Research on characteristics of biomass distribution in urban forests of Shanghai metropolis based on remote sensing and spatial analysis

Aims
Monitoring and quantifying the biomass and its distribution in urban trees and forests are crucial to understanding the role of vegetation in an urban environment. In this paper, an estimation method for biomass of urban forests was developed for the Shanghai metropolis, China, based on spatial analysis and a wide variety of data from field inventory and remote sensing.
Methods
An optimal regression model between forest biomass and auxiliary variables was established by stepwise regression analysis. The residual value of regression model was computed for each of the sites sampled and interpolated by Inverse-distance weighting (IDW) to predict residual errors of other sites not subjected to sampling. Forest biomass in the study area was estimated by combining the regression model based on remote sensing image data and residual errors of spatial distribution map. According to the distribution of plantations and management practices, a total of 93 sample plots were established between June 2011 and June 2012 in the Shanghai metropolis. To determine a suitable model, several spectral vegetation indices relating to forest biomass and structure such as normalized difference vegetation index (NDVI), ratio vegetation index (RVI), difference vegetation index (DVI), soil-adjusted vegetation index (SAVI), and modified soil-adjusted vegetation index (MSAVI), and new images synthesized through band combinations such as the sum of TM2, TM3 and TM4 (denoted Band 234), and the sum of TM3, TM4 and TM5 (denoted Band 345) were used as alternative auxiliary parameters .
Important findings
The biomass density in urban forests of the Shanghai metropolis varied from 15 to 120 t·hm^-2. The higher densities of forest biomass concentrated mostly in the urban areas, e.g. in districts of Jing’an and Huangpu, mostly ranging from 35 to 70 t·hm^-2. Suburban localities such as the districts of Jiading and Qingpu had lower biomass densities at around 15 to 50 t·hm^-2. The biomass density of Cinnamomum camphora trees across the Shanghai metropolis varied between 20 and 110 t·hm^-2. The spatial biomass distribution of urban forests displayed a tendency of higher densities in northeastern areas and lower densities in southwestern areas. The total biomass was 3.57 million tons (Tg) for urban forests and 1.33 Tg for C. camphora trees. The overall forest biomass was also found to be distributed mostly in the suburban areas with a fraction of 93.9%, whereas the urban areas shared a fraction of only 6.1%. In terms of the areas, the suburban and urban forests accounted for 95.44% and 4.56%, respectively, of the total areas in the Shanghai metropolis. Among all the administrative districts, the Chongming county and the new district of Pudong had the highest and the second highest biomass, accounting for 20.1% and 19.18% of the total forest biomass, respectively. In contrast, the Jing’an district accounted for only 0.11% of the total forest biomass. The root-mean-square error (RMSE), mean absolute error (MAE) and mean relative error (MRE) of the model for estimating urban forest biomass in this study were 8.39, 6.86 and 24.22%, respectively, decreasing by 57.69%, 55.43% and 64.00% compared to the original simple regression model and by 62.21%, 58.50%, 65.40% compared to the spatial analysis method. Our results indicated that a more efficient way to estimate urban forest biomass in the Shanghai metropolis might be achieved by combining spatial analysis with regression analysis. In fact, the estimated results based on the proposed model are also more comparable to the up-scaled forest inventory data at a city scale than the results obtained using regression analysis or spatial analysis alone.     

中国森林凋落量时空分布特征

通过构建全国尺度上森林叶凋落量和总凋落量与年平均温度、年平均降雨量和实际蒸散量的关系模型,应用地统计学分析方法,并结合遥感解译的森林覆盖信息,分析2001、2006和2012年中国森林叶凋落量和总凋落量的空间分布及其变异格局.结果表明: 与年平均温度和降雨量相比,区域尺度上实际蒸散量对森林叶凋落量和总凋落量有更好的解释预测关系;分布于中南地区的常绿阔叶林的年凋落量最高,为636.2 g·m^-2,分布于东北地区的温带落叶阔叶林的年凋落量范围为339～385 g·m^-2,其中,森林叶凋落量约占总凋落量的70％.2001、2006和2012年,全国森林凋落总量分别为801、865和1032 Tg,呈明显增长趋势,而基于遥感图像解译的森林年覆盖率分别为18％、20％和24％,呈增长趋势.我国人工林面积的迅速增加和林分生长,极大影响了森林凋落物量及其动态变化,进而对森林生态系统物质循环产生重要影响.     

