The chapter describes methodologies for harmonized phenological assessments based on a limited set of development phases: flushing, flowering, secondary flushing, color change, and leaf/needle fall. Manual phenological observations are based on a brief examination in the forest stands. More recently, the use of terrestrial digital image photography for forest phenology monitoring has been adopted. Vegetation indices, such as the normalized difference vegetation index (NDVI) have been used for many years to quantify the phenology of different ecosystems. For satellite-based remote sensing of vegetation phenology, phenological metrics are derived from time series of optical data and represent the only possible assessment of phenology over large and inaccessible regions. All indirect methods using optical vegetation indices from digital camera or NDVI sensors need to be validated against ground observations, for which manual tree phenological observations from the forest monitoring plots are often used. Examples from phenological monitoring in Slovenia, France, United Kingdom, and Finland are presented.
Vegetation-related response variables adopted in the ICP Forests are related to health, growth, phenology, and diversity. Their assessment and measurement is subject to errors, which need to be controlled and documented. To do this, data quality requirements (DQRs) and intercomparison exercises were implemented. During 2009 and 2010, 111–260 field crews took part in different exercises organized across Europe. Results revealed that, while for some variables (e.g., tree diameter, standing basal area, ozone injury, species diversity) DQRs were substantially achieved, problems still exist for other measurements/calculations (tree height, volume and increment, crown base height, crown symptoms identification and description). In some cases, achievement of DRQs was partly due to relaxed DQRs. While the recent progresses in Quality Assurance/Quality Control for field surveys are promising, further effort is necessary to sharpen DQRs, refine standard operating procedures, and reinforce training.
The chapter describes methodologies for harmonized phenological assessments based on a limited set of development phases: flushing, flowering, secondary flushing, color change, and leaf/needle fall. Manual phenological observations are based on a brief examination in the forest stands. More recently, the use of terrestrial digital image photography for forest phenology monitoring has been adopted. Vegetation indices, such as the normalized difference vegetation index (NDVI) have been used for many years to quantify the phenology of different ecosystems. For satellite-based remote sensing of vegetation phenology, phenological metrics are derived from time series of optical data and represent the only possible assessment of phenology over large and inaccessible regions. All indirect methods using optical vegetation indices from digital camera or NDVI sensors need to be validated against ground observations, for which manual tree phenological observations from the forest monitoring plots are often used. Examples from phenological monitoring in Slovenia, France, United Kingdom, and Finland are presented.
Vegetation-related response variables adopted in the ICP Forests are related to health, growth, phenology, and diversity. Their assessment and measurement is subject to errors, which need to be controlled and documented. To do this, data quality requirements (DQRs) and intercomparison exercises were implemented. During 2009 and 2010, 111–260 field crews took part in different exercises organized across Europe. Results revealed that, while for some variables (e.g., tree diameter, standing basal area, ozone injury, species diversity) DQRs were substantially achieved, problems still exist for other measurements/calculations (tree height, volume and increment, crown base height, crown symptoms identification and description). In some cases, achievement of DRQs was partly due to relaxed DQRs. While the recent progresses in Quality Assurance/Quality Control for field surveys are promising, further effort is necessary to sharpen DQRs, refine standard operating procedures, and reinforce training.
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