Space–time LAI variability in Northern Puglia (Italy) from SPOT VGT data

Abstract

The vegetation space–time variability during 1999–2010 in the North of the Apulian region (Southern Italy) was analysed using SPOT VEGETATION (VGT) sensor data. Three bands of VEGETATION (RED, NIR and SWIR) were used to implement the vegetation index named reduced simple ratio (RSR) to derive leaf area index (LAI). The monthly average LAI is an indicator of biomass and canopy cover, while the difference between the annual maximum and minimum LAI is an indicator of annual leaf turnover. The space–time distribution of LAI at the catchment scale was analysed over the examined period to detect the consistency of vegetation dynamics in the study area. A diffuse increase of LAI was observed in the examined years that cannot be directly explained only in terms of increasing water availability. Thus, in order to explain such a general behaviour in terms of climatic factors, the analysis was performed upon stratification of land cover classes, focusing on the most widespread species: forest and wheat. An interesting ascending–descending behaviour was observed in the relationship between inter-annual increments of maximum LAI and rainfall, and in particular, a strong negative correlation was found when the rainfall amount in January and February exceeded a critical threshold of about 100 mm.

Durum Wheat Cover Analysis in the Scope of Policy and Market Price Changes: A Case Study in Southern Italy

“…On the other hand, the LAI annual mean shows a dropdown in 2005 but not a marked general trend ( Figure 6A), denoting that at the annual scale, the heterogeneity in vegetation cover and local effects prevails in interannual climatic forcing. Consistently, it has been already observed in this region, that the LAI Spatial Mean behavior in time is affected more by mean temperature and rainfall monthly distribution than by annual rainfall amount [31]. In addition, ( Figure 6C) shows a combined plot of LAI Spatial Mean and LAI Spatial STD.…”

“…Mainly because of the relatively short period of the study (15 years), the results showed a prevalent impact of the human driving factors in comparison with the physical ones related previously in the literature, such as [31,33,44,69] in affecting the year-to-year changes in the vegetation cover.…”

“…Analyzing the correlation with respect to time ( Figure 6B), we observe two distinctive relationships. A nonlinear relationship during the growing phase characterized by a convex shape, where the main intra-annual divergence between LAI Spatial Mean and LAI Spatial STD is found in the earlier growth stage of durum wheat, i.e., in January and February, the crucial months in the vegetative period that provide the minimum annual temperature and a significant fraction of the total annual rainfall amount [31]. A linear relationship is found after the peaks i.e., in the harvesting phase.…”

Land Use Change Impact on Flooding Areas: The Case Study of Cervaro Basin (Italy)

“…Vegetation indices were used to enhance the detection of vegetated areas: leaf area index (LAI) and modified soil adjusted vegetation index (MSAVI). LAI is commonly used in agronomic and environmental modeling [36][37][38][39][40][41]. In this study, LAI was evaluated through a semi-empirical model derived from the Lambert-Beer law (1): LANDSAT-TM digital numbers were firstly converted to Top of Atmosphere (TOA) reflectance and corrected for sun elevation angle by using gains, offsets, and brightness temperature coefficients provided in each scene metadata and related standard procedures suggested by the USGS [34].…”

“…LAI is commonly used in agronomic and environmental modeling [36][37][38][39][40][41]. In this study, LAI was evaluated through a semi-empirical model derived from the Lambert-Beer law (1):…”