Relationship Between Land Use / Land-Cover And Normalized Difference Vegetation Index

The cultivation of rice has had an enormous impact on the natural biome. Rice has affected the natural biome that it is grown in, in a devastating way. It has caused the natural biomes to deteriorate and caused the natural way of life to collapse leaving no place for the native animals and plants. But what would happen if the cultivation of rice stopped and the natural biomes returned to their former glory? What would happen to all those people depending on rice for the next meal? Those who depend on rice to pay for and keep their families alive? This report looks at the where, effects, who and how of rice cultivation.

Statistics provides us with very useful tools and techniques that aide us in dealing with real world scenarios. I have been able to learn several useful concepts by studying statistics that can aide me in making rational and informed decisions that are supported by the analysis results. Statistics as a discipline is the application and development of various processes put in place to gather, interpret, and analyse the information. The quantification of biological, social, and scientific phenomenons, design and analysis of experiments and surveys, and application of

By comparing the topography, soil moisture levels, relative humidity, and wind speed of the upland Savanna and Bottomland forest we will be able to compare and contrast between these two vegetation zones. The data collected in the investigation will be used to test the hypothesis that the bottomland forest has a higher degree of shrub layer cover than the savanna. The higher level of shrubs in the bottomland forest might be attributed to the space available for shrub layer species. In addition, the data collected will confirm that the bottomland forest has a higher tree density than the

All data will be analyzed by the use of the Dataplot software provided by the National Institute of Standards and Technology. This is free software that can allow the user to plot the findings from their research. Several different types of plots can be formed within the program, which will give an advantage to the user to make various plots to show their

Consulting Table A.3 (in Appendix A) for a one-tailed test with alpha = .05 and df = N – 1 = 9, tcv = 1.833.

Percent cover of both Megathyrus maximus and Senna surattensis is estimated using quadrats on 11 separate plots along Wa’ahila Ridge. Percent cover of these two plant species were compared to the measured elevation using a GPS, in each plot. A p-value greater than .005 for each indicates a lack of significance between elevation and percent cover. A correlation analysis was used to determine that there is no significance between the elevation and percent cover as M. maximus had a p-value of .873 and S. surattensis had a p-value of .380. Sample size

“Arid and semi-arid environments, which represent a substantial portion of Earth's surface, have remained poorly studied. Yet water scarcity and salt content, changes in rainfall, flash floods, high rates of aquifer exploitation and growth of desert regions are all signs that suggest climate change and human activities are also affecting these arid and semi-arid zones” (Buis).

Data is presented as mean ± SEM (standard error of the mean). Statistical analysis was

They both resulted in a small p-value (<0.05), which suggest that there were significant differences among the data collected at specific locations. The null hypothesis was rejected. Figure 1 shows that area 2 and 5 had the highest radiation measurements which were close to the nearest thousand, 7000 W/m². While the other areas, 1, 3, 4 and 6, resulted in a smaller value of sunlight radiation within the range of 600-1400 W/m². In figure 2, there were difference among the six sites, however the results were did not show an outlier. The soil temperatures were fairly between a range of 220 °C and 275 °C. Although, both measurements were gathered on different days, there is a relationship between the two. The locations of which the solar irradiance and soil temperature were taken has an effect on the two

Although land use and land cover change focused on the human induced transformation of land in earlier days, it became more popular within the larger concept of global environmental change (Meyer and Turner, 1994). Since the last couple of decades major metropolitan areas are facing the urban sprawl problem combining loss of natural vegetation and open space and a general decline in the extent and connectivity of wetlands and wildlife habitat (Acevedo et al., 1999). Vitousek (1994) notes that land use land cover change is one of the three well documented global changes which will likely effect the ecological balance. Land use change is a major force that can alter the hydrological process such as precipitation, evapotranspiration, groundwater recharge dynamics etc. (Foley et al., 2005; Leng et al., 2011, Nian et al., 2014). Moreover land use change not only cause climatic variability but also reduces the quantity, quality, and diversity of stream habitat for aquatic life (Booth and Bledsoe 2009).

(2014) based on AVHRR imageries. Summary statistics for snow cover extent and timing characteristics, including SCD, SCD_ES and SCD_LS (same as SCD, SCOD and SCMD in this study) were calculated at each 100 m elevation zone within the CARs, and within different catchment groups. Trends of the summary statistics were analyzed using Mann-Kendal test. The results show that SCMD shifted earlier significantly in upstream catchments of Amu Dar’ya and Syr Dar’ya, as well as in the elevation range between 2500 and 3200 m, which is in agreement with our previous work and the results of this study in general. It is reported that SCOD moved earlier significantly all over CARs, and no significant trend of SCD change can be detected in any catchment group or elevation zone, which differs from our findings. In this study, within the boundary of CARs, significantly decreased SCD was observed in mountainous areas of Tien Shan and Pamir, while only patches in WP and WT experienced SCOD shifted earlier. The disagreement might come from the different temporal overage of the two studies, or more probably the different calculation unit used when conducting trend test. As shown in our results, the trend of change for snow cover statistics exhibits large spatial variability. Calculating the trends of change at catchment groups and 100 m elevation zones within the political boundaries might

According to the Simpson’s diversity index calculations of all the sites in figure 7, Site 3 had the highest index reading of 0.701 out of 1. With 1 being the highest possible value, it indicates very high diversity while 0; the lowest possible value, indicates no/very low diversity. This suggests that 0.701 is a high reading, indicating high diversity of plant species. The analysis of the first hypothesis ‘There is a direct correlation between the diversity of plant species and the relative humidity at each site’, will help explain why site 3 had the highest diversity index reading when compared to the other sites.

The above equation (FVC) is used to get fraction an area with vegetation cover using NDVI value.

Since the population standard deviation is not known and in our test the number of observed data (n) is more than 30, we do not qualify for using t-statistic and henceforth we use z statistic for our analysis.

Annual crops have more turbidity than the perennial crops.so the when annual crops are converted into the forested areas perennial crops & pastures cause 16.4% of turbidity.