Mapping wetlands in the Lower Mekong Basin for wetland resource and conservation management using Landsat ETM images and field survey data

The Mekong River Basin is considered to be the second most species rich river basin in the world. The 795,000 km(2) catchment encompasses several ecoregions, incorporating biodiverse and productive wetland systems. Eighty percent of the rapidly expanding population of the Lower Mekong Basin (LMB), made up in part by Lao PDR, Thailand, Cambodia and Viet Nam, live in rural areas and are heavily reliant on wetland resources. As the populations of Cambodia and Lao PDR will double in the next 20 years, pressure on natural resources and particularly wetlands can only increase. For development planning, resource and conservation management to incorporate wetland issues, information on the distribution and character of Mekong wetlands is essential. The existing but outdated wetland maps were compiled from secondary landuse-landcover data, have limited coverage, poor thematic accuracy and no meta-data. Therefore the Mekong River Commission (MRC) undertook to produce new wetland coverage for the LMB. As resources, funding and regional capacity are limited, it was determined that the method applied should use existing facilities, be easily adaptable, and replicable locally. For the product to be useful it must be accepted by local governments and decision makers. The results must be of acceptable accuracy (>75%) and the methodology should be relatively understandable to non-experts. In the first stage of this exercise, field survey was conducted at five pilot sites covering a range of typical wetland habitats (MRC wetland classification) to supply data for a supervised classification of Landsat ETM images from the existing MRC archive. Images were analysed using ERDAS IMAGINE and applying Maximum Likelihood Classification. Field data were reserved to apply formal accuracy assessment to the final wetland habitat maps, with resulting accuracy ranging from 77 to 94%. The maps produced are now in use at a Provincial and National level in three countries for resource and conservation planning and management applications, including designation of a Ramsar wetland site of international importance.     

Integration of Satellite Imagery and Forest Inventory in Mapping Dominant and Associated Species at a Regional Scale

To achieve the overall objective of restoring natural environment and sustainable resource usability, each forest management practice effect needs to be predicted using a simulation model. Previous simulation efforts were typically confined to public land. Comprehensive forest management practices entail incorporating interactions between public and private land. To make inclusion of private land into management planning feasible at the regional scale, this study uses a new method of combining Forest Inventory and Analysis (FIA) data with remotely sensed forest group data to retrieve detailed species composition and age information for the Missouri Ozark Highlands. Remote sensed forest group and land form data inferred from topography were integrated to produce distinct combinations (ecotypes). Forest types and size classes were assigned to ecotypes based on their proportions in the FIA data. Then tree species and tree age determined from FIA subplots stratified by forest type and size class were assigned to pixels for the entire study area. The resulting species composition map can improve simulation model performance in that it has spatially explicit and continuous information of dominant and associated species, and tree ages that are unavailable from either satellite imagery or forest inventory data. In addition, the resulting species map revealed that public land and private land in Ozark Highlands differ in species composition and stand size. Shortleaf pine is a co-dominant species in public land, whereas it becomes a minor species in private land. Public forest is older than private forest. Both public and private forests have deviated from historical forest condition in terms of species composition. Based on possible reasons causing the deviation discussed in this study, corresponding management avenues that can assist in restoring natural environment were recommended.     

Mapping giant salvinia with satellite imagery and image analysis

QuickBird multispectral satellite imagery was evaluated for distinguishing giant salvinia (Salvinia molesta Mitchell) in a large reservoir in east Texas. The imagery had four bands (blue, green, red, and near-infrared) and contained 11-bit data. Color-infrared (green, red, and near-infrared bands), normal color (blue, green and red bands), and four-band composite (blue, green, red, and near-infrared bands) images were studied. Unsupervised image analysis was used to classify the imagery. Accuracy assessments performed on the classification maps of the three composite images had producer’s and user’s accuracies for giant salvinia ranging from 87.8 to 93.5%. Color-infrared, normal color, and four-band satellite imagery were excellent for distinguishing giant salvinia in a complex field habitat.     

Distribution of Selected Soil and Water Conservation Practices in the U.S. as Identified with Google Earth

The proper representation of conservation practices on agricultural lands is an important factor in large‐scale assessments of water quality in the United States. Unfortunately, there are few publicly available data sources at the local level and even fewer at the national scale. In this research, randomly selected points within agricultural lands were examined for selected conservation practices using Google Earth aerial imagery by a team of interpreters. In total, 13,530 points had field boundaries digitized, and were subsequently examined and classified. The presence of terraces, grassed waterways, contour farming, center pivot irrigation, strip cropping, ponds, riparian vegetation, filter strips, and land cover were noted. Subjectivity among interpreters was evaluated using duplicate samples and was found to be similar to image misclassification rates in other research. Conservation practice adoption rates for selected major river basins compared favorably with data collected by the Conservation Effects Assessment Project. The frequency of occurrence of each conservation practice was summarized and presented by ecoregion. To facilitate future research, point level data and software source code developed in this research are available via the web at http://nlet.brc.tamus.edu/Conservation . Aerial imagery was found to be a powerful, inexpensive, and easily accessible tool to assess large‐scale conservation practice implementation for certain conservation practices.     

