基于CASA模型和MODIS数据的甘南草地NPP时空动态变化研究

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刘洁, 孟宝平, 葛静, 高金龙, 殷建鹏, 侯蒙京, 冯琦胜, 梁天刚. 基于CASA模型和MODIS数据的甘南草地NPP时空动态变化研究. 草业学报, 2019,28(6): 19-32
LIU Jie, MENG Bao-ping, GE Jing, GAO Jin-long, YIN Jian-peng, HOU Meng-jing, FENG Qi-sheng, LIANG Tian-gang. Spatio-temporal dynamic changes of grassland NPP in Gannan prefecture, as determined by the CASA model. Acta Prataculturae Sinica,2019,28(6): 19-32 复制到剪切板

Doi:10.11686/cyxb2018320
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基于CASA模型和MODIS数据的甘南草地NPP时空动态变化研究

刘洁, 孟宝平, 葛静, 高金龙, 殷建鹏, 侯蒙京, 冯琦胜, 梁天刚^*

兰州大学草地农业生态系统国家重点实验室,兰州大学农业农村部草牧业创新重点实验室,兰州大学草地农业科技学院,甘肃兰州 730020

*通信作者 E-mail: tgliang@lzu.edu.cn

作者简介:刘洁(1995-),女,陕西渭南人,在读硕士。E-mail: liuj14@lzu.edu.cn

收稿日期: 2018-05-15

基金项目:国家“十三五”重点研发计划项目(2017YFC0504801),国家自然科学基金(31672484、31702175、41801191、41805086),长江学者和创新团队发展计划(IRT 17R50),甘肃省青年科技基金(18JR3RA024)和中央高校基本科研业务费(lzujbky-2018-it17)资助

摘要

植被净初级生产力(net primary productivity, NPP)在全球气候变化及碳循环研究中扮演着重要的角色,精准快速的估算NPP对评估区域生态系统承载力以及合理利用自然资源具有重要的意义。利用2011-2014年甘南地面实测草地地上生物量(aboveground biomass, AGB)数据和根冠比系数计算的草地NPP数据,分别验证了MOD17A3 NPP产品和基于CASA(Carnegie-Ames-Stanford approach)模型估算的草地NPP的精度,分析了2000-2016年甘南地区草地NPP的时空动态变化。结果表明:基于CASA模型模拟的草地NPP精度整体上高于MOD17A3 NPP产品的精度,其均方根误差(root mean square error, RMSE)较MOD17A3 NPP小9.94 g C·m^-2;CASA模型分析的甘南地区草地NPP总体上呈现由西南向东北逐渐减少的趋势;对不同草地类型而言,沼泽类的平均NPP最高(469.07 g C·m^-2),温性草原类最低(324.18 g C·m^-2),而占研究区草地总面积比例较大的高寒草甸类和高寒灌丛草甸类草地的平均NPP分别为449.22和465.27 g C·m^-2;2000-2016年间,甘南地区大部分草地NPP稳定不变,其面积占研究区草地总面积的75.31%,NPP呈增加趋势的区域占草地面积的22.63%,而NPP呈减少趋势的区域占比最小,仅为2.06%。以上研究结果表明CASA模型在高寒地区草地NPP评估、草地资源合理利用与管理方面具有重要的应用价值。

关键词: 甘南地区; 草地净初级生产力; MOD17A3产品; CASA模型; 动态变化

Spatio-temporal dynamic changes of grassland NPP in Gannan prefecture, as determined by the CASA model

LIU Jie, MENG Bao-ping, GE Jing, GAO Jin-long, YIN Jian-peng, HOU Meng-jing, FENG Qi-sheng, LIANG Tian-gang^*

State Key Laboratory of Grassland Agro-ecosystems, Key Laboratory of Grassland Livestock Industry Innovation, Ministry of Agriculture and Rural Affairs, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou 730020, China

* Corresponding author. E-mail: tgliang@lzu.edu.cn

Abstract

Net primary productivity (NPP) plays an important role in global carbon cycle and is important to understanding drivers of climate changes. Precise and rapid estimation of vegetation NPP is important for evaluating ecological carrying capacity at a regional scale and managing natural resources reasonably. In this study, field-measured grassland above ground biomass (AGB) from 2011 to 2014, MODIS remote sensing data and meteorological data in Gannan prefecture were used. In combination with the ratio of belowground biomass to AGB, we calculate the grassland NPP, and evaluate the accuracy of the MOD17A3 product and Carnegie-Ames-Stanford approach (CASA) model, and analyze the dynamic changes of grassland NPP from 2000 to 2016 using the better method. The results show that the accuracy of grassland NPP predictions from the CASA model (root mean square error (RMSE) = 9.94 g C ·m^-2·yr^-1) is higher than that of MOD17A3 product. The average annual grassland NPP determined by the CASA model shows a decreasing trend from the southwest to northeast between 2000 and 2016 in our study area. Comparing different vegetation types, the annual NPP for marsh grassland (469.07 g C ·m^-2·yr^-1) was the highest, while that of temperate steppe grassland was the lowest (324.18 g C ·m^-2·yr^-1). In addition, the annual NPP of alpine meadow and alpine shrub meadow grasslands (which have relatively large area in Gannan prefecture) was, respectively, 370 and 430 g C ·m^-2·yr^-1. Over the past 17 years, the annual grassland NPP was generally stable in most regions, (75.31% of the total grassland area). Meanwhile, an increasing NPP trend was seen in 22.63%, and a decreasing trend in just 2.06% of the Gannan prefecture area. These results suggest that the CASA model has an important role in grassland NPP estimation and will assist in the sustainable management of grassland resources in alpine areas.

Keyword: Gannan prefecture; grassland net primary productivity (NPP); MOD17A3 product; CASA model; dynamic change

文章图片

植被净初级生产力(net primary productivity, NPP)是指单位面积的绿色植物在单位时间累积的有机物总量, 包括植被地上部分和地下部分的生物量^[1]。NPP的研究是全球变化与陆地生态系统研究的核心内容之一, 它能够反映植物群落在自然环境条件下的生产能力, 是评价植被生态系统可持续发展的重要生态指标。草地是我国最重要的土地覆盖类型之一, 约占国土总面积的41.41%, 草地生态系统具有调节气候、涵养水源、保持水土、改良土壤和维持生物多样性等重要功能^[2]。准确监测草地NPP对于评估草地资源变化, 发掘草地生产潜力及合理利用草地资源等方面具有重要的科学意义和实际应用价值^{[3, 4, 5]}。

目前, 在区域或全球尺度上NPP无法直接测量, 利用模型间接估测是一种重要途径, 已被学者们广泛接受^{[6, 7]}。在众多区域陆地NPP估测模型中, 基于植被光能利用率原理的CASA(Carnegie-Ames-Stanford approach)模型是最具潜力的研究手段之一^[8]。CASA模型中的大部分参数可以直接通过遥感数据获得, 不需要复杂的野外工作, 实用而简单, 并且从不同的侧重点出发可估测不同生态系统的NPP^{[9, 10, 11]}。已有大量研究基于CASA模型估测了青藏高原地区高寒草地的NPP状况, 但不同研究结果差异较大。王莺等^[12]发现2001-2008年甘南地区的草地NPP平均值为483.41 g C· m^-2, 7种不同草地类型NPP介于336.03~535.60 g C· m^-2。朴世龙等^[13]基于CASA模型估算得到1982-1999年青藏高原不同植被类型的平均NPP介于24~574 g C· m^-2, 其中草甸草地NPP为176 g C· m^-2。张镱锂等^[14]发现1982-2009年高寒草地年均NPP为120.8 g C· m^-2, 28年来草地NPP呈现波动上升趋势, 增幅为13.3%。孙云晓等^[15]得出1983-2012年青藏高原不同植被类型NPP介于30~707 g C· m^-2, 其中高寒草甸类草地NPP为241 g C· m^-2。邹德富^[16]估算结果表明2003-2010年间青藏高原草地NPP年均值介于为20.0~212.2 g C· m^-2, 其中高寒草甸类草地NPP为116.0 g C· m^-2。

MOD17A3 NPP产品数据集是通过Running等^[17]利用BIOME-BGC模型(biome biogeochemical model)与光能利用率模型模拟所获得的。目前, 该产品能够较好地估测全球生态系统NPP的时空变化状况, 在不同植被生长状况评价、生物量估算、环境监测、碳循环和全球变化等研究中得到验证和广泛应用。刘琳等^[18]基于MOD17A3 NPP数据分析表明2000-2010年重庆植被NPP波动范围集中在510~646 g C· m^-2之间, 大部分地区NPP呈现上升趋势, 且由南向北增加的幅度逐渐减小。王新闯等^[19]分析得到2000-2010年河南省植被年平均NPP为384 g C· m^-2, 大部分地区植被NPP呈现增加趋势, 且在空间上从南到北逐渐减少。李登科等^[20]研究得出2000-2006年陕西省植被NPP介于340~434 g C· m^-2, 呈现北低南高的分布特征。刘海江等^[21]发现2000-2010年锡林郭勒盟草地NPP总体上呈增加的趋势, 东部地区的NPP值高于西部地区。然而, 以上研究大多集中在重庆、河南、陕西和内蒙古等地区, 在青藏高原高寒草地少有涉及, 特别是在高寒草地上针对MOD17A3产品与CASA模型模拟结果的对比研究鲜有报道, 因此系统评估MOD17A3产品与CASA模型模拟高寒草地NPP的精度, 对准确分析草地NPP的时空动态变化特征具有重要作用。

综上所述, 本研究以青藏高原甘南地区作为研究区, 基于2011-2014年野外实测的草地地上生物量(aboveground biomass, AGB)数据和根冠比系数计算的草地NPP, 对MOD17A3 NPP产品和CASA模型的估测精度进行检验。研究适合甘南地区草地NPP的估测方法, 并分析高寒草地NPP的时空动态变化特征, 以期为甘南草地资源的可持续利用和草地生态环境保护等相关研究提供科学基础。

1 材料与方法

1.1 研究区概况

甘南藏族自治州(33° 06'-35° 44' N, 100° 46'-104° 44' E)位于青藏高原东北边缘区。平均海拔3000 m, 属于典型的高原大陆性气候。年均温度为1~3 ℃, 日照时数为1759.5~2583.1 h, 年均降水量为434.0~598.9 mm^[2]。境内水资源较为丰富, 主要河流有白龙江、黄河、洮河和大夏河。全州包括7县1市, 分别为东南部的舟曲和迭部县, 中部的碌曲、卓尼和临潭县, 北部的夏河县和合作市以及西部的玛曲县。甘南地区是甘肃省重要的畜牧业生产基地, 也是全国少数民族集聚的五大草原牧区之一, 拥有天然草地7.72× 10⁶ hm², 占土地总面积的67.64%, 其中可利用草地2.57× 10⁶ hm², 占草地总面积的95.86%^[22]。该地区草地类型有暖性草丛、低平地草甸、沼泽、温性草原、温性草甸草原、高寒灌丛草甸和高寒草甸类, 其中高寒灌丛草甸的面积最大, 占甘南地区草地面积的87.00%; 其次是高寒草甸类草地, 其面积比为11.06%(图1)。

	Figure Option View Download New Window
	图1 甘南地区草地类型及外业调查样点空间分布Fig.1 Spatial distribution of different grassland types and sample points in Gannan prefecture

1.2 数据采集与处理

1.2.1 草地AGB数据野外调查主要集中在2011-2014年草地生长季7-8月, 地面实测样地数据主要分为固定监测样地和随机监测样地:1)固定样地位于夏河县桑科乡央吉社区, 在研究区内选取草地植被空间分布比较均一, 具有空间代表性且与MODIS植被指数像素(大小为250 m× 250 m)所对应的区域作为采样区。2012年在采样区内设置5个大小为30 m× 30 m的样地, 在每个样地内随机布设0.5 m× 0.5 m的样方各1个; 2013年布设同样大小的样地12个, 每个样地内随机设置3个样方; 为了增加样地的空间代表性及其与MODIS植被指数数据的空间匹配性, 2014年样地增加到13个, 采用5点法布设样方^[23], 共设置65个样方。2)随机监测样地在甘南全州内布设, 样地和样方大小与固定样地相同, 两个样地之间的距离大于5 km。本研究于2011年8月在全州范围内设置随机监测样地57个, 在植被群落一致性较好的样地内设置2~3个样方, 在植被较为复杂的样地中布设3~4个样方, 共布设样方176个。记录每个样方内的草地类型、优势种、物种数、草地植被盖度、草层高度和GPS定位信息等指标, 以及齐地面刈割所有植被地上部分的鲜重并带回室内在65 ℃恒温烘箱中烘干至恒重测干重。

