Document Type: ORIGINAL RESEARCH PAPER

Authors

1 Department of Forestry and Range Management, PMAS Arid Agriculture University, Rawalpindi, Pakistan

2 Institute of Environmental Science and Engineering, National University of Science and Technology, Pakistan

3 Department of Forestry and Wildlife Management, University of Haripur, Pakistan

4 Ministry of Forestry, Environment and Wildlife, Khyber Pukhtunkhwa, Pakistan

Abstract

Forest’s ecosystem is one of the most important carbon sink of the terrestrial ecosystem. Remote sensing technology provides robust techniques to estimate biomass and solve challenges in forest resource assessment. The present study explored the potential of Sentinel-2 bands to estimate biomass and comparatively analyzed of red-edge band based and broadband derived vegetation indices. Broadband indices include normalized difference vegetation index, modified simple ratio and atmospherically resistant VI. Whereas, red-edge band indices include two red-edge normalized difference vegetation index and sentinel-2 red-edge position. Results showed that red-edge band derived spectral indices have performed better than the Broadband indices. The coefficient of correlation for normalized difference vegetation index, modified simple ratio and atmospherically resistant-VI was 0.51, 0.44 and 0.31 respectively, On the other hand, red-edge band indices showed higher correlation of R2 0.62, 0.64 and 0.55, respectively. Similarly, in stepwise regression red-edge normalized difference vegetation index (using band 6) was selected in final model (as overall R2 of the model was 0.60) while all other indices were removed because they have non-significant relationship with the biomass. Accuracy assessment shown the red-edge index has highest R2 (0.64) and least error of (31.29 t/ha) and therefore the study concluded that narrowband indices performed better to estimate biomass and thus final model contained only red-edge index to predict biomass over the study area. The study suggests that more in-depth research should be conducted to explore further properties of red-edge indices for vegetation parameters prediction.

Graphical Abstract

Highlights

  • Above ground biomass can be efficiently quantified by use of sentinel-2 sensor which provides high spatial and temporal resolution and frequent revisit time;
  • Sentinel-2 spectral indices computed from red-edge wavelength region can provide best proxy information for Above ground biomass prediction;
  • Red edge bands of sentinel-2 to estimate forest AGB should contribute to the regional estimates of carbon emissions measurement, reporting and verification.

Keywords

Main Subjects

Adan, M.S. (2017). Integrating Sentinel-2A derived indices and terrestrial laser scanner to estimate above ground biomass/carbon in Ayer Hitam tropical forest, Malaysia, Master of Science, University of Twente, The Netherlands.

Adam, E.; Mutanga, O.; Rugege, D., (2010). Multispectral and hyperspectral remote sensing for identification and mapping of wetland vegetation: a review. Wetland Ecol. Manage., 18(3): 281-296 (16 pages).

Ahmad, A.; Mirza, S. N.; Nizami, S.M., (2014). Assessment of biomass and carbon stocks in coniferous forest of Dir Kohistan, KPK. Pak. J. Agric. Sci., 51(2): 335-350 (16 pages).

Ali, A.; Ullah, S.; Bushra, S.; Ahmad, N.; Ali, A.; Khan, M.A., (2018). Quantifying forest carbon stocks by integrating satellite images and forest inventory data. Aust. J. For. Sci., 135 (2): 93–117 (25 pages).

Asadzadeh, S.; de Souza Filho, C.R., (2016). Investigating the capability of worldview-3 superspectral data for direct hydrocarbon detection. Remote Sens. Environ.  173: 162–173 (12 pages).

Calvão, T.; Palmeirim, J.M., (2011). A comparative evaluation of spectral vegetation indices for the estimation of biophysical characteristics of Mediterranean semi-deciduous shrub communities. Int. J. Remote Sens., 32(8): 2275-2296 (22 pages).

Chang, J.;  Shoshany, M., (2016). Red-edge ratio Normalized Vegetation Index for remote estimation of green biomass. In 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS),  1337-1339 (3 pages).

Chen, J.-C.; Yang, C.-M.; Wu, S.-T.; Chung, Y.-L.; Charles, A. L.; Chen, C.-T., (2007). Leaf chlorophyll content and surface spectral reflectance of tree species along a terrain gradient in Taiwan’s Kenting National Park. Stud, 48: 71-77 (7 pages).

Chen, J. M., (1996). Evaluation of vegetation indices and a modified simple ratio for boreal applications. Canad J Remot Sens., 22(3): 229-242 (14 pages).

Cho, M.A.; Skidmore, A.K., (2006). A new technique for extracting the red edge position from hyperspectral data: The linear extrapolation method. Remote Sens. Environ., 101: 181–193 (13 pages).

Cho, M.A.; Skidmore, A.K.; Atzberger, C., (2008). Towards red-edge positions less sensitive to canopy biophysical parameters for leaf chlorophyll estimation using properties optique spectrales des feuilles (prospect) and scattering by arbitrarily inclined leaves (sailh) simulated data. Int. J. Remote Sens., 29: 2241–2255 (14 pages).

