珠海凤凰山陆气相互作用与碳通量观测塔的基本观测及晴天主要观测量的日变化特征

珠海凤凰山地处北回归线以南,森林植被覆盖率达90%,植被类型为南亚热带常绿阔叶林群落,是岭南地区典型的城市或村庄周边的再生森林,我们选择在凤凰山麓森林冠层较为平缓的低矮坡地建立了陆-气相互作用和碳通量的观测铁塔塔站。本文详细介绍了观测塔的地理环境、初步的仪器布设和基本观测,并利用已获得的资料分析了旱季典型晴天主要观测量的日变化特征。太阳总辐射及其分光辐射和反射辐射的日变化都是比较常规的中午最高的对称结构;冠层接收到的长波辐射比向上长波辐射低;气温日变化的峰值比太阳辐射滞后,白天达到最高值前的气温是低层高于高层,达到最高值后到落日前气温陡然下降,夜晚的气温是低层低于高层。相对湿度凌晨最大,下午最小,夜晚是低层相对偏湿,白天正好相反;11月份,珠海地区盛行旱季的偏北季风,有明显的海陆风的作用,白天的海风较强,夜晚的陆风较弱;森林冠层向大气释放的感热和潜热的量值基本相当,潜热基本为正;感热白天为正,夜晚基本为负;森林冠层吸收的二氧化碳的最高值出现在午后,此时空气中水汽浓度达到最低,向大气释放的二氧化碳在日出后的清晨最大,此时空气中的二氧化碳浓度达到最大,同时空气密度也最大;由于森林冠层高、密度大,土壤湿度基本没有日变化;表层土壤温度日变化的振幅随土壤深度加深而变小,土壤热流的变化是下午高,清晨低。本文还发现了一些值得深入探讨的现象,需要以后根据充沛的资料分析论证。     

Comparison of sampling strategies for object-based classification of urban vegetation from Very High Resolution satellite images

Vegetation monitoring is becoming a major issue in the urban environment due to the services they procure and necessitates an accurate and up to date mapping. Very High Resolution satellite images enable a detailed mapping of the urban tree and herbaceous vegetation. Several supervised classifications with statistical learning techniques have provided good results for the detection of urban vegetation but necessitate a large amount of training data. In this context, this study proposes to investigate the performances of different sampling strategies in order to reduce the number of examples needed. Two windows based active learning algorithms from state-of-art are compared to a classical stratified random sampling and a third combining active learning and stratified strategies is proposed. The efficiency of these strategies is evaluated on two medium size French cities, Strasbourg and Rennes, associated to different datasets. Results demonstrate that classical stratified random sampling can in some cases be just as effective as active learning methods and that it should be used more frequently to evaluate new active learning methods. Moreover, the active learning strategies proposed in this work enables to reduce the computational runtime by selecting multiple windows at each iteration without increasing the number of windows needed.     

Combining QuickBird,LiDAR, and GIS topography indices to identify a single native tree species in a complex landscape using an object-based classification approach

There are now a wide range of techniques that can be combined for image analysis. These include the use of object-based classifications rather than pixel-based classifiers, the use of LiDAR to determine vegetation height and vertical structure, as well terrain variables such as topographic wetness index and slope that can be calculated using GIS. This research investigates the benefits of combining these techniques to identify individual tree species. A QuickBird image and low point density LiDAR data for a coastal region in New Zealand was used to examine the possibility of mapping Pohutukawa trees which are regarded as an iconic tree in New Zealand. The study area included a mix of buildings and vegetation types. After image and LiDAR preparation, single tree objects were identified using a range of techniques including: a threshold of above ground height to eliminate ground based objects; Normalised Difference Vegetation Index and elevation difference between the first and last return of LiDAR data to distinguish vegetation from buildings; geometric information to separate clusters of trees from single trees, and treetop identification and region growing techniques to separate tree clusters into single tree crowns. Important feature variables were identified using Random Forest, and the Support Vector Machine provided the classification. The combined techniques using LiDAR and spectral data produced an overall accuracy of 85.4% (Kappa 80.6%). Classification using just the spectral data produced an overall accuracy of 75.8% (Kappa 67.8%). The research findings demonstrate how the combining of LiDAR and spectral data improves classification for Pohutukawa trees.     

