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Forest Cover Dynamics of a Lowland Rainforest in Southwestern Nigeria Using GIS and Remote Sensing Techniques

机译:利用GIS和遥感技术,尼日利亚西南部雨林的森林覆盖动态

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The rate of forest degradation and deforestation in Nigeria has been increasing over the years and is prominent in the southwestern parts. Despite the significant change and degradation observed in a lowland rainforest in the region—Ogbese Forest Reserve, there is a great dearth of information about the level of forest cover change. Therefore, this study determined the cover dynamics of the rainforest reserve over the epoch of 20 years using Geographic Information System and remote sensing techniques. Coordinates of the boundary and some other benchmark places within the forest reserve were obtained. Secondary data collection included: Landsat imageries of 1998, 2002 and 2018. An interview guide was used to obtain information from forest officials and locals of the surrounding communities to complement the spatial data obtained. Image classification was done using the maximum likelihood algorithm. The rate of change across the epochs was determined using the area of the land cover classes. The level of vegetation disturbance in the reserve was determined through Normalized Difference Vegetation Index. Five different forest cover classes were identified in the study area: forest, plantation, farmland, grassland, and bare land. The natural forest reduced significantly from 34.43 km2 (48%) in 1998 to 8.73 km2 (12%) in 2002 and was depleted further by 2018, while other cover classes increased. NDVI value also reduced from 0.25 to 0.13. Agriculture, among others, was observed as the main driver of forest degradation and deforestation in Ogbese Forest Reserve. The study concluded that the remaining forest (i.e. plantation) could also be depleted by 2025, as it decreases by −0.94 km2 per year if proper reforestation and management practices are not introduced.
机译:多年来,尼日利亚的森林退化和森林砍伐在西南部突出。尽管在地区ogbese森林保护区的低地雨林中观察到了重大变化和降级,但有关森林覆盖水平的浅谈缺乏信息。因此,本研究确定了使用地理信息系统和遥感技术的20年的雨林储备的覆盖动态。获得了森林储备内的边界和其他一些基准的坐标。次要数据收集包括:1998年,2002年和2018年的Landsat成像。采访指南用于从周围社区的森林官员和当地人获取信息,以补充所获得的空间数据。使用最大似然算法完成图像分类。使用陆地覆盖类别确定时期跨越时期的变化率。通过归一化差异植被指数确定储备中植被扰动的水平。在研究区内确定了五个不同的森林覆盖类:森林,种植园,农田,草原和赤土土地。 1998年,自然森林从34.43公里(48%)显着减少到2002年的8.73公里(12%),到2018年将进一步耗费,而其他覆盖课程则增加。 NDVI值也从0.25降至0.13。除其他人中,农业被认为是森林退化和奥博森林储备森林砍伐的主要驱动因素。该研究得出结论,剩余的森林(即种植园)也可以耗尽2025年,因为如果没有介绍适当的重新造林和管理惯例,它每年减少-0.94 km2。

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