泛北极地区植被数据集与制图:回顾与展望

泛北极地区位于北半球高纬度地区,主要属于低温限制型生态系统,因而泛北极地区植被对全球变暖敏感。要明确泛北极地区陆地生态系统对全球变暖响应过程和机理,就需要高精度的植被类型分类数据作为基础资料。但是,目前泛北极地区植被数据的结果还不完善。为更好地认识泛北极地区植被类型分类的现状、发展历史和未来研究方向,本文综述了泛北极地区植被类型制图的数据来源、植被类型划分方法和植被类型制图研究方法。总体而言,泛北极地区的植被调查从20世纪20年代就陆续开展,目前部分区域尺度上的地表覆盖类型的结果,但具体的植被类型制图工作还存在一系列难题。主要原因包括植被野外调查实测数据较少和分布不均、植被类型分类标准不一、苔原植被群落的异质性大、制图技术和分类算法优化困难等。因此,在下一步泛北极植被类型制图工作中,需要制定标准化分类策略,规范数据获取及数据库的整理工作,并发展新的制图方法,从而促进植被类型制图研究工作。

高分辨率大气强迫和植被功能型数据对青藏高原土壤温度模拟影响

土壤温度是陆气相互作用以及陆面模式模拟的关键参量,但高分辨率时空连续的土壤温度获取困难,尤其是我国青藏高原地区,融合遥感资料的陆面模式模拟可以获得高时空分辨率的资料。研究制作了新的地表植被功能型融合数据(MVEG),然后利用最新的高时空分辨率的中国气象局陆面数据同化系统HRCLDAS-V1. 0(1 km,1 h)驱动CLM模式对青藏高原2015年10 cm的土壤温度开展了模拟研究。结果表明,HRCLDAS-V1. 0的大气强迫数据(1 km,1 h)显著降低了模式模拟的误差,MVEG可以改善对极值的模拟,并使土壤温度空间分布较为合理。CLDAS/CLM(6 km,1 h)模拟值整体比观测值偏高1℃左右,HRCLDAS/CLM(1 km,1 h)有所改进,模拟的土壤温度年平均偏差绝对值和均方根误差分别降低0. 82和0. 18℃。HR-MVEG/CLM(1 km,1 h,同时改进了植被功能型)的模拟值最接近观测值,年平均均方根误差减小0. 27℃,且可以体现出土壤温度空间分布的细节特征。

VEGETATION数据特点与基本预处理方法

VEGETATION是由法国提供的一种全球资源观测数据,空间标称分辨率1km,VE GETATION具有免费、时效性高、信息量丰富等特点,可在国家级和流域级森林资源宏观监测中发挥作用;介绍了VEGETATION数据的基本情况与基本预处理方法,并对VEGETATION数据的基本统计特征进行了分析。

LAI和FVC植被参数对VIC模型土壤含水量模拟的影响研究

植被参数是分布式水文模型的重要输入参量,对径流、土壤水分等水循环参数的模拟具有显著的影响。论文以西藏那曲为研究区,对比分析了MODIS遥感数据估算的叶面积指数(Leaf Area Index,LAI)、植被覆盖指数(Fractional vegetation cover,FVC)两种动态植被参数对VIC水文模型模拟表层土壤含水量(0~15 cm)的精度影响。结果表明,与使用静态植被参数相比,基于MODIS的动态LAI及FVC驱动的VIC模型模拟的表层土壤含水量的精度显著提高,且冰冻期的改善尤为明显,偏差Bias从0.101 cm^3·cm^(-3)下降至0.032 cm^3·cm^(-3),均方根误差RMSE从0.135 cm^3·cm^(-3)下降至0.071 cm^3·cm^(-3),相关系数R从0.483上升至0.836。进一步分析表明,两种植被参数对VIC模型的表层土壤含水量模拟精度都有一定的影响,但FVC的影响更显著。