Predicting forest carbon stocks from high resolution satellite data in dry forests of Zimbabwe: exploring the effect of the red-edge band in forest carbon stocks estimation

In this study, we tested whether the inclusion of the red-edge band as a covariate to vegetation indices improves the predictive accuracy in forest carbon estimation and mapping in savanna dry forests of Zimbabwe. Initially, we tested whether and to what extent vegetation indices (simple ratio SR, soil-adjusted vegetation index and normalized difference vegetation index) derived from high spatial resolution satellite imagery (WorldView-2) predict forest carbon stocks. Next, we tested whether inclusion of reflectance in the red-edge band as a covariate to vegetation indices improve the model's accuracy in forest carbon prediction. We used simple regression analysis to determine the nature and the strength of the relationship between forest carbon stocks and remotely sensed vegetation indices. We then used multiple regression analysis to determine whether integrating vegetation indices and reflection in the red-edge band improve forest carbon prediction. Next, we mapped the spatial variation in forest carbon stocks using the best regression model relating forest carbon stocks to remotely sensed vegetation indices and reflection in the red-edge band. Our results showed that vegetation indices alone as an explanatory variable significantly (p < 0.05) predicted forest carbon stocks with R² ranging between 45 and 63% and RMSE ranging from 10.3 to 12.9%. However, when the reflectance in the red-edge band was included in the regression models the explained variance increased to between 68 and 70% with the RMSE ranging between 9.56 and 10.1%. A combination of SR and reflectance in the red edge produced the best predictor of forest carbon stocks. We concluded that integrating vegetation indices and reflectance in the red-edge band derived from high spatial resolution can be successfully used to estimate forest carbon in dry forests with minimal error.     

Hillslope sediment production after wildfire and post-fire forest management in northern California

High severity wildfires impact hillslope processes, including infiltration, runoff, erosion, and sediment delivery to streams. Wildfire effects on these processes can impair vegetation recovery, producing impacts on headwater and downstream water supplies. To promote forest regeneration and maintain forest and aquatic ecosystem functions, land managers often undertake active post-fire land management (e.g., salvage logging, sub-soiling, re-vegetation). The primary objective of our study was to quantify and compare sediment yields eroded from (a) burned, (b) burned and salvage logged, and (c) burned, salvage logged, and sub-soiled plots following the 2015 Valley Fire in the northern California Coast Range. We distributed 25 sediment fences (~75 m² contributing area) across four hillslopes burned at high severity and representative of the three management types. We collected eroded sediment from the fences after precipitation events for 5 years. We also quantified precipitation, canopy cover, ground cover, and soil properties to characterize the processes driving erosion across the three management types. Interestingly, during the second year after the fire, sediment yields were greater in the burned-only plots compared with both the salvage logged and sub-soiled plots. By the third year, there were no differences in sediment yields among the three management types. Sediment yields decreased over the 5 years of the study, which may have occurred due to site recovery or exhaustion of mobile sediment. As expected, sediment yields were positively related to precipitation depth, bulk density, and exposed bare soil, and negatively related to the presence of wood cover on the soil surface. Unexpectedly, we observed greater sediment yields on the burned-only plots with greater canopy closure, which we attributed to increased throughfall drop size and kinetic energy related to the residual canopy. While these results will aid post-fire management decisions in areas with Mediterranean climates prone to low intensity, long duration rainstorms, additional research is needed on the comparative effects of post-fire land management approaches to improve our understanding of the mechanisms driving post-fire erosion and sediment delivery.     

Quantifying the effects of Eucalyptus plantations and management on water resources at plot and catchment scales

Our aim was to quantify the effects of forest plantation and management (clear cut or 30% partial harvest) in relation to pasture, on catchment discharge in southeast Rio Grande do Sul state, Brazil. A paired‐catchment approach was implemented in two regions (Eldorado do Sul and São Gabriel municipalities) where discharge was measured for 4 years at three catchments in each region, two of which were predominantly eucalypt plantation (mainly Eucalyptus saligna, rotation of approximately 7–9 years) with native forest and grass in streamside zones. The third catchment was covered with grazed pasture. Weather, soils, canopy interception, groundwater level, tree growth, and leaf area index were also measured. The 3‐PG process‐based forest productivity model was adapted to predict spatial daily plantation and pasture water balance including precipitation interception, soil evaporation, transpiration, soil moisture, drainage, discharge, and monthly plantation growth. The TOPMODEL framework was used to simulate water pools and fluxes in the catchments. Discharge was higher under pasture than pre‐harvesting plantation and increased for 1–2 years after complete plantation harvest; this change was less pronounced in the catchments under partial harvest. The ratio of discharge to precipitation before harvesting varied from 7% to 13% in the eucalypt catchments and 28% to 29% under pasture. The ratio increases to 23–24% after total harvest, and to 17% after partial harvesting. The ratio under pasture also increases during this period (to 32–44%) owing to increased precipitation. The baseflow, in relation to total discharge, varied from 28% to 62% under Eucalyptus and from 38% to 43% in the pasture catchments. Hence, eucalypt plantations in these regions can be expected to influence discharge regimes when compared with pasture land use, and modelling suggests that partial harvesting would moderate the magnitude of discharge variation compared with a full catchment plantation harvesting. The model efficiency coefficient (Nash–Sutcliffe model efficiency coefficient) varied from 0.665 to 0.799 for the total period of the study. Simulation of alternative harvesting scenarios suggested that at least 20% of the catchment planted area must be harvested to increase discharge. This model could be a useful practical tool in various plantation forestry contexts around the world. Copyright © 2016 John Wiley & Sons, Ltd.     

