- The most biologically diverse savanna on earth is a major frontier of LCLUC
- It is yet unknown where high-impact land-use transitions are concentrated and the main drivers of those changes
- Urgent need to develop detection methods and novel data sets to inform decision-making a to address biodiversity loss and mitigate climate change impacts
- Use of Earth Observation data, time series analyses, and machine learning algorithms to expand the spatial and temporal data on agricultural development and a novel data set on irrigated agriculture in combination with spatial analyses of socioeconomic influences
How is land cover/use changed?
South America’s largest savanna, the Cerrado, has lost 50% of its original vegetation became a new agricultural expansion frontier, particularly since savanna vegetation is easier to clear than denser, more deeply rooted tropical forests [1]. Brazil is now the world’s largest cattle exporter, with much of this production occurring in Cerrado pastures. Row crops have spread rapidly throughout the region (Fig. 1), as they became more profitable than cattle ranching [2] over the last two decades.
Figure 1: Extent and types of agricultural land‐use change in a portion of the Cerrado from 2003 to 2016 [3].
Why is this Important?
Transitions to crops from native vegetation have a negative feedback on water recycling [3], which is increasingly an issue in the Cerrado. At the same time, regional climate changes are increasing the risk of catastrophic crop failures and land abandonment [4]. These clusters of LCLUCs vary considerably in their biophysical characteristics, socioeconomic conditions, and patterns of agricultural decision‐making [5], but to date these have not been analyzed to identify high‐impact LCLUC hotspots across the Cerrado.
How satellite data are being used to inform decision making and Earth Action?
Earth Observation data, time series analyses, and machine learning are leveraged for LCLUC detection to characterize land-use transitions (2000‐2022) LCLUC will be analyzed with environmental and socioeconomic data to understand global (e.g., climate) to local (e.g. frontier age) influences. Spatial integration of these multi‐disciplinary data sets will serve as a basis to assess climatic risks to agriculture and ultimately orient better policy decisions regarding climate change adaptation.
[1] Galford et al., 2010. Earth Interactions, 14(15), 1-24., http://doi.org/10.1175/2010EI327.1. [2] Macedo et al. 2012. Proc. of the Nat. Acad. of Sciences, 109(4), 1341–1346. http://doi.org/10.1073/pnas.1111374109. [3] Spera et al., 2016. Global Change Biology, 22(10), 3405–3413. http://doi.org/10.1111/gcb.13298. [4] Rattis, et al., 2021. Nature Climate Change, 11(12), 1098–1104, http://doi.org/10.1038/s41558‐021‐01214‐3. [5] Spera et al., 2017. Tropical Conservation Science, 10, 1940082917720662.
Project Investigator: Gillian Galford, University of Vermont, VT, USA; Email: gillian.galford@uvm.edu