* Corresponding Author
Preliminary Study on the Use of Digital Surface Models for Estimating Vegetation Cover Density in Mountainous Area
https://doi.org/10.22146/ijg.60659 Projo Danoedoro(1*), Diwyacitta Dirda Gupita(2), Muhammad Zayyanul Afwani(3), Haeydar Anggara Hadi(4), William Krista Mahendra(5)
(1) Remote Sensing Laboratory, Department of Geographical Information Science, Faculty of Geography, Universitas Gadjah Mada, Indonesia
(2) Department of Geography, NTNU (Norwegian University of Science and Technology), NO-7491, Trondheim.
(3) Remote Sensing Laboratory, Department of Geographical Information Science, Faculty of Geography, Universitas Gadjah Mada.
(4) Remote Sensing Laboratory, Department of Geographical Information Science, Faculty of Geography, Universitas Gadjah Mada.
(5) Remote Sensing Laboratory, Department of Geographical Information Science, Faculty of Geography, Universitas Gadjah Mada.
(*) Corresponding Author
Abstract
Digital surface model (DSM) has been widely available for mapping and was also sometimes used for mapping vegetation height. The authors conducted a preliminary study to evaluate the potential use of DSMs derived from ASTER, ALOS, and SRTM for estimating vegetation cover density in mountainous area. This study used NDVI and SAVI vegetation indices, in addition to forest cover density (FCD) model as references for evaluation. A DSM-based volume index (Volindex) concept is introduced, which is the product of the canopy height model (CHM) and the pixel area. CHM was derived from the value difference between the DSM and the reference DEM. The Volindex model was then compared with the NDVI, SAVI and FCD. We found that all DSM-based Volindex models are not accurate enough to represent the vegetation cover density, although the ALOS Palsar-based Volindex could reach 41.53% accuracy and was finally used to predict the vegetation cover density.
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R. Almeida‐Filho, R., Shimabukuro, Y.E., Rosenqvist, A., and Sánchez, G.A. (2009). Using dual‐polarized ALOS PALSAR data for detecting new fronts of deforestation in the Brazilian Amazônia. Intrenational Journal of Remote Sensing, 30, (14): 3735-3743, DOI: 10.1080/01431160902777175
Anggara, D.W., Wicaksono, P., and Danoedoro, P. (2015). The Integration of Active and Passive Remote Sensing System: Mapping Forest Stand-Volume Using ALOS AVNIR-2 and SRTM Data. Prosiding Pertemuan Ilmiah Tahunan MAPIN XX, Bogor
Basuki, T.M. Andrew K. Skidmore, A. K., Hussin, Y. A., and van Duren, I. (2013). Estimating tropical forest biomass more accurately by integrating ALOS PALSAR and Landsat-7 ETM+ data. International Journal of Remote Sensing, 34, (13): 4871-4888, DOI: 10.1080/ 01431161.2013.777486
Chavez, P. S. (1996). Image-Based Atmospheric Corrections Revisited and Improved. Photogrammetric. Engineering and Remote Sensing., vol. 62, no. 9, pp. 1025–1036, 1996,
Congalton, R.G. and Green, K. (2009) Assessing the Accuracy of Remotely Sensed Data: Principles and Practices. 2nd Edition, Lewis Publishers, Boca Raton.
Danoedoro, P. (2019). Multidimensional Land-use Information for Local Planning and Land Resources Assessment in Indonesia: Classification Scheme for Information Extraction from High-Spatial Resolution Imagery. Indonesian Journal of Geography, 51 (2): 131 – 146
Danoedoro, P., and Gupita, D.D. (2022). Combining Pan-Sharpening and Forest Cover Density Transformation Methods for Vegetation Mapping using Landsat-8 Satellite Imagery. International Journal on Advanced Science, Engineering, and Information Technology, 12, (3): 881-891
Dewa, R.P., and Danoedoro, P. (2017). The effect of image radiometric correction on the accuracy of vegetation canopy density estimate using several Landsat-8 OLI’s vegetation indices: A case study of Wonosari area, Indonesia. Proceedings of the IOP Conference Series: Earth and Environmental Science (54): 1 012046
Drăguţ, L., Eisank, C., and Strasser, T. (2011). Local variance for multi-scale analysis in geomorphometry. Geomorphology (130): 162–172
Drăguţ, L., and Eisank, C. (2012). Automated object-based classification of topography from SRTM data. Geomorphology (142): 21–33
Eastman, J. R. (2020). Terrset 2020 Geospatial Monitoring and Modelling System Manual. Worchester, MA: Clark University
Hecht, R., Meinel, G., and Buchroithner, M.F. (2008). Estimation of Urban Green Volume Based on Single-Pulse LiDAR Data. IEEE Transactions on Geoscience and Remote Sensing, 46, (11): 1-11
Himayah, S., Hartono, Danoedoro, P.(2016). The Utilization of Landsat 8 Multitemporal Imagery and Forest Canopy Density (FCD) Model for Forest Reclamation Priority of Natural Disaster Areas at Kelud Mountain, East Java. IOP Conference Series: Earth and Environmental Science, 47(1), 012043
Huete, A.R. (1988). A Soil-Adjusted Vegetation Index (SAVI). Remote Sensing of Environment (25): 295-309
Huggett, R.G., and Cheesman, J. (2002). Topography and the Enviroment. Chichester: Prentice Hall.
