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。     

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。     

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.     

羊草草原地上生物量的预测

采用数量化理论对羊草草原地上生物量进行了预测 ,建立了 2个数学预测模型。检验结果表明 ,地上生物量预测值与实测值的相关性显著 ,地上生物量的鲜重和干重的复相关系数分别达到了 0 97和 0 95。在影响羊草草原地上生物量的生态因素和植被特征因素中 ,按其对生物量形成的贡献大小 (以得分范围衡量 ) ,贡献最大的为测定生物量时前一个月的降雨量 ,其次为土壤全氮含量 ,再次为测定生物量时前一个月≥ 1 0℃的积温 ,小于这三个生态因子影响大小的因素依次为草群总盖度、草群平均高度和羊草种群生长状况。所建模型不但包含了生态因素对地上生物量的影响 ,而且也包含了植被特征因素对地上生物量的影响 ,为准确预测草地生物量提供了一条新途径     

基于草地综合顺序分类系统（IOCSG）的中国北方草地地上生物量高精度模拟

草地生态系统是陆地生态系统中分布最广泛的生态系统类型之一,草地生物量的精确估算一直是陆地生态学研究的重点问题。针对目前草地生物量估算方法的不确定性问题,提出了不依赖于遥感植被指数,而是通过分析草地生物量影响因素的方法去构建草地生物量估算模型。根据年积温(0℃)和湿润度指标将研究区域划分为4种潜在植被类型,即微温干旱温带半荒漠类、微温微干温带典型草原类、微温微润草甸草原类和微温湿润森林草原类,然后对每一种潜在植被类型的草地生物量分析其内在影响因素,研究结果发现,微温干旱温带半荒漠类的草地生物量与年积温存在较好的线性关系,微温微干温带典型草原类的草地生物量可以用表层土壤粘粒含量的二次多项式来模拟,后两种潜在植被类型的草地生物量则随着潜在NPP的变化呈现先减小后增大的变化趋势。对4种潜在植被类型区域分别建立草地生物量与其影响因素之间的回归关系确定研究区域草地生物量的趋势面,结合HASM模型实现研究区域草地生物量的高精度模拟,结果显示上述4种潜在植被类型区的草地平均生物量分别为76.62、110.94、142.69、184.40 g/m2。     

内蒙古草原大针茅群落地上生物量与降水量的关系

根据中国科学院内蒙古草原生态系统定位研究站在大针茅样地(面积500m×500m,地理坐标43°32′20″～43°32′40″N116°33′00″～116°33′30″E)的地上生物量与气候观测资料,利用SPSS分别进行大针茅群落地上生物量与年降水量、月降水量关键时期(4～6月份与6～8月份)降水量以及1～7月份总降水量的相关分析;对年降水量接近的1982、1983和1989年(分别为283.2mm,289.9mm,287.2mm)的地上生物量进行方差分析;运用积分回归模型模拟了降水的季节分配对群落生物量的影响。结果表明:(1)群落生物量的年际变化与年降水、月降水、关键时期降水(4～6月份和6～8月份)以及1～7月份降水量的变化没有显著的相关性(p>0.1);(2)在年降水量接近的年份,群落的地上生物量之间存在显著差异。降水量季节分配的变化对地上生物量的影响还有待于进一步研究。     

绿洲生态系统生物量与植被指数分析

利用阜康绿洲野外实测的53个样方的植物生物量数据与同期陆地卫星MODIS影像的第1,2通道250 m遥感数据,分析植被指数与绿洲植物生物量的相关关系,建立植被指数与绿洲植物生物量的一元线性和非线性回归模型。结果表明,植被指数NDVI和MSAVI与绿洲生态系统植物生物量之间存在较好的相关性;所建植被指数与植物生物量的回归模型中,三次方程为所得到的回归模型中最适合用于绿洲生态系统植物生物量和生长监测。     

一种基于数码相机图像和群落冠层结构调查的草地地上生物量估算方法

草地地上生物量是影响其生态系统功能最重要的因素之一, 也是草地生态学研究中不可或缺的监测指标。草地地上生物量监测多采用收割法进行, 但这种破坏性取样方法会对研究区域带来巨大干扰, 尤其是面积较小的长期定位监测或者控制实验研究样地, 从而使得地上生物量监测的频次受到很大限制。因此, 通过获取某些原位易测变量, 建立地上生物量的估算方法具有重要意义。该研究依托内蒙古典型草地刈割控制实验平台, 通过数码照片获取不同土地利用方式下的植被覆盖度, 并对样方内的叶面积指数、植被高度、物种多样性等参数进行了测定, 最后利用一元回归模型、逐步回归模型和随机森林模型对地上生物量进行估算。结果表明, 植被覆盖度、叶面积指数、植被平均高度、植被最大高度和物种丰富度是影响地上生物量的主要驱动因素。通过构建适宜于本地的逐步回归模型, 可以实现草地地上生物量的准确预测。在该研究区域中, 预测模型的决定系数(R²) = 0.91, 均方根误差(RMSE) = 35.60 g·m^-2。该研究提供了一种快速、准确且非破坏性测定草地地上生物量的方法, 可作为传统收割法的有效补充。     

