乌兰布和沙漠东北缘荒漠-绿洲过渡带植被地上生物量估算

干旱区荒漠植被地上生物量是植被生长状况评价与荒漠化监测的重要指标。在乌兰布和沙漠东北缘的荒漠-绿洲过渡带选取典型区,基于地面调查数据构建主要植物种的异速生长方程,对样方内的植被地上生物量进行估算;基于样方调查数据和Quick Bird影像数据,分别建立植被指数与人工固沙林和荒漠植被地上生物量的回归模型,并对研究区植被地上生物量进行估算。结果表明:植冠体积V是较好的预测变量,所得荒漠植物异速生长方程精度较高,能够满足样方内荒漠植被地上生物量估算需要;采用RVI对数模型估算人工固沙林地上生物量的效果最好(R~2=0.72,RMSEP=56.15),采用RVI线性模型估算荒漠植被地上生物量的效果最好(R~2=0.82,RMSEP=15.07);研究区内荒漠植被和人工固沙林的单位面积地上生物量分别为90.73g/m~2和105.28g/m~2。该研究可以为荒漠化监测和荒漠植被遥感信息提取提供参考。     

基于MODIS数据的草地生物量估算模型比较

准确估算草地生物量对合理规划区域畜牧业、评估草地植被的生态效益有重要意义.目前,在常用的遥感估算模型中,采用的植被指数和模型函数形式多样.本文根据野外生物量调查结果和MODIS数据,分别采用归一化植被指数(NDVI)、增强植被指数(EVI)和修正的土壤调节植被指数(MSAVI)建立了内蒙古科尔沁左翼后旗草地地上生物量和地上地下总生物量估测的3种(线性、乘幂和指数)模型,并进行了比较.结果表明:3种模型能够对草地生物量进行较好的模拟,其中指数模型效果最佳;3个植被指数(NDVI,EVI和MSAVI)与草地生物量均有较高的相关性,可用于该草地产量估测,其中MSAVI对地上生物量拟合效果最好(R2=0.900);NDVI和EVI的线性模型对总生物量的模拟明显好于对地上生物量的模拟.     

阿尔泰山两河源牧区草地群落生物量及物种多样性

群落生物量和物种多样性是表征草地生态系统数量特征的重要指标。该研究以新疆阿尔泰山南麓两河源放牧区草地为研究对象,利用样方法对两河源不同放牧区的草地植被进行调查,分析研究区生物量和物种多样性变化,探讨二者与环境因子之间的关联性,为草地群落物种保护以及草地可持续利用提供理论依据。结果表明：(1) 两河源不同牧区间群落盖度、高度、植株密度、地上生物量和单位盖度生物量存在差异。(2) 两河源牧区草地群落地上生物量与群落盖度、植株密度呈显著正相关关系(P<0.05),且地上生物量主要受草地群落盖度的影响;不同牧区的物种多样性指数有一定差异,但物种分布相对均匀。(3)两河源牧区草地群落生物量及物种多样性主要受气温和降水的影响。     

基于物候相机归一化植被指数估算高寒草地植物地上生物量的季节动态

准确评估地上生物量对优化草地资源管理和理解草地碳、水和能量平衡具有重要意义。该文通过近地遥感归一化植被指数(NDVI)构建最优经验模型, 对青藏高原高寒草地地上生物量进行估算。该文利用2018-2019年5-9月野外实测的地上生物量和植物冠层光谱仪(RapidSCAN)测定的NDVI_RS数据, 构建了生长季不同时期地上生物量的估算模型; 并结合2018年NetCam物候相机测定的NDVI_Cam时间序列数据, 实现地上生物量季节动态的模拟。主要结果: (1) NDVI_Cam、NDVI_RS与地上生物量具有相似的单峰型季节变化格局, 但NDVI峰值出现的时间(7月)较地上生物量(8月)更早; (2)基于NDVI的生物量估算最优经验模型在5、7和9月是幂函数, 在6和8月是二次多项式, 估算精度为0.29-0.77; (3)基于NDVI_Cam时间序列数据, 生长季不同时期建模(R² = 0.91)较单一时期(9月)建模(R² = 0.49)对地上生物量季节动态的估算更为准确。这些结果表明, 近地遥感是估算高寒草地植物地上生物量的有效手段, 开展季节性植物生长调查将有助于准确评估草地资源。     

