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81.
林志强  马艳鲜  德庆  边多 《气象科技》2014,42(6):1147-1153
基于遥感技术(RS)和地理信息系统(GIS),利用由基于DEM演算的地面最高温度、最小相对湿度和最大风速等格点化气象要素,FY2静止气象卫星逐日降水反演产品和AVHRR积雪监测产品计算网格森林火险天气等级,结合由植被类型、NDVI、地形要素和公路、人口聚居地等要素评估的森林火险风险等级,综合计算得到网格化的西藏森林火险等级。该项业务程序基于MeteoInfo组件建立,能够实现全自动化业务运行。对于森林火灾事件,通过与基于气象站的森林火险天气等级相比,该方法的准确性更高,能为西藏林区森林防火工作提供有效参考。  相似文献   
82.
以计算结果可靠的土层反应结果为基础数据,基于随机森林算法(Random Forest,简称"RF"),建立地震动、场地土层参数特征量与土层变形特征的关系,提出一种依据常规地震动和场地土层参数为特征量的地震荷载下土层变形等级预测新方法,给出了典型场地土体发生大应变的地震动阈值.结果表明:基于RF模型对土体的变形程度预测结...  相似文献   
83.
伏牛山地区森林生态系统服务权衡/协同效应多尺度分析   总被引:3,自引:0,他引:3  
森林生态系统服务权衡与协同研究已成为当前相关学科的研究热点和前沿,对服务权衡与协同关系的多尺度分析有助于更加有效地实施森林资源管理。综合森林类型图、NDVI、气象和土壤等多源数据,借助CASA模型、InVEST 3.2模型和ArcGIS 10.2软件,开展伏牛山地区森林生态系统服务评估,运用空间叠置方法从多个空间尺度(区域、南北坡、垂直带)探讨服务权衡与协同效应。结果表明:① 研究区森林生态系统平均蓄积量为49.26 m 3/hm 2,碳密度为156.94 t/hm 2,供水深度为494.46 mm,土壤保持量为955.4 t/hm 2,生境质量指数为0.79。② 区域尺度上,28.79%的森林服务之间存在高协同效应,10.15%的森林存在低协同效应,61.06%的森林存在强权衡和弱权衡效应。③ 南北坡尺度上,南坡服务之间的协同关系优于北坡。垂直带尺度上,南坡中山落叶阔叶林带(SIII)服务之间协同关系最好,北坡低山落叶阔叶林带(NI)协同关系最差。  相似文献   
84.
为解决森林分布不连续流域森林水源涵养功能及其多时间尺度特征的定量评价问题,根据分布式水文模型(SWAT)的特点,提出了反映森林斑块空间分布的水文响应单元划分方法,以及基于水量平衡法的森林不连续分布流域森林水源涵养量计算公式。以东南沿海的晋江流域为例,构建了2006年土地利用条件下的日时间步长SWAT模型,统计分析了2002—2010年降水条件下森林水源涵养量的时空变化规律。结果表明:① 构建的晋江流域SWAT模型精度较高,面积阈值为零生成的水文响应单元比较准确地反映流域森林斑块分布,提出的森林水源涵养量计算公式适用于森林空间分布不连续流域森林水源涵养量的多时间尺度分析,为流域森林水源涵养功能评价提供了一个新的方法。② 晋江流域森林水源年涵养量271.41~565.25 mm;月涵养量-29.15~154.59 mm;日尺度的极端降水期皆为正值,极端枯水期都为负值。表明年际之间不存在森林水源涵养的蓄丰补枯调节作用,但在年内的部分月份得到体现,而日尺度的森林蓄丰补枯功能充分发挥。从而揭示了不同时间尺度森林水源涵养量及其蓄丰补枯功能的差异。  相似文献   
85.
近些年来,生态系统的服务价值研究已成为生态学以及生态经济学领域中的一个热点问题。祁连山被称为伸进西北干旱区的一座湿岛,在我国“一带一路”建设中占有重要地位。景电灌区兴建以来从祁连山移出了大量农牧民。那么,景电灌区移民对祁连山植被恢复的生态价值如何呢?以景电灌区移民涉及到的祁连山东端景泰、古浪、天祝3县山区为研究区,用价值工程方法对从祁连山区向景电灌区移民退耕退牧还林还草的生态价值做了分析,并与模型因子当量法的计算结果进行了比较。结果表明:祁连山向景电灌区移民退耕退牧还林还草总的生态价值为37.458 1×108元。其中,退耕还林还草的生态价值为37.438 6×08元,退牧后草场植被盖度增加的生态价值为194.79×104元。计算结果为用COSTANZA和谢高地模型因子当量法计算得的祁连山向景电灌区移民退耕退牧还林还草总的生态服务价值40.054 0×08元的93.52%。两种方法计算结果祁连山向景电灌区移民退耕退牧还林还草总的生态价值为景电工程年总成本1.45×08元的25.8~27.6倍。两种计算结果一致表明,景电灌区生态移民对祁连山植被恢复的生态价值十分突出。由此也可以看出,COSTANZA和谢高地模型因子当量法适用于祁连山的生态服务价值分析。  相似文献   
86.
