The impacts of land-cover composition on urban temperatures, including temperature extremes, are well documented. Much less attention has been devoted to the consequences of land-cover configuration, most of which addresses land surface temperatures. This study explores the role of both composition and configuration—or land system architecture—of residential neighborhoods in the Phoenix metropolitan area, on near-surface air temperature. It addresses two-dimensional, spatial attributes of buildings, impervious surfaces, bare soil/rock, vegetation and the “urbanscape” at large, from 50 m to 550 m at 100 m increments, for a representative 30-day high sun period. Linear mixed-effects models evaluate the significance of land system architecture metrics at different spatial aggregation levels. The results indicate that, controlling for land-cover composition and geographical variables, land-cover configuration, specifically the fractal dimension of buildings, is significantly associated with near-surface temperatures. In addition, statistically significant predictors related to composition and configuration appear to depend on the adopted level of spatial aggregation.
Since 2000, China’s urban land has expanded at a dramatic speed because of the country’s rapid urbanization. The country has been experiencing unbalanced development between rural and urban areas, causing serious challenges such as agricultural security and land resources waste. Effectively evaluating the driving factors of urban land growth is essential for improving efficient land use management and sustainable urban development. This study established a principal component regression model based on eight indicators to identify their influences on urban land growth in Guangzhou. The results provided a grouping analysis of the driving factors, and found that economic growth, urban population, and transportation development are the driving forces of urban land growth of Guangzhou, while the tertiary industry has an opposite effect. The findings led to further suggestions and recommendations for urban sustainable development. Hence, local governments should design relevant policies for achieving the rational development of urban land use and strategic planning on urban sustainable development.
Many megacities (such as Shanghai) are located in coastal areas, therefore, coastline monitoring is critical for urban security and urban development sustainability. A shoreline is defined as the intersection between coastal land and a water surface and features seawater edge movements as tides rise and fall. Remote sensing techniques have increasingly been used for coastline extraction; however, traditional hard classification methods are performed only at the pixel-level and extracting sub-pixel accuracy using soft classification methods is both challenging and time consuming due to the complex features in coastal regions. This paper presents an automatic sub-pixel coastline extraction method (ASPCE) from high-spectral satellite imaging that performs coastline extraction based on spectral mixture analysis and, thus, achieves higher accuracy. The ASPCE method consists of three main components: 1) A Water-Vegetation-Impervious-Soil (W-V-I-S) model is first presented to detect mixed W-V-I-S pixels and determine the endmember spectra in coastal regions; 2) The linear spectral mixture unmixing technique based on Fully Constrained Least Squares (FCLS) is applied to the mixed W-V-I-S pixels to estimate seawater abundance; and 3) The spatial attraction model is used to extract the coastline. We tested this new method using EO-1 images from three coastal regions in China: the South China Sea, the East China Sea, and the Bohai Sea. The results showed that the method is accurate and robust. Root mean square error (RMSE) was utilized to evaluate the accuracy by calculating the distance differences between the extracted coastline and the digitized coastline. The classifier’s performance was compared with that of the Multiple Endmember Spectral Mixture Analysis (MESMA), Mixture Tuned Matched Filtering (MTMF), Sequential Maximum Angle Convex Cone (SMACC), Constrained Energy Minimization (CEM), and one classical Normalized Difference Water Index (NDWI). The results from the three test sites indicated that the proposed ASPCE method extracted coastlines more efficiently than did the compared methods, and its coastline extraction accuracy corresponded closely to the digitized coastline, with 0.39 pixels, 0.40 pixels, and 0.35 pixels in the three test regions, showing that the ASPCE method achieves an accuracy below 12.0 m (0.40 pixels). Moreover, in the quantitative accuracy assessment for the three test sites, the ASPCE method shows the best performance in coastline extraction, achieving a 0.35 pixel-level at the Bohai Sea, China test site. Therefore, the proposed ASPCE method can extract coastline more accurately than can the hard classification methods or other spectral unmixing methods.