基于遥感影像的植被信息提取技术在测绘中的应用研究

目前国内很多测绘单位生产的1／1万比例尺DLG数据缺少面状植被数据集，但完全基于DLG数据进行人工构面时，工作量巨大，难以形成规模化生产。基于此，本文提出了利用遥感影像与DLG成果数据生产面状植被数据集的工艺流程，主要通过采用面向对象的方法进行植被数据集提取，并将提取结果进行边缘规范化处理，并分别在平原、丘陵、高山区域进行实验。结果表明该工艺流程能有效提高植被数据集的提取效率，并能有效保证提取精度要求，具有一定的可行性，有望弥补1／1万比例尺DLG数据中缺少面状植被数据集信息服务的不足。

遥感植被指数和CASA模型估算山东省冬小麦单产

准确估算区域尺度冬小麦单产对明确区域农业生产现状与保证国家粮食安全有重要意义。光能利用率模型是作物单产估算的常用模型之一,模型中最大光能利用率(ξmax)是准确估算作物单产的关键参数,作物的ξmax是否随时间发生变化需要深入探讨。首先使用Savitzky-Golay(S-G)对中分辨率成像光谱仪(MODIS)时序植被指数数据进行滤波,采用差分法结合光谱突变法提取了山东省2000年—2015年冬小麦种植面积,并使用市级尺度年鉴统计面积对提取面积进行验证,然后使用固定ξmax和变化ξmax分别驱动光能利用率模型(CASA),结合作物收获指数与冬小麦种植面积获取山东省2000年—2016年冬小麦单产时空分布特征,探讨最大光能利用率对作物单产模拟的影响。结果表明,滤波后的时序植被指数数据能够反映冬小麦生长的光谱特征,差分法与光谱突变法结合提取冬小麦面积具有较好的普适性,提取的多年冬小麦种植面积与年鉴统计冬小麦播种面积之间的决定系数(R^2)达0.71;变化ξmax情景下模拟的多年冬小麦单产与统计单产之间的决定系数更高,说明冬小麦ξmax是随时间变化的,可能与冬小麦品种更替有关。基于统计与模拟的结果均显示山东省冬小麦单产在2000年—2016年间呈现增加趋势,两者表现出来的增加速率分别为93.12和149.79 kg·hm^-2·a^-1。在空间上,山东省冬小麦单产呈现西部高于东部的分布特征。

POI和植被修正的夜间灯光城市指数提取建成区的研究

夜间灯光(Nighttime Light,NTL)影像能够提供夜间灯光信息,已被广泛应用于建成区的提取,但夜间灯光影像存在的“灯光溢出”问题会影响建成区的提取精度。因此提出一种POI数据和植被修正的夜间灯光城市指数(POI and Vegetation Adjusted NTL Urban Index,PVANUI)用于建成区的提取。以2018年南京市作为实验区域,以NPP-VIIRS影像、POI大数据、Landsat8影像作为原始数据,采用几何平均值法构建PVANUI。然后基于原始NPP-VIIRS影像、经VANUI和PVANUI修正的NPP-VIIRS影像采用阈值法提取建成区,共提取出3种结果。实验结果表明:经PVANUI修正的影像提取的南京市建成区的查全率、查准率、F1分数比仅利用NPP-VIIRS影像提高0.04,比经VANUI修正的影像提高0.03,而且提取的建成区更加完整,内部细节更加丰富。

十五个物种多样性指数的比较研究

用关帝山植被数据对１５个物种多样性指数进行了比较研究，结果表明：各指数间有着密切的关系，它们都能较好地反映多样性特征，但其中较好的丰富度指数依次为：Ｐａｔｒｉｃｋ指数、Ｍｅｎｈｉｎｉｃｋ指数、Ｍａｒｇａｌｅｆ指数。较好的均匀度指数依次为：修正的Ｈｉｌ指数、Ａｌａｔａｌｏ指数、Ｐｉｅｌｏｕ指数（ｅ）、Ｐｉｅｌｏｕ指数（Ｊｓｗ）、Ｐｉｅｌｏｕ指数（Ｊｓｉ）、ＭｃＩｎｔｏｓｈ指数。较好的多样性指数依次为：Ｓｈａｎｎｏｎ－Ｗｉｅｎｅｒ指数、Ｓｉｍｐｓｏｎ指数、Ａｕｄａｉｒ和Ｇｏｆ指数、种间相遇机率、ＭｃＩｎｔｏｓｈ指数、Ｈｕｒｌｂｅｒｔ指数。相关系数分析也很好地说明了这个问题。