陕北地区退耕还林还草工程土壤保护效应的时空特征   总被引:1,自引:1,他引:0  
This paper looks at the Green for Grain Project in northern Shaanxi Province.Based on remote sensing monitoring data,this study analyzes the locations of arable land in northern Shaanxi in the years 2000,2010 and 2013 as well as spatio-temporal changes over that period,and then incorporates data on the distribution of terraced fields to improve the input parameters of a RUSLE model and simulate and generate raster data on soil erosion for northern Shaanxi at different stages with a accuracy verification.Finally,combined with the dataset of farmland change,compared and analyzed the characteristics of soil erosion change in the converted farmland to forest(grassland)and the unconverted farmland in northern Shaanxi,so as to determine the project’s impact on soil erosion over time across the region.The results show that between 2000 and 2010,the soil erosion modulus of repurposed farmland in northern Shaanxi decreased 22.7 t/ha,equivalent to 47.08%of the soil erosion modulus of repurposed farmland in 2000.In the same period,the soil erosion modulus of non-repurposed farmland fell 10.99 t/ha,equivalent to 28.6%of the soil erosion modulus of non-repurposed farmland in 2000.The soil erosion modulus for all types of land in northern Shaanxi decreased by an average of 14.51 t/ha between 2000 and 2010,equivalent to 41.87%of the soil erosion modulus for the entire region in 2000.This suggests that the Green for Grain Project effectively reduced the soil erosion modulus,thus helping to protect the soil.In particular,arable land that was turned into forest and grassland reduced erosion most noticeably and contributed most to soil conservation.Nevertheless,in the period 2010 to 2013,which was a period of consolidation of the Green for Grain Project,the soil erosion modulus and change in volume of soil erosion in northern Shaanxi were significantly lower than in the previous decade.  相似文献   
87.
Pinus taiwanensis is a species endemic to China. This study selected four typical plots of Pinus taiwanensis in the natural secondary forest around Macheng City, in order to reveal the characteristics of and the relationships between different diameter classes (determined based on the diameter at breast height or DBH), forest densities and species diversity, as well as the similarities of species diversity of different plots within the community. The result showed that Pinus taiwanensis was the dominant species in the community. The ratio of Pinus taiwanensis trees of diameter class IV reached a peak of 19.46% of the total followed by diameter class VII at 18.92%. The study recorded 156 species of vascular plants from 130 genera of 71 families; Pinus taiwanensis was the dominant species in the community. When the forest density was 1200 trees ha -1 with the largest average diameter of DBH=36.779±4.444 cm, the diversity (Shannon index H'=1.6716) and the evenness (Pielou index E=0.6727) of the tree layer was the highest. When the forest density reached 1525 trees ha -1 with the lowest average diameter of DBH=18.957±5.141 cm, the richness (Dma=5.4308), the diversity (H'=2.9612) and the evenness (E=0.8985) of all shrub layers climbed to the maximum. When the forest density was 1325 trees ha -1, the richness (Dma=5.8132), the diversity (H'=3.0697) and the evenness (E=0.9025) of all herb layers peaked. In terms of vertical structure, the average diversity indexes were herb layer>shrub layer>tree layer. High canopy density weakened light intensity in the community, causing a reduction in the species diversities of herbs and shrubs. The average similarity coefficient between the sample plots was 0.3356, which was at the medium dissimilarity level. External disturbances and improper management were major contributors to the low species diversity of the community. The implementation of scientific management measures is urgently needed to optimize the forest structures of Pinus taiwanensis, create a benign community environment, and promote species diversities and establish a stable forest community structure.  相似文献   
88.