1.2.2 草地NPP的计算采用草地地上生物量和地下生物量之和作为草地的NPP值, 地下生物量根据地上与地下生物量的比例系数(又称根冠比)来估算。本研究中采用的根冠比系数来自朴世龙等^[24]确定的中国草地植被地上与地下部分生物量的比例系数, 其中甘南地区高寒草甸类和高寒灌丛草甸类草地的根冠比为7.92, 低平地草甸类为6.31, 换算为NPP后乘以系数0.45得到以碳为单位的草地NPP数据^[24]。

1.3 遥感数据与预处理

1.3.1 数字高程模型与太阳总辐射量的计算数字高程模型(digital elevation model, DEM)数据来源于NASA的SRTM数据(http://srtm.csi.cgiar.org/), 空间分辨率为90 m。在ArcMap下, 利用Resample工具将其重采样至1000 m, 采用甘南州边界矢量空间数据裁剪出甘南地区的DEM数据。借助Area Solar Radiation模块计算每日太阳总辐射量(solar radiation, SOL)^[25]。

1.3.2 气象数据气象数据来源于中国气象数据网(http://data.cma.cn/), 本研究下载了2000-2016年甘南及其周边地区38个气象台站的日均温数据(℃)。通过ANUSPLIN软件, 利用薄板样条插值法(thin-plate smoothing spline, ANUSPLIN)对甘南地区2000-2016年各月的平均温度进行插值处理, 建立了近17年月均温空间数据集^{[26, 27]}。

1.3.3 MODIS数据与预处理本研究主要涉及的遥感数据来自美国航空航天局对地观测系统数据与信息系统(NASA’ S Earth Observing System Data and Information System, EOSDIS)开发的MOD17A3、MOD15A2和MOD09GA数据, 数据格式为EOS-HDF。同一时相, 能够覆盖整个研究区的以上3种数据各需1景, 轨道号均为h26v05。MOD17A3数据是空间分辨率为1 km的年NPP数据; MOD09GA是空间分辨率为500 m的每日地表反射率产品, 包括MODIS 1~7波段, 本研究使用第2波段(841~876 nm)和第6波段(1628~1652 nm)的数据, 计算地表湿润指数(land surface water index, LSWI); MOD15A2是L4级8 d合成的光合有效辐射分量产品, 空间分辨率为1 km。其中, MOD15A2数据和MOD09GA计算的LSWI数据用于CASA模型估算NPP。

利用MODIS数据重投影工具(MODIS reprojection tool, MRT)对以上数据进行格式转换和定义投影, 处理后的数据为Tiff格式和WGS84/Albers投影。在ArcMap中利用Resample工具将所有数据重采样至1000 m, 进一步利用Extract by Mask工具裁剪出甘南州的MODIS影像, 用于后续分析研究。同时, 采用最大值合成法(maximum value composite, MVC), 分别合成月最大MOD15A2和MOD09GA数据集^[28]。

1.4 基于CASA模型的草地NPP估算

CASA模型是由遥感数据、温度、降水、太阳辐射以及植被类型和土壤类型共同驱动的光能利用率模型, 主要通过光合有效辐射(absorbed photosynthesis active radiation, APAR)和光能转化率(ε )来计算NPP^{[29, 30, 31]}。其表达式为:

NPP=APAR× ε (1)

其中, APAR为植被吸收的光合有效辐射; ε 指光能转化率, 即植被把所吸收的光合有效辐射(APAR)转化为有机碳的效率^[8]。

光合有效辐射的计算式如下:

APAR=SOL× FPAR× 0.5 (2)

其中, SOL为太阳总辐射量(MJ· m^-2); FPAR为植被冠层对入射光合有效辐射(photosynthetically available radiation, PAR)的吸收比例; 常数0.5表示植被所能利用的太阳有效辐射(波长0.4~0.7 μ m)占太阳总辐射的比例。

光能转化率主要受温度和水分的影响, 其计算公式是:

ε =T_ε₁× T_ε₂× W_ε× ε ^* (3)

T_ε₁=0.8+0.02× T_opt-0.0005× (T_opt)² (4)

T_ε₂={1.184/[1+ $e^{(0.2 \times (T_{opt} - 10 - T))}$ ]}× {1/[1+ $e^{(0.3 \times (- T_{opt} - 10 + T))}$ ]} (5)

其中, T_ε₁和T_ε₂表示温度对光能转化率的胁迫作用; T_opt为植物生长时的最适温度; T为月平均气温; ε ^*为理想条件下的最大光能转化率, 其值随植被类型的不同而不同。本研究选用植被最大光能转化率的全球通用值0.389 g C· m^{-2[29, 30]} 。

水分胁迫系数(W_ε)反映植物所利用的有效水分对光能转化率的影响状况。本研究通过遥感反演地表湿润指数LSWI的方法计算 $W_{ε}$ ^[32], 计算方法如下:

W_ε= $\frac{1 + LSWI}{1 + LSW I_{\max}}$ (6)

LSWI= $\frac{ρ_{nir} - ρ_{swir}}{ρ_{nir} + ρ_{swir}}$ (7)

其中, 近红外波段(ρ _nir)和短波红外波段(ρ _swir)的范围分别为841~875 nm和1628~1652 nm; LSWI的取值范围是-1~1; W_ε的取值范围为0~1。

1.5 MOD17A3 NPP和CASA模型估算的NPP精度验证

野外实测AGB数据转化的草地NPP数据作为观测NPP, 验证MOD17A3 NPP数据和CASA模型估算的NPP在甘南草地的估测精度, 评价指标选用均方根误差(root mean square error, RMSE)^{[23, 33]}。通过精度比较确定的草地NPP估算算法将用于进一步模拟甘南地区2000-2016年的草地NPP动态变化。RMSE计算公式如下:

$RMSE = \sqrt[]{\frac{\sum (Y_{i} - Y_{i} {')}^{2}}{N}}$ (8)

式中, Y_i为第i个研究位点NPP观测值; Y_i'为第i个研究位点的CASA模型估算的NPP值或MOD17A3 NPP值。RMSE值越小, 表示模型估测误差较小; 反之, 表示模型误差较大^{[34, 35]}。

1.6 草地NPP时空动态变化

基于精度评价所选的草地NPP估算算法和Slope公式模拟甘南地区2000-2016年的草地NPP动态变化。Slope趋势变化分析方法的计算公式如下:

$Slope = \frac{n \times \overset{n}{\sum_{i = 1}} i \times X_{i} - \overset{n}{\sum_{i = 1}} i \overset{n}{\sum_{i = 1}} X_{i}}{n \times \overset{n}{\sum_{i = 1}} i^{2} - {(\overset{n}{\sum_{i = 1}} i)}^{2}}$ (9)

式中, n为监测时间段的年数; X_i为第i年的NPP值^[36]。Slope数值的大小反映了NPP随时间变化的速率大小。

为了进一步反映研究区草地NPP的变化程度, 本研究使用F检验判断其变化趋势的显著性, 计算公式为:

F= $\frac{R^{2} (n - 2)}{1 - R^{2}}$ (10)

式中, R²为2000-2016年间每个像元NPP值和年份之间的复相关系数; n表示年份数, 本研究取17年。显著性水平分为3级:不显著变化(P≥ 0.05)、显著变化(0.01< P< 0.05)和极显著变化(P≤ 0.01)。结合甘南州Slope变化趋势, 将草地NPP变化显著性情况分为极显著增加、极显著减少、显著增加、显著减少和保持稳定5种情况。

2 结果与分析

2.1 基于调查样本的甘南地区草地AGB及观测NPP的统计分析

甘南地区2011-2014年7-8月的实测草地AGB统计分析结果表明, 草地AGB空间差异较大(表1)。草地AGB最小仅为4.70 g· m^-2, 最大达234.85 g· m^-2。各县市草地AGB平均值介于55.54~84.92 g· m^-2, 变异系数介于0.31~0.49。各县市平均草地AGB由大到小依次为碌曲、玛曲、夏河、合作、卓尼和临潭。其中, 临潭县的生物量最低, 为55.54 g· m^-2, 变异系数较大, 为0.32; 碌曲县的生物量最大(84.92 g· m^-2), 变异系数为0.31。相对而言, 碌曲、夏河和临潭县的变异系数较小(0.31~0.33), 标准偏差也较小, 说明这3个县草地AGB的波动相对较小。

表1 甘南地区2011-2014年实测草地地上生物量 Table 1 Measured aboveground biomass from 2011 to 2014 in Gannan prefecture (g· m^-2)

2011年不同草地类型观测NPP整体上介于36.05~942.69 g C· m^-2, 平均值范围为176.40~359.80 g C· m^-2。研究区3种主要草地类型NPP平均值由高到低依次为高寒草甸类、高寒灌丛草甸类和低平地草甸类。其中, 高寒灌丛草甸类草地的标准偏差相对较高, 为136.34 g C· m^-2, 说明该类草地的NPP变化幅度较大(表2)。分析2011-2014年高寒草甸类草地的观测NPP统计结果(表3)表明, 4年来高寒草甸类草地的观测NPP介于36.05~942.69 g C· m^-2, 平均值为294.87 g C· m^-2。整体来看, NPP呈现逐年减小的趋势, 其平均值下降了56.05 g C· m^-2, 标准偏差随时间变化逐渐减小, 说明NPP变化波动逐年变小。

表2 2011年甘南不同草地类型观测NPP统计分析 Table 2 Statistical analysis of observed NPP in different grassland types in Gannan prefecture in 2011

表3 2011-2014年甘南高寒草地观测NPP统计分析 Table 3 Statistical analysis of observed NPP from 2011 to 2014 in Gannan alpine meadow

2.2 基于MOD17A3产品和CASA模型估算的NPP精度评价

利用2011-2014年地面观测NPP数据进行精度评价的结果如表4和表5所示。对于高寒草甸类草地而言, MOD17A3 NPP产品的RMSE与CASA模型的估算结果基本一致; 而就高寒灌丛草甸类和低平地草甸类草地而言, CASA模型计算的NPP的RMSE值(分别为123.81和53.14 g C· m^-2)均低于MOD17A3产品, 其RMSE较MOD17A3 NPP产品分别降低了38.32和41.32 g C· m^-2(表4)。

表4 2011年不同草地类型MOD17A3产品和CASA模型估算的NPP精度评价 Table 4 Accuracy assessment of NPP based on MOD17A3 product and CASA model in different grassland types in 2011

表5 2011-2014年甘南高寒草甸MOD17A3产品和CASA模型估算的NPP精度评价 Table 5 Accuracy assessment based on MOD17A3 product and CASA model from 2011 to 2014 in Gannan alpine meadow

在2011-2013年间, CASA模型模拟的草地NPP的RMSE介于102.42~122.96 g C· m^-2, 而MOD17A3 NPP产品的RMSE则介于109.56~157.82 g C· m^-2, 可以看出这3年间CASA模型的模拟结果优于MOD17A3 NPP产品。但是, 2014年MOD17A3 NPP产品的RMSE略低于CASA模型模拟NPP的RMSE。总体来看, 这4年间MOD17A3 NPP产品的RMSE较CASA模型模拟NPP的RMSE高9.94 g C· m^-2(表5)。综合以上结果, CASA模型模拟的高寒灌丛草甸类和高寒草甸类草地NPP的精度高于MOD17A3 NPP产品。由于这两类草地的面积占研究区草地总面积的90%以上, 因此可以认为CASA模型更适合用于评估甘南地区草地净初级生产力。

