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This study assesses whether MODIS Vegetation Continuous Fields percent tree cover (PTC) data can detect deforestation and forest degradation. To assess the usefulness of PTC for detecting deforestation, we used a data set consisting of eight forest and seven non-forest categories. To evaluate forest degradation, we used data from two temperate forest types in three conservation states: primary (dense), secondary (moderately degraded) and open (heavily degraded) forest. Our results show that PTC can differentiate temperate forest from non-forest categories (p = 0.05) and thus suggests PTC can adequately detect deforestation in temperate forests. In contrast, single-date PTC data does not appear to be adequate to detect forest degradation in temperate forests. As for tropical forest, PTC can partially discriminate between forest and non-forest categories.  相似文献   
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Support Vector Machines (SVM) is a machine learning (ML) algorithm commonly applied to the classification of remotely sensing data and more recently for modeling land use changes. However, in most geospatial applications the current literature does not elaborate on specifications of the SVM method with respect to data sampling, attribute selection and optimal parameters choices. Therefore the main objective of this study is to present and investigate the SVM technique for modeling urban land use change. The SVM model building procedure is presented together with the detailed evaluation of the output results with respect to the choice of datasets, attributes and the change of SVM parameters. Geospatial datasets containing nine land use classes and spatial attributes for the Municipality of Zemun, Republic of Serbia were used for years 2001, 2003, 2007 and 2011. The Correlation‐based Feature Subset method, kappa coefficient, Area Under Receiver Operating Characteristic Curve (AUC) and kappa simulation were used to perform the model evaluation and compare the model outputs with the real land use datasets. The obtained results indicate that the SVM‐based models perform better when implementing balanced data sampling, reduced data sets to informative subsets of attributes and properly identify the optimal learning parameters.  相似文献   
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Leaf mechanical traits are important to understand how aquatic plants fracture and deform when subjected to abiotic (currents or waves) or biotic (herbivory attack) mechanical forces. The likely occurrence of variation during leaf ontogeny in these traits may thus have implications for hydrodynamic performance and vulnerability to herbivory damage, and may be associated with changes in morphologic and chemical traits. Seagrasses, marine flowering plants, consist of shoot bundles holding several leaves with different developmental stages, in which outer older leaves protect inner younger leaves. In this study we examined the long‐lived seagrass Posidonia oceanica to determine ontogenic variation in mechanical traits across leaf position within a shoot, representing different developmental stages. Moreover, we investigated whether or not the collection procedure (classical uprooted shoot versus non‐destructive shoot method: cutting the shoot without a portion of rhizome) and time span after collection influence mechanical measurements. Neither collection procedure nor time elapsed within 48 h of collection affected measurements of leaf biomechanical traits when seagrass shoots were kept moist in dark cool conditions. Ontogenic variation in mechanical traits in P. oceanica leaves over intermediate and adult developmental stages was observed: leaves weakened and lost stiffness with aging, while mid‐aged leaves (the longest and thickest ones) were able to withstand higher breaking forces. In addition, younger leaves had higher nitrogen content and lower fiber content than older leaves. The observed patterns may explain fine‐scale within‐shoot ecological processes of leaves at different developmental stages, such as leaf shedding and herbivory consumption in P. oceanica.  相似文献   
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This paper discusses the integration of three-dimensional (3D) geographic information systems (GIS) and video surveillance systems using augmented reality (AR) techniques. The motivation for this integration is to overcome problems faced by conventional video surveillance systems. Explicit information concerning which camera currently monitors what area in such systems is missing; therefore, insight into the situation depends heavily on the operator’s training and experience. To ensure the complete coordination and monitoring of a situation in a system with multiple cameras, it is necessary to introduce a single reference system. GIS arises as a natural solution because it not only provides a solid ground truth but also provides semantic information that can be highly important in certain video surveillance applications. To integrate information into a GIS application, that information must be georeferenced. Based on our previous research regarding the addition of georeferencing information to surveillance video, this paper introduces models that can be applied to help integrate video and GIS. With an analogy to Milgram’s continuum between the real world and virtual reality, and analogous to the augmented reality and augmented virtuality in Milgram’s continuum, two models of integration are defined here: GIS-augmented video and video-augmented GIS. Then, we define the architecture of GIS-based video surveillance based on these proposed integration models, and finally, a prototype is implemented. The implemented prototype serves as a basis for analysing possible applications of real-world systems based on the integration of GIS and video.  相似文献   
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