- author = "Joshua Bongard and Anuradha Kodali and Marcin Szubert and Kamalika Das and Sangram Ganguly",
- title = "Understanding Climate-Vegetation Interactions in Global Rainforests Through a GP-Tree Analysis",
- year = "2017",
- keywords = "genetic algorithms, genetic programming, earth resources and remote sensing",
- bibsource = "OAI-PMH server at ntrs.nasa.gov",
- coverage = "Unclassified, Unlimited, Publicly available",
- date = "20170918",
- identifier = "Document ID: 20170011183",
- oai = "oai:casi.ntrs.nasa.gov:20170011183",
- rights = "Copyright, Public use permitted",
-
URL = "
http://hdl.handle.net/2060/20170011183",
- abstract = "The tropical rainforests are the largest reserves of terrestrial carbon sink and therefore, the future of these rainforests is a question that is of immense importance in the geoscience research community. With the recent severe Amazonian droughts in 2005 and 2010 and ongoing drought since 2000 in the Congo region there is growing concern that these forests could succumb to precipitation reduction, causing extensive carbon release and feedback to the carbon cycle. Contradicting research has claimed that these forests are resilient to such extreme climatic events. A significant reason behind these diverse conclusions is the lack of a holistic spatio-temporal analysis of the remote sensing data available for these regions. Small scale studies that use statistical correlation measure and simple linear regression to model the climate-vegetation interactions have suffered from the lack of complete data representation and the use of simple (linear) models that fail to represent physical processes accurately, thereby leading to inconclusive or incorrect predictions about the future. In this paper we use a genetic programming (GP) based approach called symbolic regression for discovering equations that govern the vegetation climate dynamics in the rainforests. Expecting micro-regions within the rainforests to have unique characteristics compared to the overall general characteristics, we use a modified regression-tree based hierarchical partitioning of the space and build a nonlinear GP model for each partition. The discovery of these equations reveal very interesting characteristics about the Amazon and the Congo rainforests. Overall it shows that the rainforests exhibit tremendous resiliency in the face of severe droughts. Based on the partitioning of the observed data points, we can conclude that in the absence of adequate precipitation, the trees adopt to reach a different steady state and recover as soon as precipitation is back to normal.",
-
notes = "Submitted to ECML PKDD 2017 ecmlpkdd2017.ijs.si
ARC-E-DAA-TN42006; The European Conference on Machine
Learning \& Principles and Practice of Knowledge
Discovery in Databases (ECML-PKDD 2017) ; 18-22 Sep.
2017; Skopje; Macedonia
May 2018 This requested resource (Online NTRS full-text PDF) is no longer available from NTRS. email at help@sti.nasa.gov also known as \cite{oai:casi.ntrs.nasa.gov:20170011183}",