2.3 甘南州草地NPP时空动态变化

2.3.1 2000-2016年平均草地NPP空间分布特征利用CASA模型分析得到甘南地区2000-2016年平均草地NPP的时空变化(图2)。17年间, 草地平均NPP介于64.84~638.85 g C· m^-2, 总体上呈现由西南向东北逐渐减少的趋势。草地NPP平均值大于570 g C· m^-2的区域主要分布在玛曲县的南部和东部, 以及碌曲县的部分地区; NPP平均值在370 g C· m^-2以下的区域主要分布在夏河县的北部、舟曲县的东部以及玛曲县和合作市的部分地区; 其余大部分地区NPP平均值在370~570 g C· m^-2, 其面积占整个草地总面积的80%以上。

在所有草地类型中, 平均NPP值由大到小依次为沼泽类、高寒灌丛草甸类、高寒草甸类、温性草甸草原类、低平地草甸类、暖性草丛类和温性草原类。其中沼泽类草地面积仅占研究区草地总面积的0.41%, 其平均NPP值最大, 达469.07 g C· m^-2; 高寒草甸类和高寒灌丛草甸类的面积比例达到90%以上, 其NPP平均值分别为449.22和465.27 g C· m^-2(表6)。

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	图2 2000-2016年甘南地区年平均NPP空间分布状况Fig.2 Spatial distribution of the average NPP from 2000 to 2016 in Gannan prefecture

表6 2000-2016年不同草地类型的NPP统计分析 Table 6 Statistical analysis of NPP in different grassland types from 2000 to 2016

2.3.2 NPP年际变化特征图3为草地NPP变化趋势分布情况。整体来看, 研究区NPP的空间异质性较大。其中, 大部分地区草地NPP年变化率介于-2~2 g C· m^-2, 其面积占整个研究区草地面积的43.18%; 其次为NPP年变化率介于2~3 g C· m^-2和大于3 g C· m^-2的区域, 主要分布在夏河县、舟曲县的东南部、碌曲县的北部以及玛曲县和合作市的部分区域, 分别占比为14.90%和34.43%; 草地NPP年变化率低于-2 g C· m^-2的区域面积较小, 分布在玛曲县的东南部和碌曲县的中部区域, 所占比例仅为7.50%。2000-2016年草地NPP变化显著性检验结果如图4所示。大部分地区NPP呈现不显著变化趋势, 仅15.41%的区域呈显著和极显著性变化, 主要分布在夏河、玛曲和舟曲县的部分区域。

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	图3 2000-2016年草地NPP变化趋势斜率分布情况Fig.3 Slope spatial distribution of the grassland NPP from 2000 to 2016

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	图4 2000-2016年草地NPP变化趋势的显著性分析Fig.4 The significance analysis of slope of the grassland NPP from 2000 to 2016

根据F显著性检验结果(图4), 结合Slope变化趋势图(图3), 分析不同草地类型的NPP变化特征, 结果如表7所示。总体来看, 大部分区域的草地NPP呈现稳定状态, 面积占草地总面积的75.31%; 呈增加趋势的区域面积比例为22.63%; 而呈减少趋势的区域面积比例仅为2.06%。在不同的草地类型中, NPP处于稳定状态的面积在各草地类型中的比例介于35.52%~84.21%, 所有草地类型呈现增加趋势的区域面积均大于减少趋势的区域面积。其中, 呈增加趋势的暖性草丛类草地NPP的面积比例最大, 为63.55%, 其次是温性草甸草原类(29.31%)、高寒灌丛草甸类(22.69%)、高寒草甸类(22.63%)、沼泽类(20.72%)和温性草原类(18.00%), 而低平地草甸类占比最小, 仅为10.53%。

表7 不同草地类型NPP变化 Table 7 NPP changes of different grassland types (%)

3 讨论

3.1 CASA模型估测草地NPP存在的问题

目前不同学者对光能转化率(ε )的取值有不同的看法, 普遍比较认同的是植被最大光能转化率的全球通用值0.389 g C· M^-1^[29], ^[30]。然而, Raymood等^[37]认为光能转化率最大值应该为3.5 g C· MJ^-1; Ruimy等^[38]以及Paruelo等^[39]的研究结果则认为其最大可以达到2.16 g C· MJ^-1; 王保林等^[40]对我国18大类草原最大光能转化率取值进行调整, 其取值区间为0.608~1.000 g C· MJ^-1。本研究选用了全球通用值0.389 g C· MJ^-1计算CASA模型的NPP, 与观测数据对比发现基于该参数模拟的NPP值偏高。因此在后续研究中还需对最大光能转化率进行深入探讨, 进一步研究适合估算甘南地区草地NPP的最大光能转化率参数。

另外, 水分胁迫系数的准确性关系到模型估算NPP的精度。从计算水分胁迫的原模型到改进后的各种模型, 由于数量较多、参数较难获得, 如何对其准确模拟一直是模型发展的难点。在传统的遥感模型中, 水分胁迫系数采用土壤水分模型模拟, 涉及的众多土壤参数属于经验系数, 导致该系数精度难以保证^{[41, 42]}。张峰等^[31]利用温度、降水和纬度来估算水分胁迫系数, 结果发现CASA模型模拟值低估了地上净初级生产力。Yuan等^[41]利用感热通量(sensible heat flux, H)与潜热通量(latent heat flux, LE)之比对水分胁迫系数进行估算, 取得了很好的研究结果。周广胜等^{[43, 44]}采用实际蒸散量和潜在蒸散量的比值来计算水分胁迫系数, 对有关参数进行了简化, 使操作性更强, 朱文泉等^[45]研究证明基于该算法得到的水分胁迫系数构建的CASA模型具有较高的模拟精度。周才平等^[46]所研究的CASA模型中水分胁迫系数采用遥感反演地表湿润指数(land surface water index, LSWI)的方法来计算, 结果显示模型的模拟结果与实测数据的相关性高达80%。该方法直接利用遥感数据, 计算简便, 综合考虑了冠层结构和观测角度等多种影响因素, 从而能够比较准确地估算水分胁迫系数^[47]。因此, 本研究同样利用该方法来计算水分胁迫系数。然而, 最终结果显示CASA模型模拟的NPP值相对于观测NPP值而言整体偏高。因此, 在以后的研究中, 深入探究合适的水分胁迫计算模型, 从而增加水分胁迫系数的准确性, 将有助于CASA模型精度的提高。

3.2 实测数据的误差

由于草地NPP实测数据较难获取, 本研究基于实测AGB与根冠比系数计算样点观测NPP, 引用的根冠比是朴世龙等^[13]提出的适用于全国区域草地根冠比系数, 但是由于温度、降水、海拔、土壤质地以及人为放牧等因素的影响, 不同地区该比例系数有一定差异, 即使同一地区不同类型的草地, 其根冠比也是有差异的。另外, 草本植物的分形结构复杂, 包括块茎、块根和地下茎等, 其地下部分常常异化出具有储藏或者其他功能的结构, 这些结构也会导致根冠比的变化^{[48, 49]}。因此, 本研究直接使用该系数必将对观测NPP的计算引入不可估测的误差, 进而影响模型精度的验证与评价。考虑到小区域环境的差异, 进一步优化具有空间特异性的根冠比, 可以使观测NPP结果与研究区实际NPP更贴近, 从而为验证模型精度提供更加准确的参照。

本研究对比验证了CASA模型和MOD17A3 NPP产品的精度, 结果表明CASA模型模拟的NPP比MOD17A3 NPP的精度高, 但是与观测值相比CASA模型估算的NPP偏高。因此, 在以后的研究中, 一方面需要改进CASA模型, 另一方面也需要进一步验证其他模型, 探索更适合研究区估算草地净初级生产力的算法, 为更加精准地分析甘南地区草地NPP的时空动态变化提供科学依据。

3.3 影响甘南地区草地NPP变化的原因

本研究结果得出2000-2016年甘南地区草地NPP整体上呈现由西南向东北逐渐减少的趋势, 并且研究区内NPP空间异质性较大, 该结果与王莺等^[12]得出的结论一致。该分布规律可能与甘南地区的地形和海拔有关。甘南西部地区(玛曲、碌曲和夏河县)是甘肃省的主要牧区, 受到来自孟加拉湾西南季风气候的影响, 该地区降水充沛, 太阳辐射充足, 土壤肥沃, 草地类型主要为高寒草甸类和高寒灌丛草甸类, 这可能是造成该地区草地NPP较高的一些原因。甘南东部地区(合作、卓尼和临潭县)主要为丘陵地带, 农林牧兼营, 气候高寒阴湿, 降水量较低, 草地面积较少, 这些也许是导致该地区草地NPP值较低的原因^[7]。此外, 不同草地类型NPP的变化也与不同环境气候因子的波动密切相关。本项研究结果得出低平地草甸和温性草原类草地的NPP增幅最小, 这可能是由于这两类草地分布在降水量较低, 年均温较高的夏河县。整体而言, 草地类型呈增加趋势的面积大于呈减少趋势的面积, 这也许与研究区降水量整体呈上升趋势有关^[50]。

4 结论

本研究基于甘南地区2011-2014年实测草地AGB, 利用不同草地类型根冠比系数计算观测样地的NPP值, 结合卫星遥感资料及气象数据, 对比分析了甘南地区MOD17A3 NPP产品和CASA模型估测NPP的精度, 筛选出适合甘南地区草地NPP的算法, 并基于该算法分析了近17年甘南地区草地NPP的时空变化特征。主要结论如下:1)2011-2014年甘南实测草地AGB在不同观测区域的差异较大, 整体上介于4.70~234.85 g· m^-2。其中玛曲县的AGB均值最高, 达84.67 g· m^-2, 临潭县的AGB均值最低, 为55.54 g· m^-2; 高寒草甸类草地观测NPP介于36.05~942.69 g C· m^-2, 平均值为294.87 g C· m^-2, 标准偏差达121.78 g C· m^-2。2)总体而言, 4年间CASA模型模拟的NPP精度比MOD17A3 NPP产品高, 与观测NPP值相比, CASA模型模拟NPP的RMSE值比MOD17A3 NPP的RMSE值降低了9.94 g C· m^-2。尤其是面积比例最大的高寒灌丛草甸类草地, 其RMSE降低了约38.32 g C· m^-2。3)利用CASA模型模拟的平均草地NPP空间分布表明, 近17年草地NPP平均值介于64.84~638.85 g C· m^-2, 总体上呈现由西南向东北递减的趋势, 其中面积比例较小的沼泽类草地NPP均值最高, 达469.07 g C· m^-2, 而占面积比例较大的高寒草甸类和高寒灌丛草甸类草地NPP值分别为449.22和465.27 g C· m^-2。4)研究区内大部分地区草地NPP基本保持稳定, 其面积占研究区草地总面积的75.31%, 而呈现增加趋势和减少趋势的区域面积比例分别为22.63%和2.06%。在所有草地类型中, 呈增加趋势的区域面积比例均大于呈减少趋势的区域, 在呈增加趋势的不同草地类型中, 暖性草丛类的面积占比最大, 达63.55%, 低平地草甸类占比最小, 仅为10.53%。

The authors have declared that no competing interests exist.

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2011

0.0

. 2011, 19(2):356-366

Research progress in the estimation models of grassland net primary productivity

草地净第一性生产力估算模型研究进展

Zhang M L , Jiang W L , Chen Q G

张美玲, 蒋文兰, 陈全功

1. College of Prataculture, Gansu Agricultural University, Lanzhou, Gansu Province 730070, China; 2. College of Science, Gansu Agricultural University, Lanzhou, Gansu Province 730070, China; 3. College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou, Gansu Province 730020, China

Grassland net primary production(NPP) can directly reflect the production capacity of grassland communities in a natural environment.Grassland NPP is an important part of the grassland carbon cycle.Simulating NPP using mathematical models has become an important and widely accepted research approach.Climate-productivity statistical models,light utilization efficiency models,eco-physiological processing models and remote sensing applications coupled with the eco-physiological process models are the primary models for grassland NPP estimation.The achievements and problems of these models are reviewed comprehensively and systematically.A future development trend of NPP estimation is further proposed in this paper.The statistical models calculate NPP base on the relationships between NPP and climatic variables(i.e.,temperature,precipitation),which require simple parameters and give poor accuracy.The theoretical basis of light utilization efficiency models is an analytical technique of photosynthesis based on the concept of photosynthesis and its associated efficiency of light utilization.Its format is simple and can use remote sensing data.Therefore,light utilization efficiency models attract much attention;even so,it has questionable results.Process modelling simulates a series of plant ecophysiological and biophysical processes to reveal their active mechanisms,but it is more complex compared with other models and may have limited practicality.Remote sensing applications coupled with the eco-physiological process model can use collected sensing data to overcome disadvantages of the light utilization efficiency and ecophysiological processing models.This may well become a major developing direction for grassland NPP estimation.Studies in grassland NPP estimation are sparse and special studies in the modeling of the grassland NPP are not commonly reported.This procedure is meaningful to develop the grassland NPP for China as another tool with freedom knowledge property right and performance.

