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Remote sensing-based forest health monitoring systems a?? case studies from Czechia and Slovakia

机译：遥感森林健康监测系统a ??捷克和斯洛伐克的案例研究

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Aim of this paper is to present the remote sensing-based systems of forest health assessment in the Czech Republic and Slovakia, and to analyse both their strengths and weaknesses. Nationwide assessment of forest health in the Czech Republic is based on the interpretation of Sentinela??2 satellite data using novel approaches for cloud-free image synthesis based on all available satellite observations. A predictive statistical model to yield time series of leaf area index (LAI) from satellite observations is developed above extensive in-situ data, including LAI and forest defoliation assessment. Forest health is evaluated for each pixel from yearly changes of forest LAI, while the country-wise assessment of the health status is performed at the cadastral level. Methodology developed for Slovakia is based on a two-phase regression sampling. The first phase of the procedure provides an initial fast estimate of forest damage using only satellite observations (visible and infrared channels from Landsat or Sentinela??2 systems). The second phase refines the result of the first phase using data from a ground damage assessment (site-level defoliation from ICP Forests database). Resulting forest health assessment over the whole forest area is presented in 10 defoliation classes. The Czech Republic shows 1.6% of heavily damaged forests, 12.5% of damaged forests, 79.2% of forests with stable conditions, 6.3% of regenerated forests and 0.4% of strongly regenerated forests. In Slovakia, the total share of damaged stands (i. e. with defoliation higher than 40%) increased from 6 a?? 8% in 2003 a?? 2011 to 13 a?? 15% in 2012 a?? 2017. Both methodologies conduct nationwide assessment of forest health status in a fast and automatized way with high accuracy and minimal costs. The weaknesses are, for example, a high computational demands for production cloud free mosaics, inability to identify initial phases of forest health decline, exclusion of stands older than 80 years (in the Czech Republic) and inability to differentiate between harvested and severely damaged stands (in Slovakia). Finally, the paper outlines future development of both methodologies.
机译：本文的目的是介绍捷克共和国和斯洛伐克的遥感森林健康评估系统，并分析其优势和劣势。全国对捷克共和国森林健康的评估是基于Sentinela的解释，使用基于所有可用卫星观测的无云图像综合方法的新颖卫星数据。从卫星观测结果产生时间序列指数（LAI）的预测统计模型是在广泛的地位数据上发展的，包括赖和森林落叶评估。从林莱年度变化评估森林健康，而对健康状况的国家明智的评估是在地籍水平进行的。为斯洛伐克开发的方法基于两相回归采样。该程序的第一阶段提供了使用仅使用卫星观测的森林损伤的初始快速估计（来自Landsat或Sentinela 2系统的可见和红外通道）。第二阶段使用来自地面损伤评估的数据（来自ICP Forests数据库的站点级别脱落）改进了第一阶段的结果。导致整个森林地区的森林健康评估在10级落叶课程中呈现。捷克共和国显示了1.6％的严重损坏的森林，损坏森林的12.5％，森林稳定的森林为79.2％，再生森林的6.3％，占强再生森林的0.4％。在斯洛伐克，损坏的立场的总份额（即，脱落高于40％）增加到6A ?? 2003年8％a ?? 2011年到13个？ 2012年15％？ 2017年。两种方法都以快速和自动化的方式对森林健康状况进行全国范围，以高精度和最低的成本。例如，弱点是对生产云马赛克的高计算需求，无法识别森林健康衰退的初始阶段，排除超过80年（在捷克共和国），无法区分收获和严重损坏的障碍（在斯洛伐克）。最后，本文概述了两种方法的未来发展。

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