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。     

Images of war: using satellite images for human rights monitoring in Turkish Kurdistan

In areas of war and armed conflict it is difficult to get trustworthy and coherent information. Civil society and human rights groups often face problems of dealing with fragmented witness reports, disinformation of war propaganda, and difficult direct access to these areas. Turkish Kurdistan was used as a case study of armed conflict to evaluate the potential use of satellite images for verification of witness reports collected by human rights groups. The Turkish army was reported to be burning forests, fields and villages as a strategy in the conflict against guerrilla uprising. This paper concludes that satellite images are useful to validate witness reports of forest fires. Even though the use of this technology for human rights groups will depend on some feasibility factors such as prices, access and expertise, the images proved to be key for analysis of spatial aspects of conflict and valuable for reconstructing a more trustworthy picture.     

Effects of calibration on L-THIA GIS runoff and pollutant estimation

Urbanization can result in alteration of a watershed's hydrologic response and water quality. To simulate hydrologic and water quality impacts of land use changes, the Long-Term Hydrologic Impact Assessment (L-THIA) system has been used. The L-THIA system estimates pollutant loading based on direct runoff quantity and land use based pollutant coefficients. The accurate estimation of direct runoff is important in assessing water quality impacts of land use changes. An automated program was developed to calibrate the L-THIA model using the millions of curve number (CN) combinations associated with land uses and hydrologic soil groups. L-THIA calibration for the Little Eagle Creek (LEC) watershed near Indianapolis, Indiana was performed using land use data for 1991 and daily rainfall data for six months of 1991 (January 1-June 30) to minimize errors associated with use of different temporal land use data and rainfall data. For the calibration period, the Nash-Sutcliffe coefficient was 0.60 for estimated and observed direct runoff. The calibrated CN values were used for validation of the model for the same year (July 1-December 31), and the Nash-Sutcliffe coefficient was 0.60 for estimated and observed direct runoff. The Nash-Sutcliffe coefficient was 0.52 for January 1, 1991 to December 31, 1991 using uncalibrated CN values. As shown in this study, the use of better input parameters for the L-THIA model can improve accuracy. The effects on direct runoff and pollutant estimation of the calibrated CN values in the L-THIA model were investigated for the LEC. Following calibration, the estimated average annual direct runoff for the LEC watershed increased by 34%, total nitrogen by 24%, total phosphorus by 22%, and total lead by 43%. This study demonstrates that the L-THIA model should be calibrated and validated prior to application in a particular watershed to more accurately assess the effects of land use changes on hydrology and water quality.     

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.     

Near Real-Time Monitoring and Mapping of Specific Conductivity Levels Across Lake Texoma, USA

A submersible sonde equipped with a specific conductivity probe, linked with a global positioning satellite receiver was developed, deployed on a small boat, and used to map spatial and temporal variations in specific conductivity in a large reservoir. 7,695 sample points were recorded during 8 sampling trips. Specific conductivity ranged from 442,uS/cm to 3,378,uS/cm over the nine-month study. The data showed five statistically different zones in the reservoir: 2 different riverine zones, 2 different riverine transition zones, and a lacustrine zone (the main lake zone). These data were imported to a geographic information system where they were spatially interpolated to generate 8 maps showing specific conductivity levels across the entire surface of the lake. The highly dynamic nature of water quality, due to the widely differing nature of the rivers that flow into the reservoir and the effect of large inflows of fresh water during winter storms is easily captured and visualized using this approach.     

Use of USLE/GIS Methodology for Predicting Soil Loss in a Semiarid Agricultural Watershed

The Universal Soil Loss Equation (USLE) is an erosion model to estimate average soil loss that would generally result from splash, sheet, and rill erosion from agricultural plots. Recently, use of USLE has been extended as a useful tool predicting soil losses and planning control practices in agricultural watersheds by the effective integration of the GIS-based procedures to estimate the factor values in a grid cell basis. This study was performed in the Kazan Watershed located in the central Anatolia, Turkey, to predict soil erosion risk by the USLE/GIS methodology for planning conservation measures in the site. Rain erosivity (R), soil erodibility (K), and cover management factor (C) values of the model were calculated from erosivity map, soil map, and land use map of Turkey, respectively. R values were site-specifically corrected using DEM and climatic data. The topographical and hydrological effects on the soil loss were characterized by LS factor evaluated by the flow accumulation tool using DEM and watershed delineation techniques. From resulting soil loss map of the watershed, the magnitude of the soil erosion was estimated in terms of the different soil units and land uses and the most erosion-prone areas where irreversible soil losses occurred were reasonably located in the Kazan watershed. This could be very useful for deciding restoration practices to control the soil erosion of the sites to be severely influenced.     

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.