我国滨海湿地生态修复领域规范的现状与分析

滨海湿地是地球上十分重要的一类生态系统, 可为人类社会提供诸如调节气候、降解污染、碳汇氮汇等众多生态服务功能。近年来, 由于气候变化和围填海等开发活动的影响, 我国滨海湿地面积锐减, 功能退化, 面临多种生态问题。为应对滨海湿地退化及日益凸显的生态环境问题, 滨海湿地保护和修复工作逐渐受到重视, 滨海湿地生态修复工程项目的数量和规模也随之急剧增加。滨海湿地生态修复技术规范对滨海湿地生态修复工作具有重要意义, 然而我国滨海湿地生态修复领域规范体系尚不完善, 导致滨海湿地生态修复项目实施无序以及生态修复成效低等问题。本文全面搜集了我国滨海湿地生态修复领域规范, 对检索到的规范进行分类统计与分析, 从而揭示其存在的问题, 主要包括规范体系系统性不足、生态修复理念滞后、规范之间缺乏协调性、规范可操作性差、规范更新迟滞等。针对存在的问题提出了相应的对策建议, 为完善我国滨海湿地生态修复领域规范体系、不断推进我国生态文明建设进程提供参考。     

伏牛山地区森林生态系统服务权衡/协同效应多尺度分析

森林生态系统服务权衡与协同研究已成为当前相关学科的研究热点和前沿,对服务权衡与协同关系的多尺度分析有助于更加有效地实施森林资源管理。综合森林类型图、NDVI、气象和土壤等多源数据,借助CASA模型、InVEST 3.2模型和ArcGIS 10.2软件,开展伏牛山地区森林生态系统服务评估,运用空间叠置方法从多个空间尺度（区域、南北坡、垂直带）探讨服务权衡与协同效应。结果表明：① 研究区森林生态系统平均蓄积量为49.26 m ³/hm ²,碳密度为156.94 t/hm ²,供水深度为494.46 mm,土壤保持量为955.4 t/hm ²,生境质量指数为0.79。② 区域尺度上,28.79%的森林服务之间存在高协同效应,10.15%的森林存在低协同效应,61.06%的森林存在强权衡和弱权衡效应。③ 南北坡尺度上,南坡服务之间的协同关系优于北坡。垂直带尺度上,南坡中山落叶阔叶林带（SIII）服务之间协同关系最好,北坡低山落叶阔叶林带（NI）协同关系最差。     

中国草畜平衡状态时空演变指示的草地生态保护格局

中国草原牧区作为重要生态安全屏障和草地畜牧业生产基地,其草畜平衡状态直接影响草地退化与恢复,进而影响草地生态系统服务能力的强弱。本文分析了2000—2015年主要草原牧区草地植被覆盖、牧草供给、草畜平衡状态的时空变化特征,深入探讨草地退化与恢复及载畜压力下草地生态系统保护与恢复空间格局。结果表明：过去16年主要草原牧区草地面积净减少约163万hm²,6.7%的草地出现植被覆盖退化,而5.4%的草地呈现植被覆盖明显恢复。天然草地牧草供给量以增加为主,年增率约0.3 kg/hm²,然而其载畜压力亦持续增加,不考虑补饲的载畜压力指数高达3.8,除内蒙古东北部、青藏高原中部仍有载畜潜力,其余多处于超载状态;考虑实际冷季补饲的载畜压力指数约3.1,内蒙古中东部有所缓解;假设冷季全额补饲则载畜压力指数减至1.9,内蒙古、青藏高原等区域明显缓解。叠加上述数据,本文针对自然保护地、牧区、半农半牧区和农区等不同区域的草地生态保护格局,提出了平衡草地生态保护与畜牧生产利用的不同发展策略。     

