Fuzzy c-means （FCM） algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm is sensitive to noise because of not taking into account the spatial information in the im...Fuzzy c-means （FCM） algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm is sensitive to noise because of not taking into account the spatial information in the image. An improved FCM algorithm is proposed to improve the antinoise performance of FCM algorithm. The new algorithm is formulated by incorporating the spatial neighborhood information into the membership function for clustering. The distribution statistics of the neighborhood pixels and the prior probability are used to form a new membership func- tion. It is not only effective to remove the noise spots but also can reduce the misclassified pixels. Experimental results indicate that the proposed algorithm is more accurate and robust to noise than the standard FCM algorithm.展开更多
This paper presents an approach based on field data to model the spatial distribution of the site productivity index （SPI） of the diverse forest types in Jalisco, Mexico and the response in SPI to site and cli-matic...This paper presents an approach based on field data to model the spatial distribution of the site productivity index （SPI） of the diverse forest types in Jalisco, Mexico and the response in SPI to site and cli-matic conditions. A linear regression model was constructed to test the hypothesis that site and climate variables can be used to predict the SPI of the major forest types in Jalisco. SPI varied significantly with topog-raphy （elevation, aspect and slope）, soil attributes （pH, sand and silt）, climate （temperature and precipitation zones） and forest type. The most important variable in the model was forest type, which accounted for 35% of the variability in SPI. Temperature and precipitation accounted for 8 to 9% of the variability in SPI while the soil attributes accounted for less than 4% of the variability observed in SPI. No significant differences were detected between the observed and predicted SPI for the individual forest types. The linear regression model was used to develop maps of the spatial variability in predicted SPI for the individual forest types in the state. The spatial site productivity models developed in this study provides a basis for understanding the complex relationship that exists between forest productivity and site and climatic conditions in the state. Findings of this study will assist resource managers in making cost-effective decisions about the management of individual forest types in the state of Jalisco, Mexico.展开更多
为了促进区域经济发展、改善黄河流域生态环境质量,基于景区兴趣点(point of interest,POI)数据,采用核密度估计、标准差椭圆、地理联系率和空间叠加分析等方法,探究黄河流域中游170个3A级及以上(以下简称“3A级以上”)山地景区的空间...为了促进区域经济发展、改善黄河流域生态环境质量,基于景区兴趣点(point of interest,POI)数据,采用核密度估计、标准差椭圆、地理联系率和空间叠加分析等方法,探究黄河流域中游170个3A级及以上(以下简称“3A级以上”)山地景区的空间分布特点及影响因素.结果表明:①黄河流域中游3A级以上山地景区集中分布在晋、陕、豫三省,景区密度大.3A级山地景区高密度区主要分布在豫北、豫南、晋东南;4A级山地景区呈向右旋转90°的“Y”型分布;5A级山地景区主要集中在晋、陕、豫交界处,组团状分布,由东北向西南展布.②自然地理环境方面,3A级以上山地景区主要分布在海拔300~1200 m处,坡度为15°~45°,偏南坡.河流水系、植被指数、空气质量对景区分布的影响效果显著.③社会经济环境方面,交通区位、固定资产投资、旅游收入和文化遗产禀赋是景区发展的重要影响因素.展开更多
近几十年来新疆气候变化显著,“暖湿化”转型与“湿干转折”先后出现,势必对地区植被生产力、大气干旱状况以及二者间的响应关系产生影响。以植被总初级生产力(GPP)和大气水分亏缺(VPD)作为评价指标,分析了1982—2018年新疆地区植被GPP...近几十年来新疆气候变化显著,“暖湿化”转型与“湿干转折”先后出现,势必对地区植被生产力、大气干旱状况以及二者间的响应关系产生影响。以植被总初级生产力(GPP)和大气水分亏缺(VPD)作为评价指标,分析了1982—2018年新疆地区植被GPP、VPD的时空分布与演变规律,并揭示了大气水分胁迫对植被GPP的影响。结果表明:(1)新疆植被GPP整体具有北高南低的分布特征,年均值为256.6 g C·m^(-2)·a^(-1),呈显著上升趋势。GPP增加趋势占植被总面积的82.00%,其中增加显著区约占42.81%,多分布于南疆绿洲和北疆山前农业区;GPP下降趋势占比较小且分布零散。(2)新疆地区VPD具有“山区低、平原/盆地高”的鲜明格局,年均值为0.66 kPa,呈不显著波动上升趋势。全疆大部地区表现出VPD显著性上升,下降趋势零星出现在昆仑山脉高海拔山区。(3)新疆植被GPP对VPD的响应“正负共存”,并具有明显空间异质性。GPP与VPD的负相关占植被区总面积的54.52%,主要出现在山前草地地带;正相关则主要分布在塔里木盆地边缘和天山北坡及其东段,以栽培作物和灌木类型为主。VPD对GPP的影响在不同植被类型间差异鲜明,而在同一植被类型内正、负响应共存。分析认为,虽然大气水分胁迫尚未成为地区植被生产力变化的主导驱动力,但在新疆干旱化急剧增加背景下,仍需加强对GPP与VPD响应关系的跟踪。展开更多
基金supported by the National Natural Science Foundation of China(6087403160740430664)
文摘Fuzzy c-means （FCM） algorithm is one of the most popular methods for image segmentation. However, the standard FCM algorithm is sensitive to noise because of not taking into account the spatial information in the image. An improved FCM algorithm is proposed to improve the antinoise performance of FCM algorithm. The new algorithm is formulated by incorporating the spatial neighborhood information into the membership function for clustering. The distribution statistics of the neighborhood pixels and the prior probability are used to form a new membership func- tion. It is not only effective to remove the noise spots but also can reduce the misclassified pixels. Experimental results indicate that the proposed algorithm is more accurate and robust to noise than the standard FCM algorithm.
