Urban Vegetation Quality Assessment Using Vegetation Index and Leaf Area Index from Spot 7 Data with Fuzzy Logic Algorithm

Nurwita Mustika Sari, Tito Latif Indra, Dony Kushardono


Urban vegetation plays an essential role in the health and comfort of the urban environment. On the other hand, the decrease of urban vegetation is mostly due to land cover change from vegetation to build up the area. Detection of urban vegetation objects is essential for monitoring the distribution and the extent of vegetation in realizing a sustainable urban environment. SPOT 7 satellite image data with high spatial resolution can display objects in urban areas, including vegetation. With this capability, the extraction of vegetation objects can be conducted more accurately. This study aims to assess urban vegetation quality using vegetation index and Leaf Area Index (LAI) from SPOT 7 data. The method proposed in this study was the fuzzy logic on each vegetation index and LAI, which was extended by involving all indexes. The results showed that urban vegetation quality classification could be done using vegetation index NDVI, SR, RDVI, and another index LAI extracted from SPOT 7 data using a fuzzy logic algorithm. Based on these four variables' overlay, the highest quality of vegetation was shown with a fuzzy value of 0.928, and the lowest quality has a fuzzy value of 0.004. The highest quality of vegetation was in paddy fields and mixed garden, while the lowest quality of vegetation was in bare land with grass plantation. Based on the results, the appropriate treatment of urban vegetation in the study area can be determined.


urban vegetation; vegetation index; Leaf Area Index; fuzzy logic; SPOT 7 data.

Full Text:



J. R. Wolch, J. Byrne, and J. P. Newell, “Urban green space, public health, and environmental justice: The challenge of making cities ‘just green enough,’” Landsc. Urban Plan., vol. 125, pp. 234–244, 2014, doi: 10.1016/j.landurbplan.2014.01.017.

Mukhoriyah, N. M. Sari, M. Sharika, and L. N. Hanifati, “Identifikasi Ketersediaan Ruang Terbuka Hijau Kecamatan Kramat Jati Kodya Jakarta Timur Menggunakan Citra Pleiades,” J. Planol., vol. 16, no. 2, pp. 158–168, 2019.

F. Maselli, M. A. Gilabert, and C. Conese, “Integration of High and Low Resolution NDVI Data for Monitoring Vegetation in Mediterranean Environments,” Remote Sens. Environ., vol. 63, pp. 208–218, 1998.

R. S. Defries and J. R. G. Townshend, “NDVI-derived land cover classifications at a global scale,” Int. J. Remote Sens., vol. 15, no. 17, pp. 3567–3586, 1994, doi: 10.1080/01431169408954345.

M. Usman, R. Liedl, M. A. Shahid, and A. Abbas, “Land use / land cover classification and its change detection using multi-temporal MODIS N DVI data,” J Geogr Sci, vol. 25, no. 12, pp. 1479–1506, 2015, doi: 10.1007/s11442-015-1247-y.

O. U. Nse, C. J. Okolie, and V. O. Nse, “Dynamics of Land Cover, Land Surface Temperature and Ndvi in Uyo Capital City, Nigeria,” Sci. African, p. e00599, 2020, doi: 10.1016/j.sciaf.2020.e00599.

Y. Zhang et al., “Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016,” Remote Sens. Environ., vol. 224, pp. 74–91, 2019, doi: 10.1016/j.rse.2019.01.038.

G. L. Spadoni, A. Cavalli, L. Congedo, and M. Munafò, “Analysis of Normalized Difference Vegetation Index (NDVI) multi-temporal series for the production of forest cartography,” Remote Sens. Appl. Soc. Environ., vol. 20, 2020, doi: 10.1016/j.rsase.2020.100419.

S. A. Shammi and Q. Meng, “Use time series NDVI and EVI to develop dynamic crop growth metrics for yield modeling,” Ecol. Indic., 2020, doi: 10.1016/j.ecolind.2020.107124.

