Maintaining healthy watershed is pivotal to ensure sustainability in water resources thereby improving the carrying capacity of the earth.Understanding and identifying the spatial variability of hydrologically sensiti...Maintaining healthy watershed is pivotal to ensure sustainability in water resources thereby improving the carrying capacity of the earth.Understanding and identifying the spatial variability of hydrologically sensitive areas(HSAs)in a watershed is an important step to prioritizing the landscape to maintain water sustainability with limited resources.A spatial technique known as Soil Topographic Index(STI)was used to identify HSAs in the landscape.This study was conducted in Clinton and Tewksbury Townships in New Jersey,United States.Three different scenarios(STI>=9,STI>=10,and STI>=11)were conducted to understand the spatial distribution of HSAs in the watershed.The following conclusions were derived from this study.Firstly,a more detail representation of HSAs in the watershed was observed when applying the STI technique with a fine scale light detection and ranging(LiDAR)digitial elevation model.Secondly,all three scenarios consistently identified perennial stream corridors as HSAs;therefore,it is important to protect perennial stream corridors through implementation of various land use controls.Thirdly,this study analyzes the land use pattern of HSAs under the three scenarios and identifies the HSAs for high intensity land uses such as agriculture and urban to be the high priority locations for implementing best management practices for water quality improvements.The procedures developed in this study can be applied to watersheds in other parts of the world with similar physiographic characteristics.展开更多
Accurate impervious surface estimation(ISE)is challenging due to the diversity of land covers and the vegetation phenology and climate.This study investigates the variation of impervious surfaces estimated from differ...Accurate impervious surface estimation(ISE)is challenging due to the diversity of land covers and the vegetation phenology and climate.This study investigates the variation of impervious surfaces estimated from different seasons of satellite images and the seasonal sensitivity of different methods.Four Landsat ETM?images of four different seasons and two popular methods(i.e.artificial neural network(ANN)and support vector machine(SVM))are employed to estimate the impervious surface on the pixel level.Results indicate that winter(dry season)is the best season to estimate impervious surface even though plants are not in their growing season.Less cloud and less variable source areas(VSA)(seasonal water body)become the major advantages of winter for the ISE,as cloud is easily confusedwith bright impervious surfaces,andwater in VSA is confusedwith dark impervious surfaces due to their similar spectral reflectance.For the seasonal sensitivity of methods,ANN appears more stable as its accuracy varied less than that obtained with SVM.However,both the methods showed a general consistency of the seasonal changes of the accuracy,indicating that winter time is the best season for impervious surfaces estimation with optical satellite images in subtropical monsoon regions.展开更多
基金the funding support to New Jersey Institute of Technology by the USDA National Institute of Food and Agriculture(Grant number NJW-2012-67019-19348).
文摘Maintaining healthy watershed is pivotal to ensure sustainability in water resources thereby improving the carrying capacity of the earth.Understanding and identifying the spatial variability of hydrologically sensitive areas(HSAs)in a watershed is an important step to prioritizing the landscape to maintain water sustainability with limited resources.A spatial technique known as Soil Topographic Index(STI)was used to identify HSAs in the landscape.This study was conducted in Clinton and Tewksbury Townships in New Jersey,United States.Three different scenarios(STI>=9,STI>=10,and STI>=11)were conducted to understand the spatial distribution of HSAs in the watershed.The following conclusions were derived from this study.Firstly,a more detail representation of HSAs in the watershed was observed when applying the STI technique with a fine scale light detection and ranging(LiDAR)digitial elevation model.Secondly,all three scenarios consistently identified perennial stream corridors as HSAs;therefore,it is important to protect perennial stream corridors through implementation of various land use controls.Thirdly,this study analyzes the land use pattern of HSAs under the three scenarios and identifies the HSAs for high intensity land uses such as agriculture and urban to be the high priority locations for implementing best management practices for water quality improvements.The procedures developed in this study can be applied to watersheds in other parts of the world with similar physiographic characteristics.
基金The ETM+data from USGS are highly appreciated.This study is jointly supported by the CUHK Direct Grants(2021103)Hong Kong Research Grants Council(RGC)General Research Grants(GRF)project(CUHK 459210 and 457212)+2 种基金Hong Kong Innovation and Technology Fund(GHP/002/11GD)the funding of Shenzhen Municipal Science and Technology Innovation Council(JCYJ20120619151239947)the National Key Technol-ogies R&D Program in the 12th Five Year Plan of China(2012BAH32B03).
文摘Accurate impervious surface estimation(ISE)is challenging due to the diversity of land covers and the vegetation phenology and climate.This study investigates the variation of impervious surfaces estimated from different seasons of satellite images and the seasonal sensitivity of different methods.Four Landsat ETM?images of four different seasons and two popular methods(i.e.artificial neural network(ANN)and support vector machine(SVM))are employed to estimate the impervious surface on the pixel level.Results indicate that winter(dry season)is the best season to estimate impervious surface even though plants are not in their growing season.Less cloud and less variable source areas(VSA)(seasonal water body)become the major advantages of winter for the ISE,as cloud is easily confusedwith bright impervious surfaces,andwater in VSA is confusedwith dark impervious surfaces due to their similar spectral reflectance.For the seasonal sensitivity of methods,ANN appears more stable as its accuracy varied less than that obtained with SVM.However,both the methods showed a general consistency of the seasonal changes of the accuracy,indicating that winter time is the best season for impervious surfaces estimation with optical satellite images in subtropical monsoon regions.
