Assessment of the gross primary production dynamics of a Mediterranean holm oak forest by remote sensing time series analysis

Abstract

Agroforestry ecosystems have a significant social, economic and environmental impact on the development of many regions in the world. On the Iberian Peninsula the Mediterranean agroforestry oak forest known as the dehesa or montado (usually formed by species of the genus Quercus) is considered to be the extreme case of transformation of a Mediterranean forest by human management to provide a wide range of natural resources. The great variability of the Mediterranean climate and the different extensive management practices carried out by humans on the dehesa produces a high spatial and temporal variability in the dynamics of the ecosystem. This leads to a complex pattern of CO 2 exchange between the atmosphere and the ecosystem that can act as a sink or as a source of CO 2 over the years, depending on the various factors interacting with them. It is thus essential to assess the carbon cycle on the dehesa in order to obtain the maximum economic benefits and ensure environmental sustainability. The availability of high-frequency remote sensing time series allows the evolution of an ecosystem to be assessed at different temporal and spatial scales. In this study our overall objective is to assess the gross primary production (GPP) dynamics of a dehesa ecosystem in Central Spain by analysing the time series (2004-2008) of two models: (1) GPP provided by remote sensing images from the MODIS sensor (MOD17A2 product); and (2) GPP estimated by the implementation of a site-specific lightuse efficiency model taking into account local ecological and meteorological parameters. Both models were compared to the production provided by an eddy covariance flux tower located in our study area. Dynamic relationships between models of GPP and precipitation and soil water content were investigated by means of cross-correlations and Granger causality tests. Our results indicate that both models of GPP show a typical dehesa dynamic where there are primarily two layers, the arboreal and the herbaceous strata. However, MODIS underestimates the production of the dehesa in a Mediterranean climate, while our site-specific model produced more similar values and dynamics to those of the eddy covariance tower. The analysis of the dynamic relationships corroborated the strong dynamic link between GPP and available water for plant growth. In conclusion, we succeeded in avoiding the main source of underestimation of the MODIS model by the implementation of a site-specific model. It therefore appears that the different ecological and meteorological parameters used in the MODIS model are primarily responsible for this underestimation. Finally, the Granger causality tests indicate that GPP prediction can be improved by including precipitation or soil water in the models.

First Insights on Soil Respiration Prediction across the Growth Stages of Rainfed Barley Based on Simulated MODIS and Sentinel-2 Spectral Indices

“…The difference between years in carbon emitted versus carbon fixed could be associated to abiotic factors dynamics; better conditions at the beginning of the growing cycle in 2012 resulted in a clear advantage for the plant to produce higher biomass. The inter-annual variability in the carbon flux balances has been shown also by other studies especially in Mediterranean climate regions [87][88][89]. In this aspect, taking into account photorespiration and root biomass values would be a significant contribution to more accurate estimations.…”

Monitoring Mediterranean Oak Decline in a Peri-Urban Protected Area Using the NDVI and Sentinel-2 Images: The Case Study of Castelporziano State Natural Reserve

“…Thanks to these properties, the NDVI can be utilized as an indicator of possible vegetation stress, particularly that due to water shortage or pest diffusion. For these reasons, in the beginning phase of the monitoring, we preferred to use the NDVI index to obtain a comparable result with others similar study cases [14][15][16][17][18][19][20].…”

Assessment of different components of the Carbon flux in forest and agricultural ecosystems using remote sensing data and field measurements

“…determine growth conditions (Dass et al, 2016;Garbulsky et al, 2010) and growing season length (Black et al, 2000;Piao et al, 2007;Wu et al, 2012). Ecological factors, such as soil and vegetation traits (Poulter et al, 2009;Turner et al, 2006) as LAI or tree cover influence GPP (Cicuéndez et al, 2015a;Xie et al, 2019). showed on Mediterranean grasslands that seasonal variations of gross primary production and respiration closely followed variations in the leaf area index, which is in turn strongly determined by the availability of water in the soil.…”

“…All these factors result in a high spatial and temporal variability of the CO2 fluxes among different forest and agricultural ecosystems (Baldocchi et al, 2018;Buchmann, 1999;Han et al, 2007;Rochette et al, 1991) which can act as a sink or as a source of CO2 over the years (Belshe et al, 2013;Cicuéndez et al, 2015a;Houghton, 1996;Meng et al, 2016). Specifically, in Mediterranean climates the variability may be especially high due to the irregular distribution of rainfall and temperature (González-Ubierna et al, 2014;Ma et al, 2007).…”

“…In this climate meteorological factors are usually out of phase (Falge et al, 2002) which difficulties the assessment of GPP and Rs dynamics. Therefore, developing suitable methods and techniques for estimating GPP and Rs are crucial to obtain accurate estimations (Cicuéndez et al, 2015a;Prolingheuer et al, 2010).…”