E. Westinga, A. P. R. Beltran, C. A. J. M. de Bie, and H. A. M. J. van Gils, “A novel approach to optimize hierarchical vegetation mapping from hyper-temporal NDVI imagery, demonstrated at national level for Namibia,” Int. J. Appl. Earth Obs. Geoinf., vol. 91, no. 102152, p. 102152, 2020, doi: 10.1016/j.jag.2020.102152.

L. Sun et al., “Reconstructing daily 30 m NDVI over complex agricultural landscapes using a crop reference curve approach,” Remote Sens. Environ., 2020, doi: 10.1016/j.rse.2020.112156.

X. Zhu, G. Xiao, D. Zhang, and L. Guo, “Mapping abandoned farmland in China using time series MODIS NDVI,” Sci. Total Environ., vol. 755, 2021, doi: 10.1016/j.scitotenv.2020.142651.

R. Moreno, N. Ojeda, J. Azócar, C. Venegas, and L. Inostroza, “Application of NDVI for identify potentiality of the urban forest for the design of a green corridors system in intermediary cities of Latin America: Case study, Temuco, Chile,” Urban For. Urban Green., vol. 55, no. 126821, 2020, doi: 10.1016/j.ufug.2020.126821.

S. Testa, K. Soudani, L. Boschetti, and E. B. Mondino, “MODIS-derived EVI, NDVI and WDRVI time series to estimate phenological metrics in French deciduous forests,” Int J Appl Earth Obs Geoinf., vol. 64, no. July 2017, pp. 132–144, 2018, doi: 10.1016/j.jag.2017.08.006.

F. F. Gerard, C. T. George, G. Hayman, C. Chavana-Bryant, and G. P. Weedon, “Leaf phenology amplitude derived from MODIS NDVI and EVI : Maps of leaf phenology synchrony for Meso- and South America,” Geosci. Data J., vol. 00, pp. 1–14, 2020, doi: 10.1002/gdj3.87.

P. Karkauskaite, T. Tagesson, and R. Fensholt, “Evaluation of the Plant Phenology Index ( PPI ), NDVI and EVI for Start-of-Season Trend Analysis of the Northern Hemisphere Boreal Zone,” Remote Sens., vol. 9, no. 485, pp. 21–21, 2017, doi: 10.3390/rs9050485.

M. Gandhi G, S. Parthiban, N. Thummalu, and C. A, “Ndvi : Vegetation change detection using remote sensing and gis – A case study of Vellore District,” Procedia - Procedia Comput. Sci., vol. 57, pp. 1199–1210, 2015, doi: 10.1016/j.procs.2015.07.415.

R. Colombo, D. Bellingeri, D. Fasolini, and C. M. Marino, “Retrieval of leaf area index in different vegetation types using high resolution satellite data,” Remote Sens. Environ., vol. 86, pp. 120–131, 2003, doi: 10.1016/S0034-4257(03)00094-4.

A. Kross, H. McNairn, D. Lapen, M. Sunohara, and C. Champagne, “Assessment of RapidEye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops,” Int. J. Appl. Earth Obs. Geoinf., vol. 34, pp. 235–248, 2015, doi: 10.1016/j.jag.2014.08.002.

L. Wang et al., “Effects of growth stage development on paddy rice leaf area index prediction models,” Remote Sens., vol. 11, no. 361, pp. 1–18, 2019, doi: 10.3390/rs11030361.

J. Zhao et al., “Estimating fractional vegetation cover from leaf area index and clumping index based on the gap probability theory,” Int. J. Appl. Earth Obs. Geoinf., vol. 90, no. 102112, 2020, doi: 10.1016/j.jag.2020.102112.

Q. Wang, S. Adiku, J. Tenhunen, and A. Granier, “On the relationship of NDVI with leaf area index in a deciduous forest site,” Remote Sens. Environ., vol. 94, pp. 244–255, 2005, doi: 10.1016/j.rse.2004.10.006.

Suwarsono et al., “Pengembangan metode penentuan Indeks Luas Daun pada penutup lahan hutan dari data satelit penginderaan jauh SPOT-2,” J. Penginderaan Jauh, vol. 8, pp. 50–59, 2011.