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。     

利用冠层光谱估测烟草叶面积指数和地上生物量

综合多种烟草类型、品种及肥料处理因素,分析了17种光谱参数与烟草叶面积指数（LAI）、地上鲜生物重（AFW）、地上干生物重（ADW）的关系,建立逐步回归模型对烟草LAI、AFW、ADW进行估测并结合相关分析筛选出相应的特征变量。结果表明：5个回归方程的复确定系数R^2、回归系数相伴概率均达到显著水平。包含17个光谱参量的逐步回归方程筛选出的第一自变量均为Rg／Rr,相关分析及散点图分析亦得出Rg／Rr与LAI、AFW、ADW相关系数分别为0．759、0．611、0．647,R^2为0．576、0．3727、0．4184,均达到极显著水平,证明烟草LAI、AFW、ADW的特征变量为Rg／Rr。仅采用8种植被指数建立模型,证明利用比值植被指数（RVI）估测LAI、ADW亦是可行的。经过统计检验,建立的模型估测效果均较好,估测值与实测值的相关性均达到显著水平,其中包含特征变量Rg／Rr的回归模型估测效果优于RVI构建的模型。表明采用高分辨率光谱或宽波段光谱提取光谱变量可对烟草LAI、AFW、ADW进行监测,并可根据数据条件选择有效的估测模型,为烟草遥感数据分析提供方法。     

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.     

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。     

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。     

典型草原通量塔通量贡献区地上生物量和叶面积指数的时空变异

引入循环采样方法,在生长季(7月初、7月末和8月末)测量了典型草原植被生物量和叶面积指数,并利用地统计方法分析了二者的空间分布格局.结果表明:在7月初、7月末和8月末3个时期,地上生物量递增,叶面积指数(LAI)先增大后减小,地上生物量和LAI均具有良好的空间自相关性,二者空间格局相似且具有相似的时间演变规律,由条带状逐渐变为斑块状.从7月初到8月末,地上生物量和LAI的块金值/基台值[C0/(C+C0)]显著降低,表明其空间自相关程度从中度自相关变为强相关;变程逐渐缩小,空间连续性变差;分维值逐渐降低,空间依赖性增强.地形和草地管理方式是影响草原植被生物量和叶面积指数空间分布格局的重要因素,导致水热因子空间差异,进而对牧草生长产生影响.     

内蒙古草原植被覆盖度遥感估算

以内蒙古锡林浩特市南部中国科学院草原生态系统定位研究站周围的草场为研究对象, 分析比较了统计模型和亚像元分解模型进行草地植被覆盖度(vegetation coverage, VC)遥感估算的适用性。结果表明, 根据Landsat-5 TM影像数据计算的比值植被指数(simple ratio vegetation index, SR)与观测的VC的相关性最高(R² = 0.761); 统计模型和亚像元分解模型生成的VC空间分布特征相似, 但亚像元分解模型得到的VC平均值比统计模型的结果高0.091; 在VC的低值和高值区, 两种方法得到的VC结果相似; 但在VC的中值区, 亚像元模型得到的结果较统计模型的结果偏高。     

MODIS NDVI饱和性对高寒草甸草地生物量遥感估测的影响——以青藏高原东缘为例

自20世纪70年代开始,归一化植被指数(NDVI)在天然草地地上生物量估测研究中得到了广泛应用。然而,NDVI对高密度植被生物量遥感估测存在饱和现象,使草地生物量遥感估测有较大的不确定性。以青藏高原东缘高寒草甸为例,基于比值植被指数(RVI)探讨了NDVI的饱和性,并评估了NDVI饱和性对高寒草甸地上生物量时空动态变化分析的影响。结果表明:(1)虽然基于中分辨率成像光谱仪(MODIS) NDVI构建的草地地上生物量估测模型精度较基于RVI构建的估测模型高,但模型对高寒草地地上生物量(生物量大于2314.627 kg/hm²)灵敏度较RVI估测模型低,即NDVI阈值大于0.73时,估测模型呈现饱和现象(低估了草地地上生物量);(２)结合RVI 和 NDVI 的相关关系,对饱和部分NDVI 遥感植被指数进行校正,校正后最优地上生物量遥感估测模型为线性模型(y=5908.5x-2198.9,R=0.6190,RMSE=902.41 kg/hm²),较调整前RMSE降低了11.72 kg/hm²;(3)就NDVI饱和性空间分布而言,从全年6月-9月初(全年第161-257天)饱和性呈现先自东南向西北延伸,后自西北向东南消退的变化趋势,平均低估值介于158.45-293.92 kg/hm²之间,最大低估值出现在8月初(全年第225天),超过600 kg/hm²;(4)此外,NDVI饱和性对草地地上生物量年际动态变化趋势分析具有较大的影响,去除饱和性影响后草地地上生物量基本不变的区域减小了21.44%,而年际变化小于-10 kg/hm²和大于30 kg/hm²的区域分别增加了8.48%和16.19%。研究探讨了NDVI饱和性对草地地上生物量遥感估测的影响,以期为精确评估高寒草地地上生物量提供理论依据,同时也为高寒草地资源可持续发展提供科学依据。     