ERS-2 SAR和Landsat ETM+数据融合提取崇明东滩典型地物信息与分类

以长江口崇明东滩为研究区,在分析崇明东滩典型地物的反射光谱特性和散射特性的基础上,对ERS-2 SAR数据和Landsat ETM＋数据分别进行了处理,其中对ERS-2 SAR数据进行了小波去噪处理和纹理特征信息的提取,对ETM＋数据进行了HIS彩色空间变换,然后在特征级层次上对两者进行了信息融合,最后对融合后的图像进行了典型地物信息的提取,并且根据我们野外实地调查数据和已有的研究成果,对本文的分类结果进行了评价,取得了满意的效果。     

GIS支持的卫星图像土地利用现状分类和制图

应用卫星图像提取专题要素，综合利用多元地理信息，是克服多光谱数据在解像力和分辨率上所存在的局限性的有效途径。ＧＩＳ技术则提供多种处理功能将图像与地理信息复合，提高分类精度。分类图像数据与地形基础要素数据复合，通过彩色扫描绘图机直接绘制高质量的土地利用现状专题地图。     

EFFECTIVE USE OF LANDSAT DATA IN HYDROLOGIC MODELS1

ABSTRACT: In a cooperative demonstration project, NASA and the U.S. Army Corps of Engineers (Corps) compared conventional and Landsat-derived land-use data for use in hydrologic models, and the resulting discharge frequency curves were analyzed. When a grid-based data-management system was used on a cell-by-cell basis (size about 1.1 acres or 0.45 hectare), Landsat classification accuracy was only 64 percent, but, when the grid cells were aggregated into watersheds, the classification accuracy increased to about 95 percent. When both conventional and Landsat land-use data were input to the HEC-1 model for generating discharge frequency curves, the differences in calculated discharge were judged insignificant for subbasins as small as 1.0mi² (2.59 km²). For basins larger than 10mi² (25.9km²), use of the Landsat approach is more cost-effective than use of conventional methods. Digital Landsat data can also be used effectively by local and regional agencies for hydrologic analysis by incorporating the data into grid-based data-management systems. The transfer of this new technology is well under way through inclusion in some Corps training courses and through use by both county government personnel and private consultants.     

Spatial Assessment of the Alder Tree in Kushiro Mire, Japan using Remotely Sensed Imagery – Effects of the Surrounding Land Use on Kushiro Mire

The relationship between alder (Alnus japonica) distribution and surrounding land use in Kushiro Mire was spatially assessed using remotely sensed imagery. From the result, it was found out that the expanding area of alder trees in Kushiro Mire was affected by the agricultural land area in the upper course of the river basin and flooding in the lower course of the river. The soil sediments flowing into the Kushiro Mire from the agricultural land resulted in heavy sedimentation that favors the growth of alder trees. Consequently, the number and density of alder trees has increased. The future distribution of alder trees was predicted based on the mechanism of expansion of the alder-tree area in Kushiro Mire, and it was found that large vegetation areas in Kushiro Mire will be changed to areas with alder trees.     

Quantitative assessment of tidal wetlands using remote sensing

Effective management of tidal wetlands requires periodic data on the boundaries, extent, and condition of the wetlands. In many states, wetlands are defined wholly, or in combination with other criteria, by the presence of particular emergent halophytic plants. Many important characteristics of the wetlands ecosystem are related directly to the production of emergent plant material or may be inferred from knowledge of the distribution of emergent plant species. Remote-sensing techniques have been applied to mapping of the distribution of wetland vegetation but not to quantitative evaluation of the condition of that vegetation.Recent research in the tidal wetlands of Delaware and elsewhere has shown that spectral canopy reflectance properties can be quantitatively related to the emergent green biomass ofSpartina alterniflora (salt marsh cord grass) throughout the peak growing season (April through September, in Delaware). Periodic measurements of this parameter could be applied to calculations of net aerial primary productivity for large areas ofS. alterniflora marsh in which conventional harvest techniques may be prohibitively time consuming. The method is species specific and, therefore, requires accurate discrimination ofS. alterniflora from other vegetation types. Observed seasonal changes in species spectral signatures are shown to have potential for improving multispectral categorization of tidal wetland vegetation types.     

OPERATIONAL APPLICATIONS OF SATELLITE SNOW COVER OBSERVATIONS1

ABSTRACT: Several federal and state water resources agencies and NASA have recently completed an Applications Systems Verification and Transfer (ASVT) project on the operational applications of satellite snow cover observations. When satellite snow cover data were tested in both empirical seasonal runoff estimation and short term modeling approaches, a definite potential for reducing forecast error was evident. Three years of testing in California resulted in reduction of seasonal stream flow forecast error was evident. Three years of testing in California resulted in reduction of seasonal stream flow forecast error from 15 percent to 10 percent on three study basins; and modeling studies on the Boise River basin in Idaho indicated that satellite snow cover could be used to reduce short term forecast error by up to 9.6 percent (5 day forecast). Potential benefits from improved satellite snow cover based predictions across the 11 western states total 10 million dollars for hydropower and 28 million dollars for irrigation annually. The truly operational application of the new technology in the West, however, will only be possible when the turnaround time for all data is reduced to 72 hours, and the water management agencies can be assured of a continuing supply of operational snow cover data from space.