植物生物量与其生存竞争能力、地球物质能力循环等密切相关，是研究生态系统功能的基础与重要参数。过去100多年间，有关生物量的研究主要关注森林生物量的核算，并基于大量实测数据构建了各类树种的材积表和生物量方程，但是由于干旱区位置偏远、面积宽阔、植被稀疏、环境恶劣、区域关注度低等因素显著，一直没有效便捷的方法评估干旱区灌木生物量。有关灌木生物量的研究仍停留在物种或个别区域层次，为进一步提升干旱区灌木群落生物量估算速度及精度，本研究基于前人研究成果，以我国干旱、半干旱区常见灌木（半灌木）样地调查数据为基础，以单株形态结构变化特征作为切入点，将干旱区灌木分为高大直立茎灌木、高大丛生灌木、低矮丛生灌木、低矮丛生半灌木、低矮垫状灌木五类，期望通过对不同形态灌木生物量分布异同的比较分析，构建能够估算干旱区灌木（半灌木）地上生物量的统一生物量评估模型，并尝试与无人机影像结合，推动实现干旱区地上生物量快速估算。主要研究结果如下：
（1）灌木在生长发育过程中，冠幅长度与冠幅宽度协同生长，二者具有极强的线性相关关系。但由于干旱区多风沙，灌木经常受到风沙的影响或者其他环境因素的影响，因此造成单株灌木冠幅垂直投影多呈椭圆形。可能受干旱、风沙等因子影响，植株纵向和横向生产策略不同，导致植株高度与冠幅长、宽线性相关关系弱于冠幅长、宽。
（2）33种灌木种有17种冠幅长度、冠幅宽度、植株高度、冠幅面积、植株体积与地上生物量均呈现显著的相关关系（p
<0.05）。受植株退化、沙埋及样品偏少影响，黑果枸杞、松叶猪毛菜、细枝盐爪爪、蓍状亚菊、珍珠猪毛菜、短叶假木贼、绵刺7种灌木的形态参数与地上生物量无关（>0.05）；膜果麻黄地上生物量仅与冠层宽度呈现交较弱的相关关系（R
2=0.18, p<0.05）；蒙古扁桃、沙木蓼、荒漠锦鸡儿、白沙蒿、灌木亚菊、驼绒藜、盐穗木、中亚紫菀木、猫头刺等9种灌木，6个形态参数中存在1-4个形态参数与地上生物量无关（p>0.05）。对五类灌木地上生物量及植株对应形态结构参数取自然对数，重新进行线性拟合。结果显示，冠幅长度分别解释了五类灌木地上生物量变化的48%-81%（P<0.001）；冠幅宽度分别解释了五类灌木地上生物量变化的39%-85%（P<0.001）；植株高度分别解释了五类灌木地上生物量变化的28%-70%（P<0.001）；冠幅面积分别解释了五类灌木地上生物量变化的44%-85%（P<0.001）；植株体积分别解释了五类灌木地上生物量变化的48%-89%（P<0.001）。从线性回归可以明显看出，自然对数处理后，形态参数能更好的反映地上生物量变化。代表灌木平面水平结构的冠幅长度、宽度、及冠幅面积之间极强的共变规律，而代表垂直结构的植株高度与冠幅长度、宽度、及冠幅面积耦合关系相对更弱。在预测地上生物量方面，植株高度与冠幅长度、宽度、及冠幅面积也具有显著的差异，植株高度解释灌木地上生物量变化程度最低（解释度为37%）。对于干旱区灌木，可以选用一元一次线性模型及植株平面水平结构冠幅面积为预测干旱区灌木地上生物量的模型及预备参数。高大直立径茎？灌木地上生物量模型为：Y=e
2.83Lnx+7.62（R
2=0.85，SEE=362.61，P＜0.001）；高大丛生灌木地上生物量模型为：Y=e
1.72Lnx+6.28（R
2=0.60，SEE=84.76，P＜0.001）；低矮丛生灌木地上生物量模型为：Y=e
1.96Lnx+6.84（R
2=0.68，SEE=9.44，P＜0.001）；低矮丛生半灌木地上生物量模型为：Y=e
1.41Lnx+6.37（R
2=0.46，SEE=9.44，P＜0.001）；低矮垫状灌木地上生物量模型为：Y=e
1.73Lnx+6.86（R
2=0.71，SEE=137.31，P＜0.001）；干旱区灌木地上生物量模型为：Y=e
0.83Lnx+6.26（R
2=0.61，SEE=305.91，P＜0.001）。
（3）由于灌丛沙埋后，单株形态与地上生物量分布均存在较大的变异，因此我们专门对灌丛沙包生物量及其预测进行了研究。灌丛沙包广泛分布于西北干旱区，本研究以干旱区常见的白刺属植物灌丛沙包为研究对象，通过样地收集和文献数据整理，获取45个唐古特白刺和21泡泡刺灌丛沙包形态及生物量数据，构建白刺属植物灌丛沙包生物量预测模型。结果表明：灌丛沙包植物总生物量每增加1kg，沙包上部、沙包内部、沙包下部的生物量及枝叶生物量、根生物量分别增加0.31kg、0.57kg、0.12kg、0.44kg和0.56kg。植株高度变化能够解释各组件生物量及总生物量变化的76%–88%。沙包长度变化能够解释各组件生物量及总生物量变化的76%–92%。植株高度和沙包长度联合能够解释各组件生物量及总生物量变化的89%–94%。随灌丛沙包高度的增加，沙包上部生物量占比呈现减少，沙包内生物量占比明显增加，沙包下部生物量占比没有明显的变化趋势。另外，随沙包高度增加，沙包内部根系的迅速增加是白刺属植物根生物量占比呈增加的主要原因。白刺属植物灌丛沙包各组件生物量和总生物量之间存在极显著相关关系(P<0.01)，这种关系可用于相互间的预测，进而可以实现野外跟踪监测。