文摘This paper presents an approach based on field data to model the spatial distribution of the site productivity index （SPI） of the diverse forest types in Jalisco, Mexico and the response in SPI to site and cli-matic conditions. A linear regression model was constructed to test the hypothesis that site and climate variables can be used to predict the SPI of the major forest types in Jalisco. SPI varied significantly with topog-raphy （elevation, aspect and slope）, soil attributes （pH, sand and silt）, climate （temperature and precipitation zones） and forest type. The most important variable in the model was forest type, which accounted for 35% of the variability in SPI. Temperature and precipitation accounted for 8 to 9% of the variability in SPI while the soil attributes accounted for less than 4% of the variability observed in SPI. No significant differences were detected between the observed and predicted SPI for the individual forest types. The linear regression model was used to develop maps of the spatial variability in predicted SPI for the individual forest types in the state. The spatial site productivity models developed in this study provides a basis for understanding the complex relationship that exists between forest productivity and site and climatic conditions in the state. Findings of this study will assist resource managers in making cost-effective decisions about the management of individual forest types in the state of Jalisco, Mexico.
文摘为了促进区域经济发展、改善黄河流域生态环境质量,基于景区兴趣点(point of interest,POI)数据,采用核密度估计、标准差椭圆、地理联系率和空间叠加分析等方法,探究黄河流域中游170个3A级及以上(以下简称“3A级以上”)山地景区的空间分布特点及影响因素.结果表明:①黄河流域中游3A级以上山地景区集中分布在晋、陕、豫三省,景区密度大.3A级山地景区高密度区主要分布在豫北、豫南、晋东南;4A级山地景区呈向右旋转90°的“Y”型分布;5A级山地景区主要集中在晋、陕、豫交界处,组团状分布,由东北向西南展布.②自然地理环境方面,3A级以上山地景区主要分布在海拔300~1200 m处,坡度为15°~45°,偏南坡.河流水系、植被指数、空气质量对景区分布的影响效果显著.③社会经济环境方面,交通区位、固定资产投资、旅游收入和文化遗产禀赋是景区发展的重要影响因素.
文摘近几十年来新疆气候变化显著,“暖湿化”转型与“湿干转折”先后出现,势必对地区植被生产力、大气干旱状况以及二者间的响应关系产生影响。以植被总初级生产力(GPP)和大气水分亏缺(VPD)作为评价指标,分析了1982—2018年新疆地区植被GPP、VPD的时空分布与演变规律,并揭示了大气水分胁迫对植被GPP的影响。结果表明:(1)新疆植被GPP整体具有北高南低的分布特征,年均值为256.6 g C·m^(-2)·a^(-1),呈显著上升趋势。GPP增加趋势占植被总面积的82.00%,其中增加显著区约占42.81%,多分布于南疆绿洲和北疆山前农业区;GPP下降趋势占比较小且分布零散。(2)新疆地区VPD具有“山区低、平原/盆地高”的鲜明格局,年均值为0.66 kPa,呈不显著波动上升趋势。全疆大部地区表现出VPD显著性上升,下降趋势零星出现在昆仑山脉高海拔山区。(3)新疆植被GPP对VPD的响应“正负共存”,并具有明显空间异质性。GPP与VPD的负相关占植被区总面积的54.52%,主要出现在山前草地地带;正相关则主要分布在塔里木盆地边缘和天山北坡及其东段,以栽培作物和灌木类型为主。VPD对GPP的影响在不同植被类型间差异鲜明,而在同一植被类型内正、负响应共存。分析认为,虽然大气水分胁迫尚未成为地区植被生产力变化的主导驱动力,但在新疆干旱化急剧增加背景下,仍需加强对GPP与VPD响应关系的跟踪。
