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In this study, Yulin city and Yan’an city in northern Shaanxi Province were taken as the study area. Based on the diurnal dry–wet events abrupt alternation index DWAAI, the joint probability distribution of two characteristic variables of “urgency” and “alternation” of dry–wet events abrupt alternation was established by using copula function, and the characteristics of dry–wet events abrupt alternation were analyzed. DWAAI was calculated from daily precipitation data and the applicability of the index was verified. On this basis, the two characteristic variables of “urgency” and “alternation” were separated, and the appropriate marginal distribution function was selected to fit them, and the correlation between the two variables was evaluated. Finally, the appropriate copula function was selected to fit the bivariate of each station, and the joint cumulative probability and recurrence period of the two variables were calculated. The results show that the DWAAI index is suitable for the identification of dry–wet events abrupt alternation in the study area. Light and moderate dry–wet events abrupt alternation occurs more frequently, while severe events rarely occur in the study area. The frequency of severe dry–wet events abrupt alternation in Jingbian station and its northern area is greater than that in the southern area, and the risk of dry–wet events abrupt alternation of disasters in the northern area is higher. The greater the degree of “urgency” and “alternation”, the greater the joint cumulative probability and the greater the return period. The return period of severe dry–wet events abrupt alternation was more than five years, while the return period of light and moderate dry–wet events abrupt alternation was less than five years.
Junhui Wang; Guangzhi Rong; Kaiwei Li; Jiquan Zhang. Analysis of Characteristics of Dry–Wet Events Abrupt Alternation in Northern Shaanxi, China. Water 2021, 13, 2384 .
AMA StyleJunhui Wang, Guangzhi Rong, Kaiwei Li, Jiquan Zhang. Analysis of Characteristics of Dry–Wet Events Abrupt Alternation in Northern Shaanxi, China. Water. 2021; 13 (17):2384.
Chicago/Turabian StyleJunhui Wang; Guangzhi Rong; Kaiwei Li; Jiquan Zhang. 2021. "Analysis of Characteristics of Dry–Wet Events Abrupt Alternation in Northern Shaanxi, China." Water 13, no. 17: 2384.
Guangxi is the primary producer of sugarcane in China and provides a highly suitable habitat for sugarcane growth. However, its distribution range has changed significantly in recent years due to climate change as well as human factors. Without extensive knowledge of the changing trends in suitable sugarcane planting areas, efforts to improve its productivity in Guangxi may be insufficient. In this study, the interdecadal change in sugarcane distribution in Guangxi in response to climate change from 1960 to 2019 was estimated using the MaxEnt model and the landscape pattern of land use in the suitable sugarcane area was analyzed. In addition, we discuss the effects of global warming on sugarcane production in the sustainable development of the sugar industry in Guangxi. Our results indicate: (1) from 1960 to 2019, approximately 65% of Guangxi Province could grow sugarcane. Chongzuo City, Nanning City and Parts of Baise City, are highly suitable areas, and unsuitable areas are mainly concentrated in the north. In general, sugarcane climate suitability extended further in low-altitude areas, and then extended to high- altitude areas. However, from the 2000s to the 2010s, climate suitability showed a decreasing trend, decreasing from 16.036 × 106 ha to 15.4985 × 106 ha (2) The order of land use area in the suitable sugarcane climate range was as follows: woodland > cropland > grassland > construction land > water. With the increase in climate suitability, the distribution of cultivated land expanded. From 1980 to 2005, cropland in suitable areas showed a fragmentation trend. By 2010, the cropland patches disappeared after fragmentation. (3) Due to landscape constraints, infertile soil, and labor costs, the sugar industry faces various challenges. The evaluation of climate suitability could provide a theoretical reference for a planting layout of sugarcane, and landscape pattern analysis of suitable sugarcane climate areas is conducive to the integration of small pieces of land into large ones, making mechanization possible. Overall, strict layout and management measures are required in sugarcane planting areas.
Suri Guga; Jie Xu; Dao Riao; Kaiwei Li; Aru Han; Jiquan Zhang. Combining MaxEnt model and landscape pattern theory for analyzing interdecadal variation of sugarcane climate suitability in Guangxi, China. Ecological Indicators 2021, 131, 108152 .
AMA StyleSuri Guga, Jie Xu, Dao Riao, Kaiwei Li, Aru Han, Jiquan Zhang. Combining MaxEnt model and landscape pattern theory for analyzing interdecadal variation of sugarcane climate suitability in Guangxi, China. Ecological Indicators. 2021; 131 ():108152.
Chicago/Turabian StyleSuri Guga; Jie Xu; Dao Riao; Kaiwei Li; Aru Han; Jiquan Zhang. 2021. "Combining MaxEnt model and landscape pattern theory for analyzing interdecadal variation of sugarcane climate suitability in Guangxi, China." Ecological Indicators 131, no. : 108152.
Inner Mongolia in China is a typically arid and semi-arid region with vegetation prominently affected by global warming and human activities. Therefore, investigating the past and future vegetation change and its impact mechanism is important for assessing the stability of the ecosystem and the ecological policy formulation. Vegetation changes, sustainability characteristics, and the mechanism of natural and anthropogenic effects in Inner Mongolia during 2000–2019 were examined using moderate resolution imaging spectroradiometer normalized difference vegetation index (NDVI) data. Theil–Sen trend analysis, Mann–Kendall method, and the coefficient of variation method were used to analyze the spatiotemporal variability characteristics and sustained stability of the NDVI. Furthermore, a trend estimation method based on a Seasonal Trend Model (STM), and the Hurst index was used to analyze breakpoints and change trends, and predict the likely future direction of vegetation, respectively. Additionally, the mechanisms of the compound influence of natural and anthropogenic activities on the vegetation dynamics in Inner Mongolia were explored using a Geodetector Model. The results show that the NDVI of Inner Mongolia shows an upward trend with a rate of 0.0028/year (p< 0.05) from 2000 to 2019. Spatially, the NDVI values showed a decreasing trend from the northeast to the southwest, and the interannual variation fluctuated widely, with coefficients of variation greater than 0.15, for which the high-value areas were in the territory of the Alxa League. The areas with increased, decreased, and stable vegetation patterns were approximately equal in size, in which the improved areas were mainly distributed in the northeastern part of Inner Mongolia, the stable and unchanged areas were mostly in the desert, and the degraded areas were mainly in the central-eastern part of Inner Mongolia, it shows a trend of progressive degradation from east to west. Breakpoints in the vegetation dynamics occurred mainly in the northwestern part of Inner Mongolia and the northeastern part of Hulunbuir, most of which occurred during 2011–2014. The future NDVI trend in Inner Mongolia shows an increasing trend in most areas, with only approximately 10% of the areas showing a decreasing trend. Considering the drivers of the NDVI, we observed annual precipitation, soil type, mean annual temperature, and land use type to be the main driving factors in Inner Mongolia. Annual precipitation was the first dominant factor, and when these four dominant factors interacted to influence vegetation change, they all showed interactive enhancement relationships. The results of this study will assist in understanding the influence of natural elements and human activities on vegetation changes and their driving mechanisms, while providing a scientific basis for the rational and effective protection of the ecological environment in Inner Mongolia.
