Following flooding disasters, satellite images provide valuable information required for generating flood inundation maps. Multispectral or optical imagery can be used for generating flood maps when the inundated areas are not covered by clouds. We propose a rapid mapping method for identifying inundated areas based on the increase in the water index value between the pre- and post-flood satellite images. Values of the Normalized Difference Water Index (NDWI) and Modified NDWI (MNDWI) will be higher in the post-flood image for flooded areas compared to the pre-flood image. Based on a threshold value, pixels corresponding to the flooded areas can be separated from non-flooded areas. Inundation maps derived from differencing MNDWI values accurately captured the flooded areas. However the output image will be influenced by the choice of the pre-flood image, hence analysts have to avoid selecting pre-flood images acquired in drought or earlier flood years. Also the inundation maps generated using this method have to be overlaid on the post-flood satellite image in order to orient personnel to landscape features. Advantages of the proposed technique are that flood impacted areas can be identified rapidly, and that the pre-existing water bodies can be excluded from the inundation maps. Using pairs of other satellite data, several maps can be generated within a single flood which would enable emergency response agencies to focus on newly flooded areas.
China’s rapid economic development has initiated the deterioration of its ecological environment, posing a threat to the sustainable development of human society. As a result, an assessment of regional sustainability is critical. This paper researches China’s most forested province, Fujian Province, as the study area. We proposed a grid-based approach to assess the regional carbon footprint in accordance with the Intergovernmental Panel on Climate Change’s (IPCC) carbon emission guidelines. Our method of assessment also introduced carbon emission indicators with our improved and published Net Primary Production (NPP) based on process simulation. The carbon footprint in Fujian Province from 2005–2017 was calculated and examined from a spatiotemporal perspective. Ecological indicators were used in the sustainability assessment. The research draws the following conclusions: 1) the carbon footprint in the eastern regions of Fujian Province was higher due to rapid economic development; 2) that of the western regions was lower; 3) an uptrend in the carbon footprint of Fujian Province was observed. All five ecological indicators based on carbon emissions and economic and social data showed an ecologically unsustainable trend over 13 years in the research area due to unsustainable economic development. Therefore, it is urgent to balance the relationship between economic development and environmental protection. Our research provides scientific references for achieving ecological civilization and sustainability in a similar region.
Climate change significantly affects the environmental and socioeconomic conditions in northwest China. Here we evaluate the ability of five general circulation models (GCMs) from 6th phase of the Coupled Model Inter-comparison Project (CMIP6) to reproduce regional temperature and precipitation over northwest China from 1961 to 2014, and project the future temperature and precipitation during 2021 to 2100 under SSPs-RCPs (SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0 and SSP5-8.5). The results show that the CMIP6 models can simulate temperature better than precipitation. Projections show that the annual mean temperature will further increase under different SSPs-RCPs scenarios in the 21st century. Future climate changes in the near-term (2021–2040), mid-term (2041–2060) and long-term (2081–2100) are analyzed relative to the reference period (1995–2014). In the long term, warming will be significantly higher than the near and mid-terms. In the long term, annual mean temperature will increase by 1.4°C, 1.9°C, 3.3°C, 5.5°C, 2.7°C, 3.8°C and 6.0°C under SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0 and SSP5-8.5, respectively. Spatially, warming in the Junggar Basin will be higher than those in the Tarim Basin. Seasonally, the maximum warming zone will be in the mountainous areas of Tarim Basin during spring and autumn, in the southern basin during winter, and in the east during summer. Precipitation shows an increasing trend under different SSPs-RCPs in the 21st century. In the long term, increase in precipitation will be significantly higher than in the near and mid-terms. Increase in annual precipitation in the long term will be 4.1% under SSP1-1.9, 13.9% under SSP1-2.6, 28.4% under SSP2-4.5, 35.2% under SSP3-7.0, 6.9% under SSP4-3.4, 8.9% under SSP4-6.0, and 27.3% under SSP5-8.5 relative to the reference period of 1995–2014. Spatially, precipitation increase will be higher in the south than the north, especially higher in mountainous regions than the basin under SSP2-4.5, SSP3-7.0, and SSP5-8.5. Seasonally, highest increase can be expected for winter, followed by spring, with significant increase in mountainous regions of southern Tarim Basin. Summer precipitation will reduce in Tian Shan and basins but will significantly increase in the northern margin of the Kunlun Mountain.