Chrysafis, I.; Mallinis, G.; Siachalou, S.; Patias, P., (2017). Assessing the relationships between growing stock volume and Sentinel-2 imagery in a mediterranean forest ecosystem. Remote Sens Lett., 8(6): 508-517 (10 pages).

Clevers, J.G.W.; Gitelson, A.A., (2013). Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on sentinel-2 and -3. Int. J. Appl. Earth Obs. Geoinf., 23: 344–351 (8 Pages).

Delegido, J.; Verrelst, J.; Meza, C.M.; Rivera, J.P.; Alonso, L.; Moreno, J., (2013). A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems. Eur. J. Agron., 46: 42–52 (11 pages).

ERRA, (2007). Muzaffarabad Earthquake Reconstruction and Rehabilitation Authority. District Profile. Prime Minister’s Secretariat, Islamabad, Kamran Printers, Blue Area., 1-3 (3 pages).

Drusch, M.; Del Bello, U.; Carlier, S.; Colin, O.; Fernandez, V.; Gascon, F.; Hoersch, B.; Isola, C.; Laberinti, P.; Martimort, P., (2012). Sentinel-2: Esa’s optical high-resolution mission for gmes operational services. Remote Sens. Environ., 120: 25–36 (12 pages).

Dusseux, P.; Hubert-Moy, L.; Corpetti, T.; Vertès, F., (2015). Evaluation of Spot imagery for the estimation of grassland biomass. Int. J. Appl. Earth Obs. Geoinf., 38: 72-77 (6 pages).

Egbers, R., (2016). Sentinel-2 data processing and identifying glacial features in Sentinel-2 imagery, TU Delft, University of Technology, The Netherlands.

Foody, G.M.; Cutler, M.E.; Mcmorrow, J.; Pelz, D.; Tangki, H.; Boyd, D.S.; Douglas, I., (2001). Mapping the biomass of Bornean tropical rain forest from remotely sensed data. Global Ecol. Biogeogr., 10(4): 379-387 (9 pages).

Frampton, W.J.; Dash, J.; Watmough, G.; Milton, E.J., (2013). Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation. ISPRS J. Photogramm. Remote Sens., 82: 83–92 (10 pages).

Fukuda, M.;  Lehara, T.; Matsumoto, M., (2003). Carbon stock estimates for sugi and hinoki forests in Japan. Forest Ecol. Manage., 184(1): 1-6 (6 pages).

Gara, T.W.; Murwira, A.; Dube, T.; Sibanda, M.; Rwasoka, D.T.; Ndaimani, H.; Hatendi, C.M., (2017). Estimating forest carbon stocks in tropical dry forests of Zimbabwe: exploring the performance of high and medium spatial-resolution multispectral sensors. Southern For. J. For. Sci., 79(1): 31-40 (9 pages).

Gascon, F.; Berger, M., (2007). GMES Sentinel-2 mission requirements document. Rapport technique, ESA.

Gholizadeh, A.; Mišurec, J.; Kopačková, V.; Mielke, C.; Rogass, C., (2016). Assessment of red-edge position extraction techniques: A case study for norway spruce forests using hymap and simulated sentinel-2 data. Forests., 7(10): 226-242 (17 pages).

Heiskanen, J. (2006). Estimating aboveground tree biomass and leaf area index in a mountain birch forest using ASTER satellite data. Int. J. Remote Sens., 27(6): 1135-1158 (24 pages).

Huete, A.; Didan, K.; Miura, T.; Rodriguez, E. P.; Gao, X.; Ferreira, L. G., (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ., 83(1-2): 195-213 (19 pages).

Kaufman, Y.J.; Tanre, D., (1992). Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE Trans Geosci Remot Sens., 30(2):261-270 (10 pages).

Karakoç, A.; Karabulut, M., (2019). Ratio-Based vegetation indices for biomass estimation depending on grassland characteristics. Turk. J. Bot., 43: 619-633 (15 pages).

Karlson, M.; Ostwald, M.; Reese, H.; Bazié, H.R.; Tankoano, B., (2016). Assessing the potential of multi-seasonal worldview-2 imagery for mapping West African agroforestry tree species. Int. J. Appl. Earth Obs. Geoinf.,50: 80–88 (9 pages).

Kross, A.; McNairn, H.; Lapen, D.; Sunohara, M.; Champagne, C., (2015). Assessment of rapideye vegetation indices for estimation of leaf area index and biomass in corn and soybean crops. Int. J. Appl. Earth Obs. Geoinf., 34: 235–248 (14 pages).

May, A.M.B.; Pinder, J.E.; Kroh, G.C., (2010). A comparison of Landsat Thematic Mapper and SPOT multi-spectral imagery for the classification of shrub and meadow vegetation in northern California, U.S.A. Int. J. Remote Sens., 18 (18): 3719-3728 (10 pages).

Martins, V.S.; Barbosa, C.C.; de Carvalho, L.A.; Jorge, D.S.; Lobo, F.D.; Novo, E.M., (2017). Assessment of atmospheric correction methods for Sentinel-2 msi images applied to Amazon floodplain lakes. Remote Sens., 9(4): 322-340 (19 pages).

