Vegetation Height Estimation using Satellite Remote Sensing in Peat Land of Central Kalimantan

Authors

  • Atriyon Julzarika Universitas Gadjah Mada (UGM) & Indonesian National Institute of Aeronautics and Space (LAPAN)
  • Harintaka Harintaka Universitas Gadjah Mada (UGM)
  • Tatik Kartika Indonesian National Institute of Aeronautics and Space (LAPAN)

DOI:

https://doi.org/10.24221/jeap.6.1.2021.3001.024-034

Keywords:

Vegetation height, LiDAR, SAR, Central Kalimantan

Abstract

Vegetation height is an important parameter in monitoring peatlands. Vegetation height can be estimated using remote sensing. Vegetation height can be estimated by utilizing DSM and DTM. The data that can be used are LiDAR, X-SAR, and SRTM C. In this study, LiDAR data is used for DSM2018 and DTM2018 extraction. The purpose of this research is to detect the vegetation height in Central Kalimantan peatlands using remote sensing technology. The research location is in Bakengbongkei, Kalampangan, Central Kalimantan. The integration of X-SAR and SRTM C is used for DSM2000 and DTM2000 extraction. DSM2000, DTM2000, DSM2018, and DTM2018 performed height error correction with tolerance of 1.96? (95%). Then do the geoid undulation correction to EGM2008. The results obtained are DSM and DTM with a similar height reference field. If it meets these conditions it can be calculated the vegetation height estimation. Vegetation height can be obtained using the Differential DEM method. The Changing in vegetation height from 2000 to 2018 can be estimated from the difference in vegetation height from 2000 to vegetation height in 2018. Results of spatial information on vegetation height and its changes need to be tested for the accuracy. This accuracy-test includes a cross section test, height difference test, and comparison with measurements of vegetation height in the field. The results of this research can be used to monitor the changing the vegetation height in peatlands.

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Author Biographies

Atriyon Julzarika, Universitas Gadjah Mada (UGM) & Indonesian National Institute of Aeronautics and Space (LAPAN)

Geodesy Geomatics Engineering, Geomodeling, Geodynamics

Harintaka Harintaka, Universitas Gadjah Mada (UGM)

Photogrammetry and Remote Sensing

Tatik Kartika, Indonesian National Institute of Aeronautics and Space (LAPAN)

Remote Sensing

References

ACKERS, S. H.; DAVIS, R. J.; OLSEN, K. A.; DUGGER, K. M. 2015. The evolution of mapping habitat for northern spotted owls (Strix occidentalis caurina): A comparison of photo‐interpreted, Landsat‐based, and lidar‐based habitat maps. Remote Sensing of Environment, 156, 361–373. https://doi.org/10.1016/j.rse.2014.09.025

ASPRS, 2014. ASPRS Accuracy Standard for Digital Geospatial Data. ASPRS. United States of America.

BAE, S.; REINEKING, B., EWALD, M.; MUELLER, J. 2014. Comparison of airborne lidar, aerial photography, and field surveys to model the habitat suitability of a cryptic forest species – The hazel grouse. International Journal of Remote Sensing, 35, 6469–6489. https://doi.org/10.1080/01431161.2014.955145

BERGEN, K. M.; GOETZ, S. J.; DUBAYAH, R. O.; HENEBRY, G. M.; HUNSAKER, C. T.; IMHOFF, M. L.; … RADELOFF, V. C. 2009. Remote sensing of vegetation 3‐D structure for biodiversity and habitat: Review and implications for lidar and radar spaceborne missions. Journal of Geophysical Research: Biogeosciences, 114, G00E06. https://doi.org/10.1029/2008JG000883

BIRDAL, A.C., AVDAN, U.; TÜRK, T., 2017. Estimating tree heights with images from an unmanned aerial vehicle. Geomatics Nat. Hazards Risk 2017, 8, 1144–1156, doi:10.1080/19475705.2017.1300608.

BRENNER, A.; ZWALLY, H.; BENTLEY, C.; CSATHO B.; HARDING, D.; HOFTON, M.; MINSTER, J.; ROBERTS, L.; SABA, J.; THOMAS, J., 2019. Geoscience Laser Altimeter System (GLAS)—derivation of range and range distributions from laser pulse waveform analysis for surface elevations, roughness, slope, and vegetation heights. AlgorithmTheoretical.

BUCKLEY, D. S.; ISEBRANDS, J.; SHARIK, T. L., 1999. Practical field methods of estimating canopy cover, PAR, and LAI in Michigan Oak and pine stands. North. J. Appl. For., 16(1), pp. 25–32.