基于国情普查成果的省级基础测绘DLG实体要素快速更新方法研究

文中以甘肃省省级基础测绘DLG更新项目为例,通过比对分析省级基础测绘DLG及地理国情普查数据,设计出了一套利用地理国情普查成果快速整合更新省级基础测绘DLG实体要素的方法,并利用地表覆盖进行植被数据生产。在实际生产作业中取得了很好的效果,对同类基础测绘更新项目有参考、借鉴的价值。

Effects of Freezing Disaster on Green-up Date of Vegetation Using MODIS/EVI Time Series Data

In the field of global changes, the relationship between plant phenology and climate, which reflects the response of terrestrial ecosystem to global climate change, has become a key subject that is highly concerned. Using the moderate-resolution imaging spectroradiometer (MODIS)/enhanced vegetation index(EVI) collected every eight days during January- July from 2005 to 2008 and the corresponding remote sensing data as experimental materials, we constructed cloud-free images via the Harmonic analysis of time series (HANTS). The cloud-free images were then treated by dynamic threshold method for obtaining the vegetation phenology in green up period and its distribution pattern. And the distribution pattern between freezing disaster year and normal year were comparatively analyzed for revealing the effect of freezing disaster on vegetation phenology in experimental plot. The result showed that the treated EVI data performed well in monitoring the effect of freezing disaster on vegetation phenology, accurately reflecting the regions suffered from freezing disaster. This result suggests that processing of remote sensing data using HANTS method could well monitor the ecological characteristics of vegetation.

An Experimental Analysis of Water and Soil Conservation Effected by Micro-landscape Structure

[Objective] This comparative experiment was to explore the soil loss con- trol effects under cultivation combination of different soil and vegetation types, and to provide scientific basis for the upcoming pilot project of ecological recovery. [Method] Both the rudiment of water locomotion functioned by micro-landscape structures and different spatial combinations of various landscape constituents are considered, thus, the combination of multi-soil type, crop species and site conditions is designed in three different experimental sites. [Result] Soil loss estimates in experiments in South Wello significantly depended on various soil type, slope, vegetation and type of con- servation structure; grass cover tremendously reduces soil loss; legume cultivation performed better than cereal cultivation in soil loss control. [Conclusion] By conduct- ing the data analysis of the experiment, a scientific reference is proposed to the agri- culture planting and protective mode for the alleviation of water and soil loss in Amhara Region, Ethiopia.

Vegetation Change of Ecotone in West of Northeast China Plain Using Time-series Remote Sensing Data

Multi-temporal series of satellite SPOT-VEGETATION normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) data from 1998 to 2007 were used for analyzing vegetation change of the ecotone in the west of the Northeast China Plain. The yearly and monthly maximal values,anomalies and change rates of NDVI and NDWI were calculated to reveal the interannual and seasonal changes in vegetation cover and vegetation water content. Linear regression method was adopted to characterize the trends in vegetation change. The yearly maximal NDVI decreased from 0.41 in 1998 to 0.37 in 2007,implying the decreasing trend of vegetation activity. There was a significant decrease of maximal NDVI in spring and summer over the study period,while an increase trend was observed in autumn. The vegetation-improved regions and vegetation-degraded regions occupied 17.03% and 20.30% of the study area,respectively. The maximal NDWI over growing season dropped by 0.027 in 1998–2007,and about 15.15% of the study area showed a decreasing trend of water content. Vegetation water stress in autumn was better than that in spring. Vegetation cover and water content variations were sensitive to annual precipitation,autumn precipitation and summer temperature. The vegetation degradation trend in this ecotone might be induced by the warm-drying climate especially continuous spring and summer drought in the recent ten years.