Mohren, G.M.J.; Hasenauer, H.; Köhl, M.; Nabuurs, G.J., (2012). Forest inventories for carbon change assessments. Curr. Opin. Environ. Sustainable. 4(6): 686-695 (10 pages).

Molto, Q.; Rossi, V.; Blanc, L. (2013). Error propagation in biomass estimation in tropical forests. Methods Ecol. Evol., 4(2): 175-183 (9 pages).

Mundava, C.; Helmholtz, P.; Schut, A.G.T.; Stovold, R.; Corner, R., (2014). Evaluation of vegetation indices for rangeland biomass estimation in the kimberley area of western Australia. ISPRS Ann Photogram. Remote Sens Spat. Inf. Sci., 2(7): 47-53 (17 pages).

Mutanga, O.; Skidmore, A.K., (2004a). Hyperspectral band depth analysis for a better estimation of grass biomass (Cenchrus ciliaris) measured under controlled laboratory conditions. Int. J. Appl. Earth Obs. Geoinf., 5(2): 87-96 (10 pages).

Mutanga, O.; Adam, E.; Cho, M.A., (2012). High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm. Int. J. Appl. Earth Obs. Geoinf., 18: 399-406 (8 pages).

Paustian, K.; Ravindranath, N. H.; van Amstel, A. R., (2006). IPCC guidelines for national greenhouse gas inventories. No. Part 2.

Richter, K.; Hank, T.B.; Vuolo, F.; Mauser, W.; D’Urso, G., (2012). Optimal exploitation of the Sentinel-2 spectral capabilities for crop leaf area index mapping. Remote Sens., 4: 561–582 (22 pages).

Louis, J.; Debaecker, V.; Pflug, B.; Main-Knorn, M.; Bieniarz, J.; Mueller-Wilm, U.; Cadau, E.; Gascon, F., (2016). Sentinel-2 Sen2Cor: L2A processor for users. InProceedings Living Planet Symposium 2016,   Spacebooks Online. 1-8 (8 pages).

Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W., (1973).  Monitoring vegetation systems in the Great Plains with ERTS. In: Third ERTS Symposium. NASA, 309–317 (9 pages).

Roy, D.P.; Li, J.; Zhang, H.K.; Yan, L., (2016). Best practices for the reprojection and resampling of Sentinel-2 multi spectral instrument level 1C data. Remote Sens. Lett., 7(11): 1023-1032 (10 pages).

Shaheen, H.; Khan, R. W. A.; Hussain, K.; Ullah, T. S.; Nasir, M.; Mehmood, A., (2016). Carbon stocks assessment in subtropical forest types of Kashmir Himalayas. Pak. J. Bot., 48(6): 2351-2357 (7 pages).

Schoene, D., (2002). Terminology in assessing and reporting forest carbon change. In second expert meeting on harmonizing forest-related definitions for use by various stakeholders. FAO, Rome.

Slonecker, T.; Haack, B.; Price, S., (2009). Spectroscopic analysis of arsenic uptake in Pteris ferns. Remote Sens, 1(4): 644-675 (31 pages).

Verrelst, J.; Munoz, J.; Alonso, L.; Delegido, J.; Rivera, J.P.; Camps-Valls, G.; Moreno, J. (2012). Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3. Remote Sens. Environ., 118: 127–139 (12 pages).

Wang, C.; Feng, M.-C.; Yang, W.-D.; Ding, G.-W.; Sun, H.; Liang, Z.-Y.; Qiao, X.X., (2016). Impact of spectral saturation on leaf area index and aboveground biomass estimation of winter wheat. Spectroscopy. Lett., 49(4): 241-248 (8 pages).

Wang, J.; Liu, Z.; Yu, H.; Li, F., (2017). Mapping spartina alterniflora biomass using LiDAR and hyperspectral data. Remote Sens., 9(6): 589-601 (13 pages).

Xie, Q.; Dash, J.; Huang, W.; Peng, D.; Qin, Q.; Mortimer, H.; Casa, R.; Pignatti, S.; Laneve, G.; Pascucci, S.; Dong, Y., (2018). Vegetation indices combining the red and red-edge spectral information for leaf area index retrieval. IEEE J Top App Ear Obser Remot Sens., 11(5): 1482-1493 (12 pages).

Zhao, D.; Huang, L.; Li, J.; Qi, J., (2007). A comparative analysis of broadband and narrowband derived vegetation indices in predicting LAI and CCD of a cotton canopy. ISPRS J. Photogramm Remote Sens., 62(1): 25-33 (9 Pages). 


Letters to Editor


GJESM Journal welcomes letters to the editor for the post-publication discussions and corrections which allows debate post publication on its site, through the Letters to Editor. Letters pertaining to manuscript published in GJESM should be sent to the editorial office of GJESM within three months of either online publication or before printed publication, except for critiques of original research. Following points are to be considering before sending the letters (comments) to the editor.

[1] Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.
[2] Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.
[3] Letters can be no more than 300 words in length.
[4] Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.
[5] Anonymous letters will not be considered.
[6] Letter writers must include their city and state of residence or work.
[7] Letters will be edited for clarity and length.

CAPTCHA Image