GPM卫星探测四川“5·21”特大暴雨的降水结构特征分析

四川复杂地形容易触发对流,暴雨发生频次较高,研究该地区暴雨结构及宏微观物理特征有重要意义和价值。本文利用GPM卫星资料分析了2018年5月21日四川一次特大暴雨的三维结构及宏微观特征。结果表明:此次特大暴雨过程由一个α中尺度对流系统造成;层状云降水与对流云降水的样本比例为2.5:1,但对流云降水率为层状云降水率的5倍;环境风场垂直切变较强导致最大雨水含量的位置与最大云冰含量的位置不一致;对流云降水反射率因子高频区数值随高度变化比层状云降水显著;粒子数浓度参数和有效粒子半径的频率分布反映出在4~7 km高度对流云中雨滴碰并增长过程显著,4 km以下雨滴蒸发和破碎过程明显,层状云降水粒子数浓度参数和粒子有效半径的范围比对流云窄且随高度变化不明显。

叶绿素a荧光遥感研究进展

继叶绿素a反演的“蓝绿比值法”后,叶绿素a荧光遥感成为海水叶绿素a浓度反演的重要方法,对提高二类水体和赤潮水体的叶绿素a浓度的反演精度效果明显。本文回顾了人们对水体叶绿素a荧光的认识、测量和研究的历史过程,介绍了荧光产生的生物学机理以及它随叶绿素a浓度的正相关和“红移现象”等主要光谱特征。本文还总结了荧光量子产量、不同藻种生理状态、水体其他物质及大气的吸收等多种因素对叶绿素a荧光遥感的影响。基于对叶绿素a荧光光谱特征和影响因素的认识,人们相继建立了两种荧光遥感方法———基线荧光高度法和归一化荧光高度法。对于前景广阔的叶绿素荧光遥感领域,人们正进行着更深入的研究与探索,积累更多的现场数据和卫星同步数据,逐步完善和改进反演模型。

浅析机载LiDAR点云的滤波与分类

滤波与分类是机载LiDAR点云处理的两个关键步骤。针对人们在传统概念上的认识误区,简要阐述了两者的不同内涵。以点云分类为例,一方面,以归一化高度特征为着力点,详细分析点云滤波对分类精度的影响,并对其影响力给予客观地评估;另一方面,以点云分类结果为着力点,采用反向分析法深入探讨了点云分类结果与滤波产生的两种误差的内在联系,从而验证了两者在点云处理过程中的辩证关系,得出了相应结论。

运用融合纹理和机载LiDAR特征模型估测森林地上生物量

针对区域尺度森林地上生物量的分布情况,以大兴安岭生态观测站为例,提出了一种融合光学影像纹理和机载LiDAR点云特征的森林地上生物量遥感估测方法。该方法首先提取Landsat 8 OLI不同波段在不同运算窗口下的纹理特征;然后对机载LiDAR点云进行滤波提取地面点,并利用地面点对点云数据进行高度归一化处理,提取点云特征因子;最后结合提取的遥感特征因子,利用支持向量回归的方法对研究区森林地上生物量进行估测,并对结果进行精度验证。结果表明:不同波段和窗口尺寸的建模精度差异较大,蓝光波段在7×7运算窗口下模型精度最高(R^2=0.73,R_(MSE)=22.32 t/hm^2);点云高度分位数变量的建模精度呈正态分布,变量H_(50)的建模精度最高(R^2=0.75,R_(MSE)=19.24 t/hm^2);与单一的遥感特征变量相比,融合光学影像纹理和机载LiDAR点云特征的模型精度有了一定提高,且针叶林和混交林的估测R_(MSE)分别为19.63和20.40 t/hm^2。因此,该方法可以为区域性的森林地上生物量估测提供有效参考。

Characteristics of Spatial and Temporal Vegetation Index Variability and Its Responses to Temperature and Precipitation in Mongolia

The Mongolian Plateau,a vital ecological barrier in northern China,is of great importance for studying vegetation dynamics in Mongolia against the background of climate warming.Such studies can enhance our understanding of regional vegetation responses to global warming and contribute to the establishment of a stronger ecological barrier in northern China.Here,we analyzed the spatial and temporal characteristics of the NDVI(normalized difference vegetation index)in Mongolia using 8 km resolution GIMMS NDVI3g data from 1990 to 2022,along with temperature,precipitation,and elevation data.Trend analysis and correlation methods were used to examine the relationships between the NDVI and temperature,as well as precipitation.The results showed four important aspects of these relationships.(1)The NDVI in Mongolia increased significantly from 1990 to 2022 at a rate of 0.0015 yr^(-1)(P<0.05).(2)Mongolia’s NDVI increased from 1990 to 2022 in 60.73%of the country.Of this total,the area with a significant increase accounted for 31.67%and was concentrated on the eastern and western edges.The area experiencing a significant decrease accounted for 15.67%and was mainly located on the southwestern edges.(3)The NDVI analysis revealed significant increasing trends in all regions except for those at elevations of 1500-2000 m.The greatest rate of increase was observed between 500 and 1000 m,and the increasing trend weakened as elevation continued to increase before gradually becoming significant again.Additionally,the NDVI increased significantly across different slopes,and the rate of increase decreased as the slope increased.(4)From 1990 to 2022,Mongolia’s NDVI was mostly negatively correlated with temperature.This occurred over 66.75%of the total land area,with 17.21%of the region exhibiting a significant negative correlation,mainly in the southwest.Conversely,the NDVI demonstrated a positive correlation with precipitation,encompassing 86.71%of the total land area.Approximately 40.44%of the region had a significant positive correlation,primarily in the southwest.In conclusion,throughout the experimental period,the vegetation state in Mongolia improved.However,due to the warming and drying climate,more attention should be paid to vegetation degradation in the south-central region.

