Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

Inter-Comparison and Evaluation of the Global LAI Product (LAI3g) and the Regional LAI Product (GGRS-LAI) over the Area of Kazakhstan

View through CrossRef
Long-term global datasets of the Leaf Area Index (LAI) are important for monitoring global vegetation dynamics and are an important input for Earth system models (ESM). The comparison of long-term datasets is based on two recently available datasets both derived from AVHRR (Advanced Very High Resolution Radiometer) time series. The LAI3g dataset is developed from the new improved third generation Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) from AVHRR sensors and best-quality MODIS LAI data. The second long-term LAI dataset is based on the 8-km spatial resolution GIMMS-AVHRR data (Goettingen GIS & Remote Sensing, GGRS dataset). The GGRS-LAI product uses a satellite-based LAI. This algorithm uses a three-dimensional physical radiative transfer model, which establishes the relationship between LAI, vegetation fractional cover and given patterns of surface reflectance, view-illumination conditions and optical properties of vegetation. The model incorporates a number of site-/region-specific parameters, including the vegetation architecture variables, such as leaf angle distribution, clumping index and light extinction coefficient. For the application of the model to Kazakhstan, the vegetation architecture variables were computed at the local (pixel) level based on extensive field surveys of the biophysical properties of vegetation in representative grassland areas of Kazakhstan. As a main result of our study, we could summarize that the differences between both products are most pronounced at the start and the end of the growing season. During the spring and autumn months, the LAI difference maps showed a considerable difference of LAI GGRS and LAI3g. LAI3g is characterized by a considerably earlier start and a later finish to the growing season than LAI GGRS. Moreover, LAI3g showed LAI > 0 during the winter months when any green vegetation is absent in all land covers of Kazakhstan. A direct cause for this could be a too high base level of the LAI3g during the leafless phase.
Title: Inter-Comparison and Evaluation of the Global LAI Product (LAI3g) and the Regional LAI Product (GGRS-LAI) over the Area of Kazakhstan
Description:
Long-term global datasets of the Leaf Area Index (LAI) are important for monitoring global vegetation dynamics and are an important input for Earth system models (ESM).
The comparison of long-term datasets is based on two recently available datasets both derived from AVHRR (Advanced Very High Resolution Radiometer) time series.
The LAI3g dataset is developed from the new improved third generation Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) from AVHRR sensors and best-quality MODIS LAI data.
The second long-term LAI dataset is based on the 8-km spatial resolution GIMMS-AVHRR data (Goettingen GIS & Remote Sensing, GGRS dataset).
The GGRS-LAI product uses a satellite-based LAI.
This algorithm uses a three-dimensional physical radiative transfer model, which establishes the relationship between LAI, vegetation fractional cover and given patterns of surface reflectance, view-illumination conditions and optical properties of vegetation.
The model incorporates a number of site-/region-specific parameters, including the vegetation architecture variables, such as leaf angle distribution, clumping index and light extinction coefficient.
For the application of the model to Kazakhstan, the vegetation architecture variables were computed at the local (pixel) level based on extensive field surveys of the biophysical properties of vegetation in representative grassland areas of Kazakhstan.
As a main result of our study, we could summarize that the differences between both products are most pronounced at the start and the end of the growing season.
During the spring and autumn months, the LAI difference maps showed a considerable difference of LAI GGRS and LAI3g.
LAI3g is characterized by a considerably earlier start and a later finish to the growing season than LAI GGRS.
Moreover, LAI3g showed LAI > 0 during the winter months when any green vegetation is absent in all land covers of Kazakhstan.
A direct cause for this could be a too high base level of the LAI3g during the leafless phase.

Related Results

JIT 2023 - Jornadas de Jóvenes Investigadores Tecnológicos
JIT 2023 - Jornadas de Jóvenes Investigadores Tecnológicos
Es un honor presentar este libro que compila los trabajos de investigación y desarrollo presentados en las Jornadas de Jóvenes Investigadores Tecnológicos (JIT) 2023. Este evento s...
Raptors and Wind Energy in Kazakhstan: What are the Prospects for Eagles?
Raptors and Wind Energy in Kazakhstan: What are the Prospects for Eagles?
Wind energy is one of the most affordable energy sources worldwide and represents one of the most climate and environmentally friendly options for energy production. However, wind ...
Research on health expenditure in Kazakhstan
Research on health expenditure in Kazakhstan
Objective To understand and study Kazakhstan's resource planning and budget allocation in the field of health care through data related to Kazakhstan's health expenditure, to ensur...
Non-Recommended Publishing Lists: Strategies for Detecting Deceitful Journals
Non-Recommended Publishing Lists: Strategies for Detecting Deceitful Journals
Abstract The rapid growth of open access publishing (OAP) has significantly improved the accessibility and dissemination of scientific knowledge. However, this expansion has also c...
Kazakistan’da Bağımsızlık Sonrası Dinî Durum
Kazakistan’da Bağımsızlık Sonrası Dinî Durum
Kazakhstan has endured the aftermath of the Soviet invasion following the era of Tsarist Russia, negatively impacting the presence of Islam in the country. The repressive policies ...
Non-Regional States’ Factors of the Influence on the Foreign Policy of Republic of Kazakhstan in 2014–2024: Turkey, Great Britain and India
Non-Regional States’ Factors of the Influence on the Foreign Policy of Republic of Kazakhstan in 2014–2024: Turkey, Great Britain and India
The article analyzes the influence of non-regional states on Kazakhstan’s foreign policy in 2014–2024. Kazakhstan’s policy is similar to that of a “becoming middle power”. The auth...
Agricultural market digitalization in Kazakhstan
Agricultural market digitalization in Kazakhstan
Introduction. An increase of the Earth population leads to the necessity to increase food production. To ensure world food security, it is necessary to increase food production com...

Back to Top