Examining Forest Net Primary Productivity Dynamics and Driving Forces in Northeastern China During 1982–2010

Forest net primary productivity （NPP） is a key parameter for forest monitoring and management. In this study, monthly and annual forest NPP in the northeastern China from 1982 to 2010 were simulated by using Carnegie-Ames-Stanford Approach （CASA） model with normalized difference vegetation index （NDVI） sequences derived from Advanced Very High Resolution Radiometer （AVHRR） Global Invento y Modeling and Mapping Studies （GIMMS） and Terra Moderate Resolution Imaging Spectroradiometer （MODIS） products. To address the problem of data inconsistency between AVHRR and MODIS data, a per-pixel unary linear regres- sion model based on least ~;quares method was developed to derive the monthly NDVI sequences. Results suggest that estimated forest NPP has mean relative error of 18.97% compared to observed NPP from forest inventory. Forest NPP in the northeastern China in- creased significantly during the twenty-nine years. The results of seasonal dynamic show that more clear increasing trend of forest NPP occurred in spring and awmnn. This study also examined the relationship between forest NPP and its driving forces including the climatic and anthropogenic factors. In spring and winter, temperature played the most pivotal role in forest NPR In autumn, precipitation acted as the most importanl factor affecting forest NPP, while solar radiation played the most important role in the summer. Evaportran- spiration had a close correlation with NPP for coniferous forest, mixed coniferous broadleaved forest, and broadleaved deciduous forest. Spatially, forest NPP in the Da Hinggan Mountains was more sensitive to climatic changes than in the other ecological functional re- gions. In addition to climalie change, the degradation and improvement of forests had important effects on forest NPP. Results in this study are helpful for understanding the regional carbon sequestration and can enrich the cases for the monitoring of vegetation during long time series.

Vegetation NPP Distribution Based on MODIS Data and CASA Model——A Case Study of Northern Hebei Province

Net Primary Productivity （NPP） is one of the important biophysical variables of vegetation activity, and it plays an important role in studying global carbon cycle, carbon source and sink of ecosystem, and spatial and temporal distribution of CO2. Remote sensing can provide broad view quickly, timely and multi-temporally, which makes it an attractive and powerful tool for studying ecosystem primary productivity, at scales ranging from local to global. This paper aims to use Moderate Resolution Imaging Spectroradiometer （MODIS） data to estimate and analyze spatial and temporal distribution of NPP of the northern Hebei Province in 2001 based on Carnegie-Ames-Stanford Approach （CASA） model. The spatial distribution of Absorbed Photosynthetically Active Radiation （APAR） of vegetation and light use efficiency in three geographical subregions, that is, Bashang Plateau Region, Basin Region in the northwestern Hebei Province and Yanshan Mountainous Region in the Northern Hebei Province were analyzed, and total NPP spatial distribution of the study area in 2001 was discussed. Based on 16-day MODIS Fraction of Photosynthetically Active Radiation absorbed by vegetation （FPAR） product, 16-day composite NPP dynamics were calculated using CASA model; the seasonal dynamics of vegetation NPP in three subreglons were also analyzed. Result reveals that the total NPP of the study area in 2001 was 25.1877 × 10^6gC/（m^2.a）, and NPP in 2001 ranged from 2 to 608gC/（m^2-a）, with an average of 337.516gC/（m^2.a）. NPP of the study area in 2001 accumulated mainly from May to September （DOY 129-272）, high NIP values appeared from June to August （DOY 177-204）, and the maximum NPP appeared from late July to mid-August （DOY 209-224）.

Estimating Fraction of Photosynthetically Active Radiation of Corn with Vegetation Indices and Neural Network from Hyperspectral Data

The fraction of photosynthetically active radiation （FPAR） is a key variable in the assessment of vegetation productivity and land ecosystem carbon cycles. Based on ground-measured corn hyperspectral reflectance and FPAR data over Northeast China, the correlations between corn-canopy FPAR and hyperspectral reflectance were analyzed, and the FPAR estimation performances using vegetation index （VI） and neural network （NN） methods with different two-band-combination hyperspectral reflectance were investigated. The results indicated that the corn-canopy FPAR retained almost a constant value in an entire day. The negative correlations between FPAR and visible and shortwave infrared reflectance （SWIR） bands are stronger than the positive correlations between FPAR and near-infrared band re- flectance （NIR）. For the six VIs, the normalized difference vegetation index （NDVI） and simple ratio （SR） performed best for estimating corn FPAR （the maximum R2 of 0.8849 and 0.8852, respectively）. However, the NN method esti- mated results （the maximum Rz is 0.9417） were obviously better than all of the VIs. For NN method, the two-band combinations showing the best corn FPAR estimation performances were from the NIR and visible bands; for VIs, however, they were from the SWIR and NIR bands. As for both the methods, the SWIR band performed exceptionally well for corn FPAR estimation. This may be attributable to the fact that the reflectance of the SWIR band were strongly controlled by leaf water content, which is a key component of corn photosynthesis and greatly affects the absorption of photosynthetically active radiation （APAR）, and makes further impact on corn-canopy FPAR.