Yao Kang; Enliang Guo; Yongfang Wang; Yulong Bao; Yuhai Bao; Naren Mandula. Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019. Remote Sensing 2021, 13, 3357 .
AMA StyleYao Kang, Enliang Guo, Yongfang Wang, Yulong Bao, Yuhai Bao, Naren Mandula. Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019. Remote Sensing. 2021; 13 (17):3357.
Chicago/Turabian StyleYao Kang; Enliang Guo; Yongfang Wang; Yulong Bao; Yuhai Bao; Naren Mandula. 2021. "Monitoring Vegetation Change and Its Potential Drivers in Inner Mongolia from 2000 to 2019." Remote Sensing 13, no. 17: 3357.
This research assessed the changes in spatial patterns and the seasonal trends in temperature, precipitation, and relative humidity over 36 years (1979–2014) using Climate Forecast System Reanalysis (CFSR) datasets. The evaluation of climate deviations was the prime objective of this research. The augmented Dickey–Fuller Test (ADF) was used to scrutinize whether the data was either stationary or non-stationary. The results of the ADF test showed that all the datasets were found to be stationary at lag order 3. To observe undulations in the time series data, trend analyses were done using Sen’s slope (SS), Mann–Kendall (MK), and Cox and Stuart (CS) tests. For all the statistical analyses, we considered the 5% significance level (α = 0.05) and p< 0.05 to be statistically significant. We observed significant (p< 0.05) trends in spring (MAM) and autumn (SON) for minimum temperature (Tmin) in Punjab. We also noted a significant (p< 0.05) trend in precipitation during autumn (SON). Annually, all the variables showed a non-significant (p > 0.05) trend for Punjab, Pakistan, during the period 1979–2014. Climate variability, such as a decrease in precipitation, higher temperature, and relative humidity fluctuations, were the reasons for the imbalance in the sustainability of Punjab, Pakistan.
Alishbah Syed; Xingpeng Liu; Moniruzzaman; Iman Rousta; Warda Syed; Jiquan Zhang; Haraldur Olafsson. Assessment of Climate Variability among Seasonal Trends Using In Situ Measurements: A Case Study of Punjab, Pakistan. Atmosphere 2021, 12, 939 .
AMA StyleAlishbah Syed, Xingpeng Liu, Moniruzzaman, Iman Rousta, Warda Syed, Jiquan Zhang, Haraldur Olafsson. Assessment of Climate Variability among Seasonal Trends Using In Situ Measurements: A Case Study of Punjab, Pakistan. Atmosphere. 2021; 12 (8):939.
Chicago/Turabian StyleAlishbah Syed; Xingpeng Liu; Moniruzzaman; Iman Rousta; Warda Syed; Jiquan Zhang; Haraldur Olafsson. 2021. "Assessment of Climate Variability among Seasonal Trends Using In Situ Measurements: A Case Study of Punjab, Pakistan." Atmosphere 12, no. 8: 939.
Land use change is an important driving force factor affecting the river water environment and directly affecting water quality. To analyze the impact of land use change on water quality change, this study first analyzed the land use change index of the study area. Then, the study area was divided into three subzones based on surface runoff. The relationship between the characteristics of land use change and the water quality grade was obtained by grey correlation analysis. The results showed that the land use types changed significantly in the study area since 2000, and water body and forest land were the two land types with the most significant changes. The transfer rate is cultivated field > forest land > construction land > grassland > unused land > water body. The entropy value of land use information is represented as Area I > Area III > Area II. The shift range of gravity center is forest land > grassland > water body > unused land > construction land > cultivated field. There is a strong correlation between land use change index and water quality, which can be improved and managed by changing the land use type. It is necessary to establish ecological protection areas or functional areas in Area I, artificial lawns or plantations shall be built in the river around the water body to intercept pollutants from non-point source pollution in Area II, and scientific and rational farming in the lower reaches of rivers can reduce non-point source pollution caused by farming.
Mingxi Zhang; Guangzhi Rong; Aru Han; Dao Riao; Xingpeng Liu; Jiquan Zhang; Zhijun Tong. Spatial-Temporal Change of Land Use and Its Impact on Water Quality of East-Liao River Basin from 2000 to 2020. Water 2021, 13, 1955 .
AMA StyleMingxi Zhang, Guangzhi Rong, Aru Han, Dao Riao, Xingpeng Liu, Jiquan Zhang, Zhijun Tong. Spatial-Temporal Change of Land Use and Its Impact on Water Quality of East-Liao River Basin from 2000 to 2020. Water. 2021; 13 (14):1955.
Chicago/Turabian StyleMingxi Zhang; Guangzhi Rong; Aru Han; Dao Riao; Xingpeng Liu; Jiquan Zhang; Zhijun Tong. 2021. "Spatial-Temporal Change of Land Use and Its Impact on Water Quality of East-Liao River Basin from 2000 to 2020." Water 13, no. 14: 1955.
