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.     

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。     

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。     

An empirical InSAR-optical fusion approach to mapping vegetation canopy height

Exploiting synergies afforded by a host of recently available national-scale data sets derived from interferometric synthetic aperture radar (InSAR) and passive optical remote sensing, this paper describes the development of a novel empirical approach for the provision of regional- to continental-scale estimates of vegetation canopy height. Supported by data from the 2000 Shuttle Radar Topography Mission (SRTM), the National Elevation Dataset (NED), the LANDFIRE project, and the National Land Cover Database (NLCD) 2001, this paper describes a data fusion and modeling strategy for developing the first-ever high-resolution map of canopy height for the conterminous U.S. The approach was tested as part of a prototype study spanning some 62,000 km² in central Utah (NLCD mapping zone 16). A mapping strategy based on object-oriented image analysis and tree-based regression techniques is employed. Empirical model development is driven by a database of height metrics obtained from an extensive field plot network administered by the USDA Forest Service-Forest Inventory and Analysis (FIA) program. Based on data from 508 FIA field plots, an average absolute height error of 2.1 m (r = 0.88) was achieved for the prototype mapping zone.     

The performance of foliage mass and crown radius models in forming the input of a forest reflectance model: A test on forest growth sample plots and Landsat 7 ETM+ images

Several published foliage mass and crown radius regression models were tested on the preparation of the input for the reflectance model of Kuusk and Nilson [Kuusk, A. and Nilson, T. (2000), A directional multispectral forest reflectance model. Remote Sensing of Environment, 72(2):244–252.] for 246 forest growth sample plots in Estonia. In each test, foliage mass and crown radius for trees in the sample plots were predicted with a particular pair of allometric regression models. The forest reflectance model was then run using the estimated foliage mass and crown radius values. Reflectance factors were simulated and compared with the reflectance values obtained from three atmospherically corrected Landsat 7 Enhanced Thematic Mapper (ETM+) scenes. The statistics of linear regression between the simulated and measured reflectance factors were used to assess the performance of foliage and crown radius models. The hypothesis was that the best allometric regression models should provide the best fit in reflectance. The strongest correlation between the simulated and measured reflectance factors was found in the short-wave infrared band (ETM + 5) for all the images. The highest R² = 0.71 was observed in Picea abies dominated stands. No excellent combination of foliage mass and crown radius functions was found, but the ranking based on determination coefficients showed that some linear crown radius models are not applicable to our data. Processing of raster images, reflectance measurement for small sample plots, usage of tree-species-specific fixed parameters (specific leaf area, etc.), and the ignored influence of phenology introduced additional variation into the relationships between simulated and measured reflectance factors. Further studies are needed, but these preliminary results demonstrate that the proposed method could serve as an effective way of testing the performance of foliage mass and canopy cover regressions.     

The impact of relative radiometric calibration on the accuracy of kNN-predictions of forest attributes

The k-nearest-neighbour (kNN) algorithm is widely applied for the estimation of forest attributes using remote sensing data. It requires a large amount of reference data to achieve satisfactory results. Usually, the number of available reference plots for the kNN-prediction is limited by the size of the area covered by a terrestrial reference inventory and remotely sensed imagery collected from one overflight. The applicability of kNN could be enhanced if adjacent images of different acquisition dates could be used in the same estimation procedure. Relative radiometric calibration is a prerequisite for this. This study focuses on two empirical calibration methods. They are tested on adjacent LANDSAT TM scenes in Austria. The first, quite conventional one is based on radiometric control points in the overlap area of two images and on the determination of transformation parameters by linear regression. The other, recently developed method exploits the kNN-cross-validation procedure. Performance and applicability of both methods as well as the impact of phenology are discussed.     

Using airborne laser scanning to monitor tree migration in the boreal-alpine transition zone

The boreal tree line is expected to advance upwards into the mountains and northwards into the tundra due to global warming. The major objective of this study was to find out if it is possible to use high-resolution airborne laser scanner data to detect very small trees — the pioneers that are pushing the tree line up into the mountains and out onto the tundra. The study was conducted in a sub-alpine/alpine environment in southeast Norway. A total of 342 small trees of Norway spruce, Scots pine, and downy birch with tree heights ranging from 0.11 to 5.20 m were precisely georeferenced and measured in field. Laser data were collected with a pulse density of 7.7 m^− 2. Three different terrain models were used to process the airborne laser point cloud in order to assess the effects of different pre-processing parameters on small tree detection. Greater than 91% of all trees > 1 m tall registered positive laser height values regardless of terrain model. For smaller trees (< 1 m), positive height values were found in 5-73% of the cases, depending on the terrain model considered. For this group of trees, the highest rate of trees with positive height values was found for spruce. The more smoothed the terrain model was, the larger the portion of the trees that had positive laser height values. The accuracy of tree height derived from the laser data indicated a systematic underestimation of true tree height by 0.40 to 1.01 m. The standard deviation for the differences between laser-derived and field-measured tree heights was 0.11-0.73 m. Commission errors, i.e., the detection of terrain objects — rocks, hummocks — as trees, increased significantly as terrain smoothing increased. Thus, if no classification of objects into classes like small trees and terrain objects is possible, many non-tree objects with a positive height value cannot be separated from those actually being trees. In a monitoring context, i.e., repeated measurements over time, we argue that most other objects like terrain structures, rocks, and hummocks will remain stable over time while the trees will change as they grow and new trees are established. Thus, this study indicates that, given a high laser pulse density and a certain density of newly established trees, it would be possible to detect a sufficient portion of newly established trees over a 10 years period to claim that tree migration is taking place.