Rapid urban sprawl and re-construction of old towns have been leading to great changes of land use in cities of China. To witness short-term urban land use changes, rapid or real time remote sensing images and effective detection methods are required. With the availability of short repeat cycle, relatively high spatial resolution, and weather-independent Synthetic Aperture Radar (SAR) remotely sensed data, detection of short-term urban land use changes becomes possible. This paper adopts newly released Sentinel-1 SAR data for urban change detection in Tianhe District of Guangzhou City in Southern China, where dramatic urban redevelopment practices have been taking place in past years. An integrative method that combines the SAR time series data and a spectral angle mapping (SAM) was developed and applied to detect the short-term land use changes. Linear trend transformations of the SAR time series data were first conducted to reveal patterns of substantial changes. Spectral mixture analysis was then conducted to extract temporal endmembers to reflect the land development patterns based on the SAR backscattering intensities over time. Moreover, SAM was applied to extract the information of significant increase and decrease patterns. The results of validation and method comparison showed a significant capability of both the proposed method and the SAR time series images for detecting the short-term urban land use changes. The method received an overall accuracy of 78%, being more accurate than that using a bi-temporal image change detection method. The results revealed land use conversions due to the removal of old buildings and their replacement by new construction. This implies that SAR time series data reflects the spatiotemporal evolution of urban constructed areas within a short time period and this study provided the potential for detecting changes that requires continuously short-term capability, and could be potential in other landscapes.
Under the influence of a climate of extreme drought and the Three Gorges Dam (TGD) operation, the water levels in the middle and lower reaches of the Yangtze River in 2006 and 2011 changed significantly compared with those in the extreme drought years of 1978 and 1986. To quantitatively analyze the characteristics of water level variations in 2006 and 2011, a new calculation method was proposed, and the daily water level and discharge from 1955–2016 were collected in this study. The findings are as follows: in 2006 and 2011, the water level in the dry season significantly increased, but that in the flood season obviously decreased compared with the levels in 1978 and 1986. Here, we described this phenomenon as “no low-water-level in dry season, no high-water-level in flood season”. Based on the calculation method, the contributions of climate variability and the Three Gorges Dam operation to water level variations in the middle and lower reaches of the Yangtze River were calculated, and the contributions indicated that climate variability was the main reason for the phenomenon of “no low-water-level in dry season, no high-water-level in flood season” instead of flood peak reduction in the flood season and drought runoff implementation in the dry season, which are both induced by TGD.
Investigation of the relationship between catchment hydrology with climate is essential for understanding of the impact of future climate on hydrological extremes, which may cause frequent flooding, drought, and shortage of water supply. The purpose of this study is to investigate the effects of climate change on extreme flows in one of the subcatchments of the Ilam dam catchment, Iran. The changes in climate parameters were predicted using the outputs of HadCM3 model for up to the end of the current century in three time periods including 2020s, 2050s, and 2080s. For A2 scenario, increases of 1.09°C, 2.03°C, and 3.62°C, and for B2 scenario rises of 1.18°C, 1.84°C, and 2.55°C have been predicted. The results suggest that for A2 scenario, the amount of precipitation would decrease by 12.63, 49.13, and 63.42 and for B2 scenario by 47.02, 48.51, and 70.26 mm per year. Also the values of PET for A2 scenario would increase by 51.18, 101.47 and 108.71 and for B2 scenario by 60.09, 89.86, and 124.32 mm per year. The results of running the SWAT model revealed that the average annual runoff would decrease by 0.11, 0.41, and 0.61 m3/s and for B2 scenario by 0.39, 0.47, and 0.59 m3/s. The extreme flows were then analyzed by running WETSPRO model. According to the results, the amounts of low flows for A2 scenario will decrease by 0.02, 0.21 and 0.33 m3/s and for B2 scenario by 0.19, 0.26 and 0.29 m3/s in the 2020s, 2050s and 2080s, respectively. On the other hand, the results show an increase of peak flows by 11.5, 19.1 and 48.7 m3/s in A2 scenario and 11.12, 25.93 and 48.1 m3/s in B2 scenario, respectively. Overall, the results indicated that an increase in return period leads to elevated levels of high flows and diminished low flows.
