Freeze–thaw erosion can lead to accelerated soil loss, which is an important factor related to soil erosion in cold regions. Tibet is a typical region that is seriously affected by freeze–thaw erosion. Traditionally, the analytic hierarchy process (AHP) method is used to calculate the weight of the factors in evaluations of freeze–thaw erosion, but this method cannot accurately depict the fuzziness and randomness of the problem. To overcome this disadvantage, this study proposed an improved AHP method based on the cloud model for the evaluation of the factors impacting freeze–thaw erosion. To establish an improved evaluation method for freeze–thaw erosion in Tibet, the following six factors were selected: mean annual air temperature, mean annual ground surface temperature, average annual precipitation, aspect, vegetation coverage, and topographic relief. The traditional AHP and the cloud model were combined to assign the weights of the impacting factors, and a consistency check was performed. The comprehensive evaluation index model was used to evaluate the intensity of freeze–thaw erosion in Tibet. The results show that freeze–thaw erosion is extensive, stretching over approximately 66.1% of Tibet. Moreover, mild erosion and moderate erosion are the most widely distributed erosion intensity levels, accounting for 36.4% and 34.4% of the total freeze–thaw erosion, respectively. The intensity of freeze–thaw erosion gradually increased from slight erosion in the northwest to severe erosion in the southeast of the study region. The evaluation results for the intensity and distribution of freeze–thaw erosion in Tibet were confirmed to be consistent with the actual situation. In brief, this study supplies a new approach for quantitatively evaluating the intensity of freeze–thaw erosion in Tibet.
Analysis of urban spatial structures is an effective way to explain and solve increasingly serious urban problems. However, many of the existing methods are limited because of data quality and availability, and usually yield inaccurate results due to the unclear description of urban social functions. In this paper, we present an investigation on urban social function based spatial structure analysis using building footprint data. An improved turning function (TF) algorithm and a self-organizing clustering method are presented to generate the variable area units (VAUs) of high-homogeneity from building footprints as the basic research units. Based on the generated VAUs, five spatial metrics are then developed for measuring the morphological characteristics and the spatial distribution patterns of buildings in an urban block. Within these spatial metrics, three models are formulated for calculating the social function likelihoods of each urban block to describe mixed social functions in an urban block, quantitatively. Consequently, the urban structures can be clearly observed by an analysis of the spatial distribution patterns, the development trends, and the hierarchy of different social functions. The results of a case study conducted for Munich validate the effectiveness of the proposed method.
The ecological environment quality is an important constraint and an optimization objective for land resource allocation. Integrating ecological service value (ESV) accounting and ecological security pattern (ESP) delineation, and combining with the land use structure of 2004/2010/2016 in Ezhou City, this research laid out the urban ESP based on ESV with Net Primary Productivity (NPP), and made it as the main influence factor to simulate land use structure in 2022. The results indicated that: 1) The water body has the biggest contribution to ESV, while the construction land has the minimum; 2) 91 ecological corridors are extracted, of which 28 were important ecological corridors; there were 36 ecological nodes extracted, including 17 important nodes; 3) According to ESV, Ezhou City was divided into four security zones. The area of ecological restoration zone was the largest, and human activity core zone area was the smallest; 4) In the no ESP protection scenarios and ESP protection scenarios separately, the net increase area of construction land is from 868.5 hm2 to 52.74 hm2 in the ecological core protection area; the construction land in the human activity core area has been increased by 2342.31 hm2 in protected scene, 766.23 hm2 more than that of the unprotected scene. The results show that the division of security zones promoted the relocation of construction land from ecological protection core zone to human activity core zone, which can protect the ecological environment effectively, and the ESP-based simulation can provide the decision-making reference to coordinate the relationship of regional land resource allocation and the ecological environment protection.