LiDAR点云支持下地物精细分类的实现方法

在遥感数据分类中,获取精细的地物类别无疑能够传递更加丰富的信息量,进一步加深对遥感数据的理解和解译。在机载LiDAR点云高程数据的支持下,提出并实现了遥感影像上地物精细分类的方法。为保证高精度地同种地物再划分,综合考虑配准、辅助数据源、首次回波、点云密度及影像空间分辨率4种因素,并重点解决了点云密度与影像空间分辨率不匹配的问题,利用决策树显著地提高了影像上建筑物、植被的分类数量,使点云与影像联合分类的优势得到体现,达到了分类精度与地物类别数量相统一的目的。

Modeling Aboveground Biomass Using MODIS Images and Climatic Data in Grasslands on the Tibetan Plateau

Accurate quantification of aboveground biomass of grasslands in alpine regions plays an important role in accurate quantification of global carbon cycling.The monthly normalized difference vegetation index（NDVI）,enhanced vegetation index（EVI）,mean air temperature（Ta）,≥5℃ accumulated air temperature（AccT）,total precipitation（TP）,and the ratio of TP to AccT（TP/AccT） were used to model aboveground biomass（AGB） in grasslands on the Tibetan Plateau.Three stepwise multiple regression methods,including stepwise multiple regression of AGB with NDVI and EVI,stepwise multiple regression of AGB with Ta,AccT,TP and TP/AccT,and stepwise multiple regression of AGB with NDVI,EVI,Ta,AccT,TP and TP/Acc T were compared.The mean absolute error（MAE） and root mean squared error（RMSE） values between estimated AGB by the NDVI and measured AGB were 31.05 g m^（-2） and 44.12 g m^（-2）,and 95.43 g m^（-2） and 131.58 g m^（-2） in the meadow and steppe,respectively.The MAE and RMSE values between estimated AGB by the AccT and measured AGB were 33.61 g m^（-2） and 48.04 g m^（-2） in the steppe,respectively.The MAE and RMSE values between estimated AGB by the vegetation index and climatic data and measured AGB were 28.09 g m^（-2） and 42.71 g m^（-2）,and 35.86 g m^（-2） and 47.94 g m^（-2）,in the meadow and steppe,respectively.The study finds that a combination of vegetation index and climatic data can improve the accuracy of estimates of AGB that are arrived at using the vegetation index or climatic data.The accuracy of estimates varied depending on the type of grassland.

The Effect of Higher Warming on Vegetation Indices and Biomass Production is Dampened by Greater Drying in an Alpine Meadow on the Northern Tibetan Plateau

In order to understand whether or not the response of vegetation indices and biomass production to warming varies with warming magnitude,an experiment of field warming at two magnitudes was conducted in an alpine meadow on the northern Tibetan Plateau beginning in late June,2013.The normalized difference vegetation index（NDVI）,green normalized difference vegetation index（GNDVI） and soil adjusted vegetation index（SAVI） data were obtained using a Tetracam Agricultural Digital Camera in 2013–2014.The gross primary production（GPP） and aboveground plant biomass（AGB） were modeled using the surface measured NDVI and climatic data during the growing seasons（i.e.June–September） in 2013–2014.Both low and high warming significantly increased air temperature by 1.54 and 4.00°C,respectively,and significantly increased vapor pressure deficit by 0.13 and 0.31 kP a,respectively,in 2013-2014.There were no significant differences of GNDVI,AGB and ANPP among the three warming treatments.The high warming significantly reduced average NDVI by 23.3%（-0.06）,while the low warming did not affect average NDVI.The low and high warming significantly decreased average SAVI by 19.0%（-0.04） and 27.4%（-0.05）,respectively,and average GPP by 24.2%（i.e.0.21 g C m^（-2） d^（-1）） and 44.0%（i.e.0.39 g C m^（–2） d^（-1））,respectively.However,the differences of the average NDVI,SAVI,and GPP between low and high warming were negligible.Our findings suggest that a greater drying may dampen the effect of a higher warming on vegetation indices and biomass production in alpine meadow on the northern Tibetan Plateau.