Tea trees are the main economic crop in Zhejiang Province. However, spring cold is a frequent occurrence there, causing frost damage to the valuable tea buds. To address this, a regional frost-hazard early-warning system is needed. In this study, frost damage area was estimated based on topography and meteorology, as well as longitude and latitude. Based on support vector machine (SVM) and artificial neural networks (ANNs), a multi-class classification model was proposed to estimate occurrence of regional frost disasters using tea frost cases from 2017. Results of the two models were compared, and optimal parameters were adjusted through multiple iterations. The highest accuracies of the two models were 83.8% and 75%, average accuracies were 79.3% and 71.3%, and Kappa coefficients were 79.1% and 67.37%. The SVM model was selected to establish spatial distribution of spring frost damage to tea trees in Zhejiang Province in 2016. Pearson’s correlation coefficient between prediction results and meteorological yield was 0.79 (p< 0.01), indicating consistency. Finally, the importance of model factors was assessed using sensitivity analysis. Results show that relative humidity and wind speed are key factors influencing accuracy of predictions. This study supports decision-making for hazard prediction and defense for tea trees facing frost.
Jie Xu; Suri Guga; Guangzhi Rong; Dao Riao; Xingpeng Liu; Kaiwei Li; Jiquan Zhang. Estimation of Frost Hazard for Tea Tree in Zhejiang Province Based on Machine Learning. Agriculture 2021, 11, 607 .
AMA StyleJie Xu, Suri Guga, Guangzhi Rong, Dao Riao, Xingpeng Liu, Kaiwei Li, Jiquan Zhang. Estimation of Frost Hazard for Tea Tree in Zhejiang Province Based on Machine Learning. Agriculture. 2021; 11 (7):607.
Chicago/Turabian StyleJie Xu; Suri Guga; Guangzhi Rong; Dao Riao; Xingpeng Liu; Kaiwei Li; Jiquan Zhang. 2021. "Estimation of Frost Hazard for Tea Tree in Zhejiang Province Based on Machine Learning." Agriculture 11, no. 7: 607.
Precipitation is low and drought occurs frequently in Northern Shaanxi. To study the characteristics and occurrence and development of drought events in Northern Shaanxi is beneficial to the prevention and control of drought disasters. Based on the monthly rainfall data of 10 meteorological stations in Northern Shaanxi from 1960 to 2019, the characteristic variables of drought events at each meteorological station in Northern Shaanxi were extracted by using run theory and copula function. The joint probability distribution and recurrence period were obtained by combining the duration and intensity of drought, and the relationship between drought characteristics and crop drought affected area was studied. The results show that (1) from 1960 to 2019, drought events mainly occurred in Northern Shaanxi with long duration and low severity, short duration and high severity, or short duration and low severity, among which the frequency of drought events that occurred in Yuyang and Baota districts was higher. The frequency of light drought and extreme drought was more in the south and less in the north, while the frequency of moderate drought and severe drought was more in the north and less in the south. (2) The optimal edge distribution of drought intensity and drought duration in Northern Shaanxi is generalized Pareto distribution, and the optimal fitting function is Frank copula function. The greater the duration and intensity of drought, the greater the cumulative probability and return period. (3) The actual recurrence interval and the theoretical recurrence interval of drought events in Northern Shaanxi were close, and the error was only 0.1–0.3a. The results of the joint return period can accurately reflect the actual situation, and this study can provide effective guidance for the prevention and management of agricultural dryland in Northern Shaanxi.
Junhui Wang; Guangzhi Rong; Kaiwei Li; Jiquan Zhang. Analysis of Drought Characteristics in Northern Shaanxi Based on Copula Function. Water 2021, 13, 1445 .
AMA StyleJunhui Wang, Guangzhi Rong, Kaiwei Li, Jiquan Zhang. Analysis of Drought Characteristics in Northern Shaanxi Based on Copula Function. Water. 2021; 13 (11):1445.
Chicago/Turabian StyleJunhui Wang; Guangzhi Rong; Kaiwei Li; Jiquan Zhang. 2021. "Analysis of Drought Characteristics in Northern Shaanxi Based on Copula Function." Water 13, no. 11: 1445.
Plant phenology depends largely on temperature, but temperature alone cannot explain the Northern Hemisphere shifts in the start of the growing season (SOS). The spatio–temporal distribution of SOS sensitivity to climate variability has also changed in recent years. We applied the partial least squares regression (PLSR) method to construct a standardized SOS sensitivity evaluation index and analyzed the combined effects of air temperature (Tem), water balance (Wbi), radiation (Srad), and previous year’s phenology on SOS. The spatial and temporal distributions of SOS sensitivity to Northern Hemisphere climate change from 1982 to 2014 were analyzed using time windows of 33 and 15 years; the dominant biological and environmental drivers were also assessed. The results showed that the combined sensitivity of SOS to climate change (SCom) is most influenced by preseason temperature sensitivity. However, because of the asymmetric response of SOS to daytime/night temperature (Tmax/Tmin) and non-negligible moderating of Wbi and Srad on SOS, SCom was more effective in expressing the effect of climate change on SOS than any single climatic factor. Vegetation cover (or type) was the dominant factor influencing the spatial pattern of SOS sensitivity, followed by spring temperature (Tmin > Tmax), and the weakest was water balance. Forests had the highest SCom absolute values. A significant decrease in the sensitivity of some vegetation (22.2%) led to a decreasing trend in sensitivity in the Northern Hemisphere. Although temperature remains the main climatic factor driving temporal changes in SCom, the temperature effects were asymmetric between spring and winter (Tems/Temw). More moisture might mitigate the asymmetric response of SCom to spring/winter warming. Vegetation adaptation has a greater influence on the temporal variability of SOS sensitivity relative to each climatic factor (Tems, Temw, Wbi, Srad). More moisture might mitigate the asymmetric response of SCom to spring/winter warming. This study provides a basis for vegetation phenology sensitivity assessment and prediction.
Kaiwei Li; Chunyi Wang; Qing Sun; Guangzhi Rong; Zhijun Tong; Xingpeng Liu; Jiquan Zhang. Spring Phenological Sensitivity to Climate Change in the Northern Hemisphere: Comprehensive Evaluation and Driving Force Analysis. Remote Sensing 2021, 13, 1972 .