Three-dimensional green volume (TDGV) reflects the quality and quantity of urban green space and its provision of ecosystem services; therefore, its spatial pattern and the underlying influential factors play important roles in urban planning and management. However, little is known about the factors contributing to the spatial pattern of TDGV. In this paper, TDGV and land use intensity (LUI) extracted from high spatial resolution (0.05 m) remotely sensed data acquired by an unmanned aerial vehicle (UAV), anthropogenic factors and natural factors were utilized to identify the spatial pattern of TDGV and the potential influencing factors in Lingang New City, a rapidly developed coastal town in Shanghai. The results showed that most of the TDGV was distributed in the western part of this new city and that its spatial variations were significantly axial. TDGV corresponded well with the chronologies of land formation, urban planning, and construction in the city. Generalized least squares (GLS) analysis of TDGV (grid cell size: 100 × 100 m) and its influencing factors showed that the TDGV in this new city was significantly negatively correlated with both LUI and distance from roads and significantly positively correlated with land formation time and distance from water. Distance from buildings did not affect TDGV. Additionally, the degree of influence decreased in the following order: distance from water>land formation time>distance from roads>LUI. These results indicate that the spatial pattern of TDGV in this new town was mainly affected by natural factors (i.e., the distance from water and land formation time) and that the artificial disturbances caused by rapid urbanization did not decrease the regional TDGV. The main factors shaping the spatial distribution of TDGV in this city were local natural factors. Our findings suggest that the improvement in local soil and water conditions should be emphasized in the construction of new cities in coastal areas to ensure the efficient provision of ecological services by urban green spaces.
Understanding the coupling relationship between ecosystem services and poverty livelihoods is a prerequisite for poverty eradication and ecological protection in China. In this paper, we intended to quantify the coupling relationship between ecosystem services and poverty livelihoods in 717 poverty-stricken counties in China. First, we identified distinct levels of ecosystem services and poverty respectively in the poverty-stricken areas based on the ecosystem services subsystem index and poverty livelihoods subsystem index. Then, we calculated the coupling degree and coupling coordination degree between the ecosystem services and the poverty livelihoods in China’s poverty-stricken counties from 2000 to 2015. Finally, we analyzed the temporal and spatial evolution of the coupling between ecosystem services and poverty livelihoods. The results indicate that the ecosystem services showed a trend of first decreasing and then increasing, while the poverty level showed an increased trend. In addition, the coupling relationship between ecosystem services and poverty livelihoods in poverty-stricken counties was also improved from 2000 to 2015. However, the coupling degree value or coupling coordination degree values in some counties or contiguous impoverished areas showed a decreased trend, the coupling relationship between ecosystem services and poverty livelihoods in those counties or areas was higher conflict. Future more, the coupling relationship in China’s poverty-stricken counties was mainly intermediate conflict and low conflict, which need to improve in the future. Thus, it is suggested that effective measures are required to lift ecosystem services out of the poverty trap and into sustainable livelihoods.
Human activity transforms a land surface into a complex surface where artificial and natural landforms coexist and continuous and emergent landforms merge. In this background, the problems of conventional digital elevation models (DEMs), such as morphological distortion, complicated updates, and lack of information, are increasingly prominent. This study proposes a new idea of DEM construction based on the concept of geographic ontology. First, landforms with common features are abstracted into a certain type of topographic entity based on their morphologies and semantics. For each type of topographic entity, a DEM was constructed independently based on the available elevation information and other information about the semantics and spatial relationships. Second, individual DEMs were merged into a complete DEM following certain rules. A 1 km2 area located in the suburb of Nanjing, Jiangsu Province, China, was selected as the experimental area. The effectiveness of the model construction method proposed in this study was verified. The results show that the DEM constructed according to the idea of this study has a significantly better performance than the conventional DEMs. The constructed DEM in this study can well represent ground objects, such as slopes, farmland, and ditches. In particular, the constructed DEM ensures the morphological accuracy of the ground objects.
This study of vegetation dynamics in the coastal region of Tanzania provides a fundamental basis to better understand the nature of the factors that underlie observed changes. The Tanzanian coast, rich in biodiversity, is economically and environmentally important although the understanding of the nature and causes of vegetation change is very limited. This paper presents an investigation of the relationship between vegetation dynamics in response to climate variations and human activities using Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Index (NDVI), meteorological, and Globeland30 Landsat data sets. Spatio-temporal trends and the relationship of NDVI to selected meteorological variables were statistically analyzed for the period 2000–2018 using the Mann-Kendall test and Pearson correlation respectively. The results reveal a significant positive trend in temperature (β>0, Z = 2.87) and a non-significant trend in precipitation (|Z|<1.96). A positive relationship between NDVI and precipitation is observed. Coastal Tanzania has therefore experienced increased temperatures and variable moisture conditions which threaten natural vegetation and ecosystems at large. Classified land cover maps obtained from GlobeLand30 were analyzed to identify the nature and scale of human impact on the land. The analysis of land use and land cover in the region reveals an increase in cultivated land, shrubland, grassland, built-up land and bare land, while forests, wetland and water all decreased between 2000 and 2020. The decrease in forest vegetation is attributable to the fact that most livelihoods in the region are dependent on agriculture and harvesting of forest products (firewood, timber, charcoal). The findings of this study highlight the need for appropriate land-use planning and sustainable utilization of forest resources.