中国巨蚓科蚯蚓的起源与演化

截至2017年中国已记录巨蚓科蚯蚓579种(亚种), 优势类群为远盲蚓属(Amynthas)与腔蚓属(Metaphire), 其丰富的物种多样性值得深入研究。近年来的研究指出联合形态分类与分子系统发育可较好地探讨中国巨蚓科蚯蚓的起源、分化与扩散。本文概述了中国巨蚓科蚯蚓物种的起源、分化时间以及扩散历程, 探讨了受精囊孔、盲肠形状等重要特征的演化, 分析了现行分类系统的缺陷。多项研究表明, 中国巨蚓科主要类群的祖先起源于中南半岛, 于白垩纪末期至新生代初期进入中国, 在新生代得以繁荣发展; 受精囊孔的对数或位置是多起源的, 盲肠形状演化中有祖征重现的现象, 故使用少数形态特征进行类群划分的现行分类系统有待改进。此外, 中国巨蚓科蚯蚓的具体演化机制及影响因素仍不明确。因此, 在未来研究中整合形态特征、分子数据与地理格局、地质历史及环境因子等信息, 定量分析类群演化与古地理、生物与非生物因素间的关系, 将有助于全面厘清中国巨蚓科蚯蚓演化的具体机制。     

基于遥感和空间分析的上海城市森林生物量分布特征

城市森林发挥着改善和维护城市生态环境质量的作用, 研究城市森林生物量和分布特点对其生态系统服务评价和林分经营均具有重要意义。该文根据上海城市森林的种植分布和经营状况利用2011年6月-2012年6月样地实测森林生物量数据和同期Landsat ETM+遥感图像, 在基于逐步回归分析建立森林生物量反演模型的基础上, 引入回归残差及空间分析, 研究了城市森林及其主要优势树种樟(Cinnamomum camphora)林分的生物量分布特征, 探讨了区域尺度森林生物量的遥感估测方法。结果表明: (1)上海城市森林生物量密度总体呈现中心城区(静安区、黄浦区等)较高, 生物量密度集中在35-70 t·hm^-2之间, 郊区(嘉定区、青浦区等)空间分布状况相对较低, 生物量密度介于15-50 t·hm^-2之间的变化特征。上海优势树种樟林分生物量密度范围为20-110 t·hm^-2; 空间上呈现出东北部较高、西南部较低的变化特征。(2)上海城市森林及樟林分的生物量总量分别为3.57 Tg和1.33 Tg。林地面积小, 具有较高森林生物量密度的上海中心城区, 其森林生物量占总量的6.1%, 其中林地面积最小的静安区生物量最低, 仅占总量的0.11%。在所有区县中, 林地面积最大的崇明县、浦东新区具有较高的森林生物量, 分别占总量的20.08%和19.18%。(3)所建立的基于回归反距离插值的城市森林生物量估测模型, 其标准误差、平均绝对误差、平均相对误差分别为8.39、6.86、24.22%, 较回归模型分别降低了57.69%、55.43%、64.00%, 较空间插值的方法分别降低了62.21%、58.50%、65.40%。残差的引入减少了由于空间变异引发的城市森林生物量遥感估测的不确定性。相比基于实测数据通过空间插值的估测, 遥感为快速便捷、客观高效的森林生物量监测提供了可能, 更加完善的结果和模型的优化有待引入其他信息源如高分高光谱信息或改善残差空间分析方法获得。