Open Access Open Access  Restricted Access Subscription or Fee Access

Correlation Between Tree Diversity and Spectral Variables of Sentinel-2 Images: A Study of Langtang National Park, Nepal

Anisha Puri, Babatunde Adeniyi Osunmadewa, Ram Asheshwar Mandal, Dr. Elmar Csaplovics

Abstract


This study was objectively conducted to explore the tree diversity of Langtang National Park (LNP), Nepal and show the correlation between spectral variables and tree biodiversity. Sentinel-2 MSI of LNP in Nepal was acquired from https://scihub.copernicus.eu/. Total 60 samples were collected from forest. Pearson correlation was established to show relationship between tree species diversity and reflectance values. Bagging LASSO algorithm was used to validate the model. Result shows that Shannon-Weiner index was ranging from 2.9 to 3 and it was the highest in dense forest. Altogether, there was 18 tree species in LNP. The highest importance value index was 79.64 of Rhoderndron arborum and it was the least around 3.44 of Prunus cornuta. Classified map of tree species of LNP showed high Kappa statistics with 0.7. The NIR band B6, B7, B8 and B8A performed high separability for tree species. High reflectance values were recorded of Betula alnoides, Qurecus semicarpofolia and Castonopsis hystrix but it was the lowest of Querus lamellose. The higher reflectance value was recorded of Juniperus indica and Sorbus cuspidate in case of shortwave-infrared spectrum (SWIR, Band 11) and SWIR2 (Band 12). The highest negative correlation was recorded between Simpson’s index and B7 with -0.386 and it was the highest positive correlation between Shannon-Weiner Index (H’) and B8 of Sentinel-2 with 0.361. The highest correlation was found between Simpson index and B8A_Entrophy with -0.365. Bagging LASSO model performed better prediction to show correlation between biophysical characteristics and reflectance value of remotely sensed data.


Full Text:

PDF

References


Mori, A.S., Lertzman, K.P., & Gustafsson, L. Biodiversity and ecosystem services in forest ecosystems: a research agenda for applied forest ecology. Journal of Applied Ecology, 2017; 54(1): 12–27.

Mora, C., Tittensor, D.P., Adl, S., Simpson, A.G., & Worm, B. How many species are there on Earth and in the ocean?. PLoS biology, 2011; 9(8) : e1001127.

Cavender-Bares, J., Arroyo, M.T., Abell, R., Ackerly, D., Ackerman, D., Arim, M. & Ziller, S.R. Status, trends and future dynamics of biodiversity and ecosystems underpinning nature’s contributions to people. Intergovernmental Science-Policy Platform for Biodiversity and Ecosystem Services (IPBES). 2018.

Worboys, G.L., Francis, W.L., & Lockwood, M. Indomalayan Connectivity Initiatives. Connectivity Conservation Management Routledge, 2010; 145-168.

Chatterjee, S., Saikia, A., Dutta, P., Ghosh, D., Pangging, G., & Goswami, A.K. Biodiversity significance of North east India. WWF-India, New Delhi, 2006; 1-71.

Record, S., Dahlin, K.M., Zarnetske, P.L., Read, Q.D., Malone, S.L., Gaddis, K.D. & Hestir, E. Remote Sensing of geodiversity as a link to biodiversity. In Remote Sensing of Plant Biodiversity Springer, Cham. 2020; 225-253.

Craven, D., Eisenhauer, N., Pearse, W.D., Hautier, Y., Isbell, F., Roscher, C. & Manning, P. Multiple facets of biodiversity drive the diversity–stability relationship. Nature ecology & evolution, 2018; 2(10) : 1579-1587.

Kandel, P., Chettri, N., Chaudhary, R.P., Badola, H.K., Gaira, K.S., Wangchuk, S., & Sharma, E. Plant diversity of the Kangchenjunga Landscape, Eastern Himalayas. Plant diversity, 2019; 41(3): 153-165.

Syrbe, R.U., & Walz, U. Spatial indicators for the assessment of ecosystem services: providing, benefiting and connecting areas and landscape metrics. Ecological indicators, 2012 ;21: 80-88.

Foody, G.M., & Cutler, M.E. Tree biodiversity in protected and logged Bornean tropical rain forests and its measurement by satellite remote sensing. Journal of Biogeography, 2003; 30(7) : 1053-1066.

Hestir, E.L., Brando, V.E., Bresciani, M., Giardino, C., Matta, E., Villa, P., & Dekker, A.G. Measuring freshwater aquatic ecosystems: The need for a hyperspectral global mapping satellite mission. Remote Sensing of Environment, 2015; 167: 181-195.

Vina, A., Liu, W., Zhou, S., Huang, J., & Liu, J. Land surface phenology as an indicator of biodiversity patterns. Ecological indicators, 2016; 64: 281-288.

