Volume XLI-B8
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 1147-1152, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-1147-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B8, 1147-1152, 2016
https://doi.org/10.5194/isprs-archives-XLI-B8-1147-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

  24 Jun 2016

24 Jun 2016

AN OBJECT-BASED WORKFLOW DEVELOPED TO EXTRACT AQUACULTURE PONDS FROM AIRBORNE LIDAR DATA: A TEST CASE IN CENTRAL VISAYAS, PHILIPPINES

R. A. Loberternos1, W. P. Porpetcho1, J. C. A. Graciosa1, R. R. Violanda1,2, A. G. Diola1,3, D. T. Dy1,3, and R. E. S. Otadoy1,2 R. A. Loberternos et al.
  • 1USC Phil-LiDAR Research Center, Fr. Josef Baumgartner Learning Resource Center, University of San Carlos – Talamban Campus, Nasipit, Talamban, 6000 Cebu City, Philippines
  • 2USC Phil-LiDAR Research Center, Fr. Josef Baumgartner Learning Resource Center, University of San Carlos – Talamban Campus, Nasipit, Talamban, 6000 Cebu City, Philippines
  • 3Department of Biology, University of San Carlos, 6000 Cebu City, Philippines

Keywords: Object-Based Image Analysis (OBIA), multiresolution segmentation, coastal land use, fishpond

Abstract. Traditional remote sensing approach for mapping aquaculture ponds typically involves the use of aerial photography and high resolution images. The current study demonstrates the use of object-based image processing and analyses of LiDAR-data-generated derivative images with 1-meter resolution, namely: CHM (canopy height model) layer, DSM (digital surface model) layer, DTM (digital terrain model) layer, Hillshade layer, Intensity layer, NumRet (number of returns) layer, and Slope layer. A Canny edge detection algorithm was also performed on the Hillshade layer in order to create a new image (Canny layer) with more defined edges. These derivative images were then used as input layers to perform a multi-resolution segmentation algorithm best fit to delineate the aquaculture ponds. In order to extract the aquaculture pond feature, three major classes were identified for classification, including land, vegetation and water. Classification was first performed by using assign class algorithm to classify Flat Surfaces to segments with mean Slope values of 10 or lower. Out of these Flat Surfaces, assign class algorithm was then performed to determine Water feature by using a threshold value of 63.5. The segments identified as Water were then merged together to form larger bodies of water which comprises the aquaculture ponds. The present study shows that LiDAR data coupled with object-based classification can be an effective approach for mapping coastal aquaculture ponds. The workflow currently presented can be used as a model to map other areas in the Philippines where aquaculture ponds exist.