（4）为进一步提升干旱区灌木群落生物量估算速度，本研究把干旱区灌木（半灌木）地上生物量的统一生物量评估模型与无人机影像结合，快速估算干旱区三个样地的灌木地上生物量。研究结果为：一号样地植株数量为？，地上生物量为31.4g/m2，二号样地植株数量为，地上生物量为24.1g/m2，三号样地植株数量为，地上生物量为54.1g/m2。
Abstract
Plant biomass, which is closely related to the survival, competitiveness and the circulation of the earth's material capacity, is the basis and important parameter for the study of ecosystem functions. In the past 100 years, most studies focused on forest biomass, and constructed the volume tables and biomass equations of various species based on a large number of measured data, However, due to the obvious factors such as remote location, wide area, sparse vegetation, harsh environment and low regional attention, there has been no effective and convenient method to evaluate shrub biomass in arid areas. Studies of shrub biomass, remains in species or specific regional level .In order to improve the arid shrub community biomass estimation accuracy and speed, based on the predecessors' research results, this study in dry area or half arid areas in our country common shrub (subshrub) sample plot survey data as the foundation, use plant morphological structure characteristics as the breakthrough point, the model is expected to estimate the arid shrub (subshrub) the unity of the aboveground biomass of biomass assessment model, and try to combine with uav image, promote rapid arid the ground biomass estimation. The main research results are as follows:
（1）According to the linear fitting characteristics of 33 shrub morphological parameters, in the growth and development process of shrubs, there is no single growth of crown length or crown width, but the co-growth and development of crown length and crown width. However, due to the windy sand in the arid area, shrubs are often affected by wind sand or other environmental factors, so the crown length and crown width of a single shrub cannot grow as the synergistic growth of 1. In arid regions, when plant height was restricted by limiting factors, plants changed their growth strategy and began to grow laterally. Therefore, the synergistic growth of growing crown length and crown width was greater than that of growing crown length, crown width and plant height.