生态系统科学研究与生态系统管理

中国科学院地理科学与资源研究所生态系统学科以生态系统生态学研究为核心,通过研制生态系统观测和模拟分析的技术和方法,探索解决区域性/大尺度生态学问题的理论和方法,监测生态系统变化,认知生态系统变化规律,推动生态系统生态学、生物地理生态学、全球变化生态学和生态信息科学技术的发展,创新生态系统管理模式,服务于国家和地方的生态建设、应对全球变化及区域可持续发展。面向国家重大需求,在中国华北平原农业区、南方红壤丘陵林业区、青藏高原农牧区以及黄土高原区等典型区域开展生态系统管理技术与模式的集成与创新研究,着力解决国家生态文明建设和应对全球气候变化中的重大生态学问题,推动区域生态系统管理领域的科技进步。围绕生态系统生态学学科前沿,着重在① 生态系统联网观测、模拟与信息管理,② 生态系统结构、过程与功能,③ 生态系统空间格局与机制,④ 生态系统对全球变化的响应与适应,⑤ 生态系统管理与生态系统服务等五大主要研究方向,系统开展生态系统生态学前沿理论和实践的创新研究,研究成果处于国内和国际生态学研究的科学前沿。     

Wall-to-wall spatial prediction of growing stock volume based on Italian National Forest Inventory plots and remotely sensed data

Spatial predictions of forest variables are required for supporting modern national and sub-national forest planning strategies, especially in the framework of a climate change scenario. Nowadays methods for constructing wall-to-wall maps and calculating small-area estimates of forest parameters are becoming essential components of most advanced National Forest Inventory (NFI) programs. Such methods are based on the assumption of a relationship between the forest variables and predictor variables that are available for the entire forest area. Many commonly used predictors are based on data obtained from active or passive remote sensing technologies. Italy has almost 40% of its land area covered by forests. Because of the great diversity of Italian forests with respect to composition, structure and management and underlying climatic, morphological and soil conditions, a relevant question is whether methods successfully used in less complex temperate and boreal forests may be applied successfully at country level in Italy.For a study area of more than 48,657 km² in central Italy of which 43% is covered by forest, the study presents the results of a test regarding wall-to-wall, spatially explicit estimation of forest growing stock volume (GSV) based on field measurement of 1350 plots during the last Italian NFI. For the same area, we used potential predictor variables that are available across the whole of Italy: cloud-free mosaics of multispectral optical satellite imagery (Landsat 5 TM), microwave sensor data (JAXA PALSAR), a canopy height model (CHM) from satellite LiDAR, and auxiliary variables from climate, temperature and precipitation maps, soil maps, and a digital terrain model.Two non-parametric (random forests and k-NN) and two parametric (multiple linear regression and geographically weighted regression) prediction methods were tested to produce wall-to-wall map of growing stock volume at 23-m resolution. Pixel level predictions were used to produce small-area, province-level model-assisted estimates. The performances of all the methods were compared in terms of percent root mean-square error using a leave-one-out procedure and an independent dataset was used for validation. Results were comparable to those available for other ecological regions using similar predictors, but random forests produced the most accurate results with a pixel level R² = 0.69 and RMSE_% = 37.2% against the independent validation dataset. Model-assisted estimates were more precise than the original design-based estimates provided by the NFI.     

Tree species identification within an extensive forest area with diverse management regimes using airborne hyperspectral data

Information on tree species composition is crucial in forest management and can be obtained using remote sensing. While the topic has been addressed frequently over the last years, the remote sensing-based identification of tree species across wide and complex forest areas is still sparse in the literature. Our study presents a tree species classification of a large fraction of the Białowieża Forest in Poland covering 62 000 ha and being subject to diverse management regimes. Key objectives were to obtain an accurate tree species map and to examine if the prevalent management strategy influences the classification results. Tree species classification was conducted based on airborne hyperspectral HySpex data. We applied an iterative Support Vector Machine classification and obtained a thematic map of 7 individual tree species (birch, oak, hornbeam, lime, alder, pine, spruce) and an additional class containing other broadleaves. Generally, the more heterogeneous the area was, the more errors we observed in the classification results. Managed forests were classified more accurately than reserves. Our findings indicate that mapping dominant tree species with airborne hyperspectral data can be accomplished also over large areas and that forest management and its effects on forest structure has an influence on classification accuracies and should be actively considered when progressing towards operational mapping of tree species composition.     