The Yellow River Estuary (YRE) alternatively experienced channel aggradation and degradation during the period 1990–2016. To study the variation in flood discharge capacity during the process of river bed evolution, bankfull characteristic parameters were investigated on the basis of measured hydrological data and surveyed cross-sectional profiles, which was crucial for comprehending the processes and the key factors to cause these rapid changes. A reach-averaged method was presented in this study in order to calculate the characteristic bankfull parameters in the YRE, and this method integrated the geometric mean using the logarithmic transformation with a weighted mean based on the distance between the two successive sections. The reach-averaged bankfull parameters in the tail reach of the Yellow River Estuary (the Lijin-Xihekou reach) during the period 1990–2016 were then calculated. Calculated results indicated that the adoption of a concept of reach-averaged bankfull discharge was much more representative than the cross-sectional bankfull discharge, and the results also indicated that bankfull discharges decreased during the process of channel aggradation, and increased during the process of channel degradation. Finally, an empirical formula and a delayed response function were established between the reach-averaged bankfull discharge and the previous 4-year average fluvial erosion intensity during flood seasons, and both of them were adopted to reproduce the reach-averaged bankfull discharges, and calculated results showed high correlations (R2>0.8) of these two methods.
Nowadays, autonomous driving has been attracted widespread attention from academia and industry. As we all know, deep learning is effective and essential for the development of AI components of Autonomous Vehicles (AVs). However, it is challenging to adopt multi-source heterogenous data in deep learning. Therefore, we propose a novel data-driven approach for the delivery of high-quality Spatio-Temporal Trajectory Data (STTD) to AVs, which can be deployed to assist the development of AI components with deep learning. The novelty of our work is that the meta-model of STTD is constructed based on the domain knowledge of autonomous driving. Our approach, including collection, preprocessing, storage and modeling of STTD as well as the training of AI components, helps to process and utilize huge amount of STTD efficiently. To further demonstrate the usability of our approach, a case study of vehicle behavior prediction using Long Short-Term Memory (LSTM) networks is discussed. Experimental results show that our approach facilitates the training process of AI components with the STTD.
Along with conventional methods, this paper proposed a method in which 1D and 3D models are integrated to identify the self-sourced reservoir potential of the Farewell Formation in the Kupe Gas Field within the Taranaki Basin, New Zealand. Source rock characteristics were evaluated at both field and basin scales by investigating source rock maturity, type of organic matter, and hydrocarbon generation potential by rock pyrolysis, using Rock-Eval 2 and 6. The 1D thermal and burial history model established the rate of sedimentation and thermal history of the Kupe 4 well. Reservoir characteri-zation at field-scale was determined by seismic interpretation, well log analysis, and 3D structural and petrophysical models. The sediments of the Farewell Formation contain types II-III (oil/gas prone) and type III (gas prone) and have fair-to-excellent generation potential. The oxygen and hydrogen indices ranged from 3 to 476 mg CO2/g TOC and 26 to 356 mg HC/g TOC, respectively, whereas the thermal maturity determined by vitrinite reflectance values ranged between 0.3% and 0.72%, indicating that the Farewell Formation is in immature-to-mature hydrocarbon generation stage. Thus, Farewell Formation was verified to be a good source rock. Additionally, structural interpretations demonstrated the structural complexity of an extensional and contractional regime within the reservoir package. Multiple faults indicated a good reservoir property there with a trapping mechanism as well as migration paths. A well-log-based petrophysical analysis established the presence of up to 70% hydrocarbon saturation within the pore spaces of Farewell sandstones. The 3D models confirmed that the Farewell Formation has significant sand zones and hydrocarbon-saturated zones, thereby proving its very good reservoir characteristics. It has been proved that the 1D and 3D structural schemes, integrated with geological techniques, was vital in identifying the Kupe Field as a self-sourced reservoir.