草地净第一性生产力(NPP)直接反映了草地植物群落在自然环境条件下的生产能力,是草地碳循环的重要组成部分。利用数学模型估算草地NPP是一种重要且被广泛接受的研究方法。在总结植被NPP估算模型的基础上,分析了气候相关统计模型、光能利用率模型、生态系统过程模型和生态遥感耦合模型各自的优缺点,讨论了各类模型在草地NPP中的研究现状,探讨了草地NPP模型研究中存在的问题,并进一步提出今后的发展方向。统计模型通过NPP与温度、降水等气候因子建立统计关系计算NPP,参数简单,但估算结果较粗;光能利用率模型以植被光合作用机理作为其理论基础,建立在植物光合作用过程和光能利用率的概念上,由于模型形式简单且可以直接利用遥感数据,使其倍受关注,但在具体求算过程中存在不确定性;过程模型从机理上对植物的生理生态过程进行模拟并对NPP的影响因子进行分析,更能揭示生物生产过程以及与环境相互作用的机理,但模型较复杂,实用性不强;生态遥感耦合模型能够利用遥感资料,克服了光能利用率模型和生态系统过程模型的不足,使其成为NPP模型的一个主要发展方向。草地植被NPP估算模型的研究相对薄弱,专门针对草地NPP建模的研究较少。建立具有自主知识产权的中国草地NPP模型是一项即具有重要意义又具有挑战性的工作。

... 植被净初级生产力(net primary productivity, NPP)是指单位面积的绿色植物在单位时间累积的有机物总量,包括植被地上部分和地下部分的生物量^[1] ...

1996

0.0

... 41%,草地生态系统具有调节气候、涵养水源、保持水土、改良土壤和维持生物多样性等重要功能^[2] ...

... 9 mm^[2] ...

2016

0.0

... 准确监测草地NPP对于评估草地资源变化,发掘草地生产潜力及合理利用草地资源等方面具有重要的科学意义和实际应用价值^[3,4,5] ...

2018

0.0

... 准确监测草地NPP对于评估草地资源变化,发掘草地生产潜力及合理利用草地资源等方面具有重要的科学意义和实际应用价值^[3,4,5] ...

2014

0.0

. 2014, 23(3):39-50 DOI:10.11686/cyxb20140305

Analysis of the spatio-temporal variation in net primary productivity of grassland during the past 10 years in Xinjiang

近10年新疆草地生态系统净初级生产力及其时空格局变化研究

Yang H F , Gang C C , Mu S J

杨红飞, 刚成诚, 穆少杰

1.College of Life Science, Anhui Normal University, Wuhu 241000, China; 2.School of Life Science, Nanjing University, Nanjing 210093, China

Dynamic monitoring of regional NPP (net primary productivity) has important significance for research on global change. The NPP of Xinjiang grassland from 2001 to 2010 was estimated using an improved light use efficiency model based on MODIS-NDVI, land use classification data and meteorological data. Spatial-temporal variation features of grassland NPP from 2001 to 2010 in Xinjiang were studied and analysed. The spatial distribution of NPP in Xinjiang grassland was constrained by the regional hydrothermal conditions. Grassland types were generally distributed as alpine and sub-alpine meadow, plain grassland, meadow, desert grassland, alpine and sub-alpine grassland from north to south. The grassland NPP reduced from 395 g C/(m 2 ·a) to near 0 g C/(m 2 ·a) and 10-year average total NPP of Xinjiang grassland was 56.47 Tg C. Compared with other grassland types, mean NPP of meadow was the highest (155.29 g C/m 2 ·a), while mean NPP of desert grassland was the lowest (57.68 g C/m 2 ·a). The overall level of mean NPP was meadow>alpine and sub-alpine meadow>plain grassland>alpine and sub-alpine grassland>desert grassland. The overall level of grassland NPP in Xinjiang was rather low, where alpine and sub-alpine meadow, plain grassland and meadow belonged to lower productive ecosystems, and desert grassland, alpine and sub-alpine grassland belonged to the lowest productive ecosystems. On the main grasslands in Xinjiang, the NPP from June to August occupied 63.17% of the whole year. The monthly NPP in different grasslands changed differently but all peaked in July. Compared with other types, NPP of alpine and sub-alpine meadow increased more quickly in the first seven months, while alpine and sub-alpine grassland were the slowest. The decrease of meadow NPP was quicker than those of other grassland types in the last five months, while desert grassland was the slowest. With the exception of meadow, the mean NPP of the other four grassland types showed a downward trend from 2001 to 2010 in Xinjiang. Total NPP of grassland in Xinjiang reached the maximum (60.21 Tg C/a) in 2007, and dropped to the minimum (53.41 Tg C/a) in 2006. During the past 10 years, the grassland NPP in Xinjiang showed a great annual fluctuation but with an overall reduction trend.

基于2001-2010年遥感监测和气象数据,采用CASA(Carnegie-Ames-Stanford Approach)模型模拟分析新疆地区草地植被净初级生产力(NPP)及其时空变化特征。结果表明,新疆草地植被NPP空间分布特征受区域水热条件的制约,草地植被大体上由北向南依次出现高山与亚高山草甸、平原草地、草甸、荒漠草地和高山与亚高山草地,其NPP也逐渐由395 g C/(m 2 ·a)减少到接近0 g C/(m 2 ·a)。10年间,新疆草地NPP总量平均值为56.47 Tg C。新疆不同草地类型的NPP存在较大差异。其中,草甸的平均NPP最高,为155.29 g C/(m 2 ·a);荒漠草地的平均NPP最低,为57.68 g C/(m 2 ·a);总体表现为草甸>高山与亚高山草甸>平原草地>高山与亚高山草地>荒漠草地;新疆地区草地植被NPP整体水平较低,其中,高山与亚高山草甸、平原草地和草甸属于较低生产力的生态系统;而荒漠草地和高山与亚高山草地则属于最低生产力的生态系统。新疆主要草地植被6-8月NPP占全年NPP的63.17%。不同草地类型的平均NPP月际变化差异较大,均在7月达峰值。前7个月平均增长速度最快的是高山与亚高山草甸,最慢的是高山与亚高山草地;后5个月平均下降速度最快的是草甸,最慢的是荒漠草地。除草甸呈增长趋势外,其他4种草地类型的平均NPP总体上表现出一定的下降趋势,其中,平原草地的平均NPP下降速率最快。全区草地植被NPP总量在2007年达最高值,为60.21 Tg C/a,最低值出现在2006年,为53.41 Tg C/a。草甸是新疆5种草地类型中NPP总量唯一呈逐渐增长的草地类型,而其他4种草地类型均呈下降趋势,其中平原草地的NPP总量下降速率最快。近10年来,新疆全区草地植被总NPP的年际变化较大,有进一步下降趋势。

... 准确监测草地NPP对于评估草地资源变化,发掘草地生产潜力及合理利用草地资源等方面具有重要的科学意义和实际应用价值^[3,4,5] ...

2014

0.0

... 目前,在区域或全球尺度上NPP无法直接测量,利用模型间接估测是一种重要途径,已被学者们广泛接受^[6,7] ...

2010

0.0

. 2010, 19(1):201-210 DOI:10.11686/cyxb20100127

Spatial and temporal dynamic changes of net primary product based on MODIS vegetation index in Gannan grassland

基于MODIS植被指数的甘南草地净初级生产力时空变化研究

Wang Y , Xia W T , Liang T G

王莺, 夏文韬, 梁天刚

利用甘南地区２００６－２００７年的外业实测样方草地干物质产量和ＭＯＤＩＳ植被指数数据，建立了草地地上部分干物质产量遥感反演模型，根据根冠比和干物质转碳率对２００６－２００８年甘南地区草地净初级生产力（ＮＰＰ，ｎｅｔｐｒｉｍａｒｙｐｒｏｄｕｃｔｉｖｉｔｙ）进行了估算，绘制了甘南草地ＮＰＰ年累积量空间分布格局图和ＮＰＰ月度变化动态图，对不同草地植被类型的ＮＰＰ差异进行了评价。研究结果表明，２００６－２００８年甘南草地年ＮＰＰ分别达６３７．０４，５９９．９８和５６６．５９ｇＣ／ｍ２，其空间分布具有自西南向东北逐渐减少的趋势；年内不同草地类型的ＮＰＰ均在７－８月达到最大累积量；ＮＰＰ累积量最高的３种草地类型是沼泽、高寒灌丛草甸和高寒草甸，３年中最大月ＮＰＰ的平均值分别达到１１３７．２８，５５３．７６和５２７．６６ｇＣ／ｍ２；２００６－２００８年甘南草地ＮＰＰ持续下降，年草地ＮＰＰ总量的减少速率为１．２Ｔｇ／ａ，尤其是沼泽湿地的ＮＰＰ下降明显，年平均减少速率达到了１２５．９２ｇＣ／ｍ２。

... 目前,在区域或全球尺度上NPP无法直接测量,利用模型间接估测是一种重要途径,已被学者们广泛接受^[6,7] ...

... 甘南东部地区(合作、卓尼和临潭县)主要为丘陵地带,农林牧兼营,气候高寒阴湿,降水量较低,草地面积较少,这些也许是导致该地区草地NPP值较低的原因^[7] ...

2001

0.0

. 2001, 25(5):603-608

Application of CASA model to the estimation of Chinese terrestrial net primary productivity

利用CASA模型估算我国植被净第一性生产力

Piao S L , Fang J Y , Guo Q H

朴世龙, 方精云, 郭庆华

... 在众多区域陆地NPP估测模型中,基于植被光能利用率原理的CASA(Carnegie-Ames-Stanford approach)模型是最具潜力的研究手段之一^[8] ...

... 指光能转化率,即植被把所吸收的光合有效辐射(APAR)转化为有机碳的效率^[8] ...

2001

0.0

... CASA模型中的大部分参数可以直接通过遥感数据获得,不需要复杂的野外工作,实用而简单,并且从不同的侧重点出发可估测不同生态系统的NPP^[9,10,11] ...

2013

0.0

. 2013, 22(3):6-15 DOI:10.11686/cyxb20130302

Spatio-temporal variation analysis of grassland net primary productivity and its relationship with climate over the past 10 years in Inner Mongolia

内蒙古草地生态系统近10年NPP时空变化及其与气候的关系

Mu S J , Li J L , Yang H F

穆少杰, 李建龙, 杨红飞

植被净初级生产力（ｎｅｔｐｒｉｍａｒｙｐｒｏｄｕｃｔｉｖｉｔｙ，ＮＰＰ）及其对气候变化的响应研究是全球变化的核心内容之一。通过改进的光能利用率模型（ＣＡＳＡ模型），利用ＭＯＤＩＳＮＤＶＩ数据、土地覆盖分类数据、气象数据等，逐像元模拟２００１－２０１０年内蒙古草地生态系统ＮＰＰ的时空变化，分析其对气候因子变化的响应关系。结果表明，１）２００１－２０１０年内蒙古草地多年平均ＮＰＰ为２８１．３ｇＣ／（ｍ２ ·ａ），空间分布呈由西南向东北递增的趋势，草甸草原、典型草原和荒漠草原平均ＮＰＰ分别为４３１．８，２８８．７和１２３．５ｇＣ／（ｍ２ ·ａ）；２）２００１－２０１０年间内蒙古草地ＮＰＰ总体上呈上升趋势。ＮＰＰ上升趋势最明显的草地主要分布在毛乌素沙地、浑善达克沙地、科尔沁沙地、呼伦贝尔盟和大兴安岭南麓地区，而下降趋势最明显的草地主要分布在阴山山脉和锡林郭勒盟中部的典型草原区；３）总体而言，降水量是内蒙古草地净初级生产力的主要影响因素。草甸草原ＮＰＰ与降水量、温度的关系均很密切，而且与温度的相关性更强；典型草原和荒漠草原ＮＰＰ则主要受降水量控制，其中荒漠草原ＮＰＰ与降水量的关系更密切。

... CASA模型中的大部分参数可以直接通过遥感数据获得,不需要复杂的野外工作,实用而简单,并且从不同的侧重点出发可估测不同生态系统的NPP^[9,10,11] ...