The fractional vegetation cover (FVC), crop residue cover (CRC), and bare soil (BS) are three important parameters in vegetation–soil ecosystems, and their correct and timely estimation can improve crop monitoring and environmental monitoring. The triangular space method uses one CRC index and one vegetation index to create a triangular space in which the three vertices represent pure vegetation, crop residue, and bare soil. Subsequently, the CRC, FVC, and BS of mixed remote sensing pixels can be distinguished by their spatial locations in the triangular space. However, soil moisture and crop-residue moisture (SM-CRM) significantly reduce the performance of broadband remote sensing CRC indices and can thus decrease the accuracy of the remote estimation and mapping of CRC, FVC, and BS. This study evaluated the use of broadband remote sensing, the triangular space method, and the random forest (RF) technique to estimate and map the FVC, CRC, and BS of cropland in which SM-CRM changes dramatically. A spectral dataset was obtained using: (1) from a field-based experiment with a field spectrometer; and (2) from a laboratory-based simulation that included four distinct soil types, three types of crop residue (winter-wheat, maize, and rice), one crop (winter wheat), and varying SM-CRM. We trained an RF model [designated the broadband crop-residue index from random forest (CRRF)] that can magnify spectral features of crop residue and soil by using the broadband remote sensing angle indices as input, and uses a moisture-resistant hyperspectral index as the target. The effects of moisture on crop residue and soil were minimized by using the broadband CRRF. Then, the CRRF-NDVI triangular space method was used to estimate and map CRC, FVC, and BS. Our method was validated by using both laboratory- and field-based experiments and Sentinel-2 broadband remote-sensing images. Our results indicate that the CRRF-NDVI triangular space method can reduce the effect of moisture on the broadband remote-sensing of CRC, and may also help to obtain laboratory and field CRC, FVC, and BS. Thus, the proposed method has great potential for application to croplands in which the SM-CRM content changes dramatically.  相似文献   
89.
Wetlands have been determined as one of the most valuable ecosystems on Earth and are currently being lost at alarming rates. Large-scale monitoring of wetlands is of high importance, but also challenging. The Sentinel-1 and -2 satellite missions for the first time provide radar and optical data at high spatial and temporal detail, and with this a unique opportunity for more accurate wetland mapping from space arises. Recent studies already used Sentinel-1 and -2 data to map specific wetland types or characteristics, but for comprehensive wetland characterisations the potential of the data has not been researched yet. The aim of our research was to study the use of the high-resolution and temporally dense Sentinel-1 and -2 data for wetland mapping in multiple levels of characterisation. The use of the data was assessed by applying Random Forests for multiple classification levels including general wetland delineation, wetland vegetation types and surface water dynamics. The results for the St. Lucia wetlands in South Africa showed that combining Sentinel-1 and -2 led to significantly higher classification accuracies than for using the systems separately. Accuracies were relatively poor for classifications in high-vegetated wetlands, as subcanopy flooding could not be detected with Sentinel-1’s C-band sensors operating in VV/VH mode. When excluding high-vegetated areas, overall accuracies were reached of 88.5% for general wetland delineation, 90.7% for mapping wetland vegetation types and 87.1% for mapping surface water dynamics. Sentinel-2 was particularly of value for general wetland delineation, while Sentinel-1 showed more value for mapping wetland vegetation types. Overlaid maps of all classification levels obtained overall accuracies of 69.1% and 76.4% for classifying ten and seven wetland classes respectively.  相似文献   
90.
The mangrove forests of northeast Hainan Island are the most species diverse forests in China and consist of the Dongzhai National Nature Reserve and the Qinglan Provincial Nature Reserve. The former reserve is the first Chinese national nature reserve for mangroves and the latter has the most abundant mangrove species in China. However, to date the aboveground ground biomass (AGB) of this mangrove region has not been quantified due to the high species diversity and the difficulty of extensive field sampling in mangrove habitat. Although three-dimensional point clouds can capture the forest vertical structure, their application to large areas is hindered by the logistics, costs and data volumes involved. To fill the gap and address this issue, this study proposed a novel upscaling method for mangrove AGB estimation using field plots, UAV-LiDAR strip data and Sentinel-2 imagery (named G∼LiDAR∼S2 model) based on a point-line-polygon framework. In this model, the partial-coverage UAV-LiDAR data were used as a linear bridge to link ground measurements to the wall-to-wall coverage Sentinel-2 data. The results showed that northeast Hainan Island has a total mangrove AGB of 312,806.29 Mg with a mean AGB of 119.26 Mg ha−1. The results also indicated that at the regional scale, the proposed UAV-LiDAR linear bridge method (i.e., G∼LiDAR∼S2 model) performed better than the traditional approach, which directly relates field plots to Sentinel-2 data (named the G∼S2 model) (R2 = 0.62 > 0.52, RMSE = 50.36 Mg ha−1<56.63 Mg ha−1). Through a trend extrapolation method, this study inferred that the G∼LiDAR∼S2 model could decrease the number of field samples required by approximately 37% in comparison with those required by the G∼S2 model in the study area. Regarding the UAV-LiDAR sampling intensity, compared with the original number of LiDAR plots, 20% of original linear bridges could produce an acceptable accuracy (R2 = 0.62, RMSE = 51.03 Mg ha−1). Consequently, this study presents the first investigation of AGB for the mangrove forests on northeast Hainan Island in China and verifies the feasibility of using this mangrove AGB upscaling method for diverse mangrove forests.  相似文献   
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