R. Darvishzadeh et al., “Analysis of Sentinel-2 and rapidEye for retrieval of leaf area index in a saltmarsh using a radiative transfer model,” Remote Sens., vol. 11, no. 671, 2019, doi: 10.3390/rs11060671.

T. Mannschatz, B. P, E. Borg, K. Feger, and P. Dietrich, “Uncertainties of LAI estimation from satellite imaging due to atmospheric correction,” Remote Sens. Environ., vol. 153, pp. 24–39, 2014, doi: 10.1016/j.rse.2014.07.020.

L. A. Zadeh, “Fuzzy Sets,” Inf. Control, vol. 353, pp. 338–353, 1965.

D. Saadoud, M. Hassani, F. José, and M. Peinado, “Application of fuzzy logic approach for wind erosion hazard mapping in Laghouat region ( Algeria ) using remote sensing and GIS,” Aeolian Res., vol. 32, no. February, pp. 24–34, 2018, doi: 10.1016/j.aeolia.2018.01.002.

S. Sarkar, S. M. Parihar, and A. Dutta, “Environmental Modelling & Software Fuzzy risk assessment modelling of East Kolkata Wetland Area : A remote sensing and GIS based approach,” Environ. Model. Softw., vol. 75, pp. 105–118, 2016, doi: 10.1016/j.envsoft.2015.10.003.

G. M. Foody, “Fuzzy modelling of vegetation from remotely sensed imagery,” Ecol. Modell., vol. 85, pp. 3–12, 1996.

T. Semeraro, G. Mastroleo, A. Pomes, A. Luvisi, E. Gissi, and R. Aretano, “Modelling fuzzy combination of remote sensing vegetation index for durum wheat crop analysis,” Comput. Electron. Agric., vol. 156, no. December 2018, pp. 684–692, 2019, doi: 10.1016/j.compag.2018.12.027.

Y. Zhang, Q. Li, X. Du, and H. Wang, “Spatially explicit mapping of phenological transition zones: A fuzzy-logic approach,” Agric. For. Meteorol., vol. 295, no. 108201, 2020, doi: 10.1016/j.agrformet.2020.108201.

R. M. Gonçalves, A. Saleem, H. A. A. Queiroz, and J. L. Awange, “A fuzzy model integrating shoreline changes, NDVI and settlement influences for coastal zone human impact classification,” Appl. Geogr., vol. 113, no. 102093, 2019, doi: 10.1016/j.apgeog.2019.102093.

C. M. Rulinda, A. Dilo, W. Bijker, and A. Stein, “Characterising and quantifying vegetative drought in East Africa using fuzzy modelling and NDVI data,” J. Arid Environ., vol. 78, pp. 169–178, 2012, doi: 10.1016/j.jaridenv.2011.11.016.

S. Ghosh, A. Das, T. K. Hembram, S. Saha, B. Pradhan, and A. M. Alamri, “Impact of COVID-19 induced lockdown on environmental quality in four Indian megacities Using Landsat 8 OLI and TIRS-derived data and Mamdani fuzzy logic modelling approach,” Sustain., vol. 12, no. 5464, pp. 1–24, 2020, doi: 10.3390/su12135464.

N. M. Sari and D. Kushardono, “Analisis dampak pembangunan infrastruktur Bandara Internasional Jawa Barat terhadap alih fungsi lahan pertanian melalui citra satelit resolusi tinggi,” J. Geogr., vol. 11, no. 2, pp. 146–162, 2019, doi: 10.24114/jg.v11i2.13470.

J. L. Roujean and F. M. Breon, “Estimating PAR absorbed by vegetation from bidirectional reflectance measurements,” Remote Sens. Environ., vol. 51, no. 3, pp. 375–384, 1995, doi: 10.1016/0034-4257(94)00114-3.

DOI: http://dx.doi.org/10.18517/ijaseit.12.2.11719


  • There are currently no refbacks.

Published by INSIGHT - Indonesian Society for Knowledge and Human Development