AMA StyleKaiwei Li, Chunyi Wang, Qing Sun, Guangzhi Rong, Zhijun Tong, Xingpeng Liu, Jiquan Zhang. Spring Phenological Sensitivity to Climate Change in the Northern Hemisphere: Comprehensive Evaluation and Driving Force Analysis. Remote Sensing. 2021; 13 (10):1972.
Chicago/Turabian StyleKaiwei Li; Chunyi Wang; Qing Sun; Guangzhi Rong; Zhijun Tong; Xingpeng Liu; Jiquan Zhang. 2021. "Spring Phenological Sensitivity to Climate Change in the Northern Hemisphere: Comprehensive Evaluation and Driving Force Analysis." Remote Sensing 13, no. 10: 1972.
Urban flooding has been an alarming issue in the past around the globe, particularly in South Asia. Pakistan is no exception from this situation where urban floods with associated damages are frequently occurring phenomena. In Pakistan, rapid urbanization is the key factor for urban flooding, which is not taken into account. This study aims to identify flood sensitivity and coping capacity while assessing urban flood resilience and move a step toward the initialization of resilience, specifically for Peshawar city and generally for other cities of Pakistan. To achieve this aim, an attempt has been made to propose an integrated approach named the “urban flood resilience model (UFResi-M),” which is based on geographical information system(GIS), remote sensing (RS), and the theory of analytical hierarchy process (AHP). The UFResi-M incorporates four main factors—urban flood hazard, exposure, susceptibility, and coping capacity into two parts, i.e., sensitivity and coping capacity. The first part consists of three factors—IH , IE , and IS —that represent sensitivity, while the second part represents coping capacity (ICc ). All four indicators were weighted through AHP to obtain product value for each indicator. The result showed that in the Westzone of the study area, the northwestern and central parts have very high resilience, whereas the southern and southwestern parts have very low resilience. Similarly, in the East zone of the study area, the northwest and southwest parts have very high resilience, while the northern and western parts have very low resilience. The likelihood of the proposed model was also determined using the receiver operating characteristic (ROC) curve method; the area under the curve acquired for the model was 0.904. The outcomes of these integrated assessments can help in tracking community performance and can provide a tool to decision makers to integrate the resilience aspect into urban flood management, urban development, and urban planning.
Muhammad Tayyab; Jiquan Zhang; Muhammad Hussain; Safi Ullah; Xingpeng Liu; Shah Khan; Muhammad Baig; Waqas Hassan; Bazel Al-Shaibah. GIS-Based Urban Flood Resilience Assessment Using Urban Flood Resilience Model: A Case Study of Peshawar City, Khyber Pakhtunkhwa, Pakistan. Remote Sensing 2021, 13, 1864 .
AMA StyleMuhammad Tayyab, Jiquan Zhang, Muhammad Hussain, Safi Ullah, Xingpeng Liu, Shah Khan, Muhammad Baig, Waqas Hassan, Bazel Al-Shaibah. GIS-Based Urban Flood Resilience Assessment Using Urban Flood Resilience Model: A Case Study of Peshawar City, Khyber Pakhtunkhwa, Pakistan. Remote Sensing. 2021; 13 (10):1864.
Chicago/Turabian StyleMuhammad Tayyab; Jiquan Zhang; Muhammad Hussain; Safi Ullah; Xingpeng Liu; Shah Khan; Muhammad Baig; Waqas Hassan; Bazel Al-Shaibah. 2021. "GIS-Based Urban Flood Resilience Assessment Using Urban Flood Resilience Model: A Case Study of Peshawar City, Khyber Pakhtunkhwa, Pakistan." Remote Sensing 13, no. 10: 1864.
Proximal sensing offers a novel means for determination of the heavy metal concentration in soil, facilitating low cost and rapid analysis over large areas. In this respect, spectral data and model variables play an important role. Thus far, no attempts have been made to estimate soil heavy metal content using continuum-removal (CR), different preprocessing and statistical methods, and different modeling variables. Considering the adsorption and retention of heavy metals in spectrally active constituents in soil, this study proposes a method for determining low heavy metal concentrations in soil using spectral bands associated with soil organic matter (SOM) and visible–near-infrared (Vis–NIR). To rapidly determine the concentration of heavy metals using hyperspectral data, partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVMR) statistical methods and 16 preprocessing combinations were developed and explored to determine an optimal combination. The results showed that the multiplicative scatter correction and standard normal variate preprocessing methods evaluated with the second derivative spectral transformation method could accurately determine soil Cr and Ni concentrations. The root-mean-square error (RMSE) values of Vis–NIR model combinations with PLSR, PCR, and SVMR were 0.34, 3.42, and 2.15 for Cr, and 0.07, 1.78, and 1.14 for Ni, respectively. Soil Cr and Ni showed strong spectral responses to the Vis–NIR spectral band. The R2 value of the Vis–NIR-based PLSR model was higher than 0.99, and the RMSE value was 0.07–0.34, suggesting higher stability and accuracy. The results were more accurate for Ni than Cr, and PLSR showed the best performance, followed by SVMR and PCR. This perspective has critical implications for guiding quantitative biogeochemical analysis using proximal sensing data.
Aru Han; Xiaoling Lu; Song Qing; Yongbin Bao; Yuhai Bao; Qing Ma; Xingpeng Liu; Jiquan Zhang. Rapid Determination of Low Heavy Metal Concentrations in Grassland Soils around Mining Using Vis–NIR Spectroscopy: A Case Study of Inner Mongolia, China. Sensors 2021, 21, 3220 .
AMA StyleAru Han, Xiaoling Lu, Song Qing, Yongbin Bao, Yuhai Bao, Qing Ma, Xingpeng Liu, Jiquan Zhang. Rapid Determination of Low Heavy Metal Concentrations in Grassland Soils around Mining Using Vis–NIR Spectroscopy: A Case Study of Inner Mongolia, China. Sensors. 2021; 21 (9):3220.
