不透水层是指能够隔离地表水渗透到土壤的覆盖表面,以不透水层分布变化来研究铜川城市化进程。利用决策树分类结合生物物理成分指数(BCI)和裸土指数(MBSI)的方法对1986,1991,1996,2002,2007,2012和2017年的遥感影像数进行不透水层提取,...不透水层是指能够隔离地表水渗透到土壤的覆盖表面,以不透水层分布变化来研究铜川城市化进程。利用决策树分类结合生物物理成分指数(BCI)和裸土指数(MBSI)的方法对1986,1991,1996,2002,2007,2012和2017年的遥感影像数进行不透水层提取,采用指标分析、重心轨迹偏移等方法探究不透水层空间扩展特征,并结合统计年鉴、DEM数据研究铜川市不透水层扩展驱动机制。结果表明:文中提出的基于决策树分类模型结合BCI和MBSI的方法对不透水层提取与验证数据的拟合优度达到0.88.铜川市的不透水层面积持续增加,面积从1986年的5.7 km 2增加到2017年的61.5 km 2,年均增长速度高达1.8%,特别是2007—2017年是快速城市化时期,增长面积占31 a变化总面积的69.3%.不透水层的重心呈先北后南的阶段性变化,1986—2002年向北移动,2002—2007年向南移动,2007—2017年继续向西南方向移动。通过对驱动力指标分析表明,经济及人口增长对不透水层扩展有着直接推动作用,矿产资源分布、地理环境限制和规划政策引导为影响研究区不透水层变化的主要因素。展开更多
In an agricultural field,the water content and salt content are defined as soil moisture and soil salinity and have to be estimated precisely.The changing of these two factors can be assessed using remote sensing tech...In an agricultural field,the water content and salt content are defined as soil moisture and soil salinity and have to be estimated precisely.The changing of these two factors can be assessed using remote sensing technology.This study was conducted by analysing the Landsat 8 satellite images,soil data of field surveys,laboratory analyses and statistical computations.Soil properties such as soil moisture and soil salinity were estimated using soil moisture index(SMI)and soil salinity index(SSI),respectively.The research combined and integrated the soil data from survey and laboratory with Landsat 8 satellite images to build two multiple regression equations model named the soil pH Index(SpHI).They are based on bare soil and paddy leaf models as the explanatory factors of soil moisture and soil salinity changes.All the computation processes were replicated three times using three different dates of Landsat 8 satellite images to produce the multi-temporal analysis.Soil moisture increased after 30 days,while the salt content was only trace amounts.Both proposed models detected 4.49–7.59 of soil pH,4.66 in bare soil model and 6.62 in paddy leaf model.During the planting period,the soil pH in bare soil model decreased to 2.12–6.47 while the paddy leaf model increased to 4.49–7.59 with RMSE 1.40 and PRMSE 24%of accuracy.The spatial relationship between soil pH,soil salinity and soil moisture are linear but varied in correlation level from weak,moderate to strong.Based on the bare soil model,the relationship between soil pH and soil moisture shows a weak negative relationship with R28.37%and a strong positive relationship with R281.94%in paddy area and bare soil area respectively,as like as in paddy area based on the paddy leaf model with R2100%.The relationship between soil temperature and soil pH shows a weak negative relationship for all models and a moderate negative relationship of soil salinity and soil pH in bare soil area based on the bare soil model with R234.89%.展开更多
文摘不透水层是指能够隔离地表水渗透到土壤的覆盖表面,以不透水层分布变化来研究铜川城市化进程。利用决策树分类结合生物物理成分指数(BCI)和裸土指数(MBSI)的方法对1986,1991,1996,2002,2007,2012和2017年的遥感影像数进行不透水层提取,采用指标分析、重心轨迹偏移等方法探究不透水层空间扩展特征,并结合统计年鉴、DEM数据研究铜川市不透水层扩展驱动机制。结果表明:文中提出的基于决策树分类模型结合BCI和MBSI的方法对不透水层提取与验证数据的拟合优度达到0.88.铜川市的不透水层面积持续增加,面积从1986年的5.7 km 2增加到2017年的61.5 km 2,年均增长速度高达1.8%,特别是2007—2017年是快速城市化时期,增长面积占31 a变化总面积的69.3%.不透水层的重心呈先北后南的阶段性变化,1986—2002年向北移动,2002—2007年向南移动,2007—2017年继续向西南方向移动。通过对驱动力指标分析表明,经济及人口增长对不透水层扩展有着直接推动作用,矿产资源分布、地理环境限制和规划政策引导为影响研究区不透水层变化的主要因素。
文摘In an agricultural field,the water content and salt content are defined as soil moisture and soil salinity and have to be estimated precisely.The changing of these two factors can be assessed using remote sensing technology.This study was conducted by analysing the Landsat 8 satellite images,soil data of field surveys,laboratory analyses and statistical computations.Soil properties such as soil moisture and soil salinity were estimated using soil moisture index(SMI)and soil salinity index(SSI),respectively.The research combined and integrated the soil data from survey and laboratory with Landsat 8 satellite images to build two multiple regression equations model named the soil pH Index(SpHI).They are based on bare soil and paddy leaf models as the explanatory factors of soil moisture and soil salinity changes.All the computation processes were replicated three times using three different dates of Landsat 8 satellite images to produce the multi-temporal analysis.Soil moisture increased after 30 days,while the salt content was only trace amounts.Both proposed models detected 4.49–7.59 of soil pH,4.66 in bare soil model and 6.62 in paddy leaf model.During the planting period,the soil pH in bare soil model decreased to 2.12–6.47 while the paddy leaf model increased to 4.49–7.59 with RMSE 1.40 and PRMSE 24%of accuracy.The spatial relationship between soil pH,soil salinity and soil moisture are linear but varied in correlation level from weak,moderate to strong.Based on the bare soil model,the relationship between soil pH and soil moisture shows a weak negative relationship with R28.37%and a strong positive relationship with R281.94%in paddy area and bare soil area respectively,as like as in paddy area based on the paddy leaf model with R2100%.The relationship between soil temperature and soil pH shows a weak negative relationship for all models and a moderate negative relationship of soil salinity and soil pH in bare soil area based on the bare soil model with R234.89%.