Using multispectral landsat and sentinel-2 satellite data to investigate vegetation change at Mount St. Helens since the great volcanic eruption in 1980

Long-term analyses of vegetation succession after catastrophic events are of high interest for an improved understanding of succession dynamics. However, in many studies such analyses were restricted to plot-based measurements. Contrarily, spatially continuous observations of succession dynamics over extended areas and timeperiods are sparse. Here, we applied a change vector analysis(CVA) to investigate vegetation succession dynamics at Mount St. Helens after the great volcanic eruption in 1980 using Landsat. We additionally applied a supervised random forest classification using Sentinel-2 data to map the currently prevailing vegetation types. Change vector analysis was performed with the normalized difference vegetation index(NDVI) and the urban index(UI) for three subsequent decades after the eruption as well as for the whole observation time between 1984 and 2016. The influence of topography on the current vegetation distribution was examined by comparing altitude, slope angles and aspect values of vegetation classes derived by the random forest classification. WilcoxRank-Sum test was applied to test for significant differences between topographic properties of the vegetation classes inside and outside of the areas affected by the eruption. For the full time period, a total area of 516 km2 was identified as re-vegetated, whereas the area and magnitude of re-growing vegetation decreased during the three decades and migrated closer to the volcanic crater. Vegetation losses were mainly observed in regions unaffected by the eruption and related mostly to timber harvesting. The vegetation type classification reached a high overall accuracy of approximately 90%. 36 years after the eruption, coniferous and deciduous trees have established at formerly devastated areas dominating with a proportion of 66%, whereas shrubs are more abundant in riparian zones. Sparse vegetation dominates at regions very close to the crater. Elevation was found to have a great influence on the reestablishment and distribution of the vegetation classes within the devastated areas showing in almost all cases significant differences in altitude distribution. Slope was less important for the different classes-only representing significantly higher values for meadows, whereas aspect seems to have no notable influence on the reestablishment of vegetation at Mount St. Helens. We conclude that major vegetation succession dynamics after catastrophic events can be assessed and characterized over large areas from freely available remote sensing data and hence contribute to an improved understanding of succession dynamics.

Predictive Vegetation Mapping Approach Based on Spectral Data, DEM and Generalized Additive Models

This study aims to provide a predictive vegetation mapping approach based on the spectral data, DEM and Generalized Additive Models (GAMs). GAMs were used as a prediction tool to describe the relationship between vegetation and environmental variables, as well as spectral variables. Based on the fitted GAMs model, probability map of species occurrence was generated and then vegetation type of each grid was defined according to the probability of species occurrence. Deviance analysis was employed to test the goodness of curve fitting and drop contribution calculation was used to evaluate the contribution of each predictor in the fitted GAMs models. Area under curve (AUC) of Receiver Operating Characteristic (ROC) curve was employed to assess the results maps of probability. The results showed that: 1) AUC values of the fitted GAMs models are very high which proves that integrating spectral data and environmental variables based on the GAMs is a feasible way to map the vegetation. 2) Prediction accuracy varies with plant community, and community with dense cover is better predicted than sparse plant community. 3) Both spectral variables and environmental variables play an important role in mapping the vegetation. However, the contribution of the same predictor in the GAMs models for different plant communities is different. 4) Insufficient resolution of spectral data, environmental data and confounding effects of land use and other variables which are not closely related to the environmental conditions are the major causes of imprecision.

