Mixed Linear Analysis

On September 17th, 2015 our group went on the Orange Trail of the State Botanical Garden of Georgia in Athens, Georgia to collect data for our lab. The biome of this area that we explored fit a temperate deciduous forest type. We walked along the trail and picked random spots to start measuring our transects. We measured DBH size and canopy coverage at 10, 20, 30, and 40 meters respectively along each transect for a total of six transects. We used a densitometer to measure the percent of canopy coverage of the tree closest to the center of the sampling points along each of the transects. We used a measuring tape to measure out 10, 20, 30, and 40 meters respectively along each of the six

Existing near the equator along countries such as Brazil and Costa Rica, this zone has a combination of warm temperatures, high humidities, and abundant precipitations. Factors like these define what the tropical zone is. For example, due to the high temperatures and frequent rainfalls, the soil is considered poor. This is so, because the temperature and the rainfall causes a quick decomposition instead of building up of organic materials in the soil. Therefore, once the tropical zone is altered by human activities, it is very hard to recover with the nutrient poor soil. In addition, it is expected that in the tropical zone there would be large trees. These large trees creates a closed canopy in which light just barely reaches the forest floor. Going along the topic of sunlight, it is a crucial abiotic factor in this zone. Consequently, there is competition among trees to obtain the limited amount of sunlight through the various vertical stratification of tree levels. Some key characteristics of trees in relation to their habitat is their ginormous height. In this zone, rainfall determines the vegetation. Thus, with the abundant rainfall, trees can reach up to fifty to sixty meters in height. Another key characteristic is their diversity. It has been reported that in a 2.5 acre land, there is approximately three hundred different species of trees. The main type of

Using remote sensing, we can determine a wide variety of features for the land that we desire to study; consequently in this case, deforestation will be studied and calculated by application of interactive supervised classification.

Figure 1 demonstrates, how NMDS explains the differences in species composition based on ordination distance via a stress plot (R^2= 0.946). Figure 2 demonstrates the tree species with environmental significance to help illustrate the differences of the plots and how species are associated with one another, the tree species listed are those important for determining forest types. The environmental factors showing the highest significance are PH, Ca, Mg, Al, Mn, Sand, Silt, Clay, elevation, and distance H2O (P<0.001). Figure 3 and 4 display the four different forest types present in the plot

This article was about a mixed methods research study that focused on elementary school children with emotional and behavior problems engagement in a gardening group counseling experience. The article itself did not list a hypothesis but was clear that the expectation was that gardening group counseling would help children with emotional and behavior problems develop healthy self-esteem, learn positive coping skills, develop social skills and acquire skills in self-regulation. The sample population was thirty-three students, data was only taking from thirty-one students because two students had biased responding and repeated absences, who all attended the same school in the southeastern part of the county. The students ranged in age five to twelve and grades kindergarten to fifth-grade. Five of the students were female and twenty-six were males. Racially wise twenty-two were Black followed by seven White, one Hispanic, and one other.

When measuring the trees, we found that the height of the angiosperms varied from large to small with the large on being 16.66m, the medium sized one being 10.12 m, and the smallest one being 4.1 m. Similarly, the conifers varied as well with the large conifer being 10.97 m, the medium one being 6.5 m, and the small one being 5.26 m. The trees that required the use of a clinometer were the medium and large angiosperms along with the large conifer. The value recorded for the (h) variable were held at a constant 1.72 m. The values for the (D) variable were recorded for the medium angiosperm at 12 m, the large angiosperm at 22.15 m and the large conifer at 15.4 m. The corresponding angles for those tree using the clinometer was 34 degrees for the large angiosperm, 35 degrees for the medium angiosperm and 31 degrees for the large conifer. We then used that correlation regression statistical test to show us the

In the Smith, et al (2006) study, 26 patients were used. They all had COPD that was stable, and they all complained of coughing. Their age, mean FEV, and smoking history were all accounted and controlled for (Smith, et al, 2006). Methods used involved a cough challenge test, sound recordings of their cough day and night, and a questionnaire that involved their perceived quality of life (Smith, et al, 2006). The coughs were actually manually counted and then translated to coughs per hour. The cough seconds per hour was quantified based on the number of seconds in each recorded hour that contained a cough. The overall median time that was spent coughing was calculated to be 7.5 cough seconds for every hour, although this lessened considerably at night (Smith, et al, 2006). The subjective cough scores were taken into account and compared with the actual cough seconds per hour. It was found that the study participants often thought that they spent more time coughing than what the data indicated to b accurate (Smith, et al, 2006).

