Methodology And Procedures Used In The Lp

Lab one introduces the basic concepts and processes of remote sensing and gives a better understanding of multiband images, color compositing, and contrast enhancement. The following were the objectives for the lab to help introduce these concepts and processes:

These 16 features included 12 features calculated based on the 6 multispectral bands, which is mean value and standard deviation of these bands. In addition, we chose intensity, texture-variance, texture-mean, and NDVI (Normalized Difference Vegetation Index) for classifications. Finally, training samples were selected for each classification category based on the previously segmented and merged objects

Thus our proposed optimal feature subset selection based on multi-level feature subset selection produced better results based on number of subset feature produced and classifier performance. The future scope of the work is to use these features to annotate the image regions, so that the image retrieval system can retrieve relevant images based on image semantics.

The goal of the feature extraction and selection is to reduce the dimension of the data. In this experiment the dimension of the AVIRIS and HYDICE images reduced to 20 from 220 and 191 respectively using PCA. From the PCA analysis we can see that image of principal component 1 is brightest and sharpest than other PCA image which is illustrated in figure-2.

We live in an age where most environments on earth have been impacted by anthropogenic activities. Chapin III et al (2001) contend that, “humans have been a natural component of most ecosystems for thousands of years” (p. 14). Humans interact with the environment in many ways: landscape modification, agricultural activities, urbanization, urban sprawl, carbon dioxide (CO2) emissions, stormwater runoff, and so forth; these anthropogenic activities can have detrimental environmental results. Satellite imagery, aerial photographs, and digital data can be used to analyze how anthropogenic activities impact environments spatially and temporally. This study will investigate how land cover has changed in the D’Olive Creek Watershed, located in Baldwin County, Alabama via the use of geographic information systems (GIS) and remote sensing methods and technology. For the purpose of this study, “land cover” refers to how much of a region is covered by specified land and water types (e.g. forests, wetlands, impervious surfaces, and so forth).

Joshua Stevens, Jennifer M. Smith, and Raechel A. Bianchetti (2012), Mapping Our Changing World, Editors: Alan M. MacEachren and Donna J. Peuquet, University Park, PA: Department of Geography, The Pennsylvania State University. National Geodetic Survey (2004). SPC Utilities. Retrieved June 6, 2017, from http://www.ngs.noaa.gov/TOOLS/spc.html National Geodetic Survey (2004). UTM Utilities.

1- most of these stations are unevenly spatially distributed and not enough to extract regional and long-term records of climatic parameters. Rainfall measurements by gages are a good example for the heterogeneity of assessment in space and time (El Kenawy and McCabe 2015); 2- some stations are restricted by topographic influences, nonetheless, their data are used to comprise the climate of the entire region including its plateaus and depressions, which gives biased results. For example, Hereher (2010) found that wind data obtained from meteorological stations in some depressions within the Western Desert of Egypt do not accurately reflect the general wind conditions in the mainland of the desert and there is differences between the main sand drift calculated in these depressions and those extracted from satellite images for the upland desert; and 3- meteorological stations are generally settled in or around cities and, hence, temperature data may be under the influence of the urban heat island (UHI) effect that rises the urban temperature more than surrounding environment. Li et al. (2004) observed that the average surface air temperatures in China were considerably affected for about 50 years due to the UHI. In such cases, it is not possible to point out the

I have worked many years with people who had LBP in the past. Some of them had chronic LBP, and these groups of patients were difficult and challenging, because there were so many things that affected the dysfunction and disability. I remember that among my colleagues, nobody wanted to take the patients with CLBP. Back then, there also were many LBP who had to quit or change their job, daily life and activities. We, physical therapists, traditionally look at signs and symptoms, but systems need to take account of the fact that there are other factors too. For chronic patient, we need an integrated approach of sorting through the multiple layers of a personal presentation.

1 Cover Page . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . page 3
2 Table of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . page 3
3 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . page 3
4 Location . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . page 3
 4.1 Map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . page 3
 4.2 Description .

With new technology such as satellites systems, low altitude photography and side looking radar scientists now figure that the world is losing about twenty million hectares of tropical forests annually. It has been suggested that the high deforestation rates are caused partially by the fact that the new surveys are more accurate and thus reveal old deforestation rates that were miscalculated with previous methods (Westoby, 202).

Guided by the Nature Conservancy’s Conservation Action Planning methodology, Jane Goodall Institution assists stakeholders in the use of satellite imaging and participatory mapping techniques to implement land use planning processes. These tools provide the institution and its local partners with the scientific basis for making decisions about land use and conservation actions.

Due to involvement of multiple data sets, latest technologies like remote sensing and GIS used to quantify LU/LC. On the basis of interpretation of remote sensing imagery, field surveys, and existing study area conditions, the study area classified into five categories, that is, Urban, Agriculture, Shrub land, Barren land, and Hilly area. The study area covers 134.464 km2 and LU/LC changes were estimated from 1984 to 2015.

The refined vegetation map was classified solely on the reflectance values measured by the sensors on the satellite and did not consider the species composition. Defining and describing vegetation types for their species composition and other characteristics is required for contrasting vegetation types for the refined map and the literature (Mucina & Rutherfold 2006), however that was not the scope of this study. Likewise, accuracy assessment was not conducted because

Although LiDAR is now regarded as the preferred data source for detailing forest structure, acquiring conventional ALS data for small areas using fixed-wing aircrafts or helicopters is economically inefficient for updating operational scale inventory data. However, cost is a major factor influencing the decision to conduct routine forest inventory updates, therefore significantly limiting the practicality of its use (Wulder et al. 2008).

AVIRIS does this by discerning the fractional covers of bare soil, photosynthetic and non-photosynthetic vegetation at a sub-pixel level. AVIRIS has high spatial and spectral resolution that can map fuel materials and improve fire risk assessment, even around the complex urban/wildland interface (Jia et al, 2006). AVIRIS data is very useful to researchers and land managers, yet data availability is often a problem. Acquiring AVIRIS data for the exact area and growing season that a researcher requires is often a challenge (Goetz, 2009). For this reason, hyperspectral satellite data from the NASA EO-1 mission is more commonly used.