Impacts of Climate Change on Net Primary Productivity in Arid and Semiarid Regions of China

In recent years, with the constant change in the global climate, the effect of climate factors on net primary productivity(NPP) has become a hot research topic. However, two opposing views have been presented in this research area: global NPP increases with global warming, and global NPP decreases with global warming. The main reasons for these two opposite results are the tremendous differences among seasonal and annual climate variables, and the growth of plants in accordance with these climate variables. Therefore, it will fail to fully clarify the relation between vegetation growth and climate changes by research that relies solely on annual data. With seasonal climate variables, we may clarify the relation between vegetation growth and climate changes more accurately. Our research examined the arid and semiarid areas in China(ASAC), which account for one quarter of the total area of China. The ecological environment of these areas is fragile and easily affected by human activities. We analyzed the influence of climate changes, especially the changes in seasonal climate variables, on NPP, with Climatic Research Unit(CRU) climatic data and Moderate Resolution Imaging Spectroradiometer(MODIS) satellite remote data, for the years 2000–2010. The results indicate that: for annual climatic data, the percentage of the ASAC in which NPP is positively correlated with temperature is 66.11%, and 91.47% of the ASAC demonstrates a positive correlation between NPP and precipitation. Precipitation is more positively correlated with NPP than temperature in the ASAC. For seasonal climatic data, the correlation between NPP and spring temperature shows significant regional differences. Positive correlation areas are concentrated in the eastern portion of the ASAC, while the western section of the ASAC generally shows a negative correlation. However, in summer, most areas in the ASAC show a negative correlation between NPP and temperature. In autumn, precipitation is less important in the west, as opposed to the east, in which it is critically important. Temperatures in winter are a limiting factor for NPP throughout the region. The findings of this research not only underline the importance of seasonal climate variables for vegetation growth, but also suggest that the effects of seasonal climate variables on NPP should be explored further in related research in the future.

Sources of uncertainty in exploring rangeland phenology： A case study in an alpine meadow on the central Tibetan Plateau

Global climate change has been found to substantially influence the phenology of rangeland,especially on the Tibetan Plateau. However, there is considerable controversy about the trends and causes of rangeland phenology owing to different phenological exploration methods and lack of ground validation. Little is known about the uncertainty in the exploration accuracy of vegetation phenology.Therefore, in this study, we selected a typical alpine rangeland near Damxung national meteorological station as a case study on central Tibetan Plateau, and identified several important sources influencing phenology to better understand their effects on phenological exploration. We found man-made land use was not easily distinguished from natural rangelands, and therefore this may confound phenological response to climate change in the rangeland. Change trends of phenology explored by four methods were similar, but ratio threshold method(RTM) was more suitable for exploring vegetation phenology in terms of the beginning of growing season(BGS) and end of growing season(EGS). However, some adjustments are needed when RTM is used in extreme drought years. MODIS NDVI/EVI dataset was most suitable for exploring vegetation phenology of BGS and EGS. The discrimination capacities of vegetation phenology declined with decreasing resolution of remote sensing images from MODIS to GIMMS AVHRR datasets. Additionally, distinct trends of phenological change rates were indicated in different terrain conditions, with advance of growing season in high altitudes but delay of season in lower altitudes. Therefore, it was necessary to eliminate interference of complex terrain and man-made land use to ensure the representativeness of natural vegetation. Moreover, selecting the appropriate method to explore rangelands and fully considering the impact of topography are important to accurately analyze the effects of climate change on vegetation phenology.

A new approach of extracting vegetation points from urban airborne LiDAR data

Urban vegetation has been an important indicator for the evaluation of eco-cities, which is of great significance to promote eeo-city construction. We study and discuss the commonly used urban vegetation extrac-tion methods. The extraction of vegetation points in this study is completed through mathematical statistics, mean-square error, successive differences and iterative algorithm which are based on the analysis of different spatial morphological characteristics in urban point clouds. Linyi, a city of Shandong Province in China, is se-lected as the study area to test this method and the result shows that the proposed method has a strong practicali- ty in urban vegetation point cloud extraction. Only 3D coordinate properties of the LiDAR point clouds are used in this method and it does not require additional information, for instance, return intensity, which makes the method more applicable and operable.