CARLSON, K. M.; CURRAN, L. M.; ASNER, G. P., PITTMAN, A. M.; TRIGG, S. N.ADENEY, J. M., 2012. Carbon emissions from forest conversion by Kalimantan oil palm plantations. Nature Climate Change.17586798.

DONG, L.; WU, B., 2008. A comparison of estimating forest canopy height integrating multi-sensor data synergy — A case study in mountain area of Three Gorges. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, ISPRS, Beijing, Vol. 37, pp. 384–379.

FLYNN, T.; TABB, M.; CARANDE, R., 2002. Coherence region shape extraction for vegetation parameter estimation in polarimetric SAR interferometry, in: IEEE International Geoscience and Remote Sensing Symposium. pp. 2596–2598. https://doi.org/10.1109/IGARSS.2002.1026712

FRANKLIN, S. 2000. Remote sensing for sustainable forest management. 10.1201/9781420032857.

FOOD AND AGRICULTURE ORGANIZATION (FAO), 2009. FAO/IIASA/ISRIC/ISSCAS/JRC, harmonized world soil database (version 1.1). FAO, Rome, Italy and IIASA, Luxenburg, Austria.

GARCÍA, M.; SAATCHI, S.; USTIN, S.; BALZTER, H. 2018. Modelling forest canopy height by integrating airborne LiDAR samples with satellite Radar and multispectral imagery. Int. J. Appl. Earth Obs. Geoinf., 66, 159–173.

GEHRKE, S.; MORIN, K.; DOWNEY, M.; BOEHRER, N.; FUCHS, T., 2008. Semi-global matching: An alternative to lidar for dsm generation? Int. Arch. Photogram. Remote Sens. Spat. Inf. Sci., XXXVIII-B1, 1–6.

GILLANI, C,; WOLF, 2006. Adjustment computation: spatial data analysis. John Wiley & Sons, Inc., Hoboken, New Jersey. United States of America.

HILL, R. A.L; BROUGHTON, R. K. 2009. Mapping the understorey of deciduous woodland from leaf‐on and leaf‐off airborne LiDAR data: A case study in lowland Britain. ISPRS Journal of Photogrammetry and Remote Sensing, 64, 223–233. https://doi.org/10.1016/j. isprsjprs.2008.12.004

HOPKINSON, C.; CHASMER, L.; LIM, K.; TREITZ, P., & CREED, I. 2006. Towards a universal lidar canopy height indicator. Canadian Journal of Remote Sensing, 32(2), 139–152.

HOSCILO, A.; PAGE, S. E.; TANSEY, K. J.; RIELEY, J. O., 2011. Effect of repeated fires on land-cover change on peatland in southern Central Kalimantan, Indonesia, from 1973 to 2005. Int J Wildland Fire.

HOUGHTON, R. A.; HOUSE, J. I.; PONGRATZ, J.; VAN DER WERF, G. R.; DEFRIES, R. S.; HANSEN, M. C., 2012. Carbon emissions from land use and land-cover change. Biogeosciences.

HYDE, P.; DUBAYAH, R.; WALKER, W.; BLAIR, J. B.; HOFTON, M.; HUNSAKER, C. 2006. Mapping forest structure for wildlife habitat analysis using multi-sensor (LiDAR, SAR/InSAR, ETM+, Quickbird) synergy. Remote Sens. Environ., 102(1–2), pp. 63–73.

HYYPPÄ, J.; HYYPPÄ, H.; INKINEN, M.; ENGDAHL, M.; LINKO, S.; ZHU, Y.-H. 2000. Accuracy comparison of various remote sensing data sources in the retrieval of forest stand attributes. Forest Ecology and Management, 128, 109–120.

KENTUCKY GEOLOGICAL SURVEY (KGS), 2016. Coal information. Kentucky Geological Survey, University of Kentucky.

KOMA Z; SEIJMONSBERGEN, AC; KISSLING WD. 2019. Use and categorization of Light Detection and Ranging vegetation metrics in avian diversity and species distribution research. Divers Distrib. 2019;25: 1045–1059. https://doi.org/10.1111/ddi.12915

KONECNY, K.; BALLHORN, U.; NAVRATIL, P.; JUBANSKI, J.; PAGE, SE.; TANSEY, K. 2015. Variable carbon losses from recurrent fires in drained tropical peatlands. Glob Change Bio.

KUMAY, D.U. 2015. Remote sensing platforms and sensor. NBKRIST Vidyanagar. India.

