新疆喀纳斯国家自然保护区植被叶面积指数观测与遥感估算

叶面积指数(Leaf Area Index,LAI)是重要的植被结构参数,调控着植被与大气之间的物质与能量交换,在生态环境脆弱的我国西北部开展植被LAI的研究对阐明该地区植被对气候变化和人类活动的响应特征具有重要的科学意义.利用LAI-2200和TRAC仪器观测了新疆喀纳斯国家级自然保护区森林和草地的有效叶面积指数(LAIe)和真实LAI,构建了其遥感估算模型,生成了研究区LAIe和LAI的空间分布图.在此基础上,分析了LAI随地形因子(海拔、坡度、坡向)的变化特征,探讨了将其应用于估算研究区森林生物量密度的可行性,并评估了研究区MODIS LAI产品的精度.结果表明:研究区阔叶林、针阔混交林、针叶林、草地LAIe的平均值分别为4.40、3.18、2.57、1.76,LAI的平均值分别为4.76、3.93、3.27、2.30.LAIe和LAI的高值主要集中分布在湖泊和河流附近;植被LAI随海拔、坡度和坡向的变化表现出明显的垂直地带性的特点.LAI随海拔和坡度的增加呈现先增加后减小的变化趋势,坡向对针叶林和草地LAI的影响明显,但对阔叶林和针阔混交林LAI的影响较弱;森林生物量密度(BD)随LAI增加而线性增加(BD=44.396LAI-25.946,R2=0.83),研究区森林生物量密度平均值为120.3 t/hm2,估算的总生物量为5.0×l06 t;MODIS LAI产品与利用TM数据生成的LAI之间具有一定的相似性(森林R2=0.42,草地R2=0.53),但森林和草地的MODIS LAI产品分别比利用TM数据生成的LAI偏低16.5％和24.4％.

Measurement and retrieval of leaf area index using remote sensing data in Kanas National Nature Reserve, Xinjiang

Leaf area index (LAI) is one of the most important vegetation structural parameters in terrestrial ecosystems. It significantly regulates the exchanges of matter and energy between the atmosphere and terrestrial ecosystems due to its effects on many biophysical and physiological processes, including photosynthesis, respiration,transpiration,precipitation interception, energy transfer, and so on. The study on LAI is of scientific importance for assessing the response of vegetation to climate change and human activities in ecologically fragile areas in northwest China. In this paper, effective LAI (LAI_e) and actual LAI of forests and grasslands were measured with the LAI-2200 and TRAC instruments in Kanas National Nature Reserve, Xinjiang Autonomous Region. Based on measurements of LAI_e and LAI at 50 sampling plots, models for estimating LAI_e and LAI from Landsat 5-TM remote sensing data were developed. Then, the changes of LAI with topographic factors (including elevation, slope, and aspect) were analyzed. The possibility of estimating vegetation biomass density based on LAI estimated from TM remote sensing data (TM LAI) was explored. Finally, the quality of the MODIS LAI product in the study area was assessed using TM LAI as the benchmark. The results show that both LAI_e and LAI significantly change with land cover types. Both LAI_e and LAI exponentially change with vegetation indices. The best fitted models for estimating LAI_e and LAI are LAI_e=0.4861e^2.6801ARVI and LAI=0.4162e^3.2706ARVI for needle-leaved forests, LAI_e =2.3405e^0.0611ARVI and LAI=1.5325e^2.174MAVI for broad-leaved forests, LAI_e =0.5575e^2.499ARVI and LAI=0.5675e^2.7732ARVI for mixed forests, LAI_e=0.3627e^4.3037SR and LAI=0.5125e^4.1258ARVI for grasslands, respectively (AVRI is the atmospherically resistant vegetation index, SR is the simple ratio vegetation index, and MAVI is the moisture adjusted vegetation index). The averages of remotely sensed LAI_e of broad-leaved forests, mixed forests, needle-leaved forests, and grasslands are 4.40, 3.18, 2.57, and 1.76, respectively. The corresponding values of LAI are 4.76, 3.93, 3.27, and 2.30, respectively. High values of LAI_e and LAI mainly appears in locations nearby the lakes and rivers. The changes of LAI with altitude, slope and aspect exhibit obviously vertical patterns. LAI tends to increase first and then decrease with the increases in altitude and slope. Aspect has significant influences on the LAI of needle-leaved forests and grasslands, but less influences on LAI of broad-leaved forests and mixed forests. LAI can act as a valuable predictor of forest biomass density (BD) (BD=44.396LAI-25.946, R²=0.83, BD was derived from the forest resources inventory data). The average BD of forests estimated from remotely sensed LAI is 120.3 t/hm² and the estimated total forest biomass is 5.0×10⁶ t in the study area. The re-sampling technique was adopted here for getting 1 km resolution TM LAI data from 30 m TM LAI data of the study area. 1 km MODIS LAI show similar patterns with LAI re-sampled from the 30 m LAI map generated from TM data and measured LAI, with R²=0.42 for forests and R²=0.53 for grasslands. However, MODIS LAI is 16.5% and 24.4% lower than LAI estimated using TM remote sensing data for forests and grasslands.