科尔沁沙质草地放牧和围封条件下的土壤种子库

该文研究了科尔沁沙质草地在放牧和围封条件下土壤种子库密度、组成及其与地上植被的关系。结果表明:1)放牧草地植物种数22种,围封草地植物种数30种,围封使土壤种子库植物种数增加了36%;2)放牧草地土壤种子库密度为16 149±1 900有效种子数·m^-2, 围封草地土壤种子库密度为20 657±3 342有效种子数·m^-2,比放牧草地增加了28%。放牧和围封草地种子库组成密度均以一年生植物为主(分别占99%和98%的比例),多年生植物所占的比例很小;3)放牧草地种子库的Shannon-Wiener指数和丰富度指数分别为0.836 3和4.954 9,明显小于围封草地的0.968 2和7.226 0,表明自由放牧导致物种多样性下降;4)放牧和围封草地土壤种子库密度与地上植被密度均存在显著的相关性(p<0.001)。表明了随着土壤种子库密度的增加, 地上植被密度随之增加,放牧草地地上植被密度78%的变异可归结为土壤种子库密度的变异, 而围封草地地上植被密度58%的变异可由土壤种子库密度的变异来解释。     

放牧管理模式对高寒草甸鼢鼠鼠丘群落演替的影响

高寒草甸放牧利用下高原鼢鼠(Eospalax baileyi)等危害的发生是草地管理的关键难题,分析放牧管理模式对鼢鼠鼠丘植被群落演替的影响能为草地管理提供重要依据。研究选择划区轮牧(RG)、生长季休牧(GSG)、连续放牧(CG)和禁牧(PG)4种放牧管理模式,以及各模式下不同年限鼠丘(一年(ZM1)、两年(ZM2)、三年(ZM3)和多年鼠丘(ZMM))与对照(CM)草地。分析不同放牧管理模式对鼠丘植被群落特征和生物量等的影响,结果发现:PG和GSG下所有年限鼠丘的植被高度、地上生物量均高于RG和CG;RG和GSG下ZM1和ZM2物种Shannon-wiener指数均高于对照样地物种Shannon-wiener指数。主成分分析表明:RG下地上生物量和物种丰富度指数是影响鼠丘植被群落演替的重要因子,PG下地上生物量、盖度、Shannon-wiener指数和均匀度指数是鼠丘植被群落演替的重要因子,CG下物种丰富度和重要值是影响鼠丘植被群落演替的重要指标,GSG下Shannon-wiener指数、盖度和高度是影响鼠丘植被群落演替的重要指标。可见,不同放牧制度对鼠丘植被群落演替的影响不同,禁牧和生长季休牧管理模式能够较好地恢复鼠丘植被群落演替。     

滨海湿地植被地上生物量遥感反演

盐沼植被生物量是滨海湿地生态系统碳循环研究的重要参数,是湿地生态系统健康评价、资源可持续利用的关键指标,开展盐沼植被地上生物量监测方法研究具有重要意义。目前,遥感技术在湿地生物量监测领域已经得到广泛应用,但反演方法仍以统计模型为主,模型构建需要实测数据支撑,时空拓展性不强。选择江苏盐城丹顶鹤保护区为研究区,基于冠层辐射(PROSAIL)传输模型,通过局部和全局敏感性分析,对模型参数本地化,构建了互花米草地上生物量半经验反演模型,应用于Landsat 8 OLI遥感影像,获得了互花米草地上生物量的时空分布。研究结果表明,利用PROSAIL模型模拟互花米草冠层反射率,叶面积指数(LAI)、叶片干物质含量(Cm)、叶倾角分布参数(LIDF)、等效水厚度(Cw)、叶绿素含量(Cab)、叶片结构参数(N)为高敏感性参数,类胡萝卜素含量(Car)、土壤参数(P_soil)为低敏感性参数;利用不同时刻的遥感影像反演了地上生物量,遥感反演结果与实测数据对比,拟合度R²为0.83,均方根误差(RMSE)为0.43kg/m²,平均相对误差(MRE)为15.7%,精度较高,模型具有较好的时空普适性。研究发展了盐沼植被地上生物量遥感反演方法,解决了以往过于依赖现场实测数据构建反演模型的局限性,该方法可以为研究滨海湿地生态系统碳循环以及准确估算其碳汇潜力提供技术支持。     

