Factors Affecting the Health Condition of Spruce Forests in Central European Mountains-Study Based on Multitemporal RapidEye Satellite Images

被引:11
作者
Balazy, Radomir [1 ]
Hycza, Tomasz [1 ]
Kaminska, Agnieszka [1 ]
Osinska-Skotak, Katarzyna [2 ]
机构
[1] Forest Res Inst, Ul Braci Lesnej 3, PL-05090 Sekocin Stary, Raszyn, Poland
[2] Warsaw Univ Technol, PlacPolitechn 1, PL-00661 Warsaw, Poland
关键词
spruce; vitality; vegetation index; topography; remote sensing; BEETLE IPS-TYPOGRAPHUS; NATURAL DISTURBANCES; VEGETATION INDEXES; AIR-POLLUTION; PICEA; DEFOLIATION; BESKID; ABIES; SENSITIVITY; PATTERNS;
D O I
10.3390/f10110943
中图分类号
S7 [林业];
学科分类号
0829 ; 0907 ;
摘要
Spruce stands in the mountains of Central Europe are particularly valuable, not only because of their natural and scenic values but also because of their role in the protection of watersheds and soil. Over the past decades, these stands were frequently exposed to massive deforestation caused by various biotic, abiotic and anthropogenic factors. The current health condition of spruce stands in the Eastern Sudetes shows that the next phase of deterioration of these stands has begun. Bearing in mind past experiences and the current situation in these mountain areas, it is particularly important to understand the processes and factors that may short-term (2012-2016) spruce health analyses based on the normalized difference red-edge index and RapidEye satellite imagery, which is being delivered annually. Aided by remote sensing data and Boosted Regression Trees, it was possible to determine the topographic and tree stand features having the greatest impact on the vitality of spruce in all analyzed areas during 2012-2016. As the results show, the highest impact on the value of the normalized difference red-edge index indicator comes from a height above sea level, age of stands, terrain slopes, and exposure. In various areas, these factors may affect the vitality of spruce to varying degrees but not always in the same way. Our models accurately explained 74-81% of the randomly selected input data (predicted 72-80% of the excluded data) for site A, 50-58% for site B (48-57% for tested data) and 54-70% for site C (52-69% for excluded data).
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页数:18
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