LEE, S.; KUGLER, F.; PAPATHANASSIOU, K.; HAJNSEK, I., 2011. Multibaseline polarimetric sar interferometry forest height inversion approaches. PolINSAR2011 i.

MAUNE. D.F.; NAYEGANDHI, A., 2018. Digital Elevation Model-DEM users manual. Digital Elevation Model technologies and applications. 3rd edition. ASPRS.

NOGGLE, G.R.; FRITZ, J.F. 1983. Introductory plant physiology. 2nd Edition, Prentice Hall Inc., Englewood Cliffs, 440-442.

PAGE, S. E.; RIELEY, J. O.; BANKS, C. J., 2011. Global and regional importance of the tropical peatland carbon pool. Glob Change Bio.

PETROU, Z.; TARANTINO, C.; ADAMO, M.; BLONDA, P.; PETROU, M.. 2012. Estimation of vegetation height through satellite image texture analysis. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XXXIX-B8. 10.5194/isprsarchives-XXXIX-B8-321-2012.

POGGIO, L.; GIMONA, A., 2014. National scale 3D modelling of soil organic carbon stocks with uncertainty propagation-An example from Scotland. Geoderma 232-234. p284–299.

POGGIO, L.; GIMONA, A.; BREWER, M.J., 2013. Regional scale mapping of soil properties and their uncertainty with a large number of satellite-derived covariates. Geoderma 209-210, pp. 1–14.

POPESCU, S.C.; WYNNE, R.H., 2004. Seeing the trees in the forest: Using lidar and multispectral data fusion with local filtering and variable window size for estimating tree height. Photogram. Eng. Remote Sens. 70, 589–604, doi:10.14358/PERS.70.5.589.

POSA, M. R. C.; WIJEDASA, L. S.; CORLETT, R. T., 2011. Biodiversity and conservation of tropicalpeat swamp forests. Bio-Science.

TRIER, O. D; SALBERG, A. B.; HAARPAINTNER, J.; AARSTEN, D.; GOBAKKEN, T.; NÆSSET, E., 2018. Multi-sensor forest vegetation height mapping methods for Tanzania, European Journal of Remote Sensing, 51:1, 587-606, DOI: 10.1080/22797254.2018.1461533

REDDINGTON, C. L.; YOSHIOKA, M.; BALASUBRAMANIAN, R.; RIDLEY, D., TOH, Y. Y.; ARNOLD, S. R., 2014. Contribution of vegetation and peat fires to particulate air pollution in Southeast Asia. Environ Res Lett. 2014; 9:94006.

REUTER, H.I.; NELSON, A.; JARVIS, A., 2007. An evaluation of void-filling interpolation methods for SRTM data. Int. J. Geogr. Inf. Sci., 21, 983–1008.

RUIZ, L., HERMOSILLA, T., MAURO, F.; GODINO, M. 2014. Analysis of the influence of plot size and LiDAR density on forest structure attribute estimates. Forests, 5, 936–951. https://doi.org/10.3390/f5050936

SEAVY, N. E.; VIERS, J. H.; WOOD, J. K.; EAVY, N. A. E. S.; IERS, J. O. H. V. 2009. Riparian bird response to vegetation structure: A multiscale analysis using LiDAR measurements of canopy height. Ecological Applications, 19, 1848–1857.

SIMARD, M.; PINTO; N.; FISHER J. B.; BACCINI, A. 2011. Mapping forest canopy height globally with spaceborne lidar. Journal of Geophysical Research, 116, https://doi.org/10.1029/2011JG001708

SINGH, G., 2008. Sustainable development of peatland for oil palm – UPB‟s experience. International Symposium and Workshop on Tropical Peatland: Wise Use and Impact Management. Kuching, Sarawak.

VAN DEN EELAART, A., 2008. Swamp development in Indonesia. Integrated Swamp development Project (ISDP), IBRD Loan 3755-IND, Indonesia.

ZHOU, Y.; HONG, W.; CAO, F., 2009. An Improvement of vegetation height estimation using multi-baseline polarimetric interferometric SAR Data. PIERS Online 5, 6–10. https://doi.org/10.2529/PIERS080907033305

Published

2021-01-28

How to Cite

Julzarika, A., Harintaka, H., & Kartika, T. (2021). Vegetation Height Estimation using Satellite Remote Sensing in Peat Land of Central Kalimantan. Journal of Environmental Analysis and Progress, 6(1), 024–034. https://doi.org/10.24221/jeap.6.1.2021.3001.024-034