Search Results (5)

Great Lakes Basin landscapes are undergoing rapid land cover and land use (LCLU) change. The goal for this study was to identify changes in land cover occurring in the Great Lakes Basin over three time periods to provide insights into historical land cover changes occurring on a bi-national watershed scale. To quantify potential impacts of anthropogenic changes on important yet vulnerable Great Lakes Wetland ecosystems, the historical changes in land cover over time are assessed via remote sensing. The goal is to better understand legacy effects on current conditions, including wetland gain and loss and the impacts of upland ecosystems on wetland health and water quality. Three key time periods with respect to Great Lakes water level changes and coastal wetland plant invasions were mapped using Landsat-derived land cover maps: 1985, 1995, and 2010. To address change between the three time periods of interest, we incorporate both radiometric and categorical change analysis and open-source tools available for assessing time series data including LandTrendr and TimeSync. Results include maps of annual land cover transition from 1985 to 1995 and 1995 to 2010 basin-wide and by ecoregion and an assessment of the magnitude and direction of change by land cover type. Basin-wide validated change results show approximately 776,854 ha of land changed from c.1980–1995 and approximately 998,400 ha of land changed from c.1995–2010. Both time periods displayed large net decreases in both deciduous forest and agricultural land and net increases in suburban cover. Change by ecoregion is reviewed in this study with many of the change types in central plains showing change in and out of agriculture and suburban land covers, the mixed wood plain ecoregion consisted of a mixture of agricultural, suburban, and forestry changes, and all top five change types in the mixed wood shield consisted of various stages of the forestry cycle for both time periods. In comparison with previous LCLU change studies, overall change products showed similar trends. The discussion reviews why, while most changes had accuracies better than 84%, accuracies found for change from urban to other classes and from other classes to agriculture were lower due to unique aspects of change in these classes which are not relevant for most change analyses applications. The study found a consistent loss in the deciduous forest area for much of the time studied, which is shown to influence the aquatic nitrogen implicated in the expansion of the invasive plant Phragmites australis in the Great Lakes Basin. This underscores the importance of LCLU maps, which allow for the quantification of historical land change in the watersheds of the Great Lakes where invasive species are expanding. Full article

Stereo-video systems (hereafter SVS) have been widely applied to study fish ecology in marine coastal ecosystems and more recently in freshwater, especially in headwater streams, due to their dependence on water clarity. Here, we assess the use of these non-destructive methods to study microhabitat use, size structure, and the abundance of endangered Spanish toothcarp (Apricaphanius iberus) and the invasive mosquitofish (Gambusia holbrooki) in coastal salt marshes. Stereo-video measurements were obtained in situ by means of static pairs of GoPro HERO7 cameras in different shallow coastal lagoons of northeastern Spain. The analysis of stereo-video recordings were processed using the open-source videogrammetry software VidSync 1.661 in order to identify the species, sex, and total length of each fish as well as their relative position in the water column. A total of ninety 17.5 min long stereo-video clips containing more than 7300 fish positions were processed for this study. Fish assemblage and population size structure gathered with this method were compared with catches at the same places using fyke nets. The accuracy and precision of fish-length estimation using SVS was also tested in the lab. SVS revealed differential water-column use, with Spanish toothcarp occurring in a lower-water column. Larger mosquitofish tended to use the upper part of the water column, whereas no clear ontogenetic shift was observed for the Spanish toothcarp. Fyke nets and SVS yielded a similar species composition and considerably correlated with abundances for two species, particularly for mosquitofish, across the six coastal ponds. The size structure varied significantly with the two techniques, with fyke nets apparently being more size-selective as the smallest mosquitofish were underrepresented in fyke nets compared with SVS. Our results suggest that SVS is a non-destructive method that does not require capturing and handling the fish, and they also suggest that it is an ideal technique for studying endangered species, with enormous potential to improve the knowledge of microhabitat use and the behavior of fish species in natural conditions. Full article

Throughout the last three decades, north central Georgia has experienced significant loss in forest land and tree cover. This study revealed the temporal patterns and thematic transitions associated with this loss by augmenting traditional forest inventory data with remotely sensed observations. In the US, there is a network of field plots measured consistently through time from the USDA Forest Service’s Forest Inventory and Analysis (FIA) Program, serial photo-based observations collected through image-based change estimation (ICE) methodology, and historical Landsat-based observations collected through TimeSync. The objective here was to evaluate how these three data sources could be used to best estimate land use and land cover (LULC) change. Using data collected in north central Georgia, we compared agreement between the three data sets, assessed the ability of each to yield adequately precise and temporally coherent estimates of land class status as well as detect net and transitional change, and we evaluated the effectiveness of using remotely sensed data in an auxiliary capacity to improve detection of statistically significant changes. With the exception of land cover from FIA plots, agreement between paired data sets for land use and cover was nearly 85%, and estimates of land class proportion were not significantly different for overlapping time intervals. Only the long time series of TimeSync data revealed significant change when conducting analyses over five-year intervals and aggregated land categories. Using ICE and TimeSync data through a two-phase estimator improved precision in estimates but did not achieve temporal coherence. We also show analytically that using auxiliary remotely sensed data for post-stratification for binary responses must be based on maps that are extremely accurate in order to see gains in precision. We conclude that, in order to report LULC trends in north central Georgia with adequate precision and temporal coherence, we need data collected on all the FIA plots each year over a long time series and broadly collapsed LULC classes. Full article

