|کد مقاله||سال انتشار||مقاله انگلیسی||ترجمه فارسی||تعداد کلمات|
|154682||2018||10 صفحه PDF||سفارش دهید||7081 کلمه|
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Remote Sensing of Environment, Volume 209, May 2018, Pages 253-262
Coastal Louisiana has been facing a serious land loss problem over the past several decades, and extensive research has been undertaken to address the problem. However, the importance of landscape fragmentation on land loss has seldom been examined. This paper evaluates the effects of landscape fragmentation on land loss in the Lower Mississippi River Basin region. The research hypothesis is that the higher the degree of fragmentation in a locality, the greater the amount of land loss in the next time period. We used Landsat-TM data with a pixel size of 30Â mÂ ÃÂ 30Â m in 1996 and 2010 and transformed the images into either land or water pixels. We then calculated the fractal dimension and Moran's I spatial autocorrelation statistics and used them to represent the degree of landscape fragmentation. Four sample box sizes, including sizes of 101Â ÃÂ 101, 71Â ÃÂ 71, 51Â ÃÂ 51, and 31Â ÃÂ 31Â pixels, were used to detect if there is a relationship between fragmentation and land loss at different neighborhood (context) scales. For each box size, 100 samples were randomly selected. To isolate the fragmentation effect so that it can be better evaluated, we used only sample boxes with a 50% land-water ratio. Regression results between fragmentation and land loss show that the R2 values for box sizes of 71Â ÃÂ 71, 51Â ÃÂ 51 and 31Â ÃÂ 31 were statistically significant (0.20, 0.45, 0.35; pÂ <Â 0.001 for Moran's I) but not for the 101Â ÃÂ 101 box size. These results imply that land protection may be most effective by prioritizing areas with land patches that have the least fragmentation. Furthermore, the neighborhood scale at which the R2 value is the highest indicates the scale at which the effects are most likely to be observed (51Â ÃÂ 51 box size, approximately 1.5Â ÃÂ 1.5Â km2, R2Â =Â 0.45), which suggests that future land loss modeling using this neighborhood scale would be most effective.