2012

0.0

. 2012, 21(1):239-247 DOI:10.11686/cyxb20120131

Modelling light use efficiency of alpine meadows on the northern Tibetan Plateau based on the MODIS algorithm

基于MODIS算法的藏北高寒草甸的光能利用效率模拟

Fu G , Shen Z X , Zhang X Z

付刚, 沈振西, 张宪洲

光能利用效率（ｌｉｇｈｔｕｓｅｅｆｆｉｃｉｅｎｃｙ，ＬＵＥ）是指初级生产力与植被冠层所吸收的光合有效辐射（ａｂｓｏｒｂｅｄｐｈｏｔｏｓｙｎｔｈｅｔｉｃａｌｌｙａｃｔｉｖｅｒａｄｉａｔｉｏｎ，ＡＰＡＲ）之比，它反映了植被利用光能的能力。定量化生产力的时空变化是定量化全球碳循环的重要研究内容，而ＬＵＥ作为光能生产力模型中的一个重要参数，是定量化生产力时空变化的基础。因此，定量化全球植被的ＬＵＥ是定量化全球碳循环的重要组成部分。基于ＭＯＤＩＳ光能利用效率算法，本研究模拟了２００４－２００５年藏北高寒草甸生态系统的光能利用效率（ＬＵＥＭＯＤＩＳ），并用观测的光能利用效率（ＬＵＥＥＣ）对模型进行了验证。在ＭＯＤＩＳ算法中，日最低气温（ Ta ｍｉｎ）和饱和水汽压亏缺（ＶＰＤ）分别被用来计算温度胁迫因子（ T ｓｃａｌａｒ）和水分胁迫因子（ W ｓｃａｌａｒ）。相关分析和多重逐步回归分析结果表明，相对于 W ｓｃａｌａｒ， T ｓｃａｌａｒ更能够解释观测的ＬＵＥ的季节变化。２００４和２００５年的模拟值分别高估了约１４．９７％和１６．５７％的观测值，但配对Ｔ检验显示模拟值和观测值差异不显著，即基于ＭＯＤＩＳ的ＬＵＥ算法在模拟藏北高寒草甸ＬＵＥ方面具有较高的精度。相关分析表明，观测的ＬＵＥ与 Ta ｍｉｎ的相关性好于观测的ＬＵＥ与平均气温的相关性，这表明在反应藏北高寒草甸生态系统ＬＵＥ的季节变异方面， Ta ｍｉｎ优于平均气温。总之，基于ＭＯＤＩＳ算法的ＬＵＥ模型能够比较准确地定量化藏北高寒草甸生态系统的ＬＵＥ。

... CASA模型中的大部分参数可以直接通过遥感数据获得,不需要复杂的野外工作,实用而简单,并且从不同的侧重点出发可估测不同生态系统的NPP^[9,10,11] ...

2011

0.0

. 2011, 20(4):316-324 DOI:10.11686/cyxb20110440

Spatial-temporal dynamics simulation of grassland net primary productivity using a satellite data-driven CASA model in Gannan prefecture

基于CASA模型的甘南地区草地净初级生产力时空动态遥感模拟

Wang Y , Xia W T , Liang T G

王莺, 夏文韬, 梁天刚

植被净初级生产力（ｎｅｔｐｒｉｍａｒｙｐｒｏｄｕｃｔｉｖｉｔｙ，ＮＰＰ）及其对气候变化的响应研究是全球变化的核心内容之一。基于地理信息系统和卫星遥感应用技术，利用ＣＡＳＡ模型估算了２００１－２００８年甘南草地ＮＰＰ，在模型验证的基础上，分析了甘南草地ＮＰＰ空间分布格局和时间分布特征。结果表明，１）２００１－２００８年甘南草地多年平均ＮＰＰ为４８３．４１ｇＣ／（ｍ２ ·ａ），大体呈现由西南向东北逐渐减少的趋势，单位面积多年平均ＮＰＰ在海拔３０００～３５００ｍ最高，达到４９７．０７ｇＣ／（ｍ２ ·ａ）；２）甘南草地植被生长季节变化明显，主要生长期集中在第１７７～２４０天；３）甘南草地ＮＰＰ呈现增加趋势，增长趋势最明显的草地类型是低平地草甸类，而沼泽的变幅最小，通过与８年间温度和降水的分析可以看出，影响甘南草地ＮＰＰ变化的主要驱动力是降水量。

... 王莺等^[12]发现2001-2008年甘南地区的草地NPP平均值为483 ...

... 3 影响甘南地区草地NPP变化的原因本研究结果得出2000-2016年甘南地区草地NPP整体上呈现由西南向东北逐渐减少的趋势,并且研究区内NPP空间异质性较大,该结果与王莺等^[12]得出的结论一致 ...

2002

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. 2002, 17(3):373-380 DOI:10.11849/zrzyxb.2002.03.017

Terrestrial net primary production and its spatio-temporal patterns in Qinghai-Xizang Plateau, China during 1982-1999

1982-1999年青藏高原植被净第一性生产力及其时空变化

Piao S L , Fang J Y

朴世龙, 方精云

Department of Urban&Environmental Science,and Center for Ecological Research&Education,Peking University,Beijing100871,China

The terrestrial net primary production(NPP)and its spatio-temporal pattern in Qing-hai-Xizang Plateau during1982-1999were studied based on an ecological process model,using remote sensing data(NOAA-AVHRR),historical temperature,precipitation and solar radiation data,along with vegetation and soil texture information.As a result,a total mean annual NPP in the Plateau was0.21PgC(1Pg=10 15 g),occupying about 12.43%of China's total annual NPP.The NPP decreased from southeastern to northwestern part;this was coupled with the changes in precipitation and temperature.In the past18years,the terrestrial NPP in the Plateau increased from0.19PgC·a -1 in1982to0.24PgC·a -1 in1999,with an annual average increasing rate of about 1%.Regions with a significant increase in NPP appeared in southeastern Qinghai,Xining district,partial southwestern Qinghai,and eastern and partial southern Xizang.Monthly mean NPP for all months but not that for October and December showed an increasing trend through the past18years for most vegetation types.

基于地理信息系统技术和生态学过程模型,利用1982～1999年间NOAA-AVHRR数据(归一化植被指数,NDVI)及其相匹配的温度、降水和太阳辐射等气象数据,结合植被和土壤质地等资料,研究了青藏高原植被的净第一性生产力(NPP)及其动态变化。结果表明:青藏高原植被的总NPP为0.21PgC·a-1(1Pg=1015g),约占全国植被NPP总量的12.43%。NPP的总体分布趋势是,自东南至西北递减,这与水热条件的分布趋势一致。18年来,青藏高原植被的NPP在波动中呈上升趋势,从1982年的0.19PgC增加到1999年的0.24PgC,年平均增加速率约为1%;其中,青海省的东南部、西宁地区和西南部的部分地区,以及西藏东部的横断山区和雅鲁藏布江南部的部分地区的NPP增加显著。除10月和12月的月平均生产力呈减少趋势外,大部分植被类型的其它月份大都呈增加趋势。

... 朴世龙等^[13]基于CASA模型估算得到1982-1999年青藏高原不同植被类型的平均NPP介于24~574 g C#cod#x000b7 ...

... 2 实测数据的误差由于草地NPP实测数据较难获取,本研究基于实测AGB与根冠比系数计算样点观测NPP,引用的根冠比是朴世龙等^[13]提出的适用于全国区域草地根冠比系数,但是由于温度、降水、海拔、土壤质地以及人为放牧等因素的影响,不同地区该比例系数有一定差异,即使同一地区不同类型的草地,其根冠比也是有差异的 ...

2013

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... 张镱锂等^[14]发现1982-2009年高寒草地年均NPP为120 ...

2014

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... 孙云晓等^[15]得出1983-2012年青藏高原不同植被类型NPP介于30~707 g C#cod#x000b7 ...

2012

0.0

... 邹德富^[16]估算结果表明2003-2010年间青藏高原草地NPP年均值介于为20 ...

0.0

... MOD17A3 NPP产品数据集是通过Running等^[17]利用BIOME-BGC模型(biome biogeochemical model)与光能利用率模型模拟所获得的 ...

2013

0.0

... 刘琳等^[18]基于MOD17A3 NPP数据分析表明2000-2010年重庆植被NPP波动范围集中在510~646 g C#cod#x000b7 ...

2013

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. 2013, 32(10):2797-2805

Spatio-temporal pattern of vegetation net primary productivity in Henan Province of China based on MOD17A3

基于MOD17A3的河南省NPP时空格局

Wang X C , Wang S D , Zhang H B

王新闯, 王世东, 张合兵

(School of Surveying and Land Information Engineering, Henan Polytechnic University, Jiaozuo 454000, Henan, China)

By using the 2000-2010 annual net primary productivity (NPP) data from MOD17A3 dataset and the GIS technology, a quantitative analysis was made on the spatiotemporal variation of NPP in Henan Province of China. In the Province, the annual NPP ranged from 308 to 430 g C·m -2 ·a -1 , with an average of 384 g C·m -2 ·a -1 . The average annual NPP was higher in the south and lower in the north part of the Province. The area with high NPP (>500 g C·m -2 ·a -1 ) was mainly distributed in the forested regions of west and southeast Henan. The forested regions of Taihang Mountains and Danjiang River Basin had an annual NPP of 200-300 g C·m -2 ·a -1 . The farmland regions had an average annual NPP of 300-500 g C·m -2 ·a -1 , the ecological conservation zone of Yellow River had an average annual NPP of -2 ·a -1 , while the cities and their surrounding areas had an average annual NPP of -2 ·a -1 . As compared with that in 2000, the annual NPP in most parts of the Province in 2010 had an increase, and the areas with the increased annual NPP accounted for 93.8% of the territory of the Province. The increase of the annual NPP in the Province was mainly in linear, and the areas with an increment of >10% occupied 83.7% of the territory of the Province. The areas with a  i.e ., lower in north and higher in south. There existed a great difference in the estimation of the NPP among different NPP estimation models or among different data sources.

利用2000—2010年MOD17A3数据集的年NPP数据和GIS技术，定量分析了河南省植被NPP的时空变化特征。结果表明：河南省年NPP变化范围为308～430 g C·m -2 ·a -1 ，平均值为384 g C·m -2 ·a -1 ；年均NPP分布全省呈现北低南高趋势，NPP高值区(> 500 g C·m -2 ·a -1 )主要分布在豫西和豫东南的林区，太行山和丹江流域的林区NPP大部在200～300 g C·m -2 ·a -1 ，农田覆盖区年平均NPP在300～500 g C·m -2 ·a -1 ，黄河生态涵养带年平均NPP多小于200 g C·m -2 ·a -1 ，城市及其周边地区NPP多小于100 g C·m -2 ·a -1 ；与2000年相比，2010年河南省年NPP大部分地区是增加的，年NPP增加的面积占总土地面积的93.8%；河南省年NPP线性变化趋势以增加为主，年NPP变化百分率增加10%以上的面积所占河南省国土面积的比例为83.7%；年NPP的变化百分率

... 王新闯等^[19]分析得到2000-2010年河南省植被年平均NPP为384 g C#cod#x000b7 ...

