Mapping Land Use and Land Cover in the Upper Ciliwung Watershed Using Landsat Tree Cover (TC) Data

- Hildanus, Suria Darma Tarigan, Kukuh Murtilaksono, Baba Barus

Abstract


Land use and land cover (LULC) mapping using Landsat Tree Cover (TC) data that we employed was digital classification by converting Landsat TC raster data into Landsat TC vector data and determining LULC classes in the attribute table based on percent TC criteria (interval TC- min - TC-max). The classification was adapted from the LCCS classification and partially modified. Compared to conventional digital image classification (supervised and unsupervised classifications), our digital classification method is easier and faster because Landsat TC data does not require pre-processing and reclassification to improve classification accuracy. Landsat TC classification accuracy was assessed against the interpretation of a very high spatial resolution (VHSR) image available in Google Earth (GE). The purpose of the study was to determine the ability of Landsat TC data paired with percent TC criteria of LULC adapted from the LCCS classification and validated with VHSR in GE for mapping LULC in the tropics. This study was conducted in the Upper Ciliwung watershed, which is located in Bogor Regency, West Java Province, Indonesia. LULC mapping using Landsat TC data paired with percent TC criteria of LULC adapted from the LCCS classification and validated with VHSR in GE provided a useful tool for producing LULC map in the Upper Ciliwung watershed. This study classified LULC in the Upper Ciliwung watershed consisting of settlements, closed forests, medium forests, opened forests, mix gardens, tea plantations, shrub lands, grasslands, and rainfed croplands paddy fields, fish fonds, and bare lands with overall accuracy of 91%.


Keywords


Landsat TC; LCCS classification; percent tree cover; Google Earth; upper Ciliwung watershed.

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References


J. M. Derwin, V. A. Thomas, R. H. Wynne, J. W. Coulston, G. C. Liknes, S. Bender, C. E. Blinn, E. B. Brooks, B. Ruefenacht, R. Benton, M. V Finco, and K. Megown, “Int J Appl Earth Obs Geoinformation Estimating tree canopy cover using harmonic regression coefficients derived from multitemporal Landsat data,” Int J Appl Earth Obs Geoinf., vol. 86, no. February 2019, p. 101985, 2020, doi: 10.1016/j.jag.2019.101985.

J. A. Ejares, R. R. Violanda, A. G. Diola, D. T. Dy, J. B. Otadoy, R. E. S. Otadoy, C. Sciences, E. Group, and V. Leeuwen, “Tree Canopy Cover Mapping Using Lidar in Urban Barangays of Cebu City , Central Philippines,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. XLI, no. July, pp. 611–615, 2016, doi: 10.5194/isprsarchives-XLI-B8-611-2016.

D. J. Nowak and E. J. Green, “Urban Forestry & Urban Greening Declining urban and community tree cover in the United States,” Urban For. Urban Green., vol. 32, no. February, pp. 32–55, 2018, doi: 10.1016/j.ufug.2018.03.006.

K. J. Doick, A. Buckland, and T. Clarke, “Historic Urban Tree Canopy Cover of Great Britain,” forests, 2020.

T. Kobayashi and R. Tateishi, “Comparison of a New Percent Tree Cover Dataset with Existing One and Categorical Land Cover Datasets in Eurasia,” Adv. Remote Sens., vol. 2013, no. December, pp. 345–357, 2013.

T. Kobayashi, J. Tsend-ayush, and R. Tateishi, “A new global tree-cover percentage map using MODIS data,” Int. J. Remote Sens., vol. 1161, no. February, 2016, doi: 10.1080/01431161.2016.1142684.

X. P. Song, H. Tang, and T. Cover, “Accuracy Assessment Of Landsat-Derived Continuous Fields of Tree Cover Products Using Airborne Lidar Data in The Eastern United,” Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci., vol. XL, 2015, doi: 10.5194/isprsarchives-XL-7-W4-241-2015.

M. Karlson, M. Ostwald, H. Reese, J. Sanou, B. Takoano, and E. Matsson, “Mapping Tree Canopy Cover and Aboveground Biomass in Sudano-Sahelian Woodlands Using Landsat 8 and Random Forest,” Remote Sens., pp. 10017–10041, 2015, doi: 10.3390/rs70810017.

F. Gao, M. Anderson, C. Daughtry, and D. Johnson, “Assessing the Variability of Corn and Soybean Yields in Central Iowa Using High Spatiotemporal Resolution Multi-Satellite Imagery,” Remote Sens., 2018, doi: 10.3390/rs10091489.

