Research Papers

Comparison between WorldView-2 and SPOT-5 images in mapping the bracken fern using the random forest algorithm

[+] Author Affiliations
John Odindi, Zinhle Ngubane, Onisimo Mutanga

University of KwaZulu-Natal, School of Agricultural, Earth and Environmental Sciences, Scottsville Private Bag X01, Pietermaritzburg 3209, South Africa

Elhadi Adam

University of KwaZulu-Natal, School of Agricultural, Earth and Environmental Sciences, Scottsville Private Bag X01, Pietermaritzburg 3209, South Africa

University of Witwatersrand, School of Geography, Archaeology and Environmental Studies, Geography & Environmental Studies Division, Private Bag 3, Witwatersrand 2050, South Africa

Rob Slotow

University of KwaZulu-Natal, School of Life Sciences, Private Bag X 54001, Durban 4000, South Africa

J. Appl. Remote Sens. 8(1), 083527 (Nov 06, 2014). doi:10.1117/1.JRS.8.083527
History: Received August 4, 2014; Revised October 7, 2014; Accepted October 13, 2014
Text Size: A A A

Abstract.  Plant species invasion is known to be a major threat to socioeconomic and ecological systems. Due to high cost and limited extents of urban green spaces, high mapping accuracy is necessary to optimize the management of such spaces. We compare the performance of the new-generation WorldView-2 (WV-2) and SPOT-5 images in mapping the bracken fern [Pteridium aquilinum (L) kuhn] in a conserved urban landscape. Using the random forest algorithm, grid-search approaches based on out-of-bag estimate error were used to determine the optimal ntree and mtry combinations. The variable importance and backward feature elimination techniques were further used to determine the influence of the image bands on mapping accuracy. Additionally, the value of the commonly used vegetation indices in enhancing the classification accuracy was tested on the better performing image data. Results show that the performance of the new WV-2 bands was better than that of the traditional bands. Overall classification accuracies of 84.72 and 72.22% were achieved for the WV-2 and SPOT images, respectively. Use of selected indices from the WV-2 bands increased the overall classification accuracy to 91.67%. The findings in this study show the suitability of the new generation in mapping the bracken fern within the often vulnerable urban natural vegetation cover types.

Figures in this Article
© 2014 Society of Photo-Optical Instrumentation Engineers

Citation

John Odindi ; Elhadi Adam ; Zinhle Ngubane ; Onisimo Mutanga and Rob Slotow
"Comparison between WorldView-2 and SPOT-5 images in mapping the bracken fern using the random forest algorithm", J. Appl. Remote Sens. 8(1), 083527 (Nov 06, 2014). ; http://dx.doi.org/10.1117/1.JRS.8.083527


Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging & repositioning the boxes below.

Related Book Chapters

Topic Collections

Advertisement
  • Don't have an account?
  • Subscribe to the SPIE Digital Library
  • Create a FREE account to sign up for Digital Library content alerts and gain access to institutional subscriptions remotely.
Access This Article
Sign in or Create a personal account to Buy this article ($20 for members, $25 for non-members).
Access This Proceeding
Sign in or Create a personal account to Buy this article ($15 for members, $18 for non-members).
Access This Chapter

Access to SPIE eBooks is limited to subscribing institutions and is not available as part of a personal subscription. Print or electronic versions of individual SPIE books may be purchased via SPIE.org.