The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLII-3/W12-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 441–446, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-441-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 441–446, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-441-2020

  06 Nov 2020

06 Nov 2020

A USER-FRIENDLY REMOTE-SENSING WEB-PLATFORM FOR BIODIVERSITY CONSERVATION AND MANAGEMENT IN PROTECTED AREAS

R. O. Chávez1, J. A. Lastra1, D. Valencia2, and I. Díaz-Hormazábal2 R. O. Chávez et al.
  • 1Laboratorio de Geo-Información y Percepción Remota, Instituto de Geografía, Pontificia Universidad Católica de Valparaíso, Av. Brasil 2241, Valparaíso, Chile
  • 2Sección de Monitoreo e Información del SNASPE, Gerencia de Áreas Silvestres Protegidas, Corporación Nacional Forestal, Paseo Bulnes 259, Santiago, Chile

Keywords: npphen, shiny, MODIS, essencial biodiversity variables, phenology

Abstract. The Chilean SNASPE is a complex network of 104 protected areas covering 18.5 million hectares of continental and insular Chile in South America. The geographical complexity and high biodiversity of the SNASPE make difficult to develop a unified monitoring system for conservation and management. In this contribution, we introduce a novel and remote-sensing web-platform for monitoring SNASPE units based completely in open acces data and software. The platform was designed in close cooperation with the Chilean forest service CONAF in order to make it applicable to the whole SNASPE. Following the framework of the Group on Earth Observation - Biodiversity Observation Network (GEO-BON), we used the Essential Biodiversity Variable (EBV) Phenology and MODIS Enhanced Vegetation Index (EVI) data to detect in near-real-time anomalies from the normal annual phenological cycle of vegetation. The platform is based on a flexible non-parametric probabilistic algorithm (the “npphen” R package) capable to reconstruct any type of leaf phenology and to quantify its inter-annual variation by means of confidence intervals around the most probable annual curve. Phenological anomalies are then calculated as a deviation from the expected annual cycle and judged based on their location within the confidence intervals. Anomalies located above 95% confidence interval trigger a “red alert” which is displayed on the web application as soon as the MODIS data become available. This user-friendly platform was implemented in the La Campana National Park giving early alerts of a severe drought in 2019, warning Conaf to implement actions to protect the park from potential wild fires.