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Population data are key indicators of policymaking, public health, and land use in urban and ecological systems; however, traditional censuses are time-consuming, expensive, and laborious. This study proposes a method of modelling population density estimations based on remote sensing data in Hefei. Four models with impervious surface (IS), night light (NTL), and point of interest (POI) data as independent variables are constructed at the township scale, and the optimal model was applied to pixels to obtain a finer population density distribution. The results show that: (1) impervious surface (IS) data can be effectively extracted by the linear spectral mixture analysis (LSMA) method; (2) there is a high potential of the multi-variable model to estimate the population density, with an adjusted R2 of 0.832, and mean absolute error (MAE) of 0.420 from 10-fold cross validation recorded; (3) downscaling the predicted population density from the township scale to pixels using the multi-variable stepwise regression model achieves a more refined population density distribution. This study provides a promising method for the rapid and effective prediction of population data in interval years, and data support for urban planning and population management.
Jinyu Zang; Ting Zhang; Longqian Chen; Long Li; Weiqiang Liu; Lina Yuan; Yu Zhang; Ruiyang Liu; Zhiqiang Wang; Ziqi Yu; Jia Wang. Optimization of Modelling Population Density Estimation Based on Impervious Surfaces. Land 2021, 10, 791 .
AMA StyleJinyu Zang, Ting Zhang, Longqian Chen, Long Li, Weiqiang Liu, Lina Yuan, Yu Zhang, Ruiyang Liu, Zhiqiang Wang, Ziqi Yu, Jia Wang. Optimization of Modelling Population Density Estimation Based on Impervious Surfaces. Land. 2021; 10 (8):791.
Chicago/Turabian StyleJinyu Zang; Ting Zhang; Longqian Chen; Long Li; Weiqiang Liu; Lina Yuan; Yu Zhang; Ruiyang Liu; Zhiqiang Wang; Ziqi Yu; Jia Wang. 2021. "Optimization of Modelling Population Density Estimation Based on Impervious Surfaces." Land 10, no. 8: 791.
This study aims to integrate multisource data to model the relative soil moisture (RSM) over the Chinese Loess Plateau in 2017 by stepwise multilinear regression (SMLR) in order to improve the spatial coverage of our previously published RSM. First, 34 candidate variables (12 quantitative and 22 dummy variables) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and topographic, soil properties, and meteorological data were preprocessed. Then, SMLR was applied to variables without multicollinearity to select statistically significant (p-value < 0.05) variables. After the accuracy assessment, monthly, seasonal, and annual spatial patterns of RSM were mapped at 500 m resolution and evaluated. The results indicate that there was a high potential of SMLR to model RSM with the desired accuracy (best fit of the model with Pearson’s r = 0.969, root mean square error = 0.761%, and mean absolute error = 0.576%) over the Chinese Loess Plateau. The variables of elevation (0–500 m and 2000–2500 m), precipitation, soil texture of loam, and nighttime land surface temperature can continuously be used in the regression models for all seasons. Including dummy variables improved the model fit both in calibration and validation. Moreover, the SMLR-modeled RSM achieved better spatial coverage than that of the reference RSM for almost all periods. This is a significant finding as the SMLR method supports the use of multisource data to complement and/or replace coarse resolution satellite imagery in the estimation of RSM.
Lina Yuan; Long Li; Ting Zhang; Longqian Chen; Weiqiang Liu; Sai Hu; Longhua Yang. Modeling Soil Moisture from Multisource Data by Stepwise Multilinear Regression: An Application to the Chinese Loess Plateau. ISPRS International Journal of Geo-Information 2021, 10, 233 .
AMA StyleLina Yuan, Long Li, Ting Zhang, Longqian Chen, Weiqiang Liu, Sai Hu, Longhua Yang. Modeling Soil Moisture from Multisource Data by Stepwise Multilinear Regression: An Application to the Chinese Loess Plateau. ISPRS International Journal of Geo-Information. 2021; 10 (4):233.
Chicago/Turabian StyleLina Yuan; Long Li; Ting Zhang; Longqian Chen; Weiqiang Liu; Sai Hu; Longhua Yang. 2021. "Modeling Soil Moisture from Multisource Data by Stepwise Multilinear Regression: An Application to the Chinese Loess Plateau." ISPRS International Journal of Geo-Information 10, no. 4: 233.