The object-based against pixel-based image analysis approaches were assessed for lithological mapping in a geologically complex terrain using Visible Near Infrared (VNIR) bands of WorldView-3 (WV-3) satellite imagery. The study area is Hormuz Island, southern Iran, a salt dome composed of dominant sedimentary and igneous rocks. When performing the object-based image analysis (OBIA) approach, the textural and spectral characteristics of lithological features were analyzed by the use of support vector machine (SVM) algorithm. However, in the pixel-based image analysis (PBIA), the spectra of lithological end-members, extracted from imagery, were used through the spectral angle mapper (SAM) method. Several test samples were used in a confusion matrix to assess the accuracy of classification methods quantitatively. Results showed that OBIA was capable of lithological mapping with an overall accuracy of 86.54% which was 19.33% greater than the accuracy of PBIA. OBIA also reduced the salt-and-pepper artifact pixels and produced a more realistic map with sharper lithological borders. This research showed limitations of pixel-based method due to relying merely on the spectral characteristics of rock types when applied to high-spatial-resolution VNIR bands of WorldView-3 imagery. It is concluded that the application of an object-based image analysis approach obtains a more accurate lithological classification when compared to a pixel-based image analysis algorithm.
Timely and accurate acquisition of crop distribution and planting area information is important for making agricultural planning and management decisions. This study employed aerial imagery as a data source and machine learning as a classification tool to statically and dynamically identify crops over an agricultural cropping area. Comparative analysis of pixel-based and object-based classifications was performed and classification results were further refined based on three types of object features (layer spectral, geometry, and texture). Static recognition using layer spectral features had the highest accuracy of 75.4% in object-based classification, and dynamic recognition had the highest accuracy of 88.0% in object-based classification based on layer spectral and geometry features. Dynamic identification could not only attenuate the effects of variations on planting dates and plant growth conditions on the results, but also amplify the differences between different features. Object-based classification produced better results than pixel-based classification, and the three feature sets (layer spectral alone, layer spectral and geometry, and all three) resulted in only small differences in accuracy in object-based classification. Dynamic recognition combined with object-based classification using layer spectral and geometry features could effectively improve crop classification accuracy with high resolution aerial imagery. The methodologies and results from this study should provide practical guidance for crop identification and other agricultural mapping applications.
Urban spatial structure is an important feature for assessing the effects of urban planning. Quantifying an urban spatial structure cannot only help in identifying the problems with current planning but also provide a basic reference for future adjustments. Evaluation of spatial structure is a difficult task for planners and researchers and this has been usually carried out by comparing different land use structures. However, these methods cannot efficiently reflect the influence of human activities. With the wide application of big data, analyzing data on human travel behavior has increasingly been carried out to reveal the relationship between urban spatial structure and urban planning. In this study, we constructed a human-activity space network using the taxi trip big data. Clustering at different scales revealed the hierarchy and redundancy of the spatial structure for assessing the appropriateness and shortcomings of urban planning. This method was applied to a case study based on one-month taxi trip data of Dongguan City. Existing urban spatial structures at different scales were retrieved and utilized to assess the effectiveness of the master plan designed for 2000 to 2015 and 2008 to 2020, which can help identify the limitations and improvements in the spatial structure designed in these two versions of the master plan. We also evaluated the potential effect of the master plan designed for 2016 to 2035 by providing a reference for reconstructing and optimizing future urban spatial structure. The analysis demonstrated that the taxi trip data are important big data on social spatial perception, and taxi data should be used for evaluating spatial structures in future urban planning.
With the intensification of climate change and human activities, the watershed ecosystem is seriously fragmented, which leads to the obstruction of hydrological connectivity, and further causes the degradation of the ecosystem. As the value of wetlands continues to be exploited, hydrological connectivity becomes increasingly significant. In this paper, the characteristics and development of hydrological connectivity research from 1998 to 2018 were analyzed through the scientometric analysis based on Web of Science database. CiteSpace, an analytical software for scientific measurement, is used to visualize the results of the retrieval. The analysis results of co-occurrence, co-operative and co-cited network indicate that the hydrological connectivity is a multidisciplinary field which involves the Environment Science and Ecology, Water Resources, Environmental Sciences, Geology and Geosciences. According to Keyword co-occurrence analysis, ecosystem, floodplain, dynamics, climate change and management are the main research hotspots in each period. In addition, the co-cited analysis of references shows that “amphibians” is the largest cluster of hydrological connectivity, and the “channel network” is the most important research topic. It is worth noting that the “GIWS” (Geographically Isolated Wetlands) is the latest research topic and may be a major research direction in the future.