1980-2017年中国碳强度关键影响因子变化特征

The Chinese government ratified the Paris Climate Agreement in 2016.Accordingly,China aims to reduce carbon dioxide emissions per unit of gross domestic product(carbon intensity)to 60%–65%of 2005 levels by 2030.However,since numerous factors influence carbon intensity in China,it is critical to assess their relative importance to determine the most important factors.As traditional methods are inadequate for identifying key factors from a range of factors acting in concert,machine learning was applied in this study.Specifically,random forest algorithm,which is based on decision tree theory,was employed because it is insensitive to multicollinearity,is robust to missing and unbalanced data,and provides reasonable predictive results.We identified the key factors affecting carbon intensity in China using random forest algorithm and analyzed the evolution in the key factors from 1980 to 2017.The dominant factors affecting carbon intensity in China from 1980 to 1991 included the scale and proportion of energy-intensive industry,the proportion of fossil fuel-based energy,and technological progress.The Chinese economy developed rapidly between 1992 and 2007;during this time,the effects of the proportion of service industry,price of fossil fuel,and traditional residential consumption on carbon intensity increased.Subsequently,the Chinese economy entered a period of structural adjustment after the 2008 global financial crisis;during this period,reductions in emissions and the availability of new energy types began to have effects on carbon intensity,and the importance of residential consumption increased.The results suggest that optimizing the energy and industrial structures,promoting technological advancement,increasing green consumption,and reducing emissions are keys to decreasing carbon intensity within China in the future.These approaches will help achieve the goal of reducing carbon intensity to 60%–65%of the 2005 level by 2030.     

Suspended solid yield from forest harvesting on upland blanket peat

Forest harvesting activities, if not carefully carried out, can disturb the forest soils and can cause significant suspended solid concentration increases in receiving water. This study examined how harvesting, following forestry guidelines, influenced suspended solid concentrations and loads in the receiving water of a blanket peat salmonid catchment. The study site comprised of two forest coupes of 34‐year‐old conifers drained by a first‐order stream. The upper coupe was not felled and acted as a baseline ‘control’ catchment; the downstream coupe was completely harvested in summer 2005 and served as the ‘experimental’ catchment. Good management practices such as the proper use of brash mats and harvesting only in dry weather were implemented to minimize soil surface disturbance and streambank erosion. Stream flow and suspended solid measurements at an upstream station (US) and a downstream station (DS) in the study stream commenced over a year before felling took place. The suspended solid concentrations, yields and release patterns at US and DS were compared before and after harvesting. These showed that post‐guideline harvesting of upland blanket peat forest did not significantly increase the suspended solid concentrations in the receiving water and the aquatic zone need not be adversely affected by soil releases from sites without a buffer strip. Copyright © 2010 John Wiley & Sons, Ltd.     

Soil pore characteristics of evergreen and deciduous forests of the tropical monsoon region in Cambodia

Both evergreen and deciduous forests (Efs and Dfs) are widely distributed under similar climatic conditions in tropical monsoon regions. To clarify the hydraulic properties of the soil matrix in different forest types and their effects on soil water storage capacity, the soil pore characteristics (SPC) were investigated in Ef and Df stands in three provinces in Cambodia. Soils in the Ef group were characterized in common by large amounts of coarse pores with moderate pore size distribution and the absence of an extremely low K_s at shallow depths, compared to Df group soils. The mean available water capacity of the soil matrix (AWC_sm) for all horizons of the Ef and Df group soils was 0·107 and 0·146 m³ m^?3, respectively. The mean coarse pore volume of the soil matrix (CPV_sm) in the Ef and Df groups was 0·231 and 0·115 m³ m^?3, respectively. A water flow simulation using a lognormal distribution model for rain events in the early dry season indicated that variation in SPC resulted in a larger increase in available soil water in Ef soils than in Df soils. Further study on deeper soil layers in Ef and each soil type in Df is necessary for the deeper understanding of the environmental conditions and the hydrological modelling of each forest ecosystem. Copyright © 2010 John Wiley & Sons, Ltd.