Classification System Drives Disagreement Among Brazilian Vegetation Maps at a Sample Area of the Semiarid Caatinga


Bontempo E., DEMİREL M. C. , Corsini C., Martins F., Valeriano D.

2020 IEEE Latin American GRSS ISPRS Remote Sensing Conference (LAGIRS), Santiago, Chile, Chile, 22 - 26 March 2020 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/lagirs48042.2020.9165656
  • City: Santiago, Chile
  • Country: Chile
  • Keywords: Land Cover Classification, LCC, Classification Semantics, Caatinga Vegetation Mapping, PLANT FUNCTIONAL TYPES

Abstract

The mapping of vegetation and Land Cover (LC) is important for research and for public policy planning but, in Brazil, although diverse maps exist there are few studies comparing them. The semiarid region of the Caatinga, in northeastern Brazil is an area long neglected by scientific research and its vegetation is diverse and relatively rich despite years of human occupation and very little preservation effort. In this study we make a comparison between the main maps made for the Caatinga from four different sources: IBGE (Brazilian Institute of Geography and Statistics), TCN (Third National Communication), ProBio (Project for Conservation and Sustainable Use of Biological Biodiversity) and MapBiomas. We also test these maps against well-known Land Cover maps from ESA and NASA: ESA's GlobCover and Climate Change Initiative (CCI) Land Cover, and NASA's MODIS MCD12Q1, This was done on a sample area where many of the Caatinga's vegetation physiognomies can be found, using well-established Difference metrics and the new SPAtial EFficiency (SPAEF) algorithm as they present complementary viewpoints to test the correspondence of mapped classes as well as that of their spatial patterns, Our results show considerable disagreement between the maps tested and their class semantics, with IBGE's and ProBio's being the most similar among all national maps and Mapliiomas' the most closely related to global LC maps, The nature of the observed disagreement between these maps shows they diverge not only in the application of their classification systems, but also in their mapped spatial pattern, signaling the need for a better classification system and a better map of vegetation and land cover for the region,