Invasion of the Sensors

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While mobile computing power increases, we will continue to have an ongoing processing power gap between PCs on the one hand, and smartphones, TVs, and tablets on the other. This creates a challenge for anyone building digital experiences, as they will attempt to deliver effective experiences to mobile devices. The number of linked sensors, cameras, and complex peripheral devices will soon be exploding around us, and we will see efforts to link this information into a cohesive intelligence layers that we can interact with in our daily lives.

As you might imagine, attempts to improve our sphere of knowability for purposes of convenience will be challenged with serious resistance from those wishing to maintain our privacy. These will be tough decisions to wrestle through and the resulting public policy decisions will have far-reaching implications. The field of wearable technology and sensors is growing rapidly. Here are a few recent examples of how this field is changing.

Surveillance and control of wildlife invasion utilizing fence sensors

Band-Aid-like circuits created at University of Illinois are flexible enough to be worn on human skin. By touching a word or phrase. Person demonstrating the Touch Hear system to read books using its own unique word recognition system. WeatherMood is a personal weather armband worn on a jacket. The finger-glove named Thimble was developed as part of a collaboration between Artefact and the Industrial Design Department at the University of Washington.

Thimble is a wearable finger glove that gives the visually impaired access to ambient as well as surrounding information. Diabetics who hate their daily blood test will soon have another option. We are currently witnessing a major transformation from desktop to mobile right now. Below is a chart from a recent Morgan Stanley report, showing that desktop connections to the Internet are continuing to increase.

Even so, in the next three or four years mobile computing will soon exceed desktop computing on the Internet. All of these changes together represent a far bigger shift in computing than the personal computer revolution. The market for cloud computing services is large and growing, even if the forecasts vary widely. Visual sensors are by far the dominant mode of detection, particularly in terrestrial habitats. At small scales they include photography and video technology along with the advent of advanced image acquisition and automatic identification. At larger scales remote sensing by use of drones, satellites and video technology are being developed to map invasive species habitats in both terrestrial and aquatic environments.

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Future use will depend in part on further development of automatic detection methodology. There is also large potential in the use of acoustic methods for early detection of invasives, especially cryptic ones, but such technology is still in its infancy due to technological and analytic limitations. In conclusion, the use of visual and acoustic sensors holds much promise for detecting and monitoring invasive species, particularly in remote settings. Moreover, it is useless for discriminating initial small patches, and a lag may thus exist between patch establishment and patch detection [ 22 , 23 ].

Therefore, high-resolution imagery pixel size less than 10 m is necessary for local or finer scale research to provide more detailed information. Some researches have suggested that high-resolution imagery is a feasible and straightforward data source that can be used to pinpoint invasive plants based on their unique spatial patterns or phenological characteristics [ 24 , 25 ]. Nonetheless, in previous studies focused on monitoring S. As a public available software, Google Earth can freely provide high-resolution imagery of simulated natural color derived from commercial imaging satellites and aerial photography, which are usually expensive, and the historical imagery function can help users to traverse back in time and study earlier stages of any place [ 26 , 27 ].

Despite this, in recent years, the Zhangjiang Estuary has suffered from S. However, little research has been conducted on the dynamic changes or expansion patterns of S. Therefore, the aims of this paper are: It consists of Intertidal vegetation is dominated by invasive S.

Discoidin domain receptors: Microenvironment sensors that promote cellular migration and invasion.

The topography shows an apparent ladder-like decreasing pattern in elevation from northwest to southeast. The climate is subtropical maritime monsoon with an annual average temperature of The maximal temperature is The annual precipitation ranges from mm to mm, with an average of mm, and the rainfall is mostly distributed between April and September.

The dynamics and geomorphologic processes of the Zhangjiang Estuary are profoundly influenced by runoff and tidal semidiurnal currents.

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Up to , there were ha of S. Tidal flats with estuaries, bays, and winding coastlines are suitable for S. However, the expansion patterns of S. Therefore, based on different environmental backgrounds, six subzones were defined to study the different expansion patterns of S. The Mangrove Zone contains the largest patch of mangrove forests in this region, and the Mudflat Zone is the area that appears in front of the Mangrove Zone during low tides, where little land was reclaimed.

The Sandbank Zones include one big sandbank suffering from land reclamation and a small sandbank where most of the mudflats have been converted into aquaculture ponds. These six subzones encompass the most extensive infestations in this region. GF-1 satellite is the first flight unit of the Chinese High-Resolution program and plays an important role in high-resolution land observation and disaster monitoring.