Predictive trees are an excellent choice for data that have features which interact in a complicated way as the models sub-divide (or partition), the data space into smaller regions, making the interactions more manageable. This partitioning continues until model can no longer make a better model than the one previously made for each subset of the data.

Despite using pixels instead of direct light energy, DAP-derived point clouds share multiple similarities to those produced using ALS (White et al. 2015). DAP-derived point clouds have the capability to generate DEMs similarly to ALS; however, due to the fact that the technology is based on images, DAP’s inability to penetrate the crown canopy significantly reduce its mapping accuracy under canopy (Holopainen et al. 2015), restricting the reliability of the derivable metrics to forest canopy. The application of UAV-based DAP (UAVDAP)has increased in popularity, and the high-resolution images acquired combining these two techniques can provide supplementary information for updating EFI databases (e.g., post-harvest or post-disturbance), therefore improve the efficiency of acquisition and provide fine spatial scale data for sustainable resource management. In addition to lower material and operational costs, UAVs have a rapid operationalization capacity and an enhanced ability to control spatial and temporal resolution of the surveys (Tang et al. 2015). Furthermore, DAP-based point clouds can provide complementary information for forest inventory currently problematic for ALS data species composition and health status (Näsi et al. 2015; White et al. 2013b; Wulder et al. 2008). In fact, despite achieving promising preliminary results for example in a controlled environment (e.g., Yu et al., 2017), multispectral ALS is still at the

With the world growing and developing at an alarming rate, the need for more space is ever increasing, which is putting pressure on our worlds resources. In particular, our rainforests are under severe threat; and unfortunately, the location of most rainforests are within countries whose infrastructure and economic wealth are experiencing rapid development. Trees are a vital part of animal and human life as the provide habitats, food and help filter the carbon dioxide in our air. According to recent research, over 50 percent of the tree cover that has disappeared is due to

Gaston, T., Short, N., Ralyea, C., & Casterline, G. (2016). Promoting patient safety: Results of a TeamSTEPPS(R) initiative. The Journal of Nursing Administration, 46(4), 201-207.

The study site was located in British Columbia, Canada. The site was selected base on its abundance of lodgepole pine; a favoured tree of the mountain pine beetle. The area covered approximately 30 million hectares of the province 95 million hectare land mass. The pine distribution data collected from the AOS which was 1 km resolution raster cells where each cell is represented by the estimated percentage of pine at the location from the years 2002 to 2006. The method was carried out in three parts (i.e. organising data, predicting risk classes, risk rating evaluation). Firstly, the authors prepared the data in ArcGIS where they overlaid AOS data onto basemaps then classified the data into risk severity categories (light,

Forest is the principal constituent of terrestrial ecosystems and shows important parts in sustaining the global substance and energy cycles, adjusting carbon balance and alleviating increases in the atmospheric CO2 concentration and corresponding global climate change (Solomona et al., 2006). The productivity of forest is pigeonholed by the appropriate temporal and spatial changeability under the influence of the climate, the soil and the physiological types of the forest (Davis et al., 2012). Based on the spectral characteristics, indices have been articulated to accentuate features of interest on the remotely sensed data. Several research embrace productivity

Normalized difference vegetation index maps extracted from near infrared and red-bands of the study periods indicated that different LU/LC has different NDVI values. As NDVI is related with the vegetation condition, the value varies from area to area based on vegetation intensity of the sites. Plantation and shrub land have the highest NDVI value than other classes (Fig. 4.6).

Two TM images of year 1995 and 2000, two ETM+ images of 2005 and 2010 and one OLI image of year 2015 with the spatial resolution of 30m are used to develop LULC database of the study area. ERDAS imagine 8.5 is used to perform digital image processing tasks on image data preparation, classification and interpretation. ArcGIS 10.1 was used to create training data and for preparing statistical data for final analysis tables on areas in hectares linkage to tons of carbon per hectare. Microsoft excel is the final application used present the final forest change and carbon stock changes from 1995 to 2015.