkit was implemented with JavaScript application programming interfaces of GEE and integrated ten imagery classification algorithms. The toolkit explicitly provides the entire pipeline of land cover classification in the form of user interfaces for end-users with basic knowledge of remote sensing but little programming skills. Concretely, it enables end-users to define study area, select remote sensing data, mark classification samples, set classification algorithms, evaluate accuracy and output results through user interfaces. Three different experimental cases indicate that the toolkit can be easily applied to different fields for various applications of land cover classification. [ABSTRACT FROM AUTHOR]

Generally, traditional convolutional neural networks (CNN) models require a long training time and output high-dimensional features for content-based remote sensing image retrieval (CBRSIR). This paper aims to examine the retrieval performance of the MobileNets model and fine-tune it by changing the dimensions of the final fully connected layer to learn low dimensional representations for CBRSIR. Experimental results show that the MobileNets model achieves the best retrieval performance in term of retrieval accuracy and training speed, and the improvement of mean average precision is between 11.2% and 44.39% compared with the next best model ResNet152. Besides, 32-dimensional features of the fine-tuning MobileNet reach better retrieval performance than the original MobileNets and the principal component analysis method, and the maximum improvement of mean average precision is 11.56% and 9.8%, respectively. Overall, the MobileNets and the proposed fine-tuning models are simple, but they can indeed greatly improve retrieval performance compared with the commonly used CNN models. [ABSTRACT FROM AUTHOR]

The surface-to-borehole transient electromagnetic (TEM) method, which uses a rectangular loop as its source, has shown great potential in mineral exploration at greater depth. However, recent studies have demonstrated that a grounded-wire source can resolve the resistive targets and provide detection at greater depths than the loop source. Thus, we propose a surface-to-borehole TEM method using a grounded wire as a transmitter at short offsets. We conducted numerical experiments to explain the variation of different parameters and its comparable effects on the TEM transients. Similarly, a systematic comparison of the cross-sectional electric field distribution maps, borehole profiles of the time derivatives of the magnetic field, qualitatively reveal that the proposed method induces detectable responses for conductive and resistive targets at greater depths. In the present study, unlike the loop source configuration, the time derivative of an underground magnetic field in horizontal direction showed high sensitivity to resistive bodies. Furthermore, 1-D inversion was performed to provide quantitative interpretation of the borehole data. The 1-D inversion of the surface and borehole data was realized using an Occam-type inversion scheme. The results of both synthetic and field data indicate that inverting the surface or borehole data separately resulted in degraded resolution in both shallow and deep earth. Thus, we also compared the results from the inversions of surface and borehole data in order to explore the merits of joint inversion in resolving shallow and deep structures. [ABSTRACT FROM AUTHOR]

In northern China, thick loess strata with low resistivity are widely present and can shield the deep induced electromagnetic field signal and influence the accuracy of deep targets while electromagnetic prospecting is being performed. To achieve deep target exploration with high resolution, a new technique known as the short offset transient electromagnetic method (SOTEM) is applied in this research area. This method can be used to perform near-source configuration surveys, but shows no obvious difference between the deep magnetite deposit and low-resistivity layer in this survey area. Therefore, it cannot explore the magnetite deposit directly using the traditional magnetic component (Hz) of SOTEM. The Ex component, which can more effectively distinguish the high-resistivity surrounding rock and overlying low-resistivity layer than the Hz component, is used for exploration. First, 2D forward modeling and sensitivity analysis of the high-resistivity body are carried out to determine the different responses of the Ex and Hz components, respectively. Moreover, to better determine the interface between the surrounding rock and low-resistivity layer, 1D Occam inversion and the generalized inverse matrix inversion method are used. Furthermore, aiming at the static effect of the Ex component in field exploration, joint inversion of the Ex and Hz components is carried out for comprehensive interpretation. Finally, a field test is conducted, and the drilling results are used for verification. The research results can provide an effective geophysical model for the exploration of contact metasomatic magnetite deposits. [ABSTRACT FROM AUTHOR]

Spatial sampling design is one of the key steps in land cover accuracy assessment, and many traditional sampling approaches may not achieve credible spatial sampling due to the high spatial heterogeneity of land cover. This paper characterizes the spatial heterogeneity with three-level LSIs and determines the subsequent sample sizes and their spatial distributions. The three-level LSIs are rLSI in a region, cLSI for each land cover class and uLSI in each geographic sampling unit in the region. The rLSIs are used to derive appropriate sample sizes in the target regions. The cLSIs are used to assure that larger sample numbers are allocated to land cover classes with higher spatial heterogeneity. The uLSIs provide useful measures for selecting optimal geographic units in which sample sites will be located. This LSI-based sampling approach can derive the sample sizes and determine their distributions in an adaptive way according to the spatial heterogeneity. An experimental case study further demonstrates that the LSI-based sampling approach obtains more appropriate sample sizes for each region, sufficient sample numbers for rare classes, and optimal sample distributions in the geographical space. [ABSTRACT FROM AUTHOR]