植被与土壤特征对青藏高原不同程度退化草地的响应

高寒草地作为青藏高原高寒生态系统的重要组分之一,其退化已严重影响到高原的可持续发展和草地恢复重建。搜集了2004—2022年间关于青藏高原高寒草地退化的64篇研究结果,包含土壤有机碳、生物量和多样性指数等16个指标的1403组数据,运用meta分析解析了草地退化对土壤理化性质、植被生产力和物种多样性的影响,并对重度退化草地的土壤理化性质和植物生物量进行线性回归分析。结果表明：随着草地退化的加剧,土壤有机碳、全氮、全磷、有效氮、有效磷、有效钾、土壤含水量、地上生物量、地下生物量和植被高度显著下降;土壤容重显著上升;土壤pH、全钾在各个退化阶段没有明显差异;Shannon多样性指数、Pielou均匀度指数和Margalef丰富度指数整体呈下降趋势。土壤有机碳、全氮、全磷、有效氮、有效磷、有效钾和土壤含水量与地上生物量、地下生物量存在显著的正相关;土壤容重与地上生物量、地下生物量呈显著的负相关;土壤pH与地上生物量、地下生物量呈负相关。因此,青藏高原高寒草地退化通过改变土壤理化性质而改变地上群落多样性和生物量,为阐明植被与土壤特征对草地退化的响应机制以及高寒退化草地的恢复提供了科学依据。     

西藏自治区草地地上生物量遥感反演研究

草地地上生物量（Aboveground Biomass,AGB）是反映草地生态系统功能和质量的关键指标,大尺度地准确估算草地AGB对草地生态系统经营管理至关重要。研究以MODIS影像为数据源,提取反射率、植被指数和植被产品三种不同类型的特征变量,结合野外实测样地草地AGB数据,构建以多元线性逐步回归为代表的参数模型以及随机森林、支持向量机和kNN等非参数模型进行西藏自治区草地AGB估测及空间分布制图。结果表明：（1）多元线性逐步回归、随机森林、支持向量机和kNN模型在加入植被产品特征变量后,RMSE分别降低了15.8%、13.5%、4.1%和17.3%,表明植被产品作为建模变量用于草地AGB估测可有效提高模型精度;（2）三种组合变量构建的草地AGB估测模型中,反射率、植被指数、植被产品组合构建的模型效果最佳,其中kNN模型估测精度最高,R²达到0.60,RMSE和MAE分别为0.43、0.34 t/hm²;（3）草地AGB空间分布呈现出西北地区较低、中部地区较高且分布形态较破碎和东部地区较高的变化特征;（4）利用MODIS植被产品结合kNN模型的预测值与草地实测的AGB空间分布趋势基本一致。综上,MODIS植被产品结合kNN模型可作为大尺度区域草地AGB遥感估测的有效参考。     

呼伦贝尔草原不同草地利用方式下生物多样性与生物量的关系

生物多样性与生产力的关系是当前生态学中研究的重点之一,以呼伦贝尔草原为研究对象,通过连续两个生长季的野外监测,从草地植物功能型的角度探讨了在不同利用方式下草地物种丰富度与地上生物量的关系,结果表明：（1）不同草地利用方式显著影响草地生物多样性和生产力,在3种不同利用方式中,生物多样性总体的趋势是割草〉围封〉放牧,其中Shannon-Wiener指数、Simpson指数和物种丰富度均差异显著;割草草地地上生物量最高,围封草地次之,放牧草地最少。（2）将草地植物按照植物功能型分类,放牧草地1、2年生植物占优势,随着物种丰富度的增加,1、2年生植物生物量没有明显的变化趋势;割草草地以禾本科植物和非禾本科植物为主,随着物种丰富度的增加,禾本科植物生物量呈下降趋势,而非禾本科植物变化不明显;围封草地中禾本科植物占优势,其他功能型植物分布较均匀,多度、频度和生物量等差异不显著。（3）3种草地利用方式中只有围封草地物种丰富度和地上生物量存在显著的正相关,即随着物种丰富度的增加,生物量也随之升高。其他两种利用方式下,物种丰富度对地上生物量没有显著影响。     

The spatial pattern of grassland aboveground biomass on Xizang Plateau and its climatic controls