Chicago/Turabian StyleAru Han; Xiaoling Lu; Song Qing; Yongbin Bao; Yuhai Bao; Qing Ma; Xingpeng Liu; Jiquan Zhang. 2021. "Rapid Determination of Low Heavy Metal Concentrations in Grassland Soils around Mining Using Vis–NIR Spectroscopy: A Case Study of Inner Mongolia, China." Sensors 21, no. 9: 3220.
The bottlenecks in enhancing regional green development are resource shortages, environmental pollution, and ecological degradation. Taking the Dongliao River Basin (DRB) of Jilin Province as an example, this study explored green development from a multidimensional perspective. Based on the dimension evaluation results of REECC (resources, environment, and ecological carrying capacity), PLES (production–living–ecological space), and ER (ecological redline), the coupling coordination degree model and spatial autocorrelation model were constructed to explore the coupling coordination degree and spatial distribution of green development. The results showed that REECC had significant spatial differences, and the REECC index showed an increasing trend from northwest to southeast. In 2018, the overall level of green development in the DRB has obvious spatial dependence, but there were spatial differences, with a more obvious polarization from northwest to southeast. The spatial distribution of the coupling degree and coupling coordination degree is roughly the same, and there is a clustering distribution. The conclusions have practical significance for future environmental protection and economic production in the DRB.
Aoyang Wang; Zhijun Tong; Walian Du; Jiquan Zhang; Xingpeng Liu; Zhiyi Yang. Comprehensive Evaluation of Green Development in Dongliao River Basin from the Integration System of “Multi-Dimensions”. Sustainability 2021, 13, 4785 .
AMA StyleAoyang Wang, Zhijun Tong, Walian Du, Jiquan Zhang, Xingpeng Liu, Zhiyi Yang. Comprehensive Evaluation of Green Development in Dongliao River Basin from the Integration System of “Multi-Dimensions”. Sustainability. 2021; 13 (9):4785.
Chicago/Turabian StyleAoyang Wang; Zhijun Tong; Walian Du; Jiquan Zhang; Xingpeng Liu; Zhiyi Yang. 2021. "Comprehensive Evaluation of Green Development in Dongliao River Basin from the Integration System of “Multi-Dimensions”." Sustainability 13, no. 9: 4785.
Erlong Lake is considered one of the largest lakes in midwest Jilin, China, and one of the drinking water resources in neighboring cities. The present study aims to explore the usage of Landsat TM5, ETM7, and OLI8 images to assess water quality (V-phenol, dissolved oxygen (DO), NH4-N, NO3-N) in Erlong Lake, Jilin province, northeast China. Thirteen multispectral images were used in this study for May, July, August, and September in 2000, 2001, 2002, and October 2020. Radiometric and atmospheric corrections were applied to all images. All in situ water quality parameters were strongly correlated to each other, except DO. The in situ measurements (V-phenol, dissolved oxygen, NH4-N, NO3-N) were statistically correlated with various spectral band combinations (blue, green, red, and NIR) derived from Landsat imagery. Regression analysis reported that there are strong relationships between the estimated and retrieved water quality from the Landsat images. Moreover, in calibrations, the highest value of the coefficient of determination (R2) was ≥0.85 with (RMSE) = 0.038; the lowest value of R2 was >0.30 with RMSE= 0.752. All generated models were validated in different statistical indices; R2 was up to 0.95 for most cases, with RMSE ranging from 1.390 to 0.050. Finally, the empirical algorithms were successfully assessed (V-phenol, dissolved oxygen, NH4-N, NO3-N) in Erlong Lake, using Landsat images with very good accuracy. Both in situ and model retrieved results showed the same trends with non-significant differences. September of 2000, 2001, and 2002 and October of 2020 were selected to assess the spatial distributions of V-phenol, DO, NH4-N, and NO3-N in the lake. V-phenol, NH4-N, and NO3-N were reported low in shallow water but high in deep water, while DO was high in shallow water but low in deep water of the lake. Domestic sewage, agricultural, and urban industrial pollution are the most common sources of pollution in the Erlong Lake.
Bazel Al-Shaibah; Xingpeng Liu; Jiquan Zhang; Zhijun Tong; Mingxi Zhang; Ahmed El-Zeiny; Cheechouyang Faichia; Muhammad Hussain; Muhammad Tayyab. Modeling Water Quality Parameters Using Landsat Multispectral Images: A Case Study of Erlong Lake, Northeast China. Remote Sensing 2021, 13, 1603 .
AMA StyleBazel Al-Shaibah, Xingpeng Liu, Jiquan Zhang, Zhijun Tong, Mingxi Zhang, Ahmed El-Zeiny, Cheechouyang Faichia, Muhammad Hussain, Muhammad Tayyab. Modeling Water Quality Parameters Using Landsat Multispectral Images: A Case Study of Erlong Lake, Northeast China. Remote Sensing. 2021; 13 (9):1603.
Chicago/Turabian StyleBazel Al-Shaibah; Xingpeng Liu; Jiquan Zhang; Zhijun Tong; Mingxi Zhang; Ahmed El-Zeiny; Cheechouyang Faichia; Muhammad Hussain; Muhammad Tayyab. 2021. "Modeling Water Quality Parameters Using Landsat Multispectral Images: A Case Study of Erlong Lake, Northeast China." Remote Sensing 13, no. 9: 1603.