Li, J., & Roy, D.A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring. Remote Sensing, 2017;9(9): 902. https://doi.org/10.3390/rs9090902

Harwood, T.D., Donohue, R.J., Williams, K.J., Ferrier, S., McVicar, T.R., Newell, G., & White, M. Habitat Condition Assessment System: a new way to assess the condition of natural habitats for terrestrial biodiversity across whole regions using remote sensing data. Methods in Ecology and Evolution, 2016;7(9): 1050-1059.

Lausch, A., Bannehr, L., Beckmann, M., Boehm, C., Feilhauer, H., Hacker, J.M., & Cord, A.F. Linking Earth Observation and taxonomic, structural and functional biodiversity: Local to ecosystem perspectives. Ecological indicators, 2016; 70:317-339.

Otunga, C., Odindi, J., Mutanga, O., & Adjorlolo, C. Evaluating the potential of the red edge channel for C3 (Festuca spp.) grass discrimination using Sentinel-2 and Rapid Eye satellite image data. Geocarto International, 2019;34(10): 1123-1143.

Gauquelin, T., Michon, G., Joffre, R., Duponnois, R., Genin, D., Fady, B., Bou DagherKharrat, M., Derridj, A., Slimani, S., Badri, W., Alifriqui, M., Auclair, L., Simenel, R., Aderghal, M., Baudoin, E., Galiana, A., Prin, Y., Sanguin, H., Fernandez, C., & Baldy, V. Mediterranean forests, land use and climate change: A social-ecological perspective. Regional Environmental Change, 2018;18(3): 623–636. https://doi.org/10.1007/s10113-016-0994-3

DNPWC, Progress Report of Protected Areas in Nepal. Department of National Park and Wildlife Conservation, Babarmahal Kathmandu Nepal, 2020.

Mngadi, M., Odindi, J., Peerbhay, K., & Mutanga, O. Examining the effectiveness of Sentinel-1 and 2 imagery for commercial forest species mapping. Geocarto International, 2021.;36(1):1-12.

MoFSC, Community Forest Inventory Guideline. Ministry of Forest and Soil Conservation, Singhdurbar Kathmandu Nepal, 2004.

Odum EP. Fundamentals of Ecology. 3rd ed. W.B. Saunders Company, Philadelphia. 1971.

Schen, M., & Berger, L. Calculating Biodiversity in the Real World. The Science Teacher, 2014; 81(7): 25-43.

Curtis, J.T., & Mcintosh, R.P. The interrelations of certain analytic and synthetic phytosociological characters. Ecology, 1950; 31(3): 434-455.

Zheng, H., Du, P., Chen, J., Xia, J., Li, E., Xu, Z., Li, X., & Yokoya, N. Performance Evaluation of Downscaling Sentinel-2 Imagery for Land Use and Land Cover Classification by Spectral-Spatial Features. Remote Sensing, 2017; 9(12): 1274-1281. https://doi.org/10.3390/rs9121274

Breiman, L. [No title found]. Machine Learning, 2001; 45(1): 5–32. https://doi.org/10.1023/A:1010933404324

Thanh Noi, P., & Kappas, M. Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery. Sensors, 2017; 18(2): 18. https://doi.org/10.3390/s18010018

Chrysafis, I., Korakis, G., Kyriazopoulos, A.P., & Mallinis, G. Predicting Tree Species Diversity Using Geodiversity and Sentinel-2 Multi-Seasonal Spectral Information. Sustainability, 2020;12(21): 9250.

Nagendra, H., Rocchini, D., Ghate, R., Sharma, B., & Pareeth, S. Assessing Plant Diversity in a Dry Tropical Forest: Comparing the Utility of Landsat and Ikonos Satellite Images. Remote Sensing, 2010;2(2):478–496. https://doi.org/10.3390/rs2020478

Lazaridis, D.C., Verbesselt, J., & Robinson, A.P. Penalized regression techniques for prediction: A case study for predicting tree mortality using remotely sensed vegetation indices. Canadian Journal of Forest Research, 2011; 41(1): 24–34. https://doi.org/10.1139/X10-180

Sterlacchini, S., Ballabio, C., Blahut, J., Masetti, M., & Sorichetta, A. Spatial agreement of predicted patterns in landslide susceptibility maps. Geomorphology, 2011; 125(1): 51-61.

Neumann, M., & Starlinger, F. The significance of different indices for stand structure and diversity in forests. Forest Ecology and Management, 2001;145(1): 91–106. https://doi.org/10.1016/S0378-1127(00)00577-6

Gairola, S., Procheş, Ş., & Rocchini, D. High-resolution satellite remote sensing: a new frontier for biodiversity exploration in Indian Himalayan forests. International Journal of Remote Sensing, 2013 ;34(6):2006-2022.