2011

0.0

. 2011, 30(12):2776-2782

Variation characteristics of vegetation net primary productivity in Shaanxi Province based on MOD17A3

基于MOD17A3的陕西省植被NPP变化特征

Li D K , Fan J Z , Wang J

李登科, 范建忠, 王娟

Shaanxi Remote Sensing Information Center for Agriculture, Xi’an 710014, China

A quantitative analysis was made on the spatiotemporal characteristics of vegetation net primary productivity (NPP) in Shaanxi Province, based on the 2000-2006 average annual vegetation NPP data of MOD17A3 dataset and by using GIS technology. In the Province, the annual NPP ranged from 340 to 434 g C·m -2 ·a -1 , with an average of 383 g C·m -2 ·a -1 . The average annual NPP was higher in the south and lower in the north part of the Province, and higher in the west and lower in the east part of central and south Shaanxi, being 0-200 g C·m -2 ·a -1 in the wind-eroded area along Great Wall, 200-300 g C·m -2 ·a -1 in the hilly and gully area of Loess Plateau, 300-400 g C·m -2 ·a -1 in the dry-farming area of Weibei plateau, 400-500 g C·m -2 ·a -1 in the forest area of central part, and 400-500 g C·m -2 ·a -1 in the forest area of Qinba mountains region. As compared with that in 2000, the annual NPP in most part of the Province in 2006 had an increase, and the area with increased annual NPP accounted for 90.52% of the territory of the Province. The increase of the annual NPP in the Province was mainly in linear type, and the area with an increment of >10% occupied 50.6% of the territory of the Province, mainly distributed in the north parts from Yan’an, which suggested that through the implementation of a series of ecological construction projects such as closing hill for forestation and restoring farmland into forestland, the vegetation in these parts was improved.

利用2000—2006年MOD17A3数据集的年均NPP数据和GIS技术定量分析了陕西省植被NPP的时空变化特征，结果表明：陕西省年NPP变化范围为340～434 g C·m -2 ·a -1 ,平均值为383g C·m -2 ·a -1 ；年均NPP分布全省呈现北低南高，关中、陕南呈现西高东低的趋势。长城沿线风沙区年平均NPP在0～200 g C·m -2 ·a -1 ，黄土高原丘陵沟壑区年平均NPP在200～300 g C·m -2 ·a -1 ，中部林区年平均NPP在400～500 g C·m -2 ·a -1 ，渭北旱作农业区年平均NPP在300～400 g C·m -2 ·a -1 ，关中灌溉农作区年平均NPP大部在400～500 g C·m -2 ·a -1 ，秦巴山地林区年平均NPP >400 g C·m -2 ·a -1 。与2000年相比，2006年陕西省年NPP大部分地区是增加的，年NPP增加的面积占国土面积的90.5%。陕西省NPP线性变化趋势以增加为主，NPP变化百分率增加10%以上的面积所占陕西省国土面积的比例为50.6%；植被NPP的变化百分率>10%的植被主要分布在延安市以北地区，说明通过实施退耕还林等生态建设工程，这些地区植被状况得到较好的改善。

... 李登科等^[20]研究得出2000-2006年陕西省植被NPP介于340~434 g C#cod#x000b7 ...

2015

0.0

. 2015, 32(11):1709-1720 DOI:10.11829/j.issn.1001-0629.2015-0371

Temporal and spatial variation of net primary productivity (NPP) and its responses with climatic changes in the Xilingol grassland from 2000 to 2010

2000-2010年锡林郭勒草原NPP时空变化及其气候响应

Liu H J , Yin S Y , Sun C

刘海江, 尹思阳, 孙聪

1.China National Environmental Monitoring Centre, Beijing 10012, China; 2.Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, Beijing 10094, China; 3.Xilingol League Environmental Protection Monitoring Station, Xilingol League Environmental Protection Breau, Xilinhot 026000, China

Using MODIS MOD17A3 Net Primary Productivity (NPP) data and meteorological data, we analyzed the temporal and spatial variation of NPP, inter-annual changes of climatic factors and the correlative relationship between NPP and climatic factors in Xilingol grassland from 2000 to 2010. The NPP of the Xilingol grassland varied from 0 to 0.5 kg C·m -2 which decreased from east to west. From 2000 to 2010, the NPP fluctuated annual that increased in generaland evenly distributed. The variations of climatic factors in typical and desert grassland were similar. The cumulative average temperature, average relative humility and cumulative sunshine duration in April to August had a downward trend, while the average wind speed had an increasing trend. The cumulative precipitation hadan increasing trend in desert grassland, but had a slight decreasing trend in typical grassland. For most climatic factors, they had more dramatic fluctuations in typical grassland than in desert grassland. For the two types of grassland in the study area, the grassland NPP has very significantly negative correlation ( P <0.001) with cumulative average temperature and average daily minimum temperature in April to August, and had slight negative correlation with cumulative sunshine duration and average wind speed, while had strong positive correlation ( P <0.001) with cumulative precipitation and average relative humility. In conclusion, the typical grassland had stronger response ability to the changes of climatic factors than the desert grassland.

利用MODIS MOD17A3植被初级生产力数据产品及地面气象观测数据,研究分析了2000-2010年锡林郭勒盟草原NPP的时空变化特征、各气候因子的年际变化特征及NPP与气候因子的相关关系。结果表明,锡林郭勒盟草原NPP的分布由东向西逐渐减小,值大多分布在0~0.5 kg C·m -2 ;2001-2010年NPP呈现波动变化,整体呈增加趋势,空间分布上更加趋于均匀分布;2000-2010年,研究区内典型草原和荒漠草原各气候因子的变化趋势大致相同,4-9月累计平均气温、平均相对湿度和累计日照时数呈减小趋势,平均风速呈增加趋势,累计降水量典型草原呈轻微减少趋势,荒漠草原呈增加趋势,趋势斜率分别为-0.026和1.044 5,典型草原大部分气候因子的波动程度大于荒漠草原;研究区内两种类型的草原的NPP均与4-9月累计平均气温、平均日最低气温有较强的负相关关系(显著水平分别为 P< 0 . 001和 P <0.01),与累计降水量、平均相对湿度有较强的正相关关系(显著水平为 P <0.01),与4-9月累计日照时数、平均风速呈负相关关系但相关关系较弱,荒漠草原对气候因子变化的响应总体不如典型草原敏感。

... 刘海江等^[21]发现2000-2010年锡林郭勒盟草地NPP总体上呈增加的趋势,东部地区的NPP值高于西部地区 ...

2004

0.0

... 86%^[22] ...

2017

0.0

... 为了增加样地的空间代表性及其与MODIS植被指数数据的空间匹配性,2014年样地增加到13个,采用5点法布设样方^[23],共设置65个样方 ...

... 5 MOD17A3 NPP和CASA模型估算的NPP精度验证野外实测AGB数据转化的草地NPP数据作为观测NPP,验证MOD17A3 NPP数据和CASA模型估算的NPP在甘南草地的估测精度,评价指标选用均方根误差(root mean square error, RMSE)^[23,33] ...

2004

0.0

. 2004, 28(4):491-498

Spatial distribution of grassland biomass in China

中国草地植被生物量及其空间分布格局

Piao S L , Fang J Y , He J S

朴世龙, 方精云, 贺金生

(1 Department of Ecology, College of Environmental Sciences, Peking University, Beijing 100871, China) (2 Institute of Geographic Sciences and Natural Resources, Chinese Academy of Sciences, Beijing 100101, China)

Estimating carbon storage in terrestrial ecosystems has been a central focus of research over the past two decades because of its importance to terrestrial carbon cycles and ecosystem processes. As one of the most widespread ecosystem types, China’s grasslands play an important role in global change research. The grasslands in China, which are distributed primarily throughout the temperate regions and on the Tibetan Plateau, were classified into 17 community types. In the present study, a statistical model was established to estimate grassland biomass and its geographical distribution in China based on a grassland inventory data set and remote sensing data (Normalized Difference Vegetation Index) using GIS and RS techniques. We found that there was a significant correlation between aboveground biomass density and the maximum annual NDVI when expressed as a power function (R2=0.71, p<0.001). The aboveground biomass was estimated to be 146.16 TgC (1Tg=1012 g) and belowground biomass was estimated as 898.60 TgC (6.15 times of the above biomass) for a total biomass of 1 044.76 TgC. This value accounts for about 2.1%-3.7% of the world’s grassland biomass. The grassland biomass is distributed primarily in the arid and semiarid regions of Northern China and the Qinghai-Xizang Plateau. The average biomass density of China’s grasslands was 315.24 gC·m-2, smaller than the world average. The aboveground biomass density decreases from southeastern China toward the northwest corresponding with changes in precipitation and temperature. Furthermore, aboveground biomass density reached the lowest levels at 1 350 m elevation and peak levels at 3 750 m above sea level which most likely is related to China’s three-step topographical background. The ratio of total biomass of grassland to forest biomass in China is 1/4, much higher than that of the world, suggesting a greater contribution of grasslands to China’s carbon pool.

草地生态系统是陆地生态系统分布最广的生态系统类型之一，它在全球变化中的作用越来越受到重视。利用中国草地资源清查资料，并结合同期的遥感影像，建立了基于最新修正的归一化植被指数(NDVI)的我国草地植被生物量估测模型，并利用该模型研究了我国草地植被生物量及其空间分布特征。结果表明：草地植被地上生物量与当年最大NDVI值具有很好的相关关系，两者可以用幂函数很好地拟合(R2=0.71, p<0.001)。我国草地植被总地上生物量为146.16 TgC(1 Tg=1012 g)，主要集中在北方干旱、半干旱地区和青藏高原；总地下生物量为898.60 TgC，是地上生物量的6.15倍；而总生物量是1 044.76 TgC，占世界草地植被的2.1%～3.7%，其平均密度约等于315.24 gC·m-2，低于世界平均水平。我国草地植被单位面积地上生物量水平分布趋势为：东南地区高，西北地区低，与水热条件的分布趋势一致；从垂直分布看，在海拔1 350 m和3 750 m处分别出现了波谷和波峰，与我国特有的三级阶梯地势有着密切的关系。此外，我国草地植被生物量为森林的1/4左右，显著大于世界平均水平，说明我国草地在碳平衡中的贡献相对较大。

... 本研究中采用的根冠比系数来自朴世龙等^[24]确定的中国草地植被地上与地下部分生物量的比例系数,其中甘南地区高寒草甸类和高寒灌丛草甸类草地的根冠比为7 ...

... 45得到以碳为单位的草地NPP数据^[24] ...

2014

0.0

... 借助Area Solar Radiation模块计算每日太阳总辐射量(solar radiation, SOL)^[25] ...

1987

0.0

... 通过ANUSPLIN软件,利用薄板样条插值法(thin-plate smoothing spline, ANUSPLIN)对甘南地区2000-2016年各月的平均温度进行插值处理,建立了近17年月均温空间数据集^[26,27] ...

2006

0.0

2009

0.0

. 2009, 18(6):12-22 DOI:10.11686/cyxb20090602

Remotely sensed dynamics monitoring of grassland aboveground biomass and carrying capacity during 2001-2008 in Gannan pastoral area

2001-2008年甘南牧区草地地上生物量与载畜量遥感动态监测

Liang T G , Cui X , Feng Q S

梁天刚, 崔霞, 冯琦胜

草地地上生物量监测是草地资源空间格局动态研究的重要内容，也是草畜平衡综合分析的基础。利用２００６－２００８年甘南牧区草地调查资料和Ｔｅｒｒａ／ＭＯＤＩＳ每日地表反射率产品ＭＯＤ０９ＧＡ，建立了草地地上生物量遥感反演模型，模拟分析了甘南州及各县市草地资源在２００１－２００８年期间的各旬、月和年的生物量及理论载畜量变化动态。研究结果表明，ＭＯＤＩＳ增强型植被指数ＥＶＩ的乘幂函数可以较好地模拟甘南牧区草地地上的生物量鲜重，拟合模型平均估产精度为７６．７％，可很好地模拟牧草生长状况较好时期（５－１０月）的草地地上生物量变化动态。甘南牧区草地生长主要集中在５月上旬－１０月下旬期间，草地旬最大地上生物量数字图像可以较客观地反映草地植被生长发育的总体规律。但是，个别旬生物量受大范围长时间阴雨多云天气状况及放牧家畜数量有较大变动情况的严重影响。２００１－２００８年不同草地类型的月最大生物量动态变化曲线均呈单峰抛物线形式，全州平均最大生物量均出现在７月，但不同年份产量达到最大值的月份有所变化，主要集中在７－８月。甘南州草地地上总生物量年度之间存在较大的差异。全州８年平均总地上生物量为１０９．３１亿ｋｇ。２００５年全州草地植被年总生物量最高，达１２９．１亿ｋｇ，其次为２００６，２００７和２００２年，分别为１１３．２，１１０．７和１０９．０亿ｋｇ。由于气候条件和不同县市草地面积及生长状况等存在较大差异，其理论载畜量也存在显著差别。

... 同时,采用最大值合成法(maximum value composite, MVC),分别合成月最大MOD15A2和MOD09GA数据集^[28] ...