Comparison of leaf area index inversion for grassland vegetation through remotely sensed spectra by unmanned aerial vehicle and field-based spectroradiometer

Aims Remote sensing technology has been proved useful in mapping grassland vegetation properties. Spectral features of vegetation cover can be recorded by optical sensors on board of different platforms. With increasing popularity of applying unmanned aerial vehicle (UAV) to mapping plant cover, the study aims to investigate the possible applications and potential issues related to mapping leaf area index (LAI) through integration of remote sensing imagery collected by multiple sensors.     

气候因子通过土壤微生物生物量氮促进青藏高原高寒草地地上生态系统功能

近年来, 在人类活动和气候变化的影响下, 物种多样性丧失趋势不断加剧, 对生态系统功能带来严重后果。目前, 关于生态系统功能的研究, 忽略了土壤和微生物碳氮养分循环过程对地上生态系统功能(AEF)的重要驱动作用, 而土壤碳氮要素和微生物的任何变化都有可能改变地下群落对生态系统功能的维持作用。该研究旨在探究高寒草地AEF的主要控制因子, 以及其关键要素对AEF的作用机理。2015年7-8月, 对青藏高原地区115个样点进行了草地群落和土壤属性等要素样带调查; 综合植物地上生物量, 叶片碳、氮和磷含量等参数计算AEF值, 分析地下土壤有机碳含量、全氮含量、生物量等关键要素对AEF值的影响。结合取样点年降水量和年平均气温, 深入探讨影响AEF的主要控制因子和作用机理。结果表明降水对AEF有较大影响, 而气温影响相对较低。年降水量、土壤微生物生物量碳含量和干旱指数对AEF值的相对重要性贡献较高(重要值分别为21.1%、10.9%和10.1%), 控制青藏高原高寒草地AEF值的关键是土壤因子。在气候因子对土壤养分和微生物的作用下, 土壤微生物生物量氮含量在调控高寒草地AEF值方面发挥重要作用。     

Estimating herbaceous aboveground biomass in Sahelian rangelands using Structure from Motion data collected on the ground and by UAV

Herbaceous aboveground biomass (HAB) is a key indicator of grassland vegetation and indirect estimation tools, such as remote sensing imagery, increase the potential for covering larger areas in a timely and cost‐efficient way. Structure from Motion (SfM) is an image analysis process that can create a variety of 3D spatial models as well as 2D orthomosaics from a set of images. Computed from Unmanned Aerial Vehicle (UAV) and ground camera measurements, the SfM potential to estimate the herbaceous aboveground biomass in Sahelian rangelands was tested in this study. Both UAV and ground camera recordings were used at three different scales: temporal, landscape, and national (across Senegal). All images were processed using PIX4D software (photogrammetry software) and were used to extract vegetation indices and heights. A random forest algorithm was used to estimate the HAB and the average estimation errors were around 150 g m⁻² for fresh mass (20% relative error) and 60 g m⁻² for dry mass (around 25% error). A comparison between different datasets revealed that the estimates based on camera data were slightly more accurate than those from UAV data. It was also found that combining datasets across scales for the same type of tool (UAV or camera) could be a useful option for monitoring HAB in Sahelian rangelands or in other grassy ecosystems.     

Functional dominance rather than taxonomic diversity and functional diversity mainly affects community aboveground biomass in the Inner Mongolia grassland

The relationship between biodiversity and productivity has been a hot topic in ecology. However, the relative importance of taxonomic diversity and functional characteristics (including functional dominance and functional diversity) in maintaining community productivity and the underlying mechanisms (including selection and complementarity effects) of the relationship between diversity and community productivity have been widely controversial. In this study, 194 sites were surveyed in five grassland types along a precipitation gradient in the Inner Mongolia grassland of China. The relationships between taxonomic diversity (species richness and the Shannon–Weaver index), functional dominance (the community‐weighted mean of four plant traits), functional diversity (Rao's quadratic entropy), and community aboveground biomass were analyzed. The results showed that (1) taxonomic diversity, functional dominance, functional diversity, and community aboveground biomass all increased from low to high precipitation grassland types; (2) there were significant positive linear relationships between taxonomic diversity, functional dominance, functional diversity, and community aboveground biomass; (3) the effect of functional characteristics on community aboveground biomass is greater than that of taxonomic diversity; and (4) community aboveground biomass depends on the community‐weighted mean plant height, which explained 57.1% of the variation in the community aboveground biomass. Our results suggested that functional dominance rather than taxonomic diversity and functional diversity mainly determines community productivity and that the selection effect plays a dominant role in maintaining the relationship between biodiversity and community productivity in the Inner Mongolia grassland.