Accuracy soil moisture estimation at a relevant spatiotemporal scale is scarce but beneficial for understanding ecohydrological processes and improving weather forecasting and climate models, particularly in arid and semi-arid regions like the Chinese Loess Plateau (CLP). This study proposed Criterion 2, a new method to improve relative soil moisture (RSM) estimation by identification of normalized difference vegetation index (NDVI) thresholds optimization based on our previously proposed iteration procedure of Criterion 1. Apparent thermal inertia (ATI) and temperature vegetation dryness index (TVDI) were applied to subregional RSM retrieval for the CLP throughout 2017. Three optimal NDVI thresholds (NDVI0 was used for computing TVDI, and both NDVIATI and NDVITVDI for dividing the entire CLP) were firstly identified with the best validation results () of subregions for 8-day periods. Then, we compared the selected optimal NDVI thresholds and estimated RSM with each criterion. Results show that NDVI thresholds were optimized to robust RSM estimation with Criterion 2, which characterized RSM variability better. The estimated RSM with Criterion 2 showed increased accuracy (maximum of 0.82 ± 0.007 for Criterion 2 and of 0.75 ± 0.008 for Criterion 1) and spatiotemporal coverage (45 and 38 periods (8-day) of RSM maps and the total RSM area of 939.52 × 104 km2 and 667.44 × 104 km2 with Criterion 2 and Criterion 1, respectively) than with Criterion 1. Moreover, the additional NDVI thresholds we applied was another strategy to acquire wider coverage of RSM estimation. The improved RSM estimation with Criterion 2 could provide a basis for forecasting drought and precision irrigation management.
Lina Yuan; Long Li; Ting Zhang; Longqian Chen; Jianlin Zhao; Weiqiang Liu; Liang Cheng; Sai Hu; Longhua Yang; Mingxin Wen. Improving Soil Moisture Estimation by Identification of NDVI Thresholds Optimization: An Application to the Chinese Loess Plateau. Remote Sensing 2021, 13, 589 .
AMA StyleLina Yuan, Long Li, Ting Zhang, Longqian Chen, Jianlin Zhao, Weiqiang Liu, Liang Cheng, Sai Hu, Longhua Yang, Mingxin Wen. Improving Soil Moisture Estimation by Identification of NDVI Thresholds Optimization: An Application to the Chinese Loess Plateau. Remote Sensing. 2021; 13 (4):589.
Chicago/Turabian StyleLina Yuan; Long Li; Ting Zhang; Longqian Chen; Jianlin Zhao; Weiqiang Liu; Liang Cheng; Sai Hu; Longhua Yang; Mingxin Wen. 2021. "Improving Soil Moisture Estimation by Identification of NDVI Thresholds Optimization: An Application to the Chinese Loess Plateau." Remote Sensing 13, no. 4: 589.
The land ecosystem provides essential natural resources for the survival and development of human beings. Therefore, land ecological security (LES) acts as a vital part of the sustainable development of human society and economy. This study included a dynamic analysis of land use change in Chaohu Lake Basin (CLB) in China from 1998 to 2018, evaluating the spatiotemporal patterns of LES at both the administrative district scale and grid scale (200 m × 200 m). Then, geographic detector was applied to analyze the influence of the assessment index on LES. The results show that in the 2008–2018 period, land use changed more significantly compared to the 1998–2008 period. The continuous extension of urban land led to a decrease in the areas of other land use types. In the CLB (administrative district scale), the LES levels varied throughout the study period. In Changfeng, Feixi, and the other three regions, the LES has been significantly improved. However, the LES in six other regions showed different degrees of decline, particularly in Hexian and Urban Hefei. Simultaneously, the LES showed a gradual improvement at a 200 m × 200 m grid scale level. The influence of anthropogenic factors on the LES was stronger than natural factors. Findings from this study provide reliable guidance for improving the ecosystem environment in ecologically fragile areas.
Mingxin Wen; Ting Zhang; Long Li; Longqian Chen; Sai Hu; Jia Wang; Weiqiang Liu; Yu Zhang; Lina Yuan. Assessment of Land Ecological Security and Analysis of Influencing Factors in Chaohu Lake Basin, China from 1998–2018. Sustainability 2021, 13, 358 .
AMA StyleMingxin Wen, Ting Zhang, Long Li, Longqian Chen, Sai Hu, Jia Wang, Weiqiang Liu, Yu Zhang, Lina Yuan. Assessment of Land Ecological Security and Analysis of Influencing Factors in Chaohu Lake Basin, China from 1998–2018. Sustainability. 2021; 13 (1):358.
