利用无人机激光雷达LiDAR(Light detecting and ranging)数据,提取能反映植被垂直和水平结构变化的LiDAR特征变量,通过相关性分析和层次聚类方法构建森林健康指标来识别黄河三角洲刺槐人工林的健康状况。结果表明,森林健康指标由LAD_(c...利用无人机激光雷达LiDAR(Light detecting and ranging)数据,提取能反映植被垂直和水平结构变化的LiDAR特征变量,通过相关性分析和层次聚类方法构建森林健康指标来识别黄河三角洲刺槐人工林的健康状况。结果表明,森林健康指标由LAD_(cv)(叶面积密度的变异系数)、weibull_α(Weibull密度函数的尺度变量)、H_(99)(高度百分位数)和VCI(垂直复杂度)构成;利用森林健康指标进行刺槐林的健康等级判断可以得到较理想的结果(总精度为86.7%,Kappa系数为0.79),证实了激光雷达技术在判断森林健康状况方面的潜能。展开更多
Based on more than 300 forest sample plots surveying data and forestry statistical data, remote sensing information from the NOAA AVHRR database and the daily meteorological data of 300 stations, we selected vigor, or...Based on more than 300 forest sample plots surveying data and forestry statistical data, remote sensing information from the NOAA AVHRR database and the daily meteorological data of 300 stations, we selected vigor, organization and resilience as the indicators to assess large-scale forest ecosystem health in China and analyzed the spatial pattern of forest ecosystem health and influencing factors. The results of assessment indicated that the spatial pattern of forest ecosystem health showed a decreasing trend along latitude gradients and longitude gradients. The healthy forests are mainly distributed in natural forests, tropical rainforests and seasonal rainforests; secondarily orderly in northeast national forest zone, subtropical forest zonation and southwest forest zonation; while the unhealthy forests were mainly located in warm temperate zone and Xinjiang-Mongolia forest zone. The coefficient of correction between Forest Ecosystem Health Index (FEHI) and annual average precipitation was 0.58 (p<0.01), while the coefficient of correlation between FEHI and annual mean temperatures was 0.49 (p<0.01), which identified that the precipitation and temperatures affect the pattern of FEHI, and the precipitation’s effect was stronger than the temperature’s. We also measured the correlation coefficient between FEHI and NPP, biodiversity and resistance, which were 0.64, 0.76 and 0.81 (p<0.01) respectively. The order of effect on forest ecosystem health was vigor, organization and resistance.展开更多
文摘利用无人机激光雷达LiDAR(Light detecting and ranging)数据,提取能反映植被垂直和水平结构变化的LiDAR特征变量,通过相关性分析和层次聚类方法构建森林健康指标来识别黄河三角洲刺槐人工林的健康状况。结果表明,森林健康指标由LAD_(cv)(叶面积密度的变异系数)、weibull_α(Weibull密度函数的尺度变量)、H_(99)(高度百分位数)和VCI(垂直复杂度)构成;利用森林健康指标进行刺槐林的健康等级判断可以得到较理想的结果(总精度为86.7%,Kappa系数为0.79),证实了激光雷达技术在判断森林健康状况方面的潜能。
基金Knowledge Innovation Project of CASNo.KZCX2-405+1 种基金 National Meteorological Center ProjectNo.ZK2003C-18
文摘Based on more than 300 forest sample plots surveying data and forestry statistical data, remote sensing information from the NOAA AVHRR database and the daily meteorological data of 300 stations, we selected vigor, organization and resilience as the indicators to assess large-scale forest ecosystem health in China and analyzed the spatial pattern of forest ecosystem health and influencing factors. The results of assessment indicated that the spatial pattern of forest ecosystem health showed a decreasing trend along latitude gradients and longitude gradients. The healthy forests are mainly distributed in natural forests, tropical rainforests and seasonal rainforests; secondarily orderly in northeast national forest zone, subtropical forest zonation and southwest forest zonation; while the unhealthy forests were mainly located in warm temperate zone and Xinjiang-Mongolia forest zone. The coefficient of correction between Forest Ecosystem Health Index (FEHI) and annual average precipitation was 0.58 (p<0.01), while the coefficient of correlation between FEHI and annual mean temperatures was 0.49 (p<0.01), which identified that the precipitation and temperatures affect the pattern of FEHI, and the precipitation’s effect was stronger than the temperature’s. We also measured the correlation coefficient between FEHI and NPP, biodiversity and resistance, which were 0.64, 0.76 and 0.81 (p<0.01) respectively. The order of effect on forest ecosystem health was vigor, organization and resistance.
