天山雪岭云杉个体生物量分配及其变化规律的研究

为了探讨天山雪岭云杉林生物量在个体组织中的分配情况及其变化规律，在研究区进行了大量的野外测量，利用已有的雪岭云杉林估算方程，分析了天山雪岭云杉林生物量在各器官（干、枝、叶、皮、根）中的分配及其变化规律。结果表明：（1） 研究区雪岭云杉林的平均生物量为388.74 t·hm^-2，树木各器官中，干、枝、根、叶和皮分别占生物量的43.65%、28.60%、13.49%、11.08%和3.18%。（2）各径级生物量所占百分比为：33.53%（40~50 cm）、20.13%（20~30 cm）、19.59%（30~40 cm）、18.19%（50~60 cm）和2.05%（10~20 cm）；树木生物量在不同树高中的分配表现为：48.78%（20~30 m）＞35.27%（10~20 m）＞14.70%（30~40 m）＞1.25%（0~10 m）；地上和地下生物量的分配比例为：87.54%和12.46%，分别为340.30 t·hm^-2和48.44 t·hm^-2。（3） 随海拔升高，天山雪岭云杉林生物量呈“单峰”变化，在海拔2 100~2 400 m处达到最大值611.58 t·hm^-2；干、皮生物量所占比例随海拔升高而减小，枝生物量逐渐增加，叶、根生物量呈先减小后增加的趋势；径级20~30 cm、30~40 cm和50~60 cm的生物量随海拔升高均呈“单峰型”变化趋势，都在海拔2 100~2 400 m处达到最大；雪岭云杉林不同树高生物量随海拔的升高呈现的趋势不同。天山雪岭云杉林生物量和年均降水量随经纬度的升高均呈降低变化，研究区林分生物量自西向东总体呈现逐渐降低的趋势；林分密度、海拔和降水共同决定了森林生物量的大小及其变化规律，海拔2 100~2 400 m是本研究区雪岭云杉林生长的最适宜场所。结果可为雪岭云杉林生态系统的恢复和重建提供基础资料，对研究区进行综合管理与生态健康分析具有重要意义。     

How forest gaps shaped plant diversity along an elevational gradient in Wolong National Nature Reserve?

Journal of Geographical Sciences - Understanding the underlying ecological processes that control plant diversity within (α-diversity) and among (β-diversity) forest gaps is important for...     

基于机器学习的海洋环境预报订正方法研究

结合中尺度数值模式 WRF 预报数据和 ERA5 再分析资料，利用机器学习方法对 WRF 预报场的风场、温度、气压进行预报订正。采用 ERA5 作为真值，与原始 WRF 预报相比，利用随机森林模型可以将预报结果整体均方根误差降低 44%以 上，利用深度神经网络模型可以将预报结果整体均方根误差降低 34%以上。通过随机森林模型实验得到不同输入特征对预报要素的影响程度，分析了关键的预报订正因子。     

Reach‐scale contributions of road‐surface sediment to the Honna River,Haida Gwaii,BC

Gravel road surfaces can be a major source of fine sediment to streams, yet their contribution to channel reach sediment balances remains poorly documented. To quantify the input of road surface material and to compare this input with natural sediment sources at the reach scale, suspended sediment dynamics was examined and a 16‐month sediment balance was developed for a ~35 channel‐width (approx. 425 m) reach of the Honna River, a medium‐size, road‐affected stream located in coastal British Columbia. Of the 105 ± 33 t of suspended material passing through the reach, 18 ± 6% was attributed to the road surface. The high availability of sediment on the road surface appears to limit hysteresis in road run‐off. During rainstorms that increase streamflow, road surface material composed 0.5–15% of sediment inputs during relatively dry conditions from April to the end of September and 5–70% through wetter conditions from October to the end of March, but our data do not show evidence of major sediment accumulation on the riverbed in the reach. A comparison of modelled sediment production on the road surface with observed yields from drainage channels suggests that (1) during low intensity rainfall, ditches and drainage channels may trap sediment from road run‐off, which is subsequently released during events of greater intensity, and/or (2) production models do not effectively describe processes, such as deposition or erosion of sediment in ditches, which control sediment transport and delivery. Our findings further emphasize the risk of unpaved roads in polluting river systems and highlight the continued need for careful road design and location away from sensitive aquatic environments. Copyright © 2016 John Wiley & Sons, Ltd.     

