利用3S技术动态监测天山草地农业产量及其成因分析

在新疆阜康县大量“天地”资料观测基础上，利用3S(RS、GIS和GPS)技术和生态系统分析方法，对新疆天山草地农业资源进行了系统的动态监测和大面积估产研究。结果表明，该县草地和森林面积1998年比1988年分别下降了17．5％和51．o％，而农业用地面积和沙漠危害面积却分别增加了57．8％和21．2％，实现了利用3s技术系统准确监测新疆阜康县草地农业资源动态变化，其大面积草地遥感估产精度达到75．8％以上。最后，建立了新型PPR估产模型，其估产精度达到81．76％以上，并给出了理论生态成因解释与定量分析结果等。     

Vulnerability assessment of sea-level rise in Viti Levu, Fiji Islands

Fiji is expected to come under increasing pressure and risk from various threats resulting from climate change and sea-level rise (SLR). Fiji consists of 332 islands and thus has a predominant and large coastline. Viti Levu is the largest and most important of the islands, harboring Fiji’s capital city and most of the major towns concentrated around its coast. The objectives of this study were to evaluate the extent of possible sea-level rise using GIS, and to identify high-risk locations. Potential sea level rise was shown graphically as an output to determine where inundation or flooding would take place. This analysis allowed important areas facing risk to be highlighted for future action. Flooding/inundation can be classified into two kinds: ‘permanent inundation’, which is the result of sea-level rise with tide; and ‘temporary flooding’, also including occasional storm surge events. The inundated area was displayed under different projections and quantified. The results produced output maps showing the distribution of inundation/flooding around the island of Viti Levu as well as the extent of flooding. Six scenarios for sea-level rise were used (0.09, 0.18, 0.48, 0.50, 0.59, 0.88 m). Six scenarios for storm surge were used with return intervals of 1, 2, 5, 10, 25, 50 years. High risk and priority locations are identified as Fiji’s capital Suva, the major tourist center and arrival port of Nadi, and Fiji’s second city Lautoka. Future action, adaptation and response strategies in these identified locations must occur to reduce risk from climate change.     

Land Degradation Monitoring in Namibia: A First Approximation

This paper presents development of a first approximation of a Namibian, national level, land degradation monitoring system. The process involved a large number of stakeholders and led to the definition of four primary indicators that were regarded as related to land degradation in Namibia: population pressure, livestock pressure, seasonal rainfall and erosion hazards. These indicators were calculated annually for the period 1971–1997. Annual land degradation risk maps were produced for the same period by combining the indicators. A time series analysis of results generated by indicators was undertaken at two sites. The analysis revealed a general trend towards an increased land degradation risk over the period 1971–1997. A decrease in annual rainfall and an increase in livestock numbers caused this negative trend at one site, while decreased annual rainfall and increased human population were the causes at a second site. Evaluation of resulting maps through direct field observations and long-term monitoring at selected study sites with different conditions relevant for the indicators defined, is an essential next step.     

Sharing the geo-Referenced Results of Climate Change Impact Research

The Prairie Adaptation Research Collaborative (PARC) has implemented an Internet Map Server (IMS) at the PARC web site (www.parc.ca) to 1) disseminate the geo-referenced results of PARC sponsored research on climate change impacts and adaptation, and 2) address data, information and knowledge management within the PARC network of researchers and partners. PARC facilitates interdisciplinary research on adaptation to the impacts of climate change in the Canadian Prairie Provinces. The web site is intended as a platform for sharing information and encouraging discussion of climate change impacts and adaptation. The IMS enables scientists and stakeholders to apply simple climate change scenarios to geo-referenced biophysical and social data, and dynamically create maps that display the geographic distribution of potential impacts of climate change. With a limited capacity for spatial analysis, most geo-processing and the climate impact modeling is done offline within a GIS environment. The IMS will serve the output from climate impact models, such that the model results can be customized by the web site user and be most readily applied to the planning and analysis of adaptation strategies.     

Assessing protected area effectiveness using surrounding (buffer) areas environmentally similar to the target area

Many studies are based on the assumption that an area and its surrounding (buffer) area present similar environmental conditions and can be compared. For example, in order to assess the effectiveness of a protected area, the land use/cover changes are compared inside the park with its surroundings. However, the heterogeneity in spatial variables can bias this assessment: we have shown that most of the protected areas in Mexico present significant environmental differences between their interior and their surroundings. Therefore, a comparison that aims at assessing the effectiveness of conservation strategies, must be cautioned. In this paper, a simple method which allows the generation of a buffer area that presents similar conditions with respect to a set of environmental variables is presented. The method was used in order to assess the effectiveness of the Calakmul Biosphere Reserve, a protected area located in the south-eastern part of Mexico. The annual rate of deforestation inside the protected area, the standard buffer area (based upon distance from the protected area only) and the similar buffer area (taking into account distance along with some environmental variables) were 0.3, 1.3 and 0.6%, respectively. These results showed that the protected area was effective in preventing land clearing, but that the comparison with the standard buffer area gave an over-optimistic vision of its effectiveness.     

