חיפוש מתקדם
remote sensing (source)

Karnieli, A., The Remote Sensing Laboratory, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University, Sede Boker Campus, Sede Boker, 84990, Israel;, Midreshet Ben-Gurion, 85280, Israel; Silver, M., The Remote Sensing Laboratory, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University, Sede Boker Campus, Sede Boker, 84990, Israel;  Panov, N., The Remote Sensing Laboratory, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University, Sede Boker Campus, Sede Boker, 84990, Israel; Varghese, D., The Remote Sensing Laboratory, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University, Sede Boker Campus, Sede Boker, 84990, Israel; Chrysoulakis, N., Remote Sensing Lab, Institute of Applied and Computational Mathematics, FORTH, Vassilika Vouton, Heraklion, 70013, Greece; Provenzale, A., Institute of Geosciences and Earth Resources, National Research Council, Pisa, 56124, Italy

Water and energy are recognized as the most influential climatic vegetation growth-limiting factors. These factors are usually measured from ground meteorological stations. However, since both vary in space, time, and scale, they can be assessed by satellite-derived biophysical indicators. Energy, represented by land surface temperature (LST), is assumed to resemble air temperature; and water availability, related to precipitation, is represented by the normalized difference vegetation index (NDVI). It is hypothesized that positive correlations between LST and NDVI indicate energy-limited conditions, while negative correlations indicate water-limited conditions. The current project aimed to quantify the spatial and seasonal (spring and summer) distributions of LST-NDVI relations over Europe, using long-term (2000-2017) MODIS images. Overlaying the LST-NDVI relations on the European biome map revealed that relations between LST and NDVI were highly diverse among the various biomes and throughout the entire study period (March-August). During the spring season (March-May), 80% of the European domain, across all biomes, showed the dominance of significant positive relations. However, during the summer season (June-August), most of the biomes-except the northern ones-turned to negative correlation. This study demonstrates that the drought/vegetation/stress spectral indices, based on the prevalent hypothesis of an inverse LST-NDVI correlation, are spatially and temporally dependent. These negative correlations are not valid in regions where energy is the limiting factor (e.g., in the drier regions in the southern and eastern extents of the domain) or during specific periods of the year (e.g., the spring season). Consequently, it is essential to re-examine this assumption and restrict applications of such an approach only to areas and periods in which negative correlations are observed. Predicted climate change will lead to an increase in temperature in the coming decades (i.e., increased LST), as well as a complex pattern of precipitation changes (i.e., changes of NDVI). Thus shifts in plant species locations are expected to cause a redistribution of biomes. © 2019 by the authors.

פותח על ידי קלירמאש פתרונות בע"מ -
הספר "אוצר וולקני"
אודות
תנאי שימוש
Spatial and seasonal patterns in vegetation growth-limiting factors over Europe
20

Karnieli, A., The Remote Sensing Laboratory, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University, Sede Boker Campus, Sede Boker, 84990, Israel;, Midreshet Ben-Gurion, 85280, Israel; Silver, M., The Remote Sensing Laboratory, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University, Sede Boker Campus, Sede Boker, 84990, Israel;  Panov, N., The Remote Sensing Laboratory, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University, Sede Boker Campus, Sede Boker, 84990, Israel; Varghese, D., The Remote Sensing Laboratory, The Jacob Blaustein Institutes for Desert Research, Ben-Gurion University, Sede Boker Campus, Sede Boker, 84990, Israel; Chrysoulakis, N., Remote Sensing Lab, Institute of Applied and Computational Mathematics, FORTH, Vassilika Vouton, Heraklion, 70013, Greece; Provenzale, A., Institute of Geosciences and Earth Resources, National Research Council, Pisa, 56124, Italy

Spatial and seasonal patterns in vegetation growth-limiting factors over Europe

Water and energy are recognized as the most influential climatic vegetation growth-limiting factors. These factors are usually measured from ground meteorological stations. However, since both vary in space, time, and scale, they can be assessed by satellite-derived biophysical indicators. Energy, represented by land surface temperature (LST), is assumed to resemble air temperature; and water availability, related to precipitation, is represented by the normalized difference vegetation index (NDVI). It is hypothesized that positive correlations between LST and NDVI indicate energy-limited conditions, while negative correlations indicate water-limited conditions. The current project aimed to quantify the spatial and seasonal (spring and summer) distributions of LST-NDVI relations over Europe, using long-term (2000-2017) MODIS images. Overlaying the LST-NDVI relations on the European biome map revealed that relations between LST and NDVI were highly diverse among the various biomes and throughout the entire study period (March-August). During the spring season (March-May), 80% of the European domain, across all biomes, showed the dominance of significant positive relations. However, during the summer season (June-August), most of the biomes-except the northern ones-turned to negative correlation. This study demonstrates that the drought/vegetation/stress spectral indices, based on the prevalent hypothesis of an inverse LST-NDVI correlation, are spatially and temporally dependent. These negative correlations are not valid in regions where energy is the limiting factor (e.g., in the drier regions in the southern and eastern extents of the domain) or during specific periods of the year (e.g., the spring season). Consequently, it is essential to re-examine this assumption and restrict applications of such an approach only to areas and periods in which negative correlations are observed. Predicted climate change will lead to an increase in temperature in the coming decades (i.e., increased LST), as well as a complex pattern of precipitation changes (i.e., changes of NDVI). Thus shifts in plant species locations are expected to cause a redistribution of biomes. © 2019 by the authors.

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