Relationships between urbanization and the oak resource of the Minneapolis/St. Paul Metropolitan area from 1991 to 1998

Urbanization was associated with loss and transformation of the oak forest in the Twin Cities (Minneapolis and St. Paul) metropolitan area (TCMA) over a recent 7-year interval. Between 1991 and 1998, urbanization increased based on several indicators: population density, area of developed land, and area of impervious surface—total impervious area and area within three classes of increasing degree of imperviousness (protected, affected, and degraded). We quantified relationships between changes in urbanization and changes in several parameters describing the oak forest at the scale of ecological subsection. Increased total and affected impervious area were strongly correlated with decreased area of oak forest when changes of the urbanization indicators and oak were expressed as percentages of the subsection area. Relationships were reversed when changes were expressed as percentages of the 1991 values. Increased population density was strongly correlated with increased loss in numbers and increased isolation of oak patches, but weakly correlated with loss of oak forest area. This is the first study to quantify relationships between changes in urbanization and changes in a specific forest cover type. Our results demonstrate complexities of urbanization impacts on a metropolitan forest resource, and highlight the importance of selected variables, spatial and temporal scales, and expressions of change when quantifying these relationships.     

Retrospective mapping of burnt areas in Central Siberia using a modification of the normalised difference water index

The boreal forest contains almost half the total carbon pool of world forest ecosystems, and so has a very significant role in global biogeochemical cycles. The flux of greenhouse gases in and out of these forests is influenced strongly by disturbances such as diseases, logging and predominantly fire. It is important to quantify these disturbances to enable the modelling of major greenhouse gases. However, because of the remoteness and vastness of the boreal forest, little data is available on the type, extent, frequency and severity of these disturbances in Siberia. For burnt areas, two of the more responsive wavelengths are the short wave infra-red (SWIR) and the near infra-red (NIR). These produce a vegetation index, the normalised difference SWIR (NDSWIR) capable of detecting retrospective disturbances. Here we combine the NDSWIR from MODIS imagery acquired in the summer of 2003 with thermal anomaly data from 1992 to 2003 to detect and date areas which burnt at some point between 1992 and 2003. The semi-automated method is called SWIR and Thermal ANomalies for Detecting Disturbances (STANDD) and is complemented by an Normalised Difference Vegetation Index (NDVI) differencing method using MODIS 2002 and 2003 imagery to ensure reliable detection of area burnt in the year of image acquisition (i.e. 2003). The area of this study covers approximately 3 million km² stretching from Lake Baikal in the south to the Laptev Sea in the north, above the Arctic Circle. Landsat ETM+ images were used to validate the shape and areal extent of the burnt areas resulting in an 81% overall accuracy with a kappa coefficient of agreement of 0.63.     

Sediment chemistry and nutrient influx in a hydrologically restored bottomland hardwood forest in Midwestern USA

Exchanges of total N and C between a river and its floodplain forest have been enhanced by sedimentation processes during flood pulses at a 5‐ha bottomland hardwood forest located at the Olentangy River Wetland Research Park in central Ohio. In the spring of 2000, the forest was hydrologically restored by notching an artificial levee that had separated the Olentangy River and its floodplain forest for 100 years. One‐hundred flat sediment traps (30 cm × 30 cm) were used to collect sediment samples during spring/summer flooding events from 2003 to 2005. Results showed that sediment deposition is determined by the landscape variability during flooding events, Net sediment deposit in the wettest area averaged 134 ± 12 g‐dry wt m^?2 in 2003, 127 ± 17 g‐dry wt m^?2 in 2004 and 149 ± 23 g‐dry wt m^?2 in 2005. Total N and C sedimentation ranged from 0.49 to 0.92 g‐N m^?2 and 5.2 to 19.9 g‐C m^?2. Fe dominates the sediment chemistry and results show the elemental abundance in the order of Al > Fe > Ca > K > Mg > S > P > Na > Mn > Zn > B > Cu > Mo. A hydrologic pulsing index (HPI) could be used as an indicator for mass changes of energies with hydrologic pulsing events. Restoration of seasonally flooded bottomland forests could stimulate potentially large nutrient and Fe releases, which would eventually lead to an enhanced forest productivity and biodiversity. Copyright © 2007 John Wiley & Sons, Ltd.     