However, GE imagery has been manipulated to reduce spectral information and improve the appearance when it is displayed on the surface of the virtual globe. Therefore, GE imagery only has three bands red, green, and blue , even though the original imagery had more bands [ 26 , 31 , 32 ]. Table 1 lists the images used in this study with their characteristics and applications.

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Gram-Schmidt pan sharpening is one of the most widespread fusion methods, which usually produces high quality fusion results for most images and performs better than many other algorithms [ 33 , 34 , 35 ]. We applied this method by ENVI software to merge the panchromatic and multi-spectral bands of GF-1 images; the resulting fused images exhibited a higher sharpness and spectral quality with a spatial resolution of 2 m and four bands.

To reduce the potential position errors among these images, we initially geo-rectified the GE image obtained in with a 1: Then, using the geo-rectified GE image as a reference, we made geometric corrections for other images based on ground control points GCPs. Ground surveys were conducted in December and September , and a total of and samples of land cover types were collected in each year, respectively. The samples contained 21 and 35 points of S.

All points were used to validate the accuracy of the classification results in and In this study, an object-oriented method was applied in conjunction with visual interpretation to classify the land cover types in different years. Six land cover types were delineated: The object-oriented classification method was conducted by the eCognition Developer 8.

The first step was image segmentation, which aims to segment an image into groups of contiguous and homogeneous pixels image objects as the mapping unit [ 36 ]. It is crucial to develop an appropriate segmentation scheme because subsequent classification directly depends on the segmented image objects [ 37 ]. In this study, a multi-resolution segmentation algorithm was adopted, and the scale factor and homogeneity criterion are the most important parameters during this process.

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The scale factor determines the size of the image objects; the larger the scale parameter, the more objects can be fused and the larger the objects grow [ 38 ]. The homogeneity criterion is composed of a shape and compactness factor that controls the clustering decision process [ 27 ]. The shape factor balances the spectral homogeneity with the shape of the objects, while the compactness factor balances the compactness with smoothness [ 39 , 40 ].

Users can set values from 0 to 1 for the shape and compactness factor to determine objects at a certain level of scale. Based on our previous experiments, we segmented images with different scales that ranged from 1 to 30, and after a series of tests, we compared the segmentation results by visual inspection.

When the scale parameter was eight, a satisfactory match between the image objects and landscape features was achieved. Using a thematic layer for segmentation will cause a further splitting of the image objects, while enabling consistent access to its thematic information [ 41 ]. To ensure that the unchanged landscape patches in different years have consistent outlines, we used the image objects created by the images of latter years as thematic layers to segment the earlier image. Figure 2 shows the segmentation results of multi-source high-resolution images.

The second step was to classify land cover types by visual interpretation.

There are obvious spectral differences between S. Since newly colonized patches of S. Firstly, we classified the image of , and validated the classification results by ground survey points. Then, we took the classification results of as a thematic layer to segment the image and revised the land cover types in In this way, we took the previous classification results as a thematic layer to segment the next image until all images were classified.

The accuracy of the classification results of and were assessed by ground survey samples described in Section 2.

Sensing infection and tissue damage

Due to a lack of field survey data in , , , and , one hundred independent points for each image were generated by a random sampling scheme. These random points were classified into S. Confusion matrices were individually created for each study year to measure the agreement between our classification results and the validation points. The overall accuracy, user accuracy, producer accuracy, and Kappa coefficient calculated from the confusion matrices were used to assess the accuracy for each land cover map [ 42 ].

To quantitatively assess the possible impacts of S. In this study, five indices were selected: CA is a commonly used metric to measure landscape composition. LPI is a simple measure of dominance that equals the percentage of the total landscape area comprised by the largest patch [ 43 ]. MPS is probably best interpreted in conjunction with the total class area, patch density or number of patches , and patch size variability, which is a widely used metric in spatial pattern analysis [ 44 ]. AI is applied to quantify the level of aggregation of spatial patterns and provides a quantitative basis to correlate spatial patterns with processes that are typically class specific [ 45 ].

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These landscape metrics were calculated using the Fragstats 4. To objectively describe the dynamic degree of S. The equations are expressed as follows: Table 2 presents the accuracy assessment results of the land cover types in each study year. The classification accuracy in was lower than that of other years. Especially when considering the categories of water body and intertidal mudflat, the confusion may be attributed to the tidal activity, which can lead to turbid water in littoral areas. The overall accuracies of all the classification results are more than 0.

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