省尺度森林植被碳储量空间分布模拟不确定性分析——以江西省为例（英文）

Forest vegetation carbon patterns are significant for evaluating carbon emission and accumulation. Many methods were used to simulate patterns of forest vegetation carbon stock in previous studies, however, uncertainty apparently existed between results of different methods, even estimates of same method in different studies. Three previous methods, including Atmosphere-vegetation interaction model 2(AVIM2), Kriging, Satellite-data Based Approach(SBA), and a new method, High Accuracy Surface Modeling(HASM), were used to simulate forest vegetation carbon stock patterns in Jiangxi Province in China. Cross-validation was used to evaluate methods. The uncertainty and applicability of the four methods on provincial scale were analyzed and discussed. The results showed that HASM had the highest accuracy, which improved by 50.66%, 33.37% and 28.58%, compared with AVIM2, Kriging and SBA, respectively. Uncertainty of simulation of forest biomass carbon stock was mainly derived from modeling error, sampling error and statistical error of forest area. Total forest carbon stock, carbon density and forest area of Jiangxi were 288.62 Tg, 3.06 kg/m~2 and 94.32×109 m~2 simulated by HASM, respectively.     

周边社区经济对漳江口红树林自然保护区资源依赖程度调查研究

以漳江口红树林国家级自然保护区为例,研究周边社区经济对自然保护区资源的依赖程度。选择竹塔村和船场村为研究对象,按滩涂承包户、鱼塘养殖户及农户3大类随机分层抽样的方式选择适当数量的样本进行入户调查。调查结果表明,社区经济对红树林自然保护区依赖方式主要有滩涂承包、讨小海、受雇捡拾海产品及鱼塘养殖。用依赖系数作为衡量社区经济对自然保护区资源依赖程度的指标,从3个层面来分析依赖系数,一是以村为基本单位的层面,竹塔村、船场村两村依赖于红树林保护区资源的经济收入成份分别为48.4%和51%;二是以户为单位的层面,竹塔村调查样本依赖系数D>50%的占总样本数的46%,船场村为41%;三是以不同调查群体为单位的层面,滩涂承包户及鱼塘养殖户对红树林保护区资源的依赖系数非常高,接近于100%,而对于农户来说,对红树林保护区资源的依赖系数相对低一些,在其经济收入超过全村平均值的群体中,农户对红树林保护区资源依赖系数大于50%的,竹塔村占6.3%,船场村占16.7%。村民的经济收入结构中,依赖于红树林国家级保护区的经济成份占有重大比重。提出政府应该鼓励村民公众参与保护区管理,使得村庄与保护区和谐发展的建议。     

Management of the marine environment: integrating ecosystem services and societal benefits with the DPSIR framework in a systems approach

Ever increasing and diverse use of the marine environment is leading to human-induced changes in marine life, habitats and landscapes, making necessary the development of marine policy that considers all members of the user community and addresses current, multiple, interacting uses. Taking a systems approach incorporating an understanding of The Ecosystem Approach, we integrate the DPSIR framework with ecosystem services and societal benefits, and the focus this gives allows us to create a specific framework for supporting decision making in the marine environment. Based on a linking of these three concepts, we present a set of basic postulates for the management of the marine environment and emphasise that these postulates should hold for marine management to be achieved. We illustrate these concepts using two case studies: the management of marine aggregates extraction in UK waters and the management of marine biodiversity at Flamborough Head, UK.     

The role of ground reference data collection in the prediction of stem volume with LiDAR data in mountain areas