Simulation of site-scale water fluxes in desert and natural oasis ecosystems of the arid region in Northwest China

The study of water fluxes is important to better understand hydrological cycles in arid regions. Data-driven machine learning models have been recently applied to water flux simulation. Previous studies have built site-scale simulation models of water fluxes for individual sites separately, requiring a large amount of data from each site and significant computation time. For arid areas, there is no consensus as to the optimal model and variable selection method to simulate water fluxes. Using data from seven flux observation sites in the arid region of Northwest China, this study compared the performance of random forest (RF), support vector machine (SVM), back propagation neural network (BPNN), and multiple linear regression (MLR) models in simulating water fluxes. Additionally, the study investigated inter-annual and seasonal variation in water fluxes and the dominant drivers of this variation at different sites. A universal simulation model for water flux was built using the RF approach and key variables as determined by MLR, incorporating data from all sites. Model performance of the SVM algorithm (R² = 0.25–0.90) was slightly worse than that of the RF algorithm (R² = 0.41–0.91); the BPNN algorithm performed poorly in most cases (R² = 0.15–0.88). Similarly, the MLR results were limited and unreliable (R² = 0.00–0.66). Using the universal RF model, annual water fluxes were found to be much higher than the precipitation received at each site, and natural oases showed higher fluxes than desert ecosystems. Water fluxes were highest during the growing season (May–September) and lowest during the non-growing season (October–April). Furthermore, the dominant drivers of water flux variation were various among different sites, but the normalized difference vegetation index (NDVI), soil moisture and soil temperature were important at most sites. This study provides useful insights for simulating water fluxes in desert and oasis ecosystems, understanding patterns of variation and the underlying mechanisms. Besides, these results can make a contribution as the decision-making basis to the water management in desert and oasis ecosystems.     

极化干涉SAR森林树高提取算法的对比分析

在对极化干涉SAR森林树高反演的DEM差值算法、相干相位-幅度综合反演算法进行分析的基础上,对基于极化干涉相干优化方法的改进算法进行了探讨。利用黑龙江大兴安岭地区的一对ALOS全极化干涉SAR数据进行实验,并对比分析各算法的反演结果。结果表明,在使用改进的算法进行森林树高反演时可以获取精度较高的反演结果,并且在一定程度上提高了森林树高反演的稳定性,为森林树高反演工作的业务化运行提供一定的依据。     

Selection and quality assessment of Landsat data for the North American forest dynamics forest history maps of the US

Using the NASA Earth Exchange platform, the North American Forest Dynamics (NAFD) project mapped forest history wall-to-wall, annually for the contiguous US (1986–2010) using the Vegetation Change Tracker algorithm. As with any effort to identify real changes in remotely sensed time-series, data gaps, shifts in seasonality, misregistration, inconsistent radiometry and cloud contamination can be sources of error. We discuss the NAFD image selection and processing stream (NISPS) that was designed to minimize these sources of error. The NISPS image quality assessments highlighted issues with the Landsat archive and metadata including inadequate georegistration, unreliability of the pre-2009 L5 cloud cover assessments algorithm, missing growing-season imagery and paucity of clear views. Assessment maps of Landsat 5–7 image quantities and qualities are presented that offer novel perspectives on the growing-season archive considered for this study. Over 150,000+ Landsat images were considered for the NAFD project. Optimally, one high quality cloud-free image in each year or a total of 12,152 images would be used. However, to accommodate data gaps and cloud/shadow contamination 23,338 images were needed. In 220 specific path-row image years no acceptable images were found resulting in data gaps in the annual national map products.     

