Spatial uncertainty analysis when mapping natural resources using remotely sensed data

Guangxing Wang, George Z. Gertner

Research output: Chapter in Book/Report/Conference proceedingChapter


Combining field plot observations and remotely sensed data has been widely used to generate spatially explicit estimates of natural resources such as leaf area index, forest biomass/carbon, biodiversity, soil types, and soil erosion (Wang et al. 2007, 2009b; Mascaro et al. 2011a; Lu et al. 2012). The used data and information possess a large amount of uncertainties and errors (Wang et al. 2009b, 2011). The procedures to generate maps of natural resources also lead to uncertainties (Wang et al. 2005). Therefore, natural resource estimates are associated with uncertainties (Gertner et al. 1995, 1996, 2002a, 2002b; Fang et al. 2002; Sierra et al. 2007; Larocque et al. 2008; Nabuurs et al. 2008; Mascaro et al. 2011b). Using the obtained maps in decision supports will definitely result in numerous risks (Heath and Smith 2000). Thus, how to quantify and reduce uncertainties of natural resource estimates is becoming very important.

Original languageEnglish (US)
Title of host publicationRemote Sensing of Natural Resources
EditorsGuangxing Wang, Qihao Weng
PublisherCRC Press
Number of pages21
ISBN (Electronic)9781466556935
ISBN (Print)9781466556928
StatePublished - Jan 1 2013

ASJC Scopus subject areas

  • General Agricultural and Biological Sciences
  • General Environmental Science
  • General Engineering
  • General Earth and Planetary Sciences


Dive into the research topics of 'Spatial uncertainty analysis when mapping natural resources using remotely sensed data'. Together they form a unique fingerprint.

Cite this