D. Frantz, A. Röder, M. Stellmes, and J. Hill, “Remote Sensing of Environment Phenology-adaptive pixel-based compositing using optical earth observation imagery,” Remote Sens. Environ., vol. 190, pp. 331–347, 2017, doi: 10.1016/j.rse.2017.01.002.

Z. Zhu, M. A. Wulder, D. P. Roy, C. E. Woodcock, M. C. Hansen, V. C. Radelo, S. P. Healey, C. Schaaf, P. Hostert, P. Strobl, J. Pekel, L. Lymburner, N. Pahlevan, and T. A. Scambos, “Remote Sensing of Environment Bene fi ts of the free and open Landsat data policy,” Remote Sens. Environ., vol. 224, no. January, pp. 382–385, 2019, doi: 10.1016/j.rse.2019.02.016.

M. A. Wulder, N. C. Coops, D. P. Roy, J. C. White, M. A. Wulder, N. C. Coops, D. P. Roy, and J. C. White, “Land cover 2.0,” Int. J. Remote Sens., vol. 39, no. 12, pp. 4254–4284, 2018, doi: 10.1080/01431161.2018.1452075.

M. A. Wulder, T. R. Loveland, D. P. Roy, C. J. Crawford, G. Masek, C. E. Woodcock, R. G. Allen, M. C. Anderson, A. S. Belward, W. B. Cohen, J. Dwyer, A. Erb, F. Gao, P. Gri, D. Helder, T. Hermosilla, J. D. Hipple, P. Hostert, M. J. Hughes, et al., “Remote Sensing of Environment Current status of Landsat program , science , and applications,” Remote Sens. Environ., vol. 225, no. February, pp. 127–147, 2019, doi: 10.1016/j.rse.2019.02.015.

Hadi, A. Krasovskii, V. M. P. Yogawarna, S. Pietsch, and M. Rautiainen, “Monitoring Deforestation in Rainforests Using Satellite Data : A Pilot Study from Kalimantan , Indonesia,” forests, pp. 1–26, doi: 10.3390/f9070389.

J. O. Sexton, X. Song, M. Feng, P. Noojipady, C. Huang, D. Kim, K. M. Collins, C. Dimiceli, J. R. Townshend, J. O. Sexton, X. Song, M. Feng, P. Noojipady, C. Huang, D. Kim, K. M. Collins, and S. Channan, “Global , 30-m resolution continuous fields of tree cover : Landsat-based rescaling of MODIS vegetation continuous fields with lidar-based estimates of error,” Int. J. Digit. Earth, vol. 6, no. 5, pp. 427–448, 2013, doi: 10.1080/17538947.2013.786146.

P. M. Montesano, C. S. R. Neigh, J. Sexton, M. Feng, S. Channan, K. J. Ranson, and J. R. Townshend, “Calibration and Validation of Landsat Tree Cover in the Taiga ´ Tundra Ecotone,” Remote Sens., pp. 5–7, 2016, doi: 10.3390/rs8070551.

J. Miettinen, C. Shi, and S. C. Liew, “Towards automated 10 – 30 m resolution land cover mapping in insular South-East Asia,” Geocarto Int., vol. 6049, no. December, pp. 1–15, 2017, doi: 10.1080/10106049.2017.1408700.

S. Godinho, N. Guiomar, and A. Gil, “Estimating tree canopy cover percentage in a mediterranean silvopastoral systems using Sentinel-2A imagery and the stochastic gradient boosting algorithm,” Int. J. Remote Sens., vol. 00, no. 00, pp. 1–23, 2017, doi: 10.1080/01431161.2017.1399480.

A. Ahrends, P. M. Hollingsworth, P. Beckscha, H. Chen, R. J. Zomer, L. Zhang, M. Wang, and J. Xu, “China’s fight to halt tree cover loss,” Proceding R. Soc., pp. 1–10, 2017.

L. Morales-barquero, M. B. Lyons, S. R. Phinn, and C. M. Roelfsema, “Trends in Remote Sensing Accuracy Assessment Approaches in the Context of Natural Resources,” Remote Sens., pp. 1–16.

Z. Asrat, H. Taddese, H. O. Ørka, T. Gobakken, and E. Næsset, “Estimation of Forest Area and Canopy Cover Based on Visual Interpretation of Satellite Images in Ethiopia,” land, pp. 1–17, 2018, doi: 10.3390/land7030092.

K. Yadav and R. G.Congakton, “Issues with Large Area Thematic Accuracy Assessment for Mapping Cropland Extent : A Tale of Three Continents,” Remote Sens., 2018, doi: 10.3390/rs10010053.




DOI: http://dx.doi.org/10.18517/ijaseit.11.6.15712

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