Coban Huseyin Oguz, Koc Ayhan, Eker Mehmet
Faculty of Forestry, Süleyman Demirel University, East Campus, Cünür 32260, Isparta, Turkey.
J Environ Biol. 2010 Jan-Mar;31(1-2):169-78.
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 mapperplus (ETM+) data for the 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 conveys 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.
以往的研究已能够利用中分辨率卫星数据(如陆地卫星数据)成功检测出植被多样性低且植被覆盖均匀的平缓坡地森林区域的变化。本研究的目的是检验多时相陆地卫星数据识别植被混合且通常位于陡坡或地形不均匀地区的森林区域变化的能力。利用1987 - 2000年的陆地卫星专题制图仪(TM)和陆地卫星增强专题制图仪+(ETM+)数据,检测土耳其西部黑海地区19500公顷森林区域内的变化。这些数据与先前为研究区域森林管理计划创建的森林覆盖类型图相符。用于检测变化的方法有:分类后比较、图像差值、图像比值和归一化差异植被指数(NDVI)差值法。在监督分类过程之后,使用误差矩阵评估所获得的分类图像的准确性。1987年图像的总体准确率计算为87.59%,2000年图像的总体准确率计算为91.81%。1987年和2000年的一般kappa统计量分别计算为0.8543和0.9038。通过分类后比较法识别出的变化与其他变化检测方法进行了比较。在4/3波段比率下发现最大一致性为74.95%。NDVI差值法和第三波段差值法实现了相同的一致性,略有差异。结果表明,陆地卫星数据准确地传达了混合结构陡坡森林区域发生的时间变化,提供的细节有限,但准确性较高。此外,已确定分类后比较法比其他方法更能满足林业活动的需求,因为它提供了这些变化方向的信息。