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Investigation on changes in complex vegetation coverage using multi-temporal landsat data of Western Black sea region - A case study

机译:利用西部黑海地区多时相陆地卫星数据研究复杂植被覆盖度的变化-案例研究

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Previous studies have been able to successfully detect changes in gently-sloping forested areas with low-diversity and homogeneous vegetation cover, using medium-resolution satellite data such as landsat. The aim of the present study is to examine the capacity of multi-temporal landsat data to identify changes in forested areas with mixed vegetation and generally located on steep slopes or non-uniform topography, landsat thematic mapper (TM)and landsat enhanced thematic mapper plus (ETM~+) data forthe years 1987-2000 was used to detect changes within a 19,500 ha forested area in the Western Black sea region of Turkey The data comply with the forest cover type maps previously created for forest management plans of the research area. The methods used to detect changes were: post-classification comparison, image differencing, image rationing and NDVI (Normalized Difference Vegetation Index) differencing methods. Following the supervised classification process, error matrices were used to evaluate the accuracy of classified images obtained. The overall accuracy has been calculated as 87.59% for 1987 image and as 91.81% for 2000 image. General kappa statistics have been calculated as 0.8543 and 0.9038 for 1987 and 2000, respectively. The changes identified via the post-classification comparison method were compared with other change detetion methods. Maximum coherence was found to be 74.95% at 4/3 band rate. The NDVI difference and 3rd band difference methods achieved the same coherence with slight variations. The results suggest that landsat satellite data accurately con veys the temporal changes which occur on steeply-sloping forested areas with a mixed structure, providing a limited amount of detail but with a high level of accuracy. Moreover, it has been decided that the post-classification comparison method can meet the needs of forestry activities better than other methods as it provides information about the direction of these changes.
机译:先前的研究已经能够使用中分辨率卫星等数据,成功地检测出多样性低,植被覆盖均匀的平缓森林地区的变化。本研究的目的是检验多时相Landats数据识别具有混合植被且通常位于陡坡或地形不均匀的林地中的变化的能力,landatat thematic mapper(TM)和landat增强型专题maper plus 1987-2000年的(ETM〜+)数据用于检测土耳其西部黑海地区19,500公顷森林区域内的变化。该数据符合先前为该研究区森林管理计划创建的森林覆盖类型图。用于检测变化的方法是:分类后比较,图像差分,图像定量和NDVI(归一化植被指数)差分方法。在监督分类过程之后,使用误差矩阵来评估获得的分类图像的准确性。 1987年的图像的整体准确度为87.59%,2000年的图像为91.81%。 1987年和2000年的一般Kappa统计数据分别为0.8543和0.9038。将通过分类后比较方法识别的更改与其他更改检测方法进行比较。发现在4/3频带速率下,最大相干性为74.95%。 NDVI差异法和第三频带差异法实现了相同的相干性,但略有变化。结果表明,陆地卫星数据准确地传达了在具有混合结构的陡坡森林地区发生的时间变化,提供了有限的详细信息,但准确性很高。此外,已经决定,后分类比较方法比其他方法更能满足林业活动的需求,因为它提供了有关这些变化方向的信息。

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