Remote Sensing : Remote Sensors

Deforestation plays a big role in global warming. The “land use connection” referred to as “land use changes” is a huge contributor to global greenhouse gas emissions. Land use change involves all the destructions of land in order to produce and distribute food. Every year rainforests are demolished for agriculture intentions. “The biggest factors are the destruction of vital rainforests through burning and clearing and the elimination of wetlands” (Lappe 753). Deforestation of these habitats leads to the discharge carbon dioxide into the environment.

Some of the major technologies used in geography, have made both sorts of information far more readily available and far easier to use. Statistical analysis and modeling of spatial patterns have relied on computer technology.

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:

Proper formation of the spatial variables for the choice model considering the scale, extent and characteristics will provide realistic and useful insight about local or regional preferences. Similarly, MAU technique that can incorporate spatial data maintaining its distinct characteristic will ensure reliability of the

We believe this chapter gave a better understanding on globalization and migration. In “Remotely Sensed”, we recognized that the women that volunteer to be trafficked for sex work are usually migrant women who have difficulties providing for their families and finding jobs where they are from. This causes them to resort to prostitution, because they see it as the only job that could earn them enough money to provide for themselves and their families. We often see influential people with power speak about helping women get out of prostitution. Yet those same people in power support the state which holds on to the structures which refuses to give women equal working opportunities. This causes us to wonder if they are really interested in putting an end to human sex traffic or will continue to let it occur as it creates and puts money in the

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).

The k-function gives insight on the spatial dependence between the locations of fast-food restaurants and schools by comparing the calculated value to an expected value based on the premise of no spatial relationship between the two. This comparison was displayed on a graph showing the expected k-function value, the observed k-function value, and a 95% confidence interval. The observed value fell above both the expected k-function value and the upper boundary of the confidence

“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).

For the core indicators and methods to provide defensible resource information at multiple scales a georeferenced, statistically valid sampling framework is necessary. This sampling framework consists of a low-intensity grid that can be intensified for local monitoring needs or supplemented with other probability-based samples, all of which can be used to train remote imagery. An unbiased, statistically valid sampling framework will permit locally collected data to be combined with regional- or national-level data to improve estimates at larger scales. Further, the framework will provide the ability to defensibly answer resource questions about large tracts of land, with relatively few sample locations, and allow for sample-site relocation if an original site is disturbed (Toevs et al. 2011).

Satellite imagery can can cover much larger areas For example Landsat is able to take fairly high resolution images (30 meters) that are widely available to be used to map riparian zones. Newer remote sensing technology such as the IKONS and quick bird satellites can take even more detailed images, IKONS with a resolution of 0.6m and Quick bird with 4m resolution. Which matches arial photography resolution capabilities. LiDAR remote sensing is used to creating digital elevation models, and determining the density of vegetation, witch can be used in combination with other imagery to better understand the health of the zone (Goetz, S.

Before we can try to understand the Universe we have to observe it. Imagine you are a cave man/woman looking up into the sky at night. You would see lots of bright points scattered about (assuming it is not cloudy). The points are not the same but how can you describe the differences between them? One of the main differences is that you have to move your head to see different examples. This might lead you to define their position. Occasionally you might notice a star flashing so would realize that there are also differences not associated with position, leading to the concept of time. If you shift your attention to the world around you you’ll be able to make further close-range observations. Picking up rocks you notice some are easy to pick

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).

Deforestation and soil loss has caused the Kenyan ecosystem to change dramatically and has impacted

This is conducted mainly to address and measure the direction and location of urban sprawl in the study area using ZonalMetrics Toolbox. ZonalMetrics toolbox is a new open source tool developed by Adamczyk and Tiede (2017) and written in python script to be added as toolbox in ArcGIS 10.2 or latest versions. It is such an important python tool with a specialized functionality for calculating spatial metrics at zonal level. The tool computes selected spatial metrics such Class Area (CA), Number of patches per class (NPC), Zone Area (ZA), Percentage of zone (PZ), Total Class Edge (TE), Edge Density (ED) and Diversity (SHDI) indices in the same way they are calculated in FRAGSTATS but here indices are calculated per class and per specified zones. The toolbox has the following 3 available utilities (Adamczyk, and Tiede, 2017): it accepts user-defined vector based categorical (polygon) layers for direct analysis; it provides landscape metrics designed to calculations within any user-defined zones for example regular gridded zones, administration zones, environmental zones, and last it provides functionalities to generate specific zone (pies, hexagons) to serve as statistical

The increasingly frequent disasters in the world are damaging people’s property and security. Moreover, the catastrophes bring massive negative effects on the development of society. In the meanwhile, it is impossible to obtain the accurate data of disaster areas by traditional observations or detection modes. Though natural disaster is a tough problem to human beings, remote sensing (RS) could help people better deal with it. According to Mei (2008), remote sensing is a reliable method of measurement which could acquire information about the property of a phenomenon or object by an observation device which is not in physical contact with the phenomenon or object under study. For example, bats are good users of RS. They can emit super sonic waves at 25000-70000 Hz and receive the acoustic echo by which means they can locate their prey and navigate. There are two main categories of RS data: aerial data and satellite data. High precision is the most advantage of airborne images. Unlike aerial data, satellite data aim at collecting information from wide range of areas.