Aims Grassland is the most widely distributed vegetation type on the Xizang Plateau. Accurate remote sensing estimation of the grassland aboveground biomass (AGB) in this region is influenced by the types of vegetation indexes (VIs) used, the grain size (resolution) of the remote sensing data and the targeted ecosystem features. This study attempts to answer the following questions: (i) Which VI can most accurately reflect the grassland AGB distribution on the Xizang Plateau? (ii) How does the grain size of remote sensing imagery affect AGB reflection? (iii) What is the spatial distribution pattern of the grassland AGB on the plateau and its relationship with the climate?Methods We investigated 90 sample sites and measured site-specific AGBs using the harvest method for three grassland types (alpine meadow, alpine steppe and desert steppe). For each sample site, four VIs, namely, Normalized Difference VI (NDVI), Enhanced VI, Normalized Difference Water Index (NDWI) and Modified Soil-Adjusted VI (MSAVI) were extracted from the Moderate Resolution Imaging Spectroradiometer (MODIS) products with grain sizes of 250 m and 1 km. Linear regression models were employed to identify the best estimator of the AGB for the entire grassland and the three individual grassland types. Paired Wilcoxon tests were applied to assess the grain size effect on the AGB estimation. General linear models were used to quantify the relationships between the spatial distribution of the grassland AGB and climatic factors.Important findings The results showed that the best estimator for the entire grassland AGB on the Xizang Plateau was MSAVI at a 250 m grain size (MSAVI 250 m). For each individual grassland type, the best estimator was MSAVI at a grain size of 250 m for alpine meadow, NDWI at a grain size of 1 km for alpine steppe and NDVI at a grain size of 1 km for desert steppe. The explanation ability of each VI for the grassland AGB did not significantly differ for the two grain sizes. Based on the best fit model (AGB =^-10.80 + 139.13 MSAVI 250 m), the spatial pattern of the grassland AGB on the plateau was characterized. The AGB varied from 1 to 136g m ?2. Approximately 59% of total spatial variation in the AGB for the entire grassland was explained by the combination of the mean annual precipitation (MAP) and mean annual temperature. The explanatory power of MAP was weaker for each individual grassland type than that for the entire grassland. This study illustrated the high efficiency of the VIs derived from MODIS data in the grassland AGB estimation on the Xizang Plateau due to the vegetation homogeneity within a 1×1 km pixel in this region. Furthermore, MAP is a primary driver on the spatial variation of AGB at a regional scale.     

高寒草地生物量变化对归一化物候指数的响应

准确评价草地地上生物量(Above-ground biomass, AGB)对草地资源的可持续利用和保护具有重要意义。以甘南为典型研究区,利用2019—2021年Sentinel-2地表反射率和野外实测地上生物量数据,借助GEE(Google Earth Engine)平台和数理统计方法评价了9种植被指数对高寒草地AGB的估算精度,构建了高寒草地地上生物量反演模型,在此基础上分析了2019—2021年甘南州草地产量的时空动态变化。结果表明：在所有植被指数中,归一化物候指数(Normalized difference phenology index, NDPI)与草地AGB的R²值最高(0.72),其次为归一化植被指数(normalized difference vegetation index, NDVI)(R²=0.68),拟合效果最差的为增强型植被指数(enhanced vegetation index, EVI)(R²=0.37)和差值植被指数(different vegetation index, DVI)(R<...     

基于机器学习算法的草地地上生物量估测——以祁连山草地为例

草地地上生物量（Aboveground Biomass,AGB）是指导畜牧业生产管理的重要指标,是草畜平衡综合分析的基础。目前,有关祁连山草地AGB反演的研究较少,且多源数据间的尺度差异问题并未得到很好的解决。为了解祁连山草地AGB的空间分布状况,利用Sentinel-2多光谱数据、无人机（Unmanned Aerial Vehicle,UAV）数据以及2021年植被生长期实测草地AGB数据实现了空天地一体化监测,通过决策树回归（Decision Tree Regression,DTR）、随机森林回归（Random Forest Regression,RFR）、梯度提升决策回归树（Gradient Boosting Regression Tree,GBRT）以及极致梯度提升（eXtreme Gradient Boosting,XGBoost）共4种算法反演草地AGB的适用性分析,利用最优模型反演了祁连山草地的AGB空间分布状况。结果表明：研究区内多种植被指数所表现出的特性有所差异。祁连山地区AGB在空间分布上呈现出由西北向东南递增的趋势,平均AGB为925.43kg/hm²。6种植被指数与实测AGB之间均表现为显著正相关,适合作为祁连山草地AGB遥感反演的指标;XGBoost模型较其它模型具有最高的R²值（0.78）和精度（74.75%）、最低的均方根误差（RMSE,99.74 kg/hm²）和平均绝对误差（MAE,71.60 kg/hm²）,模型反演效果最好;UAV数据能够提供更加详细的空间细节特征,减小Sentinel-2数据和实地采样数据间的尺度差异;因此,基于6种植被指数与祁连山草地AGB间的相关性,构建XGBoost模型反演研究区草地AGB空间分布状况是具有实践意义的。研究结果将为指导祁连山草地畜牧业的发展和维护草地生态系统的平衡提供一定的参考价值与数据支撑。     

