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Detecting areas of vegetation change in the Densu River Basin, Ghana

机译:检测植被变化的植物流域,加纳

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The objectives of this study were to monitor vegetation change patterns and to determine how land management practices have contributed to vegetation change in the Densu Basin. The procedures employed involve independently classifying multi-temporal images using a hybridized classification method after geo-referencing and co-registering images. A land cover map of the basin was pre-loaded onto a Juno SB hand-held GPS receiver and geo-referenced for ground-truth delineation of vegetation classes. Readings were taken for thirty-three (33) geographical positions and corresponding classes of vegetation to enhance classification accuracy. The Normalized Difference Vegetation Indices (NDVI) of Advanced Very High Resolution Radiometer (AVHRR) images were monitored for variations in the NDVI values due to seasonal changes. The effects due to phenology were then minimized using the Normalized Difference Senescence Vegetation Index (N DSV I ) method. The Principal Component Analysis (PCA) technique was used to identify possible areas of vegetation depletion in the basin. The trend of change was further determined by a NDVI image differencing method by setting threshold values that best discriminate per pixel change. The result of this study revealed significant loss of vegetation cover in the basin. For example whereas open forest was dominant in the northern sector with a total coverage area of 490 km~2 in the 1985 classification, the same vegetation type disappeared by 1991 and was replaced by scattered trees with dense herbs. There was a further decrease of forest cover between 1991 and 2000 from 332 km~2 in 1991 to 273 km~2 in 2000 representing a loss of 59 km~2. Also, between 2000 and 2002, there was a decrease of 41 km~2 in vegetation cover. In conclusion, this study provides information to assist land managers to gather ecological information about the basin to help in planning and managing the basin's resources.
机译:本研究的目标是监测植被变更模式,并确定土地管理实践如何为DENSU盆地的植被变化做出贡献。所采用的程序涉及在地理参考和共登记图像之后使用杂交的分类方法独立分类多时间图像。盆地的陆地覆盖地图被预先装载到Juno SB手持GPS接收器上,并参考植被课程的地面真实划分。读数是三十三(33)(33)个地理位置和相应的植被类别,以提高分类准确性。由于季节性变化,监测高级高分辨率辐射计(AVHRR)图像(AVHRR)图像的归一化差异植被指数(AVHRR)图像的变化。然后使用归一化差异衰老植被指数(N DSV I)方法最小化引起的候选的效果。主要成分分析(PCA)技术用于识别盆地中植被枯竭的可能区域。通过设置每个像素改变的最佳区分的阈值来进一步通过NDVI图像差异方法进一步确定变化趋势。本研究的结果揭示了盆地中植被覆盖的显着损失。例如,在1985年分类中,开放森林在北部部门中占据了北部部门,总覆盖面积490公里〜2,到1991年,相同的植被类型消失,并被散乱的树木用浓稠的草药所取代。 1991年至2000年间,从1991年的332 km〜2之间进一步减少了1991年至2000年的273公里〜2,代表了59公里〜2的损失。此外,在2000年至2002年期间,植被覆盖率下降了41 km〜2。总之,本研究提供了有助于土地管理人员收集有关该盆地的生态信息的信息,以帮助规划和管理盆地的资源。

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