Sources of anthropogenic fire ignitions on the peat-swamp landscape in Kalimantan,Indonesia

Fire disturbance in many tropical forests, including peat swamps, has become more frequent and extensive in recent decades. These fires compromise a variety of ecosystem services, among which mitigating global climate change through carbon storage is particularly important for peat swamps. Indonesia holds the largest amount of tropical peat carbon globally, and mean annual CO₂ emissions from decomposition of deforested and drained peatlands and associated fires in Southeast Asia have been estimated at ∼2000 Mt y-1. A key component to understanding and therefore managing fire in the region is identifying the land use/land cover classes associated with fire ignitions. We assess the oft-asserted claim that escaped fires from oil palm concessions and smallholder farms near settlements are the primary sources of fire in a peat-swamp forest area in Central Kalimantan, Indonesia, equivalent to around a third of Kalimantan's total peat area. We use the MODIS Active Fire product from 2000 to 2010 to evaluate the fire origin and spread on the land use/land cover classes of legal, industrial oil palm concessions (the only type of legal concession in the study area), non-forest, and forest, as well as in relation to settlement proximity. We find that most fires (68–71%) originate in non-forest, compared to oil palm concessions (17%–19%), and relatively few (6–9%) are within 5 km of settlements. Moreover, most fires started within oil palm concessions and in close proximity to settlements stay within those boundaries (90% and 88%, respectively), and fires that do escape constitute only a small proportion of all fires on the landscape (2% and 1%, respectively). Similarly, a small proportion of fire detections in forest originate from oil palm concessions (2%) and within close proximity to settlements (2%). However, fire ignition density in oil palm (0.055 ignitions km⁻²) is comparable to that in non-forest (0.060 km-2 ignitions km-²), which is approximately ten times that in forest (0.006 ignitions km⁻²). Ignition density within 5 km of settlements is the highest at 0.125 ignitions km⁻². Furthermore, increased anthropogenic activity in close proximity to oil palm concessions and settlements produces a detectable pattern of fire activity. The number of ignitions decreases exponentially with distance from concessions; the number of ignitions initially increases with distance from settlements, and, around from 7.2 km, then decreases with distance from settlements. These results refute the claim that most fires originate in oil palm concessions, and that fires escaping from oil palm concessions and settlements constitute a major proportion of fires in this study region. However, there is a potential for these land use types to contribute substantially to the fire landscape if their area expands. Effective fire management in this area should therefore target not just oil palm concessions, but also non-forested, degraded areas where ignitions and fires escaping into forest are most likely to occur.     

Validation of MODIS Vegetation Continuous Fields for monitoring deforestation and forest degradation: two cases in Mexico

This study assesses whether MODIS Vegetation Continuous Fields percent tree cover (PTC) data can detect deforestation and forest degradation. To assess the usefulness of PTC for detecting deforestation, we used a data set consisting of eight forest and seven non-forest categories. To evaluate forest degradation, we used data from two temperate forest types in three conservation states: primary (dense), secondary (moderately degraded) and open (heavily degraded) forest. Our results show that PTC can differentiate temperate forest from non-forest categories (p = 0.05) and thus suggests PTC can adequately detect deforestation in temperate forests. In contrast, single-date PTC data does not appear to be adequate to detect forest degradation in temperate forests. As for tropical forest, PTC can partially discriminate between forest and non-forest categories.     

Forest fragmentation in Massachusetts,USA: a town-level assessment using Morphological spatial pattern analysis and affinity propagation

Forest fragmentation has been studied extensively with respect to biodiversity loss, disruption of ecosystem services, and edge effects although the relationship between forest fragmentation and human activities is still not well understood. We classified the pattern of forests in Massachusetts using fragmentation indicators to address these objectives: 1) characterize the spatial pattern of forest fragmentation in Massachusetts towns using Morphological Spatial Pattern Analysis (MSPA); and (2) identify regional trends using archetypal towns in relation to town history, geography and socioeconomic characteristics. Six fragmentation indicators were calculated using MSPA for each town to represent patterns and processes of fragmentation. We then used these indicators and the proportion of forested land to group towns across Massachusetts with similar patterns of fragmentation. Six representative towns typify different types of forest fragmentation, and illustrate the commonalities and differences between different fragmentation types. The objective selection of representative towns suggests that they might be used as the target of future studies, both in retrospective studies that seek to explain current patterns and in analyses that predict future fragmentation trends.