Soil moisture is a reliable water resource for plant growth in arid and semi-arid regions. Characterizing the interaction between soil moisture and vegetation is important for assessing the sustainability of terrestrial ecosystems. This study explores the spatiotemporal characteristics of four soil moisture layers (layer 1: 0–7 cm, layer 2: 7–28 cm, layer 3: 28–100 cm, and layer 4: 100–289 cm) and the time-lagged correlation with the normalized difference vegetation index (NDVI) for different vegetation types on an intra-annual scale on the Mongolian Plateau (MP). The most significant results indicated that: (1) the four layers of soil moisture can be roughly divided into rapid change (layers 1 and 2), active (layer 3), and stable (layer 4) layers. The soil moisture content in the different vegetation regions was forest > grassland > desert vegetation. (2) The soil moisture in layer 1 showed the strongest positive correlation with NDVI in the whole area; meanwhile, the soil moisture of layers 2 and 3 showed the strongest negative correlation with the NDVI mainly in grassland and desert, and layer 4 showed the strongest negative correlation with the NDVI in the forest. (3) Mutual responses of NDVI and deep layer soil moisture required a longer time compared with the shallow layer. In the annual time scale, the NDVI was affected by the change in soil moisture in most of the study area, except for coniferous forest and desert vegetation regions. (4) Under the different stages of vegetation change, the soil moisture changes advance than NDVI about 3 months during the greening stage, while the NDVI changes advance than soil moisture by 0.5 months during the browning stage. Regardless of the stage, changes in soil moisture are initiated from the shallow layer and advance to the deep layer. The results of this study provide deep insight into the relationship between soil moisture and vegetation in arid and semi-arid regions.
Li Na; Risu Na; Yongbin Bao; Jiquan Zhang. Time-Lagged Correlation between Soil Moisture and Intra-Annual Dynamics of Vegetation on the Mongolian Plateau. Remote Sensing 2021, 13, 1527 .
AMA StyleLi Na, Risu Na, Yongbin Bao, Jiquan Zhang. Time-Lagged Correlation between Soil Moisture and Intra-Annual Dynamics of Vegetation on the Mongolian Plateau. Remote Sensing. 2021; 13 (8):1527.
Chicago/Turabian StyleLi Na; Risu Na; Yongbin Bao; Jiquan Zhang. 2021. "Time-Lagged Correlation between Soil Moisture and Intra-Annual Dynamics of Vegetation on the Mongolian Plateau." Remote Sensing 13, no. 8: 1527.
Actinidia arguta (Siebold and Zucc.) Planch.ex Miq, called “hardy kiwifruit”, “baby kiwi” or “kiwi berry”, has a unique taste, is rich in nutrients and has high economic value and broad market prospects. Active research on the potential geographic distribution of A. arguta in China aims to provide a reference basis for its resource investigation, conservation, development and utilization and introduction of cultivation. In this study, the Maxent model was used to combine climatic factors, soil factors and geographical factors (elevation, slope and aspect) to predict the current and future (2041–2060 and 2081–2100) potential distribution of A. arguta and to analyze the impact of climate change on it. The results showed that the suitable distribution range of A. arguta in China was 23–43 N and 100–125 E, with a total area of about 3.4451 × 106 km2. The highly suitable area of A. arguta was mainly concentrated in the middle and low mountain areas of the south of Shaanxi, the east of Sichuan, the middle and west of Guizhou and the west of Yunnan, presenting a circular distribution. The Jackknife test was used to calculate the main environmental factors affecting the distribution of A. arguta. The first four main factors were annual mean temperature (bio_1), precipitation of the warmest quarter (bio_18), elevation (ELE) and mean temperature of the warmest quarter (bio_10), which provided a contribution up to 81.7%. Under the scenarios of three representative concentrations (SSP1_2.6, SSP2_4.5 and SSP5_8.5) in the future, the area of low and moderate suitable habitat decreased, while the area of highly suitable habitat increased. The migration direction of the centroid in the highly suitable habitat moved to the southwest in the future scenario period.
Yining Ma; Xiaoling Lu; Kaiwei Li; Chunyi Wang; Ari Guna; Jiquan Zhang. Prediction of Potential Geographical Distribution Patterns of Actinidia arguta under Different Climate Scenarios. Sustainability 2021, 13, 3526 .
AMA StyleYining Ma, Xiaoling Lu, Kaiwei Li, Chunyi Wang, Ari Guna, Jiquan Zhang. Prediction of Potential Geographical Distribution Patterns of Actinidia arguta under Different Climate Scenarios. Sustainability. 2021; 13 (6):3526.
Chicago/Turabian StyleYining Ma; Xiaoling Lu; Kaiwei Li; Chunyi Wang; Ari Guna; Jiquan Zhang. 2021. "Prediction of Potential Geographical Distribution Patterns of Actinidia arguta under Different Climate Scenarios." Sustainability 13, no. 6: 3526.
Floods are considered one of the world’s most overwhelming hydro meteorological disasters, which cause tremendous environmental and socioeconomic damages in a developing country such as Pakistan. In this study, we use a Geographic information system (GIS)-based multi-criteria approach to access detailed flood vulnerability in the District Shangla by incorporating the physical, socioeconomic vulnerabilities, and coping capacity. In the first step, 21 essential criteria were chosen under three vulnerability components. To support the analytical hierarchy process (AHP), the used criteria were transformed, weighted, and standardized into spatial thematic layers. Then a weighted overlay technique was used to build an individual map of vulnerability components. Finally, the integrated vulnerability map has been generated from the individual maps and spatial dimensions of vulnerability levels have been identified successfully. The results demonstrated that 25% of the western-middle area to the northern part of the study area comprises high to very high vulnerability because of the proximity to waterways, high precipitation, elevation, and other socioeconomic factors. Although, by integrating the coping capacity, the western-central and northern parts of the study area comprising from high to very high vulnerability. The coping capacities of the central and eastern areas are higher as compared to the northern and southern parts of the study area because of the numerous flood shelters and health complexes. A qualitative approach from the field validated the results of this study. This study’s outcomes would help disaster managers, decision makers, and local administration to quantify the spatial vulnerability of flood and establish successful mitigation plans and strategies for flood risk assessment in the study area.
Muhammad Hussain; Muhammad Tayyab; Jiquan Zhang; Ashfaq Shah; Kashif Ullah; Ummer Mehmood; Bazel Al-Shaibah. GIS-Based Multi-Criteria Approach for Flood Vulnerability Assessment and Mapping in District Shangla: Khyber Pakhtunkhwa, Pakistan. Sustainability 2021, 13, 3126 .
AMA StyleMuhammad Hussain, Muhammad Tayyab, Jiquan Zhang, Ashfaq Shah, Kashif Ullah, Ummer Mehmood, Bazel Al-Shaibah. GIS-Based Multi-Criteria Approach for Flood Vulnerability Assessment and Mapping in District Shangla: Khyber Pakhtunkhwa, Pakistan. Sustainability. 2021; 13 (6):3126.