Surface and deep subsurface geological structural trends, stratigraphic features, and reservoir charac-teristics play important roles in assessment of hydrocarbon potential. Here, an approach that integrates digital elevation modelling, seismic interpretation, seismic attributes, three-dimensional (3D) geological structural modeling predicated on seismic data interpretation, and petrophysical analysis is presented to visualize and analyze reservoir structural trends and determine residual hydrocarbon potential. The digital elevation model is utilized to provide verifiable predictions of the Dhulian surface structure. Seismic interpretation of synthetic seismograms use two-way time and depth contour models to perform a representative 3D reservoir geological structure evaluation. Based on Petrel structural modeling efficiency, reservoir development indexes, such as the true 3D structural trends, slope, geometry type, depth, and possibility of hydrocarbon prospects, were calculated for the Eocene limestone Chorgali, upper Paleocene limestone Lockhart, early Permian arkosic sandstone Warcha, and Precambrian Salt Range formations. Trace envelope, instantaneous frequency, and average energy attribute analyses were utilized to resolve the spatial predictions of the subsurface structure, formation extrusion, and reflector continuity. We evaluated the average porosity, permeability, net to gross ratio, water saturation, and hydrocarbon saturation of early Eocene limestone and upper Paleocene limestone based on the qualitative interpretation of well log data. In summary, this integrated study validates 3D stratigraphic structural trends and fault networks, facilitates the residual hydrocarbon potential estimates, and reveals that the Dhulian area has a NE to SW (fold axis) thrust-bounded salt cored anticline structure, which substantiates the presence of tectonic compression. The thrust faults have fold axes trending from ENE to WSW, and the petrophysical analysis shows that the mapped reservoir is of good quality and has essential hydrocarbon potential, which can be exploited economically.
Strike and dip are essential to the description of geological features and therefore play important roles in 3D geological modeling. Unevenly and sparsely measured orientations from geological field mapping pose problems for the geological modeling, especially for covered and deep areas. This study developed a new method for estimating strike and dip based on structural expansion orientation, which can be automatically extracted from both geological and geophysical maps or profiles. Specifically, strike and dip can be estimated by minimizing an objective function composed of the included angle between the strike and dip and the leave-one-out cross-validation strike and dip. We used angle parameterization to reduce dimensionality and proposed a quasi-gradient descent (QGD) method to rapidly obtain a near-optimal solution, improving the time-efficiency and accuracy of objective function optimization with the particle swarm method. A synthetic basin fold model was subsequently used to test the proposed method, and the results showed that the strike and dip estimates were close to the true values. Finally, the proposed method was applied to a real fold structure largely covered by Cainozoic sediments in Australia. The strikes and dips estimated by the proposed method conformed to the actual geological structures more than those of the vector interpolation method did. As expected, the results of 3D geological implicit interface modeling and the strike and dip vector field were much improved by the addition of estimated strikes and dips.
To visualize and analyze the impact of uncertainty on the geological subsurface, on the term of the geological attribute probabilities (GAP), a vector parameters-based method is presented. Perturbing local data with error distribution, a GAP isosurface suite is first obtained by the Monte Carlo simulation. Several vector parameters including normal vector, curvatures and their entropy are used to measure uncertainties of the isosurface suite. The vector parameters except curvature and curvature entropy are visualized as line features by distributing them over their respective equivalent structure surfaces or concentrating on the initial surface. The curvature and curvature entropy presented with color map to reveal the geometrical variation on the perturbed zone. The multiple-dimensional scaling (MDS) method is used to map GAP isosurfaces to a set of points in low-dimensional space to obtain the total diversity among these equivalent probability surfaces. An example of a bedrock surface structure in a metro station shows that the presented method is applicable to quantitative description and visualization of uncertainties in geological subsurface. MDS plots shows differences of total diversity caused by different error distribution parameters or different distribution types.
Investigation into natural fractures is extremely important for the exploration and development of low-permeability reservoirs. Previous studies have proven that abundant oil resources are present in the Upper Triassic Yanchang Formation Chang 7 oil-bearing layer of the Ordos Basin, which are accumulated in typical low-permeability shale reservoirs. Natural fractures are important storage spaces and flow pathways for shale oil. In this study, characteristics of natural fractures in the Chang 7 oil-bearing layer are first analyzed. The results indicate that most fractures are shear fractures in the Heshui region, which are characterized by high-angle, unfilled, and ENE-WSW-trending strike. Subsequently, natural fracture distributions in the Yanchang Formation Chang 7 oil-bearing layer of the study area are predicted based on the R/S analysis approach. Logs of AC, CAL, ILD, LL8, and DEN are selected and used for fracture prediction in this study, and the R(n)/S(n) curves of each log are calculated. The quadratic derivatives are calculated to identify the concave points in the R(n)/S(n) curve, indicating the location where natural fracture develops. Considering the difference in sensitivity of each log to natural fracture, gray prediction analysis is used to construct a new parameter, fracture prediction indicator K, to quantitatively predict fracture development. In addition, fracture development among different wells is compared. The results show that parameter K responds well to fracture development. Some minor errors may probably be caused by the heterogeneity of the reservoir, limitation of core range and fracture size, dip angle, filling minerals, etc.