Chicago/Turabian StyleMingxin Wen; Ting Zhang; Long Li; Longqian Chen; Sai Hu; Jia Wang; Weiqiang Liu; Yu Zhang; Lina Yuan. 2021. "Assessment of Land Ecological Security and Analysis of Influencing Factors in Chaohu Lake Basin, China from 1998–2018." Sustainability 13, no. 1: 358.
Urbanization is a key determinant of fine particulate matter (PM2.5) pollution variability. However, there is a limited understanding of different urbanization factors’ roles in PM2.5 pollution. Using satellite-derived PM2.5 data from 2002 to 2017, we investigated the spatiotemporal evolution and the spatial autocorrelation of PM2.5 pollution in the Yangtze River Delta (YRD) region. Afterwards, the impacts of three urbanization factors (population urbanization, land urbanization and economic urbanization) on PM2.5 pollution were estimated by a spatial Durbin panel data model (SDM). Obtained results showed that: (i) PM2.5 pollution was larger in the north than in the south of YRD; (ii) Lianyungang and Yancheng cities had significant increasing trends in PM2.5 pollution from 2002 to 2017; (iii) the regional median center of PM2.5 pollution was observed in the Nanjing city, with gradual shifting to the northwest during the 16-year period; (iv) PM2.5 pollution showed significant and positive spatial autocorrelation and spillover effect; (v) population urbanization contributed more to the increase in PM2.5 pollution than land urbanization, while economic urbanization had no significant impact. The present study highlights the impacts of three urbanization factors on PM2.5 pollution which represent valuable and relevant information for air pollution control and urban planning.
Liang Cheng; Ting Zhang; Longqian Chen; Long Li; Shangjiu Wang; Sai Hu; Lina Yuan; Jia Wang; Mingxin Wen. Investigating the Impacts of Urbanization on PM2.5 Pollution in the Yangtze River Delta of China: A Spatial Panel Data Approach. Atmosphere 2020, 11, 1058 .
AMA StyleLiang Cheng, Ting Zhang, Longqian Chen, Long Li, Shangjiu Wang, Sai Hu, Lina Yuan, Jia Wang, Mingxin Wen. Investigating the Impacts of Urbanization on PM2.5 Pollution in the Yangtze River Delta of China: A Spatial Panel Data Approach. Atmosphere. 2020; 11 (10):1058.
Chicago/Turabian StyleLiang Cheng; Ting Zhang; Longqian Chen; Long Li; Shangjiu Wang; Sai Hu; Lina Yuan; Jia Wang; Mingxin Wen. 2020. "Investigating the Impacts of Urbanization on PM2.5 Pollution in the Yangtze River Delta of China: A Spatial Panel Data Approach." Atmosphere 11, no. 10: 1058.
Timely and effective estimation and monitoring of soil moisture (SM) provides not only an understanding of regional SM status for agricultural management or potential drought but also a basis for characterizing water and energy exchange. The apparent thermal inertia (ATI) and Temperature Vegetation Dryness Index (TVDI) are two widely used indices to reflect SM from remote sensing data. While the ATI-based model is routinely used to estimate the SM of bare soil and sparsely vegetated areas, the TVDI-based model is more suitable for areas with dense vegetation coverage. In this study, we present an iteration procedure that allows us to identify optimal Normalized Difference Vegetation Index (NDVI) thresholds for subregions and estimate their relative soil moisture (RSM) using three models (the ATI-based model, the TVDI-based model, and the ATI/TVDI joint model) from January 1 to December 31, 2017, in the Chinese Loess Plateau. The initial NDVI (NDVI0) was first introduced to obtain TVDI value and two other thresholds of NDVIATI and NDVITVDI were designed for dividing the whole area into three subregions (the ATI subregion, the TVDI subregion, and the ATI/TVDI subregion). The NDVI values corresponding to maximum R-values (correlation coefficient) between estimated RSM and in situ RSM measurements were chosen as optimal NDVI thresholds after performing as high as 48,620 iterations with 10 rounds of 10-fold cross-calibration and validation for each period. An RSM map of the whole study area was produced by merging the RSM of each of the three subregions. The spatiotemporal and comparative analysis further indicated that the ATI/TVDI joint model has higher applicability (accounting for 36/38 periods) and accuracy than the ATI-based and TVDI-based models. The highest average R-value between the estimated RSM and in situ RSM measurements was 0.73±0.011 (RMSE—root mean square error, 3.43±0.071% and MAE—mean absolute error, 0.05±0.025) on the 137th day of 2017 (DOY—day of the year, 137). Although there is potential for improved mapping of RSM for the entire Chinese Loess Plateau, the iteration procedure of identifying optimal thresholds determination offers a promising method for achieving finer-resolution and robust RSM estimation in large heterogeneous areas.