1993

0.0

... )来计算NPP^[29,30,31] ...

... m^-2[29,30] ...

... M^-1^[29],^[30] ...

1995

0.0

... )来计算NPP^[29,30,31] ...

... m^-2[29,30] ...

... M^-1^[29],^[30] ...

2008

0.0

. 2008, 32(4):786-797 DOI:10.3773/j.issn.1005-264x.2008.04.007

Dynamics simulation of net primary productivity by a satellite data-driven CASA model in Inner Mongolian typical steppe, China

基于CASA模型的内蒙古典型草原植被净初级生产力动态模拟

Zhang F , Zhou G S , Wang Y H

张峰, 周广胜, 王玉辉

State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China

Aims Vegetation net primary productivity ( NPP ) and its responses to global change have been focuses of global change research. Accurately estimating the spatial patterns and temporal dynamics of net primary productivity ( NPP ) of terrestrial ecosystems is of great interest to human society and is necessary for understanding the carbon cycle of the terrestrial biosphere. But only a few evidences in various biomes are available on the performance of global models of terrestrial net primary productivity ( NPP ) at ecosystem level. Methods Vegetation net primary productivity ( NPP ) derived from a carbon model (Carnegie-Ames- Stanford Approach, CASA) and its inter-annual change at ecosystem level in Inner Mongolian typical steppe, China, are investigated in this study using 1982~2002 time series data sets of normalized difference vegetation index ( NDVI ) at 8 km spatial resolution and paired ground-based information on vegetation, climate, soil, and solar radiation, after CASA model is validated by the aboveground biomass of 13 years’ continuous observation. Important Findings Results show that 21-year averaged annual NPP is 290.23 g C·m –2 ·a –1 , ranging between 145.80 g C·m –2 ·a –1 and 502.84 g C·m –2 ·a –1 . From 1982 to 2002, annual NPP shows a slightly increasing trend, while from 1982 to 1999 a significant increase ( p NPP is mainly due to the increases of the amplitude of the NPP annual cycle. Annual precipitation significantly affects the variation of NPP , and there is no significant positive correlation between NPP and annual mean temperature.

植被净初级生产力及其对气候变化的响应研究是全球变化的核心内容之一。在利用内蒙古典型草原连续13年的地上生物量资料对基于遥感信息的生态系统碳循环过程CASA(Carnegie-Ames-Stanford Approach)模型验证的基础上, 分析了内蒙古典型草原1982~2002年植被净初级生产力(Net primary productivity, NPP )的时间变异及其影响因子。结果表明: 1) 1982~2002年21年间内蒙古典型草原的平均年 NPP 为290.23 g C·m –2 ·a –1 , 变化范围为 145.80~502.84 g C·m –2 ·a –1 ; 2)内蒙古典型草原 NPP 呈增加趋势, 但没有达到显著性水平, 其中1982~1999年的18年间 NPP 呈现非常显著的增加趋势( p NPP 增加的直接原因是由于生长旺季生长本身增强所致; 3)内蒙古典型草原NPP与年降水量呈极显著的相关关系, 年降水量显著影响NPP的变异, 而 NPP 与年均温无显著相关关系。

... )来计算NPP^[29,30,31] ...

... 张峰等^[31]利用温度、降水和纬度来估算水分胁迫系数,结果发现CASA模型模拟值低估了地上净初级生产力 ...

2005

0.0

... 本研究通过遥感反演地表湿润指数LSWI的方法计算 Wε^[32],计算方法如下: ...

2007

0.0

2011

0.0

. 2011, 20(5):252-258 DOI:10.11686/cyxb20110533

Review in the study of comprehensive sequential classification system of grassland

草原综合顺序分类系统研究进展

Liang T G , Feng Q S , Huang X D

梁天刚, 冯琦胜, 黄晓东

草地类型是草地资源实体的高度抽象与概括，草地分类是草地类型理论的具体实践，是草业科学研究与实践的基础问题之一。草原综合顺序分类系统（ＣＳＣＳ）以生物气候特征为依据，根据不同的热量级和湿润度将具有同一地带性农业气候特征的草地划为一类。据此所作的分类检索图是草原综合顺序分类法的图谱表达，具有独特的理论意义和应用价值。随着ＲＳ、ＧＩＳ、ＧＰＳ等技术及地统计学中气象要素空间插值方法的不断改进和完善，草原综合顺序分类系统在提出至今的５０多年中在理论及实践方面已取得不少研究成果。系统总结了草原综合顺序分类方法在草地分类中的作用及意义、研究现状、存在的问题及研究展望，以期为该方法在更大范围的推广和应用提供科学依据。

... 反之,表示模型误差较大^[34,35] ...

2008

0.0

... 反之,表示模型误差较大^[34,35] ...

2013

0.0

... X_i为第i年的NPP值^[36] ...

1994

0.0

... 然而,Raymood等^[37]认为光能转化率最大值应该为3 ...

1994

0.0

... Ruimy等^[38]以及Paruelo等^[39]的研究结果则认为其最大可以达到2 ...

1997

0.0

... Ruimy等^[38]以及Paruelo等^[39]的研究结果则认为其最大可以达到2 ...

2013

0.0

. 2013, 22(5):220-228 DOI:10.11686/cyxb20130526

Algorithm improvements for two important parameters of FPAR and maximum solar energy utilization efficiency

植被光合有效辐射吸收分量及最大光能利用率算法的改进

Wang B L , Wang J J , Yang Y

王保林, 王晶杰, 杨勇

植被冠层对入射光有效辐射的吸收系数（ＦＰＡＲ）和最大光能利用率是ＮＰＰ模拟的重要参数。利用光量子仪实测了典型草原植被类型关键生育期光合有效辐射值，根据ＮＤＶＩ与ＦＰＡＲ之间的良好相关性，建立了２个ＦＡＰＲ估算模型。结果表明，实测的ＦＰＡＲ值与模型估算的ＦＰＡＲ之间的相关性均达到极显著水平，２个模型的估测值之间没有显著性差异。因此，本研究中采取求均值后计算了我国草原的ＦＰＡＲ，并与ＮＡＳＡ－ＦＰＡＲ产品进行了嵌套和互补，形成全国完整的草原ＦＰＡＲ空间分布图。同时，基于归一化植被指数和叶面积指数，以中国草原１８大类分类结果为基础单元，对中国草原１８大类的最大光能利用率进行调整，调整后的最大光能利用率输入ＮＰＰ模型后，改善了ＮＰＰ估算精度，在大类尺度上，总体预测精度为８５．７１％。

... 王保林等^[40]对我国18大类草原最大光能转化率取值进行调整,其取值区间为0 ...

2007

0.0

... 在传统的遥感模型中,水分胁迫系数采用土壤水分模型模拟,涉及的众多土壤参数属于经验系数,导致该系数精度难以保证^[41,42] ...

... Yuan等^[41]利用感热通量(sensible heat flux, H)与潜热通量(latent heat flux, LE)之比对水分胁迫系数进行估算,取得了很好的研究结果 ...

1999

0.0

... 在传统的遥感模型中,水分胁迫系数采用土壤水分模型模拟,涉及的众多土壤参数属于经验系数,导致该系数精度难以保证^[41,42] ...

1996

0.0

. 1996, 20(1):11-19

Study on NPP of natural vegetation in China under global climate change

全球气候变化的中国自然植被的净第一性生产力研究

Zhou G S , Zhang X S

周广胜, 张新时

In this paper, the distribution of NPP of natural vegetation in China was given according to a new NPP model. This NPP model was based on ecophysiological feature and regional evaportranspiration model relating the two well known balance equations on the earth’s surface, water balance equation and heat balance equation. The changing pattern of NPP in China was also given under doubled CO2 concentration. This study would help to make good use of natural resources and to make scientific count-measures under global climate change.

本文根据已建立的植物生理生态学特点与水热平衡关系的植物净第一性生产力模型对中国自然植被的净第一性生产力现状及全球变化后的自然植被的净第一性生产力进行了分析，给出了中国陆地生态系统自然植被的净第一性生产力在全球气候变化条件下的变化图景，为合理开发、利用自然资源，以及监测和预测中国陆地生态系统自然植被净第一性生产力的变化及应采取的策略提供了科学依据。

... 周广胜等^[43,44]采用实际蒸散量和潜在蒸散量的比值来计算水分胁迫系数,对有关参数进行了简化,使操作性更强,朱文泉等^[45]研究证明基于该算法得到的水分胁迫系数构建的CASA模型具有较高的模拟精度 ...

1996

0.0

2007

0.0

. 2007, 31(3):413-424

Estimation of net primary productivity of Chinese terrestrial vegetation based on remote sensing

中国陆地植被净初级生产力遥感估算

Zhu W Q , Pan Y Z , Zhang J S

朱文泉, 潘耀忠, 张锦水

Key Laboratory of Environmental Change and Natural Disaster of Ministry of Education, College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China

Aims Net primary productivity ( NPP ) is a key component of the terrestrial carbon cycle. Model simulation is commonly used to estimate regional and global NPP given difficulties to directly measure NPP at such spatial scales. A number of NPP models have been developed in recent years as research issues related to food security and biotic response to climatic warming have become more compelling. However, large uncertainties still exist because of the complexity of ecosystems and difficulties in determining some key model parameters. Methods We developed an estimation model of NPP based on geographic information system (GIS) and remote sensing (RS) technology. The vegetation types and their classification accuracy are simultaneously introduced to the computation of some key vegetation parameters, such as the maximum value of normalized difference vegetation index (NDVI) for different vegetation types. This can remove some noise from the remote sensing data and the statistical errors of vegetation classification. It also provides a basis for the sensitivity analysis of NPP on the classification accuracy. The maximum light use efficiency ( LUE ) for some typical vegetation types in China is simulated using a modified least squares function based on NOAA/AVHRR remote sensing data and field-observed NPP data. The simulated values of LUE are greater than the value used in the CASA model and less than the values simulated with the BIOME-BGC model. The computation of the water restriction factor is driven with ground meteorological data and remote sensing data, and complex soil parameters are avoided. Results are compared with other studies and models. Important findings The simulated mean NPP in Chinese terrestrial vegetation from 1989-1993 is 3.12 Pg C (1 Pg=10 15 g). The simulated NPP is close to the observed NPP , and the total mean relative error is 4.5%for 690 NPP observation stationsdistributed in the whole country. This illustrates the utility of the model for the estimation of terrestrial primary production over regional scales.

