Browsing by Subject "VEGETATION INDEX"

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  • Abdi, Abdulhakim; Carrié, Romain; Sidemo-Holm, William; Cai, Zhanzhang; Boke Olén, Niklas; Smith, Henrik G; Eklundh, Lars; Ekroos, Johan Edvard (2021)
    Increasing land-use intensity is a main driver of biodiversity loss in farmland, but measuring proxies for land-use intensity across entire landscapes is challenging. Here, we develop a novel method for the assessment of the impact of land-use intensity on biodiversity in agricultural landscapes using remote sensing parameters derived from the Sentinel-2 satellites. We link crop phenology and productivity parameters derived from time-series of a two-band enhanced vegetation index with biodiversity indicators (insect pollinators and insect-pollinated vascular plants) in agricultural fields in southern Sweden, with contrasting land management (i.e. conventional and organic farming). Our results show that arable land-use intensity in cereal systems dominated by spring-sown cereals can be approximated using Sentinel-2 productivity parameters. This was shown by the significant positive correlations between the amplitude and maximum value of the enhanced vegetation index on one side and farmer reported yields on the other. We also found that conventional cereal fields had 17% higher maximum and 13% higher amplitude of their enhanced vegetation index than organic fields. Sentinel-2 derived parameters were more strongly correlated with the abundance and species richness of bumblebees and the richness of vascular plants than the abundance and species richness of butterflies. The relationships we found between biodiversity and crop production proxies are consistent with predictions that increasing agricultural land-use intensity decreases field biodiversity. The newly developed method based on crop phenology and productivity parameters derived from Sentinel-2 data serves as a proof of concept for the assessment of the impact of land-use intensity on biodiversity over cereal fields across larger areas. It enables the estimation of arable productivity in cereal systems, which can then be used by ecologists and develop tools for land managers as a proxy for land-use intensity. Coupled with spatially explicit databases on agricultural land-use, this method will enable crop-specific cereal productivity estimation across large geographical regions.
  • Pfeifer, Marion; Gonsamo, Alemu; Disney, Mathias; Pellikka, Petri; Marchant, Rob (2012)
  • Wang, Siyu; Lu, Xinchen; Cheng, Xiao; Li, Xianglan; Peichl, Matthias; Mammarella, Ivan (2018)
    Recent efforts have been made to monitor the seasonal metrics of plant canopy variations globally from space, using optical remote sensing. However, phenological estimations based on vegetation indices (VIs) in high-latitude regions such as the pan-Arctic remain challenging and are rarely validated. Nevertheless, pan-Arctic ecosystems are vulnerable and also crucial in the context of climate change. We reported the limitations and challenges of using MODerate-resolution Imaging Spectroradiometer (MODIS) measurements, a widely exploited set of satellite measurements, to estimate phenological transition dates in pan-Arctic regions. Four indices including normalized vegetation difference index (NDVI), enhanced vegetation index (EVI), phenology index (PI), plant phenological index (PPI) and a MODIS Land Cover Dynamics Product MCD12Q2, were evaluated and compared against eddy covariance (EC) estimates at 11 flux sites of 102 site-years during the period from 2000 to 2014. All the indices were influenced by snow cover and soil moisture during the transition dates. While relationships existed between VI-based and EC-estimated phenological transition dates, the R-2 values were generally low (0.01-0.68). Among the VIs, PPI-estimated metrics showed an inter-annual pattern that was mostly closely related to the EC-based estimations. Thus, further studies are needed to develop region-specific indices to provide more reliable estimates of phenological transition dates.
  • Räsänen, Tuomas; Juutinen, Sari; Aurela, Mika; Virtanen, Tarmo (2019)
    Remote sensing based biomass estimates in Arctic areas are usually produced using coarse spatial resolution satellite imagery, which is incapable of capturing the fragmented nature of tundra vegetation communities. We mapped aboveground biomass using field sampling and very high spatial resolution (VHSR) satellite images (QuickBird, WorldView-2 and WorldView-3) in four different Arctic tundra or peatland sites with low vegetation located in Russia, Canada, and Finland. We compared site-specific and cross-site empirical regressions. First, we classified species into plant functional types and estimated biomass using easy, non-destructive field measurements (cover, height). Second, we used the cover/height-based biomass as the response variable and used combinations of single bands and vegetation indices in predicting total biomass. We found that plant functional type biomass could be predicted reasonably well in most cases using cover and height as the explanatory variables (adjusted R-2 0.21-0.92), and there was considerable variation in the model fit when the total biomass was predicted with satellite spectra (adjusted R-2 0.33-0.75). There were dissimilarities between cross-site and site-specific regression estimates in satellite spectra based regressions suggesting that the same regression should be used only in areas with similar kinds of vegetation. We discuss the considerable variation in biomass and plant functional type composition within and between different Arctic landscapes and how well this variation can be reproduced using VHSR satellite images. Overall, the usage of VHSR images creates new possibilities but to utilize them to full potential requires similarly more detailed in-situ data related to biomass inventories and other ecosystem change studies and modelling.