Predicting Southern Appalachian overstory vegetation with digital terrain data

Vegetation in mountainous regions responds to small-scale variation in terrain, largely due to effects on both temperature and soil moisture. However there are few studies of quantitative, terrain-based methods for predicting vegetation composition. This study investigated relationships between forest composition, elevation, and a derived index of terrain shape, and evaluates methods for predicting forest composition. Trees were measured on 406 permanent plots within the boundaries of the Coweeta Hydrologic Lab, located in the Southern Appalachian Mountains of western North Carolina, USA. All plots were in control watersheds, without human or major natural disturbance since 1923. Plots were 0.08 ha and arrayed on transects, with approximately 380 meters between parallel transects. Breast-height diameters were measured on all trees. Elevation and terrain shape (cove, ridge, sideslope) were estimated for each plot. Density (trees/ha) and basal area were summarized by species and by forest type (cove, xeric oak-pine, northern hardwoods, and mixed deciduous). Plot data were combined with a digital elevation data (DEM), and a derived index of terrain shape at two sampling resolutions: 30 m (US Geological Survey), and 80 m (Defense Mapping Agency) sources. Vegetation maps were produced using each of four different methods: 1) linear regression with and without log transformations against elevation and terrain variables combined with cartographic overlay, 2) kriging, 3) co-kriging, and 4) a mosaic diagram. Predicted vegetation was compared to known vegetation at each of 77 independent, withheld data points, and an error matrix was determined for each mapping method.We observed strong relationships between some species and elevation and/or terrain shape. Cove and xeric oak/pine species basal areas were positively and negatively related to concave landscape locations, respectively, while species typically found in the mixed deciduous and northern hardwood types were not. Most northern hardwood species occurred more frequently and at higher basal areas as elevation increased, while most other species did not respond to elevation. The regression and mosaic diagram mapping approaches had significantly higher mapping accuracies than kriging and co-kriging. There were significant effects of DEM resolution on map accuracy, with maps based on 30 m DEM data significantly more accurate than those based on 80 m data. Taken together, these results indicate that both the mapping method and terrain data resolution significantly affect the resultant vegetation maps, even when using relatively high resolution data. Landscape or regional models based on 100 m or lower resolution terrain data may significantly under-represent terrain-related variation in vegetation composition.     

Simulated effects of increasing atmospheric CO2 and changing climate on the successional characteristics of Alpine forest ecosystems

Possible effects of changing climate and increasing CO₂ on forest stand development were simulated using a forest succession model of the JABOWA/FORET type. The model was previously tested for its ability to generate plausible community patterns for Alpine forest sites ranging from 220 m to 2000 m a.s.l., and from xeric to mesic soil moisture conditions. Each model run covers a period of 1000 yrs and is based on the averaged successional characteristics of 50 forest plots with an individual size of 1/12 ha. These small forest patches serve as basic units to model establishment, growth, and death of individual trees. The simulated CO₂ scenario assumes linear climate change as atmospheric CO₂ concentration increases from 310 l/l to 620 l/l and finally to 1340 l/l. Direct effects of increasing CO₂ on tree growth were modeled using tree-ring and growth chamber data. The simulation experiment proved to be a useful tool for evaluating possible vegetation changes that might occur under CO₂-induced warming. On xeric sites from the colline to the high montane belt, the simulated climate change causes drastic soil water losses due to elevated evapotranspiration rates. This translates into a significant biomass decrease and even to a loss of forest on xeric low-elevation sites. Biomass gains can be reported from mesic to intermediate sites between 600 and 2000 m a.s.l. Increasing CO₂ and warming alters the species composition of the simulated communities considerably. In today's montane and subalpine belt an invasion of deciduous tree species can be expected. They outcompete most conifers which in turn may migrate to today's alpine belt. Some of these changes occur as early as 40 yrs after climate begins to change. This corresponds to a mean annual warming of 1.5°C compared with today's mean temperatures.     

Distribution and ecology of lesser pouched rat,Beamys hindei,in Tanzanian coastal forests

The lesser pouched rat, Beamys hindei, is a small rodent that is patchily distributed in the Eastern Arc Mountains and coastal forests in East Africa. The ecology of this species and its current distribution in coastal forests is not well known. Therefore, we conducted a study in selected coastal forests to assess the current distribution of the species and to investigate the population ecology in terms of abundance fluctuations and demographic patterns. Assessments of the species distribution were conducted in 5 forests through trapping with Sherman live traps. Data on ecology were obtained from monthly capture–mark–recapture studies conducted for 5 consecutive nights per month in two 1 ha grids set in Zaraninge Forest over a 2‐year period. The results indicate the presence of B. hindei in 3 forests where it was not previously recorded. The population abundance estimates ranged from 1 to 40 animals per month, with high numbers recorded during rainy seasons. Reproduction patterns and sex ratios did not differ between months. Survival estimates were not influenced by season, and recruitment was low, with growth rate estimates of 1 animal per month. These estimates suggest a stable population of B. hindei in Zaraninge Forest. Further studies are recommended to establish the home range, diet and burrowing behavior of the species in coastal forests in East Africa.     