Lacunarity analysis is frequently used in multiscale and spatial pattern studies. However, the explanation for the lacunarity analysis results is limited mainly at a qualitative description level. In other words, this approach can be used to judge whether the spatial pattern of the objective is regular, random or aggregated in space. The lacunarity analysis, however, cannot afford many quantitative information. Therefore, this study proposed the lacunarity variation index (LVI) to reflect the rates of variation of lacunarity with the resolution. In comparison with lacunarity analysis, the simulated experiments show that the LVI analysis can distinguish the basic spatial pattern of the geography objects more clearly and detect the scale of aggregated data. The experiment showed that different slope types in the Loess Plateau display aggregated patterns, and the characteristic scales of these patterns were detected using the slope pattern in the Loess Plateau as the research data. This study can improve the spatial pattern analysis and scale detecting methods, as well as provide a new method for landscape and vegetation community pattern analyses. Lacunarity analysis is frequently used in multiscale and spatial pattern studies. However, the explanation for the lacunarity analysis results is limited mainly at a qualitative description level. In other words, this approach can be used to judge whether the spatial pattern of the objective is regular, random or aggregated in space. The lacunarity analysis, however, cannot afford many quantitative information. Therefore, this study proposed the lacunarity variation index (LVI) to reflect the rates of variation of lacunarity with the resolution. In comparison with lacunarity analysis, the simulated experiments show that the LVI analysis can distinguish the basic spatial pattern of the geography objects more clearly and detect the scale of aggregated data. The experiment showed that different slope types in the Loess Plateau display aggregated patterns, and the characteristic scales of these patterns were detected using the slope pattern in the Loess Plateau as the research data. This study can improve the spatial pattern analysis and scale detecting methods, as well as provide a new method for landscape and vegetation community pattern analyses.
Identifying geochemical anomalies related to ore deposition processes facilitates the practice of vectoring toward undiscovered mineral deposit sites. In district-scale exploration studies, analysis of dispersion patterns of ore-forming elements results in more-reliable targets. Therefore, deriving significant geochemical footprints and mapping the ensuing geochemical anomalies are of important issues that lead exploration geologists toward anomaly sources, e.g., mineralization. This paper aims to examine the effectiveness of local relative enrichment index and singularity mapping technique, as two methods of local neighborhood statistics, in the delineation of anomalous areas for further exploration. A data set of element contents obtained from stream sediment samples in Baft area, Iran, therefore was applied to illustrate the procedure proposed. The close relationship between anomalous patterns recognized and known Cu-occurrences demonstrated that the procedures proposed can efficiently model complex dispersion patterns of geochemical anomalies in the study area. The results showed that singularity mapping method is a better technique, compared to local relative enrichment index, to delineate targets for follow-up exploration in the area. We made this comparison because, as pointed out by exploration geochemists, dispersion patterns of geochemical indicators in stream sediments vary in different areas even for the same deposit type. The variety in the dispersion patterns is due to the operation of post-mineralization subsystems, which are affected by local factors such as landscape of the areas under study. Therefore, the effectiveness of the methods should be evaluated in every area for every targeted deposit.
Water stages play a critical role on flood control, water supply, navigation, and ecology in rivers. Investigation of water stages provides better understanding of riverbed evolution processes and river management. Based on the hydrological observation in past 70 years, the changes of low-flow and flood stages were investigated by a combination of Mann-Kendall test, moving t-test, and wavelet analysis. 1) In accordance with the location, the middle Yangtze River was divided into upper reach, middle reach, and lower reach. Water stages in the upper reach show a decreasing trend, while that in the middle reach present an increasing trend, and the lower reach are mainly dominated by natural evolution. 2) The mutation year of water stages in the upper reach was around 1985, indicating that the Gezhouba Dam facilitated the reduction of water stages. The trend mutation in the middle reach was in 1969, which was consistent with the implementation of Jingjiang Cutoff. 3) Human activities aggravated the change of water stages, leading the primary period of water stage time series to exceed 20 years. 4) In the upper reach, the reductions of water stages were attributed to the riverbed erosion induced by human activities. While in the middle reach, the recent falling effects of riverbed erosion can hardly offset the rising effects of the channel resistance on water stages. 5) In the future, the increasing trend in the middle reach may be arrested due to the riverbed erosion induced by the Three Gorges Dam. Long-term observation of the flood stage must be conducted in the middle Yangtze River.