Chicago/Turabian StyleMuhammad Hussain; Muhammad Tayyab; Jiquan Zhang; Ashfaq Shah; Kashif Ullah; Ummer Mehmood; Bazel Al-Shaibah. 2021. "GIS-Based Multi-Criteria Approach for Flood Vulnerability Assessment and Mapping in District Shangla: Khyber Pakhtunkhwa, Pakistan." Sustainability 13, no. 6: 3126.
In recent years, global warming and intense human activity have been responsible for significantly altering vegetation dynamics on the Mongolian Plateau. Understanding the long-term vegetation dynamics in this region is important to assess the impact of these changes on the local ecosystem. Long-term (1982–2015), satellite-derived normalized difference vegetation index (NDVI) datasets were used to analyse the spatio-temporal patterns of vegetation activities using linear regression and the breaks for additive season and trend methods. The links between these patterns and changes in temperature, precipitation (PRE), soil moisture (SM), and anthropogenic activity were determined using partial correlation analysis, the residual trends method, and a stepwise multiple regression model. The most significant results indicated that air temperature and potential evapotranspiration increased significantly, while the SM and PRE had markedly decreased over the past 34 years. The NDVI dataset included 71.16% of pixels showing an increase in temperature and evaporation during the growing season, particularly in eastern Mongolia and the southern border of the Inner Mongolia Autonomous region, China. The proportion indicating the breakpoint of vegetation dynamics was 71.34% of pixels, and the trend breakpoints mainly occurred in 1993, 2003, and 2010. The cumulative effects of PRE and SM in the middle period, coupled with the short-term effects of temperature and potential evapotranspiration, have had positive effects on vegetation greening. Anthropogenic factors appear to have positively impacted vegetation dynamics, as shown in 81.21% of pixels. We consider rapid economic growth, PRE, and SM to be the main driving factors in Inner Mongolia. PRE was the main climatic factor, and combined human and livestock populations were the primary anthropogenic factors influencing vegetation dynamics in Mongolia. This study is important in promoting the continued use of green projects to address environmental change in the Mongolian Plateau.
Enliang Guo; Yongfang Wang; Cailin Wang; Zhongyi Sun; Yulong Bao; Naren Mandula; Buren Jirigala; Yuhai Bao; He Li. NDVI Indicates Long-Term Dynamics of Vegetation and Its Driving Forces from Climatic and Anthropogenic Factors in Mongolian Plateau. Remote Sensing 2021, 13, 688 .
AMA StyleEnliang Guo, Yongfang Wang, Cailin Wang, Zhongyi Sun, Yulong Bao, Naren Mandula, Buren Jirigala, Yuhai Bao, He Li. NDVI Indicates Long-Term Dynamics of Vegetation and Its Driving Forces from Climatic and Anthropogenic Factors in Mongolian Plateau. Remote Sensing. 2021; 13 (4):688.
Chicago/Turabian StyleEnliang Guo; Yongfang Wang; Cailin Wang; Zhongyi Sun; Yulong Bao; Naren Mandula; Buren Jirigala; Yuhai Bao; He Li. 2021. "NDVI Indicates Long-Term Dynamics of Vegetation and Its Driving Forces from Climatic and Anthropogenic Factors in Mongolian Plateau." Remote Sensing 13, no. 4: 688.
An important component in improving the quality of forests is to study the interference intensity of forest fires, in order to describe the intensity of the forest fire and the vegetation recovery, and to improve the monitoring ability of the dynamic change of the forest. Using a forest fire event in Bilahe, Inner Monglia in 2017 as a case study, this study extracted the burned area based on the BAIS2 index of Sentinel-2 data for 2016–2018. The leaf area index (LAI) and fractional vegetation cover (FVC), which are more suitable for monitoring vegetation dynamic changes of a burned area, were calculated by comparing the biophysical and spectral indices. The results showed that patterns of change of LAI and FVC of various land cover types were similar post-fire. The LAI and FVC of forest and grassland were high during the pre-fire and post-fire years. During the fire year, from the fire month (May) through the next 4 months (September), the order of areas of different fire severity in terms of values of LAI and FVC was: low > moderate > high severity. During the post fire year, LAI and FVC increased rapidly in areas of different fire severity, and the ranking of areas of different fire severity in terms of values LAI and FVC was consistent with the trend observed during the pre-fire year. The results of this study can improve the understanding of the mechanisms involved in post-fire vegetation change. By using quantitative inversion, the health trajectory of the ecosystem can be rapidly determined, and therefore this method can play an irreplaceable role in the realization of sustainable development in the study area. Therefore, it is of great scientific significance to quantitatively retrieve vegetation variables by remote sensing.
Aru Han; Song Qing; Yongbin Bao; Li Na; Yuhai Bao; Xingpeng Liu; Jiquan Zhang; Chunyi Wang. Short-Term Effects of Fire Severity on Vegetation Based on Sentinel-2 Satellite Data. Sustainability 2021, 13, 432 .
AMA StyleAru Han, Song Qing, Yongbin Bao, Li Na, Yuhai Bao, Xingpeng Liu, Jiquan Zhang, Chunyi Wang. Short-Term Effects of Fire Severity on Vegetation Based on Sentinel-2 Satellite Data. Sustainability. 2021; 13 (1):432.
Chicago/Turabian StyleAru Han; Song Qing; Yongbin Bao; Li Na; Yuhai Bao; Xingpeng Liu; Jiquan Zhang; Chunyi Wang. 2021. "Short-Term Effects of Fire Severity on Vegetation Based on Sentinel-2 Satellite Data." Sustainability 13, no. 1: 432.