浙江省良性林地流转与生态公益林建设互动机制研究

对非良性林地流转的界定及危害性进行了介绍,论述了良性林地流转对于生态公益林建设的意义,并分析了浙江省良性林地流转与生态公益林建设互动促进机制的因素,提出了构建浙江省良性林地流转与生态公益林建设互动促进机制的思路和具体内容。     

辽宁东部山区森林涵养水源的生态服务功能价值估算

文章应用现代生态学和现代经济学的基本原理,参照国内外同类研究成果,对辽宁东部山区森林涵养水源的生态系统服务功能进行了比较系统研究,并采取不同的方法对森林涵养水源的能力进行了计算,并对其生态价值进行了与市场经济价值转换的尝试。经估算,辽宁东部山区森林生态系服务功能涵养水源的经济价值为1811.41元·hm-2·a-1,总价值为41.79×108元人民币。     

不同密度麻疯树林地对土壤微生物的影响

为了科学评价不同密度麻疯树林地的土壤肥力变化,通过对土壤微生物的研究,结合水肥调控技术等措施,来改善不同密度麻疯树林生态系统的生态环境,维持其林地生产力水平,为科学营造和管理麻疯树林提供基础数据和理论依据,使之加快麻疯树的规模化发展,对云南省双柏县3种不同密度麻疯树林地的土壤微生物数量和类群进行对比研究,并对麻疯树林地微生物进行聚类分析及多样性指数分析。结果表明：3种林地土壤微生物的数量有明显差异,样地2最多,样地1和样地3相差不大;不同土层的微生物数量变化也有所不同,0~15 cm之间土壤细菌、真菌数量逐渐上升,11~15 cm处达到峰值;随着土壤深度的增加,细菌、真菌数量又呈现下降趋势;样地2物种多样性指数也显著高于其他2种林地,这可能是由于适度的密度有利于土壤微生物的生长。因此,在种植麻疯树时,适宜密度为2 m×3 m。     

沈阳天柱山油松栎林群落结构调查与研究

油松栎林群落是具有沈阳地域特色的地带性植物群落,城市园林在构建植物配置时,要遵循地带性植物群落结构的规律。因此,研究油松栎林群落的结构对城市园林建设有一定的指导意义。采用典型样地调查法对油松栎林群落的结构进行研究,得出对其产生影响的相关指标。沈阳天柱山油松栎林群落有种子植物122种,分属38科74属,通过对重要值大小的分析比较,得出乔木层的主要优势种为油松、蒙古栎、辽东栎和花曲柳,灌木层的主要优势种为胡枝子、榛子、花木兰和接骨木;对油松栎林群落生活型进行分析,可知地面芽植物所占比例最多（38.12%）,其余依次为高位芽植物（27.64%）、隐芽植物（22.87%）、1年生植物（8.1%）和地上芽植物（3.27%）;油松栎林群落分为乔木、灌木和草本3个基本层,其中灌木层的优势较差;油松栎林群落水平结构的特点是同类植物成片状均匀分布,各物种间混交性差,其中乔木层的平均郁闭度为0.75,灌木层的平均盖度为0.27,草本层的平均盖度为0.46。     

小兴安岭过伐天然林结构特点及经营策略

通过标准地调查,对小兴安岭过伐天然林与原始阔叶红松林在树种组成、结构特点、生物多样性、建群种红松的数量、径级分布规律及演替趋势等方面的差异进行了分析,探讨了小兴安岭过伐天然林与原始阔叶红松林的林分结构特点,并提出了小兴安岭过伐林的经营策略.     

西北地区退耕还林工程实施情况调查分析

为了了解退耕还林工程10 a来的实施情况,根据西北地区退耕还林县和调查人员的分布,对西北6省793个退耕还林农户进行问卷调查,并且进行了深入细致的调查研究。结果发现：参照退耕前,退耕后生态环境、农户生态意识和生计都有显著改善,退耕还林政策执行顺利,受到农民的一致赞成,补助问题是农户普遍关注的问题。此外,分析了当前存在退耕补助兑现水平低,公示度和验收力度不够,树种比例要求不现实,政策宣传力度不强,退耕地收成不高,管理粗放,科技含量低等问题,并针对问题提出了政策性建议。图1表1参10     

Retrieval of forest attributes in complex successional forests of Central Indonesia: Modeling and estimation of bitemporal data

Quantification of forest parameters in different successional stages is required because of its importance as a source of global emissions and ecosystem changes. This study focuses on a successional tropical forest under logging practices in East Kalimantan province, Indonesia. We modeled the forest attributes using both a parametric multiple linear regression analysis and neural networks approach, with Landsat ETM data acquired in 2000 (ETM00). We compiled sample plot data using forest inventory data collected from 1997 to 1998. A total of 226 plots were used to train the models and 112 plots were used for the validation. The remote sensing data (spectral values, vegetation indices, texture, etc.) coupled with digital elevation model (DEM) were experimented with and selectively used to model basal area, stem volume and above ground biomass (AGB). We investigated the possibility to estimate the forest attributes from bitemporal ETM data by calibrating radiometric properties of the ETM image from 2003 (ETM03) using the multivariate alteration detection method. The Pearson correlations showed that the mean texture index is strongly correlated with the forest attributes. We show that neural networks resulted in a higher coefficient of determination (r²) and lower RMSE than multiple regressions for predicting the forest attributes. The estimated forest properties increased with the forest succession advancement (i.e. from the open forest to advanced secondary forest classes). The modeled basal area, stem volume and AGB varied from 10.7–15.1 m² ha⁻¹, 123.2–181.9 m³ ha⁻¹, and 132.7–185.3 Mg ha⁻¹, respectively. The RMSE_r values of model fitting ranged from 11.2% to 13.3%, and the test dataset estimated slightly higher RMSE_r which varied from 12% to 14.1%. The ETM03 forest attributes revealed favorable estimates, showing considerably higher estimates than the ETM00. The estimation of forest properties using neural networks makes Landsat data a valuable source of information for forest management, mainly with the recent free access to its historical dataset.