Ground reference data collection represents an important element in the prediction of stem volume with LiDAR-derived variables, and at present it is the most expensive part of such analyses. In this paper two aspects of ground reference data collection were analyzed: (1) the positioning error of the ground plots; and (2) the optimal number of training plots. A system for the prediction of stem volume at area-based level was adopted. LiDAR data were preprocessed and 13 variables describing both height and coverage were extracted. Models were defined using a stepwise ordinary least square (OLS) regression. Three experiments were conducted: (i) the role of the plots positioning error on prediction accuracy; (ii) the influence of random downsampling of plot numbers on prediction accuracy; and (iii) the influence of a stratified downsampling of plot numbers on prediction accuracy based on LiDAR-derived variables. A dataset comprising 799 ground plots was used. They were distributed throughout a mountainous area in the Southern Alps, where the presence of a complex landscape increases the uncertainty of the Global Positioning System (GPS) accuracy, and where a large variety of tree forest species and climatic environments make it necessary to have a large number of sample plots for accurate characterization of the study area. All the experiments provided important indications for LiDAR based forest inventories: the GPS error did not significantly influence the prediction accuracy and it was possible to reduce the number of training samples without compromising the generalization ability of the prediction model. Leading on from these findings, a new ground sampling protocol based on genetic algorithms was proposed. The new protocol allowed us to obtain promising results for the considered dataset: using only 53 training plots, instead of 534 in the original dataset, we obtained the same results for the validation set. These results, obtained in a complex mountainous area, are representative of Alpine environments and allow us to infer that similar (or better) results could also be obtained within non mountainous areas.     

Land-cover change in the conterminous United States from 1973 to 2000

Land-cover change in the conterminous United States was quantified by interpreting change from satellite imagery for a sample stratified by 84 ecoregions. Gross and net changes between 11 land-cover classes were estimated for 5 dates of Landsat imagery (1973, 1980, 1986, 1992, and 2000). An estimated 673,000 km²(8.6%) of the United States’ land area experienced a change in land cover at least one time during the study period. Forest cover experienced the largest net decline of any class with 97,000 km² lost between 1973 and 2000. The large decline in forest cover was prominent in the two regions with the highest percent of overall change, the Marine West Coast Forests (24.5% of the region experienced a change in at least one time period) and the Eastern Temperate Forests (11.4% of the region with at least one change). Agriculture declined by approximately 90,000 km² with the largest annual net loss of 12,000 km² yr^?1 occurring between 1986 and 1992. Developed area increased by 33% and with the rate of conversion to developed accelerating rate over time. The time interval with the highest annual rate of change of 47,000 km² yr^?1 (0.6% per year) was 1986–1992. This national synthesis documents a spatially and temporally dynamic era of land change between 1973 and 2000. These results quantify land change based on a nationally consistent monitoring protocol and contribute fundamental estimates critical to developing understanding of the causes and consequences of land change in the conterminous United States.     

A spatial approach to baseline and leakage in CDM forest carbon sinks projects

Abstract

Forestry projects under the Clean Development Mechanism (CDM) face specific challenges with regard to determination of a baseline for carbon sequestration. We propose a semi-standardized approach called PARAPIA for calculation of a baseline that is built on the concept of a reference area around the project area whose land-use characteristics determine the baseline scenario. The land-use shares in the reference area are checked at each verification. Baseline carbon stocks are then derived ex post using the average carbon content of each land-use type. The reference area is between five and ten times larger than the project area. To determine indirect effects (the so-called ‘leakage’), a political influence area such as province or state is assessed with regards to migration flows due to the project and related emissions.     

Economics of climate change mitigation forest policy scenarios for Ukraine

Abstract

This article reveals the contribution of woodland expansion in Ukraine to climate change mitigation policies. The opportunities for climate change mitigation of three policy scenarios: (1) carbon storage in forests, (2) carbon storage and additional wood-for-fuel substitution, and (3) carbon storage with additional sink policy for wood products, are investigated by using a simulation technique, in combination with cost—benefit analysis. The article concludes that the Ukraine's forests and their expansion offer a low-cost opportunity for carbon sequestration. Important factors that influence the results are the discount rate and the time horizon considered in the models. The findings provide evidence that the storage climate change mitigation forest policy scenario is most viable for the country, under the assumptions considered in this research.     