该文在综合分析已有光能利用率模型的基础上，构建了一个净初级生产力( NPP )遥感估算模型，该模型体现了3方面的特色：1)将植被覆盖分类引入模型，并考虑植被覆盖分类精度对 NPP 估算的影响，由它们共同决定不同植被覆盖类型的归一化植被指数( NDVI )最大值；2)根据误差最小的原则，利用中国的 NPP 实测数据，模拟出各植被类型的最大光能利用率，使之更符合中国的实际情况；3)根据区域蒸散模型来模拟水分胁迫因子，与土壤水分子模型相比，这在一定程度上对有关参数实行了简化，使其实际的可操作性得到加强。模拟结果表明，1989～1993年中国陆地植被 NPP 平均值为3.12 Pg C (1 Pg=10 15 g)， NPP 模拟值与观测值比较接近，690个实测点的平均相对误差为4.5%；进一步与其它模型模拟结果以及前人研究结果的比较表明，该文所构建的 NPP 遥感估算模型具有一定的可靠性，说明在区域及全球尺度上，利用地理信息系统技术将遥感数据和各种观测数据集成在一起，并对 NPP 模型进行参数校正，基本上可以实现全球范围不同生态系统 NPP 的动态监测。

2008

0.0

. 2008, 19(5):1071-1076

Estimation of net primary productivity in middle reaches of Yarlung Zangbo River and its two tributaries

#cod#x0201c;一江两河#cod#x0201d;中部流域植被净初级生产力估算

Zhou C P , Ouyang H , Cao Y

周才平, 欧阳华, 曹宇

1 Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China; 2 Department of Land Resource Management, Zhejiang University, Hangzhou 310029, China; 3 Management Center of 21st Century, Beijing 100038, China

Based on the MODIS data and field measurement, the net primary product ion (NPP) in 2000 and 2006 in the middle reaches of Yarlung Zangbo River and its two tributaries in Tibet were estimated by using a mathematic model. The results showed that in study area, the NPP decreased gradually from valley to ridge, which was accorded with the gradients of precipitation and temperature. The annul NPP per unit area was averagely 86.8 g C·m -2 ·a -1 , and was 2.15 g C·m -2 ·a -1 higher in 2006 than in 2000. Farmland ecosystem had the highest annual NPP per unit area (243.1 g C·m -2 ·a -1 ), while desert ecosystem had the lowest one (36.5 g C·m -2 ·a -1 ). The average total NPP in the two years was 512.8×10 10 g C·a -1 , with the value in 2006 being 12.7×10 10 g C·a -1 higher than that in 2000. Meadow ecosystem had the highest annual total NPP (194.4×10 10 g C·a -1 ), while desert ecosystem had the lowest one (30.3×10 10 g C·a -1 ). In 2000-2006, the NPP value in the areas with strong human di sturbance (0-4 km away from road) had a decreasing trend, whil e that in the areas difficult for human to reach was in adverse.

基于中分辨率成像光谱仪（MODIS）的遥感数据以及地面实际观测资料，采用数学模型方法，对西藏“一江两河”中部流域地区2000和2006年的植被净初级生产力（NPP）进行了估算.结果表明：研究区NPP由河谷向山脊逐渐递减，这与该区的水热梯度基本一致；该区单位面积年NPP平均为86.8 g C·m -2 ·a -1 ，2006年比2000年高2.15 g C·m -2 ·a -1 ，不同植被类型的单位面积年NPP以农田(243.1 g C·m -2 ·a -1 )最大，荒漠(35.6 g C·m -2 ·a -1 )最小；研究区两年平均总NPP为512.8×10 10 g C·a -1 ，2006年比2000年高12.7×10 10 g C·a -1 ，不同植被类型的总NPP以草甸(194.4×10 10 g C·a -1 )最高，荒漠(30.3×10 10 g C·a -1 )最低.研究期间，人类活动强烈区域（道路缓冲区0～4 km）的植被NPP呈下降趋势，而人类活动较难到达区域的植被NPP呈增加趋势.

... 周才平等^[46]所研究的CASA模型中水分胁迫系数采用遥感反演地表湿润指数(land surface water index, LSWI)的方法来计算,结果显示模型的模拟结果与实测数据的相关性高达80% ...

2008

0.0

. 2008, 28(1):72-76

Vegetation water content estimation using NDVI and LSWI from MODIS images

MODIS影像的NDVI和LSWI植被水分含量估算

Zhang Y S , Xie Y L

张友水, 谢元礼

1. College of Geography, Fujian Normal University, Fuzhou, Fujian 350007; 2. Department of Urban and Resource Sciences, Northwest University, Xi’an, Shaanxi 710069

Estimating the water status of vegetation is most important in crop irrigation and prediction of forest fire. This paper analyzes the relationships between vegetation water content (VWC) and land surface water index (LSWI) and between fractional vegetation cover and normalized difference vegetation index (NDVI) from 8-day composite MODIS (Moderate Resolution Imaging Spectroradiometer) surface reflection products (MOD09A1), and the distribution regularities of different type of vegetation and its surface water content in LSWI-NDVI space. Two years’ 8-day composite MODIS Surface Reflection products were used to estimate the minimal and maximal water content lines of the hypothetical trapezoidal shape of NDVI-LSWI in the study area. Instead of the theoretical trapezoidal shape of fractional vegetation cover and Ts-Ta that indirectly estimated water status per fractional vegetation cover (Ts-Ta is surface (soil and canopy) temperature minus air temperature at measuring time), the vegetation dryness index (VDI) developed in this study was directly used to estimate vegetation water deficits at the canopy level by the trapezoidal shape of LSWI-NDVI. The technique simply estimated vegetation water status without measuring Ts-Ta .

植被含水量估算在作物灌溉和森林火灾预警中具有重要指导意义。采用8天合成MODIS地表反射率数据,针对植被水分含量与陆表水指数,植被覆盖与归一化植被指数的关系及不同植被类型和地表水分含量状况在NDVI-LSWI二维空间中的分布规律,在NDVI-LSWI梯形特征空间中确定最大和最小含水量边界线的基础上采用植被干燥指数直接估算植被水分亏缺程度。该方法不仅简便,而且可以避开植被指数温度梯形图中陆地表面温度和气温差值的测量。

... 该方法直接利用遥感数据,计算简便,综合考虑了冠层结构和观测角度等多种影响因素,从而能够比较准确地估算水分胁迫系数^[47] ...

2014

0.0

. 2014, 34(4):769-776 DOI:10.3969/j.issn.1001-7410.2014.04.09

Above- and below-ground biomass relationships in China#cod#x02019;s grassland vegetation

中国草地植被地上和地下生物量的关系分析

Ma A N , Yu G R , He N P

马安娜, 于贵瑞, 何念鹏

Synthesis Research Center of Chinese Ecosystem Research Network, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101

Large grassland biomass stores in belowground which plays a significant role in ecosystem carbon store and carbon sequestration. Due to the lack of below-ground biomass data Root:shoot ratio(R/S)or the relationship between above-ground biomass(AGB)and below-ground biomass(BGB)have been used to estimate below-ground biomass. By available published grassland biomass data in 2004~2010 the paper explored the R/S and the relationship between above- and below-ground biomass in six grassland types such as alpine meadow alpine steppe temperate desert steppe temperate meadow steppe montane meadow temperate steppe. The results showed that R/S was significantly different in all grassland types(F=3.542 p p R 2 =0.67)>temperate desert steppe( R 2 =0.36)>alpine meadow( R 2 =0.13). The power function fitted well with the relationship between BGB and AGB for these three types of grassland. The relationship between above-and below-ground biomass was not found in other three types of grassland. It was evident that both R/S and the relationship between AGB and BGB must be considered together when BGB was estimated.

草地生物量大部分集中在地下，地下生物量对于估算草地生态系统碳储量及其碳固持效应都至关重要。由于草地地下生物量数据缺乏，利用地下与地上的根冠比（R/S）或者地下与地上生物量的关系对草地地下生物量进行估算，是目前最常用的方法。本文在综述草地生物量研究进展的同时，利用2004~2010年已发表文献的草地生物量数据对中国不同类型草地的根冠比进行了探索，同时分析了不同类型草地的地上与地下生物量的相关关系（高寒草甸、高寒草原、山地草甸、温性草甸草原、温性草原和温性荒漠草原）。分析结果表明：不同类型草地的根冠比存在显著差异（F=3.524， p p R 2 =0.67）>温性荒漠草原（ R 2 =0.36）>高寒草甸（ R 2 =0.13），其他3种类型草地几乎不存在地上与地下生物量的相关关系。这为以后估算地下生物量提供依据，即地下生物量估算应该综合考虑根冠比和地下与地上生物量关系。

... 另外,草本植物的分形结构复杂,包括块茎、块根和地下茎等,其地下部分常常异化出具有储藏或者其他功能的结构,这些结构也会导致根冠比的变化^[48,49] ...

2010

0.0

2016

0.0

. 2016, 35(9):2494-2504

Variation of vegetation coverage and its response to climate change in Gannan Plateau from 2000 to 2014

2000#cod#x02014;2014年甘南高原植被覆盖度变化及其对气候变化的响应

Wang W J , Zhao X Y , Fang W Y

王伟军, 赵雪雁, 万文玉

( 1 College of Geography and Environmental Science, Northwest Normal University, Lanzhou 730070, China;  2 Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China).

MODIS NDVI data from 2000 to 2014 were adopted to extract the time series of the vegetation coverage in Gannan Plateau, and its spatial and temporal variation were analyzed. The response of vegetation coverage to climatic variations at annual and monthly time scales was analyzed by using phenological phase, temperature and precipitation time series at Gannan meteorological stations from 2000 to 2014. The results showed that the annual mean temperature increased with an annual rate of 0.04 ℃ and the annual mean precipitation increased with an annual rate of 4.7 mm in the Gannan Plateau from 2000 to 2014. Spatially, precipitation decreased from the southwest to the northeast and the annual mean temperature decreased from the southeast to the northwest. During the past 15 years, the vegetation coverage increased from the southeast to northwest in Gannan Plateau. The mean vegetation coverage was 77.5%, and exhibited apparent spatial heterogeneity, being the highest (77.8%) in forest and the lowest (60.3%) in alpine and subalpine grassland. There was a slight increase of vegetation coverage over the study period with an annual rate of 0.32%. The increase in sloping grassland area was as high as 1.04% per year and the plain grassland area increase was 0.47% per year, while the increases in coverage of alpine and subalpine meadow and forest were lower (0.19% and 0.10% per year, respectively). On the contrary, the marsh wetlands, alpine and subalpine grassland had a slight decrease of vegetation coverage over the study period (-0.03% and -0.06% per year, respectively). At the annual time-scale, there were no significant positive correlations between vegetation coverage and annual temperature or precipitation. However, the vegetation coverage was mainly affected by temperature in alpine and sub-alpine grassland, alpine and sub-alpine meadow, sloping grassland, plain grassland and marsh wetlands, while the vegetation coverage in forest was mainly affected by precipitation. At the monthly timescale, the time-lag response of vegetation coverage to precipitation occurred mainly in early and late growing season, while the time-lag response to temperature occurred mainly in key growth stage, and such response to precipitation was better than that to temperature.

基于2000—2014年MODIS NDVI数据，分析了甘南高原植被覆盖度的时空变化格局，并结合同期气温、降水数据及物候信息，从不同时空尺度和植被类型分析了甘南高原植被覆盖度的年、月际变化及其对气候变化的响应。结果表明：甘南高原2000—2014年年均温和降水均呈波动上升趋势（0.04 ℃·a -1 ，4.7 mm·a -1 ），降水由西南向东北呈递减趋势，年均温由东南向西北呈递减趋势；近15年甘南高原植被覆盖度总体呈东南高西北低的特征，平均植被覆盖度为77.5%，其中，森林覆盖度最高（77.8%），高山亚高山草地覆盖度最低（60.3%）；甘南高原植被覆盖度总体呈上升趋势，增加速率为0.32%·a -1 。其中，坡面草地增加趋势显著，为1.04%·a -1 ，平原草地次之，为0.47%·a -1 ，高山亚高山草甸为0.19%·a -1 ，森林仅为0.10%·a -1 ，而沼泽湿地和高山亚高山草地覆盖度呈下降趋势（-0.03%·a -1 、-0.06%·a -1 ）；从年际变化来看，植被覆盖度与气温和降水均呈不显著正相关 ( R =0.45， R = 0.21)；从不同类型植被来看，高山亚高山草甸、高山亚高山草地、平原草地、坡面草地和沼泽湿地覆盖度受气温影响较大，森林覆盖度受降水影响较大；从月际变化来看，生长季植被覆盖度对降水的滞后效应主要表现在植被生长初期和末期，对气温的滞后效应主要表现在关键生长期，且对降水的滞后效应优于气温。

... 整体而言,草地类型呈增加趋势的面积大于呈减少趋势的面积,这也许与研究区降水量整体呈上升趋势有关^[50] ...