Lina Yuan; Long Li; Ting Zhang; Longqian Chen; Jianlin Zhao; Sai Hu; Liang Cheng; Weiqiang Liu. Soil Moisture Estimation for the Chinese Loess Plateau using MODIS-derived ATI and TVDI. Remote Sensing 2020, 12, 3040 .
AMA StyleLina Yuan, Long Li, Ting Zhang, Longqian Chen, Jianlin Zhao, Sai Hu, Liang Cheng, Weiqiang Liu. Soil Moisture Estimation for the Chinese Loess Plateau using MODIS-derived ATI and TVDI. Remote Sensing. 2020; 12 (18):3040.
Chicago/Turabian StyleLina Yuan; Long Li; Ting Zhang; Longqian Chen; Jianlin Zhao; Sai Hu; Liang Cheng; Weiqiang Liu. 2020. "Soil Moisture Estimation for the Chinese Loess Plateau using MODIS-derived ATI and TVDI." Remote Sensing 12, no. 18: 3040.
Land use change has a significant impact on the structure and function of ecosystems, and the transformation of ecosystems affects the mode and efficiency of land use, which reflects a mutual interaction relationship. The prediction and simulation of future land use change can enhance the foresight of land use planning, which is of great significance to regional sustainable development. In this study, future land use changes are characterized under an ecological optimization scenario based on the grey prediction (1,1) model (GM) and a future land use simulation (FLUS) model. In addition, the ecosystem service value (ESV) of Anhui Province from 1995 to 2030 were estimated based on the revised estimation model. The results indicate the following details: (1) the FLUS model was used to simulate the land use layout of Anhui Province in 2018, where the overall accuracy of the simulation results is high, indicating that the FLUS model is applicable for simulating future land use change; (2) the spatial layout of land use types in Anhui Province is stable and the cultivated land has the highest proportion. The most significant characteristic of future land use change is that the area of cultivated land continues to decrease while the area of built-up land continues to expand; and (3) the ESV of Anhui Province is predicted to increase in the future. The regulating service is the largest ESV contributor, and water area is the land use type with the highest proportion of ESV. These findings provide reference for the formulation of sustainable development policies of the regional ecological environment.
Sai Hu; Longqian Chen; Long Li; Ting Zhang; Lina Yuan; Liang Cheng; Jia Wang; Mingxin Wen. Simulation of Land Use Change and Ecosystem Service Value Dynamics under Ecological Constraints in Anhui Province, China. International Journal of Environmental Research and Public Health 2020, 17, 1 .
AMA StyleSai Hu, Longqian Chen, Long Li, Ting Zhang, Lina Yuan, Liang Cheng, Jia Wang, Mingxin Wen. Simulation of Land Use Change and Ecosystem Service Value Dynamics under Ecological Constraints in Anhui Province, China. International Journal of Environmental Research and Public Health. 2020; 17 (12):1.
Chicago/Turabian StyleSai Hu; Longqian Chen; Long Li; Ting Zhang; Lina Yuan; Liang Cheng; Jia Wang; Mingxin Wen. 2020. "Simulation of Land Use Change and Ecosystem Service Value Dynamics under Ecological Constraints in Anhui Province, China." International Journal of Environmental Research and Public Health 17, no. 12: 1.
Volcanic activity remains highly detrimental to populations, property and activities in the range of its products. In order to reduce the impact of volcanic processes and products, it is critically important to conduct comprehensive volcanic risk assessments on volcanically active areas. This study tests a volcanic risk assessment methodology based on numerical simulations of volcanic hazards and quantitative analysis of social vulnerability in the Spanish island of Tenerife, a well-known tourist destination. We first simulated the most likely volcanic hazards in the two eruptive scenarios using the Volcanic Risk Information System (VORIS) tool and then evaluated the vulnerability using a total of 19 socio-economic indicators within the Vulnerability Scoping Diagram (VSD) framework by combining the analytic hierarchy process (AHP) and the entropy method. Our results show good agreement with previous assessments. In two eruptive scenarios, the north and northwest of the island were more exposed to volcanic hazards, and the east registered the highest vulnerability. Overall, the northern municipalities showed the highest volcanic risk in two scenarios. Our test indicates that disaster risk varies greatly across the island, and that risk reduction strategies should be prioritized on the north areas. While refinements to the model will produce more accurate results, the outputs will still be beneficial to the local authorities when designing policies for volcanic risk reduction policies in Tenerife. This study tests a comprehensive volcanic risk assessment for Tenerife, but it also provides a framework that is applicable to other regions threatened by volcanic hazards.