Linkages between climate change and sustainable development

Abstract

Climate change does not yet feature prominently within the environmental or economic policy agendas of developing countries. Yet evidence shows that some of the most adverse effects of climate change will be in developing countries, where populations are most vulnerable and least likely to easily adapt to climate change, and that climate change will affect the potential for development in these countries. Some synergies already exist between climate change policies and the sustainable development agenda in developing countries, such as energy efficiency, renewable energy, transport and sustainable land-use policies. Despite limited attention from policy-makers to date, climate change policies could have significant ancillary benefits for the local environment. The reverse is also true as local and national policies to address congestion, air quality, access to energy services and energy diversity may also limit GHG emissions. Nevertheless there could be significant trade-offs associated with deeper levels of mitigation in some countries, for example where developing countries are dependent on indigenous coal and may be required to switch to cleaner yet more expensive fuels to limit emissions. The distributional impacts of such policies are an important determinant of their feasibility and need to be considered up-front. It follows that future agreements on mitigation and adaptation under the convention will need to recognise the diverse situations of developing countries with respect to their level of economic development, their vulnerability to climate change and their ability to adapt or mitigate. Recognition of how climate change is likely to influence other development priorities may be a first step toward building cost-effective strategies and integrated, institutional capacity in developing countries to respond to climate change. Opportunities may also exist in developing countries to use regional economic organisations to assist in the design of integrated responses and to exploit synergies between climate change and other policies such as those designed to combat desertification and preserve biodiversity.

© 2002 Elsevier Science Ltd. All rights reserved.     

Above ground biomass estimation in an African tropical forest with lidar and hyperspectral data

The estimation of above ground biomass in forests is critical for carbon cycle modeling and climate change mitigation programs. Small footprint lidar provides accurate biomass estimates, but its application in tropical forests has been limited, particularly in Africa. Hyperspectral data record canopy spectral information that is potentially related to forest biomass. To assess lidar ability to retrieve biomass in an African forest and the usefulness of including hyperspectral information, we modeled biomass using small footprint lidar metrics as well as airborne hyperspectral bands and derived vegetation indexes. Partial Least Square Regression (PLSR) was adopted to cope with multiple inputs and multicollinearity issues; the Variable of Importance in the Projection was calculated to evaluate importance of individual predictors for biomass. Our findings showed that the integration of hyperspectral bands (R² = 0.70) improved the model based on lidar alone (R² = 0.64), this encouraging result call for additional research to clarify the possible role of hyperspectral data in tropical regions. Replacing the hyperspectral bands with vegetation indexes resulted in a smaller improvement (R² = 0.67). Hyperspectral bands had limited predictive power (R² = 0.36) when used alone. This analysis proves the efficiency of using PLSR with small-footprint lidar and high resolution hyperspectral data in tropical forests for biomass estimation. Results also suggest that high quality ground truth data is crucial for lidar-based AGB estimates in tropical African forests, especially if airborne lidar is used as an intermediate step of upscaling field-measured AGB to a larger area.     

A hybrid framework for single tree detection from airborne laser scanning data: A case study in temperate mature coniferous forests in Ontario,Canada

This study presents a hybrid framework for single tree detection from airborne laser scanning (ALS) data by integrating low-level image processing techniques into a high-level probabilistic framework. The proposed approach modeled tree crowns in a forest plot as a configuration of circular objects. We took advantage of low-level image processing techniques to generate candidate configurations from the canopy height model (CHM): the treetop positions were sampled within the over-extracted local maxima via local maxima filtering, and the crown sizes were derived from marker-controlled watershed segmentation using corresponding treetops as markers. The configuration containing the best possible set of detected tree objects was estimated by a global optimization solver. To achieve this, we introduced a Gibbs energy, which contains a data term that judges the fitness of the objects with respect to the data, and a prior term that prevents severe overlapping between tree crowns on the configuration space. The energy was then embedded into a Markov Chain Monte Carlo (MCMC) dynamics coupled with a simulated annealing to find its global minimum. In this research, we also proposed a Monte Carlo-based sampling method for parameter estimation. We tested the method on a temperate mature coniferous forest in Ontario, Canada and also on simulated coniferous forest plots with different degrees of crown overlap. The experimental results showed the effectiveness of our proposed method, which was capable of reducing the commission errors produced by local maxima filtering, thus increasing the overall detection accuracy by approximately 10% on all of the datasets.