Based on analyses of the lithofacies palaeogeography of the Taiyuan and the Shanxi Formations in the Qinshui Basin, the spatial variations of the coal seam thickness, coal maceral composition, coal quality, and gas content, together with the lithofacies of the surrounding rocks in each palaeogeographic unit were investigated. The results show that the thick coals of the Taiyuan Formation are mainly distributed in delta and barrier island depositional units in the Yangquan area in the northern part of the basin and the Zhangzi area in the southeastern part of the basin. The thick coals of the Shanxi Formation are located within transitional areas between delta plain and delta front depositional units in the central southern part of the basin. The Taiyuan Formation generally includes mudstone in its lower part, thick, continuous coal seams and limestones in its middle part, and thin, discontinuous coal seams and limestone and sand-mud interbeds in its top part. The Shanxi Formation consists of thick, continuous sandstones in its lower part, thick coal seams in its middle part, and thin coal seams, sandstone, and thick mudstone in its upper part. From the perspective of coal-bearing sedimentology and coalbed methane (CBM) geology, the lithology and thickness of the surrounding rocks of coal seams play more significant roles in controlling gas content variation than other factors such as coal thickness, coal macerals, and coal quality. Furthermore, it is found that the key factors influencing the gas content variation are the thicknesses of mudstone and limestone overlying a coal seam. At similar burial depths, the gas content of the Taiyuan coal seams decreases gradually in the lower delta plain, barrier-lagoon, delta front, barrier-tidal flat, and carbonate platform depositional units. The CBM enrichment areas tend to be located in zones of poorly developed limestone and well-developed mudstone. In addition, the gas content of the Shanxi Formation is higher in the coals of the delta front facies compared to those in the lower delta plain. The CBM enrichment areas tend to be associated with the thicker mudstones. Therefore, based on the lithologic distribution and thickness of the rocks overlying the coal seam in each palaeogeographic unit of the Taiyuan and Shanxi Formations, the areas with higher gas content are located in the north-central basin for the Taiyuan coals and in the southern basin for the Shanxi coals. Both of these areas should be favorable for CBM exploration in the Qinshui Basin.
The expression of gully landforms can be regarded as an indicator of the evolutionary process of gullies. Most existing studies on the expression of gully landforms focus on plane characteristics. However, the vertical characteristics of a gully should be given considerable attention because gullies have mainly eroded the surface in the vertical direction. Current studies on vertical characteristics of gullies mainly focused on a single gully or rarely a few gullies, thereby failing to express the entire gully landform in a certain area. In this study, gully profile combination (GPC) was proposed to investigate the morphology and reveal the evolution of gully landforms. It was defined as the combination of vertical projection of all gully profiles in the entire drainage basin. Then, a gully evolution index and its statistic values were used to reveal the evolution of gully landforms based on GPC. The proposed method was applied and validated in three typical loess gully landform areas (i.e., loess tableland, ridge, and hill) in the Loess Plateau of China. Results show that GPC can effectively express gully landforms. The specific geomorphological feature (monoclinic loess tableland) can also be identified using GPC. The gully evolution index results also demonstrate different magnitudes of gully evolutionary stages in a certain area, which reflect the diversity of gullies. The average and median values of the gully evolution index increase in the three typical loess gully landforms. From loess tableland, loess ridge, and loess hill, the average values are 0.653, 0.703, and 0.763, and the median values are 0.661, 0.719, and 0.783, respectively. This method is also found to be stable with gully extraction thresholds for distinguishing different loess gully landforms. Accordingly, the evolution magnitudes of loess gully are obtained.
Rock geochemical information is important for mineral exploration and provides a theoretical basis for the rapid delineation of hidden minerals. Remote sensing technology provides the possibility of rapid and large-scale extraction of geochemical information from the earth’s surface. This study analyzed the relationship between copper concentration and rock spectra by first collecting 222 rock samples, and then measuring the copper concentration of rock samples in the laboratory and reflectance spectra using an ASD FieldSpec3 portable spectrometer. It finally established quantitative relationships between the original spectra, first-order derivative spectra and second-order derivative spectra and copper concentration, respectively, using the partial least squares support vector machine method (PLS-SVM). The results show that 1) The estimation accuracy of using second-order derivatives spectra as input parameters to establish a model for estimating copper concentration is the highest, and the determined coefficient (R2) between the predicted value and real value reaches 0.54. 2) When the copper concentration is less than 80 mg/kg, the inversion model of copper concentration established using PLS-SVM obtains a good result. The R2 between the predicted copper concentration and the real copper concentration reached 0.70248. When the copper concentration is greater than 80 mg/kg, the inversion model of copper concentration established using partial least squares (PLS) obtains a good result. The R2 between the predicted copper concentration and the real copper concentration reached 0.49. The R2 between real copper concentration and copper predicted by the method of piecewise separate modeling reaches 0.816. Therefore, the method of segmental modeling has great potential to improve the accuracy of copper concentration inversion.
