16/06/2020

# 美国论文代写：Edras imagine的应用

Edras imagine是一个使用基于栅格的图形的遥感应用。它有能力做光栅图形的编辑。可用于地理信息系统中用于制图的数字图像的制备、增强和处理。在本研究中，Erdas Imagine从研究区域中选取样本点。一个模型使用一个大小为101*101像素的移动窗口来检查研究区域的每个像素，以找到1996年大约50%的水和50%的土地的研究点。利用分形分析和ICAMS的空间自相关分析各点的碎片化程度。然后对比1996年的土地覆被数据，计算2001年的土地损失程度。最后，估计破碎导致土地损失的概率(Walter et al 187)。

DeWitt H Braud在Erdas Imagine中创建移动窗口模型。他用这个模型定义了海岸线;在101*101像素的正方形区域中找出50%的土地和50%的水。因为Braud博士测试的是101*101像素，每像素30m*30m，这是寻找特定的陆地/水域百分比的最佳方法。只使用了与布劳德博士模型相关的结果。

Edras imagine is a remote sensing application which uses the raster based graphics. It has the abilities to do the editing of the raster graphics. It can be used for the preparation, enhancement and processing of the digital images for the purpose of mapping in case of geographic information system. In this research, Erdas Imagine selects sample points from study area. A model applies a moving window with size 101*101 pixels will check every pixel of the research region to find the study spots, where has about 50% of water and 50% of land in 1996. Use fractal analysis, and spatial autocorrelation of ICAMS to analyze the fragmentation-degree of the points. Next, compare to the land cover data in 1996, calculate the extent of land loss in 2001. Finally, estimate the probability of fragmentation to lead land loss (Walter et al 187).

DeWitt H Braud creates the model with moving window in Erdas Imagine. He has defined the coastal line by using this model; find the 50 percent of land and 50 percent of water in 101*101 pixels square region. Since Dr. Braud tested those 101*101 pixels with 30m*30m per pixel is the best way to seek the specific percentage of land/water region. Only the results related to the Dr. Braud Model has been used.

With the help of the moving window he was successful in getting a total of 84 variables. The measurement of habitat which has been used for the purpose of research results into the analysis of habitat selection in case there is an existence of differential detection probabilities in the habitats. In order to analyze the locations of the animals they must be precise to the scale of resource selection. As location based error leads to the increase in the relative scale for selection, there is a decrease in power of deselecting. Also, if the is mis-classification error rates are not predictable, there may be a chance that both power and type I error rates may not be predictable. One of the methods to do the analysis of the estimates of use from location with telemetry errors is the moving window method (Nittrouer et al, 350). In this method, a dominant cover type is assigned within the surrounding moving window which is equal to the size of the error. The moving window model is easier as it helps in the accomplishment of GIS but it can also lead to the risk missing identification of some of the small patches and other linear figures which may also be considered of huge importance.