Speaker diarization systems aim to find ‘who spoke when?’ in multi-speaker recordings. The dataset usually consists of meetings, TV/talk shows, telephone and multi-party interaction recordings. In this paper, we propose a novel multimodal speaker diarization technique, which finds the active speaker through audio-visual synchronization model for diarization. A pre-trained audio-visual synchronization model is used to find the synchronization between a visible person and the respective audio. For that purpose, short video segments comprised of face-only regions are acquired using a face detection technique and are then fed to the pre-trained model. This model is a two streamed network which matches audio frames with their respective visual input segments. On the basis of high confidence video segments inferred by the model, the respective audio frames are used to train Gaussian mixture model (GMM)-based clusters. This method helps in generating speaker specific clusters with high probability. We tested our approach on a popular subset of AMI meeting corpus consisting of 5.4 h of recordings for audio and 5.8 h of different set of multimodal recordings. A significant improvement is noticed with the proposed method in term of DER when compared to conventional and fully supervised audio based speaker diarization. The results of the proposed technique are very close to the complex state-of-the art multimodal diarization which shows significance of such simple yet effective technique. Full article

Understanding change in forest carbon (C) is important for devising strategies to reduce emissions of greenhouse gases. National forest inventories (NFIs) are important to meet international accounting goals, but data are often incomplete going back in time, and the amount of time between remeasurements can make attribution of C flux to specific events difficult. The long time series of Landsat imagery provides spatially comprehensive, consistent information that can be used to fill the gaps in ground measurements with predictive models. To evaluate such models, we relate Landsat spectral changes and disturbance interpretations directly to C flux measured on NFI plots and compare the performance of models with and without ground-measured predictor variables. The study was conducted in the forests of southwest Oregon State, USA, a region of diverse forest types, disturbances, and landowner management objectives. Plot data consisted of 676 NFI plots with remeasured individual tree data over a mean interval (time 1 to time 2) of 10.0 years. We calculated change in live aboveground woody carbon (AWC), including separate components of growth, mortality, and harvest. We interpreted radiometrically corrected annual Landsat images with the TimeSync (TS) tool for a 90 m × 90 m area over each plot. Spectral time series were divided into segments of similar trajectories and classified as disturbance, recovery, or stability segments, with type of disturbance identified. We calculated a variety of values and segment changes from tasseled cap angle and distance (TCA and TCD) as potential predictor variables of C flux. Multiple linear regression was used to model AWC and net change in AWC from the TS change metrics. The TS attribution of disturbance matched the plot measurements 89% of the time regarding whether fire or harvest had occurred or not. The primary disagreement was due to plots that had been partially cut, mostly in vigorous stands where the net change in AWC over the measurement was positive in spite of cutting. The plot-measured AWC at time 2 was 86.0 ± 78.7 Mg C ha⁻¹ (mean and standard deviation), and the change in AWC across all plots was 3.5 ± 33 Mg C ha⁻¹ year⁻¹. The best model for AWC based solely on TS and other mapped variables had an R² = 0.52 (RMSE = 54.6 Mg C ha⁻¹); applying this model at two time periods to estimate net change in AWC resulted in an R² = 0.25 (RMSE = 28.3 Mg ha⁻¹) and a mean error of −5.4 Mg ha⁻¹. The best model for AWC at time 2 using plot measurements at time 1 and TS variables had an R² = 0.95 (RSME = 17.0 Mg ha⁻¹). The model for net change in AWC using the same data was identical except that, because the variable being estimated was smaller in magnitude, the R² = 0.73. All models performed better at estimating net change in AWC on TS-disturbed plots than on TS-undisturbed plots. The TS discrimination of disturbance between fire and harvest was an important variable in the models because the magnitude of spectral change from fire was greater for a given change in AWC. Regional models without plot-level predictors produced erroneous predictions of net change in AWC for some of the forest types. Our study suggests that, in spite of the simplicity of applying a single carbon model to multiple image dates, the approach can produce inaccurate estimates of C flux. Although models built with plot-level predictors are necessarily constrained to making predictions at plot locations, they show promise for providing accurate updates or back-calculations of C flux assessments. Full article