The Ordos Basin is the largest oil and gas producing basin in China, where tight oil, shale gas, oil shale, and other unconventional oil and gas resources have been found in the Chang7 subsection of the Triassic Yanchang Formation. However, the mechanism of formation and the distribution of unconventional oil and gas resources in the shale layers have not been systematically investigated until now. According to the type of unconventional oil and gas resources, main controlling factors, and the maturity, depth and abundance of organic matters, the shale oil and gas resources from Chang7 region are divided into five zones that include an outcrop-shallow oil shale zone, a middle-matured and medium-burial shale oil zone, a medium-matured and medium-burial in situ conversion process (ICP) shale oil zone, a high-maturity and deep-burial shale gas zone, and an adjacent-interbedded tight sandstone oil zone. By the distribution of resources, orderly evolution of oil and gas resources and coexistences in lacustrine shale formations have been put forward, and also a strategy of integrated exploration and development of resources in the shale formations is proposed. Overall, the outcome of this study may guide on the effective utilization of unconventional oil and gas resources in other shale formations.
The identifying features of ship wakes in synthetic aperture radar (SAR) remote sensing images are of great importance for detecting ships and for extracting ship motion parameters. A statistical analysis was conducted on the identifying features of ship wakes in SAR images in the Yellow Sea. In this study, 1091 ship wake sub-images were selected from 327 SAR images in the Yellow Sea near Qingdao. Analysis of the identifying features of ship wakes in SAR images revealed that both turbulent wakes and Kelvin wakes account for the majority of ship wakes, with turbulent wakes occurring approximately four times as frequently as Kelvin wakes. Narrow-V wakes and internal wave wakes were comparatively rare, which is due to the peculiarities of the radar system parameters and marine environments required to observe these wakes. Additionally, we extracted ship motion parameters from four types of ship wakes in the SAR images. Specifically, internal wave wakes in SAR images in the Yellow Sea were also used to extract ship motion parameters. Validation of the extracted parameters indicated that the extraction of these parameters from ship wakes is a viable and accurate approach for the acquisition of ship motion parameters. These results provide a solid foundation for the commercialization of SAR-based technologies for detecting ships and extracting ship motion parameters.
Adjustments of upstream river regimes are one of the main factors affecting downstream fluvial processes. However, not all adjustments of river regimes will propagate downstream. There are some distinctive river reaches where upstream and downstream adjustments have no relevance. However, the irrelevance is neither caused by different river types nor by the different conditions of water and sediment; but rather, the channel boundaries and riverbed morphologies block the propagation effect. These are referred to here as the barrier river reach phenomena. The migration of the thalweg line is the essential reason for causing the propagation effect. Numerous influencing factors for thalweg migration exist, including 1) the average flow rate above the critical bankfull discharge, the average flow rate below the critical bankfull discharge, and their ratio, 2) the ratio of the duration of the aforementioned two periods, 3) the thalweg displacement at the entrance of the river reach, 4) the deflecting flow intensity of the node, 5) the ratio of the river width to water depth, 6) the relative width of the floodplain, and 7) the Shields number. In this study, the correlativity between the measured distances and the restricting indicators of thalweg migration in the Middle Yangtze River over the years was established. The barrier degree of 27 single-thread river reaches was subsequently assessed. These reaches included 4 barrier river reaches; 5 transitional reaches transforming from barrier to non-barrier; 10 transitional reaches transforming from non-barrier to barrier; and 8 non-barrier river reaches. Barrier river reaches were found to be important for maintaining the stability of the river regime and the transverse equilibrium of sediment transport in the downstream reaches. To some extent, the barrier river reaches may protect the natural dynamical properties from being destroyed by artificial river regulation works. Thus, they are of great significance for river management.