（2）7 of 33 shrub species have significant correlations between canopy length, canopy width, plant height, canopy area, plant volume and aboveground biomass (P＜0.001). Lycium ruthenicum Murr.、Salsola laricifolia Turcz. ex Litv.、Kalidium gracile Fenzl、Ajania achilleoides Turcz.、Salsola passerina Bunge、Anabasis brevifolia C.A.Mey.、Potaninia mongolica Maxim.this 7 shrubs’ canopy length, canopy width, plant height, canopy area and plant volume were not related to aboveground biomass (P＞0.05). Ephedra przewalskii Stapf aboveground biomass was only weakly correlated with canopy width (R2=0.18, p<0.05). Amygdalus mongolica (Maxim.) Ricker atraphaxis bracteata A. Los.Caragana roborovskyi Kom.Artemisia sphaerocephala Krasch. Ajania fruticulosa (Ledeb.) Poljakov Ajania fruticulosa (Ledeb.) Poljakov Halostachys caspica C.A.Mey.ex Schrenk Asterothamnus centraliasiaticus Novopokr. Oxytropis aciphylla Ledeb., this 9 shrubs among the 5 morphological parameters, 1-4 morphological parameters were not related to aboveground biomass (P＞0.05). The aboveground biomass and the corresponding morphological and structural parameters of the five shrubs were calculated by natural logarithm and linear fitting was performed again.The results showed that crown length explained48%-81% （P<0.001）of the variation of aboveground biomass of the five shrubs，respectively ；Crown width explained 39% to 85%（P<0.001）of the variation in aboveground biomass of the five shrubs, respectively; Plant height accounted for 28%-70%（P<0.001）of the variation in aboveground biomass of the five shrubs, respectively;The canopy area accounted for 44% to 85% （P<0.001）of the variation in aboveground biomass of the five shrubs, respectively; Plant volume accounted for 48%-89%（P<0.001）of the variation in above-ground biomass of the five shrubs, respectively.It can be seen from linear regression that the morphological parameters can better reflect the aboveground biomass after natural logarithm treatment.The relationship between canopy length, canopy width and canopy area of the horizontal shrub plane was very strong, while the relationship between canopy height and canopy length, canopy width and canopy area of the vertical shrub structure was relatively weak.In predicting aboveground biomass, plant height was significantly different from the length, width and area of canopy, and plant height was the least explicable 37% of aboveground biomass. For shrubs in arid area, a one-dimensional linear model and canopy area of plant plane structure can be used as models and preliminary parameters to predict the above-ground biomass of shrubs in arid area.The aboveground biomass model of tall and erect stem shrub is as follows: Y=e2.83Lnx+7.62（R2=0.85，SEE=362.61，P＜0.001）； the aboveground biomass model of tall and tuft shrub is as follows: Y=e1.72Lnx+6.28（R2=0.60，SEE=84.76，P＜0.001）；The aboveground biomass model of short and tuft shrub is as follows: Y=e1.96Lnx+6.84（R2=0.68，SEE=9.44，P＜0.001）；The aboveground biomass model of short and tuft subshrub is as follows: Y=e1.41Lnx+6.37（R2=0.46，SEE=9.44，P＜0.001）；The aboveground biomass model of short and cushion shrub is as follows: Y=e1.73Lnx+6.86（R2=0.71，SEE=137.31，P＜0.001）；the unified prediction model of shrub aboeground biomass in arid area is as follows: Y=e0.83Lnx+6.26（R2=0.61，SEE=305.91，P＜0.001）。
（3）As the main vegetation types in desert ecosystems and an important potential carbon sink, shrub plays a vital role in the carbon cycle of desert ecosystem. Nitraria tangoturum Bob. were the most common shrub type in deserts which had the function of preventing wind and fixing sand. Harvest and literature collection method were used and each part biomass of shrub was measured in field. Our objectives were to derive appropriate regression models for shrub biomass estimation, and to reveal the biomass allocation pattern in N. tangutorum. Based on the analysis of 66 shrub nebkha morphological and biomass data of N. tangutorum, It was found that the biomass of the upper part, the inner part and the lower part of the nebkha, the branch and leaf biomass and the root biomass increased by 0.31kg, 0.57kg, 0.12kg, 0.44kg and 0.56kg, respectively as the total biomass increased 1kg. The change of plant height could explain 76-88% of the total biomass and each component biomass of the nebkha. The change of nebkha length could explain 76-92% of the total biomass and each component biomass of the shrub sand dunes. The combination of plant height and nebkha length could account for 89 -94% of the total biomass and each component biomass of the nebkha. With the increase of nebkha height, the proportion of plant biomass above the sand dune decreased, the proportion of biomass inside the nebkha increased, but the proportion of biomass under the sand dune did not change significantly. In addition, the proportion increase of the root biomass was attributed to the root increase inside the sand dune. There were significant correlations between each part biomass and total biomass of N. tangutorum(P<0.01). This relationship can be used to predict each other, thus achieving field tracking monitor.
（4）In order to further improve the estimation speed and accuracy of shrub community biomass in arid areas, based on previous research results and survey data of common shrub (sub-shrub) sample plots in arid and semi-arid areas of China, this study constructed a unified biomass assessment model that could estimate the above-ground biomass of shrub (sub-shrub) communities in arid areas.In addition, the unified biomass assessment model of shrub (sub-shrub) above-ground biomass in arid area was combined with UAV image to rapidly estimate the above-ground biomass of shrub in arid area, which saved the time and labor cost of measuring shrub morphological parameters in the field.The results showed that the aboveground biomass of sample 1 was 31.4g/m2, that of sample 2 was 24.1g/m2, and that of sample 3 was 54.1g/m2.
Key words: Arid zone, Shrub, Morphological Parameters, Biomass, Model
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