Weiqiang Liu; Long Li; Longqian Chen; Mingxin Wen; Jia Wang; Lina Yuan; Yunqiang Liu; Han Li. Testing a Comprehensive Volcanic Risk Assessment of Tenerife by Volcanic Hazard Simulations and Social Vulnerability Analysis. ISPRS International Journal of Geo-Information 2020, 9, 273 .
AMA StyleWeiqiang Liu, Long Li, Longqian Chen, Mingxin Wen, Jia Wang, Lina Yuan, Yunqiang Liu, Han Li. Testing a Comprehensive Volcanic Risk Assessment of Tenerife by Volcanic Hazard Simulations and Social Vulnerability Analysis. ISPRS International Journal of Geo-Information. 2020; 9 (4):273.
Chicago/Turabian StyleWeiqiang Liu; Long Li; Longqian Chen; Mingxin Wen; Jia Wang; Lina Yuan; Yunqiang Liu; Han Li. 2020. "Testing a Comprehensive Volcanic Risk Assessment of Tenerife by Volcanic Hazard Simulations and Social Vulnerability Analysis." ISPRS International Journal of Geo-Information 9, no. 4: 273.
Urbanization-induced land-use change will lead to variations in the demand and supply of ecosystem services, thus significantly affecting regional ecosystem services. The continuous degradation of ecosystem functions has become a serious problem for humanity to solve. Therefore, quantitative analysis of the corresponding impact of land-use change on ecosystem service value (ESV) is important to socio-economic development and ecological protection. The Anhui province in China has experienced rapid urbanization in recent years, and ecological environmental remediation and protection have become important goals for regional development. In this paper, the province of Anhui has been selected as a case of study, we analyzed the land-use change using Landsat images from 2000, 2005, 2010, and 2015. We then adjusted the equivalent factor of ESV per unit area and estimated the ESV of Anhui province from 2000 to 2015 to analyze the impact of land-use change on ESV. Our results show that (1) paddy field is the main land-use type in Anhui province, the built-up land area has continuously increased, and the water area has continuously decreased; (2) the total ESV of Anhui province decreased from 30,015.58 × 107 CNY in 2000 to 29,683.74 × 107 CNY in 2015 (the rate of change was −1.11%), and regulating services make the greatest contribution to ESV; and (3) land-use change has led to severe ESV variations, especially for the expansion of water area and built-up land. Our study results provide useful insights for the development of land-use management and environmental protection policies in Anhui province.
Sai Hu; Longqian Chen; Long Li; Bingyi Wang; Lina Yuan; Liang Cheng; Ziqi Yu; Ting Zhang. Spatiotemporal Dynamics of Ecosystem Service Value Determined by Land-Use Changes in the Urbanization of Anhui Province, China. International Journal of Environmental Research and Public Health 2019, 16, 5104 .
AMA StyleSai Hu, Longqian Chen, Long Li, Bingyi Wang, Lina Yuan, Liang Cheng, Ziqi Yu, Ting Zhang. Spatiotemporal Dynamics of Ecosystem Service Value Determined by Land-Use Changes in the Urbanization of Anhui Province, China. International Journal of Environmental Research and Public Health. 2019; 16 (24):5104.
Chicago/Turabian StyleSai Hu; Longqian Chen; Long Li; Bingyi Wang; Lina Yuan; Liang Cheng; Ziqi Yu; Ting Zhang. 2019. "Spatiotemporal Dynamics of Ecosystem Service Value Determined by Land-Use Changes in the Urbanization of Anhui Province, China." International Journal of Environmental Research and Public Health 16, no. 24: 5104.