Salsabila, H. N., and Danoedoro, P. (2021). Comparing Two Different Radiometric Corrections in a Vegetation Mapping of Mountainous Area using Forest Cover Density Transformation. Proceedings of the 42nd Asian Conference on Remote Sensing, ACRS 2021, Can Tho, Vietnam
Ismail, M. Hartono, dan Danoedoro, P. (2017). "The Application of Forest Cover Density (FCD) Model for Structural Composition of Vegetation Changes in Part of Lore Lindu National Park, Central Sulawesi Province", In Proceedings of the 5th Geoinformation Science Symposium 2017. IOP Publishing IOP Conf. Series: Earth and Environmental Science (98) 012056
International Tropical Timber Organization (ITTO). 1997. Utilization of Remote Sensing in Site Assessment and Planning for Rehabilitation of Logged-Over Forests. Project Report on PD 32193 Rev.2 (F): Rehabilitation of Logged-Over Forests in Asia/Pacific Region, Sub-Project Iii.
Julzarika, J., and Harintaka (2019). Free Global Dem: Converting DSM to DTM and Its Applications. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-4/W16, 2019 and 6th International Conference on Geomatics and Geospatial Technology (GGT 2019), 1–3 October 2019, Kuala Lumpur, Malaysia
Kellndofer, J. , Walkera, W., Pierca, L., Dobsona, C., Fitesb, J.A. (2008). Vegetation height estimation from Shuttle Radar Topography Mission and National Elevation Datasets. Remote Sensing of Environment (93): 339 – 358
Kenyi, L. Dubayah, R., Hofton, M., and Schardt, M. (2009). Comparative analysis of SRTM-NED vegetation canopy height to LIDAR-derived vegetation canopy metrics. International Journal of Remote Sensing 30,(11):2797-2811. DOI: 10.1080/01431160802555853
Margaretha, W., Danoedoro, P., Murti, S.H. Estimasi Cadangan Karbon Vegetasi Tegakan di Kota Yogyakarta dan Sekitarnya Berbasis ALOS AVNIR-2. Prosiding Simposium Nasional Sains Geoinformasi ~ III 2013
McCloy, K.R. (2005). Resource Management Information Systems: Remote Sensing, GIS, and Modelling. 2nd edition. Broken Sound Parkway, NW: CRC Press
Nikolakopoulos, K. G., and Chrysoulakis, N. (2006). Updating the 1:50.000 topographic maps using ASTER and SRTM DEM. The case of Athens, Greece. Remote Sensing for Environmental Monitoring, GIS Applications, and Geology VI, Proc. of SPIE Vol. 6366, 636606, 0277-786X/06/$15 · doi: 10.1117/12.689016
Pahlefi, M.R., Danoedoro, P., and Kamal, M. (2021). The utilization of sentinel-2A images and google earth engine for monitoring tropical Savannah grassland. Geocarto International 39, (6): 1-15
Prasannakumar, H. Vijith, S. Abinod, and N. Geetha. (2012). Estimation of soil erosion risk within a smallmountainous sub-watershed in Kerala, India, using Revised Universal Soil Loss Equation (RUSLE) andgeo-information technology. Geoscience Frontiers 3(2): 209-215
Rikimaru, A., Roy, P.S., & Miyatake, S. 2002. Tropical Forest Cover Density Mapping. Tropical Ecology. 43(1): 39-47.
Robinson, A. H., Morrison, J.L., Muehrcke, P.C., Kimerling, A.J., and Guptill, S.C. . (1995). Elements of Cartography (6th ed.). New York: John Wiley and Sons.
Taramelli, A., and Melelli, L. (2008). Detecting Alluvial Fans Using Quantitative Roughness Characterization and Fuzzy Logic Analysis. CCSA 2008, Part I, LNCS 5072: 1–15,
Trier, Ø. D. Salberg, A-B., Haarpaintner, J., Aarsten, D. (2018). Multi-sensor forest vegetation height mapping methods for Tanzania. European Journal of Remote Sensing, 51, (1): 587–606
Tobbler, W. (1988). Resolution, Resampling, and All That, In Mounsey, H., and. Tomlinson, R. (eds)., Building Data Bases for Global Science, London, Taylor and Francis.
Umarhadi, D.A., and Danoedoro, P. (2019). Correcting topographic effect on Landsat-8 images: An evaluation of using different DEMs in Indonesia. Proceedings of SPIE - The International Society for Optical Engineering, 11311, 113110L
Umarhadi, D.A., and Danoedoro, P. (2020). The effect of topographic correction on canopy density mapping using satellite imagery in mountainous area. International Journal on Advanced Science, Engineering, and Information Technology, (10), 3: 1317–1325
Umarhadi, D.A., Danoedoro, P., Wicaksono, P., Widayani, P., Nurbandi, W., Juniansah, A. (2018) The Comparison of Canopy Density Measurement Using UAV and Hemispherical Photography for Remote Sensing Based Mapping. Proceedings of the 4th International Conference on Science and Technology, ICST 2018, 8528670
Urbazaev, M., Cremer, F., Migliavacca, M., and Reichstein, M. (2018). Potential of Multi-Temporal ALOS-2 PALSAR-2 ScanSAR Data for Vegetation Height Estimation in Tropical Forests of Mexico. Remote Sensing, 10, 1277; doi:10.3390/rs10081277
Wu, M., Yang, C., Song, X., Hoffmann, W.C. (2004). Evaluation of Orthomosics and Digital Surface Models Derived from Aerial Imagery for Crop Type Mapping. Remote Sensing of Environment (93): 339 – 358
Zheng, W., Liu, Y. Yang, X., and Fan, W. (2022). Spatiotemporal Variations of Forest Vegetation Phenology and Its Response to Climate Change in Northeast China. Remote Sensing 14, (12): 2909. DOI: /10.3390/rs14122909
DOI: https://doi.org/10.22146/ijg.60659
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