Arizona residents have been dealing with the suspended particulate matter caused health issues for a long time due to Arizona’s arid climate. The state of Arizona is vulnerable to dust storms, especially in the monsoon season because of the anomalies in wind direction and magnitude. In this study, a high-resolution Weather Research and Forecasting (WRF) model coupled with a chemistry module (WRF-Chem) was simulated to compute the particulate matter spatiotemporal distribution as well as the climatic parameters for the state of Arizona. Subsequently, Ordinary Least Square (OLS), spatial lag, spatial error, and Geographically Weighted Regression (GWR) techniques were utilized to develop predictive models based on the climatic indicators that impacted the formation and dispersion of the particulate matter during dust storms. Census tracts were adopted to create local spatial averages for the chosen variables. Terrain height, temperature, wind speed, and vegetation fraction were designated as the most significant variables, whereas base state and perturbation pressures, planetary boundary layer height and soil moisture were adopted as supplementary variables. The determination coefficient for OLS, spatial lag, spatial error, and GWR models peaked at 0.92, 0.93, 0.96, and 0.97, respectively. These models provide a better understanding of the current distribution of the particulate matter and can be used to forecast future trends.
The impact of land–sea thermal contrast on the South Asian summer monsoon (SASM) was investigated by calculating the atmospheric heat sources (AHS) and baroclinic component with ERA5 data for the period 1979–2019. Using diagnostic and statistical methods, it was found that the thermal contrast between the Tibetan Plateau (TP) and the tropical Indian Ocean (TIO) affects the South Asian monsoon circulation through the meridional temperature gradient in the upper troposphere. The seasonal changes of the AHS of the TP and TIO are reversed. In summer, the TP is the strongest at the same latitude whereas the TIO is the weakest, and the thermal contrast is the most obvious. The heat sources of the TP and TIO are located on the north and south side of the strong baroclinic area of the SASM region, respectively, and both of which are dominated by deep convective heating in the upper troposphere. The TP–TIO regional meridional thermal contrast index (QI) based on the AHS, and the SASM index (MI) based on baroclinicity were found to be strongly positively correlated. In years of abnormally high QI, the thermal contrast between the TP and TIO is strong in summer, which warms the upper troposphere over Eurasia and cools it over the TIO. The stronger temperature gradient enhances the baroclinicity in the troposphere, which results in a strengthening of the low-level westerly airflow and the upper-level easterly airflow. The anomalous winds strengthen the South Asian high (SAH), with the warmer center in the upper troposphere, and the enhanced Walker circulation over the equatorial Indian Ocean. Finally, the anomalous circulation leads to much more precipitation over the SASM region. The influence of abnormally low QI is almost the opposite.
Analyzing the vegetation dynamics and its response to driving factors provides a vital reference for understanding regional ecological processes and ecosystem services. However, this issue has been poorly understood in karst areas. Taking Guizhou Province as a case study, based on the Normalized-Difference Vegetation Index of the Global Inventory Modeling and Mapping Studies and on meteorological data sets during 1982–2015, we evaluated vegetation dynamics and its response to climatic factors and human activities. We used several methods: the Mann–Kendall test, rescaled range analysis, partial correlation analysis, and residual analysis. The results are as follows: 1) the mean annual Normalized-Difference Vegetation Index was 0.46 and exhibited a significant increasing trend with a variation rate of 0.01/10a during 1982–2015 in Guizhou Province. The vegetation cover showed was spatially heterogeneous: High vegetation cover was distributed mainly in the center and western margin of the study area, while the other parts of the study area mainly distributed with low vegetation cover, although the vegetation cover was higher in the non-karst areas than in the karst areas; 2) in general, the climate was getting warmer and drier in Guizhou Province during 1982–2015. Vegetation cover was positively correlated with temperature and negatively correlated with precipitation. Compared to precipitation, temperature was the dominant climatic factor impacting vegetation dynamics; 3) large-scale ecological restoration projects have obviously increased vegetation cover in Guizhou Province in recent years. The contribution of human activities to vegetation changes was 76%, while the contribution of climatic factors was 24%. In summary, compared to natural forces such as climatic factors and geographic parameters, human activities were the main factor driving the vegetation dynamics in Guizhou Province.