Modeling grassland aboveground biomass using a pure vegetation index

Remote sensing can be the most effective means of scaling up grassland aboveground biomass (AGB) from the sample scale to the regional scale. Among the remote-sensing approaches, statistical models based on the vegetation index (VI) are frequently used to retrieve grassland AGB because of their simplicity and reliability. However, these types of models have never been comprehensively optimized to overcome VI insensitivity and soil effects. Because grassland AGB is related to grassland type, in our research the integrated orderly classification system for grassland (IOCSG) was used to differentiate grassland types. The study area, located in Inner Mongolia, China, included desert steppe, typical steppe and meadow steppe. A pure VI (PVI) was extracted from the normal VI using spectral mixture analysis (SMA). Using a proportional relationship, PVI models were then constructed based on grassland type. The results demonstrated that the PVI models can have clear advantages over the more commonly used VI models. They simplify the parameterization of VI models and thus enhance models constructed for different regions with different remote sensing data sources. Notably, detailed differentiation of grassland types can improve the accuracy of AGB estimates. The methodology proposed in this study is particularly beneficial for AGB estimates at a national scale, especially for countries such as China with many grassland types.     

Estimation of aboveground biomass using in situ hyperspectral measurements in five major grassland ecosystems on the Tibetan Plateau

Aims There are numerous grassland ecosystem types on the Tibetan Plateau. These include the alpine meadow and steppe and degraded alpine meadow and steppe. This study aimed at developing a method to estimate aboveground biomass (AGB) for these grasslands from hyperspectral data and to explore the feasibility of applying air/satellite-borne remote sensing techniques to AGB estimation at larger scales.Methods We carried out a field survey to collect hyperspectral reflectance and AGB for five major grassland ecosystems on the Tibetan Plateau and calculated seven narrow-band vegetation indices and the vegetation index based on universal pattern decomposition (VIUPD) from the spectra to estimate AGB. First, we investigated correlations between AGB and each of these vegetation indices to identify the best estimator of AGB for each ecosystem type. Next, we estimated AGB for the five pooled ecosystem types by developing models containing dummy variables. At last, we compared the predictions of simple regression models and the models containing dummy variables to seek an ecosystem type-independent model to improve prediction of AGB for these various grassland ecosystems from hyperspectral measurements.Important findings When we considered each ecosystem type separately, all eight vegetation indices provided good estimates of AGB, with the best predictor of AGB varying among different ecosystems. When AGB of all the five ecosystems was estimated together using a simple linear model, VIUPD showed the lowest prediction error among the eight vegetation indices. The regression models containing dummy variables predicted AGB with higher accuracy than the simple models, which could be attributed to the dummy variables accounting for the effects of ecosystem type on the relationship between AGB and vegetation index (VI). These results suggest that VIUPD is the best predictor of AGB among simple regression models. Moreover, both VIUPD and the soil-adjusted VI could provide accurate estimates of AGB with dummy variables integrated in regression models. Therefore, ground-based hyperspectral measurements are useful for estimating AGB, which indicates the potential of applying satellite/airborne remote sensing techniques to AGB estimation of these grasslands on the Tibetan Plateau.     

青藏高原草地地上生物量估算

及时准确评估草地产草量对草地资源的科学管理和可持续发展具有重要意义。青藏高原自然环境特殊,气候差异显著,地形复杂,仅依靠遥感信息准确监测草地地上生物量(Aboveground Biomass,AGB)变化有较大限制。基于青藏高原草地AGB野外实测数据与Landsat遥感影像,探索了植被指数表征草地AGB信息的有效性,评估了气象和地形信息对准确估算草地AGB的影响,综合利用气象、地形和遥感信息,在新一代地球科学数据和分析应用平台(Google Earth Engine)上构建了梯度增强回归树草地AGB估算模型,绘制了青藏高原多年草地AGB空间分布图。结果表明:(1)基于单因素遥感因子的线性回归模型仅能解释8%-40%的草地AGB变化情况,其中绿色归一化植被指数(Green Normalized Difference Vegetation Index, GNDVI)对草地AGB解释能力较强(40%)。(2)基于遥感因子构建的梯度增强回归树模型测试集R²为0.57。分别添加气象、地形信息,模型对草地AGB的估测准确性有所提升,测试R²为0.62和0.63。(3)基于气象、地形和遥感因子的多因素估测模型能够提高草地AGB估测精度,经递归特征消除法优选后,基于13个特征变量的梯度增强回归树模型拟合效果最好(训练数据集R²=0.79,RMSE=43.42 g/m²,P<0.01;测试数据集R²=0.66,RMSE=53.64 g/m²,P<0.01),可以解释66%草地AGB变化情况。(4)2010年青藏高原平均AGB为94.58 g/m²,2015年93.63 g/m²,2020年100.78 g/m²。青藏高原西北部草地AGB较低,东南部草地AGB较高,整体呈现自西北向东南逐渐增加的分布格局。研究结果为准确估算青藏高原草地产草量和碳储量等研究提供重要参考。     