The effect of leaf-on and leaf-off forest canopy conditions on LiDAR derived estimations of forest structural diversity

Forest structural diversity metrics describing diversity in tree size and crown shape within forest stands can be used as indicators of biodiversity. These diversity metrics can be generated using airborne laser scanning (LiDAR) data to provide a rapid and cost effective alternative to ground-based inspection. Measures of tree height derived from LiDAR can be significantly affected by the canopy conditions at the time of data collection, in particular whether the canopy is under leaf-on or leaf-off conditions, but there have been no studies of the effects on structural diversity metrics. The aim of this research is to assess whether leaf-on/leaf-off changes in canopy conditions during LiDAR data collection affect the accuracy of calculated forest structural diversity metrics. We undertook a quantitative analysis of LiDAR ground detection and return height, and return height diversity from two airborne laser scanning surveys collected under leaf-on and leaf-off conditions to assess initial dataset differences. LiDAR data were then regressed against field-derived tree size diversity measurements using diversity metrics from each LiDAR dataset in isolation and, where appropriate, a mixture of the two. Models utilising leaf-off LiDAR diversity variables described DBH diversity, crown length diversity and crown width diversity more successfully than leaf-on (leaf-on models resulted in R² values of 0.66, 0.38 and 0.16, respectively, and leaf-off models 0.67, 0.37 and 0.23, respectively). When LiDAR datasets were combined into one model to describe tree height diversity and DBH diversity the models described 75% and 69% of the variance (R² of 0.75 for tree height diversity and 0.69 for DBH diversity). The results suggest that tree height diversity models derived from airborne LiDAR, collected (and where appropriate combined) under any seasonal conditions, can be used to differentiate between simple single and diverse multiple storey forest structure with confidence.     

近30年长江口海域生态系统健康状况及变化趋势研究

为评估长江口海域生态系统健康状况,本文以鱼类浮游生物为指示生物,选择1986年、1999年、2007年和2016年4个年份的数据,选取鱼类浮游生物总种类数、底栖鱼种类数、水层鱼种类数、低耐污鱼种类数、高耐污鱼种类数百分比、杂食性鱼种类数百分比、虫食性鱼种类数百分比、肉食性鱼种类数百分比、鱼类取样个体数、天然杂交种种类数百分比等10个评价指标,采用1、3、5赋值法计算4个不同年份的河口生物完整性指数(Estuarine Biotic Integrity Index,EBI),并以此评价近30年长江口海域生态系统健康状况变化趋势。结果显示,1986年、1999年、2007年和2016年4个不同年份的EBI值分别是52、40、36、34,对应的EBI等级分别为“好”、“一般”、“一般?差”、“差”。通过分析EBI年际变化的结果,发现近30年来长江口海域生态系统健康状况呈现先下降,而后稳定在较低水平的趋势,说明长江口海域生态系统亟需及时进行保护和修复工作。     

克拉玛依市土地利用规划实施的生态效果研究

对土地利用规划实施带来的环境影响的评价属于战略环境评价范畴的回顾性评价,借助反映生态效应的生态系统服务价值的变化可以定量描述土地利用规划通过土地利用对环境带来的影响。论文采用干旱区生态资产价值核算技术和方法,估算了规划基期、近期和规划实施评价期的生态资产价值,不同时期生态资产价值的变动趋势表明区域环境因规划实施得到改善。本文最后讨论了利用生态资产价值核算技术筛选环境可行的规划备选方案的局限性和存在的问题。     

关于海洋生态系统服务的经济属性研究

当前,海洋生态系统的服务价值日益受到科学界的广泛重视。从环境经济学的相关理论出发,深入探讨了海洋生态系统服务的经济属性,以期为海洋生态服务资本的保值与增殖提供理论依据。