Rapid urban sprawl and growth led to substantial urban thermal environment changes and influenced the local climate, environment, and quality of life of residents. Taking the Chang-Zhu-Tan urban agglomeration in China as a case, this study firstly identified the spatiotemporal patterns of surface urban heat island intensity (SUHII) and the land use/cover changes (LUCC) based on multi-temporal Landsat TM satellite data over 21 years, and then investigated the relationship between LUCC and SUHII by methods of logistic regression model and centroid shift analysis. The results showed that green spaces (e.g., cropland, forestland) of 899.13 km2 had been converted to built-up land during the 1994–2015 period, which caused significant urban expansion. The SUHII was the highest for built-up land, high for unused land, low for cropland and grassland, and the lowest for forestland and open water. Many areas experienced extensive rapid urbanization because of the emergence of the urban agglomeration, which resulted in the loss of green spaces and increased SUHI effects over the 21-year study period. In addition, the results of centroid shift analysis found that the growth of SUHII and the expansion of high SUHII areas are closely related to the expansion of an existing urban area in Xiangtan, while the increases of building density and height in Changsha resulted in the decrease of SUHII and spatiotemporal change of high SUHII areas. The analysis of the effects of land use/cover types on the SUHII in this study will contribute to future urban land use allocation for the mitigation of SUHI formation.
Unsupervised machine learning methods were applied on multivariate geophysical and geochemical datasets of ocean floor sediment cores collected from the South China Sea. The well-preserved and continuous core samples comprising high resolution Cenozoic sediment records enable scientists to carry out paleoenvironment studies in detail. Bayesian age-depth chronological models constructed from biostratigraphic control points for the drilling sites are applied on cluster boundaries generated from two popular unsupervised learning methods: K-means and random forest. The unsupervised learning methods experimented have produced compact and unambiguous clusters from the datasets, indicating that previously unknown data patterns can be revealed when all variables from the datasets are taken into account simultaneously. A study of synchroneity of past events represented by the cluster boundaries across geographically separated ocean drilling sites is achieved through converting the fixed depths of cluster boundaries into chronological ranges represented by Gaussian density plots which are then compared with known past events in the region. A Gaussian density peak at around 7.2 Ma has been identified from results of all three sites and it is suggested to coincide with the initiation of the East Asian monsoon. Contrary to traditional statistical approach, a priori assumptions are not required for unsupervised learning, and the clustering results serve as a novel data-driven proxy for studying the complex and dynamic processes of the paleoenvironment surrounding the ocean sediment. This work serves as a pioneering approach to extract valuable information of regional events and opens up a systematic and objective way to study the vast global ocean sediment datasets.
Texture and its analysis methods are crucial for image feature extraction and classification. Digital elevation model (DEM) is the most important data source of digital terrain analysis and landform classification, and considerable research values are gained from texture feature extraction and analysis from DEM data. In this research, on the basis of optimal texture feature extraction, the hilly area in Shandong, China, was selected as the study area, and DEM data with a resolution of 500 m were used as the experimental data for landform classification. First, second-order texture measures and texture image were extracted from DEM data by using a gray level co-occurrence matrix (GLCM). Second, the variation characteristics of each texture measure were analyzed, and the optimal feature parameters, such as direction, gray level, and texture window, were determined. Meanwhile, the texture feature value, combined with maximum information, was calculated, and the multiband texture image was obtained by resolving three optimal texture measure images. Finally, a support vector machine (SVM) method was adopted to classify landforms on the basis of the multiband texture image. Results indicated that the texture features of DEM data can be sufficiently represented and measured via the quantitative GLCM method. However, the feature parameters during the texture feature value calculation required further optimization. Based on the image texture from DEM data, efficient classification accuracy and ideal classification effect were achieved.
Pan-sharpening is a method of integrating low-resolution multispectral images with corresponding high-resolution panchromatic images to obtain multispectral images with high spectral and spatial resolution. A novel variational model for pan-sharpening is proposed in this paper. The model is mainly based on three hypotheses: 1) the pan-sharpened image can be linearly represented by the corresponding panchromatic image; 2) the low-resolution multispectral image is down-sampled from the high-resolution multispectral image through the down-sampling operator; and 3) the satellite image has the low-rank property. Three energy components corresponding to these assumptions are integrated into a variational framework to obtain a total energy function. We adopt the alternating direction method of multipliers (ADMM) to optimize the total energy function. The experimental results show that the proposed method performs better than other mainstream methods in spectral and spatial information preserving aspect.