Large amounts of aerosol particles suspended in the atmosphere pose a serious challenge to the climate and human health. In this study, we produced a dataset through merging the Moderate Resolution Imaging Spectrometers (MODIS) Collection 6.1 3-km resolution Dark Target aerosol optical depth (DT AOD) with the 10-km resolution Deep Blue aerosol optical depth (DB AOD) data by linear regression and made use of it to unravel the spatiotemporal characteristics of aerosols over the Pan Yangtze River Delta (PYRD) region from 2014 to 2017. Then, the geographical detector method and multiple linear regression analysis were employed to investigate the contributions of influencing factors. Results indicate that: (1) compared to the original Terra DT and Aqua DT AOD data, the average daily spatial coverage of the merged AOD data increased by 94% and 132%, respectively; (2) the values of four-year average AOD were high in the north-east and low in the south-west of the PYRD; (3) the annual average AOD showed a decreasing trend from 2014 to 2017 while the seasonal average AOD reached its maximum in spring; and that (4) Digital Elevation Model (DEM) and slope contributed most to the spatial distribution of AOD, followed by precipitation and population density. Our study highlights the spatiotemporal variability of aerosol optical depth and the contributions of different factors over this large geographical area in the four-year period, and can, therefore, provide useful insights into the air pollution control for decision makers.
Liang Cheng; Long Li; Longqian Chen; Sai Hu; Lina Yuan; Yunqiang Liu; Yifan Cui; Ting Zhang. Spatiotemporal Variability and Influencing Factors of Aerosol Optical Depth over the Pan Yangtze River Delta During the 2014–2017 Period. International Journal of Environmental Research and Public Health 2019, 16, 3522 .
AMA StyleLiang Cheng, Long Li, Longqian Chen, Sai Hu, Lina Yuan, Yunqiang Liu, Yifan Cui, Ting Zhang. Spatiotemporal Variability and Influencing Factors of Aerosol Optical Depth over the Pan Yangtze River Delta During the 2014–2017 Period. International Journal of Environmental Research and Public Health. 2019; 16 (19):3522.
Chicago/Turabian StyleLiang Cheng; Long Li; Longqian Chen; Sai Hu; Lina Yuan; Yunqiang Liu; Yifan Cui; Ting Zhang. 2019. "Spatiotemporal Variability and Influencing Factors of Aerosol Optical Depth over the Pan Yangtze River Delta During the 2014–2017 Period." International Journal of Environmental Research and Public Health 16, no. 19: 3522.
It is generally acknowledged that soil erosion has become one of the greatest global threats to the human–environment system. Although the Revised Universal Soil Loss Equation (RUSLE) has been widely used for soil erosion estimation, the algorithm for calculating soil erodibility factor (K) in this equation remains limited, particularly in the context of China, which features highly diverse soil types. In order to address the problem, a modified algorithm describing the piecewise function of gravel content and relative soil erosion was used for the first time to modify the soil erodibility factor, because it has been proven that gravel content has an important effect on soil erosion. The Chaohu Lake Basin (CLB) in East China was used as an example to assess whether our proposal can improve the accuracy of soil erodibility calculation and soil erosion estimation compared with measured data. Results show that (1) taking gravel content into account helps to improve the calculation of soil erodibility and soil erosion estimation due to its protection to topsoil; (2) the overall soil erosion in the CLB was low (1.78 Mg·ha−1·year−1) the majority of which was slight erosion (accounting for 85.6%) and no extremely severe erosion; and (3) inappropriate land use such as steep slope reclamation and excessive vegetation destruction are the main reasons for soil erosion of the CLB. Our study will contribute to decision-makers to develop soil and water conservation policies.
Sai Hu; Long Li; Longqian Chen; Liang Cheng; Lina Yuan; Xiaodong Huang; Ting Zhang. Estimation of Soil Erosion in the Chaohu Lake Basin through Modified Soil Erodibility Combined with Gravel Content in the RUSLE Model. Water 2019, 11, 1806 .
AMA StyleSai Hu, Long Li, Longqian Chen, Liang Cheng, Lina Yuan, Xiaodong Huang, Ting Zhang. Estimation of Soil Erosion in the Chaohu Lake Basin through Modified Soil Erodibility Combined with Gravel Content in the RUSLE Model. Water. 2019; 11 (9):1806.
Chicago/Turabian StyleSai Hu; Long Li; Longqian Chen; Liang Cheng; Lina Yuan; Xiaodong Huang; Ting Zhang. 2019. "Estimation of Soil Erosion in the Chaohu Lake Basin through Modified Soil Erodibility Combined with Gravel Content in the RUSLE Model." Water 11, no. 9: 1806.