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, 373–378, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-373-2020
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W12-2020, 373–378, 2020
https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-373-2020

  06 Nov 2020

06 Nov 2020

A LONG-TERM LAND COVER AND LAND USE MAPPING METHODOLOGY FOR THE ANDEAN AMAZON

M. O. Borja1,4, R. Camargo2,4, N. Moreno3,4, E. Turpo3,4,5, and S. Villacis1,4 M. O. Borja et al.
  • 1Fundación EcoCiencia, San Ignacio E12-143 and Humboldt, Quito, Ecuador
  • 2Fundación Amigos de la Naturaleza/FAN, Km. 7 1/2 Doble Vía La Guardia, Bolivia
  • 3Instituto del Bien Común/IBC, Av. Salaverry 818, Lima 11, Peru
  • 4Member – Red Amazónica de Información Socioambiental Georreferenciada/RAISG
  • 5Universidad Nacional Agraria La Molina, PDRH, Av. La Molina s/n La Molina, Lima-Perú

Keywords: Andes, Remote sensing, Land use, Amazon, Monitoring, Bolivia, Ecuador, Peru

Abstract. The data developed by the MapBiomas Amazon initiative ( http://amazonia.mapbiomas.org/ ) led by the Amazon Geo-referenced Socio-environmental Information Network’s (RAISG) is of unprecedented spatial and temporal resolution for the Andes region. It’s comprised by a series of annual maps for the years 2000 to 2017 that allow to monitor the extent of transformation in this region using a single regional methodological approach. Several variables were included to solve Andes-specific methodological challenges and they represent adaptations of RAISG’s Amazonian methodology to the Andean region. Among such, is the use of the novel NDFIb index (Turpo, 2018), an adaptation of the NDFI index that aims at mapping Andean Wetlands. Glaciers identification was aided by the fractional abundance of snow (Turpo, 2018), as well as small water bodies identification with McFeeters (1996) NDWI water index. This experience unfolds promising accessibility to novel land cover and land use regional reconstructions and comparisons possible only by the use of large-scale cloud-computing data processing tools, open source technology, spatially and temporally comprehensive remote sensing data, along with RAISG’s standardized protocols and frameworks.