Biomass allocation and productivity–richness relationship across four grassland types at the Qinghai Plateau

Aboveground biomass (AGB) and belowground biomass (BGB) allocation and productivity–richness relationship are controversial. Here, we assessed AGB and BGB allocation and the productivity–richness relationship at community level across four grassland types based on the biomass data collected from 80 sites across the Qinghai Plateau during 2011–2012. The reduced major axis regression and general linear models were used and showed that (a) the median values of AGB were significantly higher in alpine meadow than in other three grassland types; the ratio of root to shoot (R/S) was significantly higher in desert grassland (36.06) than intemperate grassland (16.60), alpine meadow (13.35), and meadow steppe (19.46). The temperate grassland had deeper root distribution than the other three grasslands, with about 91.45% roots distributed in the top 30 cm soil layer. (b) The slopes between log AGB and log BGB in the temperate grassland and meadow steppe were 1.09 and 1, respectively, whereas that in the desert grassland was 1.12, which was significantly different from the isometric allocation relationship. A competitive relationship between AGB and BGB was observed in the alpine meadow with a slope of ?1.83, indicating a trade‐off between AGB and BGB in the alpine meadow. (c) A positive productivity–richness relationship existed across the four grassland types, suggesting that the positive productivity–richness relationship might not be affected by the environmental factors at the plant location. Our results provide a new insight for biomass allocation and biodiversity–ecosystem functioning research.     

Above- and belowground biomass in relation to environmental factors in temperate grasslands, Inner Mongolia

Above- and belowground biomasses of grasslands are important parameters for characterizing re- gional and global carbon cycles in grassland ecosystems. Compared with the relatively detailed in- formation for aboveground biomass (AGB), belowground biomass (BGB) is poorly reported at the re- gional scales. The present study, based on a total of 113 sampling sites in temperate grassland of the Inner Mongolia, investigated regional distribution patterns of AGB, BGB, vertical distribution of roots, and their relationships with environmental factors. AGB and BGB increased from the southwest to the northeast of the study region. The largest biomass occurred in meadow steppe, with mean AGB and BGB of 196.7 and 1385.2 g/m2, respectively; while the lowest biomass occurred in desert steppe, with an AGB of 56.6 g/m2 and a BGB of 301.0 g/m2. In addition, about 47% of root biomass was distributed in the top 10 cm soil. Further statistical analysis indicated that precipitation was the primary determinant factor in shaping these distribution patterns. Vertical distribution of roots was significantly affected by precipitation, while the effects of soil texture and grassland types were weak.     

三江源国家公园草地地上生物量时空动态及其气候影响

三江源区位于青藏高原腹地,作为长江、黄河、澜沧江三大河流的发源地,是我国重要的生态安全屏障。基于三江源区域草地AGB的野外调查数据,本研究采用多种机器学习算法集成分析的方式构建模型,实现了高精度的三江源国家公园草地AGB时空估算。基于AGB时空模拟结果,分析了近19年(2000—2018年)三江源国家公园区域草地AGB的时空动态变化。研究结果显示：(1)通过多种机器学习结合贝叶斯平均模型,草地AGB模拟值与实测值的r为0.88,RMSE为71.60g/m²,表明多模型集成分析的方式对草地AGB估算获得了较好的模拟效果。(2)三江源国家公园区域草地AGB的空间分布具有明显的空间异质性,呈从东南向西北递减的趋势。(3)2000—2018年长江源国家公园、黄河源国家公园和澜沧江国家公园区域草地AGB多年平均值分别为82.96 g/m²、117.54g/m²和168.39 g/m²。(4)近19年间,在黄河和长江源园区受到温度上升的影响草地AGB呈现出非显著性上升趋势;澜沧江区域,由于2015和2016年的...