A Watershed-based Cumulative Risk Impact Analysis: Environmental Vulnerability and Impact Criteria

Swine Concentrated Animal Feeding Operations (CAFOs) have received much attention in recent years. As a result, a watershed-based screening tool, the Cumulative Risk Index Analysis (CRIA), was developed to assess the cumulative impacts of multiple CAFO facilities in a watershedsubunit. The CRIA formula calculates an index number based on: 1) the area of one or more facilities compared to the area of the watershed subunit, 2) the average of the environmental vulnerability criteria, and 3) the average of the industry-specific impact criteria. Each vulnerability or impact criterion is ranked on a 1 to 5 scale, with a low rank indicating low environmental vulnerability or impact and a high rank indicating high environmental vulnerability or impact. The individual criterion ranks, as well as the total CRIA score, can be used to focus the environmental analysis and facilitate discussions with industry, public, and other stakeholders in the Agency decision-making process.     

Modeling the spatial pattern of farmland using GIS and multiple logistic regression: a case study of Maotiao River Basin, Guizhou Province, China

Land use change is an important topic in the field of global environmental change and sustainable development. Land use change modeling has attracted substantial attention because it helps researchers understand the mechanisms of land use change and assists regulatory bodies in formulating relevant policies. Maotiao River Basin is located in the province of Guizhou, China, which has a developed agricultural industry in the karst mountain areas. This paper selected biophysical and social–economic factors as independent variables, and constructed a multiple logistic regression of farmland spatial distribution probability by random sampling. Then, by using GIS technology and integrating the 2000 data, this study predicted the farmland spatial pattern. When the predicted map was compared with the actual farmland map for 2000, we noted that 71% of the simulation is in accordance with the 2000 farmland pattern. The result satisfactorily proves the reasonability and applicability of our model.     

Assessing land use impacts on flood processes in complex terrain by using GIS and modeling approach

A distributed hydrologic modeling and GIS approach is applied for the assessment of land use impact in the Steinsel sub-basin, Alzette, Grand-Duchy of Luxembourg. The assessment focuses on the runoff contributions from different land use classes and the potential impact of land use changes on runoff generation. The results show that the direct runoff from urban areas is dominant for a flood event compared with runoff from other land use areas in this catchment, and tends to increase for small floods and for the dry season floods, whereas the interflow from forested, pasture and agricultural field areas contributes to the recession flow. Significant variations in flood volume, peak discharge, time to the peak, etc., are found from the model simulation based on the three hypothetical land use change scenarios.     

Using environmental stressor information to predict the ecological status of Maryland non-tidal streams as measured by biological indicators

In Maryland, U.S., an interim framework has recentlybeen developed for using biologically based thresholds, or `biocriteria', to assess the health of nontidal streams statewide at watershed scales. The evaluation of impairment is based on indices of biological integrity from the Maryland Biological Stream Survey (MBSS). We applied logistic regression to quantify how the biotic integrity of streams at a local scale is affected by cumulative effects resulting from catchment land uses, point sources, and nearby transmission line rights-of-way. Indicators for land use were developed from the remote sensing National Land Cover Data and applied at different scales. We determined that the risk of local impairment in nontidal streams rapidly increases with increased urban land use in the catchment area. The average likelihood of failing biocriteria doubled with every 10% points increment in urban land, thus an increase in urban land use from 0 to 20% quadruples the risk of impairment. For the basins evaluated in this study, catchments with more than 40–50% urban land use had greater than 80% probability of failing biocriteria, on average. Inclusion of rights-of-way and point sources in the model did not significantly improve the fit for this data set, most likely because of their low numbers. The overall results indicate that our predictive modeling approach can help pinpoint stream ecosystems experiencing or vulnerable to degradation.     

Geostatistical Uncertainty Modelling for the Environmental Hazard Assessment During Single Erosive Rainstorm Events

This paper presents an environmental hazard assessment to account the impacts of single rainstorm variability on river-torrential landscape identified as potentially vulnerable mainly to erosional soil degradation processes. An algorithm for the characterisation of this impact, called Erosive Hazard Index (EHI), is developed with a less expensive methodology. In EHI modelling, we assume that the river-torrential system has adapted to the natural hydrological regime, and a sudden fluctuation in this regime, especially those exceeding thresholds for an acceptable range of flexibility, may have disastrous consequences for the mountain environment. The hazard analysis links key rainstorm energy variables expressed as a single-storm erosion index (EI_sto), with impact thresholds identified using an intensity pattern model. Afterwards, the conditional probabilities of exceeding these thresholds are spatially assessed using non-parametric geostatistical techinques, known as indicator kriging. The approach was applied to a test site in river-torrential landscape of the Southern Italy (Benevento province) for 13 November 1997 rainstorm event.