González-Espinosa, M., María Rey-Benayas, J., Ramírez-Marcial, N., Huston, M. A., & Golicher, D. Tree diversity in the northern Neotropics: Regional patterns in highly diverse Chiapas, Mexico. Ecography, 2004;27(6): 741–756. https://doi.org/10.1111/j.0906-7590.2004.04103.x

Kunwar, R. M., & Sharma, S. P. Quantitative analysis of tree species in two community forests of Dolpa district, mid-west Nepal. Himalayan Journal of Sciences, 2006; 2(3):23–28. https://doi.org/10.3126/hjs.v2i3.226

DFRS, High Mountains and High Himal Forests of Nepal. Forest Resource Assessment (FRA) Nepal, Department of Forest Research and Survey (DFRS). Kathmandu, Nepal. 2015.

Bhatta, K.P., Aryal, A, Baral, H., Khanal, S., Acharya, A.K, Phomphakdy, C., Dorji, R. Forest Structure and Composition under Contrasting Precipitation Regimes in the High Mountains, Western Nepal. Sustainability 2021; 13: 7510-7517. https:// doi.org/10.3390/su13137510

Pettorelli, N., Safi, K., & Turner, W. Satellite remote sensing, biodiversity research and conservation of the future. Philosophical Transactions of the Royal Society B: Biological Sciences, 2014; 369(1643), 20130190.

Kampouri, M., Kolokoussis, P., Argialas, D., & Karathanassi, V. Mapping of forest tree distribution and estimation of forest biodiversity using Sentinel-2 imagery in the University Research Forest Taxiarchis in Chalkidiki, Greece. Geocarto International, 2019; 34(12):1273-1285.

Chrysafis, I., Mallinis, G., Tsakiri, M., & Patias, P. Evaluation of single-date and multi-seasonal spatial and spectral information of Sentinel-2 imagery to assess growing stock volume of a Mediterranean forest. International Journal of Applied Earth Observation and Geoinformation, 2019 ;77: 1-14.

Gilmore, M. S., Wilson, E. H., Barrett, N., Civco, D. L., Prisloe, S., Hurd, J. D., & Chadwick, C. Integrating multi-temporal spectral and structural information to map wetland vegetation in a lower Connecticut River tidal marsh. Remote Sensing of Environment, 2008; 112(11): 4048–4060. https://doi.org/10.1016/j.rse.2008.05.020

Cho, M.A., Mathieu, R., Asner, G.P., Naidoo, L., van Aardt, J., Ramoelo, A., Debba, P., Wessels, K., Main, R., Smit, I. P. J., & Erasmus, B. Mapping tree species composition in South African savannas using an integrated airborne spectral and LiDAR system. Remote Sensing of Environment, 2012;125:214–226. https://doi.org/10.1016/j.rse.2012.07.010

Belgiu, M., & Csillik, O. Sentinel-2 cropland mapping using pixel-based and object-based time-weighted dynamic time warping analysis. Remote sensing of environment, 2018; 204: 509-523.

Delegido, J., Verrelst, J., Meza, C.M., Rivera, J.P., Alonso, L., & Moreno, J. A red-edge spectral index for remote sensing estimation of green LAI over agroecosystems. European Journal of Agronomy, 2013;. 46: 42–52. https://doi.org/10.1016/j.eja.2012.12.001

Lazaridis, D.C., Verbesselt, J., & Robinson, A.P. (2011). Penalized regression techniques for prediction: A case study for predicting tree mortality using remotely sensed vegetation indices. Canadian Journal of Forest Research, 41(1), 24–34. https://doi.org/10.1139/X10-180

Lopes, M., Fauvel, M., Ouin, A., & Girard, S. Spectro-Temporal Heterogeneity Measures from Dense High Spatial Resolution Satellite Image Time Series: Application to Grassland Species Diversity Estimation. Remote Sensing, 2017; 9(10): 993. https://doi.org/10.3390/rs9100993

Ingram, J.C., Dawson, T.P., & Whittaker, R.J. Mapping tropical forest structure in southeastern Madagascar using remote sensing and artificial neural networks. Remote Sensing of Environment, 2005; 94(4): 491–507. https://doi.org/10.1016/j.rse.2004.12.001

Mallinis, G., Koutsias, N., & Arianoutsou, M. Monitoring land use/land cover transformations from 1945 to 2007 in two peri-urban mountainous areas of Athens metropolitan area, Greece. Science of The Total Environment, 2014; 490:262–278. https://doi.org/10.1016/j.scitotenv.2014.04.129

Fassnacht, F.E., Latifi, H., Stereńczak, K., Modzelewska, A., Lefsky, M., Waser, L.T., Straub, C., & Ghosh, A. Review of studies on tree species classification from remotely sensed data. Remote Sensing of Environment, 2016; 186: 64–87. https://doi.org/10.1016/j.rse.2016.08.013

Mananze, S., Pôças, I., & Cunha, M. Mapping and Assessing the Dynamics of Shifting Agricultural Landscapes Using Google Earth Engine Cloud Computing, A Case Study in Mozambique. Remote Sensing, 2020; 12(8):1279. https://doi.org/10.3390/rs12081279


Refbacks

  • There are currently no refbacks.


eISSN: 2230-7990