Identification of gaps in mangrove forests with airborne LIDAR

Mangrove forests change frequently due to disturbances from tropical storms, frost, lightning, and insects. It has been suggested that the death and regeneration of trees in small gaps due to lightning may play a critical role in mangrove forest turnover; however, the large-scale quantification of spatial pattern and areas of gaps is lacking for investigating this issue. Airborne light detection and ranging (LIDAR) technology provides an effective way for identifying gaps by remotely obtaining direct measurements of ground and canopy elevations. A method based on an alternative sequential filter and black top-hat mathematical morphological transformation was developed to extract gap features. Comparison of identified gap polygons with raw LIDAR measurements and field surveys shows that the proposed method successfully extracted gap features in mangrove forests in Everglades National Park. There are 400–500 lightning gaps per square kilometer in mangrove forests at the study sites. The distribution of gap sizes follows an exponential form and the area of gaps with sizes larger than 100 m² account for 55–61% of the total area of gaps. The area of gaps in the mangrove forest in Everglades National Park is about 4–5% of the total forest area and the average gap formation rate is about 0.3% of the total forest area per year, indicating that lightning gaps play an important role in mangrove forest dynamics.     

森林火灾的气象卫星监测方法

森林火灾是造成森林资源破坏的重大灾害之一。作者根据近年来从事森林火灾卫星监刚的实践，结合国内外有关利用气象卫星监测林火的资料，介绍了林火卫星监测的原理、方法和实用效果。     

MEMORY·童年·印象--合肥市政务文化新区儿童公园概念性方案

合肥市政务文化新区儿童乐园的概念性方案,充分考虑了儿童的心理和成长交流需要,以儿童的自发活动为特色,兼顾各类活动设施,创造丰富活动空间,构筑了一个富有感染力、空间变幻多彩的都市儿童乐园.     

空间的塑造--厦门海湾公园设计

海湾公园利用植物、地形、大海与湖泊的周边环境等景观要素,运用隐喻、对比的手法,创造丰富、神秘、变化的空间,注重空间灵活的使用功能及其良好的人性化.     

森林火情监测预报研究

对现行的森林火情监测方法进行了分析比较。根据实际存在的难题，提出了料为产用的实施方案及应采取的技术措施，进行了实地试验，取得较好的效果。     

Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data

The U.S. Fish and Wildlife Service uses the term palustrine wetland to describe vegetated wetlands traditionally identified as marsh, bog, fen, swamp, or wet meadow. Landsat TM imagery was combined with image texture and ancillary environmental data to model probabilities of palustrine wetland occurrence in Yellowstone National Park using classification trees. Model training and test locations were identified from National Wetlands Inventory maps, and classification trees were built for seven years spanning a range of annual precipitation. At a coarse level, palustrine wetland was separated from upland. At a finer level, five palustrine wetland types were discriminated: aquatic bed (PAB), emergent (PEM), forested (PFO), scrub-shrub (PSS), and unconsolidated shore (PUS). TM-derived variables alone were relatively accurate at separating wetland from upland, but model error rates dropped incrementally as image texture, DEM-derived terrain variables, and other ancillary GIS layers were added. For classification trees making use of all available predictors, average overall test error rates were 7.8% for palustrine wetland/upland models and 17.0% for palustrine wetland type models, with consistent accuracies across years. However, models were prone to wetland over-prediction. While the predominant PEM class was classified with omission and commission error rates less than 14%, we had difficulty identifying the PAB and PSS classes. Ancillary vegetation information greatly improved PSS classification and moderately improved PFO discrimination. Association with geothermal areas distinguished PUS wetlands. Wetland over-prediction was exacerbated by class imbalance in likely combination with spatial and spectral limitations of the TM sensor. Wetland probability surfaces may be more informative than hard classification, and appear to respond to climate-driven wetland variability. The developed method is portable, relatively easy to implement, and should be applicable in other settings and over larger extents.