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A geographically weighted random forest approach for evaluate forest change drivers in the Northern Ecuadorian Amazon

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  • Fabián Santos
  • Valerie Graw
  • Santiago Bonilla

Abstract

The Tropical Andes region includes biodiversity hotspots of high conservation priority whose management strategies depend on the analysis of forest dynamics drivers (FDDs). These depend on complex social and ecological interactions that manifest on different space–time scales and are commonly evaluated through regression analysis of multivariate datasets. However, processing such datasets is challenging, especially when time series are used and inconsistencies in data collection complicate their integration. Moreover, regression analysis in FDD characterization has been criticized for failing to capture spatial variability; therefore, alternatives such as geographically weighted regression (GWR) have been proposed, but their sensitivity to multicollinearity has not yet been solved. In this scenario, we present an innovative methodology that combines techniques to: 1) derive remote sensing time series products; 2) improve census processing with dasymetric mapping; 3) combine GWR and random forest (RF) to derive local variables importance; and 4) report results based in a clustering and hypothesis testing. We applied this methodology in the northwestern Ecuadorian Amazon, a highly heterogeneous region characterized by different active fronts of deforestation and reforestation, within the time period 2000–2010. Our objective was to identify linkages between these processes and validate the potential of the proposed methodology. Our findings indicate that land-use intensity proxies can be extracted from remote sensing time series, while intercensal analysis can be facilitated by calculating population density maps. Moreover, our implementation of GWR with RF achieved accurate predictions above the 74% using the out-of-bag samples, demonstrating that derived RF features can be used to construct hypothesis and discuss forest change drivers with more detailed information. In the other hand, our analysis revealed contrasting effects between deforestation and reforestation for variables related to suitability to agriculture and accessibility to its facilities, which is also reflected according patch size, land cover and population dynamics patterns. This approach demonstrates the benefits of integrating remote sensing–derived products and socioeconomic data to understand coupled socioecological systems more from a local than a global scale.

Suggested Citation

  • Fabián Santos & Valerie Graw & Santiago Bonilla, 2019. "A geographically weighted random forest approach for evaluate forest change drivers in the Northern Ecuadorian Amazon," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-37, December.
  • Handle: RePEc:plo:pone00:0226224
    DOI: 10.1371/journal.pone.0226224
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    References listed on IDEAS

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    1. Cho, Seong-Hoon & Lambert, Dayton M. & Kim, Seung Gyu & Jung, Suhyun, 2009. "Extreme coefficients in Geographically Weighted Regression and their effects on mapping," 2009 Annual Meeting, July 26-28, 2009, Milwaukee, Wisconsin 49117, Agricultural and Applied Economics Association.
    2. Michael Reibel & Aditya Agrawal, 2007. "Areal Interpolation of Population Counts Using Pre-classified Land Cover Data," Population Research and Policy Review, Springer;Southern Demographic Association (SDA), vol. 26(5), pages 619-633, December.
    3. Charrad, Malika & Ghazzali, Nadia & Boiteau, Véronique & Niknafs, Azam, 2014. "NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i06).
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