Temperate Deciduous Forest Ecosystems of the World

1992 ◽  
Vol 19 (5) ◽  
pp. 584
Author(s):  
John R. Packham ◽  
E. Rohrig ◽  
B. Ulrich
Keyword(s):  
Forest Ecosystems ◽  
Deciduous Forest ◽  
Temperate Deciduous Forest ◽  
The World ◽  
Temperate Deciduous

scholarly journals Does NDVI explain spatial and temporal variability in sap velocity in temperate forest ecosystems?

2018 ◽  
Author(s):  
Anne J. Hoek van Dijke ◽  
Kaniska Mallick ◽  
Adriaan J. Teuling ◽  
Martin Schlerf ◽  
Miriam Machwitz ◽  
...  
Keyword(s):  
Temperate Forest ◽  
Vegetation Index ◽  
Forest Ecosystems ◽  
Normalized Difference Vegetation Index ◽  
Deciduous Forest ◽  
Significant Negative Correlation ◽  
Small Scale ◽  
Spatial And Temporal Variability ◽  
Temperate Deciduous Forest ◽  
Temperate Deciduous

Abstract. There is a need for a better understanding of the link between vegetation characteristics and tree transpiration to facilitate satellite derived transpiration estimation. Many studies use the normalized difference vegetation index (NDVI), a proxy for tree biophysical characteristics, to estimate evapotranspiration. In this study we investigated the link between sap velocity and 30 m resolution Landsat derived NDVI for twenty days during two contrasting precipitation years in a temperate deciduous forest catchment. Sap velocity was measured in the Attert catchment in Luxembourg in 25 plots of 20 × 20 m covering three geologies with sensors installed in 2–4 trees per plot. The results show that sap velocity and NDVI were significantly positively correlated in April, i.e., NDVI successfully captured the pattern of sap velocity during the phase of green-up. After green-up, a significant negative correlation was found during half of the studied days. During a dry period, sap velocity was uncorrelated to NDVI, but influenced by geology and aspect. In summary, in our study area, the correlation between sap velocity and NDVI was not constant, but varied with phenology and water availability. The same behaviour was found for the Enhanced Vegetation Index (EVI). This suggests that methods using NDVI or EVI to predict small-scale variability in (evapo)transpiration should be carefully applied and that NDVI and EVI cannot be used to scale sap velocity to stand level transpiration in temperate forest ecosystems.

scholarly journals Does the Normalized Difference Vegetation Index explain spatial and temporal variability in sap velocity in temperate forest ecosystems?

2019 ◽  
Vol 23 (4) ◽  
pp. 2077-2091 ◽  
Author(s):  
Anne J. Hoek van Dijke ◽  
Kaniska Mallick ◽  
Adriaan J. Teuling ◽  
Martin Schlerf ◽  
Miriam Machwitz ◽  
...  
Keyword(s):  
Temperate Forest ◽  
Vegetation Index ◽  
Forest Ecosystems ◽  
Normalized Difference Vegetation Index ◽  
Deciduous Forest ◽  
Significant Negative Correlation ◽  
Small Scale ◽  
Spatial And Temporal Variability ◽  
Temperate Deciduous Forest ◽  
Temperate Deciduous

Abstract. Understanding the link between vegetation characteristics and tree transpiration is a critical need to facilitate satellite-based transpiration estimation. Many studies use the Normalized Difference Vegetation Index (NDVI), a proxy for tree biophysical characteristics, to estimate evapotranspiration. In this study, we investigated the link between sap velocity and 30 m resolution Landsat-derived NDVI for 20 days during 2 contrasting precipitation years in a temperate deciduous forest catchment. Sap velocity was measured in the Attert catchment in Luxembourg in 25 plots of 20×20 m covering three geologies with sensors installed in two to four trees per plot. The results show that, spatially, sap velocity and NDVI were significantly positively correlated in April, i.e. NDVI successfully captured the pattern of sap velocity during the phase of green-up. After green-up, a significant negative correlation was found during half of the studied days. During a dry period, sap velocity was uncorrelated with NDVI but influenced by geology and aspect. In summary, in our study area, the correlation between sap velocity and NDVI was not constant, but varied with phenology and water availability. The same behaviour was found for the Enhanced Vegetation Index (EVI). This suggests that methods using NDVI or EVI to predict small-scale variability in (evapo)transpiration should be carefully applied, and that NDVI and EVI cannot be used to scale sap velocity to stand-level transpiration in temperate forest ecosystems.

scholarly journals Can vegetation index track the interannual variation in gross primary production of temperate deciduous forests?

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Fan Liu ◽  
Chuankuan Wang ◽  
Xingchang Wang
Keyword(s):  
Primary Production ◽  
Vegetation Index ◽  
Near Infrared ◽  
Deciduous Forest ◽  
Gross Primary Production ◽  
Growing Season ◽  
Temperate Deciduous Forest ◽  
Near Infrared Reflectance ◽  
Modis Ndvi ◽  
Temperate Deciduous

Abstract Background Vegetation indices (VIs) by remote sensing are widely used as simple proxies of the gross primary production (GPP) of vegetation, but their performances in capturing the inter-annual variation (IAV) in GPP remain uncertain. Methods We evaluated the performances of various VIs in tracking the IAV in GPP estimated by eddy covariance in a temperate deciduous forest of Northeast China. The VIs assessed included the normalized difference vegetation index (NDVI), the enhanced vegetation index (EVI), and the near-infrared reflectance of vegetation (NIRv) obtained from tower-radiometers (broadband) and the Moderate Resolution Imaging Spectroradiometer (MODIS), respectively. Results We found that 25%–35% amplitude of the broadband EVI tracked the start of growing season derived by GPP (R2: 0.56–0.60, bias < 4 d), while 45% (or 50%) amplitudes of broadband (or MODIS) NDVI represented the end of growing season estimated by GPP (R2: 0.58–0.67, bias < 3 d). However, all the VIs failed to characterize the summer peaks of GPP. The growing-season integrals but not averaged values of the broadband NDVI, MODIS NIRv and EVI were robust surrogates of the IAV in GPP (R2: 0.40–0.67). Conclusion These findings illustrate that specific VIs are effective only to capture the GPP phenology but not the GPP peak, while the integral VIs have the potential to mirror the IAV in GPP.

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