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Sensors 2009, 9(9), 6670-6700; doi:10.3390/s90906670

Detecting the Land-Cover Changes Induced by Large-Physical Disturbances Using Landscape Metrics, Spatial Sampling, Simulation and Spatial Analysis

Department of Bioenvironmental Systems Engineering, National Taiwan University, 1, Sec. 4, Roosevelt Rd., Da-an District, Taipei City 106, Taiwan
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Received: 25 May 2009 / Revised: 16 July 2009 / Accepted: 24 August 2009 / Published: 26 August 2009
(This article belongs to the Section Remote Sensors)
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Abstract

The objectives of the study are to integrate the conditional Latin Hypercube Sampling (cLHS), sequential Gaussian simulation (SGS) and spatial analysis in remotely sensed images, to monitor the effects of large chronological disturbances on spatial characteristics of landscape changes including spatial heterogeneity and variability. The multiple NDVI images demonstrate that spatial patterns of disturbed landscapes were successfully delineated by spatial analysis such as variogram, Moran’I and landscape metrics in the study area. The hybrid method delineates the spatial patterns and spatial variability of landscapes caused by these large disturbances. The cLHS approach is applied to select samples from Normalized Difference Vegetation Index (NDVI) images from SPOT HRV images in the Chenyulan watershed of Taiwan, and then SGS with sufficient samples is used to generate maps of NDVI images. In final, the NDVI simulated maps are verified using indexes such as the correlation coefficient and mean absolute error (MAE). Therefore, the statistics and spatial structures of multiple NDVI images present a very robust behavior, which advocates the use of the index for the quantification of the landscape spatial patterns and land cover change. In addition, the results transferred by Open Geospatial techniques can be accessed from web-based and end-user applications of the watershed management. View Full-Text
Keywords: spatial analysis; Latin hypercube sampling; conditional simulation; landscape metrics; land cover change; remotely sensed images; geostatistics; Google Earth spatial analysis; Latin hypercube sampling; conditional simulation; landscape metrics; land cover change; remotely sensed images; geostatistics; Google Earth
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Chu, H.-J.; Lin, Y.-P.; Huang, Y.-L.; Wang, Y.-C. Detecting the Land-Cover Changes Induced by Large-Physical Disturbances Using Landscape Metrics, Spatial Sampling, Simulation and Spatial Analysis. Sensors 2009, 9, 6670-6700.

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