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High-resolution planet satellite imagery and multi-temporal surveys to predict risk of tree mortality in tropical eucalypt forestry.
Pascual, Adrián; Tupinambá-Simões, Frederico; Guerra-Hernández, Juan; Bravo, Felipe.
Affiliation
  • Pascual A; Department of Geographical Sciences, University of Maryland, College Park, MD, 20742, USA; Universidad de Valladolid | UVA · University Institute for Research in Sustainable Forest Management, Palencia, 34004, Spain. Electronic address: adrian.pascual.arranz@gmail.com.
  • Tupinambá-Simões F; Universidad de Valladolid | UVA · University Institute for Research in Sustainable Forest Management, Palencia, 34004, Spain; Sustainable Forest Management Research Institute UVa-INIA, Avda. Madrid 50, 34071, Palencia, Spain.
  • Guerra-Hernández J; 3edata, Centro de iniciativas empresariais, Fundación CEL, 27004, Lugo, Spain; Forest Research Centre, School of Agriculture, University of Lisbon, Tapada da Ajuda, 1349-017, Lisbon, Portugal.
  • Bravo F; Universidad de Valladolid | UVA · University Institute for Research in Sustainable Forest Management, Palencia, 34004, Spain; Sustainable Forest Management Research Institute UVa-INIA, Avda. Madrid 50, 34071, Palencia, Spain.
J Environ Manage ; 310: 114804, 2022 May 15.
Article in En | MEDLINE | ID: mdl-35240567
Global high-resolution imagery is a well-assimilated technology in forest mapping. The release of the Norway's International Climate & Forests Initiative (NICFI) Planet tropical basemaps time-series starting in 2015 at a 4.77-m resolution represents a unique opportunity to forecast climate change consequences such as drought episodes. Using multi-temporal ground surveys over 144 plots and publicly available high-resolution Planet dove time-series imagery we evaluate forest mortality patterns driven by imaging spectroscopy methods in Mato Grosso (Brazil) over an area planted with eucalypts severely affected by the 2019 drought. Changes in vegetation indexes before and after the 2019 drought were modelled using the effective logistic regression modelling to explain variation in tree mortality between the surveys, the dependent variable. We aimed to straightforwardly model tree mortality using change vectors in Planet's image mosaics co-registering in time with the observed tree mortality measurements in the field. The results showed differences in Normalized Difference Vegetation Index (NDVI) as the most significant predictor variable under the effective logistic regression modelling performed. The efficacy of 80.98% in concordance pairs correctly classified represented 0.81 of area under the Receiver Operating Curve (ROC). The release of the 2015-2020 Planet imagery in the tropics at 4.77-m resolution represents a valuable dataset to better understand previous natural disturbances and a powerful technology to detect in advance, and monthly after September 2020, eucalypt areas prone to harmful and increasingly frequent water-stress episodes.
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Full text: 1 Database: MEDLINE Main subject: Trees / Satellite Imagery Type of study: Etiology_studies / Prognostic_studies / Risk_factors_studies Language: En Journal: J Environ Manage Year: 2022 Type: Article

Full text: 1 Database: MEDLINE Main subject: Trees / Satellite Imagery Type of study: Etiology_studies / Prognostic_studies / Risk_factors_studies Language: En Journal: J Environ Manage Year: 2022 Type: Article