Arginine kinase (AK, EC 2.7.3.3) plays an important role in cells with high, fluctuating energy requirements. In invertebrates, AK is the major phosphagen kinase that modulates the energy metabolism. Here, the full-length cDNA sequence encoding arginine kinase (EcAK) was obtained from the Exopalaemon carinicauda. The complete nucleotide sequence of EcAK contained a 1068 bp open reading frame (ORF) encoding EcAK precursor of 355 amino acids. The genomic DNA fragment of EcAK with the corresponding cDNA sequence is composed of 4 exons and 3 introns. The domain architecture of the deduced EcAK protein contained an ATP-gua_PtransN domain and an ATP-gua_Ptrans domain. EcAK mRNA was predominantly expressed in the muscle. The expression of EcAK in the prawns challenged with Vibrio parahaemolyticus and Aeromonas hydrophila changed in a time-dependent manner. Then, EcAK was recombinantly expressed in Pichia pastoris and the purified recombinant EcAK had the same enzymatic characterization as AK from the muscle of Euphausia superba. In conclusion, EcAK may play the same biological activity in E. carinicauda as those from other crustaceans.
Zixuan Wu; Yujie Liu; Jiaqi Zheng; Yongzhao Zhou; Kefan Xing; Yuying Sun; Jiquan Zhang. Genomic structure, expression and functional characterization of arginine kinase (EcAK) from Exopalaemon carinicauda. Fish & Shellfish Immunology 2020, 109, 82 -86.
AMA StyleZixuan Wu, Yujie Liu, Jiaqi Zheng, Yongzhao Zhou, Kefan Xing, Yuying Sun, Jiquan Zhang. Genomic structure, expression and functional characterization of arginine kinase (EcAK) from Exopalaemon carinicauda. Fish & Shellfish Immunology. 2020; 109 ():82-86.
Chicago/Turabian StyleZixuan Wu; Yujie Liu; Jiaqi Zheng; Yongzhao Zhou; Kefan Xing; Yuying Sun; Jiquan Zhang. 2020. "Genomic structure, expression and functional characterization of arginine kinase (EcAK) from Exopalaemon carinicauda." Fish & Shellfish Immunology 109, no. : 82-86.
Grassland fire dynamics are subject to myriad climatic, biological, and anthropogenic drivers, thresholds, and feedbacks and therefore do not conform to assumptions of statistical stationarity. The presence of non-stationarity in time series data leads to ambiguous results that can misinform regional-level fire management strategies. This study employs non-stationarity in time series data among multiple variables and multiple intensities using dynamic simulations of autoregressive distributed lag models to elucidate key drivers of climate and ecological change on burned grasslands in Xilingol, China. We used unit root methods to select appropriate estimation methods for further analysis. Using the model estimations, we developed scenarios emulating the effects of instantaneous changes (i.e., shocks) of some significant variables on climate and ecological change. Changes in mean monthly wind speed and maximum temperature produce complex responses on area burned, directly, and through feedback relationships. Our framework addresses interactions among multiple drivers to explain fire and ecosystem responses in grasslands, and how these may be understood and prioritized in different empirical contexts needed to formulate effective fire management policies.
Ali Hassan Shabbir; Jiquan Zhang; John W. Groninger; Eddie J. B. van Etten; Samuel Asumadu Sarkodie; James A. Lutz; Carlos F. Valencia. Seasonal weather and climate prediction over area burned in grasslands of northeast China. Scientific Reports 2020, 10, 1 -11.
AMA StyleAli Hassan Shabbir, Jiquan Zhang, John W. Groninger, Eddie J. B. van Etten, Samuel Asumadu Sarkodie, James A. Lutz, Carlos F. Valencia. Seasonal weather and climate prediction over area burned in grasslands of northeast China. Scientific Reports. 2020; 10 (1):1-11.
Chicago/Turabian StyleAli Hassan Shabbir; Jiquan Zhang; John W. Groninger; Eddie J. B. van Etten; Samuel Asumadu Sarkodie; James A. Lutz; Carlos F. Valencia. 2020. "Seasonal weather and climate prediction over area burned in grasslands of northeast China." Scientific Reports 10, no. 1: 1-11.
Among the most frequent and dangerous natural hazards, landslides often result in huge casualties and economic losses. Landslide susceptibility mapping (LSM) is an excellent approach for protecting and reducing the risks by landslides. This study aims to explore the performance of Bayesian optimization (BO) in the random forest (RF) and gradient boosting decision tree (GBDT) model for LSM and applied in Shuicheng County, China. Multiple data sources are used to obtain 17 conditioning factors of landslides, Borderline-SMOTE and Randomundersample methods are combined to solve the imbalanced sample problem. RF and GBDT models before and after BO are adopted to calculate the susceptibility value of landslides and produce LSMs and these models were compared and evaluated using multiple validation approach. The results demonstrated that the models we proposed all have high enough model accuracy to be applied to produce LSM, the performance of the RF is better than the GBDT model without BO, while after adopting the Bayesian optimized hyperparameters, the prediction accuracy of the RF and GBDT models is improved by 1% and 7%, respectively and the Bayesian optimized GBDT model is the best for LSM in this four models. In summary, the Bayesian optimized RF and GBDT models, especially the GBDT model we proposed for landslide susceptibility assessment and LSM construction has a very good application performance and development prospects.
Guangzhi Rong; Si Alu; Kaiwei Li; Yulin Su; Jiquan Zhang; Yichen Zhang; Tiantao Li. Rainfall Induced Landslide Susceptibility Mapping Based on Bayesian Optimized Random Forest and Gradient Boosting Decision Tree Models—A Case Study of Shuicheng County, China. Water 2020, 12, 3066 .
AMA StyleGuangzhi Rong, Si Alu, Kaiwei Li, Yulin Su, Jiquan Zhang, Yichen Zhang, Tiantao Li. Rainfall Induced Landslide Susceptibility Mapping Based on Bayesian Optimized Random Forest and Gradient Boosting Decision Tree Models—A Case Study of Shuicheng County, China. Water. 2020; 12 (11):3066.
Chicago/Turabian StyleGuangzhi Rong; Si Alu; Kaiwei Li; Yulin Su; Jiquan Zhang; Yichen Zhang; Tiantao Li. 2020. "Rainfall Induced Landslide Susceptibility Mapping Based on Bayesian Optimized Random Forest and Gradient Boosting Decision Tree Models—A Case Study of Shuicheng County, China." Water 12, no. 11: 3066.