MODELS FOR DESCRIBING THE DYNAMICS OF FOREST VEGETATION BASED ON REMOTE SENSING TECHNIQUES

Ciprian Buzna, M. Horablaga, M. Herbei, F. Sala
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Abstract

The study analyzed forest vegetation in the "Bazos Dendrological Park" area, Timis County, Romania, in order to describe the seasonal variation of the vegetation through imaging analysis based on satellite images (Sentinel 2). The study took place in the period 2021-2022, and each year 7 sets of images (T1 - T7) were taken between the months of April and August. NDMI, NDVI and NBR indices were calculated from the analysis of satellite images. Among the calculated indices, very strong correlations were found between NBR and NDMI (r=-0.928, year 2021), between NBR and NDVI (r=0.947, year 2021; r=0.928, year 2022). Moderate correlations were found between NDVI and NDMI (r=-0.769, year 2021), and weak correlations were found between NDMI and t (r=-0.655, year 2021), between NDVI and NDMI (r=0.617, year 2022). Other weak intensity correlations were also recorded. The variation of the NDVI indices in relation to NDMI and the NBR index in relation to NDMI or to NDVI was described by polynomial equations of 2nd degree, under statistical safety conditions (p les than 0.001, R2>0.9 for the year 2021; p=0.007, R2 >0.9 in the case of NDVI vs NDMI; p=0.014, R2=0.877 in the case of NBR vs NDVI, respectively p less than 0.001, R2 bigger than 0.9 in the case of NBR vs NDMI for the year 2022). In relation to the time interval (t, days), spline models faithfully described the variation of the calculated indices during the study period, under statistical safety conditions ( ? = .0 0061 in the case of NDMI vs t, ? = 0017.0 in the case of NDVI vs t, ? = 0067.0 in the case of NBR vs t, under the conditions of 2021; ? = 0317.0 in the case of NDMI vs t, ? = 0024.0 in the case of NDVI vs t, ? = 0077.0 in the case of NDMI vs t, under the conditions of 2022).
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基于遥感技术的森林植被动态描述模型
本研究对罗马尼亚Timis县“Bazos树木公园”地区的森林植被进行了分析,通过基于Sentinel 2卫星图像的成像分析来描述植被的季节变化。研究时间为2021-2022年,每年4月至8月拍摄7组(T1 - T7)图像。通过卫星影像分析计算NDMI、NDVI和NBR指数。计算指标中,NBR与NDMI (r=-0.928, 2021年)、NBR与NDVI (r=0.947, 2021年)呈极强相关性;R =0.928(2022年)。NDVI与NDMI呈中度相关(r=-0.769, 2021年),NDMI与t呈弱相关(r=-0.655, 2021年),NDVI与NDMI呈弱相关(r=0.617, 2022年)。其他弱强度相关性也被记录下来。在统计安全条件下,NDVI指数与NDMI、NBR指数与NDMI或NDVI的变化用二阶多项式方程描述(p < 0.001, R2>0.9);NDVI vs NDMI p=0.007, R2 >0.9;NBR与NDVI的p=0.014, R2=0.877,分别p < 0.001, 2022年NBR与NDMI的R2大于0.9)。相对于时间间隔(t,天),样条模型忠实地描述了在统计安全条件下(?在NDMI vs . t的情况下= 0.0061,?NDVI / t = 0017.0, ?在2021年的条件下,丁腈橡胶对t = 0067.0;? = 0317.0在NDMI对t的情况下,?在NDVI / t的情况下= 0024.0,?在2022条件下,NDMI vs t = 0077.0)。
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